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Cancer Genomics: Multiomic Analysis of Single Cells and Tumor Heterogeneity

Curator: Stephen J. Williams, PhD

 

scTrio-seq identifies colon cancer lineages

Single-cell multiomics sequencing and analyses of human colorectal cancer. Shuhui Bian et al. Science  30 Nov 2018:Vol. 362, Issue 6418, pp. 1060-1063

To better design treatments for cancer, it is important to understand the heterogeneity in tumors and how this contributes to metastasis. To examine this process, Bian et al. used a single-cell triple omics sequencing (scTrio-seq) technique to examine the mutations, transcriptome, and methylome within colorectal cancer tumors and metastases from 10 individual patients. The analysis provided insights into tumor evolution, linked DNA methylation to genetic lineages, and showed that DNA methylation levels are consistent within lineages but can differ substantially among clones.

Science, this issue p. 1060

Abstract

Although genomic instability, epigenetic abnormality, and gene expression dysregulation are hallmarks of colorectal cancer, these features have not been simultaneously analyzed at single-cell resolution. Using optimized single-cell multiomics sequencing together with multiregional sampling of the primary tumor and lymphatic and distant metastases, we developed insights beyond intratumoral heterogeneity. Genome-wide DNA methylation levels were relatively consistent within a single genetic sublineage. The genome-wide DNA demethylation patterns of cancer cells were consistent in all 10 patients whose DNA we sequenced. The cancer cells’ DNA demethylation degrees clearly correlated with the densities of the heterochromatin-associated histone modification H3K9me3 of normal tissue and those of repetitive element long interspersed nuclear element 1. Our work demonstrates the feasibility of reconstructing genetic lineages and tracing their epigenomic and transcriptomic dynamics with single-cell multiomics sequencing.

Fig. 1 Reconstruction of genetic lineages with scTrio-seq2.

Global SCNA patterns (250-kb resolution) of CRC01. Each row represents an individual cell. The subclonal SCNAs used for identifying genetic sublineages were marked and indexed; for details, see fig. S6B. On the top of the heatmap, the amplification or deletion frequency of each genomic bin (250 kb) of the non-hypermutated CRC samples from the TCGA Project and patient CRC01’s cancer cells are shown.

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Fig. 1 Reconstruction of genetic lineages with scTrio-seq2.

Global SCNA patterns (250-kb resolution) of CRC01. Each row represents an individual cell. The subclonal SCNAs used for identifying genetic sublineages were marked and indexed; for details, see fig. S6B. On the top of the heatmap, the amplification or deletion frequency of each genomic bin (250 kb) of the non-hypermutated CRC samples

 

 

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Lesson 9 Cell Signaling:  Curations and Articles of reference as supplemental information for lecture section on WNTs: #TUBiol3373

Stephen J. Wiilliams, Ph.D: Curator

UPDATED 4/23/2019

This has an updated lesson on WNT signaling.  Please click on the following and look at the slides labeled under lesson 10

cell motility 9b lesson_2018_sjw

Remember our lessons on the importance of signal termination.  The CANONICAL WNT signaling (that is the β-catenin dependent signaling)

is terminated by the APC-driven degradation complex.  This leads to the signal messenger  β-catenin being degraded by the proteosome.  Other examples of growth factor signaling that is terminated by a proteosome-directed include the Hedgehog signaling system, which is involved in growth and differentiation as well as WNTs and is implicated in various cancers.

A good article on the Hedgehog signaling pathway is found here:

The Voice of a Pathologist, Cancer Expert: Scientific Interpretation of Images: Cancer Signaling Pathways and Tumor Progression

All images in use for this article are under copyrights with Shutterstock.com

Cancer is expressed through a series of transformations equally involving metabolic enzymes and glucose, fat, and protein metabolism, and gene transcription, as a result of altered gene regulatory and transcription pathways, and also as a result of changes in cell-cell interactions.  These are embodied in the following series of graphics.

Figure 1: Sonic_hedgehog_pathwaySonic_hedgehog_pathway

The Voice of Dr. Larry

The figure shows a modification of nuclear translocation by Sonic hedgehog pathway. The hedgehog proteins have since been implicated in the development of internal organs, midline neurological structures, and the hematopoietic system in humans. The Hh signaling pathway consists of three main components: the receptor patched 1 (PTCH1), the seven transmembrane G-protein coupled receptor smoothened (SMO), and the intracellular glioma-associated oncogene homolog (GLI) family of transcription factors.5The GLI family is composed of three members, including GLI1 (gene activating), GLI2 (gene activating and repressive), and GLI3 (gene repressive).6 In the absence of an activating signal from either Shh, Ihh or Dhh, PTCH1 exerts an inhibitory effect on the signal transducer SMO, preventing any downstream signaling from occurring.7 When Hh ligands bind and activate PTCH1, the inhibition on SMO is released, allowing the translocation of SMO into the cytoplasm and its subsequent activation of the GLI family of transcription factors.

 

And from the review of  Elaine Y. C. HsiaYirui Gui, and Xiaoyan Zheng   Regulation of Hedgehog Signaling by Ubiquitination  Front Biol (Beijing). 2015 Jun; 10(3): 203–220.

the authors state:

Finally, termination of Hh signaling is also important for controlling the duration of pathway activity. Hh induced ubiquitination and degradation of Ci/Gli is the most well-established mechanism for limiting signal duration, and inhibiting this process can lead to cell patterning disruption and excessive cell proliferation (). In addition to Ci/Gli, a growing body of evidence suggests that ubiquitination also plays critical roles in regulating other Hh signaling components including Ptc, Smo, and Sufu. Thus, ubiquitination serves as a general mechanism in the dynamic regulation of the Hh pathway.

Overview of Hedgehog signaling showing the signal termination by ubiquitnation and subsequent degradation of the Gli transcriptional factors. obtained from Oncotarget 5(10):2881-911 · May 2014. GSK-3B as a Therapeutic Intervention in Cancer

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Note that in absence of Hedgehog ligands Ptch inhibits Smo accumulation and activation but upon binding of Hedgehog ligands (by an autocrine or paracrine fashion) Ptch is now unable to inhibit Smo (evidence exists that Ptch is now targeted for degradation) and Smo can now inhibit Sufu-dependent and GSK-3B dependent induced degradation of Gli factors Gli1 and Gli2.  Also note the Gli1 and Gli2 are transcriptional activators while Gli3 is a transcriptional repressor.

UPDATED 4/16/2019

Please click on the following links for the Powerpoint presentation for lesson 9.  In addition click on the mp4 links to download the movies so you can view them in Powerpoint slide 22:

cell motility 9 lesson_SJW 2019

movie file 1:

Tumorigenic but noninvasive MCF-7 cells motility on an extracellular matrix derived from normal (3DCntrol) or tumor associated (TA) fibroblasts.  Note that TA ECM is “soft” and not organized and tumor cells appear to move randomly if  much at all.

Movie 2:

 

Note that these tumorigenic and invasive MDA-MB-231 breast cancer cells move in organized patterns on organized ECM derived from Tumor Associated (TA) fibroblasts than from the ‘soft’ or unorganized ECM derived from normal  (3DCntrl) fibroblasts

 

The following contain curations of scientific articles from the site https://pharmaceuticalintelligence.com  intended as additional reference material  to supplement material presented in the lecture.

Wnts are a family of lipid-modified secreted glycoproteins which are involved in:

Normal physiological processes including

A. Development:

– Osteogenesis and adipogenesis (Loss of wnt/β‐catenin signaling causes cell fate shift of preosteoblasts from osteoblasts to adipocytes)

  – embryogenesis including body axis patterning, cell fate specification, cell proliferation and cell migration

B. tissue regeneration in adult tissue

read: Wnt signaling in the intestinal epithelium: from endoderm to cancer

And in pathologic processes such as oncogenesis (refer to Wnt/β-catenin Signaling [7.10]) and to your Powerpoint presentation

 

The curation Wnt/β-catenin Signaling is a comprehensive review of canonical and noncanonical Wnt signaling pathways

 

To review:

 

 

 

 

 

 

 

 

 

 

 

Activating the canonical Wnt pathway frees B-catenin from the degradation complex, resulting in B-catenin translocating to the nucleus and resultant transcription of B-catenin/TCF/LEF target genes.

Fig. 1 Canonical Wnt/FZD signaling pathway. (A) In the absence of Wnt signaling, soluble β-catenin is phosphorylated by a degradation complex consisting of the kinases GSK3β and CK1α and the scaffolding proteins APC and Axin1. Phosphorylated β-catenin is targeted for proteasomal degradation after ubiquitination by the SCF protein complex. In the nucleus and in the absence of β-catenin, TCF/LEF transcription factor activity is repressed by TLE-1; (B) activation of the canonical Wnt/FZD signaling leads to phosphorylation of Dvl/Dsh, which in turn recruits Axin1 and GSK3β adjacent to the plasma membrane, thus preventing the formation of the degradation complex. As a result, β-catenin accumulates in the cytoplasm and translocates into the nucleus, where it promotes the expression of target genes via interaction with TCF/LEF transcription factors and other proteins such as CBP, Bcl9, and Pygo.

NOTE: In the canonical signaling, the Wnt signal is transmitted via the Frizzled/LRP5/6 activated receptor to INACTIVATE the degradation complex thus allowing free B-catenin to act as the ultimate transducer of the signal.

Remember, as we discussed, the most frequent cancer-related mutations of WNT pathway constituents is in APC.

This shows how important the degradation complex is in controlling canonical WNT signaling.

Other cell signaling systems are controlled by protein degradation:

A.  The Forkhead family of transcription factors

Read: Regulation of FoxO protein stability via ubiquitination and proteasome degradation

B. Tumor necrosis factor α/NF κB signaling

Read: NF-κB, the first quarter-century: remarkable progress and outstanding questions

1.            Question: In cell involving G-proteins, the signal can be terminated by desensitization mechanisms.  How is both the canonical and noncanonical Wnt signal eventually terminated/desensitized?

We also discussed the noncanonical Wnt signaling pathway (independent of B-catenin induced transcriptional activity).  Note that the canonical and noncanonical involve different transducers of the signal.

Noncanonical WNT Signaling

Note: In noncanonical signaling the transducer is a G-protein and second messenger system is IP3/DAG/Ca++ and/or kinases such as MAPK, JNK.

Depending on the different combinations of WNT ligands and the receptors, WNT signaling activates several different intracellular pathways  (i.e. canonical versus noncanonical)

 

In addition different Wnt ligands are expressed at different times (temporally) and different cell types in development and in the process of oncogenesis. 

The following paper on Wnt signaling in ovarian oncogenesis shows how certain Wnt ligands are expressed in normal epithelial cells but the Wnt expression pattern changes upon transformation and ovarian oncogenesis. In addition, differential expression of canonical versus noncanonical WNT ligands occur during the process of oncogenesis (for example below the authors describe the noncanonical WNT5a is expressed in normal ovarian  epithelia yet WNT5a expression in ovarian cancer is lower than the underlying normal epithelium. However the canonical WNT10a, overexpressed in ovarian cancer cells, serves as an oncogene, promoting oncogenesis and tumor growth.

Wnt5a Suppresses Epithelial Ovarian Cancer by Promoting Cellular Senescence

Benjamin G. Bitler,1 Jasmine P. Nicodemus,1 Hua Li,1 Qi Cai,2 Hong Wu,3 Xiang Hua,4 Tianyu Li,5 Michael J. Birrer,6Andrew K. Godwin,7 Paul Cairns,8 and Rugang Zhang1,*

A.           Abstract

Epithelial ovarian cancer (EOC) remains the most lethal gynecological malignancy in the US. Thus, there is an urgent need to develop novel therapeutics for this disease. Cellular senescence is an important tumor suppression mechanism that has recently been suggested as a novel mechanism to target for developing cancer therapeutics. Wnt5a is a non-canonical Wnt ligand that plays a context-dependent role in human cancers. Here, we investigate the role of Wnt5a in regulating senescence of EOC cells. We demonstrate that Wnt5a is expressed at significantly lower levels in human EOC cell lines and in primary human EOCs (n = 130) compared with either normal ovarian surface epithelium (n = 31; p = 0.039) or fallopian tube epithelium (n = 28; p < 0.001). Notably, a lower level of Wnt5a expression correlates with tumor stage (p = 0.003) and predicts shorter overall survival in EOC patients (p = 0.003). Significantly, restoration of Wnt5a expression inhibits the proliferation of human EOC cells both in vitro and in vivo in an orthotopic EOC mouse model. Mechanistically, Wnt5a antagonizes canonical Wnt/β-catenin signaling and induces cellular senescence by activating the histone repressor A (HIRA)/promyelocytic leukemia (PML) senescence pathway. In summary, we show that loss of Wnt5a predicts poor outcome in EOC patients and Wnt5a suppresses the growth of EOC cells by triggering cellular senescence. We suggest that strategies to drive senescence in EOC cells by reconstituting Wnt5a signaling may offer an effective new strategy for EOC therapy.

Oncol Lett. 2017 Dec;14(6):6611-6617. doi: 10.3892/ol.2017.7062. Epub 2017 Sep 26.

Clinical significance and biological role of Wnt10a in ovarian cancer. 

Li P1Liu W1Xu Q1Wang C1.

Ovarian cancer is one of the five most malignant types of cancer in females, and the only currently effective therapy is surgical resection combined with chemotherapy. Wnt family member 10A (Wnt10a) has previously been identified to serve an oncogenic function in several tumor types, and was revealed to have clinical significance in renal cell carcinoma; however, there is still only limited information regarding the function of Wnt10a in the carcinogenesis of ovarian cancer. The present study identified increased expression levels of Wnt10a in two cell lines, SKOV3 and A2780, using reverse transcription-polymerase chain reaction. Functional analysis indicated that the viability rate and migratory ability of SKOV3 cells was significantly inhibited following Wnt10a knockdown using short interfering RNA (siRNA) technology. The viability rate of SKOV3 cells decreased by ~60% compared with the control and the migratory ability was only ~30% of that in the control. Furthermore, the expression levels of β-catenin, transcription factor 4, lymphoid enhancer binding factor 1 and cyclin D1 were significantly downregulated in SKOV3 cells treated with Wnt10a-siRNA3 or LGK-974, a specific inhibitor of the canonical Wnt signaling pathway. However, there were no synergistic effects observed between Wnt10a siRNA3 and LGK-974, which indicated that Wnt10a activated the Wnt/β-catenin signaling pathway in SKOV3 cells. In addition, using quantitative PCR, Wnt10a was overexpressed in the tumor tissue samples obtained from 86 patients with ovarian cancer when compared with matching paratumoral tissues. Clinicopathological association analysis revealed that Wnt10a was significantly associated with high-grade (grade III, P=0.031) and late-stage (T4, P=0.008) ovarian cancer. Furthermore, the estimated 5-year survival rate was 18.4% for patients with low Wnt10a expression levels (n=38), whereas for patients with high Wnt10a expression (n=48) the rate was 6.3%. The results of the present study suggested that Wnt10a serves an oncogenic role during the carcinogenesis and progression of ovarian cancer via the Wnt/β-catenin signaling pathway.

Targeting the Wnt Pathway includes curations of articles related to the clinical development of Wnt signaling inhibitors as a therapeutic target in various cancers including hepatocellular carcinoma, colon, breast and potentially ovarian cancer.

 

2.         Question: Given that different Wnt ligands and receptors activate different signaling pathways, AND  WNT ligands  can be deferentially and temporally expressed  in various tumor types and the process of oncogenesis, how would you approach a personalized therapy targeting the WNT signaling pathway?

3.         Question: What are the potential mechanisms of either intrinsic or acquired resistance to Wnt ligand antagonists being developed?

 

Other related articles published in this Open Access Online Scientific Journal include the following:

Targeting the Wnt Pathway [7.11]

Wnt/β-catenin Signaling [7.10]

Cancer Signaling Pathways and Tumor Progression: Images of Biological Processes in the Voice of a Pathologist Cancer Expert

e-Scientific Publishing: The Competitive Advantage of a Powerhouse for Curation of Scientific Findings and Methodology Development for e-Scientific Publishing – LPBI Group, A Case in Point 

Electronic Scientific AGORA: Comment Exchanges by Global Scientists on Articles published in the Open Access Journal @pharmaceuticalintelligence.com – Four Case Studies

 

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Bioinformatic Tools for Cancer Mutational Analysis: COSMIC and Beyond

Curator: Stephen J. Williams, Ph.D.

Updated 7/26/2019

Updated 04/27/2019

Signatures of Mutational Processes in Human Cancer (from COSMIC)

From The COSMIC Database

summary_circos_cosmic_38_380

The genomic landscape of cancer. The COSMIC database has a fully curated and annotated database of recurrent genetic mutations founds in various cancers (data taken form cancer sequencing projects). For interactive map please go to the COSMIC database here: http://cancer.sanger.ac.uk/cosmic

 

 

Somatic mutations are present in all cells of the human body and occur throughout life. They are the consequence of multiple mutational processes, including the intrinsic slight infidelity of the DNA replication machinery, exogenous or endogenous mutagen exposures, enzymatic modification of DNA and defective DNA repair. Different mutational processes generate unique combinations of mutation types, termed “Mutational Signatures”.

In the past few years, large-scale analyses have revealed many mutational signatures across the spectrum of human cancer types [Nik-Zainal S. et al., Cell (2012);Alexandrov L.B. et al., Cell Reports (2013);Alexandrov L.B. et al., Nature (2013);Helleday T. et al., Nat Rev Genet (2014);Alexandrov L.B. and Stratton M.R., Curr Opin Genet Dev (2014)]. However, as the number of mutational signatures grows the need for a curated census of signatures has become apparent. Here, we deliver such a resource by providing the profiles of, and additional information about, known mutational signatures.

The current set of mutational signatures is based on an analysis of 10,952 exomes and 1,048 whole-genomes across 40 distinct types of human cancer. These analyses are based on curated data that were generated by The Cancer Genome Atlas (TCGA), the International Cancer Genome Consortium (ICGC), and a large set of freely available somatic mutations published in peer-reviewed journals. Complete details about the data sources will be provided in future releases of COSMIC.

The profile of each signature is displayed using the six substitution subtypes: C>A, C>G, C>T, T>A, T>C, and T>G (all substitutions are referred to by the pyrimidine of the mutated Watson–Crick base pair). Further, each of the substitutions is examined by incorporating information on the bases immediately 5’ and 3’ to each mutated base generating 96 possible mutation types (6 types of substitution ∗ 4 types of 5’ base ∗ 4 types of 3’ base). Mutational signatures are displayed and reported based on the observed trinucleotide frequency of the human genome, i.e., representing the relative proportions of mutations generated by each signature based on the actual trinucleotide frequencies of the reference human genome version GRCh37. Note that only validated mutational signatures have been included in the curated census of mutational signatures.

Additional information is provided for each signature, including the cancer types in which the signature has been found, proposed aetiology for the mutational processes underlying the signature, other mutational features that are associated with each signature and information that may be relevant for better understanding of a particular mutational signature.

The set of signatures will be updated in the future. This will include incorporating additional mutation types (e.g., indels, structural rearrangements, and localized hypermutation such as kataegis) and cancer samples. With more cancer genome sequences and the additional statistical power this will bring, new signatures may be found, the profiles of current signatures may be further refined, signatures may split into component signatures and signatures

See their COSMIC tutorial page here for instructional videos

Updated News: COSMIC v75 – 24th November 2015

COSMIC v75 includes curations across GRIN2A, fusion pair TCF3-PBX1, and genomic data from 17 systematic screen publications. We are also beginning a reannotation of TCGA exome datasets using Sanger’s Cancer Genome Project analyis pipeline to ensure consistency; four studies are included in this release, to be expanded across the next few releases. The Cancer Gene Census now has a dedicated curator, Dr. Zbyslaw Sondka, who will be focused on expanding the Census, enhancing the evidence underpinning it, and developing improved expert-curated detail describing each gene’s impact in cancer. Finally, as we begin to streamline our ever-growing website, we have combined all information for each gene onto one page and simplified the layout and design to improve navigation

may be found in cancer types in which they are currently not detected.

mutational signatures across human cancer

Mutational signatures across human cancer

Patterns of mutational signatures [Download signatures]

 COSMIC database identifies 30 mutational signatures in human cancer

Please goto to COSMIC site to see bigger .png of mutation signatures

Signature 1

Cancer types:

Signature 1 has been found in all cancer types and in most cancer samples.

Proposed aetiology:

Signature 1 is the result of an endogenous mutational process initiated by spontaneous deamination of 5-methylcytosine.

Additional mutational features:

Signature 1 is associated with small numbers of small insertions and deletions in most tissue types.

Comments:

The number of Signature 1 mutations correlates with age of cancer diagnosis.

Signature 2

Cancer types:

Signature 2 has been found in 22 cancer types, but most commonly in cervical and bladder cancers. In most of these 22 cancer types, Signature 2 is present in at least 10% of samples.

Proposed aetiology:

Signature 2 has been attributed to activity of the AID/APOBEC family of cytidine deaminases. On the basis of similarities in the sequence context of cytosine mutations caused by APOBEC enzymes in experimental systems, a role for APOBEC1, APOBEC3A and/or APOBEC3B in human cancer appears more likely than for other members of the family.

Additional mutational features:

Transcriptional strand bias of mutations has been observed in exons, but is not present or is weaker in introns.

Comments:

Signature 2 is usually found in the same samples as Signature 13. It has been proposed that activation of AID/APOBEC cytidine deaminases is due to viral infection, retrotransposon jumping or to tissue inflammation. Currently, there is limited evidence to support these hypotheses. A germline deletion polymorphism involving APOBEC3A and APOBEC3B is associated with the presence of large numbers of Signature 2 and 13 mutations and with predisposition to breast cancer. Mutations of similar patterns to Signatures 2 and 13 are commonly found in the phenomenon of local hypermutation present in some cancers, known as kataegis, potentially implicating AID/APOBEC enzymes in this process as well.

Signature 3

Cancer types:

Signature 3 has been found in breast, ovarian, and pancreatic cancers.

Proposed aetiology:

Signature 3 is associated with failure of DNA double-strand break-repair by homologous recombination.

Additional mutational features:

Signature 3 associates strongly with elevated numbers of large (longer than 3bp) insertions and deletions with overlapping microhomology at breakpoint junctions.

Comments:

Signature 3 is strongly associated with germline and somatic BRCA1 and BRCA2 mutations in breast, pancreatic, and ovarian cancers. In pancreatic cancer, responders to platinum therapy usually exhibit Signature 3 mutations.

Signature 4

Cancer types:

Signature 4 has been found in head and neck cancer, liver cancer, lung adenocarcinoma, lung squamous carcinoma, small cell lung carcinoma, and oesophageal cancer.

Proposed aetiology:

Signature 4 is associated with smoking and its profile is similar to the mutational pattern observed in experimental systems exposed to tobacco carcinogens (e.g., benzo[a]pyrene). Signature 4 is likely due to tobacco mutagens.

Additional mutational features:

Signature 4 exhibits transcriptional strand bias for C>A mutations, compatible with the notion that damage to guanine is repaired by transcription-coupled nucleotide excision repair. Signature 4 is also associated with CC>AA dinucleotide substitutions.

Comments:

Signature 29 is found in cancers associated with tobacco chewing and appears different from Signature 4.

Signature 5

Cancer types:

Signature 5 has been found in all cancer types and most cancer samples.

Proposed aetiology:

The aetiology of Signature 5 is unknown.

Additional mutational features:

Signature 5 exhibits transcriptional strand bias for T>C substitutions at ApTpN context.

Comments:

Signature 6

Cancer types:

Signature 6 has been found in 17 cancer types and is most common in colorectal and uterine cancers. In most other cancer types, Signature 6 is found in less than 3% of examined samples.

Proposed aetiology:

Signature 6 is associated with defective DNA mismatch repair and is found in microsatellite unstable tumours.

Additional mutational features:

Signature 6 is associated with high numbers of small (shorter than 3bp) insertions and deletions at mono/polynucleotide repeats.

Comments:

Signature 6 is one of four mutational signatures associated with defective DNA mismatch repair and is often found in the same samples as Signatures 15, 20, and 26.

Signature 7

Cancer types:

Signature 7 has been found predominantly in skin cancers and in cancers of the lip categorized as head and neck or oral squamous cancers.

Proposed aetiology:

Based on its prevalence in ultraviolet exposed areas and the similarity of the mutational pattern to that observed in experimental systems exposed to ultraviolet light Signature 7 is likely due to ultraviolet light exposure.

Additional mutational features:

Signature 7 is associated with large numbers of CC>TT dinucleotide mutations at dipyrimidines. Additionally, Signature 7 exhibits a strong transcriptional strand-bias indicating that mutations occur at pyrimidines (viz., by formation of pyrimidine-pyrimidine photodimers) and these mutations are being repaired by transcription-coupled nucleotide excision repair.

Comments:

Signature 8

Cancer types:

Signature 8 has been found in breast cancer and medulloblastoma.

Proposed aetiology:

The aetiology of Signature 8 remains unknown.

Additional mutational features:

Signature 8 exhibits weak strand bias for C>A substitutions and is associated with double nucleotide substitutions, notably CC>AA.

Comments:

Signature 9

Cancer types:

Signature 9 has been found in chronic lymphocytic leukaemias and malignant B-cell lymphomas.

Proposed aetiology:

Signature 9 is characterized by a pattern of mutations that has been attributed to polymerase η, which is implicated with the activity of AID during somatic hypermutation.

Additional mutational features:

Comments:

Chronic lymphocytic leukaemias that possess immunoglobulin gene hypermutation (IGHV-mutated) have elevated numbers of mutations attributed to Signature 9 compared to those that do not have immunoglobulin gene hypermutation.

Signature 10

Cancer types:

Signature 10 has been found in six cancer types, notably colorectal and uterine cancer, usually generating huge numbers of mutations in small subsets of samples.

Proposed aetiology:

It has been proposed that the mutational process underlying this signature is altered activity of the error-prone polymerase POLE. The presence of large numbers of Signature 10 mutations is associated with recurrent POLE somatic mutations, viz., Pro286Arg and Val411Leu.

Additional mutational features:

Signature 10 exhibits strand bias for C>A mutations at TpCpT context and T>G mutations at TpTpT context.

Comments:

Signature 10 is associated with some of most mutated cancer samples. Samples exhibiting this mutational signature have been termed ultra-hypermutators.

Signature 11

Cancer types:

Signature 11 has been found in melanoma and glioblastoma.

Proposed aetiology:

Signature 11 exhibits a mutational pattern resembling that of alkylating agents. Patient histories have revealed an association between treatments with the alkylating agent temozolomide and Signature 11 mutations.

Additional mutational features:

Signature 11 exhibits a strong transcriptional strand-bias for C>T substitutions indicating that mutations occur on guanine and that these mutations are effectively repaired by transcription-coupled nucleotide excision repair.

Comments:

Signature 12

Cancer types:

Signature 12 has been found in liver cancer.

Proposed aetiology:

The aetiology of Signature 12 remains unknown.

Additional mutational features:

Signature 12 exhibits a strong transcriptional strand-bias for T>C substitutions.

Comments:

Signature 12 usually contributes a small percentage (<20%) of the mutations observed in a liver cancer sample.

Signature 13

Cancer types:

Signature 13 has been found in 22 cancer types and seems to be commonest in cervical and bladder cancers. In most of these 22 cancer types, Signature 13 is present in at least 10% of samples.

Proposed aetiology:

Signature 13 has been attributed to activity of the AID/APOBEC family of cytidine deaminases converting cytosine to uracil. On the basis of similarities in the sequence context of cytosine mutations caused by APOBEC enzymes in experimental systems, a role for APOBEC1, APOBEC3A and/or APOBEC3B in human cancer appears more likely than for other members of the family. Signature 13 causes predominantly C>G mutations. This may be due to generation of abasic sites after removal of uracil by base excision repair and replication over these abasic sites by REV1.

Additional mutational features:

Transcriptional strand bias of mutations has been observed in exons, but is not present or is weaker in introns.

Comments:

Signature 2 is usually found in the same samples as Signature 13. It has been proposed that activation of AID/APOBEC cytidine deaminases is due to viral infection, retrotransposon jumping or to tissue inflammation. Currently, there is limited evidence to support these hypotheses. A germline deletion polymorphism involving APOBEC3A and APOBEC3B is associated with the presence of large numbers of Signature 2 and 13 mutations and with predisposition to breast cancer. Mutations of similar patterns to Signatures 2 and 13 are commonly found in the phenomenon of local hypermutation present in some cancers, known as kataegis, potentially implicating AID/APOBEC enzymes in this process as well.

Signature 14

Cancer types:

Signature 14 has been observed in four uterine cancers and a single adult low-grade glioma sample.

Proposed aetiology:

The aetiology of Signature 14 remains unknown.

Additional mutational features:

Comments:

Signature 14 generates very high numbers of somatic mutations (>200 mutations per MB) in all samples in which it has been observed.

Signature 15

Cancer types:

Signature 15 has been found in several stomach cancers and a single small cell lung carcinoma.

Proposed aetiology:

Signature 15 is associated with defective DNA mismatch repair.

Additional mutational features:

Signature 15 is associated with high numbers of small (shorter than 3bp) insertions and deletions at mono/polynucleotide repeats.

Comments:

Signature 15 is one of four mutational signatures associated with defective DNA mismatch repair and is often found in the same samples as Signatures 6, 20, and 26.

Signature 16

Cancer types:

Signature 16 has been found in liver cancer.

Proposed aetiology:

The aetiology of Signature 16 remains unknown.

Additional mutational features:

Signature 16 exhibits an extremely strong transcriptional strand bias for T>C mutations at ApTpN context, with T>C mutations occurring almost exclusively on the transcribed strand.

Comments:

Signature 17

Cancer types:

Signature 17 has been found in oesophagus cancer, breast cancer, liver cancer, lung adenocarcinoma, B-cell lymphoma, stomach cancer and melanoma.

Proposed aetiology:

The aetiology of Signature 17 remains unknown.

Additional mutational features:

Comments:

Signature 1Signature 18

Cancer types:

Signature 18 has been found commonly in neuroblastoma. Additionally, Signature 18 has been also observed in breast and stomach carcinomas.

Proposed aetiology:

The aetiology of Signature 18 remains unknown.

Additional mutational features:

Comments:

Signature 19

Cancer types:

Signature 19 has been found only in pilocytic astrocytoma.

Proposed aetiology:

The aetiology of Signature 19 remains unknown.

Additional mutational features:

Comments:

Signature 20

Cancer types:

Signature 20 has been found in stomach and breast cancers.

Proposed aetiology:

Signature 20 is believed to be associated with defective DNA mismatch repair.

Additional mutational features:

Signature 20 is associated with high numbers of small (shorter than 3bp) insertions and deletions at mono/polynucleotide repeats.

Comments:

Signature 20 is one of four mutational signatures associated with defective DNA mismatch repair and is often found in the same samples as Signatures 6, 15, and 26.

Signature 21

Cancer types:

Signature 21 has been found only in stomach cancer.

Proposed aetiology:

The aetiology of Signature 21 remains unknown.

Additional mutational features:

Comments:

Signature 21 is found only in four samples all generated by the same sequencing centre. The mutational pattern of Signature 21 is somewhat similar to the one of Signature 26. Additionally, Signature 21 is found only in samples that also have Signatures 15 and 20. As such, Signature 21 is probably also related to microsatellite unstable tumours.

Signature 22

Cancer types:

Signature 22 has been found in urothelial (renal pelvis) carcinoma and liver cancers.

Proposed aetiology:

Signature 22 has been found in cancer samples with known exposures to aristolochic acid. Additionally, the pattern of mutations exhibited by the signature is consistent with the one previous observed in experimental systems exposed to aristolochic acid.

Additional mutational features:

Signature 22 exhibits a very strong transcriptional strand bias for T>A mutations indicating adenine damage that is being repaired by transcription-coupled nucleotide excision repair.

Comments:

Signature 22 has a very high mutational burden in urothelial carcinoma; however, its mutational burden is much lower in liver cancers.

Signature 23

Cancer types:

Signature 23 has been found only in a single liver cancer sample.

Proposed aetiology:

The aetiology of Signature 23 remains unknown.

Additional mutational features:

Signature 23 exhibits very strong transcriptional strand bias for C>T mutations.

Comments:

Signature 24

Cancer types:

Signature 24 has been observed in a subset of liver cancers.

Proposed aetiology:

Signature 24 has been found in cancer samples with known exposures to aflatoxin. Additionally, the pattern of mutations exhibited by the signature is consistent with that previous observed in experimental systems exposed to aflatoxin.

Additional mutational features:

Signature 24 exhibits a very strong transcriptional strand bias for C>A mutations indicating guanine damage that is being repaired by transcription-coupled nucleotide excision repair.

Comments:

Signature 25

Cancer types:

Signature 25 has been observed in Hodgkin lymphomas.

Proposed aetiology:

The aetiology of Signature 25 remains unknown.

Additional mutational features:

Signature 25 exhibits transcriptional strand bias for T>A mutations.

Comments:

This signature has only been identified in Hodgkin’s cell lines. Data is not available from primary Hodgkin lymphomas.

Signature 26

Cancer types:

Signature 26 has been found in breast cancer, cervical cancer, stomach cancer and uterine carcinoma.

Proposed aetiology:

Signature 26 is believed to be associated with defective DNA mismatch repair.

Additional mutational features:

Signature 26 is associated with high numbers of small (shorter than 3bp) insertions and deletions at mono/polynucleotide repeats.

Comments:

Signature 26 is one of four mutational signatures associated with defective DNA mismatch repair and is often found in the same samples as Signatures 6, 15 and 20.

Signature 27

Cancer types:

Signature 27 has been observed in a subset of kidney clear cell carcinomas.

Proposed aetiology:

The aetiology of Signature 27 remains unknown.

Additional mutational features:

Signature 27 exhibits very strong transcriptional strand bias for T>A mutations. Signature 27 is associated with high numbers of small (shorter than 3bp) insertions and deletions at mono/polynucleotide repeats.

Comments:

Signature 28

Cancer types:

Signature 28 has been observed in a subset of stomach cancers.

Proposed aetiology:

The aetiology of Signature 28 remains unknown.

Additional mutational features:

Comments:

Signature 29

Cancer types:

Signature 29 has been observed only in gingivo-buccal oral squamous cell carcinoma.

Proposed aetiology:

Signature 29 has been found in cancer samples from individuals with a tobacco chewing habit.

Additional mutational features:

Signature 29 exhibits transcriptional strand bias for C>A mutations indicating guanine damage that is most likely repaired by transcription-coupled nucleotide excision repair. Signature 29 is also associated with CC>AA dinucleotide substitutions.

Comments:

The Signature 29 pattern of C>A mutations due to tobacco chewing appears different from the pattern of mutations due to tobacco smoking reflected by Signature 4.

Signature 30

Cancer types:

Signature 30 has been observed in a small subset of breast cancers.

Proposed aetiology:

The aetiology of Signature 30 remains unknown.

 


 

Examples in the literature of deposits into or analysis from the COSMIC database

The Genomic Landscapes of Human Breast and Colorectal Cancers from Wood 318 (5853): 11081113 Science 2007

“analysis of exons representing 20,857 transcripts from 18,191 genes, we conclude that the genomic landscapes of breast and colorectal cancers are composed of a handful of commonly mutated gene “mountains” and a much larger number of gene “hills” that are mutated at low frequency. ”

  • found cellular pathways with multiple pathways
  • analyzed a highly curated database (Metacore, GeneGo, Inc.) that includes human protein-protein interactions, signal transduction and metabolic pathways
  • There were 108 pathways that were found to be preferentially mutated in breast tumors. Many of the pathways involved phosphatidylinositol 3-kinase (PI3K) signaling
  • the cancer genome landscape consists of relief features (mutated genes) with heterogeneous heights (determined by CaMP scores). There are a few “mountains” representing individual CAN-genes mutated at high frequency. However, the landscapes contain a much larger number of “hills” representing the CAN-genes that are mutated at relatively low frequency. It is notable that this general genomic landscape (few gene mountains and many gene hills) is a common feature of both breast and colorectal tumors.
  • developed software to analyze multiple mutations and mutation frequencies available from Harvard Bioinformatics at

 

http://bcb.dfci.harvard.edu/~gp/software/CancerMutationAnalysis/cma.htm

 

 

R Software for Cancer Mutation Analysis (download here)

 

CancerMutationAnalysis Version 1.0:

R package to reproduce the statistical analyses of the Sjoblom et al article and the associated Technical Comment. This package is build for reproducibility of the original results and not for flexibility. Future version will be more general and define classes for the data types used. Further details are available in Working Paper 126.

CancerMutationAnalysis Version 2.0:

R package to reproduce the statistical analyses of the Wood et al article. Like its predecessor, this package is still build for reproducibility of the original results and not for flexibility. Further details are available in Working Paper 126

 

 

 

 

 

 

 

 

 

Update 04/27/2019

Review 2018. The COSMIC Cancer Gene Census: describing genetic dysfunction across all human cancers. Z. Sondka et al. Nature Reviews. 2018.

The Catalogue of Somatic Mutations in Cancer (COSMIC) Cancer Gene Census (CGC) reevaluates the cancer genome landscape periodically and curates the findings into a database of genetic changes occurring in various tumor types.  The 2018 CGC describes in detail the effect of 719 cancer driving genes.  The recent expansion includes functional and mechanistic descriptions of how each gene contributes to disease etiology and in terms of the cancer hallmarks as described by Hanahan and Weinberg.  These functional characteristics show the complexity of the cancer mutational landscape and genome and suggest ” multiple cancer-related functions for many genes, which are often highly tissue-dependent or tumour stage-dependent.”  The 2018 CGC expands a second tier of genes, expanding the list of cancer related genes.

Criteria for curation of genes into CGC (curation process)

  • choosing candidate genes are selected from published literature, conference abstracts, large cancer genome screens deposited in databases, and analysis of current COSMIC database
  • COSMIC data are analyzed to determine presence of patterns of somatic mutations and frequency of such mutations in cancer
  • literature review to determine the role of the gene in cancer
  • Minimum evidence

– at least two publications from different groups shows increased mutation frequency in at least one type of cancer (PubMed)

–  at least two publications from different groups showing experimental evidence of functional involvement in at least one hallmark of cancer in order to classify the mutant gene as oncogene, tumor suppressor, or fusion partner (like BCR-Abl)

  • independent assessment by at least two postdoctoral fellows
  • gene must be classified as either Tier 1 of Tier 2 CGC gene
  • inclusion in database
  • continued curation efforts

definitions:

Tier 1 gene: genes which have strong evidence from both mutational and functional analysis as being involved in cancer

Tier 2 gene: genes with mutational patterns typical of cancer drivers but not functionally characterized as well as genes with published mechanistic description of involvement in cancer but without proof of somatic mutations in cancer

Current Status of Tier 1 and Tier 2 genes in CGC

Tier 1 genes (574 genes): include 79 oncogenes, 140 tumor suppressor genes, 93 fusion partners

Tier 2 genes (719 genes): include 103 oncogenes, 181 tumor suppressors, 134 fusion partners and 31 with unknown function

Updated 7/26/2019

The COSMIC database is undergoing an extensive update and reannotation, in order to ensure standardisation and modernisation across COSMIC data. This will substantially improve the identification of unique variants that may have been described at the genome, transcript and/or protein level. The introduction of a Genomic Identifier, along with complete annotation across multiple, high quality Ensembl transcripts and improved compliance with current HGVS syntax, will enable variant matching both within COSMIC and across other bioinformatic datasets.

As a result of these updates there will be significant changes in the upcoming releases as we work through this process. The first stage of this work was the introduction of improvedHGVS syntax compliance in our May release. The majority of the changes will be reflected in COSMIC v90, which will be released in late August or early September, and the remaining changes will be introduced over the next few releases.

The significant changes in v90 include:

  • Updated genes, transcripts and proteins from Ensembl release 93 on both the GRCh37 and GRCh38 assemblies.
  • Full reannotation of COSMIC variants with known genomic coordinates using Ensembl’s Variant Effect Predictor (VEP). This provides accurate and standardised annotation uniformly across all relevant transcripts and genes that include the genomic location of the variant.
  • New stable genomic identifiers (COSV) that indicate the definitive position of the variant on the genome. These unique identifiers allow variants to be mapped between GRCh37 and GRCh38 assemblies and displayed on a selection of transcripts.
  • Updated cross-reference links between COSMIC genes and other widely-used databases such as HGNC, RefSeq, Uniprot and CCDS.
  • Complete standardised representation of COSMIC variants, following the most recent HGVS recommendations, where possible.
  • Remapping of gene fusions on the updated transcripts on both the GRCh37 and GRCh38 assemblies, along with the genomic coordinates for the breakpoint positions.
  • Reduced redundancy of mutations. Duplicate variants have been merged into one representative variant.

Key points for you

COSMIC variants have been annotated on all relevant Ensembl transcripts across both the GRCh37 and GRCh38 assemblies from Ensembl release 93. New genomic identifiers (e.g. COSV56056643) are used, which refers to the variant change at the genomic level rather than gene, transcript or protein level and can thus be used universally. Existing COSM IDs will continue to be supported and will now be referred to as legacy identifiers e.g. COSM476. The legacy identifiers (COSM) are still searchable. In the case of mutations without genomic coordinates, hence without a COSV identifier, COSM identifiers will continue to be used.

All relevant Ensembl transcripts in COSMIC (which have been selected based on Ensembl canonical classification and on the quality of the dataset to include only GENCODE basic transcripts) will now have both accession and version numbers, so that the exact transcript is known, ensuring reproducibility. This also provides transparency and clarity as the data are updated.

How these changes will be reflected in the download files

As we are now mapping all variants on all relevant Ensembl transcripts, the number of rows in the majority of variant download files has increased significantly. In the download files, additional columns are provided including the legacy identifier (COSM) and the new genomic identifier (COSV). An internal mutation identifier is also provided to uniquely represent each mutation, on a specific transcript, on a given assembly build. The accession and version number for each transcript are included. File descriptions for each of the download files will be available from the downloads page for clarity. We have included an example of the new columns below.

For example: COSMIC Complete Mutation Data (Targeted screens)

    1. [17:Q] Mutation Id – An internal mutation identifier to uniquely represent each mutation on a specific transcript on a given assembly build.
    1. [18:R] Genomic Mutation Id – Genomic mutation identifier (COSV) to indicate the definitive position of the variant on the genome. This identifier is trackable and stable between different versions of the release.
    1. [19:S] Legacy Mutation Id – Legacy mutation identifier (COSM) that will represent existing COSM mutation identifiers.

We will shortly have some sample data that can be downloaded in the new table structure, to give you real data to manipulate and integrate, this will be available on the variant updates page.

How this affects you

We are aware that many of the changes we are making will affect integration into your pipelines and analytical platforms. By giving you advance notice of the changes, we hope much of this can be mitigated, and the end result of having clean, standardised data will be well worth any disruption. The variant updates page on the COSMIC website will provide a central point for this information and further technical details of the changes that we are making to COSMIC.

Kind Regards,
The COSMIC Team
Wellcome Sanger Institute
Wellcome Genome Campus,
Hinxton CB10 1SA

 

 

 

 

 

 

 

 

 

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The Vibrant Philly Biotech Scene: PCCI Meeting Announcement, BioDetego Presents Colon Cancer Diagnostic Tool

Reporter: Stephen J. Williams, Ph.D.

PCCI invites you to attend a presentation by:

BioDetego, A cancer diagnostics development company (see meeting announcement here)

Monday, December 14, 2015, 6:30PM; at the Chesterbrook (Wayne, PA) Embassy Suites Hotel (directions below)

Sponsored by:

To register please click on www.rxpcci.com and follow directions

BioDetego is developing the next generation of cancer diagnostics – identifying people that will benefit from chemotherapy with unprecedented accuracy.

Today there are no clear treatment guidelines for many people with cancer and routinely people are undertreated (the lack of chemotherapy treatment for people at risk of disease relapse) or overtreated (the unnecessary, harmful treatment of people not at risk). The resulting human and economic burden is enormous. Those undertreated face increased mortality at a cost of >$150,000 per relapse, and those overtreated suffer the harmful effects of unnecessary chemotherapy at a cost of $25,000 per person.

Supported by compelling clinical data in multiple cancer types, BioDetego is developing VASPfore, a disruptive cancer diagnostic platform that addresses this critical gap in cancer care by accurately determining each person’s risk of relapse and need for chemotherapy. The lead product, VASPfore-CRC, is poised to change the standard of care in colorectal cancer diagnosis and treatment. The test will:

– Accurately determine individual risk of relapse and chemotherapy need

– Provide 100% actionable information to reduce harmful under- and overtreatment

– Improve health outcomes

– Deliver savings by reducing payor costs

BioDetego’s lead product VASPfore-CRC targets 150,000 patients per year diagnosed with stage II or III a/b colorectal cancer in the US, Europe and Australia excluding those unsuitable for chemotherapy due to age or health. Based on pricing of a competitor test with payor coverage the target market is valued at $1Billion. Ongoing development of the VASPfore platform in additional cancer types (e.g. breast, lung and prostate) will substantially increase market size. The total addressable market for the VASPfore platform is comprised of the 1.5 million patients per year diagnosed with an early/intermediate stage epithelial cancer in the US, Europe and Australia and is valued at $6.5 Billion.

PROGRAM

6:30: Cocktails and Dinner; there will be a cash bar and a special two-entrée buffet

8:00 David Zuzga PhD, CEO, will deliver the Company”s “Elevator” pitch to the group.

8:20: A panel consisting of Maria Maccecchini, Dennis Fujii and Caroline Hoedemaker will address three major issues crucial to helping the Company reach the next level. BioDetego has submitted the following questions:

  1. BioDetego is a virtual company without full-time employees and is open-minded about the composition of itd eventual management team. Given the company’s planned next steps, what mix of experience and commitment (potentially draw from its founders, current advisory board members, or from outside the company) would be desirable to potential investors?
  2. VASPfore has the potential to inform oncology clinical trials where enrolling cancer patients likely to relapse may increase the power of a study to determine treatment efficacy. How might BioDetego pursue and structure a co-development deal with a potential strategic partner?
  3. Clinical development milestones, such as the completion of large clinical validation studies and expansion of the platform to additional cancers, represent significant value inflection points. How should these inflection points be integrated into an exit strategy which best manages investor risk and potential for return.

 9:00: Q&A session

Remember to register: click on www.rxpcci.com and follow directions

Dinner price for members is a flat $40; Parking is free!

Lifetime dues for new members are still $100; join PCCI and your first dinner will be ON US!

Bring a friend and/or a business colleague! You know that our meetings a livelier and more interesting than ever.

The Embassy Suites Hotel provides an excellent facility, more room and a fine menu.

Every PCCI meeting is webcast. The webcast recording of the PCCI meetings will be posted on the PCCI website “rxpcci.com” and webcast live via the internet during the event.

Directions: Take Rt 202 to the Chesterbrook exit (that’s two exits South of the Devon exit), turn Right at the end of the Exit ramp and you’ll see the hotel at your Right. If you are going North on 202, get off at the Chesterbrook Exit and turn Left at the traffic light and drive back over Rt 202. You’ll see the hotel at your Right. Proceed to the traffic light and turn Right into the parking lot of the hotel. Their phone is: 610 647 6700.

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New Generation of Platinated Compounds to Circumvent Resistance

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

Resistance to chemotherapeutic drugs continues to be a major hurdle in the treatment of neoplastic disorders, irregardless if the drug is a member of the cytotoxic “older” drugs or the cytostatic “newer” personalized therapies like the tyrosine kinase inhibitors.  For the platinatum compounds such as cisplatin and carboplatin, which are mainstays in therapeutic regimens for ovarian and certain head and neck cancers, development of resistance is often regarded as the final blow, as new options for these diseases have been limited.

Although there are many mechanisms by which resistance to platinated compounds may develop the purpose of this posting is not to do an in-depth review of this area except to refer the reader to the book   Ovarian Cancer and just to summarize the well accepted mechanisms of cisplatin resistance including:

  • Decreased cellular cisplatin influx
  • Increased cellular cisplatin efflux
  • Increased cellular glutathione and subsequent conjugation, inactivation
  • Increased glutathione-S-transferase activity (GST) and subsequent inactivation, conjugation
  • Increased γ-GGT
  • Increased metallothionenes with subsequent conjugation, inactivation
  • Increased DNA repair: increased excision repair
  • DNA damage tolerance: loss of mismatch repair (MMR)
  • altered cell signaling activities and cell cycle protein expression

Williams, S.J., and Hamilton, T.C. Chemotherapeutic resistance in ovarian cancer. In: S.C. Rubin, and G.P. Sutton (eds.), Ovarian Cancer, pp.34-44. Lippincott, Wilkins, and Williams, New York, 2000.

Also for a great review on clinical platinum resistance by Drs. Maritn, Hamilton and Schilder please see the following Clinical Cancer Research link here.

This curation represents the scientific rationale for the development of a new class of platinated compounds which are meant to circumvent mechanisms of resistance, in this case the loss of mismatch repair (MMR) and increased tolerance to DNA damage.

An early step in the production of cytotoxicity by the important anticancer drug cisplatin and its analog carboplatin is the formation of intra- and inter-strand adducts with tumor cell DNA 1-3. This damage triggers a cascade of events, best characterized by activation of damage-sensing kinases (reviewed in 4), p53 stabilization, and induction of p53-related genes involved in apoptosis and cell cycle arrest, such as bax and the cyclin-dependent kinase inhibitor p21waf1/cip1/sdi1 (p21), respectively 5,6. DNA damage significantly induces p21 in various p53 wild-type tumor cell lines, including ovarian carcinoma cells, and this induction is responsible for the cell cycle arrest at G1/S and G2/M borders, allowing time for repair 7,8.  DNA lesions have the ability of  to result in an opening of chromatin structure, allowing for transcription factors to enter 56-58.  Therefore the anti-tumoral ability of cisplatin and other DNA damaging agents is correlated to their ability to bind to DNA and elicit responses, such as DNA breaks or DNA damage responses which ultimately lead to cell cycle arrest and apoptosis.  Therefore either repair of such lesions, the lack of recognition of such lesions, or the cellular tolerance of such lesions can lead to resistance of these agents.

resistmech2

Mechanisms of Cisplatin Sensitivity and Resistance. Red arrows show how a DNA lesion results in chemo-sensitivity while the beige arrow show common mechanisms of resistance including increased repair of the lesion, effects on expression patterns, and increased inactivation of the DNA damaging agent by conjugation reactions

 

 

 

 

 

 

 

 

 

 

 

 

 

 

mechPtresistance

 

 

Increased DNA Repair Mechanisms of Platinated Lesion Lead to ChemoResistance

 

DNA_repair_pathways

Description of Different Types of Cellular DNA Repair Pathways. Nucleotide Excision Repair is commonly up-regulated in highly cisplatin resistant cells

 

 

 

 

 

 

 

 

 

 

 

Loss of Mismatch Repair Can Lead to DNA Damage Tolerance

dnadamage tolerance

 

 

 

 

 

 

 

 

In the following Cancer Research paper Dr. Vaisman in the lab of Dr. Steve Chaney at North Carolina (and in collaboration with Dr. Tom Hamilton) describe how cisplatin resistance may arise from loss of mismatch repair and how oxaliplatin lesions are not recognized by the mismatch repair system.
Cancer Res. 1998 Aug 15;58(16):3579-85.

The role of hMLH1, hMSH3, and hMSH6 defects in cisplatin and oxaliplatin resistance: correlation with replicative bypass of platinum-DNA adducts.

Abstract

Defects in mismatch repair are associated with cisplatin resistance, and several mechanisms have been proposed to explain this correlation. It is hypothesized that futile cycles of translesion synthesis past cisplatin-DNA adducts followed by removal of the newly synthesized DNA by an active mismatch repair system may lead to cell death. Thus, resistance to platinum-DNA adducts could arise through loss of the mismatch repair pathway. However, no direct link between mismatch repair status and replicative bypass ability has been reported. In this study, cytotoxicity and steady-state chain elongation assays indicate that hMLH1 or hMSH6 defects result in 1.5-4.8-fold increased cisplatin resistance and 2.5-6-fold increased replicative bypass of cisplatin adducts. Oxaliplatin adducts are not recognized by the mismatch repair complex, and no significant differences in bypass of oxaliplatin adducts in mismatch repair-proficient and -defective cells were found. Defects in hMSH3 did not alter sensitivity to, or replicative bypass of, either cisplatin or oxaliplatin adducts. These observations support the hypothesis that mismatch repair defects in hMutL alpha and hMutS alpha, but not in hMutS beta, contribute to increased net replicative bypass of cisplatin adducts and therefore to drug resistance by preventing futile cycles of translesion synthesis and mismatch correction.

 

 

The following are slides I had co-prepared with my mentor Dr. Thomas C. Hamilton, Ph.D. of Fox Chase Cancer Center on DNA Mismatch Repair, Oxaliplatin and Ovarina Cancer.

edinborough2mmrtranslesion1

 

 

 

 

 

 

Multiple Platinum Analogs of Cisplatin (like Oxaliplatin )Had Been Designed to be Sensitive in MMR Deficient Tumors

edinborough2diffptanalogs

 

 

 

 

 

 

mmroxaliplatin

 

 

 

 

 

 

edinborough2ptanalogsresist

 

 

 

 

 

 

edinborough2relresistptanalogsdifflines

 

 

 

 

 

 

edinborough2msimlmh2refract

 

 

 

 

 

 

edinborough2gogoxaliplatintrial

 

 

 

 

 

 

 

Please see below video on 2015 Nobel Laureates and their work to elucidate the celluar DNA repair mechanisms.

Clinical genetics expert Kenneth Offit gives an overview of Lynch syndrome, a genetic disorder that can cause colon (HNPCC) and other cancers by defects in the MSH2 DNA mismatch repair gene. (View Video)

 

 

References

  1. Johnson, S. W. et al. Relationship between platinum-DNA adduct formation, removal, and cytotoxicity in cisplatin sensitive and resistant human ovarian cancer cells. Cancer Res 54, 5911-5916 (1994).
  2. Eastman, A. The formation, isolation and characterization of DNA adducts produced by anticancer platinum complexes. Pharmacology and Therapeutics 34, 155-166 (1987).
  3. Zhen, W. et al. Increased gene-specific repair of cisplatin interstrand cross-links in cisplatin-resistant human ovarian cancer cell lines. Molecular and Cellular Biology 12, 3689-3698 (1992).
  4. Durocher, D. & Jackson, S. P. DNA-PK, ATM and ATR as sensors of DNA damage: variations on a theme? Curr Opin Cell Biol 13, 225-231 (2001).
  5. el-Deiry, W. S. p21/p53, cellular growth control and genomic integrity. Curr Top Microbiol Immunol 227, 121-37 (1998).
  6. Ewen, M. E. & Miller, S. J. p53 and translational control. Biochim Biophys Acta 1242, 181-4 (1996).
  7. Gartel, A. L., Serfas, M. S. & Tyner, A. L. p21–negative regulator of the cell cycle. Proc Soc Exp Biol Med 213, 138-49 (1996).
  8. Chang, B. D. et al. p21Waf1/Cip1/Sdi1-induced growth arrest is associated with depletion of mitosis-control proteins and leads to abnormal mitosis and endoreduplication in recovering cells. Oncogene 19, 2165-70 (2000).
  9. Davies, N. P., Hardman, L. C. & Murray, V. The effect of chromatin structure on cisplatin damage in intact human cells. Nucleic Acids Res 28, 2954-2958 (2000).
  10. Vichi, P. et al. Cisplatin- and UV-damaged DNA lure the basal transcription factor TFIID/TBP. Embo J 16, 7444-7456 (1997).
  11. Xiao, G. et al. A DNA damage signal is required for p53 to activate gadd45. Cancer Res 60, 1711-9 (2000).

Other articles in this Open Access Journal on ChemoResistance Include:

Cancer Stem Cells as a Mechanism of Resistance

An alternative approach to overcoming the apoptotic resistance of pancreatic cancer

Mutation D538G – a novel mechanism conferring acquired Endocrine Resistance causes a change in the Estrogen Receptor and Treatment of Breast Cancer with Tamoxifen

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

Nitric Oxide Mitigates Sensitivity of Melanoma Cells to Cisplatin

Heroes in Medical Research: Barnett Rosenberg and the Discovery of Cisplatin

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Loss of Gene Islands May Promote a Cancer Cell’s Survival, Proliferation and Evolution: A new Hypothesis (and second paper validating model) on Oncogenesis from the Elledge Laboratory

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

It is well established that a critical event in the transformation of a cell to the malignant state involves the mutation of hosts of oncogenes and tumor suppressor genes, which in turn, confer on a cell the inability to properly control its proliferation.    On a genomic scale, these mutations can result in gene amplifications, loss of heterozygosity (LOH), and epigenetic changes resulting in tumorigenesis.  The “two hit hypothesis”, proposed by Dr. Al Knudson of Fox Chase Cancer Center[1], proposes that two mutations in the same gene are required for tumorigenesis, initially proposed to explain the progression of retinoblastoma in children, indicating a recessive disease.

(Excerpts from a great article explaining the two-hit-hypothesis is given at the end of this post).

And, although many tumor genomes display haploinsufficeint tumor suppressor genes, and fit the two hit model quite nicely, recent data show that most tumors display hemizygous recurrent deletions within their genomes.  Tumors display numerous recurrent hemizygous focal deletions that seem to contain no known tumor suppressor genes. For instance a recent analysis of over three thousand tumors including breast, bladder, pancreatic, ovarian and gastric cancers averaged greater than 10 deletions/tumor and 82 regions of recurrent focal deletions,

It has been proposed these great number of hemizygous deletions may be a result of:

  • a recessive tumor suppressor gene requiring mutation or silencing of second allele
  • the mutation may recur as they are located in fragile sites (unstable genomic regions)
  • single-copy loss may provide selective advantage regardless of the other allele

Note: some definitions of hemizygosity are given below.  In general at any locus, each parental chromosome can have 3 deletion states:

  1. wild type
  2. large deletion
  3. small deletion

Hemizygous deletions only involve one allele, not both alleles which is unlike the classic tumor suppressor like TP53

To see if it is possible that only one mutated allele of a tumor suppressor gene may be a casual event for tumorigenesis, Dr. Nicole Solimini and colleagues, from Dr. Stephen Elledge’s lab at Harvard, proposed a hypothesis they termed the cancer gene island model, after analyzing the regions of these hemizygous deletions for cancer related genes[2].  Dr. Soliin and colleagues analyzed whole-genome sequence data for 526 tumors in the COSMIC database comparing to a list generated from the Cancer Gene Census for homozygous loss-of-function mutations (mutations which result in a termination codon or frame-shift mutation: {this produces a premature stop in the protein or an altered sequence leading to a nonfunctional protein}.

Results of this analysis revealed:

  1. although tumors have a wide range of deletions per tumor (most epithelial high grade like ovarian, bladder, pancreatic, and esophageal adenocarcinomas had 10-14 deletions per tumor
  2. and although tumors exhibited a wide range (2- 16 ) loss of function mutations
  3. ONLY 14 of 82 recurrent deletions contained a known tumor suppressor gene and was a low frequency event
  4. Most recurrent cancer deletions do not contain putative tumor suppressor genes.

Therefore, as the authors suggest, an alternate method to the two-hit hypothesis may account for a selective growth advantage for these types of deletions, defining these low frequency hemizygous mutations in two general classes

  1. STOP genes: suppressors of tumor growth and proliferation
  2. GO genes: growth enhancers and oncogenes

Identifying potential STOP genes

To identify the STOP and GO genes the authors performed a primary screen of an shRNA library in telomerase (hTERT) immortalized human mammary epithelial cells using increased PROLIFERATION as a screening endpoint to determine STOP genes and decreased proliferation and lethality (essential genes) to determine possible GO genes. An initial screen identified 3582 possible STOP genes.  Using further screens and higher stringency criteria which focused on:

  • Only genes which increased proliferation in independent triplicate screens
  • Validated by competition assays
  • Were enriched more than four fold in three independent shRNA screens

the authors were able to focus on and validate 878 genes to determine the molecular pathways involved in proliferation.

These genes were involved in cell cycle regulation, apoptosis, and autophagy (which will be discussed in further posts).

To further validate that these putative STOP genes are relevant in human cancer, the list of validated STOP genes found in the screen was compared to the list of loss-of-function mutations in the 526 tumors in the COSMIC databaseSurprisingly, the validated STOP gene list were significantly enriched for known and possibly NOVEL tumor suppressor genes and especially loss of function and deletion mutations but also clustered in gene deletions in cancer.  This not only validated the authors’ model system and method but suggests that hemizygous deletions in multiple STOP genes may contribute to tumorigenesis

as the function of the majority of STOP genes is to restrain tumorigenesis

A few key conclusions from this study offer strength to an alternative view of oncogenesis NAMELY:

  • Loss of multiple STOP genes per deletion optimize a cancer cell’s proliferative capacity
  • Cancer cells display an insignificant loss of GO genes, minimizing negative impacts on cellular fitness
  • Haploinsufficiency in multiple STOP genes can result in similar alteration of function similar to complete loss of both alleles of
  • Cancer evolution may result from selection of hemizygous loss of high number of STOP and low number of GO genes
  • Leads to a CANCER GENE ISLAND model where there is a clonal evolution of transformed cells due to selective pressures

A link to the supplemental data containing STOP and GO genes found in validation screens and KEGG analysis can be found at the following link:

http://www.sciencemag.org/content/337/6090/104/suppl/DC1#

A link to an interview with the authors, originally posted on Harvard’s site can be found here.

Cumulative Haploinsufficiency and Triplosensitivity Drive Aneuploidy Patterns and Shape the Cancer Genome; a new paper from the Elledge group in the journal Cell

http://www.cell.com/retrieve/pii/S0092867413012877

A concern of the authors was the extent to which gene silencing could have on their model in tumors.  The validation of the model was performed in cancer cell lines and compared to tumor genome sequence in publicly available databases however a followup paper by the same group shows that haploinsufficiency contributes a greater impact on the cancer genome than these studies have suggested.

In a follow-up paper by the Elledge group in the journal Cell[3], Theresa Davoli and colleagues, after analyzing 8,200 tumor-normal pairs, show there are many more cancer driver genes than once had been predicted.  In addition, the distribution and potency of STOP genes, oncogenes, and essential genes (GO) contribute to the complex picture of aneuploidy seen in many sporadic tumors.  The authors proposed that, together with these and their previous findings, that haploinsufficiency plays a crucial role in shaping the cancer genome.

Hemizygosity and Haploinsufficiency

Below are a few definitions from Wikipedia:

Zygosity is the degree of similarity of the alleles for a trait in an organism.

Most eukaryotes have two matching sets of chromosomes; that is, they are diploid. Diploid organisms have the same loci on each of their two sets of homologous chromosomes, except that the sequences at these loci may differ between the two chromosomes in a matching pair and that a few chromosomes may be mismatched as part of a chromosomal sex-determination system. If both alleles of a diploid organism are the same, the organism is homozygous at that locus. If they are different, the organism is heterozygous at that locus. If one allele is missing, it is hemizygous, and, if both alleles are missing, it is nullizygous.

Haploinsufficiency occurs when a diploid organism has only a single functional copy of a gene (with the other copy inactivated by mutation) and the single functional copy does not produce enough of a gene product (typically a protein) to bring about a wild-type condition, leading to an abnormal or diseased state. It is responsible for some but not all autosomal dominant disorders.

Al Knudsen and The “Two-Hit Hypothesis” of Cancer

Excerpt from a Scientist article by Eugene Russo about Dr. Knudson’s Two hit Hypothesis;

for full article please follow the link http://www.the-scientist.com/?articles.view/articleNo/19649/title/-Two-Hit–Hypothesis/

The “two-hit” hypothesis was, according to many, among the more significant milestones in that rapid evolution of biomedical science. The theory explains the relationship between the hereditary and nonhereditary, or sporadic, forms of retinoblastoma, a rare cancer affecting one in 20,000 children. Years prior to the age of gene cloning, Knudson’s 1971 paper proposed that individuals will develop cancer of the retina if they either inherit one mutated retinoblastoma (Rb) gene and incur a second mutation (possibly environmentally induced) after conception, or if they incur two mutations or hits after conception.3 If only one Rb gene functions normally, the cancer is suppressed. Knudson dubbed these preventive genes anti-oncogenes; other scientists renamed them tumor suppressors.

When first introduced, the “two-hit” hypothesis garnered more interest from geneticists than from cancer researchers. Cancer researchers thought “even if it’s right, it may not have much significance for the world of cancer,” Knudson recalls. “But I had been taught from the early days that very often we learn fundamental things from unusual cases.” Knudson’s initial motivation for the model: a desire to understand the relationship between nonhereditary forms of cancer and the much rarer hereditary forms. He also hoped to elucidate the mechanism by which common cancers, such as those of the breast, stomach, and colon, become more prevalent with age.

According to the then-accepted somatic mutation theory, the more mutations, the greater the risk of cancer. But this didn’t jibe with Knudson’s own studies on childhood cancers, which suggested that, in the case of cancers such as retinoblastoma, disease onset peaks in early childhood. Knudson set out to determine the smallest number of cancer-inducing events necessary to cause cancer and the role of these events in hereditary vs. nonhereditary cancers. Based on existing data on cancer cases and some mathematical deduction, Knudson came up with the “two-hit” hypothesis.

Not until 1986, when researchers at the Whitehead Institute for Biomedical Research in Cambridge, Mass., cloned the Rb gene, would there be solid evidence to back up Knudson’s pathogenesis paradigm.4 “Even with the cloning of the gene, it wasn’t clear how general it would be,” says Knudson. There are, it turns out, several two-hit lesions, including polyposis, neurofibromitosis, and basal cell carcinoma syndrome. Other cancers show only some correspondence with the two-hit model. In the case of Wilm’s tumor, for example, the model accounts for about 15 percent of the cancer incidence; the remaining cases seem to be more complicated.

knudsonTwoHit1600

His seminal paper on the two-hit hypothesis[1]

A.G. Knudson, “Mutation and cancer: statistical study of retinoblastoma,” Proceedings of the National Academy of Sciences, 68:820-3, 1971.

The two hit hypothesis proposed by A.G. Knudson.  A description with video of Dr. Knudson talk at AACR can be found at the following link (photo creditied to A.G. Knudson and Fox Chase Cancer Center at the following link:http://www.fccc.edu/research/research-awards/knudson/index.html

Sources

1.            Knudson AG, Jr.: Mutation and cancer: statistical study of retinoblastoma. Proceedings of the National Academy of Sciences of the United States of America 1971, 68(4):820-823.

2.            Solimini NL, Xu Q, Mermel CH, Liang AC, Schlabach MR, Luo J, Burrows AE, Anselmo AN, Bredemeyer AL, Li MZ et al: Recurrent hemizygous deletions in cancers may optimize proliferative potential. Science 2012, 337(6090):104-109.

3.            Davoli T, Xu Andrew W, Mengwasser Kristen E, Sack Laura M, Yoon John C, Park Peter J, Elledge Stephen J: Cumulative Haploinsufficiency and Triplosensitivity Drive Aneuploidy Patterns and Shape the Cancer Genome. Cell 2013, 155(4):948-962.

Other papers on this site on CANCER and MUTATION include:

Cancer Mutations Across the Landscape

Salivary Gland Cancer – Adenoid Cystic Carcinoma: Mutation Patterns: Exome- and Genome-Sequencing @ Memorial Sloan-Kettering Cancer Center

Whole exome somatic mutations analysis of malignant melanoma contributes to the development of personalized cancer therapy for this disease

Breast Cancer and Mitochondrial Mutations

Winning Over Cancer Progression: New Oncology Drugs to Suppress Passengers Mutations vs. Driver Mutations

Hold on. Mutations in Cancer do good.

Rewriting the Mathematics of Tumor Growth; Teams Use Math Models to Sort Drivers from Passengers

How mobile elements in “Junk” DNA promote cancer. Part 1: Transposon-mediated tumorigenesis.

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

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

In an earlier post entitled “Issues in Personalized Medicine in Cancer: Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing” the heterogenic nature of solid tumors was discussed.  There resulted an excellent discussion in the Oncology Pharma forum on LinkedIn so I curated the comments (below article) to foster further discussion. To summarize the original post, this was a discussion of Dr. Charles Swanton’s paper[1] in which he and colleagues had noticed that individual biopsies from primary renal tumors displayed a variety of mutations of the same and different tumor suppressor genes (TSG), thereby not only revealing the heterogeneity of individual tumors but also how tumors can evolve.  Thus it was suggested that individual cells of a primary tumor can represent individual clones, each evolving on a distinct pathway to tumorigenicity and metastasis as each clone would have accumulated different passenger mutations.  It is these passenger mutations which have been posited to be responsible for a tumor’s continued growth (as discussed in the following post Rewriting the Mathematics of Tumor Growth; Teams Use Math Models to Sort Drivers from Passengers).  Indeed, as Dr. Swanton mentioned in the posting that it is very likely a solid tumor contains discrete clones with different driver and passenger mutations and possibly different mutated TSG but also this intra-tumor heterogeneity would have great implications for personalized chemotherapeutic strategies, not only against the primary tumor but against resistant outgrowth clones, and to the metastatic disease, as Swanton and colleagues had found that the metastatic disease displayed tremendously increased genomic instability than the underlying primary disease.

Therefore it may behoove the clinical oncologist to view solid tumors as a collection of multiple clones, each having their own mutagenic spectrum and tumorigenic phenotype.  Each of these clones may acquire further mutations which provide growth advantage over other clones in the early primary tumor.  In addition, branched evolution of a clone most likely depends more on genomic instability and epigenetic factors than on solely somatic mutation.

This is echoed in a  report in Carcinogenesis back in 2005[3] Lorena Losi, Benedicte Baisse, Hanifa Bouzourene and Jean Benhatter had shown some similar results in colorectal cancer as their abstract described:

“In primary colorectal cancers (CRCs), intratumoral genetic heterogeneity was more often observed in early than in advanced stages, at 90 and 67%, respectively. All but one of the advanced CRCs were composed of one predominant clone and other minor clones, whereas no predominant clone has been identified in half of the early cancers. A reduction of the intratumoral genetic heterogeneity for point mutations and a relative stability of the heterogeneity for allelic losses indicate that, during the progression of CRC, clonal selection and chromosome instability continue, while an increase cannot be proven.”

Therefore if a tumor had evolved in time closer to the initial driver mutation multiple therapies may be warranted while tumors which had not yet evolved much from their driver mutation may be tackled with an agent directed against that driver, hence the branched evolution as shown in the following diagram:

branced chain evolution cancer

Cancer Sequencing

Unravels clonal evolution.

From Carlos Caldas. (2012).

Nature Biotechnology V.30

pp 405-410.[2] used with

permission.

 

 

 

 

 

 

 

An article written by Drs. Andrei Krivtsov and Scott Armstron entitled “Can One Cell Influence Cancer Heterogeneity”[4] commented on a study by Friedman-Morvinski[5] in Inder Verma’s laboratory discussed how genetic lesions can revert differentiated neorons and glial cells to an undifferentiated state [an important phenotype in development of glioblastoma multiforme].

In particular it is discussed that epigenetic state of the transformed cell may contribute to the heterogeneity of the resultant tumor.  Indeed many investigators (initially discovered and proposed by Dr. Beatrice Mintz of the Institute for Cancer Research, later to be named the Fox Chase Cancer Center) show the cellular microenvironment influences transformation and tumor development[6-8].

Briefly the Friedman-Morvinski study used intra-cerebral ventricular (ICV) injection of lentivirus to introduce oncogenes within the CNS and produced tumors of multiple cell origins including neuronal and glial cell origin (neuroblastoma and glioma).  The important takeaway was differentiated somatic cells which acquire genetic lesions can transform to form multiple tumor types.  As the authors state, “cellular differentiation and specialization are accompanied by gradual changes in epigenetic programs” and that “the cell of origin may influence the epigenetic state of the tumor”.   In essence this means that the success of therapy may depend on the cellular state (whether stem cell, progenitor cell, or differentiated specialized cell) at time of transformation.  In other words tumors arising from cells with an epigenetic state seen in stem cells would be more resistant to therapy unless given an epigenetic therapy, such as azacytididne, retinoic acid or HDAC inhibitors.

 

So as the Oncology Pharma forum on LinkedIn was such an excellent discussion I would like to post the comments for curation purposes and foster further discussion.  I would like to thank everyone’s great comments below.  I would especially like to thank Dr. Emanuel Petricoin from George Mason and Dr. David Anderson for supplying extra papers which will be the subject of a future post. I had curated each comment with inserted LIVE LINKS to make it easier to refer to a paper and/or company mentioned in the comment.

The comments seemed to center on three main themes:

  1. 1.      Clinicians pondering the benefit to mutational spectrum analysis to determine personalized therapy and develop biomarkers of early disease
  2. 2.      A shift in the clinicians paradigm of cancer development, diagnoses, and treatment from strictly histologic evaluation to a genetic and altered cellular pathway view
  3. 3.      Use of proteomics, microarray and epigenetics as an alternative to mutational analysis to determine aberrant cellular networks in various stages of tumor development

 

Victor Levenson • Thanks for posting this! To be honest, I am puzzled by the insistence on sequencing as a tool for tumor analysis – we know that expression patterns rather than mutations in a limited number of genes determine tumor physiology (or, even more, physiology of any tissue). Since the AACR-2012 we know that different tumors have similar or even identical mutations, so >functional< rather than >structural< differences are important. Frankly, I’d be much more excited learning about expression pattern heterogeneity in tumors.Granted that is much more challenging than NGS sequencing, but the value of the data would be incomparable, especially in its application to biomarker development.

Stephen J. Williams, Ph.D. • Dear Dr. Levenson, thanks for your comments. I agree with you and in no way am insisting on the releiance of sequencing mutations in cancer as the sole means for determining therapy. It is extremely true that tumors will show tremendous heterogeneity of mRNA expression. There are a number of studies (one which I will post on pharmaceuticalintelligence.com) that individual tumor cells will have differing expression patterns based on the levels of regional hypoxia within the tumor as well as other microenvironmental factors. I do have two posts on pharmaceuticalintelligence.com on this matter, curating various programs around the world which are using microarray expression analysis of tumors to determine personalized strategies. I believe the reliance on mutational analysis is based on the drugs that have been developed (such as Gleevec and crizotinib) which are based on mutant forms of BCR-Abl and ALK, respectively. However (as per two posts I did based on Mike Martin on our site “Mathematical Models of Driver and Passenger mutations) where he discusses how certain driver mutations will get the senescent cell over the hump to get to fully transformed and contribute to a certain level of growth while subsequent passengers are responsible for the sustained survival and expansion of the tumor.

Victor Levenson • Dr. Williams, thanks for the comments. Driving a senescent cell into proliferative stage is a tremendous change, which >may< begin with a mutation, but involves dramatic restructuring of transcription patterns that will drive the process. Hypoxia will definitely contribute to variations in the patterns, although will probably not be the main driver of the process. As to whether a mutation or a change in transcription pattern initiate the process, I am not sure we will ever be able to determine <grin>.

Vanisree Staniforth • Thanks for posting! Certainly a thought provoking article with regard to the future of personalized cancer therapies.

 

Dr. Raj Batra • If we follow Dr Levenson’s proposed conceptual approach (which we also published in 2009 and 2010), we are MUCH more likely to significantly impact tumor morbidity and mortality.

Stephen J. Williams, Ph.D. • Thanks Vanisiree and Dr. Batra for your comments. Hopefully we will see, from the future cancer statistics, how personlized therapy have improved outcomes for the solid tumors, like the hematologic cancers. 26 days ago

Emanuel Petricoin • The issue about intra and inter tumor heterogeneity is very important however since it is unknown which mutations are true drivers, an explanation of the results found in these studies simply could be the variances are all in the inconsequential mutations and the commonality is the driver mutations. Moreover, at the end of the day, its not the mRNA expression that we really care about but the functional protein signaling -phosphoprotein driven signaling architecture, that we care about since these are the drug targets directly.

Mohammad Azhar Aziz,PhD • This article addresses the potential complexity of dealing with cancer which is apparently increasing proportionally with the amount of data generated. Intratumor heterogeneity will remain there and even multiple biopsies that are randomly chosen will offer no conclusive solution.Mutations,expression profiles and functional protein signaling (as discussed above) alone can not provide any breakthrough. It will be a composite picture of all these and many other components (e.g. microenvironment, alternative splicing, epigenetics,non-coding RNAs etc.) that will hold the promises in the future. We have made phenomenal advances in understanding each of these aspects separately but definitely lack the tools to integrate all these. Developing tools to integrate all these data may provide some breakthrough in understanding and thus treating cancer.

Emanuel Petricoin • I agree Mohammad in a systems biology approach however the current compendium of drugs largely are kinase inhibitors or enzymatic inhibitors. Since most studies have shown little correlation between gene mutation and protein levels and phosphoprotein levels, for example, it is no wonder why the recent spate of failed trials (e.g. stratification by PIK3CA mutation or PTEN mutation for AKT-mTOR inhibitors) should come as any shock. We will be publishing work using protein pathway activation mapping coupled to laser dissection of a number of intra and inter tumoral analysis that indicates that the signaling architecture appears much more stable.

Stephen J. Williams, Ph.D. • Thank you Dr. Pettricoin for your comments. I eagerly await the publication of your results concerning proteomic evaluation of multiple biopsies of a tumor. I am very interested that you found limited intratuoral heterogeneity of signaling pathways given the diversity of intratumoral microenvironmental stresses (changes in regional hypoxia, blood flow, and populations of cancer stem cells). I agree with you and Mohammed that proteomic profiling will be imperative in determining personalized approaches for targeted therapy. Dr. Swanton had informed me that they had used IHC to determine if mTOR signaling had correlated with the mutational spectrum they had seen. In addition he had mentioned that there was enhanced genomic instability in the metastatic disease relative to the primary tumor and it would be very interesting to see how signaling pathways change in cohorts of matched metastatic and primary tumors. A few years ago we were looking at genes which were completely lost upon transformation of ovarian epithelial cells and worked up one of those genes (CRBP1) in cohorts of human ovarian cancer samples, using expression analysis in conjunction with laser capture microdissection and backed up by IHC analysis, and found that the expression pattern of CRBP1 was uniform in a tumor, either there was a complete loss in all cells in a tumor of CRBP1 or all the cells expressed the protein. Therefore I am curious if intratumor heterogeneity is dependent on the cell lineage and evolution of the transformed cell into a full tumor or a function of a discrete population of stem cells with varied genomic instability. Your results might suggest a more clonal evolution rather than a branched evolution which was found in this paper.
It is interesting that you mention the tough trials with the PTEN/PI3K/AKT axis of inhibitors. In high grade serous ovarian cancer we were never able to find any PI3K, PTEN, nor AKT mutations yet PI3K activity is usually overactive. If feel both your and Mohammed’s assessment that a systems biology approach instead of just relying on DNA mutational analysis will be more important in the future. In addition, there is nice work from Dr. Jefferey Peterson at Fox Chase and the development of a database of kinase inhibitors and activity effects on the kinome, showing the vast amount of crosstalk between once thought linear enzyme systems. If TKI’s will be the brunt of pharma’s development I feel they need to quickly develop as many TKI’s as they can now before we get to a clinical problem (resistance and lack of available therapeutics).

Emanuel Petricoin • Thanks Steven- yes, we are working with Charlie Swanton and Marco on the renal sets- our other studies are from breast and colon cancers. I think one of the things we do that really no one else is doing, unfortunately, is to laser capture microdissect the tumor cells from these specimens so that we have a more pure and accurate view of the signaling architecture. One confounder from the proteomic stand-point is the fact that pre-analytical variables such as post-excision delay times where the tissue is a hypoxic wound and signaling changes fluctuating as the tissue reacts to the ex-vivo condition can really effect things. When we look at tissue sets where the tissue is biopsied and immediately frozen we really dont see big differences in the signaling – the within tumor architecture is much more similar then between. We use the reverse phase array technology we invented to provide quantitative analysis on hundreds of phosphoproteins at once – so a nice view of the functional protein activation network. Your results of CRBP1 in ovarian tumors and the IHC data are very interesting. We will see how this all plays out. Of course once other confounder with the mutational data is that we really dont know what are the drivers and what are the passengers…
Yes I know Jeff Peterson’s work- its fantastic. In the end the hope I think- and in my personal opinion- will be rationally combined therapeutics based on the signaling architecture of each individual patient.

Incidentally, we just published a paper that you may be interested in from a “systems biology” standpoint-

SYSTEMS ANALYSIS OF THE NCI-60 CANCER CELL LINES BY ALIGNMENT OF PROTEIN PATHWAY ACTIVATION MODULES WITH “-OMIC” DATA FIELDS AND THERAPEUTIC RESPONSE SIGNATURES.

Federici G, Gao X, Slawek J, Arodz T, Shitaye A, Wulfkuhle JD, De Maria R, Liotta LA, Petricoin EF 3rd. Mol Cancer Res. 2013 May

also- we published a paper that speaks directly to your point where we compared the signaling network activation of patient-matched primary colorectal cancers and synchronous liver mets. indeed there is huge systemic differences in the liver metastasis compared to the primary. there is no doubt in my mind that we will need to biopsy the metastasis to know how to treat. Looking at the primary tumor as a guide for therapy is a fools errand. here is the paper reference:

Protein pathway activation mapping of colorectal metastatic progression reveals metastasis-specific network alterations.

Silvestri A, Calvert V, Belluco C, Lipsky M, De Maria R, Deng J, Colombatti A, De Marchi F, Nitti D, Mammano E, Liotta L, Petricoin E, Pierobon M.

Clin Exp Metastasis. 2013 Mar;30(3):309-16. doi: 10.1007/s10585-012-9538-5. Epub 2012 Sep 29.

Center for Applied Proteomics and Molecular Medicine, George Mason University, 10900 University Blvd., Manassas, VA, 20110, USA.

Abstract

The mechanism by which tissue microecology influences invasion and metastasis is largely unknown. Recent studies have indicated differences in the molecular architecture of the metastatic lesion compared to the primary tumor, however, systemic analysis of the alterations within the activated protein signaling network has not been described. Using laser capture microdissection, protein microarray technology, and a unique specimen collection of 34 matched primary colorectal cancers (CRC) and synchronous hepatic metastasis, the quantitative measurement of the total and activated/phosphorylated levels of 86 key signaling proteins was performed. Activation of the EGFR-PDGFR-cKIT network, in addition to PI3K/AKT pathway, was found uniquely activated in the hepatic metastatic lesions compared to the matched primary tumors. If validated in larger study sets, these findings may have potential clinical relevance since many of these activated signaling proteins are current targets for molecularly targeted therapeutics. Thus, these findings could lead to liver metastasis specific molecular therapies for CRC.

Adrian Anghel • I think both patterns (protein phosphorylation and mRNA) should be important in this complicated equation of heterogeneity. Let’s not forget the so-called functional miRNA-mRNA regulatory modules (FMRMs). Also I think we have different patterns of this heterogeneity for different evolutive stages of the tumour.

 

Alvin L. Beers, Jr., M.D. • This is a great study, but bad news for attempting to tailor treatment based on molecular markers. Dr. Swanton’s comment: “herterogeneity is likely to complicate matters” is an understatement. Intratumoral heterogeneity, branched, instead of linear, evolution of mutational events portends a nightmare in trying to predict location and volume of biopsies. I am reminded of a series of articles in Nature 491 (22 November 2012) “Physical Scientists take on Cancer”. There is a great comment by Jennie Dusheck: “Cancer researchers now recognize that taming wild cancer cells – populations of cells that evolve, cooperate, and roam freely through the body-demand a wider-angle view than molecular biology has been able to offer. Cross-disciplinary collaborations can approach cancer a greater spatial and temporal scales, using mathematical methods more typical of engineering, physics, ecology and evolutionary biology. The sense of failure so evident five years ago is giving way to the excitement of a productive intellectual partnership.” I’m not certain how well the “productive partnership” is going, but this Swanton study confirms the limitations of molecular biology.

Stephen J. Williams, Ph.D. • Thanks Dr. Beers for adding in your comment and adding in Jennie’s comment. Certainly it is something to be aware of if a cancer center’s strategy is to rely solely on gene arrays to genotype tumors. I think Dr. Pettricoin’s work on using proteomics might give some resolution to the matter however, in communicating with Dr. Swanton, I did not get the feeling of an “all hope is lost” but just that, in the case of solid tumors like renal, that careful monitoring of tumors after treatment may be warranted and, more interestingly, from a scientific standpoint, is the genetic complexity surrounding the origin of the disease, and not simple mutational spectrum of a single clone.

Burke Lillian • This is clinically a very important issue. Right now, sequencing or massive approaches such as pan-phosphorylation studies are helpful because, although we know many of the drivers, these studies are actually identifying new genes or new pathways that are activated. After a few (or several years), we truly will know which genes are typically activated and there will be panels to look for these.

Emanuel Petricoin • yes, I agree. In fact, the company that I co-founded, Theranostics Health, Inc– is launching a CLIA based protein pathway activation mapping test at ASCO that measures actionable drug targets (e.g. phospho HER2, EGFR, HER3, AKT, ERK, JAK, STAT, p70S6) and total HER2, EGFR, HER3 and PTEN. So these tests are coming even now.

 

Alvin L. Beers, Jr., M.D. • I do not think that “all hope is lost” nor did I have the impression that Dr. Swanton feels that way with regards to molecular profiling of cancer. I certainly applaud further research into the molecular aspects of cancer biology. But I do not believe that this will be sufficient. Integrating physicial sciences into cancer biology makes perfect sense toward better understanding of this complex disease.

Eleni Papadopoulos-Bergquist • I have enjoyed reading these comments and different ideas regarding genetic testing and profiling. As a nurse and researcher at heart, this is information that will make a huge impact on drug protocols, therefore allowing the best and most specific treatment to each individual rather than having a standard treatment protocol. Even with the scientific complexity of specifying genotypes of particular cancers, there is still the question of each individuals body responding to treatment. I’d love to have some dialogue regarding immune response.

Bradford Graves • I too have enjoyed reading this discussion. I am not a clinician but as a drug discovery researcher I have been struck by some parallels to the concept of virus fitness in virology – particularly as applied to HIV. Drug discovery cannot wait for the final answers to the many important questions being addressed in the discussion initiated by Dr. Williams. The best we can do is to pursue a broad range of therapeutics that will give the clinicians the armament they will need to either cure a given cancer or to at least turn it into a chronic as opposed to an acute disease. There has been a measure of success in the HIV field and it seems like it will be achievable for cancer. Obviously, to the extent that the labels of driver and passenger mutations can be correctly applied will help to prioritize the targets we address.

David W. Anderson • I would suggest that you look at the following publications:

Horn and Pao, (2009) JCO 26: 4232-4234.

Bunn and Doebele (2011) JCO:29:1-3

Boguski et al. (2009) Customized care 2020: how medical sequencing and network biology will enable personalized medicine. F1000 Bio Report 1:7.

Jones, S et al. (2010). Evolution of an adenocarcinoma in response to selection by targeted kinase inhibitors. Genome Biology. 11:R82. Marco Marra’s group in Toronto.

Also look at how companies and organizations like Foundation Medicine, Caris, Clarient, and CollabRx who are using genomics and sequencing on a large scale to address cancer from a personalized/individual approach.

Cancer is/will be a chronic disease requiring individualized/combinatorial therapies in many cases.

Alvin L. Beers, Jr., M.D. • David. These are excellent articles by Paul Bunn and Mark Boguski regarding integrating molecular markers into diagnostic evaluation, and I’ve seen other papers of similiar elk, and likely there will be more to come. Particularly in NSC lung cancer, the SOC is to use these markers up front. Diagnosis based on histology alone can no longer be recommended. The challenge for the future is how to integrate other aspects of cell biology with these markers. It remains daunting that not only do we see heterogeneity in molecular within tumors at a particularly point in time, but that there is often an evolution of markers over time, ie, a “plasticity” of markers, whether treatment is given or not. We know that targeted agents, TKI’s, enzyme inhibitors are not curative, but do give an improvement in PFS. A great deal of this resistance has to do with this “moving target” aspect of cancer cell biology..

 

References:

1.         Gerlinger M, Rowan AJ, Horswell S, Larkin J, Endesfelder D, Gronroos E, Martinez P, Matthews N, Stewart A, Tarpey P et al: Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. The New England journal of medicine 2012, 366(10):883-892.

2.         Caldas C: Cancer sequencing unravels clonal evolution. Nature biotechnology 2012, 30(5):408-410.

3.         Losi L, Baisse B, Bouzourene H, Benhattar J: Evolution of intratumoral genetic heterogeneity during colorectal cancer progression. Carcinogenesis 2005, 26(5):916-922.

4.         Krivtsov AV, Armstrong SA: Cancer. Can one cell influence cancer heterogeneity? Science 2012, 338(6110):1035-1036.

5.         Friedmann-Morvinski D, Bushong EA, Ke E, Soda Y, Marumoto T, Singer O, Ellisman MH, Verma IM: Dedifferentiation of neurons and astrocytes by oncogenes can induce gliomas in mice. Science 2012, 338(6110):1080-1084.

6.         Mintz B, Cronmiller C: Normal blood cells of anemic genotype in teratocarcinoma-derived mosaic mice. Proceedings of the National Academy of Sciences of the United States of America 1978, 75(12):6247-6251.

7.         Watanabe T, Dewey MJ, Mintz B: Teratocarcinoma cells as vehicles for introducing specific mutant mitochondrial genes into mice. Proceedings of the National Academy of Sciences of the United States of America 1978, 75(10):5113-5117.

8.         Mintz B, Cronmiller C, Custer RP: Somatic cell origin of teratocarcinomas. Proceedings of the National Academy of Sciences of the United States of America 1978, 75(6):2834-2838.

 

 

Other articles on this site on “PERSONALIZED MEDICINE” and “CANCER” and “OMICS” include:

Personalized medicine-based diagnostic test for NSCLC

Personalized medicine and Colon cancer

Helping Physicians identify Gene-Drug Interactions for Treatment Decisions: New ‘CLIPMERGE’ program – Personalized Medicine @ The Mount Sinai Medical Center

Systems Diagnostics – Real Personalized Medicine: David de Graaf, PhD, CEO, Selventa Inc.

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

Personalized Medicine: Clinical Aspiration of Microarrays

Understanding the Role of Personalized Medicine

Directions for Genomics in Personalized Medicine

Paradigm Shift in Human Genomics – Predictive Biomarkers and Personalized Medicine – Part 1

Rewriting the Mathematics of Tumor Growth; Teams Use Math Models to Sort Drivers from Passengers

Diagnosing Diseases & Gene Therapy: Precision Genome Editing and Cost-effective microRNA Profiling

Breast Cancer: Genomic profiling to predict Survival: Combination of Histopathology and Gene Expression Analysis

Proteomics and Biomarker Discovery

 

 Also please see our upcoming e-book “Genomics Orientations for Individualized Medicine” in our Medical E-book Series at https://pharmaceuticalintelligence.com/biomed-e-books/genomics-orientations-for-personalized-medicine/volume-one-genomics-orientations-for-personalized-medicine/

 

 

 

 

 

 

 

 

 

 

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