He was a co-founder of Seragon, purchased by Genentech/Roche in 2014. J.M. is a science advisor and owns company stock in Scholar Rock. H.C. is an inventor on several patents related to organoid technology. S.W.L. is a co-founder and scientific advisory board member for ORIC Pharm, Blueprint, and Mirimus. He also serves on the scientific advisory board for Constellation, Petra, and PMV and has recently served as a consultant for Forma, Boehringer Ingelheim, and Aileron. J.G.-A. has received support from Medtronic (honorarium for consultancy with Medtronic), Johnson & Johnson (honorarium for delivering a talk), and Intuitive Surgical (honorarium for participating in a webinar by Intuitive Surgical). P.B.R. has received honorarium from Corning to discuss 3D cell culture techniques, has served as a consultant for AstraZeneca, and is a consultant for EMD Serono for work on radiation sensitizers.

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Purpose Neoadjuvant chemotherapy followed by radical cystectomy (RC) is a standard of care for the management of muscle-invasive bladder cancer (MIBC). Dose-dense cisplatin-based regimens have yielded favorable outcomes compared with standard-dose chemotherapy, yet the optimal neoadjuvant regimen remains undefined. We assessed the efficacy and tolerability of six cycles of neoadjuvant dose-dense gemcitabine and cisplatin (ddGC) in patients with MIBC. Patients and Methods In this prospective, multicenter phase II study, patients received ddGC (gemcitabine 2,500 mg/m on day 1 and cisplatin 35 mg/m on days 1 and 2) every 2 weeks for 6 cycles followed by RC. The primary end point was pathologic downstaging to non-muscle-invasive disease (< pT2N0). Patients who did not undergo RC were deemed nonresponders. Pretreatment tumors underwent next-generation sequencing to identify predictors of chemosensitivity. Results Forty-nine patients were enrolled from three institutions. The primary end point was met, with 57% of 46 evaluable patients downstaged to < pT2N0. Pathologic response correlated with improved recurrence-free survival and overall survival. Nineteen patients (39%) required toxicity-related dose modifications. Sixty-seven percent of patients completed all six planned cycles. No patient failed to undergo RC as a result of chemotherapy-associated toxicities. The most frequent treatment-related toxicity was anemia (12%; grade 3). The presence of a presumed deleterious DNA damage response (DDR) gene alteration was associated with chemosensitivity (positive predictive value for < pT2N0 [89%]). No patient with a deleterious DDR gene alteration has experienced recurrence at a median follow-up of 2 years. Conclusion Six cycles of ddGC is an active, well-tolerated neoadjuvant regimen for the treatment of patients with MIBC. The presence of a putative deleterious DDR gene alteration in pretreatment tumor tissue strongly predicted for chemosensitivity, durable response, and superior long-term survival.

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.

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Background Cell-free DNA (cfDNA) profiling is increasingly used to guide cancer care, yet mutations are not always identified. The ability to detect somatic mutations in plasma depends on both assay sensitivity and the fraction of circulating DNA in plasma that is tumor-derived (i.e., cfDNA tumor fraction). We hypothesized that cfDNA tumor fraction could inform the interpretation of negative cfDNA results and guide the choice of subsequent assays of greater genomic breadth or depth. Methods Plasma samples collected from 118 metastatic cancer patients were analyzed with cf-IMPACT, a modified version of the FDA-authorized MSK-IMPACT tumor test that can detect genomic alterations in 410 cancer-associated genes. Shallow whole genome sequencing (sWGS) was also performed in the same samples to estimate cfDNA tumor fraction based on genome-wide copy number alterations using z-score statistics. Plasma samples with no somatic alterations detected by cf-IMPACT were triaged based on sWGS-estimated tumor fraction for analysis with either a less comprehensive but more sensitive assay (MSK-ACCESS) or broader whole exome sequencing (WES). Results cfDNA profiling using cf-IMPACT identified somatic mutations in 55/76 (72%) patients for whom MSK-IMPACT tumor profiling data were available. A significantly higher concordance of mutational profiles and tumor mutational burden (TMB) was observed between plasma and tumor profiling for plasma samples with a high tumor fraction (z-score≥5). In the 42 patients from whom tumor data was not available, cf-IMPACT identified mutations in 16/42 (38%). In total, cf-IMPACT analysis of plasma revealed mutations in 71/118 (60%) patients, with clinically actionable alterations identified in 30 (25%), including therapeutic targets of FDA-approved drugs. Of the 47 samples without alterations detected and low tumor fraction (z-score<5), 29 had sufficient material to be re-analyzed using a less comprehensive but more sensitive assay, MSK-ACCESS, which revealed somatic mutations in 14/29 (48%). Conversely, 5 patients without alterations detected by cf-IMPACT and with high tumor fraction (z-score≥5) were analyzed by WES, which identified mutational signatures and alterations in potential oncogenic drivers not covered by the cf-IMPACT panel. Overall, we identified mutations in 90/118 (76%) patients in the entire cohort using the three complementary plasma profiling approaches. Conclusions cfDNA tumor fraction can inform the interpretation of negative cfDNA results and guide the selection of subsequent sequencing platforms that are most likely to identify clinically-relevant genomic alterations.