Overcoming Barriers to Tumor Genomic Profiling through Direct-to-Patient Outreach.

PURPOSE: To overcome barriers to genomic testing for patients with rare cancers, we initiated a program to offer free clinical tumor genomic testing worldwide to patients with select rare cancer subtypes. EXPERIMENTAL DESIGN: Patients were recruited through social media outreach and engagement with disease-specific advocacy groups, with a focus on patients with histiocytosis, germ cell tumors (GCT), and pediatric cancers. Tumors were analyzed using the MSK-IMPACT next-generation sequencing assay with the return of results to patients and their local physicians. Whole-exome recapture was performed for female patients with GCTs to define the genomic landscape of this rare cancer subtype. RESULTS: A total of 333 patients were enrolled, and tumor tissue was received for 288 (86.4%), with 250 (86.8%) having tumor DNA of sufficient quality for MSK-IMPACT testing. Eighteen patients with histiocytosis have received genomically guided therapy to date, of whom 17 (94%) have had clinical benefit with a mean treatment duration of 21.7 months (range, 6-40+). Whole-exome sequencing of ovarian GCTs identified a subset with haploid genotypes, a phenotype rarely observed in other cancer types. Actionable genomic alterations were rare in ovarian GCT (28%); however, 2 patients with ovarian GCTs with squamous transformation had high tumor mutational burden, one of whom had a complete response to pembrolizumab. CONCLUSIONS: Direct-to-patient outreach can facilitate the assembly of cohorts of rare cancers of sufficient size to define their genomic landscape. By profiling tumors in a clinical laboratory, results could be reported to patients and their local physicians to guide treatment. See related commentary by Desai and Subbiah, p. 2339.

OncoTree: A Cancer Classification System for Precision Oncology.

PURPOSE: Cancer classification is foundational for patient care and oncology research. Systems such as International Classification of Diseases for Oncology (ICD-O), Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT), and National Cancer Institute Thesaurus (NCIt) provide large sets of cancer classification terminologies but they lack a dynamic modernized cancer classification platform that addresses the fast-evolving needs in clinical reporting of genomic sequencing results and associated oncology research. METHODS: To meet these needs, we have developed OncoTree, an open-source cancer classification system. It is maintained by a cross-institutional committee of oncologists, pathologists, scientists, and engineers, accessible via an open-source Web user interface and an application programming interface. RESULTS: OncoTree currently includes 868 tumor types across 32 organ sites. OncoTree has been adopted as the tumor classification system for American Association for Cancer Research (AACR) Project Genomics Evidence Neoplasia Information Exchange (GENIE), a large genomic and clinical data-sharing consortium, and for clinical molecular testing efforts at Memorial Sloan Kettering Cancer Center and Dana-Farber Cancer Institute. It is also used by precision oncology tools such as OncoKB and cBioPortal for Cancer Genomics. CONCLUSION: OncoTree is a dynamic and flexible community-driven cancer classification platform encompassing rare and common cancers that provides clinically relevant and appropriately granular cancer classification for clinical decision support systems and oncology research.

Development of Genome-Derived Tumor Type Prediction to Inform Clinical Cancer Care.

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.

Accelerating Discovery of Functional Mutant Alleles in Cancer.

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.

Prospective Comprehensive Molecular Characterization of Lung Adenocarcinomas for Efficient Patient Matching to Approved and Emerging Therapies.

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. 555This article is highlighted in the In This Issue feature, p. 539.

Pharmacologic androgen deprivation and cardiovascular disease risk factors: a systematic review.

BACKGROUND: Pharmacologic androgen deprivation therapy (ADT) is widely used to treat prostate cancer. Observational studies suggest ADT is associated with cardiovascular disease and its risk factors; however, such studies may be subject to bias. Our objective was to evaluate the effect of ADT on cardiovascular disease risk factors using data from randomized controlled trials (RCTs). MATERIALS AND METHODS: We conducted a systematic review using MEDLINE and MEDLINE In-Process (1950-June 2013), EMBASE (1974-June 2013) and Web of Science (1900-June 2013) for all RCTs in men with prostate cancer that compared pharmacologic ADT (i.e. use of gonadotropin-releasing hormone agonist or antagonist) with a group that did not receive ADT and reported data on cardiovascular disease risk factors including blood pressure, cholesterol, triglycerides, fibrinogen, biomarkers of insulin sensitivity, adiposity and C-reactive protein. We also searched for ongoing or unpublished trials. This study was registered at the PROSPERO International Prospective Register of Systematic Reviews (CRD42013005035). RESULTS: Of the 3272 unique publications identified in our systematic review, we did not identify a single RCT that reported data on any cardiovascular disease risk factor. We were unable to locate unreported data from corresponding authors or study sponsors. CONCLUSIONS: There is a lack of published, reliable evidence describing the effects of ADT on cardiovascular disease risk factors. RCTs have likely collected data on these risk factors as part of routine study monitoring; however, these data have not been published. To understand the effect of ADT on cardiovascular morbidity, these data must be made available to the scientific community.