Abstract LB-105: Characterization of total mutational burden in the GENIE cohort: Small and large panels can provide TMB information but to varying degrees

Introduction: Total mutation burden (TMB) correlates with response to immune checkpoint inhibition. AACR Project Genomics Evidence Neoplasia Information Exchange (GENIE) recently released clinical sequencing data for ~19000 cases from 8 institutions. We determined the ability of diverse sequencing panels to assess TMB. Methods: De-identified sequencing data from eight international academic cancer centers were used to investigate the per sample non-silent total somatic mutational burden (TMB) per 1Mb sequenced for all submitted samples. Summary: We first limited TMB characterization to three sites with sequencing data from large gene panels (sampling greater than approximately 1Mb) comprising 14472 samples. The distribution of the TMB normalized per Mb sequenced demonstrated a wide range of mutational rates both within and between tumor types (Table 1). The other five sites utilized smaller targeted sequencing panels. We then restricted the large panel data to the footprint of the smaller targeted panels and compared the number of mutations in the footprint of the small panels to the TMB of each sample from the larger panel data. We found that samples with >5 mutations in the footprint of the smaller targeted panels almost always had a high TMB (94%). In contrast, samples with no mutations in the footprint of the small panels almost never had a high TMB (2%). However, in samples with 1-5 mutations (which represented 2/3 of the samples) the footprint of the smaller targeted panels was not predictive of TMB. Conclusions: While whole exome analyses remain the gold standard for determination of total mutational burden, large sequencing panels (covering greater than approximately 1Mb) can be used to estimate TMB in diverse tumor types. Smaller targeted panels can also predict TMB for a subset of samples, with either 0 or >5 mutations, but this approach appropriately characterizes only just under a third of cases. The majority of cases need the increased genomic sampling of the larger gene panels (or whole exome analyses) to accurately quantify TMB. In summary, while the detection of high mutational burden cases by smaller targeted panel data is quite specific it lacks sensitivity. [A. S. B. and T. S. contributed equally to this work.]

Table 1Mutations per MbsMedianSpectrum[0,1)[1,10)>10nMelanoma7.804%54%42%408Skin Cancer, Non-Melanoma7.6521%32%47%182Bladder Cancer7.585%60%35%587Colorectal Cancer6.632%81%18%1170Small Cell Lung Cancer6.635%69%26%104Endometrial Cancer6.222%72%26%446Non-Small Cell Lung Cancer6.187%70%23%2069Cancer of Unknown Primary5.307%74%19%255Non-Hodgkin Lymphoma4.7811%71%18%188Cervical Cancer4.789%79%13%80Esophagogastric Cancer4.749%82%9%440Head and Neck Cancer4.309%77%14%300Breast Cancer3.868%86%6%1739Ovarian Cancer3.868%88%4%603Leukemia3.8615%84%1%349Glioma3.798%86%6%858

Table 2.Mutations per Mbs (based on full panel)[0,1)[1,10)>10n# Mutations (based on footprint of amplicon hotspot panels)033%65%2%4521[1-5]3%81%15%9806>50%6%94%145

Citation Format: Alexander S. Baras, Thomas Stricker, on behalf of the AACR Project GENIE Consortium. Characterization of total mutational burden in the GENIE cohort: Small and large panels can provide TMB information but to varying degrees [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 LB-105. doi:10.1158/1538-7445.AM2017-LB-105

Abstract LB-104: Clinical actionability and clinical trial matching for GENIE patient genotypes using My Cancer Genome, Personalized Cancer Therapy, and OncoKB

AACR Project GENIE is an international data-sharing project with the goal of enhancing precision oncology. On January 5, 2017, data from ~19,000 somatic tumor genotype reports and selected clinical information from patients (http://www.aacr.org/RESEARCH/RESEARCH/PAGES/AACR-PROJECT-GENIE-DATA.ASPX) were released to the public. The eight member institutions contributed patient data, which were then made available on a dedicated cBioPortal website and for download by Sage Bionetworks. As part of the analysis of the first data release, member institutions began work to combine and reconcile their clinical actionability knowledgebases (KBs). In this abstract, we present efforts to combine clinical actionability assertions from My Cancer Genome (MCG; https://www.mycancergenome.org; Vanderbilt-Ingram Cancer Center), Personalized Cancer Therapy (https://pct.mdanderson.org; MD Anderson Cancer Center), and OncoKB (http://oncokb.org; Memorial Sloan Kettering Cancer Center). We also present data on matching GENIE patient genotypes to clinical actionability assertions from these KBs and to biomarker-driven clinical trials to begin defining the landscape of clinical actionability across a large cohort of patients at all stages of disease.The three KBs were combined using a modification of OncoKB’s levels of evidence for clinical actionability. Categories included standard of care therapies (e.g., those with FDA labels and in NCCN guidelines) and investigational therapies with strong clinical data, both on and off the recommended diagnosis indications. Diagnoses were mapped to the OncoTree tumor type hierarchy. Initial efforts to combine the KBs resulted in >500 therapeutic assertions. Using the combined KBs, we matched these assertions to GENIE patient diagnoses and genotypes. In our preliminary results, >33% of patient samples match at least one therapeutic assertion. Of these, ~15% match at the standard of care level, and ~8% match at the level of investigational therapies. The remaining matches were exploratory or matched by biomarker but not diagnosis. Most frequently matching diagnoses at the standard-of-care level were non-small cell lung cancer, breast cancer, and melanoma. Further details will be presented.The MCG team curates diagnosis and biomarker eligibility criteria for all recruiting cancer clinical trials reported in ClinicalTrials.gov. As of January 2017, 5,201 recruiting cancer clinical trials from ClinicalTrials.gov have been reviewed, and 1,884 trials were found to have biomarker eligibility criteria. Of these, 352 trials are testing a targeted therapy and have a known driver mutation as an inclusion criterion. Based on preliminary work, ~16% of patient samples match at least one trial in the limited set. When the trial list is expanded to include all biomarker-driven trials, including those exploring the impact of mutations along an entire cell signaling pathway, ~84% of patient samples match at least one trial; matching biomarkers are often exploratory and patient benefit from the trial intervention is not necessarily expected. For the limited trial set, patients with breast cancer, non-small cell lung cancer, glioma, and melanoma were most likely to match a trial. For the expanded trial set, patients with non-small cell lung cancer, colorectal cancer, breast cancer, and glioma were most likely to match a trial. We will show how genetic testing panel size affects trial matching and present clinical trial matching by disease, gene, alteration type, drug class, and cell signaling pathway.In conclusion, the GENIE project has resulted in more than just the shared data. It has fostered collaborations between institutions to reconcile and improve precision cancer medicine KBs and provided a resource for improving cancer genetic testing and the practice of precision cancer medicine.

[C. M. and D. C. contributed equally to this work.]

Citation Format: Christine M. Micheel, Debayani Chakravarty, Jiaojiong Gao, Ian Maurer, Clinton Miller, Kenna R. Shaw, Mia A. Levy, Nikolaus Schultz, on behalf of the AACR Project GENIE Consortium. Clinical actionability and clinical trial matching for GENIE patient genotypes using My Cancer Genome, Personalized Cancer Therapy, and OncoKB [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 LB-104. doi:10.1158/1538-7445.AM2017-LB-104

Abstract LB-103: Landscape of somatic ERBB2 Mutations: Findings from AACR GENIE and comparison to ongoing ERBB2 mutant basket study

Background: AACR GENIE is an international data-sharing project that aggregates clinical-grade cancer genomic data. As a demonstration of utility, we evaluated the landscape of ERBB2 mutations in the first 18,486 patients included in this registry and compared it to the first 100 patients enrolled in an ongoing international Phase 2 SUMMIT ‘basket’ study of the pan-HER inhibitor neratinib in ERBB2 mutant solid tumors (NCT01953926). Results: ERBB2 mutations were identified in 2.8% (519/18,486) of patients in the GENIE cohort and observed at all participating centers. In total, there were 482 missense, 66 indels, 19 truncating mutations, and 14 structural variants. A total of 263 unique missense mutations were observed including 12 at previously identified hotspots which accounted for 69.2% of all missense mutations. 35 unique cancer types were represented. The tumor types with the highest proportion of ERBB2 mutations were bladder (12.8%, 82/641), breast (3.9%, 87/2230), colorectal (3.3%, 70/2102), and NSCLC (3%, 90/3006). Among patients with copy number data available (91%) 11% had concurrent ERBB2 amplification, most often in breast cancer. The most frequently observed alterations in ERBB2, adjusted for differing exon coverage between panels, was S310F/Y in 0.46% of the GENIE cohort (12.6% of samples with ERBB2 alterations), Y772_A775dup in 0.21% (6.9%), R678Q in 0.17% (4.5%), L755S in 0.16% (5.2%), V777L in 0.12% (3.8%), and V842I in 0.09% (3.1%). The distribution of specific ERBB2 variants differed significantly by tumor type with exon 20 insertions being most common in NSCLC (44.4%, 40/90), L755S (18.9%, 11/92) in breast, S310F/Y (26.9%, 28/104) in bladder, and V842I (13.9%, 10/72) in colorectal cancer. Structural variants included intragenic deletions (n=4) and fusions involving various partners including GRB7 (n=2), and one each of C1orf87, PPIL6, HEXIM2, THRA, ASIC2, BCA3, WIPF2. The frequencies of ERBB2 mutant cancer types observed in the GENIE cohort were generally comparable to those enrolled to the neratinib basket study including NSCLC (17 vs 22%, respectively), breast (16.4 vs 24%), bladder (15.5 vs 14%), colorectal (13.2 vs 17%), and endometrial (4.2 vs 6%). At the variant level, S310F/Y was less prevalent in GENIE compared to the neratinib study (12.6 vs 24%) while all other mutations were generally similar including L755S (5.2 vs 9%), R678Q (4.5 vs 2%), Y772_A775dup (6.9 vs 13%), V777L (3.8 vs 9%), and V842I (3.1 vs 6%). Conclusion: GENIE confirms that a diversity of ERBB2 mutations are prevalent across a variety of tumor types in patients with advanced cancer. The genomic landscape of ERBB2 mutations was largely similar in the population based GENIE cohort and the neratinib SUMMIT study, providing the first direct evidence that basket study enrollment accurately reflects the true landscape of the target alteration.

Citation Format: Alison Schram, Helen H. Won, Fabrice Andre, Monica Arnedos, Funda Meric - Bernstam, Philippe L. Bedard, Kenna R. Shaw, Hugo Horlings, Christine Micheel, Ben Ho Park, Grace Mann, Alshad S. Lalani, Lillian Smyth, David B. Solit, Deborah Schrag, Mia A. Levy, Barrett J. Rollins, Mark Routbort, Charles L. Sawyers, Eva Lepisto, Michael F. Berger, David M. Hyman, on behalf of the AACR Project GENIE Consortium. Landscape of somatic ERBB2 Mutations: Findings from AACR GENIE and comparison to ongoing ERBB2 mutant basket study [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 LB-103. doi:10.1158/1538-7445.AM2017-LB-103

Abstract LB-102: Landscape analysis of the initial data release from AACR Project GENIE

AACR Project Genomics Evidence Neoplasia Information Exchange (GENIE) is a multi-phase, multi-year, international data-sharing consortium whose goal is to generate an evidence base for precision cancer medicine by integrating and linking clinical-grade cancer genomic data with clinical outcome data for tens of thousands of cancer patients treated at multiple institutions worldwide. The project fulfills an unmet need in oncology by providing the statistical power necessary to identify novel therapeutic targets, to understand genomic determinants of response to therapy, to design new biomarker-driven clinical trials and ultimately, to improve clinical decision-making and the care delivered to patients. Here we describe the goals, structure and data standards of the GENIE consortium and conclusions from a high-level analysis of the first public release of genomic and limited clinical data from approximately 19,000 patients treated at eight cancer centers obtained during this initial phase of the project. We also explore the clinical utility of these genomic data by examining rates of clinical actionability across multiple cancer types and by estimating patient enrollment rates to the NCI MATCH Trial. Based on yearly rates of sequencing at each of the eight founding institutions, together with the planned addition of new members, we estimate the GENIE database could grow to >100,000 samples within five years. Consistent with the goals of the proposed Cancer Moonshot National Cancer Data Ecosystem, GENIE is committed to the principles of generating interoperable, open access data that can be widely shared across the entire scientific community.

Citation Format: Ethan Cerami, Alexander S. Baras, Justin Guinney, Eva Lepisto, Trevor J. Pugh, Nikolaus Schultz, Thomas Stricker, Shawn M. Sweeney, Laura J. van't Veer, Gerrit A. Meijer, Fabrice Andre, Victor E. Velculescu, Kenna R. Shaw, Mia A. Levy, Philippe L. Bedard, Barrett J. Rollins, Charles L. Sawyers, on behalf of the AACR Project GENIE Consortium. Landscape analysis of the initial data release from AACR Project GENIE [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 LB-102. doi:10.1158/1538-7445.AM2017-LB-102