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Posts Tagged ‘Cancer Genomics’


Human Genetics and Childhood Diseases

Curator: Larry H. Bernstein, MD, FCAP

 

 

 

Publication Roundup: HGMD

HGMD®, the Human Gene Mutation Database is used by scientists around the world to find information on reported genetic mutations. The papers below use the database to advance our understanding of disease, DNA dynamics, and more.

https://www.qiagenbioinformatics.com/blog/translational/publication-roundup-hgmd

Local DNA dynamics shape mutational patterns of mononucleotide repeats in human genomes
First author: Albino Bacolla

Scientists in the US and UK published results in Nucleic Acids Research of a detailed analysis of single-base substitutions and indels in the human genome. Their findings show that certain base positions are more susceptible to mutagenesis than others. They used HGMD Professional to find mutations in specific genomic regions for analysis; the paper includes charts showing mutation patterns, germline SNPs, and more from HGMD data.

High prevalence of CDH23 mutations in patients with congenital high-frequency sporadic or recessively inherited hearing loss
First author: Kunio Mizutari

This Orphanet Journal of Rare Diseases paper from scientists in Japan sequenced 72 patients with unexplained hearing loss, finding several CDH23 mutations, some of which were novel. Mutations in the gene have been linked to Usher syndrome and other forms of hereditary hearing loss. The scientists used HGMD to find all known CDH23 mutations within nearly 70 coding regions.

Mutation analyses and prenatal diagnosis in families of X-linked severe combined immunodeficiency caused by IL2Rγ gene novel mutation
First author: Q.L. Bai

In Genetics and Molecular Research, scientists report the utility of mutation analysis of the interleukin-2 receptor gamma gene to assess carrier status and perform prenatal diagnosis for X-linked severe combined immunodeficiency. They studied two high-risk families, along with 100 controls, to evaluate the approach. Sequence variation was determined using HGMD Professional and an X-SCID database, and a new mutation was discovered in the project.

Impact of glucocerebrosidase mutations on motor and nonmotor complications in Parkinson’s disease
First author: Tomoko Oeda

Researchers from three hospitals in Japan published this Neurobiology of Aging report that may help stratify Parkinson’s disease patients by prognosis. They sequenced mutations in the GBA gene in 215 patients, finding that those who had mutations associated with Gaucher disease suffered dementia and psychosis much earlier than those who didn’t. The team found previously reported GBA mutations using HGMD Professional.

Comprehensive Genetic Characterization of a Spanish Brugada Syndrome Cohort
First author: Elisabet Selga

In this PLoS One publication, scientists from a number of institutions in Spain examined genetic variation among patients with Brugada syndrome, a rare genetic cardiac arrhythmia. They sequenced 14 genes in 55 patients, identifying 61 variants and finding the subset that appear pathogenic. Variants were filtered against a number of databases, including HGMD.

 

 

Local DNA dynamics shape mutational patterns of mononucleotide repeats in human genomes

Albino Bacolla1Xiao Zhu2Hanning Chen3Katy Howells4David N. Cooper4 and Karen M. Vasquez1

Nucl. Acids Res. (26 May 2015) 43(10): 5065-5080.   http://dx.doi.org:/10.1093/nar/gkv364

Single base substitutions (SBSs) and insertions/deletions are critical for generating population diversity and can lead both to inherited disease and cancer. Whereas on a genome-wide scale SBSs are influenced by cellular factors, on a fine scale SBSs are influenced by the local DNA sequence-context, although the role of flanking sequence is often unclear. Herein, we used bioinformatics, molecular dynamics and hybrid quantum mechanics/molecular mechanics to analyze sequence context-dependent mutagenesis at mononucleotide repeats (A-tracts and G-tracts) in human population variation and in cancer genomes. SBSs and insertions/deletions occur predominantly at the first and last base-pairs of A-tracts, whereas they are concentrated at the second and third base-pairs in G-tracts. These positions correspond to the most flexible sites along A-tracts, and to sites where a ‘hole’, generated by the loss of an electron through oxidation, is most likely to be localized in G-tracts. For A-tracts, most SBSs occur in the direction of the base-pair flanking the tracts. We conclude that intrinsic features of local DNA structure, i.e. base-pair flexibility and charge transfer, render specific nucleotides along mononucleotide runs susceptible to base modification, which then yields mutations. Thus, local DNA dynamics contributes to phenotypic variation and disease in the human population.

INTRODUCTION

Changes in human genomic DNA in the form of base substitutions and insertions/deletions (indels) are essential to ensure population diversity, adaptation to the environment, defense from pathogens and self-recognition; they are also a critical source of human inherited disease and cancer. On a genome-wide scale, base substitutions result from the combined action of several factors, including replication fidelity, lagging versus leading strand DNA synthesis, repair, recombination, replication timing, transcription, nucleosome occupancy, etc., both in the germline and in cancer (14). On a much finer scale [(over a few base pairs (bp)], rates of base substitutions may be strongly influenced by interrelationships between base–protein and base–base interactions. For example, the mutator role of activation-induced deaminase (AID) in B-cells during class-switch recombination and somatic hypermutation (5) targets preferentially cytosines within WRC (W: A|T; R: A|G) sequences (6), whereas apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like (APOBEC) overexpression displays a preference for base substitutions at cytosines in TCW contexts (7). Other examples, such as the induction of C→T transitions at CG:CG dinucleotides by cytosine-5-methylation and the role of UV light in promoting base substitutions at pyrimidine dimers have been well documented (reviewed in (4,8)). More recently, complex patterns of base substitution at guanosines in cancer genomes have been found to correlate with changes in guanosine ionization potentials as a result of electronic interactions with flanking bases (9), suggesting a role for electron transfer and oxidation reactions in sequence-dependent mutagenesis. However, despite these advances, the increasing number of sequence-dependent patterns of mutation noted in genome-wide sequencing studies has met with a lack of understanding of most of the underlying mechanisms (10). Thus, a picture is emerging in which mutations are often heavily dependent on sequence-context, but for which our comprehension is limited.

Mononucleotide repeats comprise blocks of identical base pairs (A|T or C|G; hereafter referred to as A-tracts and G-tracts) and display distinct features: they are abundant in vertebrate genomes; mutations within the tracts occur more frequently than the genome-wide average; mutations generally increase with increasing tract length; length instability is a hallmark of mismatch repair-deficiency in cancers; and sequence polymorphism within the general population has been linked to phenotypic diversity (1115). Thus, mononucleotide repeats appear ideal for addressing the question of sequence-dependent mutagenesis since base pairs within the tracts are flanked by identical neighbors. Both historic and recent investigations concur with the conclusion that a major source of mononucleotide repeat polymorphism is the occurrence of slippage (i.e. repeat misalignment) during semiconservative DNA replication, which gives rise to the addition or deletion of repeat units (11,12). An additional and equally important source of mutation has recently been suggested to arise from errors in DNA replication by translesion synthesis DNA polymerases, such as pol η and pol κ (13), also on slipped intermediates, leading to single base substitutions.

A key question that remains unanswered in these studies and which is relevant to the issue of sequence context-dependent mutagenesis is whether all base pairs within mononucleotide repeats display identical susceptibility to single base changes and whether indels (which are consequent to DNA breakage) occur randomly within the tracts.

Herein, we combine bioinformatics analyses on mononucleotide repeat variants from the 1000 Genomes Project and cancer genomes with molecular dynamics simulations and hybrid quantum mechanics/molecular mechanics calculations to address the question of sequence-dependent mutagenesis within these tracts. We show that mutations along both A-tracts and G-tracts are highly non-uniform. Specifically, both base substitutions and indels occur preferentially at the first and last bp of A-tracts, whereas they are concentrated between the second and third G:C base pairs in G-tracts. These positions coincide with the most flexible base pairs for A-tracts and with the preferential localization of a ‘hole’ that results when one electron is lost due to an oxidation reaction anywhere along G-tracts. Thus, despite the uniformity of sequence composition, mutations occur in a sequence-dependent context at homopolymeric runs according to a hierarchy that is imposed by both local DNA structural features and long-range base–base interactions. We also show that the repair processes leading to base substitution must differ between A- and G-tracts, since in the former, but not in the latter, base substitutions occur predominantly in the direction of the base immediately flanking the tracts. Additional sequence-dependent patterns of mutation are likely to arise from studies of more heterogeneous sequence combinations, possibly involving other aspects intrinsic to the structure of DNA.

 

RESULTS

Mononucleotide repeat variation is defined by tract length and flanking base composition

We define mononucleotide repeats in the GRCh37/hg19 (hg19) human genome assembly as uninterrupted runs of A:T and G:C base pairs (hereafter referred to as A-tracts and G-tracts, respectively) from 4 to 13 base pairs in length (Figure 1A). We retrieved a total of 48,767,945 A-tracts and 13,633,781 G-tracts, both of which displayed a biphasic distribution with an inflection point between tract lengths of 8 and 9 (bp) and with the number of runs declining with length more dramatically for G-tracts than for A-tracts (Figure 1B), as noted previously (29). Both the number of short tracts and the extent of decline varied with flanking base composition, TA[n]T runs being two- to three-fold more abundant than CA[n]Cs (Supplementary Figure S1A) and AG[n]As declining the most rapidly (Supplementary Figure S1B). Thus, mononucleotide runs exist as a collection of separate pools of sequences in extant human genomes, each maintained at distinctive rates of sequence stability, as determined by factors such as bp composition (A:T versus G:C), tract length and flanking sequence composition.

Figure 1.

Mononucleotide repeat variation, evolutionary conservation and association with transcription. (A) The search algorithm was designed to retrieve runs of As or Ts (A-tracts) and Gs or Cs (G-tracts) length n (n = 4 to 13), along with their 5′ (n = 0) and 3′ (n = n + 1) nearest neighbors from hg19. Tract bases were numbered 5′ to 3′ with respect to the purine-rich sequence. The panel exemplifies the nomenclature for A- and G-tracts of length 4. (B) Logarithmic plot of the number of A-tracts (closed circles) and G-tracts (open circles) in hg19 as a function of length. (C) Normalized fractions of polymorphic tracts (F SNV) (number of SNVs divided by both hg19 number of tracts and n) from the 1KGP for A-tracts (closed circles) and G-tracts (open circles). (D) Radial plot of SNVs in the 1KGP at the 5′ and 3′ nearest neighbors of A-tracts. Periphery, tract length; horizontal axis, scale for the fraction of SNVs (F SNV). (E) Radial plot of SNVs in the 1KGP at the 5′ and 3′ nearest neighbors of G-tracts. (F) Percent difference in the numbers of A-tracts (closed circles) and G-tracts (open circles) between syntenic regions of hg19 and HN genomes. (G) The exponents of Benjamini-corrected P-values for A-tract-containing genes enriched in transcription-factor binding sites plotted as a function of A-tract length (triangles); each value represents the median of the top 11 USCS_TFBS terms. The percent A-tracts (closed circles) and G-tracts (open circles) intersecting genomic regions pulled-down by chromatin immunoprecipitation using antibodies against transcription factors are plotted as a function of tract length. (H) List of gene enrichment terms with a Benjamini-corrected P-value of <0.05 in common between genes containing A- and G-tracts of lengths 4–13, excluding the UCSC_TFBS terms.

 We examined the extent of sequence variation in the human population by mapping 38,878,546 single nucleotide variants (SNVs) from 1092 haplotype-resolved genomes (the 1000 Genomes Project, 1KGP) (30) to the hg19 A- and G-tracts. The normalized fractions of polymorphic tracts (F SNV) were greater for G-tracts than A-tracts and both displayed Gaussian-type distributions, with maxima of 0.067 for G-tracts of length 8 and 0.017 for A-tracts of length 9 (Figure 1C). CA[n]C and AG[n]A runs displayed the highest F SNV values for A- and G-tracts, respectively (Supplementary Figure S1C and D), with F SNV values for AG[n]As attaining ∼0.10 at length 8. We conclude that flanking base composition influences the rates of SNV within mononucleotide runs and, as a consequence, their representation in the reference human genome.

F SNV values at the flanking 5′ and 3′ bp were similar between A- and G-tracts, except for minor differences for the least represented (i.e. longest) tracts and did not exceed 0.02 (Supplementary Figure S1E). These fractions are expected to be greater than at more distant positions from the tracts, based on previous data (29). SNVs at G-tracts, but not at A-tracts, were more frequent than at flanking base pairs. F SNVs for base pairs flanking short (≤8 bp) tracts were at least twice as high as those flanking long tracts; F SNVs also displayed distinct sequence preference with most (∼0.1) variants occurring at Ts 3′ of G-tracts (Figure 1D and E). In summary, SNVs at mononucleotide runs do not increase monotonically with length but peak at 8–9 bp. This behavior mirrors the genomic distributions, both with respect to the total number of tracts (Figure 1B) and the subsets flanked by specific-sequence combinations (Supplementary Figure S1A–D). Variation at flanking base pairs also displayed a biphasic pattern centered at a length of 8–9 bp, with a greater chance of variation adjacent to G- than A-tracts and with characteristic sequence preferences.

Long tracts are evolutionarily conserved and associated with high transcription

To assess whether more variable monosatellite runs (Figure 1C) might have undergone a greater reduction in number in extant humans relative to extinct hominids, we compared the number of A- and G-tracts between syntenic regions of five individuals comprising hg19 and three Neanderthal (HN) specimens (31). The difference between hg19 and HN was very small (<±2%) for the short tracts, but it displayed more negative values in hg19 with increasing tract length, which reached a maximum of −11.8 and −32.7% for A- and G-tracts, respectively, of length 9. Beyond this threshold, the numbers of tracts converged for A-tracts, whereas they were more abundant in hg19 for G-tracts >11 bp (Figure 1F). In summary, the largest difference in the number of mononucleotide runs between hg19 and HN sequences was centered at 9 bp for both A- and G-tracts, suggesting that the length distributions (Figure 1A and Supplementary Figure S1A and B) reflect distinct rates of evolutionary gains and losses due to differential sequence mutability (Figure 1C) as a function of length and flanking sequence composition (12).

The fact that long (>9 bp) mononucleotide runs display low variability in the human population (Figure 1C) and sequence conservation during evolutionary divergence (Figure 1F) raises the possibility that they might serve functional roles. Through gene enrichment analyses, we found that genes containing A- and G-tracts were enriched for genes associated with the term ‘UCSC_TFBS’, which pertains to transcripts harboring frequent transcription factor binding sites (32,33). For A-tract-containing genes, the median P-values for the top 11 UCSC_TFBS terms decreased from 2.95E-26 for tracts of length 4 to 5.22E-241 for tracts of length 13 (Figure 1G). The percent of A-tracts intersecting genomic fragments amplified from chromatin immunoprecipitation using transcription-factor binding antibodies (32,33) also increased from 8.7 to 9.9 from length 6 to 13, whereas it was constant (mean ± SD, 22.4 ± 1.1) for G-tracts (Figure1G). For gene classes excluding ‘UCSC_TFBS’, a search for categories enriched at P < 0.05 and common to all A- and G-tract-containing genes returned a set of 25 terms, 22 of which were associated with high levels of tissue-specific gene expression (Figure 1H). In summary, these analyses extend prior work (14) supporting a role for mononucleotide tracts in enhancing gene expression, a function that for A-tracts appears to increase with increasing tract length.

Repeat variability is highly skewed

Next we addressed whether bp along A- and G-tracts display equal probability and type of variation. In the 1KGP dataset, the number of SNVs at each position along both A- and G-tracts of length 4 was within a two-fold difference (144,000–240,000); for both types of sequence, transitions (i.e. A→G and G→A) were the predominant (51–78%) type of base substitution (Supplementary Figure S2A and B). However, with increasing length, the number of SNVs decreased up to 30-fold more drastically for G-tracts than for A-tracts, with increasing numbers of transversions (A→T and G→C|T) being predominant. Normalizing the data for the number of tracts genome-wide revealed that the extent of SNV varied by up to 10-fold, depending upon tract length and bp position. Specifically, the highest degree of variation was observed at the first and last A within the A-tracts (i.e. A1 and An), which underwent up to 61% A→T and 43% A→C transversions, respectively, at length 9 (Figure 2A). Likewise, for G-tracts, the most polymorphic sites were G3, followed by G2, for mid-size tracts of 8–10 bp, with 44% G→C transversions at G3 for tracts of length 8 (Figure2B). Thus, the extent of SNV at mononucleotide runs is grossly skewed in human genomes, both along the sequence itself and across tract length, which must account for the bell-shape behavior in F SNV for the tracts as a whole (Figure 1C).

Figure 2.

Population variation spectra. (A) Variation spectra of A-tracts. Percent (number of SNVs at each position divided by the number of tracts in hg19 × 100) of A→T (black), A→C (red) and A→G (green) SNVs in the 1KGP dataset (left). Percent SNVs at A1 as a function of tract length (right). (B) Variation spectra of G-tracts. As in panel A with G→T (black), G→C (red) and G→A (cyan) (left). Percent SNVs at G3 as a function of tract length (right). (C) Percent A→T, A→C and A→G transitions at each position along A-tracts (stars) preceded and followed by a T (TA[n]T, left), C (CA[n]C), center) and G (GA[n]G, right) as a function of tract length. (D) Percent G→T, G→C and G→A transitions at each position along G-tracts (stars) preceded and followed by a T (TG[n]T, left), C (CG[n]C), center) and A (AG[n]A, right) as a function of tract length. (E) Percent transitions at base pairs (stars) preceding or following A-tracts (left) and G-tracts (right) as a function of tract length (n). *, mutated position.

We assessed whether SNV hypervariability was associated with specific combinations of nearest neighbors. For A-tracts flanked 5′ by a T, C or G, the highest percentage of SNVs was observed at A1 when preceded by a T, which reached 7.9% for TA[n] tracts of length 9 (Supplementary Figure S2C). By contrast, for 3′ T, C or G, the greatest effect was elicited by a C, with the highest percentage (7.1%) of SNVs at An for A[n]C tracts of length 9 (Supplementary Figure S2D). Therefore, flanking base pairs play a critical role both in the spectra and frequencies of SNVs at A-tracts. More detailed plots along A-tracts either preceded (Supplementary Figure S2E), followed (Supplementary Figure S2F) or preceded and followed (Figure 2C) by a T, C or G revealed the dramatic and long-range (up to 9–10 bp for the longest tracts, higher than the value of 4 bp predicted by mathematical models of slippage (11)) influence of flanking base pairs on variation spectra, in which up to 95% of the changes were in the direction of the base flanking the tract. Because the number of A-tracts preceded or followed by a specific base varies by up to three-fold (Supplementary Figure S2G), we conclude that for A-tracts, the overall mutation fractions and spectra are the result of at least three variables; length, position along the tract, and base composition of the 5′ and 3′ nearest-neighbors.

For G-tracts flanked 5′ by a T, C or A, high percentages (10–12%) of SNVs were observed at G1 for tracts preceded by a C, an effect that decreased with increasing tract length (Supplementary Figure S3A). This result, together with an exceedingly low number of G→A transitions at G1 for tracts not preceded by a C (Supplementary Figure S3C) relative to all tracts (Supplementary Figure S2B), is consistent with the known high mutability of CG:CG dinucleotides as a result of cytosine-5 methylation (9). The hypermutability at G2 was observed preferentially for tracts preceded by an A, and to a lesser extent T, whereas that at G3 was insensitive to flanking sequence composition. Likewise, G-tracts flanked 3′ by a T, C or A did not display marked sequence-dependent effects (Supplementary Figure S3B). Detailed plots of the SNV spectra along G-tracts either preceded (Supplementary Figure S3D), followed (Supplementary Figure S3E), or preceded and followed (Figure 2D) by a T, C or A revealed a noticeable effect only for 5′ T in association with G→T substitutions at G1for tracts of length ≥8. Thus, despite a consistent over-representation of G-tracts flanked 5′ by a T (Supplementary Figures S3F and S1B), which must account for the high absolute number of SNVs at G1 for TG[n] relative to AG[n] and CG[n] (Supplementary Figure S3G), nearest-neighbor base composition seems to play a lesser role in SNV spectra at G-tracts than at A-tracts.

With respect to SNVs at the flanking 5′ and 3′ nearest positions, no B→A or H→G substitutions (Figure 1A) were found above a length threshold of 9 for A-tracts and 8 for G-tracts (Figure 2E, gray shading) out of 5969 SNVs, implying that tract expansion by recruiting flanking base pairs is disfavored at these lengths. In summary, base substitution along mononucleotide repeats is strongly skewed towards the edges of A-tracts and within the 5′ half of G-tracts, with frequencies that peak at midsize lengths (8–9 bp). For A-tracts ≥7 bp, base substitution occurred almost exclusively in the direction of the flanking nearest-neighbors. Finally, base substitution at flanking bases did not contribute to tract expansion for mononucleotide runs longer than 8–9 bp.

Insertions and deletions display length and positional preference

In addition to SNVs, mononucleotide runs are polymorphic in length as a result of indels. Herein, we consider separately two types of indels: one in which tract length changes by ±1 and flanking bp composition is not altered (slippage); the other comprising all other cases involving the addition or removal of 1–200 bp (indels). Slippage is a widely accepted mutational mechanism (1112,34), whereby DNA replication errors at reiterated DNA motifs cause changes in the number of motifs (most often +/−1). The normalized fractions of slippage in the 1KGP dataset peaked at lengths of 8 bp for A-tracts and 9 bp for G-tracts (Figure 3A), generating bell-shaped curves similar to those observed for SNVs (Figure1C) and with no differences in the highest fraction of ‘slipped’ tracts, which peaked at ∼0.02. By contrast, +1 slippage occurred more frequently than −1 slippage at A-tracts (Figure 3B). These results support recent studies on microsatellite repeats (12) and contrast with previous conclusions that slippage increases monotonically with tract length, and that the extent of slippage differs between A- and G-tracts (35,36).

Figure 3.

Population insertions and deletions. (A) Normalized fractions of A-tracts (closed circles) and G-tracts (open circles) displaying +/−1 bp slippage in the 1KGP dataset as a function of tract length. Data were obtained by dividing the number of events by both the number of hg19 tracts and tract length (n). (B) Ratio of the number of +1 to −1 slippage for A-tracts (closed circles) and G-tracts (open circles). (C) Indels at A-tracts. For positions along the tracts (‘Tract’), ‘F Indel’ is the ratio between the number of indels and the number of tracts in hg19 multiplied by tract length. For the positions immediately flanking the tracts genomic coordinates (‘Before tract’ and ‘After tract’), ‘F Indel’ is the ratio between the number of indels and the number of tracts in hg19. (D) Indels at G-tracts, calculated as described in panel C. (E) Heatmap representation of insertions along A-tracts. The percent insertions (i.e. the number of insertions at each position divided by the number of tracts in hg19) (y-axis) plotted as a function of location (x-axis) from position 0 (insertion between the bp 5′ to the tract and the first bp of the tract) to position n + 1 (insertion between the bp 3′ to the last bp of the tract and the following bp) (see Figure 1A) and as a function of tract length (z-axis). (F) Heatmap representation of insertions along G-tracts.

With respect to indels, the normalized fractions were low (<1 × 10−3) along short (4–6 bp) A- and G-tracts, but rose to a plateau for longer tracts as reported earlier (11); this plateau was 10-fold higher for G-tracts (∼0.03) than for A-tracts (∼0.003) (Figure 3C and D). Indels also occurred more frequently (up to six-fold for A-tracts of length 11) at nearest-neighboring base pairs (‘Before tract’ and ‘After tract’ in Figure 3C and D) than along the tracts. Thus, contrary to SNVs and slippage, indels increased to a plateau with mononucleotide tract length.

We analyzed in detail the locations of insertions along the tracts and the flanking positions with respect to the 5′ to 3′ orientation of the tracts (Figure 1A). The normalized fractions demonstrated that insertions peaked at the 3′, and to a lesser extent 5′, ends of the longest A-tracts (Figure 3E), but remained low. For G-tracts, insertions occurred most efficiently at two locations (G2–3 and G5) (Figure 3F), they increased with tract length (up to ∼0.04), and attained ∼10-fold higher values than for A-tracts. In conclusion, insertion sites at A- and G-tracts followed the patterns observed for SNVs (Figure 2A and B), suggesting that factors associated with local DNA dynamics sensitize specific bases along the tracts to genetic alteration, inducing both SBS and indels.

Base pair flexibility and charge localization map to sites of sequence changes

To elucidate elements of intrinsic DNA dynamics that may be responsible for the biases in SNV and insertion sites, we performed molecular dynamics (MD) and hybrid quantum mechanics/molecular mechanics (QM/MM) simulations on model A[6], A[9], G[6] and G[9] duplex DNA fragments. We focused on water bridge coordination (Figure 4A), bp step flexibility, and for the G[6] and G[9], charge localization, as these properties are known to impact the susceptibility of DNA to base damage, repair and mutation. The fractions of one water coordination increased along the A[9] and A[6] structures in a 5′ to 3′ direction, irrespective of flanking sequence composition, in concert with a decrease in minor groove width (Figure 4B and Supplementary Figure S4A) as predicted (37). Vstep, a measure of bp structural fluctuation, displayed a prominent peak of ∼40 Å3deg3 at the 5′-TA-3′ step for both structures (Figure 4C and Supplementary Figure S4B), which together with low water occupancy points to 5′-TA-3′ being a preferred location for base modification and mutation. In the G[9] and G[6] structures water coordination involved mostly two-water bridges due to wide (∼14 Å) minor grooves (Figure 4Dand Supplementary Figure S4C), whereas flexibility was modest (∼20–22 Å3deg3, Figure 4E and Supplementary Figure S4D). Thus, bp dynamics are likely to impact mutations at A-tracts to a greater extent than at G-tracts. Guanine has the lowest ionization potential (IP) of all four bases and IP further decreases at guanine runs, rendering them targets for electron loss, charge localization, oxidation and eventually mutation (4,38). Because after electron loss the ensuing charge (hole) can migrate along the DNA double-helix and relocalize at specific guanines, we addressed whether the preferred sites of mutation along G-tracts, i.e. G2–3 and G5, would also be preferred sites for charge localization. The QM/MM determinations indicated that whereas for the short G[6] fragment the difference in the density-derived atomic partial charges (DDAPC) (i.e. the hole) localized most often (∼50%) to the first position (Figure 4F), for the long G[9] fragment charge localization shifted downstream (mostly to the second, but also to positions 6–7, Figure 4G). Importantly, the charge was found exclusively around the guanine rings (Figure 4H). Thus, the two main sites of sequence change along G-tracts, i.e. G2–3 and G5, coincide with positions where charge localization and hence one-electron oxidation reactions is predicted to occur most frequently. In summary, bp flexibility at A-tracts and charge transfer at G-tracts likely represent intrinsic DNA features underlying the bias in SNV and insertions at mononucleotide runs in human genomes.

Figure 4.

MD and QM/MM simulations. (A) Molecular modeling of one (left) and two (right) minor groove water bridge coordination. (B) Fraction of one-water bridge occupancy (left axis) at A[9] DNA sequences flanked 5′ and 3′ by a T (black circles), C (red circles) or G (green circles). Minor groove widths (right axis), as determined from intrastrand phosphate-to-phosphate distances. (C) Vstep for A[9] DNA sequences, determined as the product of the square root of the eigenvalues (λi) described by the six bp step parameters shift, slide, rise, tilt, roll and twist; i.e. Vstep=6i=1λi−−√. (D) Fraction of one- (black circles) and two-water (red circles) bridge occupancy (left axis) at G[9] DNA sequences. Minor groove widths (right axis), as assessed from intrastrand phosphate-to-phosphate distances. (E) Vstep for G9 DNA sequences. (F) Average charge redistribution (open circles and right axis) for G[6] DNA structures upon vertical ionization, examined by calculating the difference on the density-derived atomic partial charges (DDAPC) for the neutral and negatively charged states. Histogram of the number of instances (left axis) in which the largest charge redistribution occurred at a specific position along the G[6] structures. (G) DDAPC for G[9] DNA structures (open circles and right axis) and histogram of the number of instances (left axis) in which the largest charge redistribution occurred at a specific position. (H) VMD rendering of a G[9] DNA structure displaying hole localization at G2. Capped base pairs were removed for clarity.

Position and orientation along nucleosome core particles modulate sequence variation

DNA wrapped around histones in nucleosomes is subject to local deformation (39), which may impact mutation. Thus, we analyzed the 1KGP SNVs at A- and G-tracts predicted to overlap with well-positioned nucleosome core particles (NCPs) (16). In hg19, the percentage of tracts that overlap with NCPs decreased moderately from ∼90% at length of 4 to 81% and 71% for A- and G-tracts of length 13, respectively (Figure 5A), suggesting that mononucleotide runs are not depleted in NCPs in human genomes as previously proposed (40). A-tracts of lengths 4–8 base pairs displayed distinctive peaks along the NCP surface in phase with the helical repeat of DNA (10.5 bp) and with minor grooves facing toward the inner protein core (lengths 4–5) (16) (Figure 5B and Supplementary Figure S5A). A-tracts of length of 9–13 bp exhibited only half (six) the peaks evident for the shorter tracts. For the G-tracts, only small peaks with no clear minor groove-inward-facing regions were detected (Supplementary Figure S5B).

Figure 5.

Positioning along nucleosome core particles. (A) Percent of A-tract (open circles) and G-tract (closed circles) base pairs in hg19 overlapping with well-positioned NCP genomic coordinates as a function of tract length. (B) Counts of base pairs in hg19 A-tracts of length 5 overlapping with NCPs genomic regions as a function of distance from the histone octamer dyad axis. Minor groove-inward-facing regions (gray) were derived from the X-ray crystal structure of NCP147 (41). (C) Percent SNVs in the 1KGP dataset (left axis) at every bp along A-tracts of length 5 for tracts centered at maxima (black) and minima (gray) along NCPs (Figure 5B). Percent increase (right axis) of SNVs at minima relative to maxima (green). P-values for paired t-tests: 0.013 (*), 0.002 (**) and 4.7 × 10−6 (***). (D) Whisker plots of%SNVs (left axis) at A1 for A-tracts of length 5 centered at maxima and minima (black) along NCPs (Figure 5B). Percent difference (right axis) in the number of A-tracts of length 5 in hg19 preceded by C, T or G (red) between those centered at minima and those centered at maxima (Figure5B). (E) C-containing/G-containing ratios (see text) for G-tracts of length 5 in hg19 as a function of distance from the NCP dyad axis (black) and location of core histones (maroon and green). Peaks correspond to negative iSAT (i.e. tilt parameters multiplied by the corresponding sin θ) values (gray) (39). Ratios of%SNV at G1 (upshifted by 0.5 for clarity) between C-containing (5′-CCCCCG-3′ sequences on the hg19 forward strand) and G-containing (5′-CGGGGG-3′ sequences on the hg19 forward strand) (Figure 1A) CG[5] tracts mapping NCP Chip-seq genomic intervals (red) fitted by a non-parametric local regression (loess; sampling proportion, 0.100; polynomial degree, 3). (F) VMD rendering (top) of TATTT residues 34–38 (yellow) and the complementary AAATA residues 672–753 (pink) from the 1EQZ pdb nucleosomal crystal structure, corresponding to peak area from −40 to −36 in Figure 5E. The switch in G-tract (lengths of 5 and 7) orientation along NCPs (bottom) serves to position the C-containing strand on the outside (yellow) and, correspondingly, the G-containing strand on the inside (pink).

 To assess if tract-positioning along NCPs influences SNVs, we selected A-tracts of lengths 5, 7 and 9 bp and G-tracts of lengths 5 and 7 bp whose central positions coincided with either the maxima or minima (41) (Figure 5B and Supplementary Figure S5A and B) and conducted pair-wiset-tests (330 total) between permutations of ‘categories’, including ‘tracts centered at maxima versus minima’, ‘position along the tracts’, ‘flanking sequence composition’, ‘specific NCP locations’ and ‘tract orientation’. For A-tracts, 79/207 (38%) significant pairs were found, 68 (86%) of which were related to differences between tracts centered at maxima versus minima, with a preponderance (63%) of tests displaying increased %SNVs at minima (Supplementary Figure S5C and E). For example, %SNVs at length 5 bp were greater at minima than at maxima at each position along the A-tracts (Figure 5C). A→C substitutions at A1 were more abundant at maxima than at minima (mean ± SD, 18.7 ± 0.7% at max and 17.6 ± 0.8% at min; P-value 0.001), whereas A→T substitutions at the same position displayed the opposite trend (mean ± SD, 18.4 ± 0.5% at max and 19.8 ± 1.1% at min; P-value 0.0005) (Figure 5D). A-tracts of length 7 also exhibited a similar pattern at A7 (Supplementary Figure S5H). The percentages of CA[5] and A[7]C tracts in hg19 centered at maxima were greater than at minima and the reverse was observed for the TA[5] and A[7T] tracts (Figure 5D and Supplementary Figure S5H). Thus, we conclude that positioning along the NCP surface of both the double-helical grooves and junctions with flanking base pairs influence SNVs along A-tracts. However, this influence is complex and for the most part, difficult to predict.

For G-tracts, most pairwise comparisons (18/34, 53%) indicated SNV variation according to sequence orientation (Supplementary Figure S5F and G). In hg19, the ratio of the numbers of G-tracts of lengths 5 and 7 for which the C-containing strand coincided with the forward sequence (downstream example sequence in Figure 1A) to the numbers of G-tracts for which the G-containing strand coincided with the forward sequence (upstream example sequence in Figure 1A) (C-containing/G-containing ratios) displayed a prominent 10.5-bp oscillation in phase with iSAT (Figure 5E), a measure of ‘inside’ and ‘outside’ bases, according to the bp step tilt parameter (39). Analysis of the helical path of a 146-bp DNA fragment wrapped around histones showed that the oscillation in the C-containing/G-containing ratios corresponds to a preference for guanine bases to face the protein core (Figure 5F). We analyzed the subset of G-tracts preceded by a 5′ C (i.e. CG[5]) to assess whether SNVs at G1, the position known to be mutable due to CpG methylation also oscillated with the C-containing/G-containing ratios. Oscillation in SNV-C-containing/SNV-G-containing values was evident, with peaks aligning to the hg19 troughs (Figure 5E) implying that the cytosines facing the protein surface harbor more variants than those facing away. We conclude that A- and G-tracts display preferential positioning (the former) and orientation (the latter) along NCPs, which in turn modulate the rate of sequence variation.

Mutations associated with human disease

Knowing that the first and last As of long A-tracts and G2–3 in G-tracts are the major sites of SNV in the human population, we addressed whether these features are also discernible in mutated mononucleotide tracts associated with human genetic disease. We collected 9,450,456 unique SBSs (both SBSs and SNVs refer to single base changes) from sequenced cancer genomes and normalized the percent mutations along A- and G-tracts to enable a direct comparison with the 1KGP dataset. For A-tracts (Figure 6A and Supplementary Figure S6A), SBSs displayed the same trend as the 1KGP data (Figure 2A) with respect to the bell-shape increase in mutations at A1 and An and the mutation spectra, although the susceptibility to mutation as a function of tract length attained greater values (6.36% for length 11 in cancer versus 4.15% for length 9 in the 1KGP datasets at A1). The first and last 3 bp also harbored more SBSs than in the 1KGP dataset for tracts >7 bp, a feature that we found to be due exclusively to a large cancer dataset (42) containing high-level microsatellite instability (MSI) samples (Supplementary Figure S6B and C), which are known to result from mismatch-repair deficiency (15). Thus, A-tracts display similar patterns of base substitution between the germline and somatic cancer tissues. For G-tracts, mutation spectra were characterized by G→T transversions at tract lengths >7, particularly at G1, the most frequently mutated position for tracts lengths up to 11 bp (Figure 6B and Supplementary Figure S6D). This trend persisted even when the high rates of methylation-mediated deamination mutations at the CG dinucleotide were removed (Supplementary Figure S6E). Thus, mutation patterns in cancer genomes contrast with those observed in the germline, both with respect to the most mutable position (G1 versus G2–3) and the types of base substitution (G→T in cancer genomes versus G→T and G→C in the germline).

Figure 6.

Mutation patterns in cancer genomes. (A) Mutation spectra for SBSs at A-tracts. Percent values were obtained by dividing the total number of SBSs at each position by the number of tracts in hg19 and then multiplying by 3.2516 to equalize the percentage of A-tracts of length 4 between the cancer genomes and the 1KGP datasets. (B) Mutation spectra for SBSs at G-tracts in cancer genomes. Percent values were obtained as in (A) using a multiplication factor of 3.7419. (C) Normalized fractions of A-tracts (closed circles) and G-tracts (open circles) displaying +/−1 bp slippage, obtained by dividing the number of events by both the number of tracts in hg19 and tract length. (D) Indels at A-tracts, calculated as described in Figure 3C. (E) Indels at G-tracts, calculated as described in Figure3C. (F) Heatmap representation of insertions along G-tracts, as described in Figure 3E.

 With respect to slippage, the fractions for A-tracts elicited an excess at lengths 9 and 10 bp relative to the 1KGP dataset, which was also due to the MSI-containing dataset. For G-tracts, the fractions peaked at length 8, as for the 1KGP dataset (Figures 3A and 6C), implying that the propensity to undergo slippage is indistinguishable between the germline and soma. Indels were also more abundant at flanking base pairs than along the tracts (Figure 6D and E), particularly for G-tracts of length >7, similar to the 1KGP dataset (Figure 3C and D). Detailed analyses of insertions revealed that both G1 and the preceding position were the most significant sites of mutation (F-values up to 0.08 at G1 for tracts of length 8) (Figure 6F). Thus, the 5′ end of long G-tracts is the most susceptible site for both SBSs and insertions in cancer genomes, in contrast to the germline where these occur within the runs, typically at G2–3.

We also extracted the mutated A- and G-tracts from the Human Gene Mutation Database (HGMD), a collection of >150,000 germline gene mutations associated with human inherited disease. A total of 1519 genes were mutated at A- or G-tracts out of a total of 3972 (38%); 3480 SBSs and 2866 slippage events were noted within these tracts, 85 and 46% of which were predicted to be disease-causing, respectively (Figure 7A and Supplementary Table S1). Ranking genes by the number of literature reports indicated that among the top 10 entries three were associated with cancer (BRCA1, BRCA2 and APC), two with hemophilia (F8 and F9), four with debilitating lesions of the skin (COL71A), muscle (DMD), lung (CFTR) and kidney (PKD1), with one causing hypercholesterolemia (LDLR) (Figure 7B). Thus, mutations within A- and G-tracts carry a high social burden by contributing to some of the most common human pathological conditions.

Figure 7.

Mutation patterns in HGMD and model for sequence context-dependent changes. (A) Number of germline SBSs and slippage events (Slip.) at A- and G-tracts in HGMD. Gene alterations were classified as disease-causing mutation (DM), likely disease-causing mutation (DM?), disease-associated and putatively functional polymorphism (DFP), disease-associated polymorphism with additional supporting functional evidence (DP) and invitro/laboratory orinvivo functional polymorphism (FP). Codon changes (SIFT predictor) were classified as damaging (d), null (n), tolerated (t) and low-confidence prediction (l). (B) The 10 most commonly reported genes in HGMD with mutations at A- and G-tracts. Various mutated tracts were generally reported for the same gene in different reports. (C) Mutation spectra for SBSs at A- (left) and G-tracts (right) in HGMD. Percent values were obtained by dividing the total number of SBSs at each position by the number of tracts in hg19 exons. A|G→T (black), A|G→C (red), A→G (green), G→A (cyan). (D) Normalized fractions of A-tracts (closed circles) and G-tracts (open circles) displaying +/−1 bp slippage, obtained by dividing the total number of events by the number of tracts in hg19 exons and by tract length. (E) Model for sequence context-dependent changes at A-tracts (left) and G-tracts (right). *, site of base modification.

 For both A- and G-tracts, SBSs occurred mostly at tract lengths of 4–7, with patterns more similar to those in the 1KGP than in the cancer datasets, both with respect to the location of the most mutable positions (first and last As and first/second Gs) and the types of base substitution (A→T and G→H) (Figure 7C and Supplementary Figure S6F). Likewise, slippage events peaked at tract lengths of 7–9 as observed in the 1KGP dataset (Figure 7D). In summary, the patterns of both SBSs and slippage in the HGMD dataset followed the trend observed in the 1KGP dataset, suggesting that germline variants at mononucleotide repeats leading to either population variation or human inherited disease may have arisen through similar mechanisms.
DISCUSSION

Why are specific A:T and G:C base pairs within A- and G-tracts more susceptible to sequence changes than their identical neighbors? For A-tracts, bp flexibility may play a role. Chemical damage to DNA, such as by hydroxyl radicals has been shown to be proportional to the geometrical solvent-accessible surface of the atomic groups, which increases with DNA flexibility (43). Along A-tracts flexibility is restricted, but it is high at both the 5′ and 3′ junctions. Thus, the fact that the highest rates of mutation coincide with the highest degree of flexibility at the 5′-TA-3′ bp step is consistent with the view that this position may be susceptible to DNA damage as a result of flexibility. Other sources of DNA dynamics are also likely to be relevant, such as sugar flexibility at the junctions, which increases with tract length (44). Chemical modification at these junctions may then lead to base substitution and indels, the latter as a result of strand breaks.

With respect to SNV mutation spectra, these were found mostly in the direction of flanking base composition above a length of 7–8 bp. We interpret this behavior in terms of DNA slippage along A-tracts when attempts are made during translesion synthesis (TLS) to bypass a damaged site (Figure 7Ei). Two scenarios may be considered to account for A→T transitions at A1. In the first, the last tract-template base would loop out into the polymerase active site permitting base-pairing and strand elongation (Figure 7Eii) using the tract-flanking base as a template (34,4546). In the second (Figure 7Eiii), slippage would occur behind the polymerase, prompting extension past the newly created A*:T mispair generated by primer/template misalignment. Either pathway would yield a common intermediate (Figure 7Eiv) that contains the base complementary to the junction across from the damaged site upon slippage resolution (34). Following DNA synthesis (S) and/or repair (R) (Figure 7Ev and vi), this mispair will generate a base change that is always identical to the tract-flanking base.

For G-tracts, the high rates of G→T transversions at G1 in cancer genomes are also consistent with preferred chemical attack at this site due to high flexibility (Figure 7F top). Direct chemical attack at a guanine is known to result in stable products, such as 8-oxo-G and Fapy-G, both of which are known to yield G→T transversions (4750). Thus, G1 may be the most susceptible site for such reactions for G-tracts of lengths ≥7 (Figure 7Fright), which in cancer genomes would become a mutation hotspot. In the germline, SNVs peaked inside G-tract base pairs, while mutational spectra were insensitive to flanking base composition; these events are inconsistent with a role for template misalignment and slippage as noted for A-tracts. Rather, the correspondence between hotspot mutations at G2–3 and G5 and the QM/MM simulations suggest a role for charge transfer. A large body of work during the past 20 years using computational, theoretical chemistry and biophysical techniques on short oligonucleotides, has shown that guanine is the most easily oxidizable base in DNA and that indeed a guanine radical cation can be generated through long-range hole transfer from an oxidant via one-electron oxidation mechanisms (5155). GGG triplets were found to act as the most effective traps in hole transfer by both experimental and theoretical work (5659), demonstrating that the resulting guanine radical cation (or its neutral deprotonated form) became rather delocalized, but it preferentially centered at the first and second G. These well-established patterns of chemical reactivity are consistent with our experimental observation of high mutation frequencies at G1 for short G-tracts and the results from QM/MM simulations on G6. For longer tracts, the downstream shift in mutation hotspots, i.e., G2–3 and G5, also correlate well with the charge localization predicted from QM/MM simulations, which explicitly included solvent effects and structural fluctuations. Thus, in conjunction with the constrained density functional theory (60), both the neutral and oxidized forms of a guanine nucleobase can be reliably constructed to infer the accurate determination of mutational patterns of mononucleotide repeats in human genomic DNA.

The compact organization of the sperm genome (61), and presumably low levels of oxidative stress in the germline, may enable guanine oxidization through one-electron oxidation reactions rather than by direct chemical attack, thereby favoring the formation of radical cations. A charge injected at G1 by electron loss would then migrate to neighboring guanines and localize at sites of low IP, such as G2 (Figure 7F left). Guanine radical cations are known to readily undergo further chemical modification leading to products such as 8-oxo-G, oxazolone, imidazolone, guanidinohydantoin, and spiroiminodyhydantoin (62) (M in Figure 7F), to yield G→T, G→C and G→A substitutions (4,63). Our model is in line with recent observations in which mutations at guanines within short G-runs (1–4 bp) correlate with sequence-dependent IPs at the target guanine in cancer genomes (9). Interestingly, these correlations were not observed in the germline (9). We interpret these composite observations as follows. The IP values for G-runs have been shown to decrease asymptotically with tract length, although the absolute values vary according to the methods and assumptions used (we obtained a value of 5.43 eV for both G[6] and G[9]) (64,65). We suggest that short G-runs with high IPs undergo one-electron oxidation reactions in the oxidative environment of cancer cells but would be refractory to such a mechanism in the germline (Figure 7Fright yellow and left white sectors). As length increases and IP values fall, G-runs would be attacked directly by oxidants abundant in tumor cells (Figure 7F orange sector), whereas oxidation will be limited to electron loss in the germline environment (Figure 7F left yellow sector).

These models (template misalignment for A-tracts and charge transfer for G-tracts) suggest a more complex scenario for mechanisms underlying mononucleotide repeat polymorphism in the human population than recently proposed (13), in which nucleotide misincorporation by error-prone polymerases is proposed as a primary source of mutations at both A- and G-tracts. As already stated, the directionality of SNVs toward tract-flanking bases in A-tracts and the hotspot mutations at G2–3, supports multiple and distinct mechanisms of base substitution at mononucleotide repeats.

Our analyses highlight additional information, including the lack of mutations in the direction of tract-base composition for base pairs flanking long tracts, the association with gene expression and the preference of guanines for the inner NCP surface, and extend prior observations (12) such as the bell-shape character of base substitution and slippage, whose mechanisms remain to be fully clarified. Finally, we document the contribution of mononucleotide mutagenesis to key aspects of human pathology beyond the well-established MSI instability in cancer (15), including hemophilia and tissue degeneration. Our collective work supports the conclusion that as the human genome undergoes evolutionary diversification and along the way suffers disease-associated mutations, oxidation reactions including charge transfer may play a prominent role.

SUPPLEMENTARY DATA

Supplementary Data are available at NAR Online.

 

 

Mutation analyses and prenatal diagnosis in families of X-linked severe combined immunodeficiency caused by IL2Rγ gene novel mutation

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Genet. Mol. Res. 14 (2): 6164 – 6172   DOI: 10.4238/2015.June.9.2
Severe combined immunodeficiency diseases (SCIDs) are a group of primary immunodeficiency diseases characterized by a severe lack of T cells (or T cell dysfunction) caused by various gene abnormalities and accompanied by B cell dysfunction (WHO, 1992; Buckley et al., 1997). The incidence rates in infants were 1/75,000-1/10,0000 (WHO, 1992), but no morbidity statistics are available in China. The 2 genetic modes of SCID include X-linked recessive and autosomal recessive genetic inheritance. X-linked severe combined immunodeficiency (X-SCID) is the most common form, accounting for 50-60% of SCID cases (Noguchi et al., 1993). Immune system abnormalities in patients with X-SCID include T-B+NK-, in which T cells (CD3+) and natural killer (NK) cells (CD16+/CD56+) are absent or significantly reduced, and the number of B cells (CD19+) is normal or increased, causing reduced immunoglobulin production and class switching disorder (Buckley, 2004; Fischer et al., 2005). The IL- 2Rg gene mutation has been confirmed to be a major cause of X-SCID (Noguchi et al., 1993). In recent years, great progress has been made in understanding the pathogenesis of primary immunodeficiency disease and its application in clinical treatment, particularly regarding the development of critical care medicine and immune reconstruction technology. With timely control of infection and early bone marrow or stem cell transplantation, X-SCID patients can be treated, prolonging survival time. Therefore, early diagnosis of X-SCID is very important for patient treatment. Gene diagnosis has become a better early diagnosis or differential diagnosis method. In addition, familial X-SCID brings a great psychological burden to the relatives of patients. Ordinary chromosome analysis and immunological evaluation cannot be used for female carrier identification and fetal diagnosis, and gene diagnosis is the most effective method of carrier detection and prenatal diagnosis. In this study, we detected mutations in 2 families with X-SCID and identified 2 novel mutations, confirming the X-SCID pedigrees. Prenatal diagnosis was performed for the pregnant fetus in the mother of one of the probands based on gene diagnosis. Female individuals in this family were subjected to carrier detection.
IL2Rg gene mutation test Direct sequencing of 1-8 exons and the flanking region of the IL2Rg gene by PCR in family 1 showed that the 3rd exon of the proband contained the c.361-363delGAG heterozygous deletion mutation, which led to deletion of the 121st amino acid glutamate (p.E121del) in its coding product. There were no sequence variations in other coding regions or in the shear zone. The proband’s mother carried the same heterozygous mutation, while his father did not carry the mutation site (Figure 2a, b, c). This mutation was not observed in any cases of the control group, and this family was identified as an X-SCID family. The c.510-511insGAACT insertion heterozygous mutation was present in the 4th exon of the proband’s mother in family 2. This mutation was a 5-base repeat of GAACT, resulting in a change in amino acid 173 from tryptophan into a stop codon (p.W173X). While there were no sequence variations in other coding regions or in the shear zone, the patient’s father did not carry the mutation (see Figure 2d, e). We did not find this mutation in the healthy control group. We presumed that the 4th exon of the deceased child in family 2 contained the c.510-511insGAACT insertion mutation, leading to X-SCID symptoms, and thus we speculated that this family was an X-SCID pedigree. Prenatal diagnosis We verified the chorionic villus status of the fetus in family 1 using the PowerPlex 16 HS System kit. The results of prenatal diagnosis showed that the fetal tissue contained no maternal contamination and that this fetus was female. The results of prenatal diagnosis showed that there was no c.361-363delGAG (p.E121del) heterozygous mutation in the female fetus of family 1.
Figure 2. Sequencing graph of IL2Rg gene in 2 pedigrees with X-chain severe combined immunodeficiency. a.-c. Family 1. a. Normal control (rectangle indicates 3 edentulous bases of this patient). b. Proband carrying the c.361- 363delGAG (p.E121del) mutation (arrow indicates deletion of fragment connection sites). c. The proband’s mother contained a c.361-363delGAG (p.E121del) heterozygous mutation (arrow). d.-e. Family 2. d. The proband’s mother carried the c.510-511insGAACT (p.W173X) heterozygous mutation (arrow indicates that the reverse sequencing graph was positive). e. Normal control (rectangular box indicates 2 normal copies of GAACT (the mutation fragment was 3 copies). Carrier detection results For the c.361-363delGAG (p.E121del) site, the gene analysis results of the female individual in family 1 showed that I2 (proband’s grandmother) was a heterozygous carrier and that II3 (proband’s aunt) was a non-carrier and had no mutations.
IL-2 can combine with the IL-2 receptor (IL-2R) of the immune cell membrane. IL-2R is composed of 3 subunits, including the IL-2Ra chain (CD25), IL-2Rb chain (CD122), and IL- 2Rg chain (CD132). IL-2Rg functional units in common with IL-4, IL-7, IL-9, IL-15, IL-21, and other cytokine receptors, and these regions are referred to as the total chain (Li et al., 2000). The IL-2Rg chain can maintain the integrity of the IL-2R complex and is required for the internalization of the IL-2/IL-2R complex; it is also the link that contacts the cell membrane surface factor region and downstream cell signal transduction molecules. Therefore, the integrity of the IL-2Rg chain is vital for the immune function of an organism (Malka et al., 2008; Shi et al., 2009).
Mutations in the IL2Rg gene, which encodes IL-2Rg, were identified to be a major cause of X-SCID in 1993 (Noguchi et al., 1993). The IL2Rg gene is located on chromosome X q21.3-22, is 37.5 kb length, and contains 8 exons, which encode 369 IL-2Rg amino acids. The IL2Rg chain exhibits varying structural regions, such as the signal peptide [amino acids (AA) 1-22], extracellular domain (AA 23-262), transmembrane region (AA 263-283), and intracellular region (AA 284-369). The WSXWS motif is located in the extracellular region (AA 237-241), while Box 1 is located in the intracellular region (AA 286-294).
By the end of 2013, the Human Gene Mutation Database contained a total of 200 mutations in the IL2Rg gene (HGMD Professional 2013.4). The most common mutation types in the IL2Rg gene were the missense or nonsense mutations, which result from single base changes. A total of 100 missense or nonsense mutations have been identified, followed by insertion or deletion mutations in a total of 50 species. The 3rd most common type of mutations includes shear mutations in approximately 30 species. Eight exons contained mutations, and mutations in 3rd or 4th exons were the highest, accounting for a total mutation rate of 43% (86/200). According to the X-SCID gene database (IL2RGbase) (http://research.nhgri. nih.gov/scid/), the gene mutations in IL2Rg mainly occurred in the extracellular region of the IL2Rg chain (Fugmann et al., 1998). Zhang et al. (2013) reported that the IL2Rg gene mutations in 10 patients with X-SCID in China were located in the extracellular region. Two mutations reported in our study were also located in the extracellular region. The mutation of IL2Rg gene in family 1 was a codon mutation in the 3rd exon, resulting in a 3-base deletion. The c.361-363delGAG (p.E121del) mutation was located in the extracellular area of the IL- 2Rg subunit, and we inferred that the 121 glutamate deletion caused by the mutation would lead to changes in the structure of the peptide chain, affecting signal transmission and resulting in serious symptoms. The mutation of family 2 was a GAACT repeat of ILR2g gene; this repeat of 5 bases resulted in 173 codon changes from tryptophan into a stop codon. Generation of the peptide chain with the mutation lacked 196 amino acids compared to the normal chain, including the intracellular, transmembrane, and some extracellular regions, directly affecting the structure and function of receptors and causing disease. No studies have been reported regarding these 2 mutations. We combined with the mutation characteristics and clinical manifestations and diagnosed family 1 as X-SCID pedigrees. Although the patient in family 2 was deceased, it can be speculated that the 2 deceased patients in family 2 were X-SCID pedigrees caused by c.510-511insGAACT (W173X).
Prenatal diagnosis can accurately identify fetal situations and be used to avoid birth defects, which can also ease the anxiety of the pregnant mother. Gene diagnosis for pedigrees of patients based on DNA samples has advanced recently, particularly with the application of high-throughput sequencing technology (Alsina et al., 2013). We can now perform gene analysis for varied clinical infectious diseases for differential diagnosis. However, the effectiveness of prenatal diagnosis for pedigrees in which the proband is dead remains unclear. Because the gene mutations in the proband is unknown in these cases, the patient’s situation was only inferred by his mother’s genotypes. However, we considered that for the deceased, if we can define the mother was a pathogenic gene carrier, even if the proband is not X-SCID, the woman also has a risk of having X-SCID children and this pedigree may be X-linked recessive inheritance. Prenatal diagnosis may provide a choice for preventing the birth of patients in these families in the premise of informed consent.
Gene diagnosis of IL2Rg can also be used for carrier detection of suspected females in the family.
In the present study, we performed carrier detection of the patient’s grandmother and aunt in family 1 and determined that the patient’s pathogenic mutations were from his grandmother. His aunt did not inherit the pathogenic gene, and thus she was a non-carrier and her fertility will not be affected. In this study, we used direct sequencing of PCR products and identified IL2Rg gene mutations in 2 pedigrees with X-SCID. We found 2 unreported mutations in the IL2Rg gene, and prenatal diagnosis and carrier detection were conducted in 1 X-SCID family. Because the incidence rate of X-SCID is extremely low, it is difficult to promote the widespread use and application of genetic diagnosis. However, this study may provide some implications for the diagnosis of infants with immunodeficiency, and gene diagnosis techniques such as conventional or high-throughput sequencing should be used as soon as possible during pregnancy, which can be used to guide treatment. This method can also provide reliable prenatal diagnosis and carrier detection service for these families.
MEF2A gene mutations and susceptibility to coronary artery disease in the Chinese population
J. Li1 , H.-X. Chen2 , J.-G. Yang3 , W. Li3 , R. Du3 and L. Tian3       DOI http://dx.doi.org/10.4238/2014.October.20.15
Coronary artery disease (CAD) has high morbidity and mortality rates worldwide. Thus, the pathogenesis of CAD has long been the focus of medical studies. Myocyte enhancer factor 2A (MEF2A) was first discovered as a CAD-related gene by Wang (2005) and Wang et al. (2003, 2005). Three mutation points in exon 7 of MEF2A were subsequently identified by Bhagavatula et al. (2004); however, Altshuler and Hirschhorn (2005) and Weng et al. (2005) predicted that the MEF2A gene lacked mutations. Zhou et al. (2006a,b) analyzed the mutations and polymorphisms in exons 7 and 11 of the MEF2A gene in the Han population in Beijing, and various rare mutations were found in exon 11 rather than in exon 7. The clinical significance of specific 21-bp deletions in MEF2A was also explored, and previous studies have shown mixed results. In this study, polymerase chain reaction-singlestrand conformation polymorphism (PCR-SSCP) and DNA sequencing were used to detect exon 11 of the MEF2A gene in samples collected from 210 CAD patients and 190 healthy controls and to investigate the function of the MEF2A gene in CAD pathogenesis and their correlation.
CAD, a common disease in China, is induced by multiple factors, such as genetics, the environment, and lifestyle. Thus, a multi-faceted approach is necessary in the study of CAD pathogenesis, particularly in molecular biology research, which is important for developing comprehensive treatment of CAD based on gene therapy. The MEF2A gene was first identified as a CAD-related gene through linkage analysis of a large family with CAD (9 of 13 patients developed MI) in 2003.
In this study, we found the following mutations: 1) codon 451G/T (147191) heterozygous or homozygous mutation; 2) loss of 1 (Q), 2 (QQ), 3 (QQP), 6 (425QQQQQQ430), and 7 (424QQQQQQQ430) amino acids (147108-147131); and 3) codon 435G/A (147143) heterozygous mutation. Among these mutations, the synonymous mutation at locus 147191 was confirmed by reference to the National Center for Biotechnology Information (NCBI) database to be a single nucleotide polymorphism, which was also demonstrated in our study by the extensive presence of this polymorphism in healthy controls. However, the heterozygous mutation at locus 147143 was only found in the genomes of CAD patients, and was therefore identified as a mutation.
Given that MEF2A is a CAD-related gene, the results of various studies are controversial among several countries. Weng et al. (2005) screened gene mutations in exon 11 of the MEF2A gene from 300 CAD patients and 1500 healthy controls. They hypothesized that the changes in 5-12 CAG repeats are genetic polymorphisms and that the 21-base deletion in exon 11 of the MEF2A gene did not induce autosomal dominant genetic CAD. Gonzalez et al. (2006) suggested that the CAG repeat polymorphism was independent of MI susceptibility in Spanish patients. Kajimoto et al. (2005) reported that the CAG repeat sequence was not correlated with MI susceptibility in Japanese patients. Horan et al. (2006) also found that the CAG repeat sequence was not associated with the susceptibility to early-onset familial CAD in an Irish population. Hsu et al. (2010) identified no correlation between the CAG repeat sequence and CAD susceptibility in the Taiwanese population. Dai et al. (2010) found that the structural change in exon 11 was not related to CAD in the Chinese Han population. Lieb et al. (2008) and Guella et al. (2009) hypothesized that MEF2A was independent of CAD. However, Yuan et al. (2006) and Han et al. (2007) suggested that the CAG repeat sequence was correlated with CAD because 9 CAG repeats was an independent predictor of CAD. Elhawari et al. (2010) and Maiolino et al. (2011) suggested that MEF2A is a susceptibility gene for CAD. Dai et al. (2013) showed that mutations in exon 12 are associated with the early onset of CAD in the Chinese population. Liu et al. (2012) failed to demonstrate a correlation between the CAG repeat sequence and CAD through case-control analysis, systematic review, and meta-analysis, but found that the 21- base deletion in exon 11 was strongly associated with CAD, and that genetic variations in MEF2A may be a relatively rare, but specific, pathogenic gene for CAD/MI. Kajimoto et al. (2005) reported 4-15 CAG repeats. However, only 4-11 CAG repeats were observed in our study, possibly because of genetic differences in patients in this study. Eleven CAG repeats were observed in most samples from the control group, and the proportion of 10, 9, and 8 repeats exceeded 1%. The heterozygous mutation at 147143, as well as the 4 and 5 CAG repeats, was only observed in CAD patients. Thus, we speculated that the CAG repeat sequence is correlated with CAD susceptibility, and the presence of 4 or 5 repeats may be a risk factor for CAD, which was inconsistent with the results obtained by Han et al. (2007). The inconsistency in these results may be explained by the differences in subjects and sample sizes among studies.
Impact of glucocerebrosidase mutations on motor and nonmotor complications in Parkinson’s disease

Homozygous and compound heterozygous mutations in GBA encoding glucocerebrosidase lead to Gaucher disease (GD). A link between heterozygous GBAmutations and Parkinson’s disease (PD) has been suggested ( Bembi et al., 2003,Goker-Alpan et al., 2004, Halperin et al., 2006, Machaczka et al., 1999, Neudorfer et al., 1996, Tayebi et al., 2001 and Tayebi et al., 2003). In 2009, a 16-center worldwide analysis of GBA revealed that heterozygous GBA mutation carriers have a strong risk of PD ( Sidransky et al., 2009).

In addition, heterozygote GBA mutations not only carry a risk for PD development but also the possibility of some risk burden on the progression of PD clinical course. In cross-sectional analyses of GBA mutations in PD patients, earlier disease onset, increased cognitive impairment, a greater family history of PD, and more frequent pain were reported in patients with mutations, compared with no mutations ( Chahine et al., 2013,Clark et al., 2007, Gan-Or et al., 2008, Kresojevic et al., 2015, Lwin et al., 2004, Malec-Litwinowicz et al., 2014, Mitsui et al., 2009, Neumann et al., 2009, Nichols et al., 2009,Seto-Salvia et al., 2012, Sidransky et al., 2009, Swan and Saunders-Pullman, 2013 and Wang et al., 2012). Recently, a few prospective studies have investigated clinical features of PD with GBA and showed a more rapid progression of motor impairment and cognitive decline in GBA mutation cases than in PD controls ( Beavan et al., 2015, Brockmann et al., 2015 and Winder-Rhodes et al., 2013). However, in terms of motor complications such as wearing-off and dyskinesia, no studies exist in the longitudinal course of PD with GBA mutations.

Here, we conducted a multicenter retrospective cohort analysis, and the data were investigated by survival time analysis to show the impact of GBA mutations on PD clinical course. We also investigated regional cerebral blood flow (rCBF) and cardiac sympathetic nerve degeneration of subjects with GBA mutations, compared with matched PD controls.

3.1. Subjects

Among the 224 eligible PD patients (the subjects were not related to each other), 9 subjects were excluded from the analysis (4 due to multiple system atrophy findings on subsequent brain MRI and 5 because of insufficient clinical information). Therefore, 215 PD patients [female, 52.1%; age, 66.7 ± 10.8 (mean ± standard deviation)] were analyzed. For non-PD healthy controls, 126 patients’ spouses (female, 58.7%; age, 67.3 ± 10.3) without a family history of PD or GD were enrolled.

3.2. GBA mutations and risk ratios for PD

In the PD subjects, we identified 10 nonsynonymous and 2 synonymous GBA variants. Within the nonsynonymous variants, 7 mutations were previously reported in GD [R120W, L444P-A456P-V460 (RecNciI), L444P, D409H, A384D, D380N, and444L(1447-1466 del 20, insTG)] as GD-associated mutations. Three nonsynonymous mutations have never been reported in GD patients [I(-20)V, I489V, and there was one novel mutation (Y11H)].

GD-associated GBA mutations were found in 19 of the 215 (8.8%) PD patients but none in the healthy controls. The risk of PD development relative to these GD-associated mutations was estimated as an OR of 25.1 [95% confidence interval (CI), 1.50–420,p = 0.0001] with 0-cell correction. The nonsynonymous mutations that were not reported in GD patients had no association with PD development (p = 0.506; OR, 1.3; 95% CI, 0.7–2.6) ( Table 1). Four subjects had double mutations. For subsequent analyses, 2 subjects with double mutations of I (-20)V and K466K were adopted to the group of mutations unreported in GD, and 2 subjects with double mutations of R120W and I(-20)V, and of R120W and L336L were adopted to the group of GD-associated mutations.

Table 1.Frequency of glucocerebrosidase gene allele in Parkinson’s disease patients and controls

Allele name PD (n = 215) Controls (n = 126) p Odds ratio (95% CI)
GD-associated mutations
 R120W 7a 0 0.050 9.1 (0.5–160.8)
 RecNciI (L444P-A456P-V460) 4 0
 L444P 4 0
 D409H 1 0
 A384D 1 0
 D380N 1 0
444L(1447-1466 del 20, insTG) 1 0
 Subtotal, n (%) 19 (8.8%) 0 (0%) <0.001 25.1 (1.5–419.8)b
Nonsynonymous mutations not reported in GD
 I(-20)V 27a 13 0.603 1.3 (0.6–2.5)
 I489V 3 0
 Y11Hc 0 1
 Subtotal, n (%) 30 (14.0%) 14 (11.1%) 0.506 1.3 (0.7–2.6)
Synonymous, n
 K466K 2a 1
 L336L 1a 0
Allele names refer to the processed protein (excluding the 39-residue signal peptide).

Key: CI, confidence interval; GD, Gaucher disease; PD, Parkinson’s disease.

a Four subjects had double mutations; 2 of I(-20)V and K466K, 1 of I(-20)V and R120W, and 1 of R120W and L336L.
b Odds ratio was calculated by adding 0.5 to each value.
c Novel mutation.
3.3. Clinical features of PD patients by GBA mutation groups

The clinical features of PD patients with GD-associated mutations, those with mutations unreported in GD, and those without mutations are shown in Table 2. In the GD-associated mutation group, females, those with a family history and those with dementia (DSM IV) were significantly more frequent than those in the no-mutation group (p = 0.047, 0.012, and 0.020, respectively). The age of PD onset was lower in patients with GD-associated mutations (55.2 ± 9.9 years ± standard deviation), compared with those without mutations (59.3 ± 11.5), although the statistical difference was not significant. There were no differences in clinical manifestations between subjects with mutations unreported in GD and those without mutations, except for dopamine agonist dosage (p = 0.026) ( Table 2).

Table 2.Epidemiological and clinical features of PD patients with Gaucher disease–associated GBA mutations, those with mutations previously unreported in GD and those without mutations

Variables Total n = 215 Mutation (-) GD-associated mutations


Mutations unreported in GD


167 19a pb 29c pd
Sex Female, n (%) 83 (49.7) 14 (73.7) 0.047 15 (51.7) ns
Age Mean (SD) 67.0 (10.8) 62.2 (10.7) 0.063e 67.5 (11.2) nsf
Disease duration (y) Mean (SD) 7.7 (5.5) 6.9 (4.6) nsf 7.2 (4.9) nsf
Onset age Mean (SD) 59.3 (11.5) 55.2 (9.9) ns 60.3 (11.8) ns
Family history Yes, n (%) 17 (11.0)g 6 (31.6) 0.012 0 (0.0) ns
Dementia (DSM-IV) Yes, n (%) 29 (17.4) 9 (47.4) 0.020 5 (17.2) ns
MMSE Mean (SD) 25.8 (5.4)h 23.3 (7.7) nsf 27.0 (3.4)i nsf
Onset symptom (tremor vs. others) Tremor, n (%) 78 (46.8) 9 (47.4) ns 15 (51.7) ns
Modified H-Y on (<3 vs. ≥3) ≥3, n (%) 82 (49.1) 14 (73.7) 0.042 16 (55.2) ns
UPDRS part 3 Mean (SD) 23.6 (12.2)j 28.5 (13.8) nsf 21.9 (8.7) nsf
Wearing off Yes, n (%) 70 (41.9) 9 (47.4) ns 13 (44.8) ns
Dyskinesia Yes, n (%) 49 (29.3) 8 (42.1) ns 8 (27.6) ns
Mood disorder Yes, n (%) 43 (25.7) 8 (42.1) ns 7 (24.1) ns
Orthostatic hypotension symptom Yes, n (%) 21 (12.6) 5 (26.3) ns 7 (24.1) ns
Psychosis history Yes, n (%) 59 (35.3) 10 (52.6) ns 7 (24.1) ns
ICD history Yes, n (%) 8 (4.8) 1 (5.3) ns 1 (3.4) ns
Stereotactic brain surgery for PD Yes, n (%) 4 (2.4) 0 (0.0) ns 0 (0.0) ns
Agonist LED mg/d Mean (SD) 92.8 (114.2) 72.1 (137.7) nse 163.7 (155.6) 0.026e
Levodopa LED mg/d Mean (SD) 400.7 (184.2) 456.7 (206.9) nsf 369.2 (230.3) nse
Total LED mg/d Mean (SD) 496.4 (233.7) 537.9 (258.9) nsf 525.7 (287.4) nsf
Categorical data were examined by Fisher’s exact test.

Key: DSM-IV, Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition; GBA, glucocerebrosidase gene; GD, Gaucher disease; H-Y, Hoehn and Yahr; ICD, impulse control disorder; LED, levodopa equivalent dose; ns, not significant; MMSE, Mini-Mental State Examination; PD, Parkinson’s disease; SD, standard deviation; UPDRS, Unified Parkinson’s Disease Rating Scale.

a Including a double-mutation subject (with a mutation unreported in GD).
b GD-associated mutations versus mutation (-).
c Two subjects with double mutation, including GD-associated mutations, were assigned to GD-associated mutation group.
d Other mutations versus mutation (-).
e Examined by Student t test after Levene’s test for equality of variances.
f Examined by Mann-Whitney U-test because of non-Gaussian distribution.
g    n = 155 due to 10 missing data.
h    n = 164 due to 3 missing data.
i     n = 28 due to 1 missing datum.
j     n = 165 due to 2 missing data.

3.4. Survival time analyses to develop dementia, psychosis, dyskinesia, and wearing-off

Time to develop clinical outcomes (dementia, psychosis, dyskinesia, and wearing-off) was compared in 19 subjects with GD-associated mutations, 29 with mutations unreported in GD, and 167 without mutation. The median observation time was 6.0 years. The subjects with GD-associated mutations showed a significantly earlier development of dementia and psychosis, compared with subjects without mutation (p < 0.001 and p = 0.017) ( Supplementary Table e-1, Fig. 1A and B). We rereviewed the clinical record of the subject who showed early dementia (defined by DSM IV) ( Fig. 1A) and made sure it did not satisfy the criteria of DLB ( McKeith et al., 2005).

Kaplan–Meier curves of dementia and psychosis in Parkinson's disease (PD) ...

Fig. 1.

Kaplan–Meier curves of dementia and psychosis in Parkinson’s disease (PD) patients with Gaucher disease (GD)-associated glucocerebrosidase gene (GBA) mutations and those without mutations. PD patients with GD-associated GBA mutations and those without GBA mutations were compared to investigate the time taken to develop dementia (A) and psychosis (B). Because of insufficient information in several patients, the numbers in each analysis were different. The patients with and without mutations were 17 and 165 (A), 18 and 165 (B) against a total of 19 and 167. DSM IV, Diagnostic and Statistical Manual of Mental Disorders, revised fourth edition. p-Values were calculated by log-rank tests.

The associations of GBA mutations and these symptoms were estimated as HRs, adjusting for sex and age at PD onset. HRs were 8.3 for dementia (95% CI, 3.3–20.9; p < 0.001) and 3.1 for psychosis (95% CI, 1.5–6.4; p = 0.002). The time until development of wearing-off and dyskinesia complications was not statistically significant, with HRs of 1.5 (95% CI, 0.8–3.1; p = 0.219) and 1.9 (95% CI, 0.9–4.1; p = 0.086) ( Table 3).

Table 3.Hazard ratios of GBA pathogenic mutations for clinical symptoms

Model Clinical feature Hazard ratio 95% CI p
1 Dementia (DSM-IV) 8.3 3.3–20.9 <0.001
2 Psychosis 3.1 1.5–6.4 0.002
3 Wearing-off 1.5 0.8–3.1 0.219
4 Dyskinesia 1.9 0.9–4.1 0.086
Each model was adjusted for sex and age at onset.

Key: CI, confidence interval; DSM-IV; The Diagnostic and Statistical Manual of Mental Disorders part 1IV; GBA, glucocerebrosidase.

Subjects with mutations unreported in GD did not show significant differences in time to develop all 4 outcomes, compared with no mutation subjects. Therefore, subjects with GD-unreported mutations were regarded as subjects without GBA mutations in further analyses.

3.5. rCBF on SPECT in patients with GD-associated GBA mutations

We conducted pixel-by-pixel comparisons of rCBF on SPECT between PD subjects with mutations (cases) and sex-, age-, and disease duration-matched PD subjects without any mutations in GBA (controls). Four controls were adopted for each case (except for a 34-year-old female case who was matched to a control), and in total 12 cases (female 50%, age at SPECT mean ± standard error (SE); 58.9 ± 3.3 years, disease duration at SPECT 7.3 ± 1.5 years) and 45 controls (female 64.4%, age at SPECT mean ± SE; 61.0 ± 1.3 years, disease duration at SPECT 7.1 ± 0.7 years) were analyzed. As a result, a significantly lower rCBF was seen in the cases compared to the controls in the bilateral parietal cortex, including the precuneus ( Fig. 2).

Regional cerebral blood flow in the group with GD-associated mutations compared ...

Fig. 2.

Regional cerebral blood flow in the group with GD-associated mutations compared with the matched Parkinson’s disease group without mutations. Regions with lower regional cerebral blood flow in the group with GD-associated mutations displayed on an anatomic reference map. Abbreviation: GD, Gaucher disease.

3.6. H/M ratios on MIBG scintigraphy in patients with GD-associated GBA mutations

Cardiac MIBG scintigraphy visualizes catecholaminergic terminals in vivo that are reduced as well as brain dopaminergic neurons in PD patients. We also investigated MIBG scintigraphy between 16 cases (female 68.8%, age at examination mean ± SE; 60.2 ± 2.6 years, disease duration at examination 6.2 ± 1.2 years) and sex-, age- and disease duration-matched 61 controls [(63.8 %, age 62.0 ± 1.1 years, disease duration 5.5 ± 0.6 years) (1:4 except for 1 young 34-year-old female case who was matched to a control)]. In the results, both early and late H/M ratios declined in both groups and did not show any significant differences (p = 0.309 and 0.244) ( Supplementary Table e-2).

4. Discussion

4.1. Contributions of GD-associated GBA mutations to the development of PD

In the analysis of 215 PD patients and 126 non-PD controls, we identified 10 nonsynonymous heterozygous GBA mutations, including 1 novel mutation. Among these mutations, 7 were GD-associated, and the patients carrying these mutations represented 8.8% of the PD cohort. No significant association was found between the GD-unreported mutations and PD development, which suggests that only the GD-associated mutations are a genetic risk for PD. According to a worldwide multicenter analysis of 1883 fully sequenced PD patients, 7% of the GD-associated mutations are found in non-Ashkenazi Jewish PD patients ( Sidransky et al., 2009). Although the mutation frequency in the present study was similar to previous results, the OR of GD-associated heterozygous mutations (25.1) was significantly greater than the OR (5.43) of other ethnic cohorts (Sidransky et al., 2009) and was consistent with an OR of 28.0 from a previous Japanese report ( Mitsui et al., 2009). These results, taken together, suggest the possibility thatGBA mutations are at a distinct risk for PD in the Japanese population. However, a larger Japanese cohort study is required to confirm this.

4.2. Cross-sectional clinical figures of PD with GBA mutations

Before the survival time analyses, we investigated clinical features at enrollment between mutation groups. The lower onset age, more frequent family history and dementia, and worse disease severity of PD in patients with GBA mutations, compared with those without mutations, were consistent with previous cross-sectional case-control reports ( Anheim et al., 2012, Brockmann et al., 2011, Chahine et al., 2013, Lesage et al., 2011, Li et al., 2013, Mitsui et al., 2009, Neumann et al., 2009, Seto-Salvia et al., 2012 and Sidransky et al., 2009). In contrast, female-predominance (73.7%, p = 0.047) in patients with mutations observed in the present study is inconsistent ( Neumann et al., 2009 and Seto-Salvia et al., 2012).

4.3. Impact of GBA mutations on the clinical course of PD

To investigate the impact of GBA mutations on the clinical course of PD, a prospective-designed study over a long period is preferred. Although there has been a few longitudinally designed study to date, follow-up clinical data for a median of 6 years of 121 PD cases from a community-based incident cohort was recently reanalyzed; results demonstrate that progression to dementia defined by DSM IV (HR 5.7) and Hoehn and Yahr stage 3 (HR 3.2) are significantly earlier in 4 GBA mutation-carrier patients compared with 117 patients with wild-type GBA ( Winder-Rhodes et al., 2013). A 2-year follow-up clinical report of 28 heterozygous GBA carriers who were recruited from relatives of GD-patients shows slight but significant deterioration of cognition and smelling, compared to healthy controls ( Beavan et al., 2015). Brockmann et al. (2015)assessed motor and nonmotor symptoms including cognitive and mood disturbances for 3 years in 20 PD patients with GBA mutations and showed a more rapid disease progression of motor impairment and cognitive decline in GBA mutation cases comparing to sporadic PD controls. The current long-term retrospective cohort study up to 12 years reinforced these results. It revealed that dementia and psychosis developed significantly earlier in subjects with GD-associated mutations compared with those without mutation, and the HRs of GBA mutations were estimated at 8.3 for dementia and 23.1 for psychosis, with adjustments for sex and PD onset age. In contrast, the results showed no significant difference in developing wearing-off and dyskinesia.

In this study, we also investigated whether GD-unreported mutations affected the clinical course of PD. In both cross-sectional and survival time analyses, the mutations unreported in GD carried no increased burden on clinical symptoms such as dementia, psychosis, wearing-off, and dyskinesia.

4.4. Reduced rCBF in PD with GBA mutations compared with matched PD controls

We found a significantly decreased rCBF, reflecting decreased synaptic activity, in the bilateral parietal cortex including the precuneus, in subjects with GD-associated mutations compared with matched subjects without mutations. The pattern of reduced rCBF was very similar to the pattern of H215O positron-emission tomography that Goker-Alpan et.al. (2012) reported, showing decreased resting rCBF in the lateral parietal association cortex and the precuneus bilaterally in GD subjects with parkinsonism (7 subjects with homozygous or compound heterozygous GBA mutations), compared with 11 PD without GBA mutations. Results suggest that PD with heterozygous GBAmutations and GD patients presenting parkinsonism had a common reduced pattern of rCBF. Interestingly, in their study, rCBF in the precuneus—but not in the lateral parietal cortex—correlated with IQ, suggesting that the involvement of the precuneus is critical for defining GBA-associated patterns.

4.5. Reduced cardiac MIBG H/M ratios as well as matched PD controls

We also showed that cardiac MIBG H/M ratios in subjects with GD-associated mutations were lower than the cutoff point for PD discrimination (Sawada et al., 2009), suggesting that postganglionic sympathetic nerve terminals to the epicardium were denervated, as well as in PD without mutations.

4.6. Mechanisms of impact on PD clinical course by GD-associated GBA mutations

Experimental studies suggesting a bidirectional pathogenic loop between α-synuclein and glucocerebrosidase have been accumulated (Fishbein et al., 2014, Gegg et al., 2012, Mazzulli et al., 2011, Noelker et al., 2015, Schondorf et al., 2014 and Uemura et al., 2015). Loss of glucocerebrosidase function compromises α-synuclein degradation in lysosome, whereas aggregated α-synuclein inhibits normal lysosomal function of glucocerebrosidase. The pathogenic loop may facilitate neurodegeneration in GD-associated PD brain, resulting in early development of dementia or psychosis as shown in the present study. Several recent researches propose the possibility that the similar mechanism as in PD with GBA mutations exists even in idiopathic PD brain ( Alcalay et al., 2015, Chiasserini et al., 2015, Gegg et al., 2012 and Murphy et al., 2014). On the other hand, the impacts of GD-associated GBA mutations for the development of motor complications such as wearing-off and dyskinesia were not statistically significant, suggesting other pathophysiological mechanisms in the striatal circuit brought out after long-term therapy especially by l-dopa.

4.7. Limitations

Our study has several limitations. In the design of the study, we assumed that the sample size was 215 (PD patients) for survival time analyses and investigated 224 PD patients. We assumed that the mutation prevalence would be 9.4%, and in fact, we found 19 patients with mutations (8.5%) of the 224 patients. Based on these figures, we estimated the risk ratios of heterozygous GBA mutations for the risk of PD development and PD clinical symptoms as ORs in the cross-sectional multivariate analyses, although the 95% CIs were broad. More of subject numbers will be needed to determine robust risk ratios.

Comprehensive Genetic Characterization of a Spanish Brugada Syndrome Cohort

PLOS   Published: July 14, 2015   DOI: http://dx.doi.org:/10.1371/journal.pone.0132888

Brugada syndrome (BrS) was identified as a new clinical entity in 1992 [1]. Six years later, the first genetic basis for the disease was identified, with the discovery of genetic variations inSCN5A [2]. Nowadays, more than 300 pathogenic variations in this first gene are known to be associated with BrS [3]. SCN5A encodes for the α subunit of the cardiac voltage-dependent sodium channel (Nav1.5), which is responsible for inward sodium current (INa), and thus plays an essential role in phase 0 of the cardiac action potential (AP). Genetic variations in this gene can explain around 20–25% of BrS cases [3].

Since BrS was classified as a genetic disease, several other genes have been described to confer BrS-susceptibility [47]. Pathogenic variations have been mainly described in: 1) genes encoding proteins that modulate Nav1.5 function, and 2) other calcium and potassium channels and their regulatory subunits. All these proteins participate, either directly or indirectly, in the development of the cardiac AP. Although the incidence of pathogenic variations in these BrS-associated genes is low [6], it is considered that, among all of them, they could provide a genetic diagnosis for up to an extra 5–10% of BrS cases. Hence, altogether, a genetic diagnosis can be achieved approximately in 35% of clinically diagnosed BrS patients.
Other types of genetic abnormalities have been suggested to explain the remaining percentage of undiagnosed patients. Indeed, multiplex ligation-dependent probe amplification (MLPA) has allowed the detection of large-scale gene rearrangements involving one or several exons ofSCN5A in BrS cases. However, the low proportion of BrS patients carrying large genetic imbalances identified to date suggests that this type of rearrangements will provide a genetic diagnosis for a modest percentage of BrS cases [810].
BrS has been associated with an increased risk of sudden cardiac death (SCD), despite the reported variability in disease penetrance and expressivity [11]. The prevalence of BrS is estimated at about 1.34 cases per 100 000 individuals per year, with a higher incidence in Asia than in the United States and Europe [12]. However, the dynamic nature of the typical electrocardiogram (ECG) and the fact that it is often concealed, hinder the diagnosis of BrS. Therefore, an exhaustive genetic testing and subsequent family screening may prove to be crucial in identifying silent carriers. A large percentage of these pathogenic variation carriers are clinically asymptomatic, and may be at risk of SCD, which is, sometimes, the first manifestation of the disease [13].
In the present work, we aimed to determine the spectrum and prevalence of genetic variations in BrS-susceptibility genes in a Spanish cohort diagnosed with BrS, and to identify variation carriers among relatives, which would enable the adoption of preventive measures to avoid SCD in their families.

Results  
Study population 

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Table 1. Demographics of the 55 Spanish BrS patients included in the study.

The table shows the demographic characteristics of all the patients included in the study. Numbers in parentheses represent the relative percentages for each condition. T1 ECG refers to Type 1 BrS diagnostic electrocardiogram (ECG), obtained either spontaneously, or after drug challenge. The information regarding both the electrophysiological studies (EPS) and the treatment was not available for all the patients. Two of the patients that didn’t receive any treatment died, and were not taken into account for the calculations of percentages (+2 dead). ICD, intracardiac cardioverter defibrillator.

http://dx.doi.org:/10.1371/journal.pone.0132888.t001

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Table 2. Characteristics of the Spanish BrS patients carrying rare genetic variations.

The table shows the clinical characteristics of the probands who carried rare genetic variations in SCN5A, SCN2B, or RANGRF. All of them are potentially pathogenic except that found in RANGRF, which is of unknown significance (see discussion). All the potentially pathogenic variations (PPVs) that had been previously reported, except p.P1725L and p.R1898C, had been identified in BrS patients. p.P1725L had been associated with Long QT Syndrome and p.R1898C was found in Exome Variant Server with a MAF of 0.0079%. No rare variations were identified in the control population. Patient’s age is expressed in years. Bold identifies the patients carrying variations that had not been described previously. M, male; F, female; S, syncope; ICD, intracardiac cardioverter defibrillator; UK, unknown; EPS, electrophysiological studies (+, positive response;-, negative response; N/P, not performed). The two patients who carried two PPVs each are identified by a and b, respectively.

http://dx.doi.org:/10.1371/journal.pone.0132888.t002

Sequencing of genes associated with BrS

We performed a genetic screening of 14 genes (SCN5A, CACNA1C, CACNB2, GPD1L,SCN1B, SCN2B, SCN3B, SCN4B, KCNE3, RANGRF, HCN4, KCNJ8, KCND3, and KCNE1L), which allowed the identification of 61 genetic variations in our cohort. Of these, 20 were classified as potentially pathogenic variations (PPVs), one variation of unknown significance, and 40 common or synonymous variants considered benign.

The 20 PPVs were found in 18 of the 55 patients (32.7% of the patients, 83.3% males; Table 2). Sixteen patients (88.9%) carried one PPV, and two patients (11.1%) carried two different PPVs each. Nineteen out of the 20 PPVs identified were localized in SCN5A and one in SCN2B.

The vast majority of the PPVs identified were missense (70%). We also detected 2 nonsense variations (10%), 3 insertions or deletions causing frameshifts (15%), and one splicing variation (5%). The three frameshifts (p.R569Pfs*151, p.E625Rfs*95 and p.R1623Efs*7) were identified in SCN5A. These were not found in any of the databases consulted (see Methods), and were thus considered potentially pathogenic (see below). The other 16 rare variations identified inSCN5A had been previously described, and hence were also considered potentially pathogenic. Fourteen of them had been identified in BrS patients. Of these, 6 had also been identified in individuals diagnosed with other cardiac electric diseases (i.e. Sick Sinus Syndrome, Long QT Syndrome, Sudden Unexplained Nocturnal Death Syndrome or Idiopathic Ventricular Fibrillation [2,15,16,20,21,25]). The other 2, p.P1725L and p.R1898C, had only been associated with Long QT Syndrome or found in Exome Variant Server with a MAF of 0.0079%, respectively. Furthermore, we identified a variation in SCN2B (c.632A>G in exon 4 of the gene, resulting in p.D211G) which was considered pathogenic. This patient was included within our cohort, but the functional characterization of channels expressing SCN2B p.D211G was object of a previous study from our group [7]. We also identified a nonsense variation in RANGRFwhich has been formerly reported as rare genetic variation of unknown significance [29].

Additionally, we screened the relatives of those probands carrying a PPV. We analysed a total of 129 relatives, 69 of which (53.5%) were variation carriers. Genotype-phenotype correlations evidenced that 8 of the families displayed complete penetrance (S3 Table). Additionally, no relatives were available for one of the probands carrying a PPV, thus hampering genotype-phenotype correlation assessment. The other 12 families showed incomplete penetrance.

 

MLPA analysis

The 37 patients with negative results after the genetic screening of the 14 BrS-associated genes underwent MLPA analyses of SCN5A. This technique did not reveal any large exon deletion or duplication in this gene for any of the patients.

 

SCN5A p.R569Pfs*151 (c.1705dupC), a novel PPV

A 41-year-old asymptomatic male presented a type 3 BrS ECG which was suggestive of BrS. Flecainide challenge unmasked a type 1 BrS ECG (Fig 1A, left), which was also spontaneously observed sometimes during medical follow up. Sequencing of SCN5A revealed a duplication of a cytosine at position 1705 (c.1705dupC; Fig 1A, right), which originated a frameshift that lead to a truncated Nav1.5 channel (p.R569Pfs*151). The proband’s sister also carried this duplication, but had never presented signs of arrhythmogenesis. The proband’s twin daughters were also variation carriers, displayed normal ECGs and, to date, are asymptomatic (Fig 1A, middle). Thus, p.R569Pfs*151 represents a novel genetic alteration in the Nav1.5 channel that could potentially lead to BrS, but with incomplete penetrance.

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Fig 1. Characteristics of the probands carrying non-reported potentially pathogenic variations (PPVs) in SCN5A and their families.

Left: Electrocardiograms of the probands: (A) patient carrying the p.R569Pfs*151 variation, showing the ST elevation characteristic of BrS in V1 at the time of the flecainide test; (B) patient carrying the p.E625Rfs*95 variation, showing the spontaneous ST elevation characteristic of BrS in V1 and V2; and (C) patient carrying the p.R1623Efs*7 variation, showing the spontaneous ST elevation characteristic of BrS in V1 and V2. Middle: Family pedigrees. Open symbols designate clinically normal subjects, filled symbols mark clinically affected individuals and question marks identify subjects without an available clinical diagnosis. Plus signs indicate the carriers of the PPVs and minus signs, non-carriers. The crosses mark deceased individuals and arrows identify the proband. Right: Detail of the electropherograms obtained after SCN5Asequence analysis of a control subject (left panels) and of the probands (right panels).

http://dx.doi.org:/10.1371/journal.pone.0132888.g001

SCN5A p.E625Rfs*95 (c.1872dupA), a novel PPV

A 51-year-old asymptomatic male was diagnosed with BrS since he presented a spontaneous ST segment elevation in leads V1 and V2 characteristic of type 1 BrS ECG (Fig 1B, left). The sequencing of SCN5A evidenced an adenine duplication at position 1872 (c.1872dupA, Fig 1B, right). This genetic variation results in a truncated Nav1.5 channel (p.E625Rfs*95). The genetic analysis of the proband’s relatives proved that only her mother carried the variation (Fig 1B, middle). She was asymptomatic, but a BrS ECG was unmasked upon ajmaline challenge. The proband’s sister was found dead in her crib at 6 months of age, which suggests that her death might be compatible with BrS. Therefore, the p.E625Rfs*95 variation in the Nav1.5 channel represents a novel genetic alteration potentially causing BrS.

SCN5A p.R1623Efs*7 (c.4867delC), a novel PPV

The proband, a 31-year-old male, was admitted to hospital after suffering a syncope. His baseline 12-lead ECG showed a ST segment elevation in leads V1 and V2 that strongly suggested BrS type 1 (Fig 1C, left). A deletion of the cytosine at position 4867 (c.4867delC) was observed upon SCN5A sequencing (Fig 1C, right). This base deletion leads to a frameshift that originates a truncated Nav1.5 channel (p.R1623Efs*7). Genetic screening of his parents and sisters evidenced that none of them carried this novel variation (Fig 1C, middle). None of them had presented any signs of arrhythmogenicity, nor had a BrS ECG. Nevertheless, in uterogenetic analysis of one of his daughters proved that she had inherited the variation. She died when she was 1 year of age of non-arrhythmogenic causes. Hence, the p.R1623Efs*7 variation in the Nav1.5 channel is a novel genetic alteration originated de novo in the proband that could potentially lead to BrS.

Synonymous and common genetic variations portrayal

In our cohort, we identified 40 single nucleotide variations which were common genetic variants and/or synonymous variants (S2 Table). Twenty-nine had a minor allele frequency (MAF) over 1%, and were thus considered common genetic variants.

We also identified 11 variants with MAF less than 1%. Of them, 9 were synonymous variants, what made us assume that they were not disease-causing. Four of these synonymous variants were not found in any of the databases consulted, and thus their MAF was considered to be less than 1%. Each of these synonymous variations was identified in 1 patient of the cohort. A similar proportion of individuals carrying these novel variations was detected upon sequencing of 300 healthy Spanish individuals (600 alleles). The remaining 2 variants were missense, and although they had either a MAF of less than 1% or an unknown MAF according to the Exome Variant Server and dbSNP websites, they were common in our cohort (29.2 and 50%, respectively; S2 Table), and a similar MAF was detected in a Spanish cohort of healthy individuals (26.7% and 48.8%, respectively).

Influence of phenotype and age on PPV discovery

To assess if a connection existed between the probands’ phenotype and the PPV detection yield, we classified the patients in our cohort according to their ECG (spontaneous or induced type 1), the presence of BrS cases within their families, and the presence/absence of symptoms. Even though the overall PPV detection yield was 32.7%, it was even higher for symptomatic patients (Fig 2). Indeed, in this group of patients, having a family history of BrS was identified as a factor for increased PPV discovery yield. In the case of absence of BrS in the family, the variation discovery yield was almost double for those patients having a spontaneous type 1 BrS ECG than for patients with drug-induced type 1 ECG (45.5% vs 25%, respectively). In addition, we identified a PPV in 44.4% of the asymptomatic patients who presented family history of BrS and a spontaneous type 1 BrS ECG. When the patient presented drug-induced type 1 ECG or in the absence of family history of BrS, the PPV discovery yield was of around 15%.

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Fig 2. Influence of the phenotype on PPV discovery yield.

Bar graph comparing the PPV detection yield in 8 different clinical categories (stated below the graph). Each bar shows the total number of patients for each clinical category divided in those with a PPV (black) and those without an identified PPV (white). The number of patients (in brackets) and percentages are given. Pos, positive; Neg, negative; Spont, spontaneous type 1 BrS ECG; Drug, drug-induced type 1 BrS ECG; n, number of patients.

http://dx.doi.org/:10.1371/journal.pone.0132888.g002

We also investigated the role of age on the PPV occurrence. No significant age differences were observed between variation carriers and non-carriers (38.6±10.3 and 43.5±14.4, respectively, p = 0.16). However, the PPV discovery yield was higher for patients with ages between 30 and 50 years: out of the total of patients carrying a PPV, 83.3% of the patients were in this age range, while 11.1% were younger and 5.6% were older patients (Fig 3A, upper panel). The PPV discovery yield was significantly higher for symptomatic than for asymptomatic patients (42.3% vs 24.1%, respectively; Fig 3A, lower panels).

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Fig 3. Influence of the age on PPVs discovery yield.

(A) Pie charts showing the distribution of patients in the overall population as well as in the categories of symptomatic and asymptomatic patients regarding PPV discovery. The percentage and the number of patients (in brackets) are given for each group. The small pie charts correspond to the age distribution of patients with an identified PPV. (B) Bar graphs of the PPV detection yields obtained for each of the age groups (< 30 years, 30–50 years and > 50 years). Numbers inside each bar correspond to the number of patients carrying a PPV for each category and the percentages represent the variation detection yield.

http://dx.doi.org:/10.1371/journal.pone.0132888.g003

Noteworthy, in the 30–50 age range, 52.9% (9/17) of the symptomatic patients and 35.3% (6/17) of asymptomatic patients carried one PPV (Fig 3B, middle). Additionally, 40% (2/5) of the symptomatic young patients (< 30 years) were variation carriers, while no PPVs were identified in asymptomatic patients within this age range.

Overall, 55 unrelated Spanish patients clinically diagnosed with BrS were included in our study.Table 1 shows the demographics of this cohort, and Table 2 and S1 Table show the clinical and genetic characteristics of all the patients included in the study. The mean age at clinical diagnosis was of 41.9±13.3 years. Although the majority of patients were males (74.5%), their age at diagnosis was not different than that of females (41.8±12.1 years and 42.3±16.3 years, respectively; p = 0.92). A type 1 BrS ECG was present spontaneously in 37 patients (67.3%), and drug challenge revealed a type 1 BrS ECG for the remaining 18 patients (32.7%). Almost half of the patients had experienced symptoms, including 2 SCD and 4 aborted SCD. Patients who had not previously experienced any signs of arrhythmogenicity despite having a BrS ECG were considered asymptomatic. Comparison of symptomatic vs asymptomatic patients evidenced a similar percentage of males (73.1% and 75.9%, respectively). However, the mean age at diagnosis was different between the two groups of patients (37.7±14.3 and 45.7±11.4, respectively; p<0.05).

Discussion

To the best of our knowledge, this is the first comprehensive genetic evaluation of 14 BrS-susceptibility genes and MLPA of SCN5A in a Spanish cohort. Well delimited BrS cohorts from Japan, China, Greece and even Spain have been genetically studied [24,3032]. Additionally, an international compendium of BrS genetic variations identified in more than 2100 unrelated patients from different countries was published in 2010 [3]. However, all these studies screenedSCN5A exclusively. In 2012, Crotti et al. reported the spectrum and prevalence of genetic variations in 12 BrS-susceptibility genes in a BrS cohort [5]. However, this study included patients of different ethnicity. Here, we report the analysis of 14 genes which has been conducted on a well-defined BrS cohort of the same ethnicity.

Our results confirm that SCN5A is still the most prevalent gene associated with BrS. Indeed,SCN5A-mediated BrS in our cohort (30.9%) is higher than the proportion described in other European reports [3,23], where a potentially causative variation is identified in only 20–25% of BrS patients. The reason for this discrepancy is unclear but could point towards a higher prevalence of SCN5A PPVs in the Spanish population or to selection bias. Additionally, we identified a genetic variation in SCN2B (c.632A>G, which results in p.D211G). We have formerly published the comprehensive electrophysiological characterization of this variation, and showed that indeed this variation could be responsible of the phenotype of the patient, thus linking SCN2B with BrS for the first time [7]. Also, we identified a variation in RANGRF. This variation (c.181G>T leading to p.E61X) had been previously reported in a Danish atrial fibrillation cohort [33]. Surprisingly, the authors reported an incidence of 0.4% for this variation in the healthy Danish population, which brought into question its pathogenicity. Our finding of this variation in an asymptomatic patient displaying a type 2 BrS ECG also points toward considering it as a rare genetic variation with a potential modifier effect on the phenotype but not clearly responsible for the disease [29].

No PPVs were identified in the other genes tested. Certainly, it is well accepted that the contribution of these genes to the disease is minor, and thus should only be considered under special circumstances [13,34]. In addition, recent studies have questioned the causality of variations identified in some of these minority genes [35].

We also used the MLPA technique for the detection of large exon duplications and/or deletions in SCN5A in patients without PPVs, and no large rearrangements were identified. This is in accordance with previous reports, which revealed that such imbalances are uncommon [810].

Kapplinger et al. [3] reported a predominance of PPVs in transmembrane regions of Nav1.5. Indeed, it has been proposed that most rare genetic variations in interdomain linkers may be considered as non-pathogenic [36]. In contrast, PPVs identified in this study are mainly located in extracellular loops and cytosolic linker regions of Nav1.5 (Fig 4). Additionally, 2 of our non-previously reported frameshifts are located in the DI-DII linker. These 2 genetic variations lead to truncated proteins, which would lack around 75% of the protein sequence, and thus are presupposed to be pathogenic.

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Fig 4. Nav1.5 channel scheme showing the relative position of the SCN5A PPVs identified in our cohort.

Open symbols indicate already described variations and closed symbols locate novel variations reported in this study. DI to DIV designate the 4 domains of the protein, and numbers 1–6 identify the different segments within each domain. Crosses mark the voltage sensor.

http://dx.doi.org:/10.1371/journal.pone.0132888.g004

In our cohort, we have identified 40 synonymous or common genetic variations, 4 of which have not been previously reported. These variations are gradually becoming more and more important in the explanation of certain phenotypes of genetic diseases. Only a few common variations identified here are already published as phenotypic modifiers [37,38]. The effect of these and other common variants identified in our cohort on BrS phenotype should be further studied.

Unexpectedly, almost 40% (7/18) of the PPV carriers did not present signs of arrhythmogenicity. We also performed genotype-phenotype correlations of the PPVs identified in the families (S3 Table). These studies uncovered relatives, most of whom were young individuals, who carried a familial variation but had never exhibited any clinical manifestations of the disease. This is in agreement with Crotti et al. and Priori et al. [5,23], who postulated that a positive genetic testing result is not always associated with the presence of symptoms. Indeed, the existence of asymptomatic patients carrying genetic variations described to cause a severe Nav1.5 channel dysfunction has been reported [39]. The identification of silent carriers is of paramount importance since it allows the adoption of preventive measures before any lethal episode takes place. Unknown environmental factors, medication and modifier genes have been suggested to influence and/or predispose to arrhythmogenesis [11]. Hence, this group of patients has to be cautiously followed in order to avoid fatal events.

Our studies on the connection between patients’ phenotype and the PPV detection yield highlighted the presence of symptoms as a factor for an increased variation discovery yield. Within the group of symptomatic individuals, a PPV was identified in a higher proportion of patients displaying a spontaneous type 1 BrS ECG than for patients showing a drug-induced ECG. Likewise, within the asymptomatic patients with family history of BrS, those who presented spontaneous type 1 BrS ECG carried a PPV more often than those with a drug-induced ECG (Fig 2). Referring to age, the vast majority (17/20, 85%) of the PPVs were identified in patients around their fourth decade of age (30–50 years). This is in accordance with the accepted mean age of disease manifestation. Moreover, in this age range, more than 50% of the patients who presented symptoms carried a variation that could be pathogenic (Fig 3). Importantly, 35.3% of asymptomatic patients of around 40 years of age also carried one of such variations. These data highlight the importance of performing a genetic test even in the absence of clinical manifestations of the disease, and particularly when in the 30–50 years range, which is in accordance with consensus recommendations [13,34].

In conclusion, we have analysed for the first time 14 BrS-susceptibility genes and performed MLPA of SCN5A in a Spanish BrS cohort. Our cohort showed male prevalence with a mean age of disease manifestation around 40 years. BrS in this cohort was almost exclusivelySCN5A-mediated. The mean PPV discovery yield in our Spanish BrS patients is higher than that described for other BrS cohorts (32.7% vs 20–25%, respectively), and is even higher for patients in the 30–50 years age range (up to 53% for symptomatic patients). All these evidences support the genetic testing, at least of SCN5A, in all clinically well diagnosed BrS patients.

 

Study Limitations

First of all, drug challenge tests were not performed for all the relatives who were asymptomatic variation carriers. This fact hampered their clinical diagnosis and represents an impediment to definitely assess the link between PPVs and BrS. These patients are nowadays under follow-up.

New PPVs have been identified in our cohort. The clinical information available for the families suggests that these new variations could be pathogenic. Still, in vitro studies of these variations are required in order to evaluate their functional effects and verify their pathogenic role. Additionally, genotyping in an independent cohort would help reduce the likelihood of type I (false positive) error in genetic variant discovery.

We have to acknowledge that the study set is relatively small. Consequently, the classification of patients according to the different clinical categories rendered rather small sub-groups, which may lead to over-interpretation of the results. Future studies will be directed to the genetic screening of additional Spanish BrS patients, which will probably reinforce the significance of the tendencies observed here.

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Craig Venter

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

Interview: J.Craig Venter

http://www.frontlinegenomics.com/2328/j-craig-venter-from-sequencing-the-first-human-genome-to-sequencing-all-the-human-genomes/

FLG: You recently told the Graduating class of 2015 at UC San Diego School of Medicine that pretty soon they’ll find that most of what they’ve learned is “just plain wrong”. What would you say is the first thing in our understanding of human medicine that is going to change significantly?

JCV: One of the areas that’s changing the fastest right now is cancer – as we drill down to the genome level we’re getting more information and understanding than has ever been possible before. Every single cancer is a genetic disease. Not necessarily inherited from your parents, but it’s genetic changes which cause cancer. So as we sequence the genomes of tumours and compare those to the sequence of patients, we’re getting down to the fundamental basis of each individual person’s cancer. And that’s truly my definition of my view of precision medicine. For example, at Human Longevity (HLI), we sequence the whole genome of the patient; we sequence the genome of the tumour to a very high, adept, coverage; we sequence the RNA in the tumour to understand which genes in the tumour are being expressed and modified; and we sequence the entire immune system. From that picture we understand the patients susceptibility in the first place for cancer, and why they probably got it, and whether their immune system responded to the cancer – and usually it doesn’t which is why cancer shows up. From the modified proteins that show up from the genetic changes, we get a whole new view of which drugs will work, and will not work, on that tumour. Also, we’re taking that further, developing personalised cancer vaccines for that individual against their specific tumour. So, it’s getting very precise – very data and information driven, versus what standard practise is today; doing surgery and trying to diagnose things using a microscope. It’s a different level resolution. It’s like trying to look through a telescope on Earth at Pluto versus the photos we just saw from that flyby.

FLG: Your new company, Human Longevity, is aiming to play a significant part in changing the human experience. What got you excited enough to buy all those hi-seq machines and set out to build the world’s largest genomic database?

JCV: Well, you might recall that 15 years ago I announced the first human genome that my team sequenced at Celera. The trouble is, that genome cost $100 million and took 9 months to do, with a large dedicated team. That seems extraordinary today, now that we can do thousands a month for little over $1,000 each, but 15 years ago there was a $3 billion 15 year government program to try and do the same thing. So we’ve changed from that 15 year $3 billion dollar effort, down to 9 months and $100 million, and down to thousands a month. It’s always been the dream, but technology didn’t allow it until recently.

FLG: You guys already have some great partnerships out there giving you access to samples to get you to that 1 million genomes mark. Are you still on course to hit your total by 2020? What are you looking for when you approach organisations whose samples you want to sequence and analyse?

JCV: The way it’s starting to look, we may greatly exceed the 1 million! The technology is still changing – we’re exceeding Moore’s Law still with technology change. We have more transistors per unit that change the compute capacity; we’re getting higher and higher throughput per machine; there’s new technologies coming – I’ve never had sequencing machines last more than 3 years in the last 20 years of my career, before they were replaced by a new, faster and better, technology. 5 years from now, this will still look like the end of the dark era.

FLG: From a technology standpoint, what are you hoping to see in the next 5 years that can help you better reach your goal?

JCV: We need a combination of the cost and the throughput of the Illumina sequencers, with the quality and long sequence reads on single molecules that we get with PacBio. Future technologies can still improve substantially on the quality of the data, the percentage of the genome that’s covered, and how well that’s done. In my talk at the Festival of Genomics, I talked about haplotype phasing, where on sequencing your genome we can separate your chromosomes into the parts you got from your mother and the parts you got from your father. We need much better technology to do that routinely, rapidly, and cheaply.

Craig Venter_quote3

FLG: At a personal level, the idea of staying healthy for longer is very appealing. However, we already have some major social and economic factors to deal with as a result of longer life expectancies. Here in the UK, we just had our general election. One of the topics for discussion was how the government was going to address some of the challenges being faced by my generation of 20-30 something year olds. People are living longer, so the government has to pay more for pensions, which in turn are funded by those working today on comparatively lower salaries. People are working further into their life times, so some of those big opportunities for vertical movement can be harder to come by. And then you have the general problems associated with an every increasing population. So, if you’re successful in increasing healthy lifespan for people, what kind of knock-on effect do you think it will have at the population level?

JCV: I’m glad you picked up on our emphasis on healthy lifespan versus just increasing human longevity. Even though that’s our name, our goal is totally focused on the healthy lifespan.

Healthcare is the biggest rising cost, certainly in the US, and in the UK I think as well. So we don’t bankrupt our entire economies, we need to switch to preventative medicine. One of the challenges with a government health system, like in the UK, with all of this data, is that you have a government making decisions on which treatments they’ll pay for and which ones they won’t. That’s a dangerous, dangerous, place to get into society. The UK health system is already there, insurance companies are already there – but countries where that isn’t an issue right now, are where there is good competition and different paying systems. So there’s a lot of reform that’s going to be needed across the board, there. But if we can prevent disease – it solves a lot of the social dilemmas about the government deciding you’re not worthy of getting a new kidney or getting a new treatment.

On the other hand, if we live longer healthier lives – in a few months I turn 69, I have relatives who are younger than me, who have retired already – it would be an incredible thought to me, to even consider stopping what I’m doing. I have a very exciting job and career. But we could solve a lot of these economic problems as well (the US has a bigger problem with this than the UK I think) if we just changed the retirement age to 75. With this notion that you work 20 years and then retire, it’s pretty stunning. My science career has already been close to 40 odd years, and I’m hoping for at least another 20. We need to have opportunities, not just for labourers to labour another decade, but having an education system that helps people move up the economic ladder. Knowing you’re going to be working a much longer period of time, you get incentivised to get retraining and take on something new, rather than assuming the government is going to take care of you at age 65.

FLG: One of the first things that brought your name to public attention was the congressional briefing back in 1991 where you mentioned that the NIH were planning on filing patent applications on thousands of genes based on expressed sequence tags. Amongst the numerous arguments against this plan, was the notion that this would impede the open exchange of information and increase the price of obtaining the sequence of the human genome. Ultimately, the NIH didn’t go ahead with the plan, and you’ve been carrying the ‘egomaniac’ tag ever since. By having that patent and license protection in place so early on, what do you think would be different today if the plan had gone ahead?

JCV: Well, even though the US government abandoned their patents, I think it put the taxpayer at an economic disadvantage. It’s well documented history that as the UK and US public genome labs, with the $5 billion funding, dumped their data nightly – every single pharmaceutical company downloaded that data nightly, and patented it. So it just shifted it from US taxpayers owning it directly, to the worldwide pharmaceutical companies owning that data directly. It’s led to the development of a lot of drugs and tests that are currently available in the market. I’ve said so publically, and am delighted by the recent Supreme Court ruling saying that these naturally occurring DNA sequences are not patentable – like Myriad have done with their breast cancer test.

What we’re doing with whole genome sequencing was going to make them obsolete anyway, because they’re multi thousand dollar tests, while we get the entire genome for a little over a thousand dollars. The patents wouldn’t have allowed them to block us looking at that data. So one way or another, they were going to become obsolete. I think it quite interesting now – some of the biggest critics from 20 years ago, are using the economic models that they criticised me for. In fact the Wellcome Trust, is now charging subscriptions to get access to data. So the world has come around. All this stuff was in the heat of a competition that most academic scientists never expected – that somebody would just come along and take their 15-year project away from them and just do it!

That created a lot more heat than light at the time. Some of the arguments that came out then were the weapons of the rhetoric of the time that had nothing to do with reality. Point to drug after drug, and test after test – even Myriad’s test with breast cancer – that have helped hundreds of thousands of people understand their risk for cancer and have new drugs to treat them. So if it was such an oppressive system, it would have disappeared a long time ago. Academic scientists have never been limited in their access to any of this data, so all of these were political arguments for rhetoric.

One of the things I’ve said several times recently, with these anniversaries of our first genome announcement, is that if you look at all of the rhetoric of the time – Francis Collins calling what we were doing, generating the “Mad Magazine” version and that whole genome shotgunning wasn’t going to work. All you have to do is take a look around the world, and every genome that’s been sequenced by us and what every other group has done with the methods that we published 20 years ago. That’s the nice thing about the field of science – the test of time sorts out the truth. Sometimes it takes the test of time to get away from the emotion and the rhetoric, but the fact that we’re now sequencing 3,000 genomes a month with this technique, and globally millions of genomes of countless different species… Every one of them has been sequenced with the technology we first described with the first genome in 1995.

Craig Venter_quote2

FLG: There’s a worry out there that today’s political and commercial interest in genomics is not always in the best interest of scientific pursuit?

JCV: You’re probably hearing that because you’re in the UK! We don’t hear that so much in the US. But there’s this constant left wing thinking that comes out of academia in the UK, that companies are inherently evil. It’s just bull****. The leading edge of the best science in the world is being driven by private money, and investment money because of the scarcity of government money to do this. It’s not only by far the best and most advanced science, we’re driving the equation at Human Longevity that everyone else is beginning to follow as well. I think those are old world thinkings of academia versus industry versus government, and just has nothing whatsoever to do with reality outside of perhaps a totalitarian communist regime!

FLG: We touched on it before, but, for better or for worse, you do seem to be seen as one of modern science’s greatest egomaniacs. Is there any factual basis to that allegation, or is it just part of being at the top?

JCV: Show me a highly successful person in any field that has gotten there having a weak ego. You have to believe in yourself, and you have to believe in what you’re doing. I think because of all that early rhetoric, and because my teams have been continuously successful at the very leading edge of this field for that last 20 years, it’s easy to label anyone at the front of things. I do have a healthy belief in my teams and the science that we’re doing, and that it’s going to change what’s going on. If I had a weak ego, and doubts about this, the first genome would not yet have been completed with US and UK government funding.

FLG: You’ve already had a pretty storied career in genomics, and it certainly seems far from over. When it does come to an end, is there any one thing in particular you hope people will remember you for? What is it, ultimately, that you’ve been trying to achieve?

JCV: I think you should ask me that in another 20 years! I think I’ve achieved some good things; doing the first genome in history – my team on that was phenomenal and all the things they pulled together; writing the first genome with a synthetic cell; my teams at the Venter Institute, Human Longevity, and before that Celera. These are all team sports. I’m the captain of the team, or the orchestra conductor, but the only reason I’ve been successful is because of having the most extraordinary scientists, mathematicians and engineers excited about working on some of the ideas I put forward. I’m hoping that these next 20 years will show what we did 20 years ago in sequencing the first human genome, was the beginning of the health revolution that will have more positive impact in people’s lives than any other health event in history.

Craig Venter_quoteFLG: In the build up to The Festival of Genomics, we asked people who they were most looking forward to seeing present. Perhaps a little unsurprisingly, your name was almost always mentioned. So we thought it would be a nice idea to have some of our previous interviewees and contributors to the magazine put some questions to you:

Richard Lumb, CEO, Front Line Genomics: One of your partners, Peter Diamandis, talks about the need of businesses to regularly “disrupt their own business model”. The stated purpose of Human Longevity is already differentiating and your approach already appears disruptive [an impressive combination of stem cell technology and genomics in a commercial enterprise]. Is this concept of ‘self-disruption’ something that you recognize in your past work, and how would you anticipate Human Longevity disrupting your own business model over the next few years?

JCV: That’s an excellent, thoughtful, question from somebody who’s obviously put some unique things together. If you ask anybody that works at Human Longevity, and on my other projects, I disrupt things daily. There’s no complacency. We modified our business model, relatively substantially, from 18 months ago when that was first put together. We’re adapting to the data in real-time, and that’s what happens in the best of science. All the things I’ve done are because the data we got has told us what the next direction was going to be, and what was possible, and the kinds of questions to ask. We have new data here on tens of thousands of human genomes. The machine learning team here, headed up by Franz Och whom I hired out of Google (you’re aware of his work if you use Google Translate), have already come up with some amazing associations out of the data. Now we’re trying to predict somebody’s voice from their genetic code, pictures of them, and their precise biological age. If you’d asked me about a year ago if these would be highly probably in the next year or two, I would have said “I’m doubtful!” We have great scientists making real nice breakthroughs, modifying how we think about the data going forward.

Some of the biggest companies of the past have disappeared because they stuck with their technology and have refused to evolve. Our genomes are evolving and changing every single day. I think that is somewhat of a surprise for me. I thought we’d just sequence the genome once and that would be sufficient for most things in people’s lifetimes. Now we’re seeing how changeable and adaptable it is, which is why we’re surviving and evolving as a species. If we don’t adapt and change constantly, then we will become one of the relics of evolution. So it’s not just a nice thing to do for survival, it’s essential in building for the next stage of success.

Jean-Claude Marshall, Director Clinical Pharmacology Laboratory, Pfizer: What are your thoughts around how the FDA could regulate both LDTs (laboratory developed tests) and NGS (next generation sequencing)? Additionally, what do you foresee as the next set of challenges in the field of both companion diagnostics based on genomic analysis of patients, and the challenge of direct to consumer genetic offerings?

JCV: That’s a sophisticated question, and an important one. We have a staff of several people who’s job it is to help work out a good regular trade path. I’ve met personally with the FDA commissioner. This is an area that’s very key to us. We want to help educate the FDA on these changes. We’re working with companies, and Pfizer is one of the ones we’re in discussions with, to use our technology to change how they do clinical trials. We’re working with several pharmaceutical companies on sequencing the genomes of patients from failed phase III clinical trials, to rescue them. In fact, Pfizer is probably more familiar with this than any other company. They did a large clinical trial for one of their drugs to treat lung cancer. The trial failed pretty badly. But then they did retrospective analysis of lung cancer patients with a translocation in the ALK gene. It turns out it’s in around 4-6% of lung cancer patients. Over 60% of those individuals, respond extremely well to the Pfizer drug. And now Pfizer have a blockbuster drug, totally because of that genetic segregation to rescue that failed trial.

As to the question on companion diagnostics; if you measure whether people have the ALK translocation, that’s a companion diagnostic for prescribing the Pfizer ALK targeted drug. To me, it will become the standard of care. Not an unusual abnormality. Pfizer’s path to this helped pave the way for others to see it.

Brian Dougherty, Translational Genomics Lead – Oncology, AstraZeneca: What’s different for you this time around? Sequencing and analysis is more sophisticated. The first human genome is done. Will similar business models work a decade later?

JCV: Well Brian was one of the key contributors back in the early days at TIGR and he participated in the very first human genome. He came in from Ham Smith’s lab, and saw first hand and contributed to the very first stages.

So what’s different today? Well the world has had my genome for 15 years, done with Sanger sequencing. Others have been added to it, Jim Watson was the second one done with the 454 technology. One, or two, or even a few dozen genomes, have proven to give great targets for pharmaceutical analysis. But they don’t give you enough to answer fundamental questions about what’s unique to you, what’s unique to me, and how do we interpret that data? So we concluded that the only route to get to that data, was rather than wait for the academic community to do one genetic study at a time, was to build a very large database so we can comprehensively and globally understand the 3% differences amongst all of us. It’s already starting to pay out. Doing more of the same in a highly homogenous species doesn’t really make sense. When you sequence sperm cells, no two sperm are alike. No two eggs are a like. No two people’s genomes are alike. Even Identical twins have some spontaneous mutations that make their genomes different. So we’re now able to get down to the resolution to start seeing those differences. I’d say that this is actually the most exciting era of genomics!

Anna Middleton, Principal Staff Scientists, Genetic Counsellor, Wellcome Trust Sanger Institute:What hooks do you use to start a conversation about genomics with people who know nothing about genomics, i.e. what, in a nutshell, do you think people want to connect to?

JCV: That’s a very interesting question. What we’re trying to design, with helping to introduce the data to people, is that we’re ultimately trying to describe them at the most comprehensive level. The interpretation of medicine today is ‘do your clinical values fall within a normal range?’ Everything in the globe right now is in the law of averages, which mean absolutely nothing to individuals.

Larry Page told me that even if we cured all cancer, it would only change average human lifespan by a few years. But you can see what a meaningless statistic that is if you’re a 9-month-old child and you die from a neuroblastoma tumour. That doesn’t shift the averages, but it’s a huge individual effect. Genomics are about individuals. It’s about what’s specific to you, not your siblings, not your parents – each of us is totally unique. We will only see that uniqueness by drilling down to the genetic code. Like I said in my talk, we’re a genetically, DNA driven software species. Every parent knows that when they see their children on day zero. We all come out totally unique, and everyone comes out differently. We understand it at an intuitive level, we are now developing the scientific data to help all of us understand what’s unique and different about us, and how we can use that information to have better, healthier lives.

Alka Chaubey, Director Cytogenetics Laboratory, Greenwood Genetic Center: You played the most important role in not only the Human Genome but also getting your diploid genome sequenced and available to the public. With all the human genome information available and the ability to identify rare genetic (constitutional) disorders, what are your thoughts on approaches to reducing the burden and improving the quality of life of individuals with disorders persisting as lifelong disabilities (e.g. Autism, Intellectual disability, etc.)?

JCV: That’s a nice compliment and another important question. It’s going to be the challenges of medicine, and of this technology. Not every disease or disorder is going to be amenable to cure and treatment. Particularly for diseases that result in a dramatic reordering of brain structures and functions. For autism in particular, we’re doing a large cohort where we’re sequencing the entire genome of autistic individuals. It appears that no two are alike. But we classify it as one disease under that name. It doesn’t have a single cause. So if you call any disease the ‘unlucky disease’, you might call that one the unlucky one. It seems to be primarily driven by spontaneous mutations in that individual’s genome. The rate of those spontaneous mutations is accelerated by having older parents. Perhaps that’s why we’re seeing more of it?

Sequencing the genomes of individuals with autism, and trying to find which genes are affected – in some cases will lead to some pharmaceutical therapies that might help them. But it won’t be across the board. So, I am not one to promise that genomics is the savour for all of medicine and all of humanity. That’s why prevention becomes more important than treatment. If we can prevent the miswiring of the brain, either by early screening, or selecting embryonic cells that don’t have mutations, we increase the chance of healthier outcomes for everybody. But there won’t be magic drug treatments for every disorder. But Alzheimer’s disease might be an important exception if it’s treated early enough. We can detect changes through a combination of the RNAi imaging we’re doing here of the brain, and the genome, that indicate a high risk of Alzheimer’s disease 20 years before somebody would experience their first symptoms. If that’s what we target for preventing the development of the disease, it might yield, as some recent trials are beginning to show, a very different outcome compared to trying to treat late stage Alzheimer’s disease where a third of the functioning neurons have already been lost in the brain, and pathways are gone – you can’t just instantly restore those with a magic pill. So prevention is probably the single most important word to come out of the genome era.

Keith Bradnam, Associate Project Scientists, UC Davis: What do you see as the limits of synthetic biology? Could we assemble a functional eukaryotic genome, and what are the practical applications of such technology?

JCV: That’s a great question! The limitations will ultimately be more society limitations, ethical limitations, and standards rather than technology. I think a synthetic single eukaryotic cell would be very straightforward to do today. Various groups of scientists have been trying to build the yeast genome. It’s kind of like rebuilding a house one brick at a time, but they’re making a synthetic version of yeast. That’s not quite the same as writing the genetic code and then booting it up as we did, but that’s just because of the limitations on writing the genetic code now.

I think understanding what makes a multicellular organism, and all the regulation associated with that, are so far away from design that we’re going to have to learn a whole lot more biology before we get to that stage of deliberate design. I think about 10% of the genes in our designed synthetic bacterial cell, are of unknown function. All we know is that you can’t get life without them. That problem expands tremendously with eukaryotic cells. If you extrapolate to the challenge of interpreting the human genome, we only understand a tiny fraction of the human genome today.

Nick McCooke, former CEO of Solexa also asked to remind you that you still owe him for tea at Claridges, in London, back in 2003.

JCV: Ha ha ha! Well it’s interesting…My cofounder at HLI is Peter Diamandis, who is also the CEO of the XPRIZE organization. I started a prize out of the Venter Institute early on, which was a half million dollars to spur on technology development. Today, Solexa would clearly be the winner of that. But things progressed so fast. The economics changed so dramatically, that nobody cared about a half million dollar prize anymore. XPRIZE made it a $10 million prize, but that wasn’t big enough to influence anything that Illumina or Life Technologies was doing. So the economic scale of the field has changed in part due to the tremendous success of Solexa.

FLG: That’s it for the questions, so thank you very much for your time! Is there anything else you’d like to mention?

JCV: No, I think you’ve covered the waterfront pretty nicely! It was fun talking to you and an enjoyable conversation. I was impressed by the quality of questions you guys put together!

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Can IntraTumoral Heterogeneity Be Thought of as a Mechanism of Resistance?

Curator/Reporter: Stephen J. Williams, Ph.D.

Therapeutic resistance remains one of the most challenging problems for the oncologist, despite the increase of new therapeutics in the oncologist’s toolkit. As new targeted therapies are developed, and new novel targets are investigated as potential therapies, especially cytostatic therapies which it has become evident our understanding of chemoresistance is expanding beyond mechanisms to circumvent a drug’s pharmacologic mechanism of action (i.e. increased DNA repair and cisplatin) or pharmacokinetic changes (i.e. increased efflux by acquisition of a MDR phenotype).

In a talk at the 2015 AACR National Meeting, Dr. Charles Swanton discusses the development of tumor heterogeneity in the light of developing, or acquired, drug resistance. Chemoresistance is either categorized as acquired resistance (where resistance develops upon continued exposure to drug) or inherent resistance (related to a tumor being refractory or unresponsive to drug). Dr Swanton discusses findings where development of this heterogeneity (discussed here in a posting on Issues in Personalized Medicine in Cancer: Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing) and here (Notes On Tumor Heterogeneity: Targets and Mechanisms, from the 2015 AACR Meeting in Philadelphia PA) on recent findings on Branched Chain Heterogeneity) is resulting in clones resistant to the initial drug treatment.

To recount a bit of background I list the overall points of the one of previous posts on tumor heterogeneity (and an interview with Dr. Charles Swanton) are as follows:

Multiple biopsies of primary tumor and metastases are required to determine the full mutational landscape of a patient’s tumor

The intratumor heterogeneity will have an impact on the personalized therapy strategy for the clinician

Metastases arising from primary tumor clones will have a greater genomic instability and mutational spectrum than the tumor from which it originates

Tumors and their metastases do NOT evolve in a linear path but have a branched evolution and would complicate biomarker development and the prognostic and resistance outlook for the patient

 

The following is a curation of various talks and abstracts from the 2015 AACR National Meeting in Philadelphia on effects of clonal evolution and intratumoral heterogeneity of a tumor with respect to development of chemoresistance. As this theory of heterogeneity and clonal evolution is particularly new I attempted to present all works (although apologize for the length upfront) to forgo bias and so the reader may extract any information pertinent to their clinical efforts and research. However I will give a brief highlight summary below:

 

From the 2015 AACR National Meeting in Philadelphia

 

 

 

 

PresentationNumber:NGO2

Presentation Title: Polyclonal and heterogeneous resistance to targeted therapy in leukemia
Presentation Time: Monday, Apr 20, 2015, 10:40 AM -10:55 AM
Location: Room 201, Pennsylvania Convention Center
Author Block: Catherine C. Smith, Amy Paguirigan, Chen-Shan Chin, Michael Brown, Wendy Parker, Mark J. Levis, Alexander E. Perl, Kevin Travers, Corynn Kasap, Jerald P. Radich, Susan Branford, Neil P. Shah. University of California, San Francisco, CA, Fred Hutchinson Cancer Research Center, Seattle, WA, Pacific Biosciences, Menlo Park, CA, Royal Adelaide Hospital, Adelaide, Australia, Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore, MD, Abramson Cancer Center of the University of Pennsylvania, Philadelphia, PA, University of California, San Francisco, CA
Abstract Body: Genomic studies in solid tumors have revealed significant branching intratumoral clonal genetic heterogeneity. Such complexity is not surprising in solid tumors, where sequencing studies have revealed thousands of mutations per tumor genome. However, in leukemia, the genetic landscape is considerably less complex. Chronic myeloid leukemia (CML) is the human malignancy most definitively linked to a single genetic lesion, the BCR-ABL gene fusion. Genome wide sequencing of acute myeloid leukemia (AML) has revealed that AML is the most genetically straightforward of all extensively sequenced adult cancers to date, with an average of 13 coding mutations and 3 or less clones identified per tumor.
In CML, tyrosine kinase inhibitors (TKIs) of BCR-ABL have resulted in high rates of remission. However, despite excellent initial response rates with TKI monotherapy, patients still relapse, including virtually all patients with Philadelphia-positive acute lymphoblastic leukemia and blast crisis CML. Studies of clinical resistance highlight BCR-ABL as the sole genetic driver in CML as secondary kinase domain (KD) mutations that prevent drug binding are the predominant mechanism of relapse on BCR-ABL TKIs.
In AML, a more diverse panel of disease-defining genetic mutations has been uncovered. However, in individual patients, a single oncogene can still drive disease. This is the case in FLT3 mutant AML, in which the investigational FLT3 TKI quizartinib achieved an initial response rate of ~50% in relapsed/refractory AML patients with activating FLT3 internal tandem duplication (ITD) mutations, though most patients eventually relapsed. Confirming the importance of FLT3 in disease maintenance, we showed that 8 of 8 patients who relapsed on quizartinib did so due to acquired drug-resistant FLT3 KD mutations.
Studies in CML have revealed that sequential TKI therapy is associated with additional complexity where multiple mutations can coexist separately in an individual patient (“polyclonality”) or in tandem on a single allele (“compound mutations”). In AML, we observed polyclonal FLT3-ITD KD mutations in 2 of 8 patients examined in our initial study of quizartinib resistance.
In light of the polyclonal KD mutations observed in CML and AML at the time of TKI relapse, we undertook next generation sequencing studies to determine the true genetic complexity in CML and AML patients at the time of relapse on targeted therapy. We used Pacific Biosciences RS Single Molecule Real Time (SMRT) third generation sequencing technology to sequence the entire ABL KD or the entire FLT3 juxtamembrane and KD on a single strand of DNA. Using this method, we assessed a total of 103 samples from 79 CML patients on ABL TKI therapy and 36 paired pre-treatment and relapse samples from 18 FLT3-ITD+ AML patients who responded to investigational FLT3 TKI therapy.
In CML, using SMRT sequencing, we detected all mutations previously detected by direct sequencing. Of samples in which multiple mutations were detectable by direct sequencing, 85% had compound mutant alleles detectable in a variety of combinations. Compound mutant alleles were comprised of both dominant and minor mutations, some which were not detectable by direct sequencing. In the most complex case, 12 individual mutant alleles comprised of 7 different mutations were identified in a single sample.
For 12 CML patients, we interrogated longitudinal samples (2-4 time points per patient) and observed complex clonal relationships with highly dynamic shifts in mutant allele populations over time. We detected compound mutations arising from ancestral single mutant clones as well as parallel evolution of de novo polyclonal and compound mutations largely in keeping with what would be expected to cause resistance to the second generation TKI therapy received by that patient.
We used a phospho-flow cytometric technique to assesses the phosphorylation status of the BCR-ABL substrate CRKL in as a method to test the ex vivo biochemical responsiveness of individual mutant cell populations to TKI therapy and assess functional cellular heterogeneity in a given patient at a given timepoint. Using this technique, we observed co-existing cell populations with differential ex vivo response to TKI in 2 cases with detectable polyclonal mutations. In a third case, we identified co-existence of an MLL-AF9 containing cell population that retained the ability to modulate p-CRKL in response to BCR-ABL TKIs along with a BCR-ABL containing only population that showed biochemical resistance to all TKIs, suggesting the co-existence of BCR-ABL independent and dependent resistance in a single patient.
In AML, using SMRT sequencing, we identified acquired quizartinib resistant KD mutations on the FLT3-ITD (ITD+) allele of 9 of 9 patients who relapsed after response to quizartinib and 4 of 9 patients who relapsed after response to the investigational FLT3 inhibitor, PLX3397. In 4 cases of quizartinib resistance and 3 cases of PLX3397 resistance, polyclonal mutations were observed, including 7 different KD mutations in one patient with PLX3397 resistance. In 7 quizartinib-resistant cases and 3 PLX3397-resistant cases, mutations occurred at the activation loop residue D835. When we examined non-ITD containing (ITD-) alleles, we surprisingly uncovered concurrent drug-resistant FLT3 KD mutations on ITD- alleles in 7 patients who developed quizartinib resistance and 4 patients with PLX3397 resistance. One additional PLX3397-resistant patient developed a D835Y mutation only in ITD- alleles at the time of resistance, suggesting selection for a non-ITD containing clone. All of the individual substitutions found on ITD- alleles were the same substitutions identified on ITD+ alleles for each individual patient.
Given that the same individual mutations found on ITD- alleles were also found on ITD+ alleles, we sought to determine whether these mutations were found in the same cell or were indicative of polyclonal blast populations in each patient. To answer this question, we performed single cell sorting of viably frozen blasts from 3 quizartinib-resistant patients with D835 mutations identified at the time of relapse and genotyped single cells for the presence or absence of ITD and D835 mutations. This analysis revealed striking genetic heterogeneity. In 2/3 cases, polyclonal D835 mutations were found in both ITD+ and ITD- cells. In all cases, FLT3-ITD and D835 mutations were found in both heterozygous and homozygous combinations. Most surprisingly, in all 3 patients, approximately 30-40% of FLT3-ITD+ cells had no identified quizartinib resistance-causing FLT3 KD mutation to account for resistance, suggesting the presence of non-FLT3 dependent resistance in all patients.
To determine that ITD+ cells lacking FLT3 KD mutations observed in patients relapsed on quizartinib are indeed consistent with leukemic blasts functionally resistant to quizartinib and do not instead represent a population of differentiated or non-proliferating cells, we utilized relapse blasts from another patient who initially achieved clearance of bone marrow blasts on quizartinib and developed a D835Y mutation at relapse. We performed a colony assay in the presence of 20nM quizartinib. As expected, this dose of quizartinib was unable to suppress the colony-forming ability of blasts from this relapsed patient when compared to DMSO treatment. Genotyping of individual colonies grown from this relapse sample in the presence of 20nM quizartinib again showed remarkable genetic heterogeneity, including ITD+ and ITD- colonies with D835Y mutations in homozygous and heterozygous combinations as well as ITD+ colonies without D835Y mutations, again suggesting the presence of blasts with non-FLT3 dependent resistance. Additionally, 4 colonies with no FLT3 mutations at all were identified in this sample, suggesting the presence of a quizartinib-resistant non-FLT3 mutant blast population. To see if we could identify specific mechanisms of off-target resistance, we performed targeted exome sequencing 33-AML relevant genes from relapse and pre-treatment DNA from all four patients and detected no new mutations in any genes other than FLT3 acquired at the time of disease relapse. Clonal genetic heterogeneity is not surprising in solid tumors, where multiple driver mutations frequently occur, but in CML and FLT3-ITD+ AML, where disease has been shown to be exquisitely dependent on oncogenic driver mutations, our studies suggest a surprising amount of clonal diversity. Our findings show that clinical TKI resistance in these diseases is amazingly intricate on the single allele level and frequently consists of both polyclonal and compound mutations that give rise to an complicated pool of TKI-resistant alleles that can change dynamically over time. In addition, we demonstrate that cell populations with off-target resistance can co-exist with other TKI-resistant populations, underscoring the emerging complexity of clinical TKI resistance. Such complexity argues strongly that monotherapy strategies in advanced CML and AML may be ultimately doomed to fail due to heterogeneous cell intrinsic resistance mechanisms. Ultimately, combination strategies that can address both on and off target resistance will be required to effect durable therapeutic responses.
Session Title: Tumor Heterogeneity and Evolution
Session Type: Educational Session
Session Start/End Time: Saturday, Apr 18, 2015, 1:00 PM – 3:00 PM
Location: Terrace Ballroom II-III (400 Level), Pennsylvania Convention Center
CME: CME-Designated
CME/CE Hours: 2
Session Description: One of the major challenges for both the measurement and management of cancer is its heterogeneity. Recent studies have revealed both extensive inter- and intra-tumor heterogeneity at the genotypic and phenotypic levels. Leaders in the field will discuss this challenge, its origins, dynamics and clinical importance. They will also review how we can best measure and deal with tumor heterogeneity, particularly intra-tumor heterogeneity.
Presentations:
Chairperson
Saturday, Apr 18, 2015, 1:00 PM – 3:00 PM
Carlo C. Maley. UCSF Helen Diller Family Comp. Cancer Center, San Francisco, CA
Universal biomarkers: How to handle tumor heterogeneity
Saturday, Apr 18, 2015, 1:00 PM – 1:25 PM
Carlo C. Maley. UCSF Helen Diller Family Comp. Cancer Center, San Francisco, CA
Discussion
Saturday, Apr 18, 2015, 1:25 PM – 1:30 PM
Heterogeneity of resistance to cancer therapy
Saturday, Apr 18, 2015, 1:30 PM – 1:55 PM
Ivana Bozic. HARVARD UNIV., Cambridge, MA
Discussion
Saturday, Apr 18, 2015, 1:55 PM – 2:00 PM
Determinants of phenotypic intra-tumor heterogeneity: integrative approach
Saturday, Apr 18, 2015, 2:00 PM – 2:25 PM
Andriy Marusyk, Michalina Janiszewska, Doris Tabassum. Dana-Farber Cancer Institute, Boston, MA, Dana-Farber Cancer Institute, Boston, MA
Discussion
Saturday, Apr 18, 2015, 2:25 PM – 2:30 PM
Cancer clonal complexity and evolution at the macro- and microheterogeneity scale
Saturday, Apr 18, 2015, 2:30 PM – 2:55 PM
Marco Gerlinger. Institute of Cancer Research, London, United Kingdom
Discussion
Saturday, Apr 18, 2015, 2:55 PM – 3:00 PM

From Ivana Bozic:

A spatial model predicts that dispersal and cell turnover limit intratumour heterogeneity.

Waclaw B, Bozic I, Pittman ME, Hruban RH, Vogelstein B, Nowak MA.

Nature. 2015 Sep 10;525(7568):261-4. doi: 10.1038/nature14971. Epub 2015 Aug 26.

PMID:

26308893

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Bozic I, Nowak MA.

Proc Natl Acad Sci U S A. 2014 Nov 11;111(45):15964-8. doi: 10.1073/pnas.1412075111. Epub 2014 Oct 27.

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Session Title: Mechanisms of Cancer Therapy Resistance
Session Type: Educational Session
Session Start/End Time: Saturday, Apr 18, 2015, 1:00 PM – 3:00 PM
Location: Room 204, Pennsylvania Convention Center
CME: CME-Designated
CME/CE Hours: 2
Session Description: Despite dramatic advances in the treatment of cancer, therapy resistance remains the most significant hurdle in improving the outcome of cancer patients. In this session, we will discuss many different aspects of therapy resistance, including a summary of our current understanding of therapy resistant tumor cell populations as well as analyses of the challenges associated with intratumoral heterogeneity and adaptive responses to targeted therapies.
Presentations:
Chairperson
Saturday, Apr 18, 2015, 1:00 PM – 3:00 PM
Charles Swanton. Cancer Research UK London Research Institute, London, United Kingdom
Tumor heterogeneity and drug resistance
Saturday, Apr 18, 2015, 1:00 PM – 1:30 PM
Charles Swanton. Cancer Research UK London Research Institute, London, United Kingdom
Discussion

Saturday, Apr 18, 2015, 1:30 PM – 1:40 PM
Discussion Discussion, Discussion

Principles of resistance to targeted therapy
Saturday, Apr 18, 2015, 1:40 PM – 2:10 PM
Levi A. Garraway. Dana-Farber Cancer Institute, Boston, MA
Discussion

Saturday, Apr 18, 2015, 2:10 PM – 2:20 PM
Discussion Discussion, Discussion

Adaptive re-wiring of signaling pathways driving drug resistance to targeted therapies
Saturday, Apr 18, 2015, 2:20 PM – 2:50 PM
Taru E. Muranen. Harvard Medical School, Boston, MA
Discussion

Saturday, Apr 18, 2015, 2:50 PM – 3:00 PM
Discussion Discussion, Discussion

Presentation Abstract  

 

 

 

Abstract Number: 737
Presentation Title: Clonal evolution of the HER2 L755S mutation as a mechanism of acquired HER-targeted therapy resistance
Presentation Time: Sunday, Apr 19, 2015, 1:00 PM – 5:00 PM
Location: Section 30
Poster Board Number: 29
Author Block: Xiaowei Xu1, Agostina Nardone1, Huizhong Hu1, Lanfang Qin1, Sarmistha Nanda1, Laura Heiser2, Nicholas Wang2, Kyle Covington1, Edward Chen1, Alexander Renwick1, Tamika Mitchell1, Marty Shea1, Tao Wang1, Carmine De Angelis1, Alejandro Contreras1, Carolina Gutierrez1, Suzanne Fuqua1, Gary Chamness1, Chad Shaw1, Marilyn Li1, David Wheeler1, Susan Hilsenbeck1, Mothaffar Fahed Rimawi1, Joe Gray2, C.Kent Osborne1, Rachel Schiff1. 1Baylor College of Medicine, Houston, TX; 2Oregon Health & Science University, Portland, OR
Abstract Body: Background: Targeting HER2 with lapatinib (L), trastuzumab (T), or the LT combination, is effective in HER2+ breast cancer (BC), but acquired resistance commonly occurs. In our 12-week neoadjuvant
trial (TBCRC006) of LT without chemotherapy in HER2+ BC, the overall pathologic complete response (pCR) rate was 27%. To investigate resistance mechanisms, we developed 10 HER2+ BC cell line
models resistant (R) to one or both drugs (LR/TR/LTR). To discover potential predictive markers/therapeutic targets to circumvent resistance, we completed genomic profiling of the cell lines and a
subset of pre-treatment specimens from TBCRC006.
Methods: Parental (P) and LR/TR/LTR lines of 10 cell line models were profiled with whole exome/RNA sequencing. Mutations detected in R lines but not in P lines of the same model were identified. Mutation-specific Q-PCR was designed for sensitive quantification. Resistant cell and xenograft tumor growth were measured in response to drugs. Whole exome sequencing (>100X) and Ampliseq of 17 baseline tumor/normal pairs from TBCRC006 were performed.
Results: We found and validated the HER2 L755S mutation in the BT474/ATCC-LTR line and BT474/AZ-LR line (in ~30% of DNA/RNA), in which the HER pathway was reactivated for resistance. Overexpression of this mutation was previously shown to induce LR in HER2-negative BC cell lines, and resistant growth of BT474/AZ-LR line is significantly inhibited by HER2-L755S-specific siRNA knock-down, suggesting its role as an acquired L/LT resistance driver in HER2+ BC. Sequencing of BT474/AZ-LR single cell clones found the mutation in ~30% of HER2 copies in every cell. Using mutation-specific Q-PCR, we found statistically higher HER2 L755S levels in two BT474 parentals compared to P lines of SKBR3, AU565, and UACC812. These data suggest that HER2 L755S resistant subclones preexist in the BT474 parentals and were selected by L treatment to become the major clone in the two R lines. The HER1/2 irreversible tyrosine kinase inhibitor (TKI) afatinib (Afa) robustly inhibited growth of BT474/AZ-LR and BT474/ATCC-LTR cells (IC50: Afa 0.02µM vs. L 3 µM) and BT474/AZ-LR xenografts. Whole exome sequencing/Ampliseq of TBCRC006 found the HER2 L755S mutation in 1/17 primaries. This patient did not achieve pCR. The variant was present in 2% of DNA on both platforms, indicating a subclonal event of the resistance mutation.
Conclusion: Acquired L/LT resistance in the two BT474 R lines is due to selection of HER2 L755S subclones present in parental cells. The higher HER2 L755S
levels in BT474 parentals compared with other parentals, and detection of its subclonal presence in a pre-treatment HER2+ BC patient, suggest that sensitive mutation detection methods will be needed to identify patients with potentially actionable HER family mutations in primary tumor. Treating this patient group
with an irreversible TKI like Afa may prevent resistance and improve clinical outcome of this subset of HER2+ BC.
Presentation Number: SY07-04
Presentation Title: The evolutionary landscape of CLL: Therapeutic implications
Presentation Time: Sunday, Apr 19, 2015, 2:25 PM – 2:45 PM
Location: Grand Ballroom (300 Level), Pennsylvania Convention Center
Author Block: Catherine J. Wu. Dana-Farber Cancer Institute, Boston, MA
Abstract Body: Clonal evolution is a key feature of cancer progression and relapse. Recent studies across cancers have demonstrated the extensive degree of intratumoral heterogeneity present within individual cancers. We hypothesized that evolutionary dynamics contribute to the variations in disease tempo and response to therapy that are highly characteristic of chronic lymphocytic leukemia (CLL). We have recently investigated this phenomenon by developing a pipeline that estimates the fraction of cancer cells harboring each somatic mutation within a tumor through integration of whole-exome sequence (WES) and local copy number data (Landau et al., Cell 2013). By applying this analysis approach to 149 CLL cases, we discovered earlier and later cancer drivers, uncovered patterns of clonal evolution in CLL and linked the presence of subclones harboring driver mutations with adverse clinical outcome. Thus, our study, generated from a heterogeneous sample cohort, strongly supports the concept that CLL clonal evolution arises from mass extinction and therapeutic bottlenecks which lead to the emergence of highly fit (and treatment resistant) subclones. We further hypothesized that epigenetic heterogeneity also shapes CLL clonal evolution through interrelation with genetic heterogeneity. Indeed, in recent work, we have uncovered stochastic methylation disorder as the primary cause of methylation changes in CLL and cancer in general, and that this phenomena impacts gene transcription, genetic evolution and clinical outcome. Thus, integrated studies of genetic and epigenetic heterogeneity in CLL have revealed the complex and diverse evolutionary trajectories of these cancer cells.
Immunotherapy is exquisitely suited for specifically and simultaneously targeting multiple lesions. We have developed an approach that leverages whole-exome sequencing to systematically identify personal tumor mutations with immunogenic potential, which can be incorporated as antigen targets in multi-epitope personalized therapeutic vaccines. We are pioneering this approach in an ongoing trial in melanoma and will now expand this concept to address diverse malignancies. Our expectation is that the choice of tumor neoantigens for a vaccine bypasses thymic tolerance and thus generates highly specific and potent high-affinity T cell responses to eliminate tumors in any cancer, including both ‘trunk’ and ‘branch’ lesions.

 

Abstract Number: LB-056
Presentation Title: TP53 and RB1 alterations promote reprogramming and antiandrogen resistance in advanced prostate cancer
Presentation Time: Sunday, Apr 19, 2015, 4:50 PM – 5:05 PM
Location: Room 122, Pennsylvania Convention Center
Author Block: Ping Mu, Zhen Cao, Elizabeth Hoover, John Wongvipat, Chun-Hao Huang, Wouter Karthaus, Wassim Abida, Elisa De Stanchina, Charles Sawyers. Memorial Sloan Kettering Cancer Center, New York, NY
Abstract Body: Castration-resistant prostate cancer (CRPC) is one of the most difficult cancers to treat with conventional methods and is responsible for nearly all prostate cancer deaths in the US. The Sawyers laboratory first showed that the primary mechanism of resistance to antiandrogen therapy is elevated androgen receptor (AR) expression. Research based on this finding has led to the development of next-generation antiandrogen: enzalutamide. Despite the exciting clinical success of enzalutamide, about 60% of patients exhibit various degrees of resistance to this agent. Highly variable responses to enzalutamide limit the clinical benefit of this novel antiandrogen, underscoring the importance of understanding the mechanisms of enzalutamide resistance. Most recently, an unbiased SU2C-Prostate Cancer Dream Team metastatic CRPC sequencing project led by Dr. Sawyers and Dr. Chinnaiyan revealed that mutations in the TP53 locus are the most significantly enriched alteration in CRPC tumors when compared to primary prostate cancers. Moreover, deletions and decreased expressions of the TP53 and RB1 loci (co-occurrence and individual occurrence) are more commonly associated with CRPC than with primary tumors. These results established that alteration of the TP53 and RB1 pathways are associated with the development of antiandrogen resistance.
By knockdowning TP53 or/and RB1 in the castration resistant LNCaP/AR model, we demonstrate that the disruption of either TP53 or RB1 alone confers significant resistance to enzalutamide both in vitro and in vivo. Strikingly, the co-inactivation of these pathways confers the most dramatic resistance. Since up-regulation of either AR or AR target genes is not observed in the resistant tumors, loss of TP53 and RB1 function confers enzalutamide resistance likely through an AR independent mechanism. In the clinic, resistance to enzalutamide is increasingly being associated with a transition to a poorly differentiated or neuroendocrine-like histology. Interestingly, we observed significant up-regulations of the basal cell marker Ck5 and the neuroendocrine-like cell marker Synaptophysin in the TP53 and RB1 inactivated cells, as well as down-regulation of the luminal cell marker Ck8. The differences between these markers became even greater after enzalutamide treatment. By using the p53-stabilizing drug Nutlin, level of p53 is rescued and consequently the the decrease of AR protein caused by RB1 and TP53 knockdown is reversed. These results strongly suggest that interference of TP53 and RB1 pathways confers antiandrogen resistance by “priming” prostate cancer cells to reprogramming or transdifferentiation, likely neuroendocrine-like differentiation, in response to treatment. Futher experiments will be performed to assess the molecular mechanism of TP53/RB1 alterations in mediating cell programming and conferring antiandrogen resistance.

 

Abstract Number: LB-146
Presentation Title: TGF-β-induced tumor heterogeneity and drug resistance of cancer stem cells
Presentation Time: Monday, Apr 20, 2015, 1:00 PM – 5:00 PM
Location: Section 41
Author Block: Naoki Oshimori1, Daniel Oristian1, Elaine Fuchs2. 1Rockefeller University, New York, NY; 2HHMI/Rockefeller University, New York, NY
Abstract Body: Among the most common and life-threatening cancers world-wide, squamous cell carcinoma (SCC) exhibit high rates of tumor recurrence following anti-cancer therapy. Subsets of cancer stem cells (CSCs) often escape anti-cancer therapeutics and promote recurrence. However, its sources and mechanisms that generate tumor heterogeneity and therapy-resistant cell population are largely unknown. Tumor microenvironment may drive intratumor heterogeneity by transmitting signaling factors, oxygen and metabolites to tumor cells depending on their proximity to the local sources. While the hypothesis is attractive, experimental evidence is lacking, and non-genetic mechanisms that drive functional heterogeneity remain largely unknown. As a potential non-genetic factor, we focused on TGF-β because of its multiple roles in tumor progression.
Here we devise a functional reporter system to monitor, track and modify TGF-β signaling in mouse skin SCC in vivo. Using this approach, we found that perivascular TGF-β in the tumor microenvironment generates heterogeneity in TGF-β signaling in neighboring CSCs. This heterogeneity is functionally important: small subsets of TGF-β-responding CSCs proliferate more slowly than their non-responding counterparts. They also exhibit invasive morphology and a malignant differentiation program compared to their non-responding neighbors. By lineage tracing, we show that although TGF-β-responding CSCs clonally expand more slowly they gain a growth advantage in a remarkable ability to escape cisplatin-induced apoptosis. We show that indeed it is their progenies that make a substantial contribution in tumor recurrence. Surprisingly, the slower proliferating state of this subset of CSCs within the cancer correlated with but did not confer the survival advantage to anti-cancer drugs. Using transcriptomic, biochemical and genetic analyses, we unravel a novel mechanism by which heterogeneity in the tumor microenvironment allows a subset of CSCs to respond to TGF-β, and evade anti-cancer drugs.
Our findings also show that TGF-β established ability to suppress proliferation and promote invasion and metastasis do not happen sequentially, but rather simultaneously. This new work build upon the roles of this factor in tumor progression, and sets an important paradigm for a non-genetic factor that produces tumor heterogeneity.
Abstract Number: LB-129
Presentation Title: Identifying tumor subpopulations and the functional consequences of intratumor heterogeneity using single-cell profiling of breast cancer patient-derived xenografts
Presentation Time: Monday, Apr 20, 2015, 1:00 PM – 5:00 PM
Location: Section 41
Author Block: Paul Savage1, Sadiq M. Saleh1, Ernesto Iacucci1, Timothe Revil1, Yu-Chang Wang1, Nicholas Bertos1, Anie Monast1, Hong Zhao1, Margarita Souleimanova1, Keith Szulwach2, Chandana Batchu2, Atilla Omeroglu1, Morag Park1, Ioannis Ragoussis1. 1McGill University, Montreal, QC, Canada; 2Fluidigm Corporation, South San Francisco, CA
Abstract Body: Human breast tumors have been shown to exhibit extensive inter- and intra-tumor heterogeneity. While recent advances in genomic technologies have allowed us to deconvolute this heterogeneity, few studies have addressed the functional consequences of diversity within tumor populations. Here, we identified an index case for which we have derived a patient-derived xenograft (PDX) as a renewable tissue source to identify subpopulations and perform functional assays. On pathology, the tumor was an invasive ductal carcinoma which was hormone receptor-negative, HER2-positive (IHC 2+, FISH average HER2/CEP17 2.4), though the FISH signal was noted to be heterogeneous. On gene expression profiling of bulk samples, the primary tumor and PDX were classified as basal-like. We performed single cell RNA and exome sequencing of the PDX to identify population structure. Using a single sample predictor of breast cancer subtype, we have identified single basal-like, HER2-enriched and normal-like cells co-existing within the PDX tumor. Genes differentially expressed between these subpopulations are involved in proliferation and differentiation. Functional studies distinguishing these subpopulations are ongoing. Microfluidic whole genome amplification followed by whole exome capture of 81 single cells showed high and homogeneous target enrichment with >75% of reads mapping uniquely on target. Variant calling using GATK and Samtools revealed founder mutations in key genes as BRCA1 and TP53, as well as subclonal mutations that are being investigated further. Loss of heterozygocity was observed in 16 TCGA cancer driver genes and novel mutations in 7 cancer driver genes. These findings may be important in understanding the functional consequences of intra-tumor heterogeneity with respect to clinically important phenotypes such as invasion, metastasis and drug-resistance.
Abstract Number: 2847
Presentation Title: High complexity barcoding to study clonal dynamics in response to cancer therapy
Presentation Time: Monday, Apr 20, 2015, 4:35 PM – 4:50 PM
Location: Room 118, Pennsylvania Convention Center
Author Block: Hyo-eun C. Bhang1, David A. Ruddy1, Viveksagar Krishnamurthy Radhakrishna1, Rui Zhao2, Iris Kao1, Daniel Rakiec1, Pamela Shaw1, Marissa Balak1, Justina X. Caushi1, Elizabeth Ackley1, Nicholas Keen1, Michael R. Schlabach1, Michael Palmer1, William R. Sellers1, Franziska Michor2, Vesselina G. Cooke1, Joshua M. Korn1, Frank Stegmeier1. 1Novartis Institutes for BioMedical Research, Cambridge, MA; 2Dana-Farber Cancer Institute, Boston, MA
Abstract Body: Targeted therapies, such as erlotinib and imatinib, lead to dramatic clinical responses, but the emergence of resistance presents a significant challenge. Recent studies have revealed intratumoral heterogeneity as a potential source for the emergence of therapeutic resistance. However, it is still unclear if relapse/resistance is driven predominantly by pre-existing or de novo acquired alterations. To address this question, we developed a high-complexity barcode library, ClonTracer, which contains over 27 million unique DNA barcodes and thus enables the high resolution tracking of cancer cells under drug treatment. Using this library in two clinically relevant resistance models, we demonstrate that the majority of resistant clones pre-exist as rare subpopulations that become selected in response to therapeutic challenge. Furthermore, our data provide direct evidence that both genetic and non-genetic resistance mechanisms pre-exist in cancer cell populations. The ClonTracer barcoding strategy, together with mathematical modeling, enabled us to quantitatively dissect the frequency of drug-resistant subpopulations and evaluate the impact of combination treatments on the clonal complexity of these cancer models. Hence, monitoring of clonal diversity in drug-resistant cell populations by the ClonTracer barcoding strategy described here may provide a valuable tool to optimize therapeutic regimens towards the goal of curative cancer therapies.
Abstract Number: 3590
Presentation Title: Resistance mechanisms to ALK inhibitors
Presentation Time: Tuesday, Apr 21, 2015, 8:00 AM -12:00 PM
Location: Section 31
Poster Board Number: 13
Author Block: Ryohei Katayama1, Noriko Yanagitani1, Sumie Koike1, Takuya Sakashita1, Satoru Kitazono1, Makoto Nishio1, Yasushi Okuno2, Jeffrey A. Engelman3, Alice T. Shaw3, Naoya Fujita1. 1Japanese Foundation for Cancer Research, Tokyo, Japan; 2Graduate School of Medicine, Kyoto University, Kyoto, Japan; 3Massachusetts General Hospital Cancer Center, Boston, MA
Abstract Body: Purpose: ALK-rearranged non-small cell lung cancer (NSCLC) was first reported in 2007. Approximately 3-5% of NSCLCs harbor an ALK gene rearrangement. The first-generation ALK tyrosine kinase inhibitor (TKI) crizotinib is a standard therapy for patients with advanced ALK-rearranged NSCLC. Several next-generation ALK-TKIs have entered the clinic and have shown promising antitumor activity in crizotinib-resistant patients. As patients still relapse even on these next-generation ALK-TKIs, we examined mechanisms of resistance to one next-generation ALK-TKI – alectinib – and potential strategies to overcome this resistance.
Experimental Procedure: We established a cell line model of alectinib resistance, and analyzed resistant tumor specimens from patients who had relapsed on alectinib. Cell lines were also established under an IRB-approved protocol when there was sufficient fresh tumor tissue. We established Ba/F3 cells expressing EML4-ALK and performed ENU mutagenesis to compare potential crizotinib or alectinib-resistance mutations. In addition, we developed Ba/F3 models harboring ALK resistance mutations and evaluated the potency of multiple next-generation ALK-TKIs including 3rd generation ALK inhibitor in these models and in vivo. To elucidate structure-activity-relationships of ALK resistance mutations, we performed computational thermodynamic simulation with MP-CAFEE.
Results: We identified multiple resistance mutations, including ALK I1171N, I1171S, and V1180L, from the ENU mutagenesis screen and the cell line model. In addition we found secondary mutations at the I1171 residue from the Japanese patients who developed resistance to alectinib or crizotinib. Both ALK mutations (V1180L and I1171 mutations) conferred resistance to alectinib as well as to crizotinib, but were sensitive to ceritinib and other next-generation ALK-TKIs. Based on thermodynamics simulation, each resistance mutation is predicted to lead to distinct structural alterations that decrease the binding affinity of ALK-TKIs for ALK.
Conclusions: We have identified multiple alectinib-resistance mutations from the cell line model, patient derived cell lines, and tumor tissues, and ENU mutagenesis. ALK secondary mutations arising after alectinib exposure are sensitive to other next generation ALK-TKIs. These findings suggest a potential role for sequential therapy with multiple next-generation ALK-TKIs in patients with advanced, ALK-rearranged cancers.
Session Title: Mechanisms of Resistance: From Signaling Pathways to Stem Cells
Session Type: Major Symposium
Session Start/End Time: Tuesday, Apr 21, 2015, 10:30 AM -12:30 PM
Location: Terrace Ballroom II-III (400 Level), Pennsylvania Convention Center
CME: CME-Designated
CME/CE Hours: 2
Session Description: Even the most effective cancer therapies are limited due to the development of one or more resistance mechanisms. Acquired resistance to targeted therapies can, in some cases, be attributed to the selective propagation of a small population of intrinsically resistant cells. However, there is also evidence that cancer drugs themselves can drive resistance by triggering the biochemical- or genetic-reprogramming of cells within the tumor or its microenvironment. Therefore, understanding drug resistance at the molecular and biological levels may enable the selection of specific drug combinations to counteract these adaptive responses. This symposium will explore some of the recent advances addressing the molecular basis of cancer cell drug resistance. We will address how tumor cell signaling pathways become rewired to facilitate tumor cell survival in the face of some of our most promising cancer drugs. Another topic to be discussed involves how drugs select for or induce the reprogramming of tumor cells toward a stem-like, drug resistant fate. By targeting the molecular driver(s) of rewired signaling pathways and/or cancer stemness it may be possible to select drug combinations that prevent the reprogramming of tumors and thereby delay or eliminate the onset of drug resistance.
Presentations:
Chairperson
Tuesday, Apr 21, 2015, 10:30 AM -12:30 PM
David A. Cheresh. UCSD Moores Cancer Center, La Jolla, CA
Introduction
Tuesday, Apr 21, 2015, 10:30 AM -10:40 AM
Resistance to tyrosine kinase inhibitors: Heterogeneity and therapeutic strategies.
Tuesday, Apr 21, 2015, 10:40 AM -10:55 AM
Jeffrey A. Engelman. Massachusetts General Hospital, Boston, MA
Discussion
Tuesday, Apr 21, 2015, 10:55 AM -11:00 AM
NG04: Clinical acquired resistance to RAF inhibitor combinations in BRAF mutant colorectal cancer through MAPK pathway alterations
Tuesday, Apr 21, 2015, 11:00 AM -11:15 AM
Ryan B. Corcoran, Leanne G. Ahronian, Eliezer Van Allen, Erin M. Coffee, Nikhil Wagle, Eunice L. Kwak, Jason E. Faris, A. John Iafrate, Levi A. Garraway, Jeffrey A. Engelman. Massachusetts General Hospital Cancer Center, Boston, MA, Dana-Farber Cancer Institute, Boston, MA
Discussion
Tuesday, Apr 21, 2015, 11:15 AM -11:20 AM
SY27-02: Tumour heterogeneity and therapy resistance in melanoma
Tuesday, Apr 21, 2015, 11:20 AM -11:35 AM
Claudia Wellbrock. Univ. of Manchester, Manchester, United Kingdom

Presentation Number: SY27-02
Presentation Title: Tumour heterogeneity and therapy resistance in melanoma
Presentation Time: Tuesday, Apr 21, 2015, 11:20 AM -11:35 AM
Location: Terrace Ballroom II-III (400 Level), Pennsylvania Convention Center
Author Block: Claudia Wellbrock. Univ. of Manchester, Manchester, United Kingdom
Abstract Body: Solid tumors are structurally very complex; they consist of heterogeneous cancer cell populations, other non-cancerous cell types and a distinct extracellular matrix. Interactions of cancer cells with non-cancerous cells is well investigated, and our recent work in melanoma has demonstrated that the cellular environment that surrounds cancer cells has a major impact on the way a patient responds to MAP-kinase pathway targeting therapy.
We have shown that intra-tumor signaling within a heterogeneous tumor can have a major impact on the efficacy of BRAF and MEK inhibitors. With the increasing evidence of genetic and phenotypic heterogeneity within tumors, intra-tumor signaling between individual cancer-cell subpopulations is therefore a crucial factor that needs to be considered in future therapy approaches. Our work has identified the ‘melanocyte-lineage survival oncogene’ MITF as an important player in phenotypic heterogeneity (MITFhigh and MITFlow cells) in melanoma, and MITF expression levels are crucial for the response to MAP-kinase pathway targeted therapy. We found that ‘MITF heterogeneity’ can be caused by cell-autonomous mechanisms or by the microenvironment, including the immune-microenvironment.
We have identified various mechanisms underlying MITF action in resistance to BRAF and MEK inhibitors in melanoma. In MITFhigh expressing cells, MITF confers cell-autonomous resistance to MAP-kinase pathway targeted therapy. Moreover, it appears that in melanomas heterogeneous for MITF expression (MITFhigh and MITFlow cells), individual subpopulations of resistant and sensitive cells communicate and MITF can contribute to overall tumor-resistance through intra-tumor signaling. Finally, we have identified a novel approach of interfering with MITF action, which profoundly sensitizes melanoma to MAP-kinase pathway targeted therapy.
Discussion
Tuesday, Apr 21, 2015, 11:35 AM -11:40 AM
SY27-03: Breast cancer stem cell state transitions mediate therapeutic resistance
Tuesday, Apr 21, 2015, 11:40 AM -11:55 AM
Max S. Wicha. University of Michigan, Comprehensive Cancer Center, Ann Arbor, MI
Discussion
Tuesday, Apr 21, 2015, 11:55 AM -12:00 PM
SY27-04: Induction of cancer stemness and drug resistance by EGFR blockade
Tuesday, Apr 21, 2015, 12:00 PM -12:15 PM
David A. Cheresh. UCSD Moores Cancer Center, La Jolla, CA

 

Cellular Reprogramming in Carcinogenesis: Implications for Tumor Heterogeneity, Prognosis, and Therapy
Session Type: Major Symposium
Session Start/End Time: Tuesday, Apr 21, 2015, 10:30 AM -12:30 PM
Location: Room 103, Pennsylvania Convention Center
CME: CME-Designated
CME/CE Hours: 2
Session Description: Cancers, both solid and liquid, consist of phenotypically heterogeneous cell types that make up the full cellular complement of disease. Deep sequencing of bulk cancers also frequently reveals a genetic intratumoral heterogeneity that reflects clonal evolution in space and in time and under the influence of treatment. How the distinct phenotypic and genotypic cells contribute to individual cancer growth and progression is incompletely understood. In this symposium, we will discuss issues of cancer heterogeneity and effects on growth and treatment resistance, with emphasis on cancer cell functional properties and influences of the microenvironment, interclonal genomic heterogeneity, and lineage relationships between cancer cells with stem cell and differentiated properties. Understanding these complex cellular relationships within cancers will have critical implications for devising more effective treatments.
Presentations:
Chairperson
Tuesday, Apr 21, 2015, 10:30 AM -12:30 PM
Peter B. Dirks. Univ. of Toronto Hospital for Sick Children, Toronto, ON, Canada
Introduction

Tuesday, Apr 21, 2015, 10:30 AM -10:40 AM

Origins, evolution and selection in childhood leukaemia
Tuesday, Apr 21, 2015, 10:40 AM -11:00 AM
Tariq Enver. Cancer Research UK, London, United Kingdom
Discussion

Tuesday, Apr 21, 2015, 11:00 AM -11:05 AM

Cytokine-controlled stem cell plasticity inintestinal tumorigenesis
Tuesday, Apr 21, 2015, 11:05 AM -11:25 AM
Florian Greten. Georg-Speyer-Haus, Frankfurt, Germany
Discussion

Tuesday, Apr 21, 2015, 11:25 AM -11:30 AM

SY23-03: Intratumoural heterogeneity in human serous ovarian carcinoma
Tuesday, Apr 21, 2015, 11:30 AM -11:50 AM
John P. Stingl. Cancer Research UK Cambridge Research Inst., Cambridge, United Kingdom
Discussion

Tuesday, Apr 21, 2015, 11:50 AM -11:55 AM

Functional and genomic heterogeneity in brain tumors
Tuesday, Apr 21, 2015, 11:55 AM -12:15 PM

 

Proc Natl Acad Sci U S A. 2015 Jan 20;112(3):851-6. doi: 10.1073/pnas.1320611111. Epub 2015 Jan 5.

Single cell-derived clonal analysis of human glioblastoma links functional and genomic heterogeneity.

Meyer M1, Reimand J2, Lan X3, Head R1, Zhu X1, Kushida M1, Bayani J4, Pressey JC5, Lionel AC6, Clarke ID7, Cusimano M8, Squire JA9, Scherer SW6, Bernstein M10, Woodin MA5, Bader GD11, Dirks PB12.

Author information

Abstract

Glioblastoma (GBM) is a cancer comprised of morphologically, genetically, and phenotypically diverse cells. However, an understanding of the functional significance of intratumoral heterogeneity is lacking. We devised a method to isolate and functionally profile tumorigenic clones from patient glioblastoma samples. Individual clones demonstrated unique proliferation and differentiation abilities. Importantly, naïve patient tumors included clones that were temozolomide resistant, indicating that resistance to conventional GBM therapy can preexist in untreated tumors at a clonal level. Further, candidate therapies for resistant clones were detected with clone-specific drug screening. Genomic analyses revealed genes and pathways that associate with specific functional behavior of single clones. Our results suggest that functional clonal profiling used to identify tumorigenic and drug-resistant tumor clones will lead to the discovery of new GBM clone-specific treatment strategies.

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739: Tumor cell plasticity with transition to a mesenchymal phenotype is a mechanism of chemoresistance that is reversed by Notch pathway inhibition in lung adenocarcinoma
Sunday, Apr 19, 2015, 1:00 PM – 5:00 PM
Khaled A. Hassan. University Of Michigan, Ann Arbor, MI

745: Oncostatin M receptor activation leads to molecular targeted therapy resistance in non-small cell lung cancer
Sunday, Apr 19, 2015, 1:00 PM – 5:00 PM
Kazuhiko Shien1, Vassiliki A. Papadimitrakopoulou1, Dennis Ruder1, Nana E. Hanson1, Neda Kalhor1, J. Jack Lee1, Waun Ki Hong1, Ximing Tang1, Roy S. Herbst2, Luc Girard3, John D. Minna3, Jonathan M. Kurie1, Ignacio I. Wistuba1, Julie G. Izzo1. 1University of Texas MD Anderson Cancer Center, Houston, TX; 2Yale Cancer Center, Yale School of Medicine, New Haven, CT; 3Hamon Center for Therapeutic Oncology Research, University of Texas Southwestern Medical Center, Dallas, TX

746: Activation of EGFR bypass signaling through TGFα overexpression induces acquired resistance to alectinib in ALK-translocated lung cancer cells
Sunday, Apr 19, 2015, 1:00 PM – 5:00 PM
Tetsuo Tani, Hiroyuki Yasuda, Junko Hamamoto, Aoi Kuroda, Daisuke Arai, Kota Ishioka, Keiko Ohgino, Ichiro Kawada, Katsuhiko Naoki, Hayashi Yuichiro, Tomoko Betsuyaku, Kenzo Soejima. Keio University, Tokyo, Japan

752: Elucidating the mechanisms of acquired resistance in lung adenocarcinomas
Sunday, Apr 19, 2015, 1:00 PM – 5:00 PM
Sandra Ortiz-Cuarán1, Lynnette Fernandez-Cuesta1, Christine M. Lovly2, Marc Bos1, Matthias Scheffler3, Sebastian Michels3, Kerstin Albus4, Lydia Meyer4, Katharina König4, Ilona Dahmen1, Christian Mueller1, Luca Ozretić4, Lars Tharun4, Philipp Schaub1, Alexandra Florin4, Berit Pinther1, Nike Bahlmann1, Sascha Ansén3, Martin Peifer1, Lukas C. Heukamp4, Reinhard Buettner4, Martin L. Sos1, Jürgen Wolf3, William Pao2, Roman K. Thomas1. 1University of Cologne, Cologne, Germany; 2Department of Medicine, Vanderbilt University, Nashville, TN; 3Department of Internal Medicine, Center for Integrated Oncology Köln-Bonn, University Hospital Cologne, Cologne, Germany; 4Institute of Pathology, University Hospital Cologne, Cologne, Germany

760: On the evolution of erlotinib-resistant NSCLC subpopulations
Sunday, Apr 19, 2015, 1:00 PM – 5:00 PM
Michael E. Ramirez1, Robert J. Steininger, III1, Lani F. Wu2, Steven J. Altschuler2. 1UT Southwestern, Dallas, TX; 2UCSF, San Francisco, CA
763: Implications of resistance patterns with NSCLC targeted agents
Sunday, Apr 19, 2015, 1:00 PM – 5:00 PM
David J. Stewart, Paul Wheatley-Price, Rob MacRae, Jason Pantarotto. University of Ottawa, Ottawa, ON, Canada

 

768: A kinome-wide siRNA screen identifies modifiers of sensitivity to the EGFR T790M-targeted tyrosine kinase inhibitor (TKI), AZD9291, in EGFR mutant lung adenocarcinoma
Sunday, Apr 19, 2015, 1:00 PM – 5:00 PM
Eiki Ichihara1, Joshua A. Bauer2, Pengcheng Lu3, Fei Ye3, Darren Cross4, William Pao1, Christine M. Lovly1. 1Vanderbilt University School of Medicine, Nashville, TN; 2Vanderbilt Institute of Chemical Biology High-Throughput Screening Facility, Nashville, TN; 3Vanderbilt University Medical Center, Nashville, TN; 4AstraZeneca Oncology Innovative Medicines, United Kingdom

LB-055: Clinical acquired resistance to RAF inhibitor combinations in BRAF-mutant colorectal cancer through MAPK pathway alterations
Sunday, Apr 19, 2015, 4:35 PM – 4:50 PM
Leanne G. Ahronian1, Erin M. Sennott1, Eliezer M. Van Allen2, Nikhil Wagle2, Eunice L. Kwak1, Jason E. Faris1, Jason T. Godfrey1, Koki Nishimura1, Kerry D. Lynch3, Craig H. Mermel1, Elizabeth L. Lockerman1, Anuj Kalsy1, Joseph M. Gurski, Jr.1, Samira Bahl4, Kristin Anderka4, Lisa M. Green4, Niall J. Lennon4, Tiffany G. Huynh3, Mari Mino-Kenudson3, Gad Getz1, Dora Dias-Santagata3, A. John Iafrate3, Jeffrey A. Engelman1, Levi A. Garraway2, Ryan B. Corcoran1. 1Massachusetts General Hospital Cancer Center, Boston, MA; 2Dana Farber Cancer Institute, Boston, MA; 3Massachusetts General Hospital Department of Pathology, Boston, MA; 4Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA

 

Other Articles on this Site Related to Tumor Heterogeneity Include

Notes On Tumor Heterogeneity: Targets and Mechanisms, from the 2015 AACR Meeting in Philadelphia PA

Issues in Personalized Medicine: Discussions of Intratumor Heterogeneity from the Oncology Pharma forum on LinkedIn

Issues in Personalized Medicine in Cancer: Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing

CANCER COMPLEXITY: Heterogeneity in Tumor Progression and Drug Response – 2015 Annual Symposium @Koch Institute for Integrative Cancer Research at MIT – W34, 6/12/2015 9:00 AM EDT – 4:30 PM EDT

In vitro Models of Tumor Microenvironment for New Cancer Target and Drug Discovery, 11/17 – 11/19/2014, Hyatt Boston Harbor

What can we expect of tumor therapeutic response?

 

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Hematological Cancer Classification

Author and Curator: Larry H. Bernstein, MD, FCAP

 

 

Introduction to leukemias and lymphomas

 

2.4.1 Ontogenesis of the blood elements: hematopoiesis

http://www.britannica.com/EBchecked/topic/69747/blood-cell-formation

Blood cells are divided into three groups: the red blood cells (erythrocytes), the white blood cells (leukocytes), and the blood platelets (thrombocytes). The white blood cells are subdivided into three broad groups: granulocytes, lymphocytes, and monocytes.

Blood cells do not originate in the bloodstream itself but in specific blood-forming organs, notably the marrow of certain bones. In the human adult, the bone marrow produces all of the red blood cells, 60–70 percent of the white cells (i.e., the granulocytes), and all of the platelets. The lymphatic tissues, particularly the thymus, the spleen, and the lymph nodes, produce the lymphocytes (comprising 20–30 percent of the white cells). The reticuloendothelial tissues of the spleen, liver, lymph nodes, and other organs produce the monocytes (4–8 percent of the white cells). The platelets, which are small cellular fragments rather than complete cells, are formed from bits of the cytoplasm of the giant cells (megakaryocytes) of the bone marrow.

In the human embryo, the first site of blood formation is the yolk sac. Later in embryonic life, the liver becomes the most important red blood cell-forming organ, but it is soon succeeded by the bone marrow, which in adult life is the only source of both red blood cells and the granulocytes. Both the red and white blood cells arise through a series of complex, gradual, and successive transformations from primitive stem cells, which have the ability to form any of the precursors of a blood cell. Precursor cells are stem cells that have developed to the stage where they are committed to forming a particular kind of new blood cell.

In a normal adult the red cells of about half a liter (almost one pint) of blood are produced by the bone marrow every week. Almost 1 percent of the body’s red cells are generated each day, and the balance between red cell production and the removal of aging red cells from the circulation is precisely maintained.

Cells-in-the-Bone-Marrow-1024x747

http://interactive-biology.com/wp-content/uploads/2012/07/Cells-in-the-Bone-Marrow-1024×747.png

Erythropoiesis

http://www.interactive-biology.com/3969/erythropoiesis-formation-of-red-blood-cells/

Erythropoiesis – Formation of Red Blood Cells

Because of the inability of erythrocytes (red blood cells) to divide to replenish their own numbers, the old ruptured cells must be replaced by totally new cells. They meet their demise because they don’t have the usual specialized intracellular machinery, which controls cell growth and repair, leading to a short life span of 120 days.

This short life span necessitates the process erythropoiesis, which is the formation of red blood cells. All blood cells are formed in the bone marrow. This is the erythrocyte factory, which is soft, highly cellar tissue that fills the internal cavities of bones.

Erythrocyte differentiation takes place in 8 stages. It is the pathway through which an erythrocyte matures from a hemocytoblast into a full-blown erythrocyte. The first seven all take place within the bone marrow. After stage 7 the cell is then released into the bloodstream as a reticulocyte, where it then matures 1-2 days later into an erythrocyte. The stages are as follows:

  1. Hemocytoblast, which is a pluripotent hematopoietic stem cell
  2. Common myeloid progenitor, a multipotent stem cell
  3. Unipotent stem cell
  4. Pronormoblast
  5. Basophilic normoblast also called an erythroblast.
  6. Polychromatophilic normoblast
  7. Orthochromatic normoblast
  8. Reticulocyte

These characteristics can be seen during the course of erythrocyte maturation:

  • The size of the cell decreases
  • The cytoplasm volume increases
  • Initially there is a nucleus and as the cell matures the size of the nucleus decreases until it vanishes with the condensation of the chromatin material.

Low oxygen tension stimulates the kidneys to secrete the hormone erythropoietin into the blood, and this hormone stimulates the bone marrow to produce erythrocytes.

Rarely, a malignancy or cancer of erythropoiesis occurs. It is referred to as erythroleukemia. This most likely arises from a common myeloid precursor, and it may occur associated with a myelodysplastic syndrome.

Summary of erythrocyte maturation

White blood cell series: myelopoiesis

http://www.nlm.nih.gov/medlineplus/ency/presentations/100151_3.htm

http://www.nlm.nih.gov/medlineplus/ency/images/ency/fullsize/15220.jpg

There are various types of white blood cells (WBCs) that normally appear in the blood: neutrophils (polymorphonuclear leukocytes; PMNs), band cells (slightly immature neutrophils), T-type lymphocytes (T cells), B-type lymphocytes (B cells), monocytes, eosinophils, and basophils. T and B-type lymphocytes are indistinguishable from each other in a normal slide preparation. Any infection or acute stress will result in an increased production of WBCs. This usually entails increased numbers of cells and an increase in the percentage of immature cells (mainly band cells) in the blood. This change is referred to as a “shift to the left” People who have had a splenectomy have a persistent mild elevation of WBCs. Drugs that may increase WBC counts include epinephrine, allopurinol, aspirin, chloroform, heparin, quinine, corticosteroids, and triamterene. Drugs that may decrease WBC counts include antibiotics, anticonvulsants, antihistamine, antithyroid drugs, arsenicals, barbiturates, chemotherapeutic agents, diuretics and sulfonamides.   (Updated by: David C. Dugdale, III, MD)

https://www.med-ed.virginia.edu/courses/path/innes/nh/wcbmaturation.cfm

Note that the mature forms of the myeloid series (neutrophils, eosinophils, basophils), all have lobed (segmented) nuclei. The degree of lobation increases as the cells mature.

The earliest recognizable myeloid cell is the myeloblast (10-20m dia) with a large round to oval nucleus. There is fine diffuse immature chromatin (without clumping) and a prominant nucleolus.

The cytoplasm is basophilic without granules. Although one may see a small golgi area adjacent to the nucleus, granules are not usually visible by light microscopy. One should not see blast cells in the peripheral blood.

myeloblast x100b

https://www.med-ed.virginia.edu/courses/path/innes/images/nhjpeg/nh%20myeloblast%20x100b.jpeg

The promyelocyte (10-20m) is slightly larger than a blast. Its nucleus, although similar to a myeloblast shows slight chromatin condensation and less prominent nucleoli. The cytoplasm contains striking azurophilic granules or primary granules. These granules contain myeloperoxidase, acid phosphatase, and esterase enzymes. Normally no promyelocytes are seen in the peripheral blood.

At the point in development when secondary granules can be recognized, the cell becomes a myelocyte.

promyelocyte x100

https://www.med-ed.virginia.edu/courses/path/innes/images/nhjpeg/nh%20promyelocyte%20×100%20a.jpeg

Myelocytes (10-18m) are not normally found in the peripheral blood. Nucleoli may not be seen in the late myelocyte. Primary azurophilic granules are still present, but secondary granules predominate. Secondary granules (neut, eos, or baso) first appear adjacent to the nucleus. In neutrophils this is the “dawn” of neutrophilia.

Metamyelocytes (10-18m) have kidney shaped indented nuclei and dense chromatin along the nuclear membrane. The cytoplasm is faintly pink, and they have secondary granules (neutro, eos, or baso). Zero to one percent of the peripheral blood white cells may be metamyelocytes (juveniles).

metamyelocyte x100

https://www.med-ed.virginia.edu/courses/path/innes/images/nhjpeg/nh%20metamyelocyte%20×100.jpeg

Bands, slightly smaller than juveniles, are marked by a U-shaped or deeply indented nucleus.

band neutrophilx100a

https://www.med-ed.virginia.edu/courses/path/innes/images/nhjpeg/nh%20band%20x100a.jpeg

Segmented (segs) or polymorphonuclear (PMN) leukocytes (average 14 m dia) are distinguished by definite lobation with thin thread-like filaments of chromatin joining the 2-5 lobes. 45-75% of the peripheral blood white cells are segmented neutrophils.

https://www.med-ed.virginia.edu/courses/path/innes/images/nhjpeg/nh%20neutrophil%20×100%20d.jpeg

Thrombocytogenesis

The incredible journey: From megakaryocyte development to platelet formation

Kellie R. Machlus1,2 and Joseph E. Italiano Jr
JCB 2013; 201(6): 785-796
http://dx.doi.org:/10.1083/jcb.201304054

Large progenitor cells in the bone marrow called megakaryocytes (MKs) are the source of platelets. MKs release platelets through a series of fascinating cell biological events. During maturation, they become polyploid and accumulate massive amounts of protein and membrane. Then, in a cytoskeletal-driven process, they extend long branching processes, designated proplatelets, into sinusoidal blood vessels where they undergo fission to release platelets.

megakaryocyte production of platelets

http://dm5migu4zj3pb.cloudfront.net/manuscripts/26000/26891/medium/JCI0526891.f4.jpg

platelets and the immune continuum nri2956-f3

http://www.nature.com/nri/journal/v11/n4/images/nri2956-f3.jpg

2.4.2 Classification of hematological malignancies
Practical Diagnosis of Hematologic Disoreders. 4th edition. Vol 2.
Kjeldsberg CR, Ed.  ASCP Press.  2006. Chicago, IL.

2.4.2.1 Primary Classification

Acute leukemias

Myelodysplastic syndromes

Acute myeloid leukemia

Acute lymphoblastic leukemia

Myeloproliferative Disorders

Chronic myeloproliferative disorders

Chronic myelogenous leukemia and related disorders

Myelofibrosis, including chronic idiopathic

Polycythemia, including polycythemia rubra vera

Thrombocytosis, including essential thrombocythemia

Chronic lymphoid leukemia and other lymphoid leukemias

Lymphomas

Non-Hodgkin Lymphoma

Hodgkin lymphoma

Lymphoproliferative disorders associated with immunodeficiency

Plasma Cell dyscrasias

Mast cell disease and Histiocytic neoplasms

2.4.2.2 Secondary Classification

2.4.2.3 Nuance – PathologyOutlines
Nat Pernick, Ed.

Leukemia – Acute

Primary referencesacute leukemia-generalAML generalAML classificationtransient abnormal myelopoiesis

Recurrent genetic abnormalities: AML with t(6;9)AML with t(8;21)AML with 11q23 abnormalitiesAML with inv(16) or t(16;16)AML with Down syndromeAML with FLT3 mutationsAML with myelodysplastic related changesAML therapy relatedAPL microgranular variantAPL with t(15;17)APL with t(V;17)APL therapy related

AML not otherwise categorized: minimally differentiated (M0)without maturation (M1)with maturation (M2)M3myelomonocyticmonoblastic and monocyticerythroidmegakaryoblasticCD13/CD33 negativebasophilicmyeloid sarcomaacute panmyelosis with myelofibrosiswith Philadelphia chromosomewith pseudo Chediak-Higashi anomalyhypocellular

ALL: generalWHO classificationwith eosinophilia

PreB ALL: generalt(9;22)t(v;11q23)t(1;19)t(5;14)t(12;21)hyperdiploidyhypodiploidymature B ALL/Burkitt

Other ALL: T ALLambiguous lineagemixed phenotype

AML and related malignancies

Acute myeloid leukemias with recurrent genetic abnormalities:

  • AML with t(8;21)(q22;q22); RUNX1-RUNX1T1
  • AML with inv(16)(p13.1;q22) or t(16;16)(p13.1;q22); CBF&beta-MYH11
  • Acute promyelocytic leukemia with t(15;17)(q22;q12); PML/RAR&alpha and variants
  • AML with t(9;11)(p22;q23); MLLT3-MLL
  • AML with t(6;9)(p23;q34); DEK-NUP214
  • AML with inv(3)(q21q26.2) or t(3;3)(q21;q26.2); RPN1-EVI1
  • AML (megakaryoblastic) with t(1;22)(p13;q13); RBM15-MKL1
  • AML with mutated NPM1*
  • AML with mutated CEBPA*

* provisional

Acute myeloid leukemia with myelodysplasia related changes

Therapy related acute myeloid leukemia

  • Alkylating agent related
  • Topoisomerase II inhibitor related (some maybe lymphoid)

Acute myeloid leukemia not otherwise categorized:

  • AML minimally differentiated (M0)
  • AML without maturation (M1)
  • AML with maturation (M2)
  • Acute myelomonocytic leukemia (M4)
  • Acute monoblastic and monocytic leukemia (M5a, M5b)
  • Acute erythroid leukemia (M6)
  • Acute megakaryoblastic leukemia (M7)
  • Acute basophilic leukemia
  • Acute panmyelosis with myelofibrosis

Myeloid Sarcoma

Myeloid proliferations related to Down syndrome:

  • Transient abnormal myelopoeisis
  • Myeloid leukemia associated with Down syndrome

Blastic plasmacytoid dentritic cell neoplasm:

Acute leukemia of ambiguous lineage:

  • Acute undifferentiated leukemia
  • Mixed phenotype acute leukemia with t(9;22)(q34;q11.2); BCR-ABL1
  • Mixed phenotype acute leukemia with t(v;11q23); MLL rearranged
  • Mixed phenotype acute leukemia, B/myeloid, NOS
  • Mixed phenotype acute leukemia, T/myeloid, NOS
  • Mixed phenotype acute leukemia, NOS, rare types
  • Other acute leukemia of ambiguous lineage
  • References: WHO Classification of Tumours of Haematopoietic and Lymphoid Tissue (IARC, 2008), Discovery Medicine 2010, eMedicine

Acute lymphocytic leukemia

General
=================================================================

  • WHO classification system includes former FAB classifications ALL-L1 and L2
    ● FAB L3 is now considered Burkitt lymphoma

WHO classification of acute lymphoblastic leukemia
=================================================================

Precursor B lymphoblastic leukemia / lymphoblastic lymphoma:
● ALL with t(9;22)(q34;q11.2); BCR-ABL (Philadelphia chromosome)
● ALL with t(v;11q23) (MLL rearranged)
● ALL with t(1;19)(q23;p13.3); TCF3-PBX1 (E2A-PBX1)
● ALL with t(12;21)(p13;q22); ETV6-RUNX1 (TEL-AML1)
● Hyperdiploid > 50
● Hypodiploid
● t(5;14)(q31;q32); IL3-IGH

Precursor T lymphoblastic leukemia / lymphoma

Additional references
=================================================================

Mixed phenotype acute leukemia (MPAL)

General
=================================================================

  • De novo acute leukemia containing separate populations of blasts of more than one lineage (bilineal or bilineage), or a single population of blasts co-expressing antigens of more than one lineage (biphenotypic)Excludes:
    ● Acute myeloid leukemia (AML) with recurrent translocations t(8;21), t(15;17) or inv(16)
    ● Leukemias with FGFR1 mutations
    ● Chronic myelogenous leukemia (CML) in blast crisis
    ● Myelodysplastic syndrome (MDS)-related AML and therapy-related AML, even if they have MPAL immunophenotypeCriteria for biphenotypic leukemia:
    ● Score of 2 or more for each of two separate lineages:The European Group for the Immunological Classification of Leukemias (EGIL) scoring system2008 WHO classification of acute leukemias of ambiguous lineage 

Prognosis
=================================================================

  • Poor, overall survival of 18 months
    ● Young age, normal karyotype and ALL induction therapy are associated with favorable survival
    ● Ph+ is a predictor for poor prognosis
    ● Bone marrow transplantation should be considered in first remission

Major Categories

MPAL with t(9;22)(q34;q11.2); BCR-ABL1
=================================================================

  • 20% of all MPAL
    ● Blasts with t(9;22)(q34;q11.2) translocation or BCR-ABL1 rearrangement (Ph+) without history of CML
    ● Majority in adults
    ● High WBC counts● Most of the cases B/myeloid phenotype
    ● Rare T/myeloid, B and T lineage, or trilineage leukemiasMorphology:
    ● Many cases show a dimorphic blast population, one resembling myeloblasts and the other lymphoblastsCytogenetic abnormalities:
    ● Conventional karyotyping for t(9;22), FISH or PCR for BCR-ABL1 translocation
    ● Additional complex karyotypes
    ● Ph+ is a poor prognostic factor for MPAL, with a reported median survival of 8 months
    ● Worse than patients of all other types of MPAL

MPAL with t(v;11q23); MLL rearranged
=================================================================

  • Meeting the diagnostic criteria for MPAL with blasts bearing a translocation involving the 11q23 breakpoint (MLL gene)
    ● MPAL with MLL rearranged rare
    ● More often seen in children and relatively common in infancy
    ● High WBC counts
    ● Poor prognosis
    ● Dimorphic blast population, with one resembling monoblasts and the other resembling lymphoblasts
    ● Lymphoblast population often shows a CD19+, CD10- B precursor immunophenotype, frequently CD15+
    ● Expression of other B markers usually weak
    ● Translocations involving MLL gene include t(4;11)(q21;q23), t(11;19)(q23;p13), and t(9;11)(p22;q23)
    ● Cases with chromosome 11q23 deletion should not be classified in this category

B cell acute lymphoblastic leukemia (ALL) / lymphoblastic lymphoma (LBL)

General

=================================================================

  • Current 2008 WHO classification: B lymphoblastic leukemia / lymphoma, NOS or B lymphoblastic leukemia / lymphoma with recurrent genetic abnormalities
  • See also lymphomas: B cell chapter
  • Also called B cell acute lymphoblastic leukemia / lymphoblastic lymphoma, pre B ALL / LBL
  • Usually children
  • B acute lymphoblastic leukemia presents with pancytopenia due to extensive marrow involvement, stormy onset of symptoms, bone pain due to marrow expansion, hepatosplenomegaly due to neoplastic infiltration, CNS symptoms due to meningeal spread and testicular involvement
  • B acute lymphoblastic lymphoma often presents with cutaneous nodules, bone or nodal involvement, < 25% lymphoblasts in bone marrow and peripheral blood; aleukemic cases are usually asymptomatic
  • Depending on specific leukemia, arises in either hematopoietic stem cell or B-cell progenitor
  • Tumors are derived from pre-germinal center naive B cells with unmutated VH region genes
  • Have multiple immunophenotyping aberrancies relative to normal B cell precursors (hematogones); at relapse, 73% show loss of 1+ aberrance and 60% show new aberrancies (Am J Clin Pathol 2007;127:39)

Prognostic features

=================================================================

  • Favorable prognosis: age 1-10 years, female, white; preB phenotype, hyperdiploidy>50, t(12,21), WBC count at presentation <50×108/L, non-traumatic tap with no blasts in CNS, rapid response to chemotherapy < 5% blasts on morphology on day 15, remission status after induction <5% blasts on morphology and <0.01% blast on flow or PCR, CD10+
  • Intermediate prognosis: hyperdiploidy 47-50, diploid, 6q- and rearrangements of 8q24
  • Unfavorable prognosis: under age 1 (usually have 11q23 translocations) or over age 10; t(9;22) (but not if age 59+ years, Am J Clin Pathol 2002;117:716); male, > 50×108/L WBC at presentation, hypodiploidy, near tetraploidy, 17p- and MLL rearrangements t(v;11q23); CD10-; non-traumatic tap with > 5% blasts or traumatic tap (7%); also increased microvessel staining using CD105 in children (Leuk Res 2007;31:1741), MDR1 expression in children (Oncol Rep 2004;12:1201) and adults (Blood 2002;100:974), 25%+ blasts on morphology on day 15, remission status after induction ≥ 5% blasts on morphology and ≥ 0.1% blasts on flow or PCR

Case reports

=================================================================

  • 12 month old girl and 13 month old boy with mature phenotype but no translocations (Arch Pathol Lab Med 2003;127:1340)
  • 56 year old man with ALL arising from follicular lymphoma (Arch Pathol Lab Med 2002;126:997)
  • 76 year old man with basal cell carcinoma (Diagn Pathol 2007;2:32)
  • With hemophagocytic lymphohistiocytosis (Pediatr Blood Cancer 2008;50:381)

Treatment

================================================================

  • Chemotherapy cures more children than adults; adolescents benefit from intensive regimens (Hematology Am Soc Hematol Educ Program 2005:123)

Micro description

=================================================================

  • Bone marrow smears: small to intermediate blast-like cells with scant, variably basophilic cytoplasm, round / oval or convoluted nuclei, fine chromatin and indistinct nucleoli; frequent mitotic figures; may have “starry sky” appearance similar to Burkitt lymphoma; may have large lymphoblasts with 1-4 prominent nucleoli resembling myeloblasts; usually no sclerosis
  • Bone marrow biopsy: usually markedly hypercellular with reduction of trilinear maturation; cells have minimal cytoplasm, medium sized nuclei that are often convoluted, moderately dense chromatin and indistinct nucleoli, brisk mitotic activity
  • Other tissues: may have “starry sky” appearance similar to Burkitt lymphoma; collagen dissection, periadipocyte growth pattern and single cell linear filing

Chronic Leukemia

Chronic Myeloid Neoplasms

Myelodysplastic syndromes (MDS): general, WHO classification, childhood, refractory anemia, refractory anemia with ringed sideroblasts, refractory cytopenia with multilineage dysplasia, refractory anemia with excess blasts, 5q-syndrome, therapy related, unclassified, arsenic toxicity

Myeloproliferative neoplasms (MPN): general, WHO classification, chronic eosinophilic leukemia, chronic myelogenous leukemia, chronic neutrophilic leukemia, essential thrombocythemia, hypereosinophilic syndrome, mast cell disease, polycythemia vera, primary myelofibrosis, unclassifiable

MDS/MPN: general, WHO classification, atypical CML, chronic myelomonocytic leukemia (CMML), chronic myelomonocytic leukemia with eosinophilia, juvenile myelomonocytic leukemia, unclassifiable

Myeloid neoplasms associated with eosinophilia and abnormalities of PDGFRA, PDGFRB, or FGFR1: PDGFRA, PDGFRB, FGFR1

Miscellaneous: transient myeloproliferative disorder of Downís syndrome

Lymphoma and plasma cell neoplasms

Lymph nodes: normal development-generalB cellsT cellsNK cellsnormal histologygrossing lymph nodesfeatures to report

Molecular testing: theoryFISHnorthern blotPCRsouthern blot

Non-Hodgkin lymphoma: generalcytogeneticsstagingstaging-pediatricmorphologic clueshemophagocytic syndromechemotherapeutic atypia

B cell disorders: generalpost-rituximabbone marrow biopsyclassification-historicalWHO classification

B cell lymphoma subtypes: age-related EBV-associatedALK positive large cellBurkittunclassifiable-intermediate between Burkitt and diffuse large B cell lymphomaCLL
diffuse large B cell: 
diffuse-NOSCD5+T cell / histiocyte richprimary cutaneous-generalprimary cutaneous-legprimary sites-other
follicular: 
generalchildhoodcutaneousGI
hairy cell leukemiaHCL variantintravascular large B celllymphomatoid granulomatosislymphoplasmacyticmantle cell-classicmantle cell-blastoidmarginal zone-generalmarginal zone-MALTMALT-primary sitesmarginal zone-nodalmediastinal (thymic)plasmablasticpre B lymphoblastic leukemia/lymphomaprimary effusionprolymphocytic leukemiapyothorax associatedSLLsplenic marginal zonesplenic lymphoma with villous lymphocytes

Plasma cell neoplasms: generalmyelomaplasmacytomaheavy chain diseaseprimary amyloidosisMGUSOsteosclerotic myeloma (POEMS)cryoglobulinemia

T/NK cell disorders: generalWHO classificationadult T cellaggressive NK cell leukemiaanaplastic large cell ALK+ALK-angioimmunoblastic T cellblastic plasmacytoidchronic lymphoproliferative disorders of NK cellscutaneous CD4+ small/medium sized T cell lymphomacutaneous CD30 positive T cell lymphoproliferative disorderscutaneous gamma delta T cell lymphomaenteropathyepidermotropic CD8+ T cell lymphomahepatosplenicindolent T cell proliferationsmycosis fungoidesNK/T cell lymphoma-nasal typenodal CD8+ cytotoxic T cellnonB nonT lymphoblasticperipheral T cell lymphoma, NOSprimary effusion lymphomaSezary syndromestagingsubcutaneous panniculitis-likeT cell large granular lymphocytic leukemiaT cell lymphoblastic leukemia/lymphomaT cell prolymphocytic leukemia

Hodgkin lymphoma: general/stagingclassiclymphocyte depletedlymphocyte rich classicalmixed cellularitynodular lymphocyte predominantnodular sclerosis

Post-transplantation: generalWHO classificationplasmacytic hyperplasia/IM-like lesionspolymorphic B cell lymphoproliferative disordersmonomorphic B cell lymphoproliferative disordersothergraft versus host disease

Other: AIDS associated-generalAIDS associated-examplesEBV+ T cell lymphoproliferative disorders of childhoodprimary immune disorders related

Myeloproliferative neoplasms (MPN)

WHO 2008 – Myeloproliferative neoplasms (MPN) 

General
=================================================================

  • Chronic myelogenous leukemia
    ● Polycythemia vera
    ● Essential thrombocythemia
    ● Primary myelofibrosis
    ● Chronic neutrophilic leukemia
    ● Chronic eosinophilic leukemia, not otherwise categorized
    ● Mast cell disease
    ● MPNs, unclassifiable

WHO 2001 – Chronic myeloproliferative diseases 

Definition
=================================================================

  • Chronic myelogenous leukemia (Philadelphia chromosome, t(9;22)(q34;q11), BCR-ABL positive)
    ● Chronic neutrophilic leukemia
    ● Chronic eosinophilic leukemia (and the hypereosinophilic syndrome)
    ● Polycythemia vera
    ● Chronic idiopathic myelofibrosis (with extramedullary hematopoiesis)
    ● Essential thrombocythemia
    ● Chronic myeloproliferative disease, unclassifiable

Additional references
=================================================================

The World Health Organization (WHO) classification of the myeloid neoplasms  James W. Vardiman, Nancy Lee Harris, and Richard D. Brunning
Blood 2002; 100(7)  http://dx.doi.org/10.1182/blood-2002-04-1199

Lymphoma – Non B cell neoplasms

T/NK cell disorders/WHO classification (2008)

Principles of classification
=================================================================

  • Based on all available information (morphology, immunophenotype, genetics, clinical)
    ● No one antigenic marker is specific for any neoplasm (except ALK1)
    ● Immune profiling less helpful in subclassification of T cell lymphomas then B cell lymphomas
    ● Certain antigens commonly associated with specific disease entities but not entirely disease specific
    ● CD30: common in anaplastic large cell lymphoma but also classic Hodgkin lymphoma and other B and T cell lymphomas
    ● CD56: characteristic for nasal NK/T cell lymphoma, but also other T cell neoplasms and plasma cell disorders
    ● Variation of immunophenotype within a given disease (hepatosplenic T cell lymphoma: usually γδ but some are αβ)
    ● Recurrent genetic alterations have been identified for many B cell lymphomas but not for most T cell lymphomas
    ● No attempt to stratify lymphoid malignancies by grade
    ● Recognize the existence of grey zone lymphomas
    ● This multiparameter approach has been validated in international studies as highly reproducible

WHO 2008 classification of tumors of hematopoietic and lymphoid tissues (T/NK)
=================================================================

Precursor T-lymphoid neoplasms
● T lymphoblastic leukemia/lymphoma, 9837/3

Mature T cell and NK cell neoplasms
● T cell prolymphocytic leukemia, 9834/3
● T cell large granular lymphocytic leukemia, 9831/3
● Chronic lymphoproliferative disorder of NK cells, 9831/3
● Aggressive NK cell leukemia, 9948/3
● Systemic EBV-positive T cell lymphoproliferative disease of childhood, 9724/3
● Hydroa vacciniforme-like lymphoma, 9725/3
● Adult T cell leukemia/lymphoma, 9827/3
● Extranodal NK/T cell lymphoma, nasal type, 9719/3
● Enteropathy-associated T cell lymphoma, 9717/3
● Hepatosplenic T cell lymphoma, 9716/3
● Subcutaneous panniculitis-like T cell lymphoma, 9708/3
● Mycosis fungoides, 9700/3
● Sézary syndrome, 9701/3
● Primary cutaneous CD30-positive T cell lymphoproliferative disorders
● Lymphomatoid papulosis, 9718/1
● Primary cutaneous anaplastic large cell lymphoma, 9718/3
● Primary cutaneous gamma-delta T cell lymphoma, 9726/3
● Primary cutaneous CD8-positive aggressive epidermotropic cytotoxic T cell lymphoma, 9709/3
● Primary cutaneous CD4-positive small/medium T cell lymphoma, 9709/3
● Peripheral T cell lymphoma, NOS, 9702/3
● Angioimmunoblastic T cell lymphoma, 9705/3
● Anaplastic large cell lymphoma, ALK-positive, 9714/3
● Anaplastic large cell lymphoma, ALK-negative, 9702/3

Chronic Lymphocytic Leukemia

Chronic Lymphocytic Leukemia Staging
Author: Sandy D Kotiah, MD; Chief Editor: Jules E Harris, MD
Medscape Sep 6, 2013
http://emedicine.medscape.com/article/2006578-overview

General considerations in the staging of chronic lymphocytic leukemia (CLL) and the revised Rai (United States) and Binet (Europe) staging systems for CLL are provided below.[1, 2, 3]

See Chronic Leukemias: 4 Cancers to Differentiate, a Critical Images slideshow, to help detect chronic leukemias and determine the specific type present.

General considerations

  • CLL and small lymphocytic lymphoma (SLL) are different manifestations of the same disease; SLL is diagnosed when the disease is mainly nodal, and CLL is diagnosed when the disease is seen in the blood and bone marrow
  • CLL is diagnosed by > 5000 monoclonal lymphocytes/mm3 for longer than 3mo; the bone marrow usually has more than 30% monoclonal lymphocytes and is either normocellular or hypercellular
  • Monoclonal B lymphocytosis is a precursor form of CLL that is defined by a monoclonal B cell lymphocytosis < 5000 monoclonal lymphocytes/mm3; all lymph nodes smaller than 1.5 cm; no anemia; and no thrombocytopenia

Revised Rai staging system (United States)

Low risk (formerly stage 0)[1] :

  • Lymphocytosis, lymphocytes in blood > 15000/mcL, and > 40% lymphocytes in the bone marrow

Intermediate risk (formerly stages I and II):

  • Lymphocytosis as in low risk with enlarged node(s) in any site, or splenomegaly or hepatomegaly or both

High risk (formerly stages III and IV):

  • Lymphocytosis as in low risk and intermediate risk with disease-related anemia (hemoglobin level < 11.0 g/dL or hematocrit < 33%) or platelets < 100,000/mcL

Binet staging system (Europe)

Stage A:

  • Hemoglobin ≥ 10 g/dL, platelets ≥ 100,000/mm3, and < 3 enlarged areas

Stage B:

  • Hemoglobin ≥ 10 g/dL, platelets ≥ 100,000/mm3, and ≥ 3 enlarged areas

Stage C:

  • Hemoglobin < 10 g/dL, platelets < 100,000/mm3, and any number of enlarged areas

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Notes On Tumor Heterogeneity: Targets and Mechanisms, from the 2015 AACR Meeting in Philadelphia PA

Reporter: Stephen J. Williams, Ph.D.

The following contain notes from the Sunday April 19, 2015 AACR Meeting (Pennsylvania Convention Center, Philadelphia PA) 1 PM Major Symposium Session on Tumor Heterogeneity: Targets and Mechanism chaired by Dr. Charles Swanton.

Speakers included: Mark J. Smyth, Charles Swanton, René H. Medema, and Catherine J. Wu

Tumor heterogeneity is a common feature of many malignancies, especially the solid tumors and can drive the evolution and adaptation of the growing tumor, complicating therapy and resulting in therapeutic failure, including resistance. This session at AACR described the mechanisms, both genetic and epigenetic, which precipitate intratumor heterogeneity and how mutational processes and chromosomal instability may impact the tumor progression and the origin of driver events during tumor evolution. Finally the session examined possible therapeutic strategies to take advantage of, and overcome, tumor evolution. The session was chaired by Dr. Charles Swanton. For a more complete description of his work, tumor heterogeneity, and an interview on this site please click on the link below:

Issues in Personalized Medicine in Cancer: Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing

and

Issues in Personalized Medicine: Discussions of Intratumor Heterogeneity from the Oncology Pharma forum on LinkedIn

 

Notes from Charles Swanton, Cancer Research UK; Identifying Drivers of Cancer Diversity

Dr. Swanton’s lecture focused on data from two recent papers from his lab by Franseco Favero and Nicholas McGranahan:

  1. Glioblastoma adaptation Traced Through Decline of an IDH1 clonal driver and macro-evolution of a double-minute chromosome (Annals of Oncology, 2015)[1]

This paper described the longitudinal Whole Genome Sequencing (WGS) study of a 35 year old female whose primary glioblastoma (GBM) was followed through temozolomide treatment and ultimately recurrence.

  • In 2008 patient was diagnosed with primary GBM (three biopsies of unrelated sites were Grade II and Grade IV; temozolomide therapy for three years then relapse in 2011
  • WGS of 2 areas of primary tumor showed extensive mutational and copy number heterogeneity; was able to identify clonal TP53 mutations and clonal IDH1 mutation in primary tumor with different patterns of clonality based on grade
  • Amplifications on chromosome 4 and 12 (PDGFRA, KIT, CDK4)
  • After three years of temozolomide multiple translocations found in chromosome 4 and 12 (6 translocations)
  • Clonal IDH1 R132H mutation in primary tumor only at very low frequency in recurrent tumor
  • The WGS on recurrent tumor (sequencing took ONLY 9 days from tumor resection to sequence results) showed mutation cluster in KIT/PDGFRA.PI3K.mTOR axis so patient treated with imatinib
  • However despite rapid sequencing and a personalized approach based on WGS results, tumor progressed and patient died shortly: tumor evolution is HUGE hurdle for personalized medicine

As Dr. Swanton stated:

“we are underestimating the frequency of polyclonal evolution”

  1. Clonal status of actionable driver events and the timing of mutational processes in cancer evolution (Science Translational Medicine, 2015)[2]
  • analyzed nine cancer types to determine the subclonal frequencies of driver events, to time mutational processes during cancer evolution, and to identify drivers of subclonal expansions.
  • identified later subclonal “actionable” mutations, including BRAF (V600E), IDH1 (R132H), PIK3CA (E545K), EGFR (L858R), and KRAS (G12D), which may compromise the efficacy of targeted therapy approaches.
  • > 20% of IDH1 mutations in glioblastomas, and 15% of mutations in genes in the PI3K (phosphatidylinositol 3-kinase)–AKT–mTOR (mammalian target of rapamycin) signaling axis across all tumor types were subclonal
  • Mutations in the RAS–MEK (mitogen-activated protein kinase kinase) signaling axis were less likely to be subclonal than mutations in genes associated with PI3K-AKT-mTOR signaling

Branched chain can converge on single resistance mechanism; clonal resistance (for example to PI3K inhibitors can get multiple PTEN mutations in various metastases

Targeting Tumor Heterogeneity

  • Identify high risk occupants (have to know case history)
  • Mutational landscape interferes with anti-PD1 therapies
  • Low frequency mutations affect outcome

Notes from Dr. Catherine J. Wu, Dana-Farber Cancer Institute: The evolutionary landscape of CLL: Therapeutic implications

  • Clonal evolution a key feature of cancer progression and relapse
  • Hypothesis: evolutionary dynamics (heterogeneity) in chronic lymphocytic leukemia (CLL) contributes to variations in response and disease “tempo”
  • Used whole exome sequencing and copy number data of 149 CLL cases to discover early and late cancer drivers: clonal patterns (Landau et. al, Cell 2013); some drivers correspond to poor clinical outcome
  • Methylation studies suggest that there is epigenetic heterogeneity which may drive CLL clonal evolution
  • Developing methodology to integrate WES to determine mutations with immunogenic potential for development of personalized immunotherapy for CLL and other malignancies

References

  1. Favero F, McGranahan N, Salm M, Birkbak NJ, Sanborn JZ, Benz SC, Becq J, Peden JF, Kingsbury Z, Grocok RJ et al: Glioblastoma adaptation traced through decline of an IDH1 clonal driver and macro-evolution of a double-minute chromosome. Annals of oncology : official journal of the European Society for Medical Oncology / ESMO 2015, 26(5):880-887.
  2. McGranahan N, Favero F, de Bruin EC, Birkbak NJ, Szallasi Z, Swanton C: Clonal status of actionable driver events and the timing of mutational processes in cancer evolution. Science translational medicine 2015, 7(283):283ra254.

 

Other related articles on Tumor Heterogeneity were published in this Open Access Online Scientific Journal, include the following:

 

Issues in Personalized Medicine: Discussions of Intratumor Heterogeneity from the Oncology Pharma forum on LinkedIn

Issues in Personalized Medicine in Cancer: Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing

CANCER COMPLEXITY: Heterogeneity in Tumor Progression and Drug Response – 2015 Annual Symposium @Koch Institute for Integrative Cancer Research at MIT – W34, 6/12/2015 9:00 AM EDT – 4:30 PM EDT

My Cancer Genome from Vanderbilt University: Matching Tumor Mutations to Therapies & Clinical Trials

Tumor Imaging and Targeting: Predicting Tumor Response to Treatment: Where we stand?

Mitochondrial Isocitrate Dehydrogenase and Variants

War on Cancer Needs to Refocus to Stay Ahead of Disease Says Cancer Expert

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War on Cancer Needs to Refocus to Stay Ahead of Disease Says Cancer Expert


War on Cancer Needs to Refocus to Stay Ahead of Disease Says Cancer Expert

Writer, Curator: Stephen J. Williams, Ph.D.

UPDATED 1/08/2020

Is one of the world’s most prominent cancer researchers throwing in the towel on the War On Cancer? Not throwing in the towel, just reminding us that cancer is more complex than just a genetic disease, and in the process, giving kudos to those researchers who focus on non-genetic aspects of the disease (see Dr. Larry Bernstein’s article Is the Warburg Effect the Cause or the Effect of Cancer: A 21st Century View?).

 

National Public Radio (NPR) has been conducting an interview series with MIT cancer biology pioneer, founding member of the Whitehead Institute for Biomedical Research, and National Academy of Science member and National Medal of Science awardee Robert A. Weinberg, Ph.D., who co-discovered one of the first human oncogenes (Ras)[1], isolation of first tumor suppressor (Rb)[2], and first (with Dr. Bill Hahn) proved that cells could become tumorigenic after discrete genetic lesions[3].   In the latest NPR piece, Why The War On Cancer Hasn’t Been Won (seen on NPR’s blog by Richard Harris), Dr. Weinberg discusses a comment in an essay he wrote in the journal Cell[4], basically that, in recent years, cancer research may have focused too much on the genetic basis of cancer at the expense of multifaceted etiology of cancer, including the roles of metabolism, immunity, and physiology. Cancer is the second most cause of medically related deaths in the developed world. However, concerted efforts among most developed nations to eradicate the disease, such as increased government funding for cancer research and a mandated ‘war on cancer’ in the mid 70’s has translated into remarkable improvements in diagnosis, early detection, and cancer survival rates for many individual cancer. For example, survival rate for breast and colon cancer have improved dramatically over the last 40 years. In the UK, overall median survival times have improved from one year in 1972 to 5.8 years for patients diagnosed in 2007. In the US, the overall 5 year survival improved from 50% for all adult cancers and 62% for childhood cancer in 1972 to 68% and childhood cancer rate improved to 82% in 2007. However, for some cancers, including lung, brain, pancreatic and ovarian cancer, there has been little improvement in survival rates since the “war on cancer” has started.

(Other NPR interviews with Dr. Weinberg include How Does Cancer Spread Through The Body?)

As Weinberg said, in the 1950s, medical researchers saw cancer as “an extremely complicated process that needed to be described in hundreds, if not thousands of different ways,”. Then scientists tried to find a unifying principle, first focusing on viruses as the cause of cancer (for example rous sarcoma virus and read Dr. Gallo’s book on his early research on cancer, virology, and HIV in Virus Hunting: AIDS, Cancer & the Human Retrovirus: A Story of Scientific Discovery).

However (as the blog article goes on) “that idea was replaced by the notion that cancer is all about wayward genes.”

“The thought, at least in the early 1980s, was that were a small number of these mutant, cancer-causing oncogenes, and therefore that one could understand a whole disparate group of cancers simply by studying these mutant genes that seemed to be present in many of them,” Weinberg says. “And this gave the notion, the illusion over the ensuing years, that we would be able to understand the laws of cancer formation the way we understand, with some simplicity, the laws of physics, for example.”

According to Weinberg, this gene-directed unifying theory has given way as recent evidences point back once again to a multi-faceted view of cancer etiology.

But this is not a revolutionary or conflicting idea for Dr. Weinberg, being a recipient of the 2007 Otto Warburg Medal and focusing his latest research on complex systems such as angiogenesis, cell migration, and epithelial-stromal interactions.

In fact, it was both Dr. Weinberg and Dr. Bill Hanahan who formulated eight governing principles or Hallmarks of cancer:

  1. Maintaining Proliferative Signals
  2. Avoiding Immune Destruction
  3. Evading Growth Suppressors
  4. Resisting Cell Death
  5. Becoming Immortal
  6. Angiogenesis
  7. Deregulating Cellular Energy
  8. Activating Invasion and Metastasis

Taken together, these hallmarks represent the common features that tumors have, and may involve genetic or non-genetic (epigenetic) lesions … a multi-modal view of cancer that spans over time and across disciplines. As reviewed by both Dr. Larry Bernstein and me in the e-book Volume One: Cancer Biology and Genomics for Disease Diagnosis, each scientific discipline, whether the pharmacologist, toxicologist, virologist, molecular biologist, physiologist, or cell biologist has contributed greatly to our total understanding of this disease, each from their own unique perspective based on their discipline. This leads to a “multi-modal” view on cancer etiology and diagnosis, treatment. Many of the improvements in survival rates are a direct result of the massive increase in the knowledge of tumor biology obtained through ardent basic research. Breakthrough discoveries regarding oncogenes, cancer cell signaling, survival, and regulated death mechanisms, tumor immunology, genetics and molecular biology, biomarker research, and now nanotechnology and imaging, have directly led to the advances we now we in early detection, chemotherapy, personalized medicine, as well as new therapeutic modalities such as cancer vaccines and immunotherapies and combination chemotherapies. Molecular and personalized therapies such as trastuzumab and aromatase inhibitors for breast cancer, imatnib for CML and GIST related tumors, bevacizumab for advanced colorectal cancer have been a direct result of molecular discoveries into the nature of cancer. This then leads to an interesting question (one to be tackled in another post):

Would shifting focus less on cancer genome and back to cancer biology limit the progress we’ve made in personalized medicine?

 

In a 2012 post Genomics And Targets For The Treatment Of Cancer: Is Our New World Turning Into “Pharmageddon” Or Are We On The Threshold Of Great Discoveries? Dr. Leonard Lichtenfield, MD, Deputy Chief Medical Officer for the ACS, comments on issues regarding the changes which genomics and personalized strategy has on oncology drug development. As he notes, in the past, chemotherapy development was sort of ‘hit or miss’ and the dream and promise of genomics suggested an era of targeted therapy, where drug development was more ‘rational’ and targets were easily identifiable.

To quote his post

That was the dream, and there have been some successes–even apparent cures or long term control–with the used of targeted medicines with biologic drugs such as Gleevec®, Herceptin® and Avastin®. But I think it is fair to say that the progress and the impact hasn’t been quite what we thought it would be. Cancer has proven a wily foe, and every time we get answers to questions what we usually get are more questions that need more answers. The complexity of the cancer cell is enormous, and its adaptability and the genetic heterogeneity of even primary cancers (as recently reported in a research paper in the New England Journal of Medicine) has been surprising, if not (realistically) unexpected.

                                                                               ”

Indeed the complexity of a given patient’s cancer (especially solid tumors) with regard to its genetic and mutation landscape (heterogeneity) [please see post with interview with Dr. Swanton on tumor heterogeneity] has been at the forefront of many clinicians minds [see comments within the related post as well as notes from recent personalized medicine conferences which were covered live on this site including the PMWC15 and Harvard Personalized Medicine conference this past fall].

In addition, Dr. Lichtenfeld makes some interesting observations including:

  • A “pharmageddon” where drug development risks/costs exceed the reward so drug developers keep their ‘wallets shut’. For example even for targeted therapies it takes $12 billion US to develop a drug versus $2 billion years ago
  • Drugs are still drugs and failure in clinical trials is still a huge risk
  • “Eroom’s Law” (like “Moore’s Law” but opposite effect) – increasing costs with decreasing success
  • Limited market for drugs targeted to a select mutant; what he called “slice and dice”

The pros and cons of focusing solely on targeted therapeutic drug development versus using a systems biology approach was discussed at the 2013 Institute of Medicine’s national Cancer Policy Summit.

  • Andrea Califano, PhD – Precision Medicine predictions based on statistical associations where systems biology predictions based on a physical regulatory model
  • Spyro Mousses, PhD – open biomedical knowledge and private patient data should be combined to form systems oncology clearinghouse to form evolving network, linking drugs, genomic data, and evolving multiscalar models
  • Razelle Kurzrock, MD – What if every patient with metastatic disease is genomically unique? Problem with model of smaller trials (so-called N=1 studies) of genetically similar disease: drugs may not be easily acquired or re-purposed, and greater regulatory burdens

So, discoveries of oncogenes, tumor suppressors, mutant variants, high-end sequencing, and the genomics and bioinformatic era may have led to advent of targeted chemotherapies with genetically well-defined patient populations, a different focus in chemotherapy development

… but as long as we have the conversation open I have no fear of myopia within the field, and multiple viewpoints on origins and therapeutic strategies will continue to develop for years to come.

References

  1. Parada LF, Tabin CJ, Shih C, Weinberg RA: Human EJ bladder carcinoma oncogene is homologue of Harvey sarcoma virus ras gene. Nature 1982, 297(5866):474-478.
  2. Friend SH, Bernards R, Rogelj S, Weinberg RA, Rapaport JM, Albert DM, Dryja TP: A human DNA segment with properties of the gene that predisposes to retinoblastoma and osteosarcoma. Nature 1986, 323(6089):643-646.
  3. Hahn WC, Counter CM, Lundberg AS, Beijersbergen RL, Brooks MW, Weinberg RA: Creation of human tumour cells with defined genetic elements. Nature 1999, 400(6743):464-468.
  4. Weinberg RA: Coming full circle-from endless complexity to simplicity and back again. Cell 2014, 157(1):267-271.

 

UPDATED 1/08/2020

from the Washington Post

Cancer death rate posts biggest one-year drop ever

The 2.2 percent decline in 2017 is part of a long-term decrease in mortality rates.

Jan. 8, 2020 at 7:00 a.m. EST

The cancer death rate in the United States fell 2.2 percent in 2017 — the biggest single-year drop ever reported — propelled by gains against lung cancer, the American Cancer Society said Wednesday.

Declines in the mortality rate for lung cancer have accelerated in recent years in response to new treatments and falling smoking rates, said Rebecca Siegel, lead author of Cancer Statistics 2020, the latest edition of the organization’s annual report on cancer trends.

The improvement in 2017, the most recent year for which data is available, is part of a long-term drop in cancer mortality that reflects, to a large extent, the smoking downturn. Since peaking in 1991, the cancer death rate has fallen 29 percent, which translates into 2.9 million fewer deaths.

Norman “Ned” Sharpless, director of the National Cancer Institute, which was not involved in the report, said the data reinforces that “we are making steady progress” on cancer. For lung cancer, he pointed to new immunotherapy treatments and so-called targeted therapies that stop the action of molecules key to cancer growth. He predicted that the mortality rate would continue to fall “as we get better at using these therapies.” Multiple clinical trials are exploring how to combine the new approaches with older ones, such as chemotherapy.

Sharpless expressed concern, however, that progress against cancer would be undermined by increased obesity, which is a risk factor for several malignancies.

The cancer society report projected 1.8 million new cases of cancer in the United States this year and more than 606,000 deaths. Nationally, cancer is the second-leading cause of death after heart disease in both men and women. It is the No. 1 cause in many states, and among Hispanic and Asian Americans and people younger than 80, the report said.

The cancer death rate is defined as deaths per 100,000 people. The cancer society has been reporting the rate since 1930.

Because lung cancer is the leading cause of cancer deaths, accounting for 1 in 4, any change in the mortality rate has a large effect on the overall cancer death rate, Siegel noted.

She described the gains against lung cancer, and against another often deadly cancer, melanoma, as “exciting.” But, she added, “the news this year is mixed” because of slower progress against colorectal, breast and prostate cancers. Those cancers often can be detected early by screening, she said.

The report said substantial racial and geographic disparities remain for highly preventable cancers, such as cervical cancer, and called for “the equitable application” of cancer control measures.

The five-year survival rate for all cancers diagnosed from 2009 through 2015 was 67 percent overall — 68 percent for whites and 62 percent for African Americans.

In recent years, melanoma has showed the biggest mortality-rate drop of any cancer. That’s largely a result of breakthrough treatments such as immunotherapy, which unleashes the patient’s own immune system to fight the cancer and was approved for advanced melanoma in 2011.

Other posts on this site on The War on Cancer and Origins of Cancer include:

 

2013 Perspective on “War on Cancer” on December 23, 1971

Is the Warburg Effect the Cause or the Effect of Cancer: A 21st Century View?

World facing cancer ‘tidal wave’, warns WHO

2013 American Cancer Research Association Award for Outstanding Achievement in Chemistry in Cancer Research: Professor Alexander Levitzki

Genomics and Metabolomics Advances in Cancer

The Changing Economics of Cancer Medicine: Causes for the Vanishing of Independent Oncology Groups in the US

Cancer Research Pioneer, after 71 years of Immunology Lab Research, Herman Eisen, MD, MIT Professor Emeritus of Biology, dies at 96

My Cancer Genome from Vanderbilt University: Matching Tumor Mutations to Therapies & Clinical Trials

Articles on Cancer-Related Topic in http://pharmaceuticalintelligence.com Scientific Journal

Issues in Personalized Medicine in Cancer: Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing

Issues in Personalized Medicine: Discussions of Intratumor Heterogeneity from the Oncology Pharma forum on LinkedIn

Introduction – The Evolution of Cancer Therapy and Cancer Research: How We Got Here?

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10:15AM 11/13/2014 – 10th Annual Personalized Medicine Conference at the Harvard Medical School, Boston

REAL TIME Coverage of this Conference by Dr. Aviva Lev-Ari, PhD, RN – Director and Founder of LEADERS in PHARMACEUTICAL BUSINESS INTELLIGENCE, Boston http://pharmaceuticalintelligence.com

10:15 a.m. Panel Discussion — IT/Big Data

IT/Big Data

The human genome is composed of 6 billion nucleotides (using the genetic alphabet of T, C, G and A). As the cost of sequencing the human genome is decreasing at a rapid rate, it might not be too far into the future that every human being will be sequenced at least once in their lifetime. The sequence data together with the clinical data are going to be used more and more frequently to make clinical decisions. If that is true, we need to have secure methods of storing, retrieving and analyzing all of these data.  Some people argue that this is a tsunami of data that we are not ready to handle. The panel will discuss the types and volumes of data that are being generated and how to deal with it.

IT/Big Data

   Moderator:

Amy Abernethy, M.D.
Chief Medical Officer, Flatiron

Role of Informatics, SW and HW in PM. Big data and Healthcare

How Lab and Clinics can be connected. Oncologist, Hematologist use labs in clinical setting, Role of IT and Technology in the environment of the Clinicians

Compare Stanford Medical Center and Harvard Medical Center and Duke Medical Center — THREE different models in Healthcare data management

Create novel solutions: Capture the voice of the patient for integration of component: Volume, Veracity, Value

Decisions need to be made in short time frame, documentation added after the fact

No system can be perfect in all aspects

Understanding clinical record for conversion into data bases – keeping quality of data collected

Key Topics

Panelists:

Stephen Eck, M.D., Ph.D.
Vice President, Global Head of Oncology Medical Sciences,
Astellas, Inc.

Small data expert, great advantage to small data. Populations data allows for longitudinal studies,

Big Mac Big Data – Big is Good — Is data been collected suitable for what is it used, is it robust, limitations, of what the data analysis mean

Data analysis in Chemical Libraries – now annotated

Diversity data in NOTED by MDs, nuances are very great, Using Medical Records for building Billing Systems

Cases when the data needed is not known or not available — use data that is available — limits the scope of what Valuable solution can be arrived at

In Clinical Trial: needs of researchers, billing clinicians — in one system

Translation of data on disease to data object

Signal to Noise Problem — Thus Big data provided validity and power

 

J. Michael Gaziano, M.D., M.P.H., F.R.C.P.
Scientific Director, Massachusetts Veterans Epidemiology Research
and Information Center (MAVERIC), VA Boston Healthcare System;
Chief Division of Aging, Brigham and Women’s Hospital;
Professor of Medicine, Harvard Medical School

at BWH since 1987 at 75% – push forward the Genomics Agenda, VA system 25% – VA is horizontally data integrated embed research and knowledge — baseline questionnaire 200,000 phenotypes – questionnaire and Genomics data to be integrated, Data hierarchical way to be curated, Simple phenotypes, validate phenotypes, Probability to have susceptibility for actual disease, Genomics Medicine will benefit Clinicians

Data must be of visible quality, collect data via Telephone VA – on Med compliance study, on Ability to tolerate medication

–>>Annotation assisted in building a tool for Neurologist on Alzheimer’s Disease (AlzSWAN knowledge base) (see also Genotator , a Disease-Agnostic Tool for Annotation)

–>>Curation of data is very different than statistical analysis of Clinical Trial Data

–>>Integration of data at VA and at BWH are tow different models of SUCCESSFUL data integration models, accessing the data is also using a different model

–>>Data extraction from the Big data — an issue

–>>Where the answers are in the data, build algorithms that will pick up causes of disease: Alzheimer’s – very difficult to do

–>>system around all stakeholders: investment in connectivity, moving data, individual silo, HR, FIN, Clinical Research

–>>Biobank data and data quality

 

Krishna Yeshwant, M.D.
General Partner, Google Ventures;
Physician, Brigham and Women’s Hospital

Computer Scientist and Medical Student. Were the technology is going?

Messy situation, interaction IT and HC, Boston and Silicon Valley are focusing on Consumers, Google Engineers interested in developing Medical and HC applications — HUGE interest. Application or Wearable – new companies in this space, from Computer Science world to Medicine – Enterprise level – EMR or Consumer level – Wearable — both areas are very active in Silicon Valley

IT stuff in the hospital HARDER that IT in any other environment, great progress in last 5 years, security of data, privacy. Sequencing data cost of big data management with highest security

Constrained data vs non-constrained data

Opportunities for Government cooperation as a Lead needed for standardization of data objects

 

Questions from the Podium:

  • Where is the Truth: do we have all the tools or we don’t for Genomic data usage
  • Question on Interoperability
  • Big Valuable data — vs Big data
  • quality, uniform, large cohort, comprehensive Cancer Centers
  • Volume of data can compensate quality of data
  • Data from Imaging – Quality and interpretation – THREE radiologist will read cancer screening

 

 

 

– See more at: http://personalizedmedicine.partners.org/Education/Personalized-Medicine-Conference/Program.aspx#sthash.qGbGZXXf.dpuf

 

@HarvardPMConf

#PMConf

@SachsAssociates

@Duke_Medicine

@AstellasUS

@GoogleVentures

@harvardmed

@BrighamWomens

@kyeshwant

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