Genomic Heterogeneity as a Barrier to Precision Medicine in Gastroesophageal Adenocarcinoma.

Gastroesophageal adenocarcinoma (GEA) is a lethal disease where targeted therapies, even when guided by genomic biomarkers, have had limited efficacy. A potential reason for the failure of such therapies is that genomic profiling results could commonly differ between the primary and metastatic tumors. To evaluate genomic heterogeneity, we sequenced paired primary GEA and synchronous metastatic lesions across multiple cohorts, finding extensive differences in genomic alterations, including discrepancies in potentially clinically relevant alterations. Multiregion sequencing showed significant discrepancy within the primary tumor (PT) and between the PT and disseminated disease, with oncogene amplification profiles commonly discordant. In addition, a pilot analysis of cell-free DNA (cfDNA) sequencing demonstrated the feasibility of detecting genomic amplifications not detected in PT sampling. Lastly, we profiled paired primary tumors, metastatic tumors, and cfDNA from patients enrolled in the personalized antibodies for GEA (PANGEA) trial of targeted therapies in GEA and found that genomic biomarkers were recurrently discrepant between the PT and untreated metastases. Divergent primary and metastatic tissue profiling led to treatment reassignment in 32% (9/28) of patients. In discordant primary and metastatic lesions, we found 87.5% concordance for targetable alterations in metastatic tissue and cfDNA, suggesting the potential for cfDNA profiling to enhance selection of therapy.Significance: We demonstrate frequent baseline heterogeneity in targetable genomic alterations in GEA, indicating that current tissue sampling practices for biomarker testing do not effectively guide precision medicine in this disease and that routine profiling of metastatic lesions and/or cfDNA should be systematically evaluated. Cancer Discov; 8(1); 37-48. ©2017 AACR.See related commentary by Sundar and Tan, p. 14See related article by Janjigian et al., p. 49This article is highlighted in the In This Issue feature, p. 1.

Gauging NOTCH1 Activation in Cancer Using Immunohistochemistry.

Fixed, paraffin-embedded (FPE) tissues are a potentially rich resource for studying the role of NOTCH1 in cancer and other pathologies, but tests that reliably detect activated NOTCH1 (NICD1) in FPE samples have been lacking. Here, we bridge this gap by developing an immunohistochemical (IHC) stain that detects a neoepitope created by the proteolytic cleavage event that activates NOTCH1. Following validation using xenografted cancers and normal tissues with known patterns of NOTCH1 activation, we applied this test to tumors linked to dysregulated Notch signaling by mutational studies. As expected, frequent NICD1 staining was observed in T lymphoblastic leukemia/lymphoma, a tumor in which activating NOTCH1 mutations are common. However, when IHC was used to gauge NOTCH1 activation in other human cancers, several unexpected findings emerged. Among B cell tumors, NICD1 staining was much more frequent in chronic lymphocytic leukemia than would be predicted based on the frequency of NOTCH1 mutations, while mantle cell lymphoma and diffuse large B cell lymphoma showed no evidence of NOTCH1 activation. NICD1 was also detected in 38% of peripheral T cell lymphomas. Of interest, NICD1 staining in chronic lymphocytic leukemia cells and in angioimmunoblastic lymphoma was consistently more pronounced in lymph nodes than in surrounding soft tissues, implicating factors in the nodal microenvironment in NOTCH1 activation in these diseases. Among carcinomas, diffuse strong NICD1 staining was observed in 3.8% of cases of triple negative breast cancer (3 of 78 tumors), but was absent from 151 non-small cell lung carcinomas and 147 ovarian carcinomas. Frequent staining of normal endothelium was also observed; in line with this observation, strong NICD1 staining was also seen in 77% of angiosarcomas. These findings complement insights from genomic sequencing studies and suggest that IHC staining is a valuable experimental tool that may be useful in selection of patients for clinical trials.