Warner, Jeremy L.

Abstract Background : Patients (pts) with COVID-19 are reported to have increased risk of venous thromboembolism yet bleeding has been an under recognized complication. Rates of bleeding remain unexamined in all patients especially in pts with cancer and COVID-19. Aim: To estimate the incidence of bleeding complication in patients with cancer and COVID 19 Methods: The CCC19 international registry (NCT04354701) aims to investigate complications of COVID-19 in pts with cancer. Our aim was to investigate the frequency of bleeding in hospitalized adult pts with cancer andCOVID-19, enrolled between March 16, 2020 and Feb 8, 2021. The incidence of bleeding complications was captured as defined by CCC19 and included both major and non major bleeding . Associated baseline clinic-pathologic prognostic factors and outcomes such as need for mechanical ventilation, intensive care unit (ICU) admission and mortality rates were assessed Results :3849 pts met analysis inclusion criteria. Bleeding was reported in 276 (7%) pts with median age of 70years; incidence was 6.6 % in females and 7.6 % in males, 6.5% in non-Hispanic white pts, 8.2 % in non-Hispanic Black pts, and 7.8 % in Hispanic pts. 74% had solid cancer and 29% had hematologic malignancies, 33% had received anti-cancer therapy in preceding 30 days, and 8% had surgery within 4weeks. In pts taking antiplatelet or anticoagulant medications at baseline, 7.2% developed bleeding. Need for mechanical ventilation, ICU admission, 30-day mortality, and total mortality were significantly higher in those with bleeding complications compared to those without, p<0.05 Conclusion : We describe the incidence of bleeding in a large cohort of pts with cancer and COVID-19. Bleeding events were observed in those with adverse outcomes including mechanical ventilation, ICU admission, and high mortality; the overall mortality of 43% in patients with bleeding complications is especially notable. This important complication may reflect underlying COVID-19 pathophysiology as well as iatrogenic causes. Figure 1 Figure 1. Disclosures Kumar: Diagnostica Stago: Honoraria. Zon: AMAGMA AND RLZ: Consultancy, Current holder of individual stocks in a privately-held company. Byeff: Pfizer, BMS, Takeda,Teva, Merck, United health: Consultancy, Current equity holder in publicly-traded company, Current holder of stock options in a privately-held company. Nagaraj: Novartis: Research Funding. Hwang: astrazaneca,Merck,bayer, Genentech: Consultancy, Research Funding. McKay: Myovant: Consultancy; Bayer: Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees; Exelixis: Consultancy, Membership on an entity's Board of Directors or advisory committees; Calithera: Membership on an entity's Board of Directors or advisory committees; Tempus: Research Funding; Merck: Consultancy, Membership on an entity's Board of Directors or advisory committees; Tempus: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees; Bristol Myers Squibb: Consultancy, Membership on an entity's Board of Directors or advisory committees; Sanofi: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees; Dendreon: Consultancy; Caris: Other: Serves as a molecular tumor board ; Vividion: Consultancy; Sorrento Therapeutics: Consultancy; Bayer: Research Funding. Warner: Westat, Hemonc.org: Consultancy, Current holder of stock options in a privately-held company. Connors: Pfizer: Honoraria; CSL Behring: Research Funding; Alnylam: Consultancy; Bristol-Myers Squibb: Honoraria; takeda: Honoraria; Abbott: Consultancy. Rosovsky: Janssen: Consultancy, Research Funding; BMS: Consultancy, Research Funding; Inari: Consultancy, Membership on an entity's Board of Directors or advisory committees; Dova: Consultancy, Membership on an entity's Board of Directors or advisory committees.

6549 Background: Representation and outcomes of women, older adults, and racial minorities in ICI trials has not been previously described. Methods: MEDLINE and Embase were searched to identify ICI RCTs. Data for trial characteristics, proportion of trials reporting race, age and sex as well as the proportion of patients by race, age and sex enrolled in ICI trials was collected. Descriptive statistics were reported for trials reporting minority representation and proportion of included patients by race, age and sex. Disparities in representation were calculated using enrollment incidence disparity (EID) and enrollment incidence ratios (EIR) by comparing trial enrollment against U.S. population-based estimates acquired from the SEER 18 incidence database. The relationship of EID to key trial characteristics were compared using standard parametric and non-parametric statistical tests. Trends in EIR were analyzed using the Joinpoint Regression Analysis software. Results: 108 ICI trials from 2009 to 2020 with 48,360 patients were included in this analysis. All RCTs reported sex (101/101). 78 trials reported race (72%), of which only 41 trials (38%) reported data on all 5 U.S. racial categories (Black, White, Asian, Pacific Islander and Native American). Participation of Black patients was reported in 66 trials (61%), White participants in 78 trials (72%), Asians in 69 trials (64%), Native Americans and Pacific Islanders in 41 trials (38%), and Hispanics in 24 trials (22%). Age categories were inconsistently defined, and 80 trials (74%) reported the proportion of patient by age categories. Subgroup analyses of clinical outcomes by race, age and sex were reported in 17 (22%), 62 (79%) and 57 (73%) trials respectively. Women (trial proportion [TP]: 32%; EIR: 0.77), patients aged ≥ 65 years (TP: 42%; EIR: 0.74), Black participants (TP: 1.8%; EIR: 0.17) and Hispanic participants (TP: 5.9%; EIR: 0.67) were largely underrepresented, and Asians were overrepresented (TP: 15.9%; EIR: 2.64). Black patients were underrepresented across all cancer types. Similarly, women, older adults ( > 65 years of age) and Hispanic patients were consistently underrepresented across cancer types with few exceptions. Representation of older adults increased significantly from 2010-2020 (APC: 2.72), while representation of Black patients decreased significantly from 2009-2020 (APC: -23.37). Black patients were found to be significantly underrepresented in phase III trials (p = 0.0005), trials with OS as the primary endpoint (p = 0.004), and PD1 inhibitor trials (p = 0.002). Hispanics were significantly underrepresented in PD1 inhibitor trials (p = 0.003). Conclusions: There is both suboptimal reporting about participation and underrepresentation of women, racial minorities (particularly Black patients) and older adults in ICI trials as compared to their cancer incidence.

PURPOSE Multiple large clinical trials have investigated adjuvant tyrosine kinase inhibitors (TKIs) to reduce the risk of cancer recurrence and progression to metastasis in high-risk renal cell carcinoma. We sought to maintain living and interactive evidence on this topic, until a high level of certainty is reached for key clinical outcomes such that further updates become unnecessary and unlikely to change clinical practice. METHODS We created a living interactive evidence synthesis platform to maintain a continuously updated meta-analysis on TKI monotherapy in adjuvant renal cell carcinoma. We implemented an automated search strategy with weekly updates to identify randomized phase 2 and 3 clinical trials. Study selection, appraisal, and data extraction were done in duplicate. Cumulative meta-analysis was performed using Analyzer Module in Living Interactive Evidence platform. For each outcome (overall survival [OS], disease-free survival [DFS], and all-cause and treatment-related adverse events), we assessed certainty of evidence using GRADE approach and conducted trial sequential analysis. RESULTS This final update includes five randomized trials including recently updated data from PROTECT trial. Meta-analysis shows that adjuvant TKI monotherapy offers no benefit in OS (hazard ratio, 1.01; 95% CI, 0.91 to 1.12, high certainty) or DFS (hazard ratio, 0.92; 95% CI, 0.86 to 1.00, high certainty) and significantly increases adverse event risk. Lack of benefit was consistent across subgroups including highest-risk patients (test for subgroup differences: P = .32). Optimal information size criteria were met, and there was high certainty of evidence for lack of DFS and OS benefit for adjuvant TKIs. CONCLUSION There is no guidance on when to stop maintaining a living review. In this example, we used trial sequential analysis and high certainty of evidence (future clinical trials unlikely to change current conclusions) as a benchmark to conclude a living review in view of convincing evidence.

Abstract Background: Overall, patients with cancer experience a greater risk of adverse outcomes following SARS-CoV-2 infection; however, little is known for those with BC. Methods: CCC19 (NCT04354701) is an international cohort study aimed at investigating the impact of COVID-19 in patients with a history of or active cancer using de-identified data on patient demographics, cancer history, clinical course and outcomes of COVID-19. The current analysis includes patients from U.S. and Canada with invasive BC and laboratory-confirmed SARS-CoV-2 entered between March 17, 2020 and July 2, 2020. Co-primary outcomes were hospitalization during COVID-19 illness and 30-day all-cause mortality. Frequencies for categorical variables and medians (range) for continuous variables were estimated. Final presentation will include bivariable and multivariable Cox proportional hazards regression analysis to identify risk factors (including BC subtypes and therapies, for which data collection is ongoing) associated with 30-day all-cause mortality and severe COVID-19 illness (composite of any hospitalization requiring supplemental oxygen, admission to an intensive care unit [ICU], use of mechanical ventilation, or death). Results: During the study period, a total of 2683 patients with cancer and COVID-19 were accrued in the CCC19 registry including 529 (20%) with invasive BC. Among patients with BC, 352 (67%) were 60 years or older; 518 (98%) were women; 275 (52%) non-Hispanic White, 116 (22%) non-Hispanic Black, 70 (13%) Hispanic, 56 (11%) in other categorizations; 178 (34%) had a smoking history; 75 (14%) ECOG performance status ≥2; 191 (36%) with >2 active comorbidities; 64 (12%) with AJCC stage IV disease at BC diagnosis; and 267 (50%) were on anti-cancer treatment including systemic therapy, radiation, or surgery within 3 months of COVID-19 diagnosis. At least 323 (61%) patients with BC had 30-day follow-up after COVID-19 diagnosis, and 35 (7%) had 90-day follow-up. COVID-19 illness at initial diagnosis required outpatient care in 288 (54%), inpatient care in 193 (36%), and ICU care in 40 (8%). Overall, 247 (47%) were hospitalized and 30-day all-cause mortality was 9%. 30-day all-cause mortality rates by receipt of major BC treatment modalities (within past 3 months) were: 10% for those on cytotoxic systemic therapy vs 5% and 12% for noncytotoxic systemic therapy and local therapy, respectively. The table shows hospitalization and mortality outcomes by major demographic and BC treatment strata. The final presentation will incorporate the latest patient accrual and evaluate independent clinical risk factors associated with serious COVID-19 outcomes in patients with BC. Conclusions: This represents the largest study to date of COVID-19 outcomes in patients with invasive BC. Nearly half of the patients with BC required hospitalization during their COVID-19 disease course and we observed a 9% 30-day all-cause mortality. Submitted on behalf of the COVID-19 and Cancer Consortium (ccc19us.org) Table 1. COVID-19 related hospitalization and 30-day all-cause mortality for all patients with invasive BCNAny Hospitalization30-day all-cause MortalityN% [95% CI]N% [95% CI]Total population52924747 [42-51]499 [7-12]Age<601774425 [19-32]21 [0-4]60-691155144 [35-54]87 [3-13]70-791206655 [46-64]1714 [8-22]80+1178674 [65-81]2219 [12-27]ECOG PS0-132212238 [33-43]165 [3-8]2+756181 [71-89]1621 [13-32]Active Comorbidities0741014 [7-23]00 [0-5]1-22239442 [36-49]178 [5-12]>219112968 [60-74]2915 [10-21]Treatment IntentCurative1866434 [28-42]95 [2-9]Palliative743953 [41-64]1216 [9-27]Cancer StatusRemission/NED33115447 [41-52]247 [5-11]Active disease, stable or responding to treatment1194840 [31-50]76 [2-12]Active disease, progressing372773 [56-86]1130 [16-47]Treatment Modality (within 3 months)Cytotoxic chemotherapy833542 [31-54]810 [4-18]Noncytotoxic therapy1856435 [28-42]105 [3-10]Local therapy (surgery or radiation)341750 [32-68]412 [3-27] Citation Format: Ali Raza Khaki, Dimpy P Shah, Maryam B Lustberg, Melissa K Accordino, Daniel G Stover, Gayathri Nagaraj, Donna R Rivera, Edith A Perez, Sara M Tolaney, Jeffrey Peppercorn, Petros Grivas, Jeremy L Warner, Corrie A Painter, Gilberto de Lima Lopes, Jr, Solange Peters, Michael A Thompson, Toni K Choueiri, Brian I Rini, Gary H Lyman, Nicole M Kuderer, Shaveta Vinayak. Characteristics and outcomes of SARS-CoV-2 infection in patients with invasive breast cancer (BC) from the COVID-19 and cancer consortium (CCC19) cohort study [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS7-01.

Abstract Comprehensive interpretation of a patient's cancer genome is a cross-disciplinary process requiring the expertise of oncologists, molecular pathologists, genetic counselors, genomic scientists, and bioinformaticians. The Variant Interpretation for Cancer Consortium (VICC) has created a non-clinical forum for the variant interpretation process by connecting experts from multiple academic institutions and industry to create a Virtual Molecular Tumor Board (VMTB). During VICC-VMTB sessions, experts retrospectively discuss de-identified, challenging patient cases in the context of genomic interpretation guidelines to evaluate a patient's profile across several distinct components: genomic variants; disease context; therapeutic history; family history; demographic information; genomic assay information; and variant allele frequency. These components are processed through the prism of publically available knowledge and expertise of VMTB members, resulting in a broad spectrum of clinically relevant assertions about the patient's disease that may be of prognostic, diagnostic, or predictive relevance. Through these evaluations, the VICC-VMTB primarily works as a means of illuminating challenges in genomic interpretation and advancing the objectives of domain-specific VICC working groups. Examples of challenges that have been highlighted by the VICC-VMTB forum include: 1) inconsistent disease and therapy ontologies across resources are being addressed by Disease Harmonization & Drug Harmonization working groups, respectively; 2) use of machine-learning and natural language processing into clinical-grade variant interpretation are being addressed by the AI-Assisted Curation working group; 3) aggregation and harmonization of knowledgebase data into a centralized resource is being addressed by the Data Licensing and Variant Harmonization working groups; 4) inefficiencies in knowledgebase search functions are being addressed by the Search working group; 5) determination of accurate prediction of biological consequences of rare variants is being addressed by the In Silico Interpretation working group; and 6) standardization of genomic variant curation is being addressed by the Knowledge Curation and Interpretation Standards working group. These VICC working groups provide dedicated expert assemblies intended to specifically tackle these challenges, further increasing the breadth for which genomic information can be made useful in cancer variant interpretation. The VICC-VMTB has been a pertinent link between cancer genomes and clinical interpretation. We are confident VICC-VMTB will continue to be a gateway of acceleration and progress needed in the field of cancer interpretation to provide accurate and efficient answers for our patients in the future. Citation Format: Beth A. Pitel, Shruti Rao, Catherine Del Vecchio Fitz, Subha Madhavan, Rodrigo Dientsmann, Peter Horak, Ian King, Susan M. Mockus, Gordana Raca, Damian T. Rieke, Peter Rogan, Dmitriy Sonkin, David Tamborero, Ioannis S. Vlachos, Brian Walsh, Jeremy L. Warner, Malachi Griffith, Obi L. Griffith, Debyani Chakravarty, Alex H. Wagner. The Virtual Molecular Tumor Board of the Variant Interpretation for Cancer Consortium: A systematic gateway connecting cancer genome interpretation and progress in genomic knowledgebases in cancer [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 3222.

Abstract The Clinical Genome (ClinGen) Resource is a US National Human Genome Research Institute (NHGRI)-funded program dedicated to building an expert curated and freely available central resource that defines the clinical relevance of genes and variants for use in precision medicine. Teams of experts in various clinical domains come together as working groups within ClinGen to facilitate the interpretation, annotation and utilization of genes-variants for clinical application. Somatic Cancer is one such ClinGen Clinical Domain Working Group (CDWG) that consists of over 90 members worldwide, including clinicians, clinical laboratory diagnosticians, genomic scientists and bioinformaticians. Members of this CDWG identify high priority somatic variants in different cancer types that require expert curation and consensus in their clinical interpretation. In order to accurately implement practice guidelines/standards for variant interpretation, the Somatic Cancer CDWG recently established a somatic Variant Curation Expert Panel (VCEP) approval process. Based on their interest and clinical domain expertise, a subset of the CDWG members formed somatic VCEPs to perform authoritative curation on the shortlisted genes-somatic variants within the context of a disease, therapeutic indication or biological pathway. Expert curation within these VCEPs is performed by utilizing guidelines such as those recommended by AMP/ASCO/CAP (Li et al. 2017) and the Somatic Cancer CDWG (Ritter et al. 2016). Wherever necessary, the somatic VCEPs will develop: 1) gene- and disease-specific modifications to address gaps in existing variant assessment guidelines, 2) quantitative approaches for variant interpretation, and 3) implement standardized protocols for annotating somatic variants in genes for a specific disease, drug or biological pathway. Furthermore, ClinGen recently formed the Cancer Variant Interpretation (CVI) committee to provide support, review and feedback on the provisional somatic variant interpretation proposals developed by the VCEPs. The CVI provides somatic VCEPs with a preliminary approval before the final approval by the ClinGen CDWG oversight committee and ultimately ‘expert panel' status in ClinVar, an NCBI-maintained database of clinically relevant gene variants. NTRK fusions in cancer is the first Somatic VCEP going through the ClinGen Somatic Expert Panel approval process. Alterations in the FGFR pathway in GU cancers is the second somatic VCEP under consideration. The Somatic CDWG uses the CIViC (Clinical Interpretation of Variants in Cancer) platform for curation of somatic variants. To date, the CDWG has curated 268 evidence items relating to cancer variants in CIViC, 6 assertions, and 33 evidence source suggestions. The ultimate goal of the Somatic CDWG is to enhance the usability, dissemination and implementation of cancer somatic changes in the ClinGen resource and other cancer variant knowledgebases. Citation Format: Shruti Rao, Deborah Ritter, Arpad Danos, Gordana Raca, Angshumoy Roy, Kilannin Krysiak, Wan-Hsin Lin, Erica Barnell, Matthew McCoy, Beth Pitel, Dmitriy Sonkin, Jue Wang, Seyed Ali Hosseini, Shamini Selvarajah, Ian King, Rashmi Kanagal-Shamana, Xinjie Xu, Jeremy L. Warner, Funda Meric-Bernstam, Jason D. Merker, Marilyn Li, Alex H. Wagner, Malachi Griffith, Obi L. Griffith, Shashikant Kulkarni, Subha Madhavan. ClinGen somatic cancer working group: Disseminating standardized cancer molecular diagnostic data and evidence through global collaboration and expert curation [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 3215.

Abstract Defining patient-to-patient similarity is essential for the development of precision medicine in clinical care and research. Conceptually, the identification of similar patient cohorts appears straightforward; however, universally accepted definitions remain elusive. Simultaneously, an explosion of vendors and published algorithms have emerged and all provide varied levels of functionality in identifying patient similarity categories. To provide clarity and a common framework for patient similarity, a workshop at the American Medical Informatics Association 2019 Annual Meeting was convened. This workshop included invited discussants from academics, the biotechnology industry, the FDA, and private practice oncology groups. Drawing from a broad range of backgrounds, workshop participants were able to coalesce around 4 major patient similarity classes: (1) feature, (2) outcome, (3) exposure, and (4) mixed-class. This perspective expands into these 4 subtypes more critically and offers the medical informatics community a means of communicating their work on this important topic.