Metastatic breast cancer is usually diagnosed after becoming symptomatic, at which point it is rarely curable. Cell-free circulating tumor DNA (ctDNA) contains tumor-specific chromosomal rearrangements that may be interrogated in blood plasma. We evaluated serial monitoring of ctDNA for earlier detection of metastasis in a retrospective study of 20 patients diagnosed with primary breast cancer and long follow-up. Using an approach combining low-coverage whole-genome sequencing of primary tumors and quantification of tumor-specific rearrangements in plasma by droplet digital PCR, we identify for the first time that ctDNA monitoring is highly accurate for postsurgical discrimination between patients with (93%) and without (100%) eventual clinically detected recurrence. ctDNA-based detection preceded clinical detection of metastasis in 86% of patients with an average lead time of 11 months (range 0–37 months), whereas patients with long-term disease-free survival had undetectable ctDNA postoperatively. ctDNA quantity was predictive of poor survival. These findings establish the rationale for larger validation studies in early breast cancer to evaluate ctDNA as a monitoring tool for early metastasis detection, therapy modification, and to aid in avoidance of overtreatment.

BackgroundBreast cancer exhibits significant molecular, pathological, and clinical heterogeneity. Current clinicopathological evaluation is imperfect for predicting outcome, which results in overtreatment for many patients, and for others, leads to death from recurrent disease. Therefore, additional criteria are needed to better personalize care and maximize treatment effectiveness and survival.MethodsTo address these challenges, the Sweden Cancerome Analysis Network - Breast (SCAN-B) consortium was initiated in 2010 as a multicenter prospective study with longsighted aims to analyze breast cancers with next-generation genomic technologies for translational research in a population-based manner and integrated with healthcare; decipher fundamental tumor biology from these analyses; utilize genomic data to develop and validate new clinically-actionable biomarker assays; and establish real-time clinical implementation of molecular diagnostic, prognostic, and predictive tests. In the first phase, we focus on molecular profiling by next-generation RNA-sequencing on the Illumina platform.ResultsIn the first 3 years from 30 August 2010 through 31 August 2013, we have consented and enrolled 3,979 patients with primary breast cancer at the seven hospital sites in South Sweden, representing approximately 85% of eligible patients in the catchment area. Preoperative blood samples have been collected for 3,942 (99%) patients and primary tumor specimens collected for 2,929 (74%) patients. Herein we describe the study infrastructure and protocols and present initial proof of concept results from prospective RNA sequencing including tumor molecular subtyping and detection of driver gene mutations. Prospective patient enrollment is ongoing.ConclusionsWe demonstrate that large-scale population-based collection and RNA-sequencing analysis of breast cancer is feasible. The SCAN-B Initiative should significantly reduce the time to discovery, validation, and clinical implementation of novel molecular diagnostic and predictive tests. We welcome the participation of additional comprehensive cancer treatment centers.Trial registrationClinicalTrials.gov identifier NCT02306096.Electronic supplementary materialThe online version of this article (doi:10.1186/s13073-015-0131-9) contains supplementary material, which is available to authorized users.

Predicting the clinical outcome of cancer patients based on the expression of marker genes in their tumors has received increasing interest in the past decade. Accurate predictors of outcome and response to therapy could be used to personalize and thereby improve therapy. However, state of the art methods used so far often found marker genes with limited prediction accuracy, limited reproducibility, and unclear biological relevance. To address this problem, we developed a novel computational approach to identify genes prognostic for outcome that couples gene expression measurements from primary tumor samples with a network of known relationships between the genes. Our approach ranks genes according to their prognostic relevance using both expression and network information in a manner similar to Google's PageRank. We applied this method to gene expression profiles which we obtained from 30 patients with pancreatic cancer, and identified seven candidate marker genes prognostic for outcome. Compared to genes found with state of the art methods, such as Pearson correlation of gene expression with survival time, we improve the prediction accuracy by up to 7%. Accuracies were assessed using support vector machine classifiers and Monte Carlo cross-validation. We then validated the prognostic value of our seven candidate markers using immunohistochemistry on an independent set of 412 pancreatic cancer samples. Notably, signatures derived from our candidate markers were independently predictive of outcome and superior to established clinical prognostic factors such as grade, tumor size, and nodal status. As the amount of genomic data of individual tumors grows rapidly, our algorithm meets the need for powerful computational approaches that are key to exploit these data for personalized cancer therapies in clinical practice.

T cell non-Hodgkin lymphomas (T-NHLs) represent a heterogeneous group of highly aggressive malignancies with poor clinical outcomes1. T-NHLs originate from peripheral T lymphocytes and are frequently characterized by genetic gain-of-function variants in T cell antigen receptor (TCR) signalling molecules1–4. Although these oncogenic alterations are thought to drive TCR pathways to induce chronic proliferation and survival programmes, it remains unclear whether T cells harbour tumour suppressors that can counteract these events. Using a murine model of human T cell lymphoma, we demonstrate that the acute enforcement of oncogenic TCR signalling in lymphocytes drives the strong expansion of these cells in vivo. However, this response is short-lived and robustly counteracted by cell-intrinsic mechanisms. A subsequent genome-wide in vivo screen using T cell-specific transposon mutagenesis identified PDCD1, which encodes the inhibitory receptor Programmed Death-1 (PD-1), as a master gene suppressing oncogenic T cell signalling. Mono- and bi-allelic PDCD1 deletions are also recurrently observed in human T cell lymphomas with frequencies that can exceed 30%, indicating high clinical relevance. Mechanistically, PD-1 activity enhances PTEN levels and attenuates AKT and PKC signalling in pre-malignant cells. In contrast, a homo- or heterozygous deletion of PD-1 allows unrestricted T cell growth after an oncogenic insult and leads to the rapid development of highly aggressive lymphomas in vivo that are readily transplantable to recipients. Altogether, these results indicate that the inhibitory PD-1 receptor is a potent haploinsufficient tumour suppressor in T-NHLs that is frequently altered in human disease. These findings extend the known physiological functions of PD-1 beyond the prevention of immunopathology after antigen-induced T cell activation and have implications for T cell lymphoma therapies and for current strategies that target PD-1 in the broader context of immuno-oncology.

SCOPPI, the structural classification of protein–protein interfaces, is a comprehensive database that classifies and annotates domain interactions derived from all known protein structures. SCOPPI applies SCOP domain definitions and a distance criterion to determine inter-domain interfaces. Using a novel method based on multiple sequence and structural alignments of SCOP families, SCOPPI presents a comprehensive geometrical classification of domain interfaces. Various interface characteristics such as number, type and position of interacting amino acids, conservation, interface size, and permanent or transient nature of the interaction are further provided. Proteins in SCOPPI are annotated with Gene Ontology terms, and the ontology can be used to quickly browse SCOPPI. Screenshots are available for every interface and its participating domains. Here, we describe contents and features of the web-based user interface as well as the underlying methods used to generate SCOPPI's data. In addition, we present a number of examples where SCOPPI becomes a useful tool to analyze viral mimicry of human interface binding sites, gene fusion events, conservation of interface residues and diversity of interface localizations. SCOPPI is available at .