Noushin Niknafs

Cell-free DNA in the blood provides a non-invasive diagnostic avenue for patients with cancer1. However, characteristics of the origins and molecular features of cell-free DNA are poorly understood. Here we developed an approach to evaluate fragmentation patterns of cell-free DNA across the genome, and found that profiles of healthy individuals reflected nucleosomal patterns of white blood cells, whereas patients with cancer had altered fragmentation profiles. We used this method to analyse the fragmentation profiles of 236 patients with breast, colorectal, lung, ovarian, pancreatic, gastric or bile duct cancer and 245 healthy individuals. A machine learning model that incorporated genome-wide fragmentation features had sensitivities of detection ranging from 57% to more than 99% among the seven cancer types at 98% specificity, with an overall area under the curve value of 0.94. Fragmentation profiles could be used to identify the tissue of origin of the cancers to a limited number of sites in 75% of cases. Combining our approach with mutation-based cell-free DNA analyses detected 91% of patients with cancer. The results of these analyses highlight important properties of cell-free DNA and provide a proof-of-principle approach for the screening, early detection and monitoring of human cancer. Analyses of fragmentation patterns of cell-free DNA in the blood of patients with cancer and healthy individuals using a machine learning algorithm provide a proof-of principle approach for the early detection and screening of human cancer.

Abstract Immune checkpoint inhibitors have shown significant therapeutic responses against tumors containing increased mutation-associated neoantigen load. We have examined the evolving landscape of tumor neoantigens during the emergence of acquired resistance in patients with non–small cell lung cancer after initial response to immune checkpoint blockade with anti–PD-1 or anti–PD-1/anti–CTLA-4 antibodies. Analyses of matched pretreatment and resistant tumors identified genomic changes resulting in loss of 7 to 18 putative mutation-associated neoantigens in resistant clones. Peptides generated from the eliminated neoantigens elicited clonal T-cell expansion in autologous T-cell cultures, suggesting that they generated functional immune responses. Neoantigen loss occurred through elimination of tumor subclones or through deletion of chromosomal regions containing truncal alterations, and was associated with changes in T-cell receptor clonality. These analyses provide insight into the dynamics of mutational landscapes during immune checkpoint blockade and have implications for the development of immune therapies that target tumor neoantigens. Significance: Acquired resistance to immune checkpoint therapy is being recognized more commonly. This work demonstrates for the first time that acquired resistance to immune checkpoint blockade can arise in association with the evolving landscape of mutations, some of which encode tumor neoantigens recognizable by T cells. These observations imply that widening the breadth of neoantigen reactivity may mitigate the development of acquired resistance. Cancer Discov; 7(3); 264–76. ©2017 AACR. See related commentary by Yang, p. 250. This article is highlighted in the In This Issue feature, p. 235

High-grade serous ovarian carcinoma (HGSOC) is the most frequent type of ovarian cancer and has a poor outcome. It has been proposed that fallopian tube cancers may be precursors of HGSOC but evolutionary evidence for this hypothesis has been limited. Here, we perform whole-exome sequence and copy number analyses of laser capture microdissected fallopian tube lesions (p53 signatures, serous tubal intraepithelial carcinomas (STICs), and fallopian tube carcinomas), ovarian cancers, and metastases from nine patients. The majority of tumor-specific alterations in ovarian cancers were present in STICs, including those affecting TP53, BRCA1, BRCA2 or PTEN. Evolutionary analyses reveal that p53 signatures and STICs are precursors of ovarian carcinoma and identify a window of 7 years between development of a STIC and initiation of ovarian carcinoma, with metastases following rapidly thereafter. Our results provide insights into the etiology of ovarian cancer and have implications for prevention, early detection and therapeutic intervention of this disease.

Background: Neoadjuvant anti-PD-1 may improve outcomes for patients with resectable NSCLC and provides a critical window for examining pathologic features associated with response. Resections showing major pathologic response to neoadjuvant therapy, defined as ≤10% residual viable tumor (RVT), may predict improved long-term patient outcome. However, %RVT calculations were developed in the context of chemotherapy (%cRVT). An immune-related %RVT (%irRVT) has yet to be developed. Patients and methods: The first trial of neoadjuvant anti-PD-1 (nivolumab, NCT02259621) was just reported. We analyzed hematoxylin and eosin-stained slides from the post-treatment resection specimens of the 20 patients with non-small-cell lung carcinoma who underwent definitive surgery. Pretreatment tumor biopsies and preresection radiographic 'tumor' measurements were also assessed. Results: We found that the regression bed (the area of immune-mediated tumor clearance) accounts for the previously noted discrepancy between CT imaging and pathologic assessment of residual tumor. The regression bed is characterized by (i) immune activation-dense tumor infiltrating lymphocytes with macrophages and tertiary lymphoid structures; (ii) massive tumor cell death-cholesterol clefts; and (iii) tissue repair-neovascularization and proliferative fibrosis (each feature enriched in major pathologic responders versus nonresponders, P < 0.05). This distinct constellation of histologic findings was not identified in any pretreatment specimens. Histopathologic features of the regression bed were used to develop 'Immune-Related Pathologic Response Criteria' (irPRC), and these criteria were shown to be reproducible amongst pathologists. Specifically, %irRVT had improved interobserver consistency compared with %cRVT [median per-case %RVT variability 5% (0%-29%) versus 10% (0%-58%), P = 0.007] and a twofold decrease in median standard deviation across pathologists within a sample (4.6 versus 2.2, P = 0.002). Conclusions: irPRC may be used to standardize pathologic assessment of immunotherapeutic efficacy. Long-term follow-up is needed to determine irPRC reliability as a surrogate for recurrence-free and overall survival.