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DISCUSSION – Genomics-driven personalized medicine for Pancreatic Cancer

Reporter: Aviva Lev-Ari, PhD, RN

[bold face added, ALA]

Integrated Patient-Derived Models Delineate Individualized Therapeutic Vulnerabilities of Pancreatic Cancer –>>> Personalized Tumor Models Could Help Identify Combination Therapies for Hard-to-Treat Cancers

 

Original article

Pancreatic Cancer – Genomics-driven personalized medicine

 

PDAC has a particularly poor prognosis, and even with new targeted therapies and chemotherapy, the survival is poor. Here, we show that patient-derived models can be developed and used to investigate therapeutic sensitivities determined by genetic features of the disease and to identify empirical therapeutic vulnerabilities. These data reveal several key points that are of prime relevance to pancreatic cancer and tumor biology in general.

The Challenges of Using Genetic Analysis to Inform Treatment in PDAC

Precision oncology is dependent on the existence of known vulnerabilities encoded by high-potency genetic events and drugs capable of exploiting these vulnerabilities. At present, the repertoire of actionable genetic events in PDAC is limited.

  • Rare BRAF V600E mutations are identified in PDAC and could represent the basis for targeted inhibition, as our group and others have previously published (Collisson et al., 2012; Witkiewicz et al., 2015).

Similarly,

  • germline BRCA deficiency is the basis for ongoing poly(ADP-ribose) polymerase (PARP) inhibitor clinical trials (Lowery et al., 2011).

As shown here, out of 28 cases, only one genetic event was identified that yielded sensitivity to a therapeutic strategy. In this case, existence of the matched model allowed us to confirm the biological relevance of the

  • STAG2 mutation by showing sensitivity of the model to a DNA cross-linking agent.

Therefore, annotated patient-derived models provide a substrate upon which to functionally dissect the significance of novel and potentially actionable genetic events that occur within a tumor.

Another challenge of genomics-driven personalized medicine is

  • assessing the effect of specific molecular aberrations on therapeutic response in the context of complex genetic changes present in individual tumors.
  • KRAS has been proposed to modify therapeutic dependency to EZH2 inhibitors (Kim et al., 2015), and in the models tested, responses to this class of drugs were not uniformly present in cases harboring mutations in chromatin-remodeling genes.

This finding suggests that, although tumors acquire genetic alterations in specific genes, the implicated pathway may not be functionally inactive or therapeutically actionable. Therefore, annotated patient derived models provide a unique test bed for interrogating specific therapeutic dependencies in a genetically tractable system.

Empirical Definition of Therapeutic Sensitivities and Clinical Relevance

Cell lines offer the advantage of the ability to conduct high throughput approaches to interrogate many therapeutic agents. A large number of failed clinical trials have demonstrated the difficulty in treating PDAC. Based on the data herein, the paucity of clinical success is, most probably, due to the diverse therapeutic sensitivity of individual PDAC cases, suggesting that, with an unselected patient population, it will be veritably impossible to demonstrate clinical benefit. Additionally,

  •  very few models exhibited an exceptional response to single agents across the breadth of a library encompassing 305 agents.
  • We could identify only one tumor that was particularly sensitive to MEK inhibition and another model that was sensitive to
  • EGFR and
  • tyrosine kinase inhibitors.

In contrast to the limited activity of single agents, combination screens yielded responses at low-dose concentrations in the majority of models. Specific combinations were effective across several models, indicating that, by potentially screening more models, therapeutic sensitivity clades of PDAC will emerge. In the pharmacological screens performed in this study,

  • MEK inhibition, coupled with MTOR, docetaxel, or tyrosine kinase inhibitors, was effective in _30% of models tested.
  • Resistance to MEK inhibitors occurs through several mechanisms, including
  • Upregulation of oncogenic bypass signaling pathways such as AKT, tyrosine kinase, or MTOR (mammalian target of rapamycin) signaling.

In the clinic, the MEK and MTOR inhibitors (e.g., NCT02583542) are being tested. An intriguing finding from the drug screen was

  • sensitivity of a subset of models to combined MEK and docetaxel inhibition. This combination has been observed to synergistically enhance apoptosis and inhibit tumor growth in human xenograft tumor models (Balko et al., 2012; McDaid et al., 2005) and is currently being tested in a phase III study in patients with KRAS-mutated, advanced non-small-cell lung adenocarcinoma (Ja¨ nne et al., 2016).

Interestingly, in the models tested herein, there was limited sensitivity imparted through

  •  the combination of gemcitabine and MEK inhibition.

This potentially explains why the combination of MEK inhibitor and gemcitabine tested in the clinic did not show improved efficacy over gemcitabine alone (Infante et al., 2014).

Another promising strategy that emerged from this study involves using

  • CHK or BCL2 inhibitors as agents that drive enhanced sensitivity to chemotherapy.

Together, the data suggest that the majority of PDAC tumors have intrinsic therapeutic sensitivities, but the challenge is to prospectively identify effective treatment.

Patient-Derived Model-Based Approach to Precision Medicine

This study supports a path for guiding patient treatment based on the integration of genetic and empirically determined sensitivities of the patient’s tumor (Figure S7). In reference to defined genetic susceptibilities, the models provide a means to interrogate the voracity of specific drug targets. Parallel unbiased screening enables the discovery of sensitivities that could be exploited in the clinic. The model-guided treatment must be optimized, allowing for the generation of data in a time frame compatible with clinical decision making and appropriate validation.

In the present study, the majority of models were developed, cell lines were drug screened, and select hits were validated in PDX models within a 10- to 12-month window (Figure S7). This chronology would allow time to inform frontline therapy for recurrent disease for most patients who were surgically resected and treated with a standard of care where the median time to recurrence is approximately 14 months (Saif, 2013).

Although most models were generated from surgically resected specimens, two of the models (EMC3226 and EMC62) were established from primary tumor biopsies, indicating that this approach could be used with only a limited amount of tumor tissue available.

In the context of inoperable pancreatic cancer, application of data from a cell-line screen without in vivo validation in PDX would permit the generation of sensitivity data in the time frame compatible with treatment.

[We] acknowledge that model-guided treatment is also not without significant logistical hurdles, including the availability of drugs for patient treatment, clinically relevant time frames, patient-performance status, toxicity of combination regiments, and quality metrics related to model development and therapeutic response evaluation.

Additionally, it will be very important to monitor ex vivo genetic and phenotypic divergence with passage and try to understand the features of tumor heterogeneity that could undermine the efficacy of using models to direct treatment. As shown here, drug sensitivities remained stable with passage in cell culture and, importantly, were confirmed in PDX models, suggesting that the dominant genetic drivers and related therapeutic sensitivities are conserved.

In spite of these challenges, progressively more effort is going into the development of patient-derived models for guidance of disease treatment (Aparicio et al., 2015; Boj et al., 2015; Crystal et al., 2014; van de Wetering et al., 2015).

Several ongoing trials use PDX models to direct a limited repertoire of agents (e.g., NCT02312245, NCT02720796, and ERCAVATAR2015). Given the experience here, PDAC cell lines would provide the opportunity to rapidly interrogate a larger portfolio of combinations that could be used to guide patient care and provide a novel approach to precision medicine.

Validation of this approach would require the establishment of challenging multi-arm or N-of-1 clinical trials. However, considering the dire outcome for PDAC patients and the long-lasting difficulty in developing effective treatments, this non-canonical approach might be particularly impactful in pancreatic cancer.

SOURCE

Witkiewicz et al., 2016, Cell Reports 16, 1–15

August 16, 2016 ª 2016 The Author(s).

http://dx.doi.org/10.1016/j.celrep.2016.07.023

Agnieszka K. WitkiewiczPress enter key for correspondence information
Uthra Balaji
Cody Eslinger
Elizabeth McMillan
William Conway
Bruce Posner
Gordon B. Mills
Eileen M. O’Reilly
Erik S. KnudsencorrespondencePress enter key for correspondence information
Publication stage: In Press Corrected Proof
Open Access

Resource Integrated Patient-Derived Models Delineate Individualized Therapeutic Vulnerabilities of Pancreatic Cancer

Correspondence

awitki@email.arizona.edu (A.K.W.),

eknudsen@email.arizona.edu (E.S.K.)

Accession Numbers: GSE84023

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

Pancreatic Cancer: Articles of Note @PharmaceuticalIntelligence.com

Curator: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2016/05/26/pancreatic-cancer-articles-of-note-pharmaceuticalintelligence-com/

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Crowdsourcing Genetic Data Yields Discovery of DNA loci associated with Major Depressive Disorder (MDD) in European Descendants

 

Reporter: Kelly Perlman, Life Sciences Student and Research Assistant, McGill University

 

Researchers from Pfizer Global Research and Development, 23andMe, and the Massachusetts General Hospital have published a study in Nature Genetics, pinpointing 15 genetic loci associated with the risk of developing major depressive disorder (MDD) in individuals of European ancestry. Evidence from previous research suggests that MDD is heritable, but the details of the specific gene correlates are unclear. The identification of loci where single nucleotide polymorphisms (SNPs) related to MDD exist could provide better insight into the neurobiology of depression, and therefore better treatment options.

23andMe, a private biotechnology company situated in California, offers a DNA sequencing service in which consumers send in a saliva swab for testing, and later receive a report listing the findings of the analysis related to ancestry, physical and behavioral traits, along with risk of inheriting certain diseases. The participants of this study had agreed to provide the results of their genetic testing for scientific research.

The results of 75,607 participants with self-reported diagnoses of depression were compared to the results of 231,747 participants reporting having never experienced depression. This data was combined with the results of previously published MDD genome-wide association studies (GWAS). To test the whether these results could be replicated, another set of results from 23andMe was analyzed, in which there were 45,773 MDD subjects, and 106,354 controls.

After the joint analysis, 17 SNPs were identified at 15 different loci. Tissue and gene enrichment assays showed that the genes that were over-expressed in the CNS were related to functions including neurodevelopment, histone methylation, neurogenesis and synaptic modification.

The team then created a weighted genetic risk score (GRS) in which they compared the 17 SNPs with factors including medication use, comorbid diseases and behavioral phenotypes, all of which were correlated with the GRS. Of note, the GRS was very highly correlated with age of onset of MDD.

The crowdsourcing of genetic data proves to be an efficient and powerful tool for large-scale MDD studies. Pooling large subject databases together is essential in order to account for the heterogeneous nature of the disease. Despite not being able to precisely assess each subject’s disease phenotype, scientists can make more rapid headway by collaborating with biotechnology companies in the quest to better understand the biological mechanisms of depression. Ron Perlis, M.D., M.Sc., of the Massachusetts General Hospital and co-author of this paper explained that “finding genes associated with depression should help make clear that this is a brain disease, which we hope will decrease the stigma still associated with these kinds of illnesses”.

 

Details on specific significant genes:

http://www.genecards.org/cgi-bin/carddisp.pl?gene=OLFM4

http://www.genecards.org/cgi-bin/carddisp.pl?gene=TMEM161B

http://www.genecards.org/cgi-bin/carddisp.pl?gene=MEF2C

http://www.genecards.org/cgi-bin/carddisp.pl?gene=MEIS2

http://www.genecards.org/cgi-bin/carddisp.pl?gene=TMCO5A

http://www.genecards.org/cgi-bin/carddisp.pl?gene=NEGR1

 

SOURCES

Hyde, C. L., Nagle, M. W., Tian, C., Chen, X., Paciga, S. A., Wendland, J. R., . . . Winslow, A. R. (2016). Identification of 15 genetic loci associated with risk of major depression in individuals of European descent. Nature Genetics Nat Genet. doi:10.1038/ng.3623

Major Depressive Disorder Loci Discovered in Large GWAS Enabled by 23andMe Participants’ Data. (2016, August 01). Retrieved August 09, 2016, from https://www.genomeweb.com/microarrays-multiplexing/major-depressive-disorder-loci-discovered-large-gwas-enabled-23andme

 

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Using Online Mendelian Inheritance in Man (OMIM) database and the Human Genome Mutation Database (HGMD) Pro 2015.2 for Quantification of the growth in gene-disease and variant-disease associations

Reporter: Aviva Lev-Ari, PhD, RN

 

Reanalysis of Clinical Exome Data Over Time Could Yield New Diagnoses

NEW YORK (GenomeWeb) – Clinical exomes that are re-evaluated in a systematic way could yield new diagnoses and prove useful to clinicians, according to a study published yesterday in Genetics in Medicine.

A team of researchers from Stanford University set out to examine whether nondiagnostic clinical exomes could provide new information for patients if they were re-examined with current bioinformatics software and knowledge of disease-related variants as presented in the literature.

Clinical exome sequencing yields no diagnosis for about 75 percent of patients evaluated for possible Mendelian disorders, wrote senior author Gill Bejerano and his colleagues. But a reanalysis of exome and phenotypic data from 40 such individuals using current methods identified a definitive diagnosis for four of them — 10 percent — the team said.

In these cases, the causative variant was de novo and found in a relevant autosomal-dominant disease gene. At the time these exomes were first sequenced, the researchers wrote, the existing literature on these causative genes was either “weak, nonexistent, or not readily located.” When the exomes were re-examined by his team, Bejerano noted, the supporting literature was more robust.

SOURCE

https://www.genomeweb.com/sequencing/reanalysis-clinical-exome-data-over-time-could-yield-new-diagnoses?utm_source=SilverpopMailing&utm_medium=email&utm_campaign=Daily%20News:%20Reanalysis%20of%20Clinical%20Exome%20Data%20Over%20Time%20Could%20Yield%20New%20Diagnoses%20-%2007/22/2016%2011:20:00%20AM

At ACMG, Researchers Report Data Re-Analysis, Matchmaking Boosts Solved Exome Cases

In addition to re-analyzing exome data, the researchers have been working on establishing causality for novel candidate disease genes through patient matches. For this, the team has been using the GeneMatcher website, which allows them to find other clinicians and researchers around the world who have patients, or animal models, with mutations in the same genes as their own patients. Through an API developed by the Matchmaker Exchange project, GeneMatcher submitters can also query the PhenomeCentral and Decipher databases. As of March, more than 4,000 genes had been submitted to GeneMatcher from more than 1,300 submitters in 48 countries, and 1,900 matches had been made, Sobreira reported.

Her team has so far submitted data from 104 families, involving 280 genes, and has had 314 matches so far, involving 113 genes. Several cases have been successes, meaning the researchers could establish that a candidate gene is indeed disease causing, and several others are pending, both from Hopkins and from other groups. The total number of solved cases tracing their success to GeneMatcher is currently unknown, Sobreira said, but the organizers are planning to survey submitters about their success rate in the near future.

SOURCE

https://www.genomeweb.com/molecular-diagnostics/acmg-researchers-report-data-re-analysis-matchmaking-boosts-solved-exome-cases

 

Related Articles

 

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mRNA Data Survival Analysis

Curators: Larry H. Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN

 

 

SURVIV for survival analysis of mRNA isoform variation

Shihao ShenYuanyuan WangChengyang WangYing Nian Wu & Yi Xing
Nature Communications7,Article number:11548
 Feb 2016      doi:10.1038/ncomms11548

The rapid accumulation of clinical RNA-seq data sets has provided the opportunity to associate mRNA isoform variations to clinical outcomes. Here we report a statistical method SURVIV (Survival analysis of mRNA Isoform Variation), designed for identifying mRNA isoform variation associated with patient survival time. A unique feature and major strength of SURVIV is that it models the measurement uncertainty of mRNA isoform ratio in RNA-seq data. Simulation studies suggest that SURVIV outperforms the conventional Cox regression survival analysis, especially for data sets with modest sequencing depth. We applied SURVIV to TCGA RNA-seq data of invasive ductal carcinoma as well as five additional cancer types. Alternative splicing-based survival predictors consistently outperform gene expression-based survival predictors, and the integration of clinical, gene expression and alternative splicing profiles leads to the best survival prediction. We anticipate that SURVIV will have broad utilities for analysing diverse types of mRNA isoform variation in large-scale clinical RNA-seq projects.

Eukaryotic cells generate remarkable regulatory and functional complexity from a finite set of genes. Production of mRNA isoforms through alternative processing and modification of RNA is essential for generating this complexity. A prevalent mechanism for producing mRNA isoforms is the alternative splicing of precursor mRNA1. Over 95% of the multi-exon human genes undergo alternative splicing2, 3, resulting in an enormous level of plasticity in the regulation of gene function and protein diversity. In the last decade, extensive genomic and functional studies have firmly established the critical role of alternative splicing in cancer4, 5, 6. Alternative splicing is involved in a full spectrum of oncogenic processes including cell proliferation, apoptosis, hypoxia, angiogenesis, immune escape and metastasis7, 8. These cancer-associated alternative splicing patterns are not merely the consequences of disrupted gene regulation in cancer but in numerous instances actively contribute to cancer development and progression. For example, alternative splicing of genes encoding the Bcl-2 family of apoptosis regulators generates both anti-apoptotic and pro-apoptotic protein isoforms9. Alternative splicing of the pyruvate kinase M (PKM) gene has a significant impact on cancer cell metabolism and tumour growth10. A transcriptome-wide switch of the alternative splicing programme during the epithelial–mesenchymal transition plays an important role in cancer cell invasion and metastasis11, 12.

RNA sequencing (RNA-seq) has become a popular and cost-effective technology to study transcriptome regulation and mRNA isoform variation13, 14. As the cost of RNA-seq continues to decline, it has been widely adopted in large-scale clinical transcriptome projects, especially for profiling transcriptome changes in cancer. For example, as of April 2015 The Cancer Genome Atlas (TCGA) consortium had generated RNA-seq data on over 11,000 cancer patient specimens from 34 different cancer types. Within the TCGA data, breast invasive carcinoma (BRCA) has the largest sample size of RNA-seq data covering over 1,000 patients, and clinical information such as survival times, tumour stages and histological subtypes is available for the majority of the BRCA patients15. Moreover, the median follow-up time of BRCA patients is ~400 days, and 25% of the patients have more than 1,200 days of follow-up. Collectively, the large sample size and long follow-up time of the TCGA BRCA data set allow us to correlate genomic and transcriptomic profiles to clinical outcomes and patient survival times.

To date, systematic analyses have been performed to reveal the association between copy number variation, DNA methylation, gene expression and microRNA expression profiles with cancer patient survival16, 17. By contrast, despite the importance of mRNA isoform variation and alternative splicing, there have been limited efforts in transcriptome-wide survival analysis of alternative splicing in cancer patients. Most RNA-seq studies of alternative splicing in cancer transcriptomes focus on identifying ‘cancer-specific’ alternative splicing events by comparing cancer tissues with normal controls (see refs 18, 19, 20, 21, 22, 23 for examples). A recent analysis of TCGA RNA-seq data identified 163 recurrent differential alternative splicing events between cancer and normal tissues of three cancer types, among which five were found to have suggestive survival signals for breast cancer at a nominal P-value cutoff of 0.05 (ref. 21). Some other studies reported a significant survival difference between cancer patient subgroups after stratifying patients with overall mRNA isoform expression profiles24, 25. However, systematic cancer survival analyses of alternative splicing at the individual exon resolution have been lacking. Two main challenges exist for survival analyses of mRNA isoform variation and alternative splicing using RNA-seq data. The first challenge is to account for the estimation uncertainty of mRNA isoform ratios inferred from RNA-seq read counts. The statistical confidence of mRNA isoform ratio estimation depends on the RNA-seq read coverage for the events of interest, with larger read coverage leading to a more reliable estimation14. Modelling the estimation uncertainty of mRNA isoform ratio is an essential component of RNA-seq analyses of alternative splicing, as shown by various statistical algorithms developed for detecting differential alternative splicing from multi-group RNA-seq data14, 26, 27, 28,29. The second challenge, which is a general issue in survival analysis, is to properly model the association of mRNA isoform ratio with survival time, while accounting for missing data in survival time because of censoring, that is, patients still alive at the end of the survival study, whose precise survival time would be uncertain. To date, no algorithm has been developed for survival analyses of mRNA isoform variation that accounts for these sources of uncertainty simultaneously.

Here we introduce SURVIV (Survival analysis of mRNA Isoform Variation), a statistical model for identifying mRNA isoform ratios associated with patient survival times in large-scale cancer RNA-seq data sets. SURVIV models the estimation uncertainty of mRNA isoform ratios in RNA-seq data and tests the survival effects of isoform variation in both censored and uncensored survival data. In simulation studies, SURVIV consistently outperforms the conventional Cox regression survival analysis that ignores the measurement uncertainty of mRNA isoform ratio. We used SURVIV to identify alternatively spliced exons whose exon-inclusion levels significantly correlated with the survival times of invasive ductal carcinoma (IDC) patients from the TCGA breast cancer cohort. Survival-associated alternative splicing events are identified in gene pathways associated with apoptosis, oxidative stress and DNA damage repair. Importantly, we show that alternative splicing-based survival predictors outperform gene expression-based survival predictors in the TCGA IDC RNA-seq data set, as well as in TCGA data of five additional cancer types. Moreover, the integration of clinical information, gene expression and alternative splicing profiles leads to the best prediction of survival time.

SURVIV statistical model

The statistical model of SURVIV assesses the association between mRNA isoform ratio and patient survival time. While the model is generic for many types of alternative isoform variation, here we use the exon-skipping type of alternative splicing to illustrate the model (Fig. 1a). For each alternative exon involved in exon-skipping, we can use the RNA-seq reads mapping to its exon-inclusion or -skipping isoform to estimate its exon-inclusion level (denoted as ψ, or PSI that is Per cent Spliced In14). A key feature of SURVIV is that it models the RNA-seq estimation uncertainty of exon-inclusion level as influenced by the sequencing coverage for the alternative splicing event of interest. This is a critical issue in accurate quantitative analyses of mRNA isoform ratio in large-scale RNA-seq data sets14, 26, 27, 28, 29. Therefore, SURVIV contains two major components: the first to model the association of mRNA isoform ratio with patient survival time across all patients, and the second to model the estimation uncertainty of mRNA isoform ratio in each individual patient (Fig. 1a).

Figure 1: The statistical framework of the SURVIV model.

(a) For each patient k, the patient’s hazard rate λk(t) is associated with the baseline hazard rate λ0(t) and this patient’s exon-inclusion level ψk. The association of exon-inclusion level with patient survival is estimated by the survival coefficient β. The exon-inclusion level ψk is estimated from the read counts for the exon-inclusion isoform ICk and the exon-skipping isoform SCk. The proportion of the inclusion and skipping reads is adjusted by a normalization function f that considers the lengths of the exon-inclusion and -skipping isoforms (see details in Results and Supplementary Methods). (b) A hypothetical example to illustrate the association of exon-inclusion level with patient survival probability over time Sk(t), with the survival coefficient β=−1 and a constant baseline hazard rate λ0(t)=1. In this example, patients with higher exon-inclusion levels have lower hazard rates and higher survival probabilities. (c) The schematic diagram of an exon-skipping event. The exon-inclusion reads ICk are the reads from the upstream splice junction, the alternative exon itself and the downstream splice junction. The exon-skipping reads SCk are the reads from the skipping splice junction that directly connects the upstream exon to the downstream exon.

Briefly, for any individual exon-skipping event, the first component of SURVIV uses a proportional hazards model to establish the relationship between patient k’s exon-inclusion level ψk and hazard rate λk(t).

For each exon, the association between the exon-inclusion level and patient survival time is reflected by the survival coefficient β. A positive β means increased exon inclusion is associated with higher hazard rate and poorer survival, while a negative β means increased exon inclusion is associated with lower hazard rate and better survival. λ0(t) is the baseline hazard rate estimated from the survival data of all patients (see Supplementary Methods for the detailed estimation procedure). A particular patient’s survival probability over time Sk(t) can be calculated from the patient-specific hazard rate λk(t) as . Figure 1b illustrates a simple example with a negative β=−1 and a constant baseline hazard rate λ0(t)=1, where higher exon-inclusion levels are associated with lower hazard rates and higher survival probabilities.

The second component of SURVIV models the exon-inclusion level and its estimation uncertainty in individual patient samples. As illustrated in Fig. 1c, the exon-inclusion level ψk of a given exon in a particular sample can be estimated by the RNA-seq read count specific to the exon inclusion isoform (ICk) and the exon-skipping isoform (SCk). Other types of alternative splicing and mRNA isoform variation can be similarly modelled by this framework29. Given the effective lengths (that is, the number of unique isoform-specific read positions) of the exon-inclusion isoform (lI) and the exon-skipping isoform (lS), the exon-inclusion level ψk can be estimated as . Assuming that the exon-inclusion read count ICk follows a binomial distribution with the total read count nk=ICk+SCk, we have:

The binomial distribution models the estimation uncertainty of ψk as influenced by the total read count nk, in which the parameter pk represents the proportion of reads from the exon-inclusion isoform, given the exon-inclusion level ψk adjusted by a length normalization function f(ψk) based on the effective lengths of the isoforms. The definitions of effective lengths for all basic types of alternative splicing patterns are described in ref. 29.

Distinct from conventional survival analyses in which predictors do not have estimation uncertainty, the predictors in SURVIV are exon-inclusion levels ψk estimated from RNA-seq count data, and the confidence of ψk estimate for a given exon in a particular sample depends on the RNA-seq read coverage. We use the statistical framework of survival measurement error model30 to incorporate the estimation uncertainty of isoform ratio in the proportional hazards model. Using a likelihood ratio test, we test whether the exon-inclusion levels have a significant association with patient survival over the null hypothesis H0:β=0. The false discovery rate (FDR) is estimated using the Benjamini and Hochberg approach31. Details of the parameter estimation and likelihood ratio test in SURVIV are described in Supplementary Methods.

 

Figure 2: Simulation studies to assess the performance of SURVIV and the importance of modelling the estimation uncertainty of mRNA isoform ratio.

We compared our SURVIV model with Cox regression using point estimates of exon-inclusion levels, which does not consider the estimation uncertainty of the mRNA isoform ratio. (a) To study the effect of RNA-seq depth, we simulated the mean total splice junction read counts equal to 5, 10, 20, 50, 80 and 100 reads. We generated two sets of simulations with and without data-censoring. For each simulation, the true-positive rate (TPR) at 5% false-positive rate is plotted. The inset figure shows the empirical distribution of the mean total splice junction read counts in the TCGA IDC RNA-seq data (x axis in the log10 scale). (b) To faithfully represent the read count distribution in a real data set, we performed another simulation with read counts directly sampled from the TCGA IDC data. Sampled read counts were then multiplied by different factors ranging from 10 to 300% to simulate data sets with different RNA-seq read depth. Continuous and dashed lines represent the performance of SURVIV and Cox regression, respectively. Red lines represent the area under curve (AUC) of the ROC curve (TPR versus false-positive rate plot). Black lines represent the TPR at 5% false-positive rate.

 

Using these simulated data, we compared SURVIV with Cox regression in two settings, without or with censoring of the survival time. In the setting without censoring, the death and survival time of each individual is known. In the setting with censoring, certain individuals are still alive at the end of the survival study. Consequently, these patients have unknown death and survival time. Here, in the simulation with censoring, we assumed that 85% of the patients were still alive at the end of the study, similar to the censoring rate of the TCGA IDC data set. In both settings and with different depths of RNA-seq coverage, SURVIV consistently outperformed Cox regression in the true-positive rate at the same false-positive rate of 5% (Fig. 2a). As expected, we observed a more significant improvement in SURVIV over Cox regression when the RNA-seq read coverage was low (Fig. 2a).

To more faithfully recapitulate the read count distribution in a real cancer RNA-seq data set, we performed another simulation study with read counts directly sampled from the TCGA IDC data. To assess the influence of RNA-seq read depth on the performance of SURVIV and Cox regression, sampled read counts were then multiplied by different factors ranging from 10 to 300% to simulate data sets with different RNA-seq read depths (Fig. 2b). The TCGA IDC data set has an average RNA-seq depth of ~60 million paired-end reads per patient. Thus, the read depth of these simulated RNA-seq data sets ranged from ~6 million reads to 180 million reads per patient, representing low-coverage RNA-seq studies designed primarily for gene expression analysis32 up to high-coverage RNA-seq studies designed primarily for alternative isoform analysis29. At all levels of RNA-seq depth, SURVIV consistently outperformed Cox regression, as reflected by the area under curve of the receiver operating characteristic (ROC) curve as well as the true-positive rate at 5% false-positive rate (Fig. 2b). The improvement of SURVIV over Cox regression was particularly prominent when the read depth was low. For example, at 10% read depth, SURVIV had 7% improvement in area under curve (68% versus 61%) and 8% improvement in the true-positive rate at 5% false-positive rate (46% versus 38%). Collectively, these simulation results suggest that SURVIV achieves a higher accuracy by accounting for the estimation uncertainty of mRNA isoform ratio in RNA-seq data.

SURVIV analysis of TCGA IDC breast cancer data

To illustrate the practical utility of SURVIV, we used it to analyse the overall survival time of 682 IDC patients from the TCGA breast cancer (BRCA) RNA-seq data set (see Methods for details of the data source and processing pipeline). We chose to analyse IDC because it is the most frequent type of breast cancer33, comprising ~70% of patients in the TCGA breast cancer data set. To control for the effects of significant clinical parameters such as tumour stage and subtype and identify alternative splicing events associated with patient outcomes across multiple molecular and clinical subtypes, we followed the procedure of Croce and colleagues in analysing mRNA and microRNA prognostic signature of IDC33 and stratified the patients according to their clinical parameters. We then conducted SURVIV analysis in 26 clinical subgroups with at least 50 patients in each subgroup. We identified 229 exon-skipping events associated with patient survival in multiple clinical subgroups that met the criteria of SURVIV P-value≤0.01 in at least two subgroups of the same clinical parameter (cancer subtype, stage, lymph node, metastasis, tumour size, oestrogen receptor status, progesterone receptor status, HER2 status and age as shown in Fig. 3). DAVID (Database for Annotation, Visualization and Integrated Discovery) Gene Ontology analyses34 of the 229 alternative splicing events suggest an enrichment of genes in cancer-related functional categories such as intracellular signalling, apoptosis, oxidative stress and response to DNA damage (Supplementary Fig. 1). Table 1 shows a few selected examples of survival-associated alternative splicing events in cancer-related genes. Using two-means clustering of each individual exon’s inclusion levels, the 682 IDC patients can be segregated into two subgroups with significantly different survival times as illustrated by the Kaplan–Meier survival plot (Fig. 4). We also carried out hierarchical clustering of IDC patients using 176 survival-associated alternative exons (P≤0.01; SURVIV analysis of all IDC patients). Using the exon-inclusion levels of these 176 exons, we clustered IDC patients into three major subgroups, with 95, 194 and 389 patients, respectively. As illustrated by the Kaplan–Meier survival plots, the three subgroups had significantly different survival times (Supplementary Fig. 2).

Figure 3: SURVIV analysis of exon-skipping events in the TCGA IDC RNA-seq data set.

IDC patients are stratified into multiple clinical subgroups based on clinical parameters including cancer subtype, stage, lymph node status, metastasis, tumour size, oestrogen receptor status, progesterone receptor status, HER2 status and age. Only clinical subgroups with at least 50 patients are included in further analyses. Numbers of patients in the subgroups are indicated next to the names of the subgroups. Shown in the heatmap are the log10 SURVIV P-values of the 229 exons associated with patient survival (P≤0.01) in at least two subgroups of the same class of clinical parameters. Turquoise colour indicates positive correlation that higher exon-inclusion levels are associated with higher survival probabilities. Magenta colour indicates negative correlation that lower exon-inclusion levels are associated with higher survival probabilities.

TABLE 1 (not shown)

Figure 4: Kaplan–Meier survival plots of IDC patients stratified by two-means clustering of the exon-inclusion levels of four survival-associated alternative splicing events.

Clustering was generated for each of the four exons separately. Black lines represent patients with high exon-inclusion levels. Red lines represent patients with low exon-inclusion levels. The P-values are from SURVIV analysis of the TCGA IDC RNA-seq data. (a) ATRIP. (b) BCL2L11. (c) CD74. (d) PCBP4.

 

Figure 5: Alternative splicing of STAT5A exon 5 is significantly associated with IDC patient survival.

(a) The gene structure of the STAT5A full-length isoform compared to the ΔEx5 isoform skipping the 5th exon. (b) Kaplan–Meier survival plot of IDC patients stratified by two-means clustering using exon-inclusion levels of STAT5A exon 5. The 420 patients in Group 1 (average exon 5 inclusion level=95%) have significantly higher survival probabilities than the 262 patients in Group 2 (average exon 5 inclusion level=85%) (SURVIV P=6.8e−4). (c) Exon 5 inclusion levels of IDC patients stratified by two-means clustering using exon 5 inclusion levels. Group 1 has 420 patients with average exon-inclusion level at 95%. Group 2 has 262 patients with average exon-inclusion level at 85%. (d) STAT5A exon 5 inclusion levels in normal breast tissues versus breast cancer tumour samples. Exon-inclusion levels are extracted from 86 TCGA breast cancer patients with matched normal and tumour samples. Normal breast tissues have average exon 5 inclusion level at 95%, compared to 91% average exon-inclusion level in tumour samples. Error bars represent 95% confidence interval of the mean.

Network of survival-associated alternative splicing events

…see http://www.nature.com/ncomms/2016/160609/ncomms11548/full/ncomms11548.html

Figure 6: Splicing factor regulatory network of survival-associated alternative splicing events in IDC.

(ac) Kaplan–Meier survival plots of IDC patients stratified by the gene expression levels of three splicing factors: TRA2B (a, Cox regression P=1.8e−4), HNRNPH1 (b, P=3.4e−4) and SFRS3 (c, P=2.8e−3). Black lines represent patients with high gene expression levels. Red lines represent patients with low gene expression levels. (d) The exon-inclusion levels of a DHX30 alternative exon are negatively correlated with TRA2B gene expression levels (robust correlation coefficient r=−0.26, correlation P=1.2e−17). (e) The exon-inclusion levels of a MAP3K4 alternative exon are positively correlated withHNRNPH1 gene expression levels (robust correlation coefficient r=0.16, correlation P=2.6e−06). (f) A splicing co-expression network of the three splicing factors and their correlated survival-associated alternative exons. In total, 84 survival-associated alternative exons are significantly correlated with the three splicing factors. The positive/negative correlation between splicing factors and alternative exons is represented by blue/red lines, respectively. Exons whose inclusion levels are positively/negatively correlated with survival times are represented by blue/red dots, respectively. The size of the splicing factor circles is proportional to the number of correlated exons within the network.

…..

Alternative splicing predictors of cancer patient survival

see http://www.nature.com/ncomms/2016/160609/ncomms11548/full/ncomms11548.html

Figure 7: Cross-validation of different classes of IDC survival predictors measured by the C-index

A C-index of 1 indicates perfect prediction accuracy and a C-index of 0.5 indicates random guess. The plots indicate the distribution of C-indexes from 100 rounds of cross-validation. The centre value of the box plot is the median C-index from 100 rounds of cross-validation. The notch represents the 95%confidence interval of the median. The box represents the 25 and 75% quantiles. The whiskers extended out from the box represent the 5 and 95% quantiles. Two-sided Wilcoxon test was used to compare different survival predictors. The different classes of predictors are: (a) clinical information (median C-index 0.67). (b) Gene expression (median C-index 0.68). (c) Alternative splicing (median C-index 0.71). (d) Clinical information+gene expression (median C-index 0.69). (e) Clinical information+alternative splicing (median C-index 0.73). (f) Clinical information+gene expression+alternative splicing (median C-index 0.74). Note that ‘Gene’ refers to ‘Gene-level expression’ in these plots.

Next, we carried out the SURVIV analysis in five additional cancer types in TCGA, including GBM (glioblastoma multiforme), KIRC (kidney renal clear cell carcinoma), LGG (lower grade glioma), LUSC (lung squamous cell carcinoma) and OV (ovarian serous cystadenocarcinoma). As expected, the number of significant events at different FDR or P-value significance cutoffs varied across cancer types, with LGG having the strongest survival-associated alternative splicing signals with 660 significant exon-skipping events at FDR≤5% (Supplementary Data 3 and 4). Strikingly, regardless of the number of significant events, alternative splicing-based survival predictors outperformed gene expression-based survival predictors across all cancer types (Supplementary Fig. 3), consistent with our initial observation on the IDC data set.

 

Alternative processing and modification of mRNA, such as alternative splicing, allow cells to generate a large number of mRNA and protein isoforms with diverse regulatory and functional properties. The plasticity of alternative splicing is often exploited by cancer cells to produce isoform switches that promote cancer cell survival, proliferation and metastasis7, 8. The widespread use of RNA-seq in cancer transcriptome studies15, 47, 48 has provided the opportunity to comprehensively elucidate the landscape of alternative splicing in cancer tissues. While existing studies of alternative splicing in large-scale cancer transcriptome data largely focused on the comparison of splicing patterns between cancer and normal tissues or between different subtypes of cancer18, 21, 49, additional computational tools are needed to characterize the clinical relevance of alternative splicing using massive RNA-seq data sets, including the association of alternative splicing with phenotypes and patient outcomes.

We have developed SURVIV, a novel statistical model for survival analysis of alternative isoform variation using cancer RNA-seq data. SURVIV uses a survival measurement error model to simultaneously model the estimation uncertainty of mRNA isoform ratio in individual patients and the association of mRNA isoform ratio with survival time across patients. Compared with the conventional Cox regression model that uses each patient’s mRNA isoform ratio as a point estimate, SURVIV achieves a higher accuracy as indicated by simulation studies under a variety of settings. Of note, we observed a particularly marked improvement of SURVIV over Cox regression for low- and moderate-depth RNA-seq data (Fig. 2b). This has important practical value because many clinical RNA-seq data sets have large sample size but relatively modest sequencing depth.

Using the TCGA IDC breast cancer RNA-seq data of 682 patients, SURVIV identified 229 alternative splicing events associated with patient survival time, which met the criteria of SURVIVP-values≤0.01 in multiple clinical subgroups. While the statistical threshold seemed loose, several lines of evidence suggest the functional and clinical relevance of these survival-associated alternative splicing events. These alternative splicing events were frequently identified and enriched in the gene functional groups important for cancer development and progression, including apoptosis, DNA damage response and oxidative stress. While some of these events may simply reflect correlation but not causal effect on cancer patient survival, other events may play an active role in regulating cancer cell phenotypes. For example, a survival-associated alternative splicing event involving exon 5 of STAT5A is known to regulate the activity of this transcription factor with important roles in epithelial cell growth and apoptosis37. Using a co-expression network analysis of splicing factor to exon correlation across all patients, we identified three splicing factors (TRA2B, HNRNPH1 and SFRS3) as potential hubs of the survival-associated alternative splicing network of IDC. The expression levels of all three splicing factors were negatively associated with patient survival times (Fig. 6a–c), and both TRA2B and HNRNPH1 were previously reported to have an impact on cancer-related molecular pathways40, 41, 42, 43, 44, 45. Finally, despite the limited power in detecting individual events, we show that the survival-associated alternative splicing events can be used to construct a predictor for patient survival, with an accuracy higher than predictors based on clinical parameters or gene expression profiles (Fig. 7). This further demonstrates the potential biological relevance and clinical utility of the identified alternative splicing events.

We performed cross-validation analyses to evaluate and compare the prognostic value of alternative splicing, gene expression and clinical information for predicting patient survival, either independently or in combination. As expected, the combined use of all three types of information led to the best prediction accuracy. Because we used penalized regression to build the prediction model, combining information from multiple layers of data did not necessarily increase the number of predictors in the model. The perhaps more surprising and intriguing result is that alternative splicing-based predictors appear to outperform gene expression-based predictors when used alone and when either type of data was combined with clinical information (Fig. 7). We observed the same trend in five additional cancer types (Supplementary Fig. 3). We note that this finding was consistent with a previous report that cancer subtype classification based on splicing isoform expression performed better than gene expression-based classification25. While this trend seems counterintuitive because accurate estimation of gene expression requires much lower RNA-seq depth than accurate estimation of alternative splicing29, one possible explanation may be the inherent characteristic of isoform ratio data. By definition, mRNA isoform ratio is estimated as the ratio of multiple mRNA isoforms from a single gene. Therefore, mRNA isoform ratio data have a ‘built-in’ internal control that could be more robust against certain artefacts and confounding issues that influence gene expression estimates across large clinical RNA-seq data sets, such as poor sample quality and RNA degradation12. Regardless of the reasons, our data call for further studies to fully explore the utility of mRNA isoform ratio data for various clinical research applications.

The SURVIV source code is available for download at https://github.com/Xinglab/SURVIV. SURVIV is a general statistical model for survival analysis of mRNA isoform ratio using RNA-seq data. The current statistical framework of SURVIV is applicable to RNA-seq based count data for all basic types of alternative splicing patterns involving two isoform choices from an alternatively spliced region, such as exon-skipping, alternative 5′ splice sites, alternative 3′ splice sites, mutually exclusive exons and retained introns, as well as other forms of alternative isoform variation such as RNA editing. With the rapid accumulation of clinical RNA-seq data sets, SURVIV will be a useful tool for elucidating the clinical relevance and potential functional significance of alternative isoform variation in cancer and other diseases.

 

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Durable responses with checkpoint inhibitor

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

Immunotherapy Active in ‘Universally Lethal’ Glioblastoma

ACTIVATE CME    by Kristin Jenkins 

  • Contributing Writer, MedPage Today

http://www.medpagetoday.com/HematologyOncology/BrainCancer/57641

 

Two pediatric siblings with recurrent multifocal glioblastoma multiforme (GBM) refractory to current standard therapies exhibited “remarkable and durable” responses to immune checkpoint inhibition with single-agent nivolumab (Opdivo), researchers said.

Following pre-clinical testing in 37 biallelic mismatch repair deficiency (bMMRD) cancers, a regimen of 3 mg/kg nivolumab every 2 weeks resulted in clinically significant responses and a profound radiologic response, Uri Tabori, MD, of The Hospital for Sick Children, Toronto, Ontario, Canada, and colleagues reported in the Journal of Clinical Oncology.

The 6-year-old white female patient and her 3.5-year-old brother resumed normal schooling and daily activities after 9 and 5 months of therapy, respectively, the researchers said.

“This observation is especially encouraging because these children are still clinically stable, whereas most relapsed pediatric GBMs will progress within 1 to 2 months despite salvage treatment, and survival is usually 3 to 6 months post-recurrence. It also highlights the utility of germline predisposition in guiding novel treatment options — in this case, immunotherapy — for cancer treatment.”

Findings from this lab study and small case series report may have implications for GBM as well as for other hypermutant cancers arising from primary (genetic predisposition) or secondary MMRD, the researchers said. “Given the increasing availability of commercial sequencing platforms, analysis of mutation burden and neoantigens can play a role in transforming treatment of these patients.”

Still, they added that these results, while encouraging, need to be validated in multinational prospective clinical trials of these “universally lethal” bMMRD-driven hypermutant cancers.

“Sometimes very small studies can yield meaningful results,” Robert Fenstermaker, MD, of Roswell Park Cancer Institute in Buffalo, N.Y., told MedPage Today via email. “Although anecdotal, the results of this study are quite encouraging because they tend to confirm current theory about immunotherapy for glioblastoma.”

Although these kinds of clinical responses to single-agent drug therapy in GBM are uncommon and the results may not be broadly applicable to all glioblastoma patients, this paper “is of much greater importance than just these few cases,” Fenstermaker emphasized. “The excellent responses in these particular cases suggest that an immune checkpoint inhibitor (nivolumab) may have enabled the immune system to respond fully.”

This “very small case series” report of a “compelling clinical experience” is a “fascinating and beautiful example of how mechanistic insight can be linked to rationally designed clinical applications — in turn, stimulating new downstream ideas,” Stephanie Weiss, MD, a radiation oncologist at Fox Chase Cancer Center in Philadelphia, commented in an email.

“This series also tests ‘proof of principle,’ that bMMRD tumors are hypermutated and associated with a high neoantigen load, and therefore may respond much like other immune checkpoint inhibitor-sensitive tumors. In this sense, the results reveal a tantalizing glimpse into the disease process of at least a subset of GBMs and can guide high-quality study of novel treatment for GBM.”

For the study, Tabori and colleagues performed exome sequencing and neoantigen prediction on 37 bMMRD-associated tumors, including 21 GBMs, and compared them with childhood and adult brain neoplasms.

The bMMRD GBMs were found to be hypermutant and to have an extremely strong neoantigen load — up to 16 times higher than the signature commonly seen in known immune checkpoint inhibitors (P<.001).

The female patient, diagnosed with a left parietal GBM, underwent near-total resection and focal irradiation over 6.5 weeks. After a clinical remission lasting 3 months, surveillance MRI revealed recurrence in the initial tumor bed and a second lesion in the left temporal lobe.

Six months earlier, the index patient’s brother had been diagnosed with a right frontoparietal GBM and treated with surgery, focal irradiation, and temozolomide (Temodal). Ten months after diagnosis, surveillance MRI revealed an asymptomatic diffuse multinodular GBM recurrence.

When given nivolumab as a last-resort therapeutic agent, both children initially experienced serious symptoms that on imaging mimicked tutor progression. After symptomatic management and observation, both stabilized, and follow-up imaging demonstrated significant improvement in tumor-related abnormalities.

Fenstermaker said that important next steps lie ahead, such as combining immune checkpoint inhibitors with specific cancer vaccines designed to immunize patients with glioblastomas other than this rare hypermutated type. “There are a number of prospective vaccines currently in the glioblastoma drug pipeline that would be candidates for this kind of approach,” he told MedPage Today. Examples include SurVaxM, NeoVax, HSPPC-96, and various dendritic cell vaccines.

In addition, newer genomic techniques are being developed that could make it possible to create a personalized profile of the mutant proteins in a given patient’s tumor, he noted. “One can imagine combining such a personalized vaccine against these mutant proteins together with an immune checkpoint inhibitor. Such a combination might result in many more responses like the ones seen in this small study.”

 

PD-1 Blockade in Tumors with Mismatch-Repair Deficiency

Dung T. Le, Jennifer N. Uram, Hao Wang, Bjarne R. Bartlett, Holly Kemberling, Aleksandra D. Eyring, et al.
http://www.nejm.org/doi/full/10.1056/NEJMoa1500596

BACKGROUND

Somatic mutations have the potential to encode “non-self” immunogenic antigens. We hypothesized that tumors with a large number of somatic mutations due to mismatch-repair defects may be susceptible to immune checkpoint blockade.

METHODS

We conducted a phase 2 study to evaluate the clinical activity of pembrolizumab, an anti–programmed death 1 immune checkpoint inhibitor, in 41 patients with progressive metastatic carcinoma with or without mismatch-repair deficiency. Pembrolizumab was administered intravenously at a dose of 10 mg per kilogram of body weight every 14 days in patients with mismatch repair–deficient colorectal cancers, patients with mismatch repair–proficient colorectal cancers, and patients with mismatch repair–deficient cancers that were not colorectal. The coprimary end points were the immune-related objective response rate and the 20-week immune-related progression-free survival rate.

RESULTS

The immune-related objective response rate and immune-related progression-free survival rate were 40% (4 of 10 patients) and 78% (7 of 9 patients), respectively, for mismatch repair–deficient colorectal cancers and 0% (0 of 18 patients) and 11% (2 of 18 patients) for mismatch repair–proficient colorectal cancers. The median progression-free survival and overall survival were not reached in the cohort with mismatch repair–deficient colorectal cancer but were 2.2 and 5.0 months, respectively, in the cohort with mismatch repair–proficient colorectal cancer (hazard ratio for disease progression or death, 0.10 [P<0.001], and hazard ratio for death, 0.22 [P=0.05]). Patients with mismatch repair–deficient noncolorectal cancer had responses similar to those of patients with mismatch repair–deficient colorectal cancer (immune-related objective response rate, 71% [5 of 7 patients]; immune-related progression-free survival rate, 67% [4 of 6 patients]). Whole-exome sequencing revealed a mean of 1782 somatic mutations per tumor in mismatch repair–deficient tumors, as compared with 73 in mismatch repair–proficient tumors (P=0.007), and high somatic mutation loads were associated with prolonged progression-free survival (P=0.02).

CONCLUSIONS

This study showed that mismatch-repair status predicted clinical benefit of immune checkpoint blockade with pembrolizumab. (Funded by Johns Hopkins University and others; ClinicalTrials.gov number, NCT01876511.)

Eric BouffetValérie LaroucheBrittany B. CampbellDaniele MericoRichard de Borja, et al.

Purpose Recurrent glioblastoma multiforme (GBM) is incurable with current therapies. Biallelic mismatch repair deficiency (bMMRD) is a highly penetrant childhood cancer syndrome often resulting in GBM characterized by a high mutational burden. Evidence suggests that high mutation and neoantigen loads are associated with response to immune checkpoint inhibition.

Patients and Methods We performed exome sequencing and neoantigen prediction on 37 bMMRD cancers and compared them with childhood and adult brain neoplasms. Neoantigen prediction bMMRD GBM was compared with responsive adult cancers from multiple tissues. Two siblings with recurrent multifocal bMMRD GBM were treated with the immune checkpoint inhibitor nivolumab.

Results All malignant tumors (n = 32) were hypermutant. Although bMMRD brain tumors had the highest mutational load because of secondary polymerase mutations (mean, 17,740 ± standard deviation, 7,703), all other high-grade tumors were hypermutant (mean, 1,589 ± standard deviation, 1,043), similar to other cancers that responded favorably to immune checkpoint inhibitors. bMMRD GBM had a significantly higher mutational load than sporadic pediatric and adult gliomas and all other brain tumors (P < .001). bMMRD GBM harbored mean neoantigen loads seven to 16 times higher than those in immunoresponsive melanomas, lung cancers, or microsatellite-unstable GI cancers (P < .001). On the basis of these preclinical data, we treated two bMMRD siblings with recurrent multifocal GBM with the anti–programmed death-1 inhibitor nivolumab, which resulted in clinically significant responses and a profound radiologic response.

Conclusion This report of initial and durable responses of recurrent GBM to immune checkpoint inhibition may have implications for GBM in general and other hypermutant cancers arising from primary (genetic predisposition) or secondary MMRD.

Glioblastoma multiforme (GBM) is a highly malignant brain tumor and the most common cause of death among children with CNS neoplasms.1 Despite primary management, which consists of surgical resection followed by radiation therapy and chemotherapy, most GBMs will recur, resulting in rapid death. Patients with recurrent disease have a particularly poor prognosis, with a median survival of fewer than 6 months; no effective therapies currently exist.

In contrast to adult CNS malignancies, a significant proportion of childhood brain tumors occur in the context of cancer predisposition syndromes.2 Pediatric GBMs are associated with germline mutations in TP53 (Li-Fraumeni syndrome)1 and the mismatch repair (MMR) genes (biallelic MMR deficiency syndrome [bMMRD]).3 Patients with bMMRD are unique in both the molecular events that lead to GBM formation and opportunities for innovative management of these tumors to possibly improve survival.

bMMRD is caused by homozygous germline mutations in one of the four MMR genes (PMS2, MLH1, MSH2, and MSH6) and is arguably the most penetrant cancer predisposition syndrome, with 100% of biallelic mutation carriers developing cancers in the first two decades of life. These are most commonly malignant gliomas, hematologic malignancies, and GI cancers.3,4 Understanding the relationship between the bMMRD somatic mutational landscape and tumor biology can lead to development of novel therapies and improved patient outcomes.

bMMRD GBMs harbor the highest mutation load among human cancers.5 Combined germline mutations in the MMR genes and somatic mutations in DNA polymerase result in complete ablation of proofreading during DNA replication and underpin this phenomenon. bMMRD GBMs, in contrast to other childhood cancers and adult MMR-proficient gliomas, exhibit a molecular signature characterized by single-nucleotide changes present in exponentially higher numbers. An important characteristic of non-bMMRD cancers exhibiting high mutation loads—subsets of malignant melanomas and lung, bladder, and microsatellite-unstable GI cancers—is responsiveness to immune checkpoint inhibitors.69

Checkpoint inhibitors target the immunomodulatory effect of CTLA-4 (cytotoxic T lymphocyte–associated protein 4) and programmed death-1 (PD-1)/programmed death-ligand 1, restoring effector T-cell function and antitumor activity. Recent reports have shown that patients whose tumors bear a high mutation load and/or definedtumor-associated antigen (neoantigen) signatures derive enhanced clinical benefit from checkpoint inhibitor therapy.10

Nivolumab is an anti–PD-1–directed immune checkpoint inhibitor approved for use in the treatment of non–small-cell lung cancer11and melanoma and under clinical investigation in multiple adult and pediatric tumors.12,13 However, this response is currently unknown in bMMRD-associated cancers and the uniformly lethal GBM.

 

Fig 1.

Fig 1.   Clinical and molecular features of the biallelic mismatch repair (MMR) deficiency (bMMRD) family. (A) Pedigree of the family with both bMMRD-affected children (solid square and circle). Both siblings presented with glioblastoma multiforme (GBM), whereas parents remained unaffected, as observed in other bMMRD families. (B) Immunohistochemistry staining of the index patient’s GBM for the four MMR genes: MSH2, MSH6,MLH1, and PMS2. A PMS2-negative stain in both tumor and normal cells prompted subsequent genetic testing that confirmed the diagnosis of bMMRD. NF1, neurofibromatosis type 1.   http://jco.ascopubs.org/content/early/2016/03/17/JCO.2016.66.6552/F1.small.gif

 

To examine whether immune checkpoint inhibitors would be applicable for bMMRD cancers, we surveyed the extent of hypermutation across bMMRD tumors form various tissues. Exome sequencing of 37 cancers collected from the bMMRD consortium revealed that all malignant tumors (n = 32) were hypermutant. Although bMMRD brain tumors had the highest mutational load resulting from secondary polymerase mutations (mean, 17,740 ± standard deviation [SD], 7,703), all other high-grade tumors were hypermutant, harboring more than 100 exonic mutations (mean, 1,589 ± SD, 1,043; Fig 2A). Lower-grade bMMRD tumors (n = 5) did not exhibit hypermutation (mean, 40 ± SD, 18). Importantly, bMMRD GBMs had a significantly higher mutational load than sporadic pediatric and adult gliomas and all other brain tumors (P < .001; Fig 2A). To test the extent to which hypermutation translates to a strong neoantigen signature, a current predictor of response to immune checkpoint inhibition, we performed genome-wide somatic neoepitope analysis using similar algorithms previously used for melanoma, lung, and colon cancers.9,14,15 For each study, we compared our cohort of tumors with other tumors that were reported to respond to immune checkpoint inhibitors (Fig 2B). Strikingly, bMMRD GBMs had a significantly higher number of predicted neoantigens, whereas other tumors responded with a fraction of the neoantigens found in our patients (P < .001; Fig 2B). The mean neoantigen load was seven to 16 times higher than those of immunoresponsive melanomas, lung cancers, and microsatellite-unstable GI cancers.

 

Fig 2.

Fig 2.  Tumor mutation and neoantigen analysis. (A) Boxplot comparing the number of mutations per tumor exome in several biallelic mismatch repair deficiency (bMMRD) cancer types with pediatric and adult brain tumors. (B) Ratio of the number of neoantigens found in immunoresponsive tumors from melanoma (n = 27), lung cancer (n = 14), and colon cancer (n = 7) data sets compared with median number of neoantigens in bMMRD glioblastoma multiforme (GBM; n = 13). ATRT, atypical teratoid rhabdoid tumor; DIPG, diffuse intrinsic pontine glioma; L/L, leukemia/lymphoma; LGG, low-grade glioma; MB, medulloblastoma; PA, pilocytic astrocytoma; PNET, primitive neuroectodermal tumor.

 

We describe two pediatric patients with recurrent multifocal GBM refractory to current standard therapies who exhibited remarkable and durable responses to immune checkpoint inhibition with single-agent nivolumab. This observation is especially encouraging because these children are still clinically stable, whereas most relapsed pediatric GBMs will progress within 1 to 2 months19 despite salvage treatment, and survival is usually 3 to 6 months postrecurrence.20 Furthermore, bMMRD GBMs have outcomes similar to those of sporadic childhood GBMs,21 and data gathered from the consortium reveal a mean time from relapse to death of 2.6 months in bMMRD GBM. To our knowledge, this is the first report of such a response in childhood or adult GBM. It also highlights the utility of germline predisposition in guiding novel treatment options—in this case, immunotherapy—for cancer treatment. ….

sjwilliamspa

Not sure if the link between PD-L1 response and MMR status is causal in this ase. there are many tumors with MMR and especially all tumors had high degree of MMR. Perhaps they need to look at tumors that have a more stable genome like certain hepatocarcinomas.

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CRISPR-Cas9 Screening by Horizon Discovery, Cambridge, UK – HDx™ Reference Standards

Reporters: David Orchard-Webb, PhD and Aviva Lev-Ari, PhD, RN

 

VIEW VIDEO

https://www.horizondiscovery.com/resources/webinars/how-crispr-cas9-screening-will-revolutionise-your-drug-development-programs?_cldee=ZC5vcmNoYXJkLXdlYmJAYmF0aC5lZHU%3d&utm_source=ClickDimensions&utm_medium=email&utm_campaign=201605%20Email%20-%207%20must-read%20CRISPR%20screening%20papers&urlid=3

Seven essential CRISPR screening papers

https://www.horizondiscovery.com/knowledge-base/essential-crispr-screening-papers?_cldee=ZC5vcmNoYXJkLXdlYmJAYmF0aC5lZHU%3d&utm_source=ClickDimensions&utm_medium=email&utm_campaign=201605%20Email%20-%207%20must-read%20CRISPR%20screening%20papers&urlid=1

  • CRISPR Screens… the beginning!
  •  …moving beyond DNA cleavage
  •  …changing the way we think about library design
  •  …in primary immune cells
  •  …pushing the technology in vivo
  •  …improving screen resolution
  •  So where next?

 

Related pages:

Read our short guide to CRISPR Screening CLICK HERE
Watch our short introductory video to Horizon’s CRISPR screening platform CLICK HERE
Explore our collection of ready-made sgRNA libraries available as part of our CRISPR screening service CLICK HERE
Read about how we’ve combined KRAS isogenic cell lines, 3D cell culture and CRISPR screening for more definitive target validation CLICK HERE

SOURCE

https://www.horizondiscovery.com/knowledge-base/essential-crispr-screening-papers?_cldee=ZC5vcmNoYXJkLXdlYmJAYmF0aC5lZHU%3d&utm_source=ClickDimensions&utm_medium=email&utm_campaign=201605%20Email%20-%207%20must-read%20CRISPR%20screening%20papers&urlid=1

Immuno-Oncology Platform at Horizon Discovery

  • They have leveraged our cutting-edge technologies to build a ground-breaking platform for Immuno-Oncology drug discovery.
  • Their collection of high throughput assays allows you to rapidly identify single agents or combinations that affect T cell and natural killer (NK) cell function.

 

Gene-Editing in Immune Cells

Gene-editing in immune cells for your Immuno-Oncology project is now even faster than before. Leveraging the latest genome engineering tools, including CRISPR-Cas9, we’re able to precisely edit over 10 pre-characterized T-cell, B-cell, and monocytes lines.

CLICK HERE TO LEARN MORE

Gene-Editing for Immuno-Oncology

Power Your Immune-Oncology Projects with Custom Engineered Immune Cells

Gene-editing in immune cell lines can be challenging due to low targeting efficiency and difficulties in single cell derivation of suspension cells. Horizon has validated 10+ immune cell lines including THP-1, Jurkat and NALM-6 cells for gene-editing projects. CRISPR, rAAV and ZFN gene-editing technologies are available depending on project requirements.

Take advantage of the largest panel of pre-characterized immune cell lines available and benefit from Horizon’s exceptional knowhow and experience in completing over 2,000 gene-editing projects.

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SOURCE

https://www.horizondiscovery.com/research-services/immuno-oncology

Gene-Editing for Immuno-Oncology Offering

  • Modifications: Knockin, knockout (point mutations, tags and reporter genes)
  • Technologies: CRISPR, rAAV or ZFN depending on project requirements
  • Project types: Standard or Premium
  • Estimated timeline: 13-32 weeks
  • Deliverables: Isogenic cell line pair and a summary report

Cell Lines Including

Cell line Species Cell Type Disease

See Table in the Source, below

SOURCE

https://www.horizondiscovery.com/research-services/immuno-oncology/gene-editing-for-immuno-oncology

Scientific Literature

https://www.horizondiscovery.com/resources/scientific-literature

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Exosomes – History and Promise

Reporter: Aviva Lev-Ari, PhD, RN
It was discovered some time ago that eukaryotic cells regularly secrete such structures as microvesicles, macromolecular complexes, and small molecules into their ambient environment. Exosomes are one of the types of natural nanoparticles (or nanovesicles) that have shown promise in many areas of research, diagnostics and therapy. They are small lipid membrane vesicles (30-120 nm) generated by fusion of cytoplasmic endosomal multivesicular bodies within the cell surface. Exosomes are found throughout the body in such fluids as blood, saliva, urine, and breast milk. Furthermore, all types of cells secrete them in in vitro culture. It is believed that they have many natural functions, including acting as transporters of nucleic acids (mostly RNA), cytosolic proteins and metabolites to many cells, tissues or organs throughout the body. Much remains to be understood regarding how they are formed, as well as of their targeting and ultimate physiological activity. But many don’t realize that some activities have been rather thoroughly demonstrated─ such as their function in some sort of either local or more systemic intercellular communication.

Exosomes as Tools

General interest in exosomes is now growing for many reasons. One is because of the observation of their natural activity with antigen-presenting cells and in immune responses in the body. Their potential as very powerful biomedical tools of both diagnostic and therapeutic value is now being more widely reported. Applications described include using them as immunotherapeutic reagents, vectors of engineered genetic constructs, and vaccine particles. They’ve also been described as tools in the diagnosis or prognosis of a wide variety of disorders, such as cancer and neurodegenerative diseases. Also, their potential in tissue-level microcommunication is driving interest in such therapeutic activities as cardiac repair following heart attacks. Their potential as biomarkers is being explored because their content has been described as a “fingerprint” of differentiation or signaling or regulation status of the cell generating them. For example, by monitoring the exosomes secreted by transplanted cells, one may be able to predict the status or potentially even the outcome of cell therapy procedures. Clinical trials are in progress for exosomes in many therapeutic functions, for many indications. One example is using dendritic cell-derived exosomes to initiate immune response to cancers.

Exosome Manufacturing

Exosome product manufacturing involves many distinct areas of study. First of all, we are interested in their efficient and robust generation at a sufficient scale. Also, because they are found in such raw materials as animal serum, avoiding process-related contaminants is a concern. Finally, a variety of means of separating them from other types of extracellular vesicles and cell debris is under study. As exosomes are being examined in so many applications, their production involves many distinct platforms and concerns. First of all, an appropriate and effective culture mode is required for any cell line that is specifically required by the application. Also, one must consider the quality systems and regulatory status of the materials and manufacturing environment for the particular product addressed. Finally, a robust process must be described for the scale and duration of production demanded. As things exist now, their production can be described as
1) the at-scale expansion and culture of the parent cell-line,
2) the collection or harvest of the culture media containing the secreted exosomes, and
3) the isolation or purification of the desired exosomes from not only other macrovesicles, macromolecular complexes, and small molecules, but from such other process contaminants as cellular debris and culture media components.

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