Search

Tumor molecular profiling of single gene-variant (‘first-order’) genomic alterations informs potential therapeutic approaches. Interactions between such first-order events and global molecular features (for example, mutational signatures) are increasingly associated with clinical outcomes, but these ‘second-order’ alterations are not yet accounted for in clinical interpretation algorithms and knowledge bases. We introduce the Molecular Oncology Almanac (MOAlmanac), a paired clinical interpretation algorithm and knowledge base to enable integrative interpretation of multimodal genomic data for point-of-care decision making and translational-hypothesis generation. We benchmarked MOAlmanac to a first-order interpretation method across multiple retrospective cohorts and observed an increased number of clinical hypotheses from evaluation of molecular features and profile-to-cell line matchmaking. When applied to a prospective precision oncology trial cohort, MOAlmanac nominated a median of two therapies per patient and identified therapeutic strategies administered in 47% of patients. Overall, we present an open-source computational method for integrative clinical interpretation of individualized molecular profiles. Van Allen and colleagues develop a data-integration framework with an underlying knowledge base supporting clinical decision making and also serving as a hypothesis-generating platform, which the authors benchmark and validate across several retrospective cohorts and a prospective precision oncology trial.

When ordered by discriminant, it is known that about 83% of quartic fields over Q have associated Galois group S_4, while the remaining 17% have Galois group D_4. We study these proportions over a general number field F. We find that asymptotically 100% of quadratic number fields have more D_4 extensions than S_4 and that the ratio between the number of D_4 and S_4 quartic extensions is biased arbitrarily in favor of D_4 extensions. Under GRH, we give a lower bound that holds for general number fields., Fixed a typo with Theorem 1.3 that is present in the published version of the paper. The main results remain unchanged

We generalize Mertens' product theorem to Chebotarev sets of prime ideals in Galois extensions of number fields. Using work of Rosen, we extend an argument of Williams from cyclotomic extensions to this more general case. Additionally, we compute these products for Cheboratev sets in abelian extensions, $S_3$ sextic extensions, and sets of primes represented by some quadratic forms., 19 pages, comments welcome

Individual tumor molecular profiling is routinely used to detect single gene-variant (“first-order”) genomic alterations that may inform therapeutic actions -- for instance, a tumor with aBRAFp.V600E variant might be considered for RAF/MEK inhibitor therapy. Interactions between such first-order events (e.g., somatic-germline) and global molecular features (e.g. mutational signatures) are increasingly associated with clinical outcomes, but these “second order” alterations are not yet generally accounted for in clinical interpretation algorithms and knowledge bases. Here, we introduce the Molecular Oncology Almanac (MOAlmanac), a clinical interpretation algorithm paired with a novel underlying knowledge base to enable integrative interpretation of genomic and transcriptional cancer data for point-of-care treatment decision-making and translational hypothesis generation. We compared MOAlmanac to first-order interpretation methodology in multiple retrospective patient cohorts and observed that the inclusion of preclinical and inferential evidence as well as second-order molecular features increased the number of nominated clinical hypotheses. MOAlmanac also performed matchmaking between patient molecular profiles and cancer cell lines to further expand individualized clinical actionability. When applied to a prospective precision oncology trial cohort, MOAlmanac nominated a median of two therapies per patient and identified therapeutic strategies administered in 46% of patient profiles. Overall, we present a novel computational method to perform integrative clinical interpretation of individualized molecular profiles. MOAlmanc increases clinical actionability over conventional approaches by considering second-order molecular features and additional evidence sources, and is available as an open-source framework.

Tumor mutational burden correlates with response to immune checkpoint blockade in multiple solid tumors, though in microsatellite stable tumors this association is weak and of limited clinical utility. Here, we uniformly analyzed whole exome sequencing (WES) of 249 tumors and matched normal tissue from patients with clinically annotated outcomes to immune checkpoint therapy, including radiographic response, across multiple cancer types to examine additional tumor genomic features that contribute to selective response. Our analyses identified genomic correlates of response beyond mutational burden, including somatic events in individual driver genes, certain global mutational signatures, and specific HLA-restricted neoantigens. However, these features were often inter-related, highlighting the complexity of identifying genetic driver events that generate an immunoresponsive tumor environment. This study lays a path forward in analyzing large clinical cohorts in an integrated and multifaceted manner to enhance our ability to discover clinically meaningful predictive features of response to immune checkpoint blockade.

BACKGROUND. Understanding the integrated immunogenomic landscape of advanced prostate cancer (APC) could impact stratified treatment selection. METHODS. Defective mismatch repair (dMMR) status was determined by either loss of mismatch repair protein expression on IHC or microsatellite instability (MSI) by PCR in 127 APC biopsies from 124 patients (Royal Marsden [RMH] cohort); MSI by targeted panel next-generation sequencing (MSINGS) was then evaluated in the same cohort and in 254 APC samples from the Stand Up To Cancer/Prostate Cancer Foundation (SU2C/PCF). Whole exome sequencing (WES) data from this latter cohort were analyzed for pathogenic MMR gene variants, mutational load, and mutational signatures. Transcriptomic data, available for 168 samples, was also performed. RESULTS. Overall, 8.1% of patients in the RMH cohort had some evidence of dMMR, which associated with decreased overall survival. Higher MSINGS scores associated with dMMR, and these APCs were enriched for higher T cell infiltration and PD-L1 protein expression. Exome MSINGS scores strongly correlated with targeted panel MSINGS scores (r = 0.73, P < 0.0001), and higher MSINGS scores associated with dMMR mutational signatures in APC exomes. dMMR mutational signatures also associated with MMR gene mutations and increased immune cell, immune checkpoint, and T cell–associated transcripts. APC with dMMR mutational signatures overexpressed a variety of immune transcripts, including CD200R1, BTLA, PD-L1, PD-L2, ADORA2A, PIK3CG, and TIGIT. CONCLUSION. These data could impact immune target selection, combination therapeutic strategy selection, and selection of predictive biomarkers for immunotherapy in APC. FUNDING. We acknowledge funding support from Movember, Prostate Cancer UK, The Prostate Cancer Foundation, SU2C, and Cancer Research UK.

Approximately 20,000 new cases of muscle-invasive bladder cancer (MIBC), a localized but potentially lethal stage of disease, are diagnosed annually in the US alone. Standard-of-care therapy for MIBC includes neoadjuvant cisplatin-based chemotherapy followed by definitive bladder resection. In prior work, we identified and validated genomic alterations in DNA repair genes such as ERCC2, which predict response to cisplatin-based chemotherapy (Van Allen et al. Cancer Discovery 2014; Liu et al. JAMA Oncology 2016). However, the majority of patients have disease resistant to chemotherapy with a poor prognosis of 50x, and a tumor purity minimum threshold of 10%, we had data from 30 trios available for analysis. We hypothesized that DNA-damaging chemotherapy may lead to increased mutational load in the post-treatment tumor. However, we observed that while some tumors gained mutations, others lost mutations, with no overall change (mean change = -17.3 mutations, paired t-test p = 0.20) in total mutational load. We found that clonal mutations (found in all tumor cells) were virtually unchanged from matched pre- to post-treatment tumors. In contrast, subclonal mutations (found only in a subset of tumor cells) were private to pre- and post-treatment tumors. These pre- and post-treatment mutation differences may reflect tumor sampling heterogeneity (i.e. taking from different parts of the tumor), but may be also due to selection pressure from therapy (e.g. loss of subclones) and cisplatin-induced mutations.To investigate the latter possibility, we adapted a non-negative matrix factorization (NMF) approach (Lee and Seung Nature 1999) to discover mutational signatures (Alexandrov et al Nature 2013) in the mutations unique to post-treatment tumors. Along with signatures known to be operant in bladder cancers, we discovered a mutational signature dissimilar to any other previously described mutational signature which accounted for ~15% of post-treatment mutations. This signature exhibited a transcriptional strand bias consistent with known mechanisms of cisplatin-induced DNA damage and repair, and was enriched in subclonal mutations consistent with the relatively short time frame between cisplatin exposure and cystectomy. This signature also exhibited similar activity to a cisplatin-induced mutational signature derived in a preclinical model (DT40) exposed to cisplatin therapy (Pearson rho = 0.95, empiric p = 0.004). Finally, we were able to validate this signature in a separate cohort of pre- and post-cisplatin treated bladder cancers (Faltas et al Nature Genetics 2016). We further hypothesized that the degree of tumor heterogeneity itself may be a prognostic factor. We calculated two different measures of intratumor heterogeneity: (1) the proportion of mutations in each tumor that was subclonal; and (2) the number of unique subclones in each tumor, and examined the association of survival with these measures of intratumor heterogeneity using Cox survival analyses. We found that overall survival was associated with heterogeneity, with a 6.6% increase in mortality rate for each 10% increase in post-treatment proportion of subclonal mutations (p=0.013), and 64% increase in mortality rate for each additional inferred subclone (p=0.02). Tumor heterogeneity continued to be associated with survival after adjusting for clinical covariates (p=0.03, p=0.014, respectively).Finally, we analyzed our tumors for genomic alterations associated with resistance. While we did not discover highly recurrent post-treatment mutations in specific genes, we found drivers of cell cycle progression (E2F3 amplification, JUN amplification), biallelic loss of FBXW7 (regulator of protein degradation of multiple onco-proteins including c-MYC, Notch, Cyclin E, and c-JUN), and focal amplification of PD-L1/2 in individual post-treatment resistant tumors.In this study, we found that cisplatin-based chemotherapy did not induce a large increase in the number of mutations. Thus, while there is good empiric data for the efficacy of combination of chemotherapy and immune checkpoint inhibition in specific tumor type and clinical settings (e.g. platinum-doublet therapy + ICB in first-line therapy of non-small cell lung cancer (NSCLC)), our data suggests that alternative mechanisms other than increased neoantigen burden are responsible. We discovered a cisplatin-induced mutational signature in post-treatment tumors which has subsequently been found in other cisplatin-treated tumors (e.g. NSCLC and ovarian cancer). Interestingly, the proportion of mutations inferred to be cisplatin-induced was quite different between resistant tumors, and an area for further inquiry is whether these differences could be associated with different mechanisms of resistance (e.g. upregulation of efflux pumps vs. anti-apoptotic adaptations). Tumor heterogeneity, which has been associated with worse outcomes and resistance in multiple contexts, was prognostic for survival in our cohort, suggesting that this may be clinically useful as part of a prognostic biomarker. We discovered additional association of drivers of cell-cycle progression with resistance, and further identified acquisition of a focal amplification in a region containing PD-L1/PD-L2, suggesting a potential biomarker for a subset of bladder cancers for response to immune checkpoint blockade. Broadly, this study represents the development of algorithms to dissect genomic features associated with survival and resistance in a carefully curated cohort of matched patient tumors within a specific clinical context. These types of approaches can be applied across tumor types, therapies, and clinical contexts to shed light onto biological mechanisms underpinning response and resistance and inform the development of biomarkers to guide clinical management. Citation Format: David Liu, Philip Abbosh, Daniel Keliher, Brendan Reardon, Diana Miao, Kent Mouw, Amaro Weiner-Taylor, Stephanie Wankowicz, Garam Han, Min-Yuen Teo, Catharine Cipolla, Jaegil Kim, Gopa Iyer, Hikmat Al-Ahmadie, Essel Dulaimi, David Y.T. Chen, R. Katherine Alpaugh, Jean Hoffman-Censits, Levi A. Garraway, Gad Getz, Scott L. Carter, Joaquim Bellmunt, Elizabeth Plimack, Jonathan E. Rosenberg, Eliezer M. Van Allen. Dissecting genomic correlates of response and resistance to chemotherapy in bladder cancer through clinical computational oncology [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr SY05-03.

Background: Mutational signatures of exposure to DNA damaging agents such as UV irradiation, tobacco, and temozolomide have been described; however a cisplatin mutational signature has not been identified. We analyzed mutational changes in matched pre- and post-cisplatin based neoadjuvant chemotherapy (NAC)-treated muscle-invasive bladder cancer (MIBC) samples to identify a cisplatin mutational signature. Methods: Whole exome sequencing and mutation calling was performed on matched germline and pre- and post-cisplatin based NAC (MVAC and GC) tumor samples from 46 MIBC patients with gross residual disease (≥ pT2) at cystectomy. After quality control, samples from 30 patients were analyzed. For each tumor, we characterized single nucleotide mutations (C>A, C>T, C>G, T>A, T>C, T>G) within a tri-nucleotide context, and used an optimized non-negative matrix factorization (NMF) to discover signatures of mutational processes in pre-NAC tumors, post-NAC tumors, and among mutations unique to the post-NAC samples. We compared our discovered signatures to previously described human mutational signatures and to a signature of cisplatin exposure in a DT40 (chicken lymphoblast) cell line (normalized for a human exome context). We also performed strand asymmetry analysis to search for evidence of transcription-coupled repair. Results: In both pre and post-NAC tumors we identified mutational signatures matching those attributed to APOBEC activity and nucleotide excision repair pathway defects, consistent with past studies in MIBC. In post-NAC tumors we identified an additional novel mutational signature, with modest overall cosine similarity (0.61) to the DT40 cisplatin signature, but similar C>A and T>A motifs. There was a strong correlation in inferred mutational activity (Pearson R = 0.98) when we replaced the novel signature with the DT40 signature in the post-NAC tumors, which was highly unlikely to be due to chance (p < 0.001, empiric null distribution). Further, we found evidence of transcriptional strand bias in C>A (p = 0.00025) and T>A (p = 4.2e-06) motifs with depletion of coding strand mutations, consistent with transcription coupled repair of platinum crosslinks at GpG and ApG motifs in the non-coding strand. Finally, we were able to rediscover the novel signature when limiting analyses to mutations unique to post-NAC tumors, consistent with mutational activity during chemotherapy. Conclusions: Analysis of matched pre- and post- cisplatin treated MIBC identified a novel signature in post-cisplatin treated samples that (1) has mutational activity similar to a preclinical cisplatin mutational signature; (2) has a transcription strand bias consistent with known repair characteristics of platinum-induced DNA damage; and (3) arises in tumors following cisplatin chemotherapy. This may represent a cisplatin-induced mutational signature in human tumors. Citation Format: David Liu, Daniel Keliher, Philip Abbosh, Kent Mouw, Diana Miao, Mariel Boyd, Jean Hoffman-Censits, Gopa Iyer, Sara Tolaney, Jaegil Kim, Gad Getz, Scott Carter, Joaquim Bellmunt, Elizabeth R. Plimack, Jonathan E. Rosenberg, Eliezer M. Van Allen. Analysis of matched pre and post cisplatin-treated muscle-invasive bladder cancer reveals a candidate cisplatin mutational signature [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2918. doi:10.1158/1538-7445.AM2017-2918

Introduction: Immune checkpoint therapies benefit a subset of patients with metastatic melanoma, but ability to predict clinical outcomes is limited. This meta-analysis of genomic predictors of outcomes to aPD1 and aCTLA4 in melanoma combines 220 sequenced tumors from 3 published cohorts, aiming to validate existing hypotheses regarding response to immune checkpoint therapies and discover new relationships with greater power. Methods: Genomic data and clinical annotations from published cohorts were analyzed with standardized pipelines for somatic variant calling, mutational signature deconvolution, and neoantigen prediction. Patients were stratified into clinical benefit (CB) and no clinical benefit (NCB) as described in Van Allen et al. 2015. Analyses were repeated using two other published response metrics (CB=PFS>6 months; CB=CR or PR). Results: Nonsynonymous mutational burden was significantly higher in CB vs. NCB using all 3 response metrics, though significance was less pronounced using PFS alone (pC substitutions (S5), mismatch repair (S6), alkylating agents (S11), UV (S7), and T>G substitutions (S17). The proportion of mutations in S7 or S11 was positively correlated with mutational burden (Spearman’s rho=0.66), while S5 and S1 were anti-correlated (rho=-0.62). In a multivariate logistic model, S7 and S11 activity were independent predictors of CB adjusting for mutational load (p1/2 of mutations in S7 or S11, compared to only 36/71 of low-mutation NCB (p500 genes were mutated more frequently in either CB or NCB (p Conclusions: In this meta-analysis of 220 patients, harmonized clinical and whole exome analysis confirmed that mutational burden correlates with CB from aPD1 and aCTLA4 therapy, while mutational signatures and alterations in specific genes potentially provide additional predictive power. Citation Format: Diana Miao, David Liu, Daniel Keliher, Sachet Shukla, Bastian Schilling, Claire Margolis, Alicia Smart, Levi Garraway, Stephen Hodi, Dirk Schadendorf, Eliezer M. Van Allen. Meta-analysis of genomic predictors of response to immune checkpoint therapy in metastatic melanoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 571. doi:10.1158/1538-7445.AM2017-571

4512 Background: Biomarkers of survival and resistance in chemotherapy-resistant muscle-invasive bladder cancer (MIBC) are not well-characterized, but may inform management in this setting. Methods: Matched pre- and post-neoadjuvant cisplatin-based chemotherapy (NAC) tumor samples were obtained from 30 MIBC patients with gross residual disease (≥ pT2) at cystectomy, followed by whole exome sequencing of these “trios” (pre- and post-NAC tumor with matched germline samples). Phylogenetic analysis of matched tumor samples was performed to identify subclones, their associated mutations, and the corresponding enrichment in post-treatment tumors. Intratumoral heterogeneity was assessed by the proportion of mutations that were subclonal; the number of inferred subclones; and associated with overall survival using a Cox Proportional Hazards model. Results: Increased proportion of subclonal mutations in post-treatment tumors was associated with worse overall survival (HRR 1.86 [95% CI 1.12-3.06], p = 0.02), whereas pre-treatment proportion of subclonal mutations was only borderline statistically significant (HRR 1.48 [95% CI 0.99-2.20], p = 0.052). The total number of inferred tumor subclones in pre- or post-treatment tumor (or both) was associated with overall survival (HRR 1.60 [95% CI 1.05-2.43], p = 0.03), interpreted as a 60% increase in death rate per additional inferred subclone. While no single gene was statistically significantly enriched for new alterations in the post-chemotherapy resistant samples, we observed new post-treatment amplifications in cell-cycle genes ( E2F3, c-JUN), biallelic events in cell-cycle regulators ( FBXW7), and amplification of immune checkpoint genes ( PDL1/2). Conclusions: These results suggest that intratumoral heterogeneity (particularly post-therapy) predicts survival in a chemotherapy-resistant cohort. Further, alterations in cell cycle regulation may contribute to the mechanism of chemotherapy resistance. Finally, we observe evidence of immune checkpoint gene amplification post-treatment, suggesting that testing immune checkpoint blockade during NAC or, in high risk patients, following NAC may be warranted.