Discovery of New Fusion Transcripts in a Cohort of Pediatric Solid Cancers at Relapse and Relevance for Personalized Medicine.

We hypothetized that pediatric cancers would more likely harbor fusion transcripts. To dissect the complexity of the fusions landscape in recurrent solid pediatric cancers, we conducted a study on 48 patients with different relapsing or resistant malignancies. By analyzing RNA sequencing data with a new in-house pipeline for fusions detection named ChimComp, followed by verification by real-time PCR, we identified and classified the most confident fusion transcripts (FTs) according to their potential biological function and druggability. The majority of FTs were predicted to affect key cancer pathways and described to be involved in oncogenesis. Contrary to previous descriptions, we found no significant correlation between the number of fusions and mutations, emphasizing the particularity to study pre-treated pediatric patients. A considerable proportion of FTs containing tumor suppressor genes was detected, reflecting their importance in pediatric cancers. FTs containing non-receptor tyrosine kinases occurred at low incidence and predominantly in brain tumors. Remarkably, more than 30% of patients presented a potentially druggable high-confidence fusion. In conclusion, we detected new oncogenic FTs in relapsing pediatric cancer patients by establishing a robust pipeline that can be applied to other malignancies, to detect and prioritize experimental validation studies leading to the development of new therapeutic options.

Mutational Profile of Metastatic Breast Cancers: A Retrospective Analysis.

BACKGROUND: Major advances have been achieved in the characterization of early breast cancer (eBC) genomic profiles. Metastatic breast cancer (mBC) is associated with poor outcomes, yet limited information is available on the genomic profile of this disease. This study aims to decipher mutational profiles of mBC using next-generation sequencing. METHODS AND FINDINGS: Whole-exome sequencing was performed on 216 tumor-blood pairs from mBC patients who underwent a biopsy in the context of the SAFIR01, SAFIR02, SHIVA, or Molecular Screening for Cancer Treatment Optimization (MOSCATO) prospective trials. Mutational profiles from 772 primary breast tumors from The Cancer Genome Atlas (TCGA) were used as a reference for comparing primary and mBC mutational profiles. Twelve genes (TP53, PIK3CA, GATA3, ESR1, MAP3K1, CDH1, AKT1, MAP2K4, RB1, PTEN, CBFB, and CDKN2A) were identified as significantly mutated in mBC (false discovery rate [FDR] < 0.1). Eight genes (ESR1, FSIP2, FRAS1, OSBPL3, EDC4, PALB2, IGFN1, and AGRN) were more frequently mutated in mBC as compared to eBC (FDR < 0.01). ESR1 was identified both as a driver and as a metastatic gene (n = 22, odds ratio = 29, 95% CI [9-155], p = 1.2e-12) and also presented with focal amplification (n = 9) for a total of 31 mBCs with either ESR1 mutation or amplification, including 27 hormone receptor positive (HR+) and HER2 negative (HER2-) mBCs (19%). HR+/HER2- mBC presented a high prevalence of mutations on genes located on the mechanistic target of rapamycin (mTOR) pathway (TSC1 and TSC2) as compared to HR+/HER2- eBC (respectively 6% and 0.7%, p = 0.0004). Other actionable genes were more frequently mutated in HR+ mBC, including ERBB4 (n = 8), NOTCH3 (n = 7), and ALK (n = 7). Analysis of mutational signatures revealed a significant increase in APOBEC-mediated mutagenesis in HR+/HER2- metastatic tumors as compared to primary TCGA samples (p < 2e-16). The main limitations of this study include the absence of bone metastases and the size of the cohort, which might not have allowed the identification of rare mutations and their effect on survival. CONCLUSIONS: This work reports the results of the analysis of the first large-scale study on mutation profiles of mBC. This study revealed genomic alterations and mutational signatures involved in the resistance to therapies, including actionable mutations.

Integrating deep learning CT-scan model, biological and clinical variables to predict severity of COVID-19 patients.

The SARS-COV-2 pandemic has put pressure on intensive care units, so that identifying predictors of disease severity is a priority. We collect 58 clinical and biological variables, and chest CT scan data, from 1003 coronavirus-infected patients from two French hospitals. We train a deep learning model based on CT scans to predict severity. We then construct the multimodal AI-severity score that includes 5 clinical and biological variables (age, sex, oxygenation, urea, platelet) in addition to the deep learning model. We show that neural network analysis of CT-scans brings unique prognosis information, although it is correlated with other markers of severity (oxygenation, LDH, and CRP) explaining the measurable but limited 0.03 increase of AUC obtained when adding CT-scan information to clinical variables. Here, we show that when comparing AI-severity with 11 existing severity scores, we find significantly improved prognosis performance; AI-severity can therefore rapidly become a reference scoring approach.

Frequent Homologous Recombination Deficiency in High-grade Endometrial Carcinomas.

PURPOSE: The elevated levels of somatic copy-number alterations (SCNAs) in a subset of high-risk endometrial cancers are suggestive of defects in pathways governing genome integrity. We sought to assess the prevalence of homologous recombination deficiency (HRD) in endometrial cancers and its association with histopathologic and molecular characteristics. EXPERIMENTAL DESIGN: Fresh tumor tissue was prospectively collected from 36 endometrial cancers, and functional HRD was examined by the ability of replicating tumor cells to accumulate RAD51 protein at DNA double-strand breaks (RAD51 foci) induced by ionizing radiation. Genomic alterations were determined by next-generation sequencing and array comparative genomic hybridization/SNP array. The prevalence of BRCA-associated genomic scars, a surrogate marker for HRD, was determined in the The Cancer Genome Atlas (TCGA) endometrial cancer cohort. RESULTS: Most endometrial cancers included in the final analysis (n = 25) were of non-endometrioid (52%), grade 3 (60%) histology, and FIGO stage I (72%). HRD was observed in 24% (n = 6) of cases and was restricted to non-endometrioid endometrial cancers (NEEC), with 46% of NEECs being HRD compared with none of the endometrioid endometrial cancers (EEC, P = 0.014). All but 1 of the HRD cases harbored either a pathogenic BRCA1 variant or high somatic copy-number (SCN) losses of HR genes. Analysis of TCGA cases supported these results, with BRCA-associated genomic scars present in up to 48% (63/132) of NEEC versus 12% (37/312) of EEC (P < 0.001). CONCLUSIONS: HRD occurs in endometrial cancers and is largely restricted to non-endometrioid, TP53-mutant endometrial cancers. Evaluation of HRD may help select patients that could benefit from treatments targeting this defect, including platinum compounds and PARP inhibitors.

Added Value of Whole-Exome and Transcriptome Sequencing for Clinical Molecular Screenings of Advanced Cancer Patients With Solid Tumors.

Comprehensive genomic profiling using high-throughput sequencing brings a wealth of information, and its place in the clinical setting has been increasingly prominent. This review emphasizes the utility of whole-exome sequencing (WES) and transcriptome sequencing (RNAseq) in patient care and clinical research, based on published reports as well as our experience with the MOSCATO-01 (MOlecular Screening for CAncer Treatment Optimization) molecular triage trial at Gustave Roussy Cancer Center. In this trial, all contributive samples of patients with advanced solid tumors were analyzed prospectively with targeted gene sequencing (TGS) and comparative genomic hybridization. In addition, 92 consecutive metastatic patients with contributive biopsies were sequenced for WES and RNAseq and compared with TGS and comparative genomic hybridization. Whole-exome sequencing allowed the reporting of additional variants in relevant genes in 38% of patients. Mutation detection sensitivity of WES was 95% compared with TGS. Additional information derived from WES and RNAseq could influence clinical decision, including fusion transcripts, expression levels, allele-specific expression, alternate transcripts, RNA-based pathogen diagnostic, tumor mutation load, mutational signatures, expression signatures, HLA genotyping, and neoepitope prediction. The current challenge is to be able to process the large-scale data from these comprehensive genome-wide technologies in an efficient way.

Improving the Performance of Somatic Mutation Identification by Recovering Circulating Tumor DNA Mutations.

DNA extracted from cancer patients' whole blood may contain somatic mutations from circulating tumor DNA (ctDNA) fragments. In this study, we introduce cmDetect, a computational method for the systematic identification of ctDNA mutations using whole-exome sequencing of a cohort of tumor and corresponding peripheral whole-blood samples. Through the analysis of simulated data, we demonstrated an increase in sensitivity in calling somatic mutations by combining cmDetect to two widely used mutation callers. In a cohort of 93 breast cancer metastatic patients, cmDetect identified ctDNA mutations in 54% of the patients and recovered somatic mutations in cancer genes EGFR, PIK3CA, and TP53 We further showed that cmDetect detected ctDNA in 89% of patients with confirmed mutated cell-free tumor DNA by plasma analyses (n = 9) within 46 pan-cancer patients. Our results prompt immediate consideration of the use of this method as an additional step in somatic mutation calling using whole-exome sequencing data with blood samples as controls. Cancer Res; 76(20); 5954-61. ©2016 AACR.