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Supplementary Figures 1-7

Breakdown of RET and ROS1 fusion positive patients

Co-occurring Level 1-3 alterations by MSK-IMPACT in 52 patients

Types of Clinical trials patients enrolled to according to level 1-4 and UMD assigned samples

Supplementary Table S1: List of actionable genes and potential matched therapy Supplementary Table S2: List of co-occurring Level 1-4 mutations Supplementary Table S3: EGFR mutations identified and associated therapy Supplementary Table S4: List of 410 genes in MSK-IMPACT assay

Supplementary Tables 1-4

Breakdown of MAP2K1, RAF1, ARAF, FGFR3-TACC3 fusion and CDKN2A loss patients

Patient 1 Concurrent EGFR and KRAS mutations in a patient identified by MSK-IMPACT

Median number of mutations in samples sequenced by MSK-IMPACT in patients with a Level 1-4 alteration or assigned to the unknown mitogenic driver cohort.

Patient 2 Concurrent EGFR and KRAS mutations in a patient identified by MSK-IMPACT

Most mutations in cancer are rare, which complicates the identification of therapeutically significant mutations and thus limits the clinical impact of genomic profiling in patients with cancer. Here, we analyzed 24,592 cancers including 10,336 prospectively sequenced patients with advanced disease to identify mutant residues arising more frequently than expected in the absence of selection. We identified 1,165 statistically significant hotspot mutations of which 80% arose in 1 in 1,000 or fewer patients. Of 55 recurrent in-frame indels, we validated that novel AKT1 duplications induced pathway hyperactivation and conferred AKT inhibitor sensitivity. Cancer genes exhibit different rates of hotspot discovery with increasing sample size, with few approaching saturation. Consequently, 26% of all hotspots in therapeutically actionable oncogenes were novel. Upon matching a subset of affected patients directly to molecularly targeted therapy, we observed radiographic and clinical responses. Population-scale mutant allele discovery illustrates how the identification of driver mutations in cancer is far from complete.Significance: Our systematic computational, experimental, and clinical analysis of hotspot mutations in approximately 25,000 human cancers demonstrates that the long right tail of biologically and therapeutically significant mutant alleles is still incompletely characterized. Sharing prospective genomic data will accelerate hotspot identification, thereby expanding the reach of precision oncology in patients with cancer. Cancer Discov; 8(2); 174–83. ©2017 AACR.This article is highlighted in the In This Issue feature, p. 127

Background Genomic profiling is increasingly incorporated into oncology research and the clinical care of cancer patients. We sought to determine physician perception and use of enterprise-scale clinical sequencing at our center, including whether testing changed management and the reasoning behind this decision-making. Patients and methods All physicians who consented patients to MSK-IMPACT, a next-generation hybridization capture assay, in tumor types where molecular profiling is not routinely performed were asked to complete a questionnaire for each patient. Physician determination of genomic ‘actionability’ was compared to an expertly curated knowledgebase of somatic variants. Reported management decisions were compared to chart review. Results Responses were received from 146 physicians pertaining to 1932 patients diagnosed with 1 of 49 cancer types. Physicians indicated that sequencing altered management in 21% (331/1593) of patients in need of a treatment change. Among those in whom treatment was not altered, physicians indicated the presence of an actionable alteration in 55% (805/1474), however, only 45% (362/805) of these cases had a genomic variant annotated as actionable by expert curators. Further evaluation of these patients revealed that 66% (291/443) had a variant in a gene associated with biologic but not clinical evidence of actionability or a variant of unknown significance in a gene with at least one known actionable alteration. Of the cases annotated as actionable by experts, physicians identified an actionable alteration in 81% (362/445). In total, 13% (245/1932) of patients were enrolled to a genomically matched trial. Conclusion Although physician and expert assessment differed, clinicians demonstrate substantial awareness of the genes associated with potential actionability and report using this knowledge to inform management in one in five patients. Clinical Trial number NCT01775072.

Tumor genetic testing is standard of care for patients with advanced lung adenocarcinoma, but the fraction of patients who derive clinical benefit remains undefined. Here, we report the experience of 860 patients with metastatic lung adenocarcinoma analyzed prospectively for mutations in >300 cancer-associated genes. Potentially actionable genetic events were stratified into one of four levels based upon published clinical or laboratory evidence that the mutation in question confers increased sensitivity to standard or investigational therapies. Overall, 37.1% (319/860) of patients received a matched therapy guided by their tumor molecular profile. Excluding alterations associated with standard-of-care therapy, 14.4% (69/478) received matched therapy, with a clinical benefit of 52%. Use of matched therapy was strongly influenced by the level of preexistent clinical evidence that the mutation identified predicts for drug response. Analysis of genes mutated significantly more often in tumors without known actionable mutations nominated STK11 and KEAP1 as possible targetable mitogenic drivers. Significance: An increasing number of therapies that target molecular alterations required for tumor maintenance and progression have demonstrated clinical activity in patients with lung adenocarcinoma. The data reported here suggest that broader, early testing for molecular alterations that have not yet been recognized as standard-of-care predictive biomarkers of drug response could accelerate the development of targeted agents for rare mutational events and could result in improved clinical outcomes. Cancer Discov; 7(6); 596–609. ©2017 AACR. See related commentary by Liu et al., p. 555. This article is highlighted in the In This Issue feature, p. 539

Tumor molecular profiling is a fundamental component of precision oncology, enabling the identification of genomic alterations in genes and pathways that can be targeted therapeutically. The existence of recurrent targetable alterations across distinct histologically defined tumor types, coupled with an expanding portfolio of molecularly targeted therapies, demands flexible and comprehensive approaches to profile clinically relevant genes across the full spectrum of cancers. We established a large-scale, prospective clinical sequencing initiative using a comprehensive assay, MSK-IMPACT, through which we have compiled tumor and matched normal sequence data from a unique cohort of more than 10,000 patients with advanced cancer and available pathological and clinical annotations. Using these data, we identified clinically relevant somatic mutations, novel noncoding alterations, and mutational signatures that were shared by common and rare tumor types. Patients were enrolled on genomically matched clinical trials at a rate of 11%. To enable discovery of novel biomarkers and deeper investigation into rare alterations and tumor types, all results are publicly accessible.

Importance Diagnosing the site of origin for cancer is a pillar of disease classification that has directed clinical care for more than a century. Even in an era of precision oncologic practice, in which treatment is increasingly informed by the presence or absence of mutant genes responsible for cancer growth and progression, tumor origin remains a critical factor in tumor biologic characteristics and therapeutic sensitivity. Objective To evaluate whether data derived from routine clinical DNA sequencing of tumors could complement conventional approaches to enable improved diagnostic accuracy. Design, Setting, and Participants A machine learning approach was developed to predict tumor type from targeted panel DNA sequence data obtained at the point of care, incorporating both discrete molecular alterations and inferred features such as mutational signatures. This algorithm was trained on 7791 tumors representing 22 cancer types selected from a prospectively sequenced cohort of patients with advanced cancer. Results The correct tumor type was predicted for 5748 of the 7791 patients (73.8%) in the training set as well as 8623 of 11 644 patients (74.1%) in an independent cohort. Predictions were assigned probabilities that reflected empirical accuracy, with 3388 cases (43.5%) representing high-confidence predictions (>95% probability). Informative molecular features and feature categories varied widely by tumor type. Genomic analysis of plasma cell-free DNA yielded accurate predictions in 45 of 60 cases (75.0%), suggesting that this approach may be applied in diverse clinical settings including as an adjunct to cancer screening. Likely tissues of origin were predicted from targeted tumor sequencing in 95 of 141 patients (67.4%) with cancers of unknown primary site. Applying this method prospectively to patients under active care enabled genome-directed reassessment of diagnosis in 2 patients initially presumed to have metastatic breast cancer, leading to the selection of more appropriate treatments, which elicited clinical responses. Conclusions and Relevance These results suggest that the application of artificial intelligence to predict tissue of origin in oncologic practice can act as a useful complement to conventional histologic review to provide integrated pathologic diagnoses, often with important therapeutic implications.

Tumor molecular profiling is a fundamental component of precision oncology, enabling the identification of genomic alterations in genes and pathways that can be targeted therapeutically. The existence of recurrent targetable alterations across distinct histologically-defined tumor types, coupled with an expanding portfolio of molecularly-targeted therapies, demands flexible and comprehensive approaches to profile clinically significant genes across the full spectrum of cancers. We established a large-scale, prospective clinical sequencing initiative utilizing a comprehensive assay, MSK-IMPACT, through which we have compiled matched tumor and normal sequence data from a unique cohort of more than 10,000 patients with advanced cancer and available pathological and clinical annotations. Using these data, we identified clinically relevant somatic mutations, novel non-coding alterations, and mutational signatures that were shared among common and rare tumor types. Patients were enrolled on genomically matched clinical trials at a rate of 11%. To enable discovery of novel biomarkers and deeper investigation into rare alterations and tumor types, all results are publicly accessible.

At Memorial Sloan Kettering, we have used a clinically validated custom hybridization capture-based NGS assay (MSK-IMPACT) to sequence the tumors of more than 10,000 patients. We sought to determine physician perception and use of these results, including whether they changed management and the reasoning behind this decision-making. All physicians who ordered MSK-IMPACT testing for patients where it was not considered routine were asked to complete a questionnaire quarterly (Table). Physician determination of genomic “actionability” was compared to OncoKB, a curated knowledge base of somatic variants (OncoKB.org). Responses were received from 146 of 258 physicians emailed (57%) regarding 1932 of 9147 patients (21%). Physician respondents comprised a diversity of cancer specialties including medical oncology (67%), pediatric oncology (8%), surgery (6%), radiation oncology (5%), and neuro-oncology (5%). A total of 49 cancer types were represented. Physicians indicated that sequencing altered management in 331 (20%) of profiled patients in need of a treatment change. Among those in whom treatment was reportedly not altered, physicians indicated the presence of at least one actionable alteration in 55% (805/1474) of cases. However, only 45% of these cases harbored a genomic variant annotated as actionable by OncoKB. Among patients in whom physicians deemed the report non-actionable, 12% had OncoKB annotated actionable variants. Across the cases annotated as potentially actionable by OncoKB, physicians identified an actionable alteration in 81% of cases. At the time of data analysis, 297 (15%) patients had been enrolled in at least one clinical trial of targeted therapy at MSKCC including 224 (12%) patients on genomically-matched trials, 76% of whom participated after IMPACT profiling. As the clinical adoption of NGS panels expands, continued education of physicians as well as maintained knowledge bases for annotation will be necessary to expand the utility of this approach and the opportunity for precision medicine. QuestionsResponses, N (%)DID alter treatment, as follows:1. Patient enrolled to a therapeutic protocol at MSKCC265 (14)2. Patient enrolled to a therapeutic protocol at another institution15 (1)3. Patient treated with off-label use of an FDA approved therapy43 (2)DID NOT alter treatment, as follows:4. Actionable mutation(s) identified, but no therapeutic protocol was available175 (9)5. Actionable mutation(s) identified, but patient declined participation in, or was ineligible for, available therapeutic protocol115 (6)6. Actionable mutation(s) identified, but patient deteriorated, progressed, or died before results could be used176 (9)7. Actionable mutation(s) identified and therapeutic study available, but patient has not recurred/progressed since MSK-IMPACT result339 (18)8. No actionable mutation identified669 (35)Other135 (7)TOTAL1932 Citation Format: Alison M. Schram, Dalicia Reales, Jesse Galle, Roy Cambria, Robert Durany, Darren Feldman, Eric Sherman, Jonathan Rosenberg, Gabriella D'Andrea, Shrujal Baxi, Yelena Janjigian, William Tap, Maura Dickler, José Baselga, Barry Taylor, Debyani Chakravarty, Jianjiong Gao, Nikolaus D. Schultz, David B. Solit, Michael F. Berger, David M. Hyman. Oncologist use and perception of large panel next generation tumor sequencing [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 375. doi:10.1158/1538-7445.AM2017-375

LBA11516 Background: ctDNA assays can noninvasively assess tumor burden and biology by identifying tumor-derived somatic alterations. For broad applicability, including early cancer detection, an unprecedented high-intensity approach (ultra-deep sequencing of plasma cell-free DNA (cfDNA) with broad genomic coverage) is needed to address intra-patient and population-level heterogeneity. We present initial results with this approach in patients (pts) with metastatic breast (BC), non-small cell lung (NSCLC), and castration-resistant prostate cancer (CRPC). Methods: Blood and tissue were prospectively collected w/in 6 wks with no intervening therapy change from pts with de novo or progressive cancer. cfDNA and white blood cell (WBC) genomic DNA from each pt were sequenced with a 508-gene panel (2 Mb; >60,000X raw depth). cfDNA variant calling used molecular barcoding for error correction and filtering for WBC variants. Tissue was sequenced using the MSK-IMPACT assay (410 genes, 1.4 Mb, >500X depth) blinded to plasma/WBC sequencing. Variants were classified as clonal or subclonal based on tumor sequencing in BC and NSCLC. Results: Of 161 eligible pts, 124 (39 BC, 41 NSCLC, and 44 CRPC) were evaluable for concordance. In tissue, 864 variants were detected across the 3 tumor types, with 627 (73%) also detected in plasma: single nucleotide variants/indels - 75%, fusions - 67%, and copy number alterations - 58%. In 90% of pts, at least 1 of the variants detected in tumor tissue was also detected in plasma: BC - 97%, NSCLC - 85%, CRPC - 84%. Most actionable mutations detected in tissue were also detected in plasma (54/71, 76%; SNVs only: 28/31, 90%). A subset of driver mutations (eg. in ESR1, PIK3CA, ERBB2, EGFR) were observed in plasma but not tissue. Clonal variants in tissue were more likely to be detected in plasma than subclonal variants (p

8017 Background: Pts with EGFR-mutant lung cancers treated with EGFR tyrosine kinase inhibitors (TKI) develop clinical resistance, most frequently due to acquisition of EGFR T790M. Modeling suggest...