Tumor Sequencing Data Research Articles

Abstract P160: MatchMiner: An open-source platform for cancer precision medicine

Abstract With the advent of next generation sequencing in cancer care, patients’ tumors can be genomically profiled and specific genetic alterations can be targeted with precision medicine drugs. However, the abundance of patient sequencing data coupled with complex clinical trial eligibility has made it challenging to match patients to precision medicine trials. To facilitate interpretation of complex tumor sequencing data and clinical trial genomic eligibility criteria, we developed MatchMiner, an open-source platform to computationally match cancer patients to precision medicine clinical trials. MatchMiner has several modes of clinical use: (1) patient-centric, where clinicians look up trial matches for their patient, (2) trial-centric, where clinical trial investigators identify patients for their clinical trials, and (3) trial search, where clinicians identify available trials based on any criteria, including external genomic reports. To support users in all three modes, MatchMiner also displays full genomic reports for patients and detailed trial information in user-friendly formats. MatchMiner trial matching is performed via the MatchEngine, an algorithm that computes matches based on patient genomic and clinical data and trial eligibility criteria. The MatchEngine accepts many different data inputs for patient-trial matching, and is easily customized to work with data available at any institution. At Dana-Farber Cancer Institute (DFCI), MatchMiner supports the following data: 1) patient-specific genomic sequencing data, including mutations, copy number alterations, structural variants, tumor mutational burden and mutational signatures including mismatch repair deficiency or microsatellite instability, 2) patient-specific clinical data, including primary cancer type, gender, age, and vital status, and 3) trial eligibility criteria including genomic targets, cancer type, and age. Unique to MatchMiner, each trial’s eligibility criteria is encoded in clinical trial markup language (CTML), a structured format that encodes detailed information about a trial and utilizes boolean logic to encode inclusion and exclusion criteria. Although MatchMiner has been operational at DFCI since early 2017, its impact on patient care has not yet been extensively studied. Thus far, MatchMiner has facilitated 181 precision medicine trial consents (MatchMiner consents, MMC) for 159 patients. To quantify MatchMiner’s impact on trial consent, we retrospectively measured time from genomic sequencing report date to trial consent date for a subset of the 181 MMC (166 MMC). We compared time to trial consent date for the 166 MMC to a group of 353 consents for the same trials not facilitated by MatchMiner (non-MatchMiner consents, non-MMC). MMC consented to trials 22% faster (P=0.004, median=195 days, IQR=85-34) than non-MMC (median=250 days; IQR=99-491). Thus, clinical use of MatchMiner decreased time to enroll in a precision medicine study, and suggests that use of precision medicine trial matching tools such as MatchMiner are important for the future of patient care. Citation Format: Harry Klein, Tali Mazor, Priti Kumari, James Lindsay, Andrea Ovalle, Pavel Trukhanov, Joyce Yu, Michael Hassett, Ethan Cerami. MatchMiner: An open-source platform for cancer precision medicine [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2021 Oct 7-10. Philadelphia (PA): AACR; Mol Cancer Ther 2021;20(12 Suppl):Abstract nr P160.

Quantifying smoking exposure, genomic correlates, and related risk of treatment failure in p16+ squamous cell carcinoma of the oropharynx.

ObjectivesHPV‐associated (p16+) squamous cell carcinoma of the oropharynx (OPSCC) has improved survival as compared to HPV‐negative, smoking‐associated disease. Intermediate outcomes have been noted in patients with p16+ tumors and smoking exposure. However, the extent of smoking exposure required for outcomes to decrease has not been delineated due to low failure rates and poor availability of quantitative tobacco smoke exposure data. Our primary objective is to characterize the dose‐dependent relationship between recurrence‐free survival (RFS) and tobacco smoke exposure in p16+ OPSCC and secondarily correlate tobacco smoke exposure with genomic alterations.MethodsSingle institution chart review was performed of patients diagnosed with p16+ OPSCC from 2003 to 2015. Patients were excluded if staging, treatment details, recurrence status, or smoking exposure in pack‐years were not available. Two hundred and forty‐four patients were included.ResultsPatients with 25 pack‐years or greater smoking history exhibited a dose‐dependent decrease in RFS compared to never smokers. This was robust to multivariate analysis for including staging and demographic factors. Forty‐three patients with available targeted tumor sequencing data were identified. A strong trend was observed for increased C to A transversion mutations above 25 pack‐years, which are known to be associated with exposure to tobacco smoke. Similarly, the proportion of COSMIC Signature 4 mutations were also found to be more common in patients with more than 25 pack‐years of smoking exposure.ConclusionEvidence‐based smoking exposure thresholds are needed to define inclusion criteria for trials of de‐escalation therapy for p16+ OPSCC. Patients with smoking exposure greater than 20 pack‐years have increased risk of recurrence and a distinct pattern of genomic alterations. Further studies are needed to delineate the potential consequences of mild smoking exposure. Smoking‐related mutational signatures may hold potential for biomarker development in p16+ OPSCC.Level of Evidence2B

Germline Sequencing Improves Tumor-Only Sequencing Interpretation in a Precision Genomic Study of Patients With Pediatric Solid Tumor

PURPOSEMolecular tumor profiling is becoming a routine part of clinical cancer care, typically involving tumor-only panel testing without matched germline. We hypothesized that integrated germline sequencing could improve clinical interpretation and enhance the identification of germline variants with significant hereditary risks.MATERIALS AND METHODSTumors from pediatric patients with high-risk, extracranial solid malignancies were sequenced with a targeted panel of cancer-associated genes. Later, germline DNA was analyzed for a subset of these genes. We performed a post hoc analysis to identify how an integrated analysis of tumor and germline data would improve clinical interpretation.RESULTSOne hundred sixty participants with both tumor-only and germline sequencing reports were eligible for this analysis. Germline sequencing identified 38 pathogenic or likely pathogenic variants among 35 (22%) patients. Twenty-five (66%) of these were included in the tumor sequencing report. The remaining germline pathogenic or likely pathogenic variants were single-nucleotide variants filtered out of tumor-only analysis because of population frequency or copy-number variation masked by additional copy-number changes in the tumor. In tumor-only sequencing, 308 of 434 (71%) single-nucleotide variants reported were present in the germline, including 31% with suggested clinical utility. Finally, we provide further evidence that the variant allele fraction from tumor-only sequencing is insufficient to differentiate somatic from germline events.CONCLUSIONA paired approach to analyzing tumor and germline sequencing data would be expected to improve the efficiency and accuracy of distinguishing somatic mutations and germline variants, thereby facilitating the process of variant curation and therapeutic interpretation for somatic reports, as well as the identification of variants associated with germline cancer predisposition.

The Intratumor Microbiota Signatures Associate With Subtype, Tumor Stage, and Survival Status of Esophageal Carcinoma.

Altered human microbiome characteristic has been linked with esophageal carcinoma (ESCA), analysis of microbial profiling directly derived from ESCA tumor tissue is beneficial for studying the microbial functions in tumorigenesis and development of ESCA. In this study, we identified the intratumor microbiome signature and investigated the correlation between microbes and clinical characteristics of patients with ESCA, on the basis of data and information obtained from The Cancer Microbiome Atlas (TCMA) and The Cancer Genome Atlas (TCGA) databases. A total of 82 samples were analyzed for microbial composition at various taxonomic levels, including 40 tumor samples of esophageal squamous cell carcinoma (ESCC), 20 tumor samples of esophageal adenocarcinoma (EAD), and 22 adjacent normal samples. The results showed that the relative abundance of several microbes changed in tumors compared to their paired normal tissues, such as Firmicutes increased significantly while Proteobacteria decreased in tumor samples. We also identified a microbial signature composed of ten microbes that may help in the classification of ESCC and EAD, the two subtypes of ESCA. Correlation analysis demonstrated that compositions of microbes Fusobacteria/Fusobacteriia/Fusobacteriales, Lactobacillales/Lactobacillaceae/Lactobacillus, Clostridia/Clostridiales, Proteobacteria, and Negativicutes were correlated with the clinical characteristics of ESCA patients. In summary, this study supports the feasibility of detecting intratumor microbial composition derived from tumor sequencing data, and it provides novel insights into the roles of microbiota in tumors. Ultimately, as the second genome of human body, microbiome signature analysis may help to add more information to the blueprint of human biology.

Comprehensive Pan-Cancer Analysis of Heat Shock Protein 110, 90, 70, and 60 Families.

Background: Here we carried out a panoramic analysis of the expression and prognosis of HSP110, HSP90, HSP70, and HSP60 families in 33 types of cancer, with the aim of deepening the systematic understanding of heat shock proteins (HSPs) in cancer. Materials and Methods: Next-generation sequencing data of multiple tumors were downloaded from TCGA, CCLE and Oncomine databases. RStudio 3.6.1 was used to analyze HSP110, HSP90, HSP70 and HSP60 families based on their expression in 33 types of cancer. The validations in vivo (stomach adenocarcinoma and colon adenocarcinoma tissues) were performed by qRT-PCR. Results: HSPs were differentially expressed in different cancers. The results revealed mainly positive correlations among the expressions of HSPs in different cancers. Expressions of HSP family members were generally associated with poor prognosis in respiratory, digestive, urinary and reproductive system tumors and associated with good prognosis in cholangiocarcinoma, pheochromocytoma and paraganglioma. TCGA mutation analysis showed that HSP gene mutation rate in cancers was 0–23%. CCLE mutation analysis indicated that HSP gene mutation rate in 828 cell lines from 15 tumors was 0–17%. CNV analysis revealed that HSPs have different degrees of gene amplifications and deletions in cancers. Gene mutations of 15 HSPs influenced their protein expressions in different cancers. Copy number amplifications and deletions of 22 HSPs also impacted protein expression levels in pan-cancer. HSP gene mutation was generally a poor prognosis factor in cancers, except for uterine corpus endometrial carcinoma. CNVs in 14 HSPs showed varying influences on survival status in different cancers. HSPs may be involved in the activation and inhibition of multiple cancer-related pathways. HSP expressions were closely correlated with 22 immune cell infiltrations in different cancers. The qRT-PCR validation results in vivo showed that HSPA2 was down-regulated in stomach adenocarcinoma and colon adenocarcinoma; HSPA7 and HSPA1A also were down-regulated in colon adenocarcinoma. HSPA2-HSPA7 (r = 0.031, p = 0.009) and HSPA1A-HSPA7 (r = 0.516, p < 0.001) were positive correlation in colon adenocarcinoma. Conclusion: These analysis and validation results show that HSP families play an important role in the occurrence and development of various tumors and are potential tumor diagnostic and prognostic biomarkers as well as anti-cancer therapeutic targets.

CloneSig can jointly infer intra-tumor heterogeneity and mutational signature activity in bulk tumor sequencing data

Systematic DNA sequencing of cancer samples has highlighted the importance of two aspects of cancer genomics: intra-tumor heterogeneity (ITH) and mutational processes. These two aspects may not always be independent, as different mutational processes could be involved in different stages or regions of the tumor, but existing computational approaches to study them largely ignore this potential dependency. Here, we present CloneSig, a computational method to jointly infer ITH and mutational processes in a tumor from bulk-sequencing data. Extensive simulations show that CloneSig outperforms current methods for ITH inference and detection of mutational processes when the distribution of mutational signatures changes between clones. Applied to a large cohort of 8,951 tumors with whole-exome sequencing data from The Cancer Genome Atlas, and on a pan-cancer dataset of 2,632 whole-genome sequencing tumor samples from the Pan-Cancer Analysis of Whole Genomes initiative, CloneSig obtains results overall coherent with previous studies.

RNA-seq profiling reveals PBMC RNA as a potential biomarker for hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors and has extremely high morbidity and mortality. Although many existing studies have focused on the identification of biomarkers, little information has been uncovered regarding the PBMC RNA profile of HCC. We attempted to create a profile throughout using expression of peripheral blood mononuclear cell (PBMC) RNA using RNA-seq technology and compared the transcriptome between HCC patients and healthy controls. Seventeen patients and 17 matched healthy controls were included in this study, and PBMC RNA was sequenced from all samples. Sequencing data were analyzed using bioinformatics tools, and quantitative reverse transcription PCR (qRT-PCR) was used for selected validation of DEGs. A total of 1,578 dysregulated genes were found in the PBMC samples, including 1,334 upregulated genes and 244 downregulated genes. GO enrichment and KEGG studies revealed that HCC is closely linked to differentially expressed genes (DEGs) implicated in the immune response. Expression of 6 selected genes (SELENBP1, SLC4A1, SLC26A8, HSPA8P4, CALM1, and RPL7p24) was confirmed by qRT-PCR, and higher sensitivity and specificity were obtained by ROC analysis of the 6 genes. CALM1 was found to gradually decrease as tumors enlarged. Nearly the opposite expression modes were obtained when compared to tumor sequencing data. Immune cell populations exhibited significant differences between HCC and controls. These findings suggest a potential biomarker for the diagnosis of HCC. This study provides new perspectives for liver cancer development and possible future successful clinical diagnosis.

Distinguishing linear and branched evolution given single-cell DNA sequencing data of tumors

BackgroundCancer arises from an evolutionary process where somatic mutations give rise to clonal expansions. Reconstructing this evolutionary process is useful for treatment decision-making as well as understanding evolutionary patterns across patients and cancer types. In particular, classifying a tumor’s evolutionary process as either linear or branched and understanding what cancer types and which patients have each of these trajectories could provide useful insights for both clinicians and researchers. While comprehensive cancer phylogeny inference from single-cell DNA sequencing data is challenging due to limitations with current sequencing technology and the complexity of the resulting problem, current data might provide sufficient signal to accurately classify a tumor’s evolutionary history as either linear or branched.ResultsWe introduce the Linear Perfect Phylogeny Flipping (LPPF) problem as a means of testing two alternative hypotheses for the pattern of evolution, which we prove to be NP-hard. We develop Phyolin, which uses constraint programming to solve the LPPF problem. Through both in silico experiments and real data application, we demonstrate the performance of our method, outperforming a competing machine learning approach.ConclusionPhyolin is an accurate, easy to use and fast method for classifying an evolutionary trajectory as linear or branched given a tumor’s single-cell DNA sequencing data.

Abstract 176: Detecting neoepitopes from tumor RNA sequencing datasets

Abstract Epitopes are peptides that present on the surface of the cell and can be recognized by immune cells to initiate the immune response. Identification of neoepitopes – tumor-specific, MHC-bound epitopes recognized specifically by T-cells – is valuable for predicting response to immunotherapies, including checkpoint blockade therapies. Tumors with more neoepitopes tend to be more responsive to immune checkpoint therapies compared to tumors with fewer neoepitopes. ProTECT is a previously published computational method that uses Illumina whole genome and transcriptome sequencing data from tumor and matched normal tissues to identify neoepitopes. Tumor and normal whole genome sequencing data are used to infer a patient's HLA haplotypes, as well as annotate variants as either somatic or germline. While whole genome sequencing is comprehensive, it is quite costly and not available for many samples. Here we adapt ProTECT to use only tumor RNA sequencing data and HLA haplotype information available to the clinician to identify neoepitopes in a tumor sample. Prior to running ProTECT, we use the computational tools Opossum and Platypus for variant calling instead of Radia (which is designed for variant calling using both RNA and DNA sequencing data as input). To determine which variants are somatic and therefore could represent tumor neoepitopes, variants found in RNA are compared to a panel of normals, for example the Genome Aggregation Database (gnomAD; containing variants from 125,748 exome sequences and 15,708 whole-genome sequences). With the resulting somatic variants and the HLA type, ProTECT proceeds as usual, with translation of variants into proteins, MHC:Peptide binding predictions and neoepitope ranking. We find that high quality neoepitopes are identifiable using an RNA-only approach, when genomic data is absent. Future work will validate the sensitivity of our method by benchmarking it against the original ProTECT predictions in the TCGA Prostate Adenocarcinoma cohort. Citation Format: Drew Thompson, Olena M. Vaske, Arjun Rao, Holly C. Beale. Detecting neoepitopes from tumor RNA sequencing datasets [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 176.

Abstract 1198: MatchMiner: An open-source computational platform that accelerates patient enrollment on to precision medicine trials

Abstract With the advent of next generation sequencing in cancer care, patients' tumors can be genomically profiled and specific genetic alterations can be targeted with precision medicine drugs. However, the abundance of patient sequencing data coupled with complex clinical trial eligibility has made it challenging to match patients to precision medicine trials. To facilitate interpretation of complex tumor sequencing data and clinical trial genomic eligibility criteria, we developed MatchMiner, an open-source platform to computationally match cancer patients to precision medicine clinical trials. MatchMiner supports two distinct workflows: (1) patient-centric mode, in which an oncologist can find clinical trial matches for a specific patient, and (2) trial-centric mode, in which a clinical trial investigator can identify and recruit patients for a specific trial. In MatchMiner at DFCI, there are currently 330+ precision medicine trials and genomic and genomic and clinical data from 39,000+ patients. Although MatchMiner has been operational at Dana-Farber Cancer Institute since early 2017, its impact on patient care has not yet been extensively studied. In this study, we analyzed temporal trends of 170 MatchMiner-driven trial enrollments. We compared these 170 MatchMiner-driven trial enrollments to non-MatchMiner-driven trial enrollments to determine how MatchMiner has impacted patient enrollments. To compare MatchMiner-driven trial enrollments to non-MatchMiner-driven enrollments, we limited the non-MatchMiner group by choosing patients who enrolled on the same trials. We also ensured that all patients in both enrollment groups had a genomic report present in MatchMiner before their consent date. We then analyzed temporal trends between genomic report dates, patient consent and on-study dates, and patient views in MatchMiner. MatchMiner-driven enrollments had a significant decrease in time from genomic report date to consent date compared to non-MatchMiner-driven enrollments. Thus, clinical use of MatchMiner decreased time to enroll in a precision medicine study, and suggests that use of precision medicine trial matching tools such as MatchMiner are important for the future of patient care. The MatchMiner open-source software package is available through GitHub (https://github.com/dfci/matchminer). We are committed to supporting MatchMiner as an open-source software; to our knowledge, at least five cancer centers are implementing MatchMiner. Citation Format: Harry Klein, Tali Mazor, Priti Kumari, James Lindsay, Andrea Ovalle, Ethan Siegel, Pavel Trukhanov, Joyce Yu, Michael Hassett, Ethan Cerami. MatchMiner: An open-source computational platform that accelerates patient enrollment on to precision medicine trials [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1198.

Abstract 642: Genomes for Kids: Comprehensive DNA and RNA sequencing defining the scope of actionable mutations in pediatric cancer

Abstract Clinical genomic studies of pediatric cancer have primarily focused on specific tumor types or high-risk disease. In the Genomes for Kids study (NCT02530658) we used a three-platform sequencing approach, including whole genome (WGS), whole exome (WES) and RNA sequencing, to examine tumor and paired germline genomes from prospectively identified children with cancer. The goal of the study was to assess the potential of comprehensive next generation sequencing to elucidate the molecular mechanisms underlying tumor formation and investigate the potential of this information to influence clinical decision-making.The cohort, with a median age of 6 yrs, range 0 - 26 yrs, included 301 patients with newly diagnosed (85%) or relapsed/refractory (15%) cancers, unselected for tumor type or stage. Patients with hematologic malignancies accounted for 41% of cases, 31% had CNS tumors, and 28% had other non-CNS solid tumors. This cohort also included 18 patients with very rare tumor types, defined here as occurring in less than 2 cases per million person per year.Two hundred fifty three patients (84%) had sufficient tumor for three-platform sequencing and all 301 had adequate paired germline samples. Following analysis, 86% of patients harbored diagnostic (53%), prognostic (57%), therapeutically relevant (25%), and/or cancer predisposing (18%) variants. The inclusion of WGS enabled detection of oncogenic gene fusions, as well as 22 cases in which oncogenes were activated through enhancer hijacking, a particularly frequent occurrence in hematologic malignancies. In addition, WGS effectively detected clinically relevant small intragenic deletions (15% of tumors) and a variety of mutational signatures, which were not detectable through analysis of whole exome data. Evaluation of 56 pathogenic germline variants in the context of paired tumor sequence data helped establish the disease relevance of several genes that are not typically associated with the cancer type in question, providing critical insights on a case-by-case basis. Examples include a pathogenic germline variant in MUTYH in a patient with retinoblastoma whose tumor exhibited a mutation signature associated with reactive oxygen species indicative of loss of MUTYH function; and conversely, a likely pathogenic variant in PMS2 in a rare brain cancer, which did not exhibit a mutation signature associated with microsatellite instability. This study successfully demonstrated the power of this three-platform approach to interrogate and interpret the full range of genomic variants across newly diagnosed as well as relapsed/refractory pediatric cancers. As a result of these findings, we have incorporated this three-platform approach into our routine real-time clinical service at St. Jude Children's Hospital. Citation Format: David A. Wheeler, Scott Newman, Joy Nakitandwe, Chimene A. Kesserwan, Elizabeth M. Azzato, Michael C. Rusch, Sheila Shurtleff, Armita Bahrami, Brent Orr, Jeffery M. Klco, Dale J. Hedges, Kayla V. Hamilton, Scott G. Foy, Michael N. Edmonson, Andrew Thrasher, Jiali Gu, Lynn W. Harrison, Lu Wang, Roya Mostafavi, Manish Kubal, Jamie Maciaszek, Michael Clay, Annastasia Ouma, Antonina Silkov, Yanling Liu, Zhaojie Zhang, Yu Liu, Samuel W. Brady, Xin Zhou, Mark Wilkinson, Delaram Rahbarinia, Jay Knight, Jian Wang, Charles G. Mullighan, Rose B. McGee, Emily A. Quinn, Elsie L. Gerhardt, Leslie M. Taylor, Regina Nuccio, Jessica M. Valdez, Stacy J. Hines-Dowell, Alberto Pappo, Giles Robinson, Liza-Marie Johnson, Ching-Hon Pui, David W. Ellison, James R. Downing, Jinghui Zhang, Kim E. Nichols. Genomes for Kids: Comprehensive DNA and RNA sequencing defining the scope of actionable mutations in pediatric cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 642.

Improving tumor mutational burden calibration in non-European patients.

1559 Background: Tumor mutational burden (TMB), has been recently granted FDA approval as a biomarker for ICI treatment in tumors with a high mutation load (≥ 10/MB). To leverage this biomarker in the clinical setting, it is necessary to evaluate its application in diverse patient populations. In particular, tumor-only sequencing may overestimate TMB in non-EUR populations for which reference panels are small or unavailable, and thus have poorer predictive performance. Herein, we investigate the effect of TMB overestimation in non-EURs on patient diagnosis and clinical outcomes in a real world patient cohort. Methods: TMB was computed using a tumor-only NGS platform (Oncopanel) for 8349 cancer patients (pts) of 7 cancer types (Table). Genetic ancestry was inferred directly from tumor sequencing data and confirmed for a subset of pts using germline SNP arrays. TMB was compared using Wilcoxon rank-sum test between European (EUR) and non-EUR pts. TMB percentile rank by ancestral group (East Asian, African, European) and tumor histology was computed. In non-EUR pts, TMB was calibrated by reassigning its value to the corresponding percentile rank in EUR pts having the same cancer (AH-TMB). Tumors with raw TMB ≥ 10/MB were assigned as TMB-high (TMBH; with the rest referred to as TMBL for TMB-low) and those with calibrated AH-TMB ≥ 10/MB as AH-TMBH. A subset of pts treated with ICIs at DFCI and of EUR ancestry were analyzed. TMB was intentionally mis-calibrated in EURs (MC-TMB), to mimic the TMB overestimation observed in non-EURs. Associations between TMBH status and overall survival (OS) was assessed using Cox regression. Results: Uncalibrated TMB was significantly higher in tumors from non-EUR pts overall (p &lt;0.0001, Table) as anticipated, whereas this difference was not observed in tumors with matched germline filtering. We reassigned non-EUR pts into low/high groups using calibrated AH-TMB, which changed the assignment of 67/670 non-EURs, most of which (65/67) were downgraded from TMBH to AH-TMBL. In the ICI-treated cohort, there was a strong association between TMBH status and OS (p = 2e-11, HR = 0.6) whereas MC-TMBH (mimicking the miscalibration in non-EUR samples) had a weaker association with OS (p = 0.0013, HR = 0.8). Importantly, pts assigned to TMB-H due to miscalibration (90 TMB-L/MC-TMBH pts) had shorter OS compared to the true TMBH group (p = 3e-6, HR = 1.9). Conclusions: In this analysis, we showed that tumor-only sequencing platforms can overestimate TMB in non-EUR pts, impacting treatment decisions and outcomes. Replacing raw TMB counts with an ancestry-adjusted measure could optimize TMB application in the clinical setting when germline sequencing is not available. Accurate ancestry inference can be performed using tumor-only sequencing.[Table: see text]

Genetic ancestry and clinical outcomes to immune checkpoint inhibitors among seven common cancers.

10536 Background: Prior studies and clinical trials report associations between self-reported race and clinical outcomes to Immune Checkpoint Inhibitors (ICIs). However, comprehensive studies of ancestry-associated differences in clinical outcomes have not been performed. We derived genetic ancestry scores and assessed clinical outcomes in 1341 patients with cancer treated with ICIs. Methods: Patients at the Dana-Farber Cancer Institute treated with ICIs only and with relevant cancer types and targeted exome sequencing data (Oncopanel) were included. Relevant cancer types included colorectal adenocarcinoma (CRC), esophagogastric adenocarcinoma (EGC), head and neck squamous cell carcinoma (HNSCC), melanoma, non-small cell lung cancer (NSCLC), renal cell carcinoma (RCC), and urothelial carcinoma (UC). We developed a bioinformatics pipeline to infer fine-scale genetic ancestry for each patient (n=1341) directly from tumor sequencing data by leveraging off and on-target sequenced reads and external ancestry reference panels. Three ancestry scores were determined (African, East Asian, European). Overall survival (OS) and time-to-treatment failure (TTF) were compared by Cox logistic regression between ancestral populations. Hazard ratio (HR) was derived using multivariable analysis, adjusted for single versus combination therapy, prior lines of therapy, and tumor mutational burden (TMB, as percentiles). Results: Median follow-up was 37.8 months (m; interquartile range: 35.7-39.5m). Common cancer types included CRC (n=52), EGC (n=114), HNSCC (n=88), melanoma (n=274), NSCLC (n=571), RCC (n=99), and UC (n=143). A higher East Asian ancestry (EAS) was significantly associated with worse OS ( p=0.03) and TTF ( p=0.002) in patients with RCC, independent of the histologic subtype (Table). There was no significant association between any of the three ancestral populations and clinical outcomes in the other 6 cancer types. Conclusions: We described clinical outcomes to ICIs across three global populations in 7 cancers. As the medical field re-evaluates the use of self-reported race in clinical decision-making, we utilize a novel ancestry pipeline that can be readily applied to tumor-only sequencing panels and better characterize non-white populations. We find no ancestry differences in clinical outcomes except in patients with RCC treated with ICIs which will require future validation. We plan to analyze genomic correlates of response by ancestry in each of the cancer types to better understand these diverge clinical behaviors.[Table: see text]

Tumor Signature Analysis Implicates Hereditary Cancer Genes in Endometrial Cancer Development.

Simple SummaryWomen with a family history of cancer are at increased risk of cancer, including endometrial cancer (affecting the womb lining). In some of the women with such family history, the risk can be explained by deleterious changes in mismatch repair genes that cause Lynch syndrome. This study explored the role of other genes in risk of endometrial cancer, using several approaches. The number and type of changes in gene sequence information in women with endometrial cancer was compared to that from individuals in the general population. Gene sequence changes in endometrial cancer patients with a family history of cancer were also analyzed. Lastly, endometrial cancers from individuals with gene changes were examined for distinctive genomic patterns expected to be seen if a gene change was driving the cancer. This study has identified several additional genes for further exploration in relation to endometrial cancer risk and therapy.Risk of endometrial cancer (EC) is increased ~2-fold for women with a family history of cancer, partly due to inherited pathogenic variants in mismatch repair (MMR) genes. We explored the role of additional genes as explanation for familial EC presentation by investigating germline and EC tumor sequence data from The Cancer Genome Atlas (n = 539; 308 European ancestry), and germline data from 33 suspected familial European ancestry EC patients demonstrating immunohistochemistry-detected tumor MMR proficiency. Germline variants in MMR and 26 other known/candidate EC risk genes were annotated for pathogenicity in the two EC datasets, and also for European ancestry individuals from gnomAD as a population reference set (n = 59,095). Ancestry-matched case–control comparisons of germline variant frequency and/or sequence data from suspected familial EC cases highlighted ATM, PALB2, RAD51C, MUTYH and NBN as candidates for large-scale risk association studies. Tumor mutational signature analysis identified a microsatellite-high signature for all cases with a germline pathogenic MMR gene variant. Signature analysis also indicated that germline loss-of-function variants in homologous recombination (BRCA1, PALB2, RAD51C) or base excision (NTHL1, MUTYH) repair genes can contribute to EC development in some individuals with germline variants in these genes. These findings have implications for expanded therapeutic options for EC cases.

Inflation of tumor mutation burden by tumor-only sequencing in under-represented groups

With the recent FDA approval of tumor mutational burden-high (TMB-H) status as a biomarker for treatment with a PD-1 inhibitor regardless of tumor type, accurate assessment of patient-specific TMB is more critical now more than ever. Using paired tumor and germline exome sequencing data from 701 patients newly diagnosed with multiple myeloma, including 575 self-reported White patients and 126 self-reported Black patients, we observed that compared to the gold standard of filtering germline variants with patient-paired germline sequencing data, TMB estimates were significantly higher in both Black and White patients when using public databases for filtering non-somatic mutations; however, TMB was more significantly inflated in Black patients compared to White patients. TMB as a biomarker for patient selection to receive immune checkpoint inhibitors (ICIs) therapy without patient-paired germline sequencing may introduce racial bias due to the under-representation of minority groups in public databases.

Association between tumor mutational burden (TMB) and immune-related adverse events (irAEs) in patients (pts) with metastatic urothelial carcinoma (mUC) during checkpoint immunotherapy.

489 Background: Immune checkpoint inhibition (ICI) has greatly improved clinical outcomes for pts with mUC and other cancers. ICI is associated with a class of AEs, deemed irAEs due to immune activation. Nonetheless, biomarkers associated with irAE are still lacking. We hypothesized that the immune response against neoantigens is partly responsible for irAEs and investigated the association between irAEs, TMB and response to ICI. Methods: We identified patients with mUC at Dana Farber Cancer Institute who were treated with ICI (monotherapy or combination) and had available tumor sequencing data through Oncopanel. TMB was calculated using the number of non-synonymous exonic mutations per megabase. The severity of irAEs was graded using CTCAE v.5.0. Mann-Whitney U test was performed to identify association between TMB, incidence and grade of irAEs. A cut-off of 10/mb was assigned for TMB. Fisher’s exact test was used to evaluate the radiologic response between pts with and without irAEs and low vs. high TMB. Multivariable linear regression was used to assess the relationship between TMB, irAEs and response. p-values were adjusted using Benjamini-Hochberg method. Results: Of 101 pts with mUC who met the inclusion criteria, 32 (32%) reported irAEs. 6 (6%) were grade (G)1, 20 (20%) were G2, and 6 (6%) were G3. Median(m) time on therapy was 84 days for pts without irAEs and 88 days for pts with irAEs. Pts with irAEs had higher mTMB (15.4/mb) compared to pts with no irAEs (9.8) ( p = 0.01). In pts on monotherapy (93), those with irAEs (n=27) had a higher mTMB (15.13/mb) compared to pts with no irAEs (n=66) (mTMB = 10.20/mb) ( p = 0.01). Out of 94 pts with radiological data, response was achieved in 16 (50%) pts with irAE vs 10 (16%) pts with no irAE ( p &lt; 0.001). When both irAE and response were included in a multivariable regression, the association between irAE and TMB was not significant ( p = 0.4). Pts with both irAE and high TMB had a response rate of 56% which was significantly higher than those with either irAE but low TMB (28.6%) or high TMB but no irAE (21.2 %) or low TMB and no irAE (10.3%) (Chi-square test p = 0.002; FDR corrected p-values for individual comparisons in Table). There was no association between TMB and irAE grade. Conclusions: Higher TMB was associated with higher incidence of irAEs in pts with mUC on ICIs. Moreover, pts with both high TMB and irAEs exhibited better response rates than those with only high TMB or irAEs, suggesting that they may provide complementary tumor and host characteristics. Further evaluation in mUC is needed to confirm this relationship between TMB, irAEs and response in a larger cohort and explore specific mutational signatures that may be associated with irAEs. [Table: see text]

Bladder tumor metabolic alterations in response to IFNα gene therapy.

468 Background: Intravesical interferon-alpha (IFNα) gene therapy with Nadofaragene firadenovec has shown clinical efficacy in patients with non-muscle invasive bladder cancer (NMIBC) in a phase III clinical trial, highlighting the therapeutic potential of this approach in a disease with significant unmet clinical need. Optimizing the clinical efficacy of IFNα gene therapy requires an understanding of the underlying therapeutic mechanisms. Here, we investigate the impact of IFNα gene therapy on tumor metabolism using in vitro and orthotopic murine preclinical models and clinical trial data to elucidate mechanisms of tumor resistance and identify predictive biomarkers. Methods: In vitro murine bladder cancer cell lines treated with recombinant IFNα (rIFNα) and lentiviral IFNα (LV-IFNα) were analyzed by whole-transcriptome sequencing, glucose uptake, and lactate production. Preclinical murine bladder cancer models were treated with LV-IFNα (orthotopic tumor model) or Poly(I:C) (flank tumor model), a potent IFN inducer. Disease response was monitored by in vivo real-time luciferase imaging. Tumors were harvested and whole-transcriptome sequencing performed to assess effects of IFNα therapy on tumor metabolism and lipidomics. Lipidomic profiling was performed on patient urine samples from a phase II clinical trial of intravesical Nadofaragene firadenovec (7 clinical responders and 6 non-responders) to assess for clinically-relevant differences in lipid metabolism. Results: Following IFNα therapy in vitro and in murine orthotopic bladder cancer models, we identified downregulation of genes involved in fatty acid synthesis and upregulation of genes involved in glycolysis by whole-transcriptome sequencing. This was confirmed by higher glucose uptake and lactate production by IFNα-treated cells in vitro. These findings were recapitulated in whole-transcriptome sequencing data of human bladder tumors treated with intravesical Nadofaragene firadenovec. Lipidomics performed on murine MB49 tumors treated with poly(I:C) identified 79 upregulated lipids, including phosphotidyl choline, spingomyelin and phosphatidyl ethanolamine, and 12 downregulated lipids, notably the cardiolipin class. Lipidomics performed on patient urine samples collected pre- and post-treatment with intravesical Nadofaragene firadenovec detected &gt;592 lipids with distinct expression profiles differentiating clinical responders and non-responders at both timepoints. Conclusions: We describe novel modulation of glucose and lipid metabolism by bladder tumor cells in response to IFNα gene therapy. These metabolic changes were reproducible across in vitro, in vivo and clinical trial studies and improve our mechanistic understanding of IFNα gene therapy, identify tumor escape pathways targetable with combination therapy regimens, and identify a new class of biomarkers for predicting clinical response of NMIBC to IFNα gene therapy.

Abstract PR006: Correlation of immunohistochemistry with TP53 sequence and clinical outcomes in GOG-086P

Abstract Introduction: GOG-086P was one of the first attempts to combine molecular inhibitors such as bevacizumab or temsirolimus with chemotherapy in patients with advanced endometrial cancer. An exploratory analysis of this study indicates that bevacizumab combined with chemotherapy is superior to temsirolimus plus chemotherapy in patients with TP53 mutated tumors as determined by next generation sequencing. The functional status of p53 in a tumor can be inferred by sequence analysis and/or by immunohistochemistry (IHC). In this next study of the GOG-086P cohort, we sought to determine whether p53 IHC alone or integrated with TP53 sequencing was similarly predictive of outcome and whether IHC was predictive of the sequence analysis. Methods: A total of 349 patients were enrolled on GOG-086P. Two hundred and forty-three patients have TP53 sequence data, and 213 have p53 protein expression data measured by IHC. Tumor histology and sequence data were correlated with p53 IHC categorizing each case as p53 negative or null, wild type (WT) or over-expressed (OE), and these variables were further correlated with progression-free survival (PFS) and overall survival (OS) in the chemotherapy + bevacizumab arms versus the chemotherapy + temsirolimus arm. Results: The majority of patients (118) had tumors with WT p53 protein expression by IHC and non-mutated or WT TP53 sequence, with expected concordance between IHC and sequence analysis. However, there were five discordant cases with OE p53 protein in the absence of a TP53 mutation. Fourteen patients had null IHC protein expression and a concordant TP53 sequence mutation expected to result in the total loss of protein. Four cases that were p53 null by IHC had a discordant WT TP53 sequence. Seventeen patients had tumors with a missense TP53 mutation but had WT p53 protein expression by IHC, reflecting a potential discordance between sequencing and IHC versus a missense mutation that did not result in loss of protein function. Six of these 17 discordant cases may be explained by co-mutations in TP53 and POLE or microsatellite instability where p53 proteins, though mutated, function more like WT. Reassuringly and as expected, high concordance was found in the 55 cases that had OE protein by IHC and a missense mutation in TP53. On integrated analysis, a TP53 mutation with p53 protein OE was associated with significantly lower hazard ratio for PFS (HR=0.41; 95% CI 0.22-0.83) and OS (HR=0.28; 95% CI 0.14-0.59), i.e., longer PFS and OS, for patients receiving bevacizumab + chemotherapy relative to temsirolimus + chemotherapy. Conclusions: Mutations in TP53, which are common in patients with advanced endometrial cancer, represent a developing platform upon which to design personalized treatment regimens. We demonstrate that IHC is often but not always predictive of the TP53 sequence. However, most cases with over-expressed p53 protein by IHC had a mutation in TP53, and bevacizumab + chemotherapy prolonged PFS and OS in such patients. Citation Format: Kristina W. Thiel, Eric J. Devor, Virginia Filiaci, Douglas A. Levine, Fanny Dao, Narciso Olvera, David Mutch, Carol Aghajanian, Kimberly K. Leslie. Correlation of immunohistochemistry with TP53 sequence and clinical outcomes in GOG-086P [abstract]. In: Proceedings of the AACR Virtual Special Conference: Endometrial Cancer: New Biology Driving Research and Treatment; 2020 Nov 9-10. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(3_Suppl):Abstract nr PR006.

Accucopy: accurate and fast inference of allele-specific copy number alterations from\xa0low-coverage low-purity tumor sequencing data

BackgroundCopy number alterations (CNAs), due to their large impact on the genome, have been an important contributing factor to oncogenesis and metastasis. Detecting genomic alterations from the shallow-sequencing data of a low-purity tumor sample remains a challenging task.ResultsWe introduce Accucopy, a method to infer total copy numbers (TCNs) and allele-specific copy numbers (ASCNs) from challenging low-purity and low-coverage tumor samples. Accucopy adopts many robust statistical techniques such as kernel smoothing of coverage differentiation information to discern signals from noise and combines ideas from time-series analysis and the signal-processing field to derive a range of estimates for the period in a histogram of coverage differentiation information. Statistical learning models such as the tiered Gaussian mixture model, the expectation–maximization algorithm, and sparse Bayesian learning were customized and built into the model. Accucopy is implemented in C++ /Rust, packaged in a docker image, and supports non-human samples, more at http://www.yfish.org/software/.ConclusionsWe describe Accucopy, a method that can predict both TCNs and ASCNs from low-coverage low-purity tumor sequencing data. Through comparative analyses in both simulated and real-sequencing samples, we demonstrate that Accucopy is more accurate than Sclust, ABSOLUTE, and Sequenza.

Mutstats: An Ultra-fast Computational Method to Determine Clonal Status of Somatic Mutations

Tumor cell population is a mixture of heterogeneous cell subpopulations, known as subclones. Identification of clonal status of mutations, i.e., whether a mutation occurs in all tumor cells or in a subset of tumor cells, is crucial for understanding tumor progression and developing personalized treatment strategies. We make three major contributions in this paper: (1) we summarize terminologies in the literature based on a unified mathematical representation of subclones; (2) we develop a simulation algorithm to generate hypothetical sequencing data that are akin to real data; and (3) we present an ultra-fast computational method, Mutstats, to infer clonal status of somatic mutations from sequencing data of tumors. The inference is based on a Gaussian mixture model for mutation multiplicities. To validate Mutstats, we evaluate its performance on simulated datasets as well as two breast carcinoma samples from The Cancer Genome Atlas project.