Added value of whole-exome and RNA sequencing in advanced and refractory cancer patients with no molecular-based treatment recommendation based on a 90-gene panel.

INTRODUCTION: The objective was to determine the added value of comprehensive molecular profile by whole-exome and RNA sequencing (WES/RNA-Seq) in advanced and refractory cancer patients who had no molecular-based treatment recommendation (MBTR) based on a more limited targeted gene panel (TGP) plus array-based comparative genomic hybridization (aCGH). MATERIALS AND METHODS: In this retrospective analysis, we selected 50 patients previously included in the PROFILER trial (NCT01774409) for which no MBT could be recommended based on a targeted 90-gene panel and aCGH. For each patient, the frozen tumor sample mirroring the FFPE sample used for TGP/aCGH analysis were processed for WES and RNA-Seq. Data from TGP/aCGH were reanalyzed, and together with WES/RNA-Seq, findings were simultaneously discussed at a new molecular tumor board (MTB). RESULTS: After exclusion of variants of unknown significance, a total of 167 somatic molecular alterations were identified in 50 patients (median: 3 [1-10]). Out of these 167 relevant molecular alterations, 51 (31%) were common to both TGP/aCGH and WES/RNA-Seq, 19 (11%) were identified by the TGP/aCGH only and 97 (58%) were identified by WES/RNA-Seq only, including two fusion transcripts in two patients. A MBTR was provided in 4/50 (8%) patients using the information from TGP/aCGH versus 9/50 (18%) patients using WES/RNA-Seq findings. Three patients had similar recommendations based on TGP/aCGH and WES/RNA-Seq. CONCLUSIONS: In advanced and refractory cancer patients in whom no MBTR was recommended from TGP/aCGH, WES/RNA-Seq allowed to identify more alterations which may in turn, in a limited fraction of patients, lead to new MBTR.

Sorafenib in Molecularly Selected Cancer Patients: Final Analysis of the MOST-Plus Sorafenib Cohort.

BACKGROUND: MOST-plus is a multicenter, randomized, open-label, adaptive Phase II trial evaluating the clinical benefit of targeted treatments matched to molecular alteration in advanced/metastatic solid tumors. Sorafenib was tested on patients with tumors harboring sorafenib-targeted genes. METHODS: The MOST-plus trial used a randomized discontinuation design. After 12 weeks of sorafenib (400 mg, po BID), patients with progressive disease discontinued study, patients with objective response were proposed to continue sorafenib, whereas patients with stable disease (SD) were randomly assigned (1:1) to the maintenance or interruption of treatment. The primary endpoint was RECIST version 1.1 progression-free rate at 16 weeks after randomization (PFR-16w). Secondary endpoints included progression-free survival (PFS), overall survival (OS), and toxicity. Statistical analyses used a sequential Bayesian approach with interim efficacy analyses. The enrolment could be stopped in the case of a 95% probability for the estimated PFR-16w to be higher in the maintenance than in the interruption arm (NCT02029001). RESULTS: 151 patients were included, of whom 35 had SD at 12 weeks of Sorafenib. For the 35 patients with SD on sorafenib, the PFR-16w was 65% [95% credibility interval 43.4-83.7] in the continuation arm and 25% [7.8-48.1] in the interruption arm. Median PFS and OS were improved in the maintenance versus the interruption arm (mPFS: 5.6 [95%CI 1.97-6.77] months versus 2.0 [95%CI 1.61-3.91] months (p = 0.0231) and mOS: 14.3 [95%CI 8.9-23.8] versus 8.0 months [95%CI 3.5-15.2] (p = 0.0857)). CONCLUSION: Sorafenib showed activity in progressive patients with solid tumors harboring somatic genomic alterations in sorafenib-targeted genes. Continuing sorafenib when SD is achieved improves PFR compared to interruption.

Dissecting the Origin of Heterogeneity in Uterine and Ovarian Carcinosarcomas.

Gynecologic carcinosarcomas (CS) are biphasic neoplasms composed of carcinomatous (C) and sarcomatous (S) malignant components. Because of their rarity and histologic complexity, genetic and functional studies on CS are scarce and the mechanisms of initiation and development remain largely unknown. Whole-genome analysis of the C and S components reveals shared genomic alterations, thus emphasizing the clonal evolution of CS. Reconstructions of the evolutionary history of each tumor further reveal that C and S samples are composed of both ancestral cell populations and component-specific subclones, supporting a common origin followed by distinct evolutionary trajectories. However, while we do not find any recurrent genomic features associated with phenotypic divergence, transcriptomic and methylome analyses identify a common mechanism across the cohort, the epithelial-to-mesenchymal transition (EMT), suggesting a role for nongenetic factors in inflicting changes to cellular fate. Altogether, these data accredit the hypothesis that CS tumors are driven by both clonal evolution and transcriptomic reprogramming, essential for susceptibility to transdifferentiation upon encountering environmental cues, thus linking CS heterogeneity to genetic, transcriptomic, and epigenetic influences. Significance: We have provided a detailed characterization of the genomic landscape of CS and identified EMT as a common mechanism associated with phenotypic divergence, linking CS heterogeneity to genetic, transcriptomic, and epigenetic influences.

Multiomic analysis of malignant pleural mesothelioma identifies molecular axes and specialized tumor profiles driving intertumor heterogeneity.

Malignant pleural mesothelioma (MPM) is an aggressive cancer with rising incidence and challenging clinical management. Through a large series of whole-genome sequencing data, integrated with transcriptomic and epigenomic data using multiomics factor analysis, we demonstrate that the current World Health Organization classification only accounts for up to 10% of interpatient molecular differences. Instead, the MESOMICS project paves the way for a morphomolecular classification of MPM based on four dimensions: ploidy, tumor cell morphology, adaptive immune response and CpG island methylator profile. We show that these four dimensions are complementary, capture major interpatient molecular differences and are delimited by extreme phenotypes that-in the case of the interdependent tumor cell morphology and adapted immune response-reflect tumor specialization. These findings unearth the interplay between MPM functional biology and its genomic history, and provide insights into the variations observed in the clinical behavior of patients with MPM.

Genomics to select treatment for patients with metastatic breast cancer.

Cancer progression is driven in part by genomic alterations1. The genomic characterization of cancers has shown interpatient heterogeneity regarding driver alterations2, leading to the concept that generation of genomic profiling in patients with cancer could allow the selection of effective therapies3,4. Although DNA sequencing has been implemented in practice, it remains unclear how to use its results. A total of 1,462 patients with HER2-non-overexpressing metastatic breast cancer were enroled to receive genomic profiling in the SAFIR02-BREAST trial. Two hundred and thirty-eight of these patients were randomized in two trials (nos. NCT02299999 and NCT03386162) comparing the efficacy of maintenance treatment5 with a targeted therapy matched to genomic alteration. Targeted therapies matched to genomics improves progression-free survival when genomic alterations are classified as level I/II according to the ESMO Scale for Clinical Actionability of Molecular Targets (ESCAT)6 (adjusted hazards ratio (HR): 0.41, 90% confidence interval (CI): 0.27-0.61, P < 0.001), but not when alterations are unselected using ESCAT (adjusted HR: 0.77, 95% CI: 0.56-1.06, P = 0.109). No improvement in progression-free survival was observed in the targeted therapies arm (unadjusted HR: 1.15, 95% CI: 0.76-1.75) for patients presenting with ESCAT alteration beyond level I/II. Patients with germline BRCA1/2 mutations (n = 49) derived high benefit from olaparib (gBRCA1: HR = 0.36, 90% CI: 0.14-0.89; gBRCA2: HR = 0.37, 90% CI: 0.17-0.78). This trial provides evidence that the treatment decision led by genomics should be driven by a framework of target actionability in patients with metastatic breast cancer.

Comprehensive Genome Profiling in Patients With Metastatic Non-Small Cell Lung Cancer: The Precision Medicine Phase II Randomized SAFIR02-Lung/IFCT 1301 Trial.

PURPOSE: Targeted therapies (TT) and immune checkpoint blockers (ICB) have revolutionized the approach to non-small cell lung cancer (NSCLC) treatment in the era of precision medicine. Their impact as switch maintenance therapy based on molecular characterization is unknown. PATIENTS AND METHODS: SAFIR02-Lung/IFCT 1301 was an open-label, randomized, phase II trial, involving 33 centers in France. We investigated eight TT (substudy-1) and one ICB (substudy-2), compared with standard-of-care as a maintenance strategy in patients with advanced EGFR, ALK wild-type (wt) NSCLC without progression after first-line chemotherapy, based on high-throughput genome analysis. The primary outcome was progression-free survival (PFS). RESULTS: Among the 175 patients randomized in substudy-1, 116 received TT (selumetinib, vistusertib, capivasertib, AZD4547, AZD8931, vandetanib, olaparib, savolitinib) and 59 standard-of-care. Median PFS was 2.7 months [95% confidence interval (CI), 1.6-2.9] with TT versus 2.7 months (1.6-4.1) with standard-of-care (HR, 0.97; 95% CI, 0.7-1.36; P = 0.87). There were no significant differences in PFS within any molecular subgroup. In substudy-2, 183 patients were randomized, 121 received durvalumab and 62 standard-of-care. Median PFS was 3.0 months (2.3-4.4) with durvalumab versus 3.0 months (2.0-5.1) with standard-of-care (HR, 0.86; 95% CI, 0.62-1.20; P = 0.38). Preplanned subgroup analysis showed an enhanced benefit with durvalumab in patients with PD-L1 tumor proportion score (TPS) ≥1%, (n = 29; HR, 0.29; 95% CI, 0.11-0.75) as compared with PD-L1 <1% (n = 31; HR, 0.71; 95% CI, 0.31-1.60; Pinteraction = 0.036). CONCLUSIONS: Molecular profiling can feasibly be implemented to guide treatment choice for the maintenance strategy in EGFR/ALK wt NSCLC; in this study it did not lead to substantial treatment benefits beyond durvalumab for PD-L1 ≥ 1 patients.

Epithelial-to-mesenchymal transition promotes immune escape by inducing CD70 in non-small cell lung cancer.

INTRODUCTION: Epithelial-to-mesenchymal transition (EMT) is associated with tumor aggressiveness, drug resistance, and poor survival in non-small cell lung cancer (NSCLC) and other cancers. The identification of immune-checkpoint ligands (ICPLs) associated with NSCLCs that display a mesenchymal phenotype (mNSCLC) could help to define subgroups of patients who may benefit from treatment strategies using immunotherapy. METHODS: We evaluated ICPL expression in silico in 130 NSCLC cell lines. In vitro, CRISPR/Cas9-mediated knockdown and lentiviral expression were used to assess the impact of ZEB1 expression on CD70. Gene expression profiles of lung cancer samples from the TCGA (n = 1018) and a dataset from MD Anderson Cancer Center (n = 275) were analyzed. Independent validation was performed by immunohistochemistry and targeted-RNA sequencing in 154 NSCLC whole sections, including a large cohort of pulmonary sarcomatoid carcinomas (SC, n = 55). RESULTS: We uncover that the expression of CD70, a regulatory ligand from the tumor necrosis factor ligand family, is enriched in mNSCLC in vitro models. Mechanistically, the EMT-inducer ZEB1 impacted CD70 expression and fostered increased activity of the CD70 promoter. CD70 overexpression was also evidenced in mNSCLC patient tumor samples and was particularly enriched in SC, a lung cancer subtype associated with poor prognosis. In these tumors, CD70 expression was associated with decreased CD3+ and CD8+ T-cell infiltration and increased T-cell exhaustion markers. CONCLUSION: Our results provide evidence on the pivotal roles of CD70 and ZEB1 in immune escape in mNSCLC, suggesting that EMT might promote cancer progression and metastasis by not only increasing cancer cell plasticity but also reprogramming the immune response in the local tumor microenvironment.

Precision medicine for patients with gastro-oesophageal cancer: A subset analysis of the ProfiLER program.

BACKGROUND: Chemotherapy, anti-HER2 and PD-1 antibodies are standard treatments but only a minority of patients derive long-term benefit from these agents. METHODS: In this report we describe the mutational landscape and outcome of patients with gastroesophageal cancers enroled in the ProfiLER program. RESULTS: Adenocarcinoma (n = 86, 59%), signet-cell (n = 37, 25%) and squamous-cell (n = 21, 14%) were the dominant histology amongst 147 patients. Genomic analyses could be performed for 114 (78%) patients. The most common genomic alterations involved ERBB2 (15%), KRAS (12%), CCND1 (7%), FGFR1-3 (8%), EGFR (5%) and MET (3%), TP53 (51%) and CDKN2A/B (10%). ERBB2, MET and FGFR alterations were found exclusively in the adenocarcinoma and signet-cell subtypes, while CCND1 amplification, TP53 mutations and CDKN2A/B loss were found in both adenocarcinoma and squamous-cell subtypes. Nine patients (8%) received therapy matched to their genomic alteration, with 5 of them achieving disease control. In an exploratory analysis, patients with stage IV disease at diagnosis who had an actionable alteration had longer overall survival compared to those without. CONCLUSION: Genomic profiling for patients with advanced gastroesophageal cancers allows the identification of actionable alterations in large proportion of patients. Increased accessibility to molecularly matched therapy may improve survival in this disease.

Early changes in the immune microenvironment of oral potentially malignant disorders reveal an unexpected association of M2 macrophages with oral cancer free survival.

Understanding the dynamics of the immune microenvironment is critical to the development of immuno-based strategies for the prevention of oral potentially malignant disorders transformation to oral squamous cell carcinoma (OSCC). We used laser capture microdissection and RNA-sequencing to profile the expression of 13 matched pairs of epithelial versus stromal compartments from normal mucosa, hyperplasia, dysplasia, and invasive tumors in the 4-nitroquinolein (4-NQO) murine model of oral carcinogenesis. Genes differentially expressed at each step of transformation were defined. Immune cell deconvolution and enrichment scores of various biological processes including immune-related ones were computed. Immunohistochemistry was also performed to characterize the immune infiltrates by T-cells (T-cells CD3+, helper CD4+, cytotoxic CD8+, regulatory FoxP3+), B-cells (B220+), and macrophages (M1 iNOS+, M2 CD163+) at each histological step. Enrichment of three independent M2 macrophages signatures were computed in 86 oral leukoplakia with available clinical outcome. Most gene expression changes were observed in the stromal compartment and related to immune biological processes. Immune cell deconvolution identified infiltration by the macrophage population as the most important quantitatively especially at the stage of dysplasia. In 86 patients with oral leukoplakia, three M2 macrophages signatures were independently associated with improved oral cancer-free survival. This study provides a better understanding of the dynamics of the immune microenvironment during oral carcinogenesis and highlights an unexpected association of M2 macrophages gene expression signatures with oral cancer free survival in patients with oral leukoplakia.

Exploring the Complementarity of Pancreatic Ductal Adenocarcinoma Preclinical Models.

Purpose: Compare pancreatic ductal adenocarcinoma (PDAC), preclinical models, by their transcriptome and drug response landscapes to evaluate their complementarity. Experimental Design: Three paired PDAC preclinical models-patient-derived xenografts (PDX), xenograft-derived pancreatic organoids (XDPO) and xenograft-derived primary cell cultures (XDPCC)-were derived from 20 patients and analyzed at the transcriptomic and chemosensitivity level. Transcriptomic characterization was performed using the basal-like/classical subtyping and the PDAC molecular gradient (PAMG). Chemosensitivity for gemcitabine, irinotecan, 5-fluorouracil and oxaliplatin was established and the associated biological pathways were determined using independent component analysis (ICA) on the transcriptome of each model. The selection criteria used to identify the different components was the chemosensitivity score (CSS) found for each drug in each model. Results: PDX was the most dispersed model whereas XDPO and XDPCC were mainly classical and basal-like, respectively. Chemosensitivity scoring determines that PDX and XDPO display a positive correlation for three out of four drugs tested, whereas PDX and XDPCC did not correlate. No match was observed for each tumor chemosensitivity in the different models. Finally, pathway analysis shows a significant association between PDX and XDPO for the chemosensitivity-associated pathways and PDX and XDPCC for the chemoresistance-associated pathways. Conclusions: Each PDAC preclinical model possesses a unique basal-like/classical transcriptomic phenotype that strongly influences their global chemosensitivity. Each preclinical model is imperfect but complementary, suggesting that a more representative approach of the clinical reality could be obtained by combining them. Translational Relevance: The identification of molecular signatures that underpin drug sensitivity to chemotherapy in PDAC remains clinically challenging. Importantly, the vast majority of studies using preclinical in vivo and in vitro models fail when transferred to patients in a clinical setting despite initially promising results. This study presents for the first time a comparison between three preclinical models directly derived from the same patients. We show that their applicability to preclinical studies should be considered with a complementary focus, avoiding tumor-based direct extrapolations, which might generate misleading conclusions and consequently the overlook of clinically relevant features.

Prediction of Breast Cancer Treatment-Induced Fatigue by Machine Learning Using Genome-Wide Association Data.

BACKGROUND: We aimed at predicting fatigue after breast cancer treatment using machine learning on clinical covariates and germline genome-wide data. METHODS: We accessed germline genome-wide data of 2799 early-stage breast cancer patients from the Cancer Toxicity study (NCT01993498). The primary endpoint was defined as scoring zero at diagnosis and higher than quartile 3 at 1 year after primary treatment completion on European Organization for Research and Treatment of Cancer quality-of-life questionnaires for Overall Fatigue and on the multidimensional questionnaire for Physical, Emotional, and Cognitive fatigue. First, we tested univariate associations of each endpoint with clinical variables and genome-wide variants. Then, using preselected clinical (false discovery rate < 0.05) and genomic (P < .001) variables, a multivariable preconditioned random-forest regression model was built and validated on a hold-out subset to predict fatigue. Gene set enrichment analysis identified key biological correlates (MetaCore). All statistical tests were 2-sided. RESULTS: Statistically significant clinical associations were found only with Emotional and Cognitive Fatigue, including receipt of chemotherapy, anxiety, and pain. Some single nucleotide polymorphisms had some degree of association (P < .001) with the different fatigue endpoints, although there were no genome-wide statistically significant (P < 5.00 × 10-8) associations. Only for Cognitive Fatigue, the predictive ability of the genomic multivariable model was statistically significantly better than random (area under the curve = 0.59, P = .01) and marginally improved with clinical variables (area under the curve = 0.60, P = .005). Single nucleotide polymorphisms found to be associated (P < .001) with Cognitive Fatigue belonged to genes linked to inflammation (false discovery rate adjusted P = .03), cognitive disorders (P = 1.51 × 10-12), and synaptic transmission (P = 6.28 × 10-8). CONCLUSIONS: Genomic analyses in this large cohort of breast cancer survivors suggest a possible genetic role for severe Cognitive Fatigue that warrants further exploration.

Redefining malignant pleural mesothelioma types as a continuum uncovers immune-vascular interactions.

BACKGROUND: Malignant Pleural Mesothelioma (MPM) is an aggressive disease related to asbestos exposure, with no effective therapeutic options. METHODS: We undertook unsupervised analyses of RNA-sequencing data of 284 MPMs, with no assumption of discreteness. Using immunohistochemistry, we performed an orthogonal validation on a subset of 103 samples and a biological replication in an independent series of 77 samples. FINDINGS: A continuum of molecular profiles explained the prognosis of the disease better than any discrete model. The immune and vascular pathways were the major sources of molecular variation, with strong differences in the expression of immune checkpoints and pro-angiogenic genes; the extrema of this continuum had specific molecular profiles: a "hot" bad-prognosis profile, with high lymphocyte infiltration and high expression of immune checkpoints and pro-angiogenic genes; a "cold" bad-prognosis profile, with low lymphocyte infiltration and high expression of pro-angiogenic genes; and a "VEGFR2+/VISTA+" better-prognosis profile, with high expression of immune checkpoint VISTA and pro-angiogenic gene VEGFR2. We validated the gene expression levels at the protein level for a subset of five selected genes belonging to the immune and vascular pathways (CD8A, PDL1, VEGFR3, VEGFR2, and VISTA), in the validation series, and replicated the molecular profiles as well as their prognostic value in the replication series. INTERPRETATION: The prognosis of MPM is best explained by a continuous model, which extremes show specific expression patterns of genes involved in angiogenesis and immune response.

Author Correction: Landscape of somatic mutations in 560 breast cancer whole-genome sequences.

In the Methods section of this Article, 'greater than' should have been 'less than' in the sentence 'Putative regions of clustered rearrangements were identified as having an average inter-rearrangement distance that was at least 10 times greater than the whole-genome average for the individual sample. '. The Article has not been corrected.

Mutational Profile of Aggressive, Localised Prostate Cancer from African Caribbean Men Versus European Ancestry Men.

Causes of high mortality of prostate cancer in men of African ancestry living in the French West Indies are still debated, between suspicions of environmental factors and genetic susceptibility. We report an integrated genomic study of 25 tumour tissues from radical prostatectomy of aggressive (defined by International Society of Urological Pathology ≥3) prostate cancer patients (10 African Caribbean and 15 French Caucasian) using single nucleotide polymorphism arrays, whole-genome sequencing, and RNA sequencing. The results show that African Caribbean tumours are characterised by a more frequent deletion at 1q41-43 encompassing the DNA repair gene PARP1, and a higher proportion of intrachromosomal rearrangements including duplications associated with CDK12 truncating mutations. Transcriptome analyses show an overexpression of genes related to androgen receptor activity in African Caribbean tumours, and of PVT1, a long non-coding RNA located at 8q24 that confirms the strong involvement of this region in prostate tumours from men of African ancestry. Patient summary: Mortality of prostate cancer is higher in African Caribbean men than in French Caucasian men. Specificities of the former could be explained by genomic events linked with key genes such as DNA damage pathway genes PARP1, CDK12, and the oncogenic long non-coding RNA gene PVT1 at the 8q24 prostate cancer susceptibility locus.

A gene-expression profiling score for prediction of outcome in patients with follicular lymphoma: a retrospective training and validation analysis in three international cohorts.

BACKGROUND: Patients with follicular lymphoma have heterogeneous outcomes. Predictor models to distinguish, at diagnosis, between patients at high and low risk of progression are needed. The objective of this study was to use gene-expression profiling data to build and validate a predictive model of outcome for patients treated in the rituximab era. METHODS: A training set of fresh-frozen tumour biopsies was prospectively obtained from 160 untreated patients with high-tumour-burden follicular lymphoma enrolled in the phase 3 randomised PRIMA trial, in which rituximab maintenance was evaluated after rituximab plus chemotherapy induction (median follow-up 6·6 years [IQR 6·0-7·0]). RNA of sufficient quality was obtained for 149 of 160 cases, and Affymetrix U133 Plus 2.0 microarrays were used for gene-expression profiling. We did a multivariate Cox regression analysis to identify genes with expression levels associated with progression-free survival independently of maintenance treatment in a subgroup of 134 randomised patients. Expression levels from 95 curated genes were then determined by digital expression profiling (NanoString technology) in 53 formalin-fixed paraffin-embedded samples of the training set to compare the technical reproducibility of expression levels for each gene between technologies. Genes with high correlation (>0·75) were included in an L2-penalised Cox model adjusted on rituximab maintenance to build a predictive score for progression-free survival. The model was validated using NanoString technology to digitally quantify gene expression in 488 formalin-fixed, paraffin-embedded samples from three independent international patient cohorts from the PRIMA trial (n=178; distinct from the training cohort), the University of Iowa/Mayo Clinic Lymphoma SPORE project (n=201), and the Barcelona Hospital Clinic (n=109). All tissue samples consisted of pretreatment diagnostic biopsies and were confirmed as follicular lymphoma grade 1-3a. The patients were all treated with regimens containing rituximab and chemotherapy, possibly followed by either rituximab maintenance or ibritumomab-tiuxetan consolidation. We determined an optimum threshold on the score to predict patients at low risk and high risk of progression. The model, including the multigene score and the threshold, was initially evaluated in the three validation cohorts separately. The sensitivity and specificity of the score for the prediction of the risk of lymphoma progression at 2 years were assessed on the combined validation cohorts. FINDINGS: In the training cohort, the expression levels of 395 genes were associated with a risk of progression. 23 genes reflecting both B-cell biology and tumour microenvironment with correlation coefficients greater than 0·75 between the two technologies and sample types were retained to build a predictive model that identified a population at an increased risk of progression (p<0·0001). In a multivariate Cox model for progression-free survival adjusted on rituximab maintenance treatment and Follicular Lymphoma International Prognostic Index 1 (FLIPI-1) score, this predictor independently predicted progression (adjusted hazard ratio [aHR] of the high-risk group compared with the low-risk group 3·68, 95% CI 2·19-6·17 [p<0·0001]). The 5-year progression-free survival was 26% (95% CI 16-43) in the high-risk group and 73% (64-83) in the low-risk group. The predictor performances were confirmed in each of the individual validation cohorts (aHR comparing high-risk to low-risk groups 2·57 [95% CI 1·65-4·01] in cohort 1; 2·12 [1·32-3·39] in cohort 2; and 2·11 [1·01-4·41] in cohort 3). In the combined validation cohort, the median progression-free survival was 3·1 years (95% CI 2·4-4·8) in the high-risk group and 10·8 years (10·1-not reached) in the low-risk group (p<0·0001). The risk of lymphoma progression at 2 years was 38% (95% CI 29-46) in the high-risk group and 19% (15-24) in the low-risk group. In a multivariate analysis, the score predicted progression-free survival independently of anti-CD20 maintenance treatment and of the FLIPI score (aHR for the combined cohort 2·30, 95% CI 1·72-3·07). INTERPRETATION: We developed and validated a robust 23-gene expression-based predictor of progression-free survival that is applicable to routinely available formalin-fixed, paraffin-embedded tumour biopsies from patients with follicular lymphoma at time of diagnosis. Applying this score could allow individualised therapy for patients according to their risk category. FUNDING: Roche, SIRIC Lyric, LYSARC, National Institutes of Health, the Henry J Predolin Foundation, and the Spanish Plan Nacional de Investigacion.

HRDetect is a predictor of BRCA1 and BRCA2 deficiency based on mutational signatures.

Approximately 1-5% of breast cancers are attributed to inherited mutations in BRCA1 or BRCA2 and are selectively sensitive to poly(ADP-ribose) polymerase (PARP) inhibitors. In other cancer types, germline and/or somatic mutations in BRCA1 and/or BRCA2 (BRCA1/BRCA2) also confer selective sensitivity to PARP inhibitors. Thus, assays to detect BRCA1/BRCA2-deficient tumors have been sought. Recently, somatic substitution, insertion/deletion and rearrangement patterns, or 'mutational signatures', were associated with BRCA1/BRCA2 dysfunction. Herein we used a lasso logistic regression model to identify six distinguishing mutational signatures predictive of BRCA1/BRCA2 deficiency. A weighted model called HRDetect was developed to accurately detect BRCA1/BRCA2-deficient samples. HRDetect identifies BRCA1/BRCA2-deficient tumors with 98.7% sensitivity (area under the curve (AUC) = 0.98). Application of this model in a cohort of 560 individuals with breast cancer, of whom 22 were known to carry a germline BRCA1 or BRCA2 mutation, allowed us to identify an additional 22 tumors with somatic loss of BRCA1 or BRCA2 and 47 tumors with functional BRCA1/BRCA2 deficiency where no mutation was detected. We validated HRDetect on independent cohorts of breast, ovarian and pancreatic cancers and demonstrated its efficacy in alternative sequencing strategies. Integrating all of the classes of mutational signatures thus reveals a larger proportion of individuals with breast cancer harboring BRCA1/BRCA2 deficiency (up to 22%) than hitherto appreciated (∼1-5%) who could have selective therapeutic sensitivity to PARP inhibition.

A Systematic Evaluation of Blood Serum and Plasma Pre-Analytics for Metabolomics Cohort Studies.

The recent thriving development of biobanks and associated high-throughput phenotyping studies requires the elaboration of large-scale approaches for monitoring biological sample quality and compliance with standard protocols. We present a metabolomic investigation of human blood samples that delineates pitfalls and guidelines for the collection, storage and handling procedures for serum and plasma. A series of eight pre-processing technical parameters is systematically investigated along variable ranges commonly encountered across clinical studies. While metabolic fingerprints, as assessed by nuclear magnetic resonance, are not significantly affected by altered centrifugation parameters or delays between sample pre-processing (blood centrifugation) and storage, our metabolomic investigation highlights that both the delay and storage temperature between blood draw and centrifugation are the primary parameters impacting serum and plasma metabolic profiles. Storing the blood drawn at 4 °C is shown to be a reliable routine to confine variability associated with idle time prior to sample pre-processing. Based on their fine sensitivity to pre-analytical parameters and protocol variations, metabolic fingerprints could be exploited as valuable ways to determine compliance with standard procedures and quality assessment of blood samples within large multi-omic clinical and translational cohort studies.

Direct Transcriptional Consequences of Somatic Mutation in Breast Cancer.

Disordered transcriptomes of cancer encompass direct effects of somatic mutation on transcription, coordinated secondary pathway alterations, and increased transcriptional noise. To catalog the rules governing how somatic mutation exerts direct transcriptional effects, we developed an exhaustive pipeline for analyzing RNA sequencing data, which we integrated with whole genomes from 23 breast cancers. Using X-inactivation analyses, we found that cancer cells are more transcriptionally active than intermixed stromal cells. This is especially true in estrogen receptor (ER)-negative tumors. Overall, 59% of substitutions were expressed. Nonsense mutations showed lower expression levels than expected, with patterns characteristic of nonsense-mediated decay. 14% of 4,234 rearrangements caused transcriptional abnormalities, including exon skips, exon reusage, fusions, and premature polyadenylation. We found productive, stable transcription from sense-to-antisense gene fusions and gene-to-intergenic rearrangements, suggesting that these mutation classes drive more transcriptional disruption than previously suspected. Systematic integration of transcriptome with genome data reveals the rules by which transcriptional machinery interprets somatic mutation.

A whole-genome sequence and transcriptome perspective on HER2-positive breast cancers.

HER2-positive breast cancer has long proven to be a clinically distinct class of breast cancers for which several targeted therapies are now available. However, resistance to the treatment associated with specific gene expressions or mutations has been observed, revealing the underlying diversity of these cancers. Therefore, understanding the full extent of the HER2-positive disease heterogeneity still remains challenging. Here we carry out an in-depth genomic characterization of 64 HER2-positive breast tumour genomes that exhibit four subgroups, based on the expression data, with distinctive genomic features in terms of somatic mutations, copy-number changes or structural variations. The results suggest that, despite being clinically defined by a specific gene amplification, HER2-positive tumours melt into the whole luminal-basal breast cancer spectrum rather than standing apart. The results also lead to a refined ERBB2 amplicon of 106 kb and show that several cases of amplifications are compatible with a breakage-fusion-bridge mechanism.

Genomic Copy Number Profiling Using Circulating Free Tumor DNA Highlights Heterogeneity in Neuroblastoma.

PURPOSE: The tumor genomic copy number profile is of prognostic significance in neuroblastoma patients. We have studied the genomic copy number profile of cell-free DNA (cfDNA) and compared this with primary tumor arrayCGH (aCGH) at diagnosis. EXPERIMENTAL DESIGN: In 70 patients, cfDNA genomic copy number profiling was performed using the OncoScan platform. The profiles were classified according to the overall pattern, including numerical chromosome alterations (NCA), segmental chromosome alterations (SCA), and MYCN amplification (MNA). RESULTS: Interpretable and dynamic cfDNA profiles were obtained in 66 of 70 and 52 of 70 cases, respectively. An overall identical genomic profile between tumor aCGH and cfDNA was observed in 47 cases (3 NCAs, 22 SCAs, 22 MNAs). In one case, cfDNA showed an additional SCA not detected by tumor aCGH. In 4 of 8 cases with a silent tumor aCGH profile, cfDNA analysis revealed a dynamic profile (3 SCAs, 1 NCA). In 14 cases, cfDNA analysis did not reveal any copy number changes. A total of 378 breakpoints common to the primary tumor and cfDNA of any given patient were identified, 27 breakpoints were seen by tumor aCGH, and 54 breakpoints were seen in cfDNA only, including two cases with interstitial IGFR1 gains and two alterations targeting TERT CONCLUSIONS: These results demonstrate the feasibility of cfDNA copy number profiling in neuroblastoma patients, with a concordance of the overall genomic profile in aCGH and cfDNA dynamic cases of 97% and a sensitivity of 77%, respectively. Furthermore, neuroblastoma heterogeneity is highlighted, suggesting that cfDNA might reflect genetic alterations of more aggressive cell clones. Clin Cancer Res; 22(22); 5564-73. ©2016 AACRSee related commentary by Janku and Kurzrock, p. 5400.