Correlation between variant allele frequency and mean tumor molecules with tumor burden in patients with solid tumors.

Several studies have demonstrated the prognostic value of circulating tumor DNA (ctDNA); however, the correlation of mean tumor molecules (MTM)/ml of plasma and mean variant allele frequency (mVAF; %) with clinical parameters is yet to be understood. In this study, we analyzed ctDNA data in a pan-cancer cohort of 23 543 patients who had ctDNA testing performed using a personalized, tumor-informed assay (Signatera™, mPCR-NGS assay). For ctDNA-positive patients, the correlation between MTM/ml and mVAF was examined. Two subanalyses were performed: (a) to establish the association of ctDNA with tumor volume and (b) to assess the correlation between ctDNA dynamics and patient outcomes. On a global cohort, a positive correlation between MTM/ml and mVAF was observed. Among 18 426 patients with longitudinal ctDNA measurements, 13.3% had discordant trajectories between MTM/ml and mVAF at subsequent time points. In metastatic patients receiving immunotherapy (N = 51), changes in ctDNA levels expressed both in MTM/ml and mVAF showed a statistically significant association with progression-free survival; however, the correlation with MTM/ml was numerically stronger.

Analysis of Plasma Cell-Free DNA by Ultradeep Sequencing in Patients With Stages I to III Colorectal Cancer.

IMPORTANCE: Novel sensitive methods for detection and monitoring of residual disease can improve postoperative risk stratification with implications for patient selection for adjuvant chemotherapy (ACT), ACT duration, intensity of radiologic surveillance, and, ultimately, outcome for patients with colorectal cancer (CRC). OBJECTIVE: To investigate the association of circulating tumor DNA (ctDNA) with recurrence using longitudinal data from ultradeep sequencing of plasma cell-free DNA in patients with CRC before and after surgery, during and after ACT, and during surveillance. DESIGN, SETTING, AND PARTICIPANTS: In this prospective, multicenter cohort study, ctDNA was quantified in the preoperative and postoperative settings of stages I to III CRC by personalized multiplex, polymerase chain reaction-based, next-generation sequencing. The study enrolled 130 patients at the surgical departments of Aarhus University Hospital, Randers Hospital, and Herning Hospital in Denmark from May 1, 2014, to January 31, 2017. Plasma samples (n = 829) were collected before surgery, postoperatively at day 30, and every third month for up to 3 years. MAIN OUTCOMES AND MEASURES: Outcomes were ctDNA measurement, clinical recurrence, and recurrence-free survival. RESULTS: A total of 130 patients with stages I to III CRC (mean [SD] age, 67.9 [10.1] years; 74 [56.9%] male) were enrolled in the study; 5 patients discontinued participation, leaving 125 patients for analysis. Preoperatively, ctDNA was detectable in 108 of 122 patients (88.5%). After definitive treatment, longitudinal ctDNA analysis identified 14 of 16 relapses (87.5%). At postoperative day 30, ctDNA-positive patients were 7 times more likely to relapse than ctDNA-negative patients (hazard ratio [HR], 7.2; 95% CI, 2.7-19.0; P < .001). Similarly, shortly after ACT ctDNA-positive patients were 17 times (HR, 17.5; 95% CI, 5.4-56.5; P < .001) more likely to relapse. All 7 patients who were ctDNA positive after ACT experienced relapse. Monitoring during and after ACT indicated that 3 of the 10 ctDNA-positive patients (30.0%) were cleared by ACT. During surveillance after definitive therapy, ctDNA-positive patients were more than 40 times more likely to experience disease recurrence than ctDNA-negative patients (HR, 43.5; 95% CI, 9.8-193.5 P < .001). In all multivariate analyses, ctDNA status was independently associated with relapse after adjusting for known clinicopathologic risk factors. Serial ctDNA analyses revealed disease recurrence up to 16.5 months ahead of standard-of-care radiologic imaging (mean, 8.7 months; range, 0.8-16.5 months). Actionable mutations were identified in 81.8% of the ctDNA-positive relapse samples. CONCLUSIONS AND RELEVANCE: Circulating tumor DNA analysis can potentially change the postoperative management of CRC by enabling risk stratification, ACT monitoring, and early relapse detection.

Early Detection of Metastatic Relapse and Monitoring of Therapeutic Efficacy by Ultra-Deep Sequencing of Plasma Cell-Free DNA in Patients With Urothelial Bladder Carcinoma.

PURPOSE: Novel sensitive methods for early detection of relapse and for monitoring therapeutic efficacy may have a huge impact on risk stratification, treatment, and ultimately outcome for patients with bladder cancer. We addressed the prognostic and predictive impact of ultra-deep sequencing of cell-free DNA in patients before and after cystectomy and during chemotherapy. PATIENTS AND METHODS: We included 68 patients with localized advanced bladder cancer. Patient-specific somatic mutations, identified by whole-exome sequencing, were used to assess circulating tumor DNA (ctDNA) by ultra-deep sequencing (median, 105,000×) of plasma DNA. Plasma samples (n = 656) were procured at diagnosis, during chemotherapy, before cystectomy, and during surveillance. Expression profiling was performed for tumor subtype and immune signature analyses. RESULTS: Presence of ctDNA was highly prognostic at diagnosis before chemotherapy (hazard ratio, 29.1; P = .001). After cystectomy, ctDNA analysis correctly identified all patients with metastatic relapse during disease monitoring (100% sensitivity, 98% specificity). A median lead time over radiographic imaging of 96 days was observed. In addition, for high-risk patients (ctDNA positive before or during treatment), the dynamics of ctDNA during chemotherapy was associated with disease recurrence (P = .023), whereas pathologic downstaging was not. Analysis of tumor-centric biomarkers showed that mutational processes (signature 5) were associated with pathologic downstaging (P = .024); however, no significant correlation for tumor subtypes, DNA damage response mutations, and other biomarkers was observed. Our results suggest that ctDNA analysis is better associated with treatment efficacy compared with other available methods. CONCLUSION: ctDNA assessment for early risk stratification, therapy monitoring, and early relapse detection in bladder cancer is feasible and provides a basis for clinical studies that evaluate early therapeutic interventions.

Personalized Detection of Circulating Tumor DNA Antedates Breast Cancer Metastatic Recurrence.

PURPOSE: Up to 30% of patients with breast cancer relapse after primary treatment. There are no sensitive and reliable tests to monitor these patients and detect distant metastases before overt recurrence. Here, we demonstrate the use of personalized circulating tumor DNA (ctDNA) profiling for detection of recurrence in breast cancer. EXPERIMENTAL DESIGN: Forty-nine primary patients with breast cancer were recruited following surgery and adjuvant therapy. Plasma samples (n = 208) were collected every 6 months for up to 4 years. Personalized assays targeting 16 variants selected from primary tumor whole-exome data were tested in serial plasma for the presence of ctDNA by ultradeep sequencing (average >100,000X). RESULTS: Plasma ctDNA was detected ahead of clinical or radiologic relapse in 16 of the 18 relapsed patients (sensitivity of 89%); metastatic relapse was predicted with a lead time of up to 2 years (median, 8.9 months; range, 0.5-24.0 months). None of the 31 nonrelapsing patients were ctDNA-positive at any time point across 156 plasma samples (specificity of 100%). Of the two relapsed patients who were not detected in the study, the first had only a local recurrence, whereas the second patient had bone recurrence and had completed chemotherapy just 13 days prior to blood sampling. CONCLUSIONS: This study demonstrates that patient-specific ctDNA analysis can be a sensitive and specific approach for disease surveillance for patients with breast cancer. More importantly, earlier detection of up to 2 years provides a possible window for therapeutic intervention.

Corrigendum: Phylogenetic ctDNA analysis depicts early-stage lung cancer evolution.

This corrects the article DOI: 10.1038/nature22364.

Phylogenetic ctDNA analysis depicts early-stage lung cancer evolution.

The early detection of relapse following primary surgery for non-small-cell lung cancer and the characterization of emerging subclones, which seed metastatic sites, might offer new therapeutic approaches for limiting tumour recurrence. The ability to track the evolutionary dynamics of early-stage lung cancer non-invasively in circulating tumour DNA (ctDNA) has not yet been demonstrated. Here we use a tumour-specific phylogenetic approach to profile the ctDNA of the first 100 TRACERx (Tracking Non-Small-Cell Lung Cancer Evolution Through Therapy (Rx)) study participants, including one patient who was also recruited to the PEACE (Posthumous Evaluation of Advanced Cancer Environment) post-mortem study. We identify independent predictors of ctDNA release and analyse the tumour-volume detection limit. Through blinded profiling of postoperative plasma, we observe evidence of adjuvant chemotherapy resistance and identify patients who are very likely to experience recurrence of their lung cancer. Finally, we show that phylogenetic ctDNA profiling tracks the subclonal nature of lung cancer relapse and metastasis, providing a new approach for ctDNA-driven therapeutic studies.

Fast and scalable inference of multi-sample cancer lineages.

Somatic variants can be used as lineage markers for the phylogenetic reconstruction of cancer evolution. Since somatic phylogenetics is complicated by sample heterogeneity, novel specialized tree-building methods are required for cancer phylogeny reconstruction. We present LICHeE (Lineage Inference for Cancer Heterogeneity and Evolution), a novel method that automates the phylogenetic inference of cancer progression from multiple somatic samples. LICHeE uses variant allele frequencies of somatic single nucleotide variants obtained by deep sequencing to reconstruct multi-sample cell lineage trees and infer the subclonal composition of the samples. LICHeE is open source and available at http://viq854.github.io/lichee .

Mutations in early follicular lymphoma progenitors are associated with suppressed antigen presentation.

Follicular lymphoma (FL) is incurable with conventional therapies and has a clinical course typified by multiple relapses after therapy. These tumors are genetically characterized by B-cell leukemia/lymphoma 2 (BCL2) translocation and mutation of genes involved in chromatin modification. By analyzing purified tumor cells, we identified additional novel recurrently mutated genes and confirmed mutations of one or more chromatin modifier genes within 96% of FL tumors and two or more in 76% of tumors. We defined the hierarchy of somatic mutations arising during tumor evolution by analyzing the phylogenetic relationship of somatic mutations across the coding genomes of 59 sequentially acquired biopsies from 22 patients. Among all somatically mutated genes, CREBBP mutations were most significantly enriched within the earliest inferable progenitor. These mutations were associated with a signature of decreased antigen presentation characterized by reduced transcript and protein abundance of MHC class II on tumor B cells, in line with the role of CREBBP in promoting class II transactivator (CIITA)-dependent transcriptional activation of these genes. CREBBP mutant B cells stimulated less proliferation of T cells in vitro compared with wild-type B cells from the same tumor. Transcriptional signatures of tumor-infiltrating T cells were indicative of reduced proliferation, and this corresponded to decreased frequencies of tumor-infiltrating CD4 helper T cells and CD8 memory cytotoxic T cells. These observations therefore implicate CREBBP mutation as an early event in FL evolution that contributes to immune evasion via decreased antigen presentation.

Inference of tumor phylogenies with improved somatic mutation discovery.

Next-generation sequencing technologies provide a powerful tool for studying genome evolution during progression of advanced diseases such as cancer. Although many recent studies have employed new sequencing technologies to detect mutations across multiple, genetically related tumors, current methods do not exploit available phylogenetic information to improve the accuracy of their variant calls. Here, we present a novel algorithm that uses somatic single-nucleotide variations (SNVs) in multiple, related tissue samples as lineage markers for phylogenetic tree reconstruction. Our method then leverages the inferred phylogeny to improve the accuracy of SNV discovery. Experimental analyses demonstrate that our method achieves up to 32% improvement for somatic SNV calling of multiple, related samples over the accuracy of GATK's Unified Genotyper, the state-of-the-art multisample SNV caller.

Genome evolution during progression to breast cancer.

Cancer evolution involves cycles of genomic damage, epigenetic deregulation, and increased cellular proliferation that eventually culminate in the carcinoma phenotype. Early neoplasias, which are often found concurrently with carcinomas and are histologically distinguishable from normal breast tissue, are less advanced in phenotype than carcinomas and are thought to represent precursor stages. To elucidate their role in cancer evolution we performed comparative whole-genome sequencing of early neoplasias, matched normal tissue, and carcinomas from six patients, for a total of 31 samples. By using somatic mutations as lineage markers we built trees that relate the tissue samples within each patient. On the basis of these lineage trees we inferred the order, timing, and rates of genomic events. In four out of six cases, an early neoplasia and the carcinoma share a mutated common ancestor with recurring aneuploidies, and in all six cases evolution accelerated in the carcinoma lineage. Transition spectra of somatic mutations are stable and consistent across cases, suggesting that accumulation of somatic mutations is a result of increased ancestral cell division rather than specific mutational mechanisms. In contrast to highly advanced tumors that are the focus of much of the current cancer genome sequencing, neither the early neoplasia genomes nor the carcinomas are enriched with potentially functional somatic point mutations. Aneuploidies that occur in common ancestors of neoplastic and tumor cells are the earliest events that affect a large number of genes and may predispose breast tissue to eventual development of invasive carcinoma.

Module cover - a new approach to genotype-phenotype studies.

Uncovering and interpreting phenotype/genotype relationships are among the most challenging open questions in disease studies. Set cover approaches are explicitly designed to provide a representative set for diverse disease cases and thus are valuable in studies of heterogeneous datasets. At the same time pathway-centric methods have emerged as key approaches that significantly empower studies of genotype-phenotype relationships. Combining the utility of set cover techniques with the power of network-centric approaches, we designed a novel approach that extends the concept of set cover to network modules cover. We developed two alternative methods to solve the module cover problem: (i) an integrated method that simultaneously determines network modules and optimizes the coverage of disease cases. (ii) a two-step method where we first determined a candidate set of network modules and subsequently selected modules that provided the best coverage of the disease cases. The integrated method showed superior performance in the context of our application. We demonstrated the utility of the module cover approach for the identification of groups of related genes whose activity is perturbed in a coherent way by specific genomic alterations, allowing the interpretation of the heterogeneity of cancer cases.

Inferring cancer subnetwork markers using density-constrained biclustering.

MOTIVATION: Recent genomic studies have confirmed that cancer is of utmost phenotypical complexity, varying greatly in terms of subtypes and evolutionary stages. When classifying cancer tissue samples, subnetwork marker approaches have proven to be superior over single gene marker approaches, most importantly in cross-platform evaluation schemes. However, prior subnetwork-based approaches do not explicitly address the great phenotypical complexity of cancer. RESULTS: We explicitly address this and employ density-constrained biclustering to compute subnetwork markers, which reflect pathways being dysregulated in many, but not necessarily all samples under consideration. In breast cancer we achieve substantial improvements over all cross-platform applicable approaches when predicting TP53 mutation status in a well-established non-cross-platform setting. In colon cancer, we raise prediction accuracy in the most difficult instances from 87% to 93% for cancer versus non-cancer and from 83% to (astonishing) 92%, for with versus without liver metastasis, in well-established cross-platform evaluation schemes. AVAILABILITY: Software is available on request.