Highest OncoKB level of evidence per (a) tumor type and per (b) tumor type and gene for a subset of solid tumor samples from the MSK-IMPACT cohort with n≥100.

Among genes found to have limited to no clinical actionability (OncoKB Levels 3B or 4 as the highest level of clinical actionability, or no alteration with an OncoKB level) and altered in >1% of samples in the MSK-IMPACT subset of the AACR Project GENIE dataset, left: the breakdown of genes into different gene-function categories and right: the breakdown of genes as oncogenes, tumor suppressors, both or neither per www.oncokb.org.

Table S6: Gene-alteration-tumor types associated with an OncoKB Therapeutic Level of Evidence (Levels 1-4) in March 2017, and changes made to the dataset to ensure 2017 OncoKB clinically actionable variants are consistent with the OncoKB Standard Operating Procedure v2.2.

A list of precision oncology therapies (per the definition provided in the manuscript) approved by the US FDA between January 1998 and November 2022, as well as the year each drug was first FDA approved, the FDA-recognized biomarker(s), the method of biomarker detection, and classification of each drug as first-in-class, mechanistically-distinct, follow-on, or resistance (per definitions provided in the manuscript).

Supplementary Note 1: The Methods on FDA-drug curation taken directly from Olivier T, Haslam A, Prasad V. Anticancer drugs approved by the US food and drug administration from 2009 to 2020 according to their mechanism of action. JAMA Netw Open. 2021;4:e2138793. Supplementary Note 2: The Methods and References on FDA-drug curation taken directly from Sun J, Wei Q, Zhou Y, Wang J, Liu Q, Xu H. A systematic analysis of FDA-approved anticancer drugs. BMC Syst Biol. 2017;11:87.

A breakdown of the number and percentage of tumor type-specific samples included in the MSK-IMPACT subset of the AACR Project GENIE dataset.

For solid tumor samples from the MSK-IMPACT subset of the AACR Project GENIE dataset with n≥100, (a) the median number of oncogenic and actionable (gray), oncogenic and non-actionable (light blue), or total (orange) mutations per sample per cancer type, and (b) the percentage of samples that carry zero, one, two, or three or more actionable mutations (as defined by OncoKB version October 2022) per sample.

Table S5: Genes associated with an OncoKB Therapeutic Level of Evidence (1, 2 or 3A) in March of 2017

Table S4: Genes found to have limited to no clinical actionability (OncoKB Levels 3B or 4 as the highest level of clinical actionability, or no alteration with an OncoKB level) were categorized by 1. the gene/protein function and 2. assignment as an oncogene, tumor suppressor, both or neither per www.oncokb.org.

A list of oncology drugs first approved by the US FDA between January 1998 and November 2022, as well as the year each drug was first FDA approved, the class of agent and mechanism of action, and categorization of each drug as a targeted therapy (Y/N) and precision oncology therapy (Y/N) per definitions provided in the manuscript.

Abstract There is a continuing debate about the proportion of cancer patients that benefit from precision oncology, attributable in part to conflicting views as to which molecular alterations are clinically actionable. To quantify the expansion of clinical actionability since 2017, we annotated 47,271 solid tumors sequenced with the MSK-IMPACT clinical assay using two temporally distinct versions of the OncoKB knowledge base deployed 5 years apart. Between 2017 and 2022, we observed an increase from 8.9% to 31.6% in the fraction of tumors harboring a standard care (level 1 or 2) predictive biomarker of therapy response and an almost halving of tumors carrying nonactionable drivers (44.2% to 22.8%). In tumors with limited or no clinical actionability, TP53 (43.2%), KRAS (19.2%), and CDKN2A (12.2%) were the most frequently altered genes. Significance: Although clear progress has been made in expanding the availability of precision oncology-based treatment paradigms, our results suggest a continued unmet need for innovative therapeutic strategies, particularly for cancers with currently undruggable oncogenic drivers. See related commentary by Horak and Fröhling, p. 18. This article is featured in Selected Articles from This Issue, p. 5

List of AACR Project GENIE BPC Core Team Members

Abstract International cancer registries make real-world genomic and clinical data available, but their joint analysis remains a challenge. AACR Project GENIE, an international cancer registry collecting data from 19 cancer centers, makes data from >130,000 patients publicly available through the cBioPortal for Cancer Genomics (https://genie.cbioportal.org). For 25,000 patients, additional real-world longitudinal clinical data, including treatment and outcome data, are being collected by the AACR Project GENIE Biopharma Collaborative using the PRISSMM data curation model. Several thousand of these cases are now also available in cBioPortal. We have significantly enhanced the functionalities of cBioPortal to support the visualization and analysis of this rich clinico-genomic linked dataset, as well as datasets generated by other centers and consortia. Examples of these enhancements include (i) visualization of the longitudinal clinical and genomic data at the patient level, including timelines for diagnoses, treatments, and outcomes; (ii) the ability to select samples based on treatment status, facilitating a comparison of molecular and clinical attributes between samples before and after a specific treatment; and (iii) survival analysis estimates based on individual treatment regimens received. Together, these features provide cBioPortal users with a toolkit to interactively investigate complex clinico-genomic data to generate hypotheses and make discoveries about the impact of specific genomic variants on prognosis and therapeutic sensitivities in cancer. Significance: Enhanced cBioPortal features allow clinicians and researchers to effectively investigate longitudinal clinico-genomic data from patients with cancer, which will improve exploration of data from the AACR Project GENIE Biopharma Collaborative and similar datasets.

10535 Background: Homologous recombination is a major mechanism of defective DNA repair, but it remains uncertain whether homologous repair deficient (HRD) tumors have favorable prognosis or are more/less likely to respond to treatment than tumors lacking such mutations. Objective: To determine whether lung (NSCLC) and colorectal (CRC) HRD+ tumors have better survival or response to chemotherapy than HRD- tumors. Methods: Patients with de novo stage IV NSCLC or CRC who had next generation sequencing (NGS) between 2015-2018 from one of four cancer centers were identified. Records were curated using the PRISSMM framework to ascertain treatment, overall survival (OS) and progression free survival based on imaging (PFS-I) and oncologists’ notes (PFS-M). Each NSCLC or CRC tumor was categorized as HRD+ if NGS revealed an oncogenic/likely oncogenic mutation in: ATM, BAP1, BARD1, BLM, BRCA1, BRCA2, BRIP1, CHEK2, FAM175A, FANCA, FANCC, NBN, PALB2, RAD50, RAD51, RAD51C, RTEL1, or MRE11A based on the OncoKB database. The tumor was categorized as HRD- if no oncogenic mutation in any of these genes was evident and HRD indeterminate (HRD?) if no mutation was identified but the panel did not include all genes. OS, PFS-I and PFS-M from start of first line therapy were reported by HRD status. The percentage with a good response to first line therapy (≥2x the median) and exceptional response (≥3x the median) was estimated for each endpoint. Results: For NSCLC 4% were HRD+, 59% HRD- and 37% HRD?. For CRC there were 5% HRD+, 60% HRD- and 35% HRD?. There were no significant differences for any survival endpoint between patients who were HRD+ vs HRD- in univariable analyses. The proportion of good and exceptional responders to first line systemic chemotherapy also did not vary by HRD status, though patients with HRD+ CRC were potentially more likely to be exceptional responders. Similarly, no differences between HRD+ and HRD- tumors were apparent for the subgroup receiving platinum containing therapy. Conclusions: NSCLC and CRC patients with somatic mutations in HRD oncogenic genes did not differ from patients lacking such a mutation with respect to OS or PFS. CRC patients with HRD+ tumors may be more likely to be exceptional responders, but sample sizes are limited. By May, the analysis will include breast and pancreatic cancer cases.[Table: see text]

IMPORTANCE Contemporary observational cancer research requires associating genomic biomarkers with reproducible end points; overall survival (OS) is a key end point, but interpretation can be challenging when multiple lines of therapy and prolonged survival are common. Progressionfree survival (PFS), time to treatment discontinuation (TTD), and time to next treatment (TTNT) are alternative end points, but their utility as surrogates for OS in real-world clinicogenomic data sets has not been well characterized. OBJECTIVE To measure correlations between candidate surrogate end points and OS in a multiinstitutional clinicogenomic data set. DESIGN, SETTING, AND PARTICIPANTS A retrospective cohort study was conducted of patients with non-small cell lung cancer (NSCLC) or colorectal cancer (CRC) whose tumors were genotyped at 4 academic centers from January 1, 2014, to December 31, 2017, and who initiated systemic therapy for advanced disease. Patients were followed up through August 31, 2020 (NSCLC), and October 31, 2020 (CRC). Statistical analyses were conducted on January 5, 2021. EXPOSURES Candidate surrogate end points included TTD; TTNT; PFS based on imaging reports only; PFS based on medical oncologist ascertainment only; PFS based on either imaging or medical oncologist ascertainment, whichever came first; and PFS defined by a requirement that both imaging and medical oncologist ascertainment have indicated progression. MAIN OUTCOMES AND MEASURES The primary outcome was the correlation between candidate surrogate end points and OS. RESULTS There were 1161 patients with NSCLC (648 women [55.8%]; mean [SD] age, 63 [11] years) and 1150 with CRC (647 men [56.3%]; mean [SD] age, 54 [12] years) identified for analysis. Progression-free survival based on both imaging and medical oncologist documentation was most correlated with OS (NSCLC:. = 0.76; 95% CI, 0.73-0.79; CRC:. = 0.73; 95% CI, 0.69-0.75). Time to treatment discontinuationwas least associated with OS (NSCLC:. = 0.45; 95% CI, 0.40-0.50; CRC:. = 0.13; 95% CI, 0.06-0.19). Time to next treatment was modestly associated with OS (NSCLC:. = 0.60; 0.55-0.64; CRC:. = 0.39; 95% CI, 0.32-0.46). CONCLUSIONS AND RELEVANCE This cohort study suggests that PFS based on both a radiologist and a treating oncologist determining that a progression event has occurred was the surrogate end point most highly correlated with OS for analysis of observational clinicogenomic data.

Abstract Obtaining information regarding cancer recurrence from a retrospective, EHR-based dataset poses several challenges primarily due to the lack of structured data. Patients are at risk for cancer recurrence beginning at a time point at which they are characterized as having no evidence of disease. The absence of cancer may be indicated on a radiology report or a medical oncologist assessment, requiring manual review and interpretation of potentially ambiguous free text. Further, the recurrence event itself can be defined based on several distinct data sources including pathology, imaging, clinician assessments, or tumor markers. The likelihood of ascertaining recurrence is dependent on the frequency and type of surveillance performed and varies based on tumor type and based on clinicians' thresholds for pursuing workup of borderline or suspicious findings; if follow up assessments are infrequent, there are fewer opportunities to detect recurrence. Given these challenges, there is currently no standardized approach to evaluating cancer recurrence in EHR data, impeding analyses of rare molecular tumor subtypes in multi-institutional linked clinico-genomic databases. For this analysis, we leveraged the AACR Project GENIE Biopharma Collaborative data based on the PRISSMM curation model to develop an algorithm for identifying recurrence among patients diagnosed with stage I-III non-small cell lung cancer or with stage I-III colorectal cancer. This algorithm involves using curated pathology report data to identify a definitive surgery as the time at which patients have completed curative intent treatment. Subsequent imaging reports, pathology reports, medical oncologist assessments and tumor marker data are then evaluated in order to characterize the timing of specific recurrence events. We will present the real-world recurrence algorithm, its underlying rationale and discuss applications of recurrence endpoints. Beyond enabling estimates of recurrence-free survival, identifying cancer recurrence will allow for estimation of progression-free survival among stage I-III patients in addition to estimation of PFS among de novo stage IV patients. Estimating PFS in a large cohort of patients with linked phenomic and genomic data has historically been a limitation of these types of datasets. Overcoming this limitation will allow for precision medicine advances in oncology by facilitating data pooling across institutions and enabling examination of rare molecular subtypes in relation to clinically meaningful endpoints. Citation Format: Jessica A. Lavery, Samantha Brown, Eva Lepisto, Michele L. Lenoue-Newton, Caroline McCarthy, Hira Rizvi, Celeste Yu, Kenneth L. Kehl, Shawn M. Sweeney, Julia E. Rudolph, Nikolaus Schultz, Brooke Mastrogiacomo, Ritika Kundra, Jeremy Warner, Philippe Bedard, Gregory J. Riely, Katherine S. Panageas, Deborah Schrag, AACR Project GENIE Consortium. Defining real-world recurrence in the AACR Project GENIE Biopharma Collaborative Data [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 2619.

Abstract Studies linking genomic and phenomic data are subject to selection biases, including delayed entry or immortal time bias. Delayed entry can be problematic for time-to-event analyses, but utilization of appropriate statistical methods to account for delayed entry are underutilized. Delayed entry commonly occurs when genomic sequencing results are obtained after the start time for survival estimation. To evaluate the impact of left truncation on overall survival (OS) estimates, we explored outcomes in patients with de novo stage IV non-small cell lung cancer (NSCLC) and colorectal cancer (CRC) from the AACR GENIE Biopharma Collaborative, who had genomic sequencing within a specified timeframe. We analyzed OS from diagnosis and from start of the most common first-line regimen, carboplatin/pemetrexed for NSCLC (N = 212 patients) and FOLFOX for CRC (N = 369 patients). We compared median OS using standard Kaplan-Meier methods to median OS using left truncation methods to account for delayed entry. All NSCLC and CRC patients underwent genomic sequencing after their diagnosis date. Among NSCLC patients on carboplatin/pemetrexed, 41% and among CRC patients on FOLFOX, 14% had sequencing determined after starting first-line regimen. The survfit function in R package survival was used, and the absolute differences and percent differences in median OS estimates were calculated. Failure to account for delayed entry leads to an overestimation of OS, regardless of cohort and start date. Adjusting survival outcomes using left truncation methods reduces the influence of some aspects of selection bias and results in better estimates of time to event outcomes. Analyses from these cohorts can provide meaningful insights about survival outcomes outside the clinical trial setting and may support trial design and reliable selection of control arms. As such, it is imperative that analytic methods to account for the inflated survival estimates are incorporated. EstimateCRC Stage IV (N = 658)NSCLC Stage IV (N = 722)Unadjusted Median (IQR) Overall Survival from Diagnosis (Years)3.2 (2.9, 3.4)2.3 (2.0, 2.5)Median (IQR) Overall Survival from Diagnosis in Years, Adjusting for Delayed Entry2.1 (1.9, 2.4)1.3 (1.1, 1.6)Difference in Medians (Years)1.11.0% Difference in Medians34%44%EstimateCRC Stage IV (N = 369)NSCLC Stage IV (N = 212)Unadjusted Median (IQR) Overall Survival from Most Common First-Line Regimen (Years)2.9 (2.6, 3.4)1.3 (1.0, 1.6)Median (IQR) Overall Survival from Most Common First-Line Regimen in Years, Adjusting for Delayed Entry2.1 (1.8, 2.5)0.9 (0.7, 1.2)Difference in Medians (Years)0.80.4% Difference in Medians28%31% Citation Format: Samantha Brown, Jessica A. Lavery, Eva M. Lepisto, Caroline McCarthy, Hira Rizvi, Celeste Yu, Kenneth L. Kehl, Shawn M. Sweeney, Julia E. Rudolph, Nikolaus Schultz, Ritika Kundra, Brooke Mastrogiacomo, Phillipe Bedard, Jeremy L. Warner, Gregory J. Riely, Deborah Schrag, Katherine S. Panageas, The AACR Project GENIE Consortium. Ignoring left truncation in overall survival within real-world genomic-phenomic data leads to inflated survival estimates [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 2620.

9622 Background: Molecular tumor profiling has become an integral component of oncology practice but linked genomic-phenomic data remain scarce. Recurrence, treatment response and progression are not structured consistently in medical records and this deficit has been a roadblock to discovery of biomarkers that are associated with favorable outcomes. Methods: The Genomics Evidence Neoplasia Information Exchange (GENIE) consortium is an AACR sponsored project to link and share genomic and phenomic data to promote discovery in precision medicine. 3 cancer centers that routinely perform somatic tumor profiling for advanced cancers agreed to curate anti-neoplastic treatment exposures and outcomes including recurrence, progression, response and survival using a standard method. 6 cancer types (lung, colorectal, breast, prostate, pancreas and bladder) were selected and a REDCAP database captures anti-neoplastic treatments, and specific elements from pathology, radiology and oncology reports. Curators abstract data using data fields that rely on the PRISSMM standard. “Real world” progression free survival (PFS) was identified based on curation of: 1) the text of radiologists’ reports for CT, PET/CT, PET and MRI scans (PFSI) and 2) medical oncologists’ notes (PFSM). PFSI and PFSM were estimated from the start of 1st line anti-neoplastic systemic therapy until progression or death for all patients with molecularly characterized non-small cell lung cancer (NSCLC). Results: Genomic sequencing was performed between 2015 and 2017 for 748 patients with NSCLC treated at three major cancer centers. Median age at diagnosis was 66 years (interquartile range 58, 73) and 43% were male. As shown in the table, when RECIST assessments are unavailable, estimates of PFS vary based on whether they are derived from radiologists’ or oncologists’ interpretations. Conclusions: Radiologists’ reports and oncologists’ reports provide different PFS estimates. Cohort studies should specify the method used to define “real world” endpoints. Project GENIE will have 1800 NSCLC patients with curated endpoints by the ASCO meeting. [Table: see text]

OncoKB is a precision oncology knowledge base that annotates the oncogenic effects and clinical actionability of somatic alterations in cancer. Initially focused on solid tumors, OncoKB was introduced in 2016 with >200 genes and almost 3000 somatic alterations via a public website (oncokb.org) and through the cBioPortal for Cancer Genomics. OncoKB now contains annotations for >5000 alterations in 642 genes. This includes 30 Level 1 alterations (included in the FDA drug label; a growth of 114% since 2016), 15 Level 2 alterations (included in the NCCN guidelines; 50% growth), and 38 Level 3A alterations (predictive of drug response in well-powered clinical studies; 65% growth). OncoKB now also supports hematologic malignancies with two new levels of evidence systems that encompass diagnostic and prognostic implications (in addition to therapeutic implications) and 288 heme-specific alterations in 156 newly curated cancer-associated genes. At MSK, OncoKB is used for the annotation of 1000 molecular patient reports per month. To assess the clinical utility of OncoKB and changes in the frequency of actionable alterations, we performed a comparison between the AACR Project GENIE cohort from 2017 and the most recent one (Table 1). With an increased number of tumor types and greater inclusion of hematologic malignancies, the overall potential actionability rate increased by 3.6 percentage points. A shift in access to targeted cancer therapies is also observed, where Level 1 or 2 alterations increased over 5 percentage points and Level 3 alterations decreased by ~3 percentage points, perhaps reaping the benefits of recent successful phase III trials. August 2017 (v1.1)December 2019 (v7.2)AACR Project GENIE cohort size18,80480,248Tumor types with >100 samples3151Hematologic malignancies included29Level 1 or 2 annotation7.3%12.9%Level 3A annotation6.4%4.7%Level 3B annotation17.8%17.5%Total potential actionability31.5%35.1% While only a subset of patients with targetable alterations will benefit from treatments, there is ample evidence that targeted cancer therapies can have profound and durable clinical activity. Knowledgebases such as OncoKB have become a key component to support clinical decision making, and there is a continued need to expand their capabilities while maintaining a nuanced approach to annotation. Citation Format: Sarah Suehnholz, Hongxin Zhang, Moriah Nissan, Ritika Kundra, Jing Su, Lindsay LaFave, Kinisha Gala, Chad Vanderbilt, Maria Arcila, Marc Ladanyi, Michael Berger, Ahmet Zehir, Julia E. Rudolph, Paul Sabbatini, Ross Levine, Ahmet Dogan, Jianjiong Gao, David B. Solit, Nikolaus Schultz, Debyani Chakravarty. OncoKB, a precision oncology knowledgebase [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3208.

Abstract OncoKB is a precision oncology knowledge base that annotates the oncogenic effects and clinical actionability of somatic alterations in cancer. Initially focused on solid tumors, OncoKB was introduced in 2016 with >200 genes and almost 3000 somatic alterations via a public website (oncokb.org) and through the cBioPortal for Cancer Genomics. OncoKB now contains annotations for >5000 alterations in 642 genes. This includes 30 Level 1 alterations (included in the FDA drug label; a growth of 114% since 2016), 15 Level 2 alterations (included in the NCCN guidelines; 50% growth), and 38 Level 3A alterations (predictive of drug response in well-powered clinical studies; 65% growth). OncoKB now also supports hematologic malignancies with two new levels of evidence systems that encompass diagnostic and prognostic implications (in addition to therapeutic implications) and 288 heme-specific alterations in 156 newly curated cancer-associated genes. At MSK, OncoKB is used for the annotation of 1000 molecular patient reports per month. To assess the clinical utility of OncoKB and changes in the frequency of actionable alterations, we performed a comparison between the AACR Project GENIE cohort from 2017 and the most recent one (Table 1). With an increased number of tumor types and greater inclusion of hematologic malignancies, the overall potential actionability rate increased by 3.6 percentage points. A shift in access to targeted cancer therapies is also observed, where Level 1 or 2 alterations increased over 5 percentage points and Level 3 alterations decreased by ~3 percentage points, perhaps reaping the benefits of recent successful phase III trials. August 2017 (v1.1)December 2019 (v7.2)AACR Project GENIE cohort size18,80480,248Tumor types with >100 samples3151Hematologic malignancies included29Level 1 or 2 annotation7.3%12.9%Level 3A annotation6.4%4.7%Level 3B annotation17.8%17.5%Total potential actionability31.5%35.1% While only a subset of patients with targetable alterations will benefit from treatments, there is ample evidence that targeted cancer therapies can have profound and durable clinical activity. Knowledgebases such as OncoKB have become a key component to support clinical decision making, and there is a continued need to expand their capabilities while maintaining a nuanced approach to annotation. Citation Format: Sarah Suehnholz, Hongxin Zhang, Moriah Nissan, Ritika Kundra, Jing Su, Lindsay LaFave, Kinisha Gala, Chad Vanderbilt, Maria Arcila, Marc Ladanyi, Michael Berger, Ahmet Zehir, Julia E. Rudolph, Paul Sabbatini, Ross Levine, Ahmet Dogan, Jianjiong Gao, David B. Solit, Nikolaus Schultz, Debyani Chakravarty. OncoKB, a precision oncology knowledgebase [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3208.