microRNA Expression during Trophectoderm Specification

Segregation of the trophectoderm from the inner cell mass of the embryo represents the first cell-fate decision of mammalian development. Transcription factors essential for specifying trophectoderm have been identified, but the role of microRNAs (miRNAs) in modulating this fate-choice has been largely unexplored. We have compared miRNA expression in embryonic stem cell (ESC)-derived trophectoderm and in staged murine embryos to identify a set of candidate miRNAs likely to be involved in trophectoderm specification.We profiled embryonic stem cells (ESCs) as they were induced to differentiate into trophectodermal cells by ectopic expression of HRas/Q61L. We also profiled murine embryos at progressive stages of preimplantation development (zygote, 2-cell, 4-cell, 8-cell, morula, and blastocyst), which includes the time window in which the trophectoderm is specified in vivo Q61L/H.We describe miRNA expression changes that occur during trophectoderm specification and validate that our in vitro system faithfully recapitulates trophectoderm specification in vivo. By comparing our in vitro and in vivo datasets, we have identified a minimal set of candidate miRNAs likely to play a role in trophectoderm specification. These miRNAs are predicted to regulate a host of development-associated target genes, and many of these miRNAs have previously reported roles in development and differentiation. Additionally, we highlight a number of miRNAs whose tight developmental regulation may reflect a functional role in other stages of embryogenesis. Our embryo profiling data may be useful to investigators studying trophectoderm specification and other stages of preimplantation development

Discovery and saturation analysis of cancer genes across 21 tumor types

Summary While a few cancer genes are mutated in a high proportion of tumors of a given type (>20%), most are mutated at intermediate frequencies (2–20%). To explore the feasibility of creating a comprehensive catalog of cancer genes, we analyzed somatic point mutations in exome sequence from 4,742 tumor-normal pairs across 21 cancer types. We found that large-scale genomic analysis can identify nearly all known cancer genes in these tumor types. Our analysis also identified 33 genes not previously known to be significantly mutated, including genes related to proliferation, apoptosis, genome stability, chromatin regulation, immune evasion, RNA processing and protein homeostasis. Down-sampling analysis indicates that larger sample sizes will reveal many more genes, mutated at clinically important frequencies. We estimate that near-saturation may be achieved with 600–5000 samples per tumor type, depending on background mutation rate. The results help guide the next stage of cancer genomics

Deep learning-based survival prediction for multiple cancer types using histopathology images

Prognostic information at diagnosis has important implications for cancer treatment and monitoring. Although cancer staging, histopathological assessment, molecular features, and clinical variables can provide useful prognostic insights, improving risk stratification remains an active research area. We developed a deep learning system (DLS) to predict disease specific survival across 10 cancer types from The Cancer Genome Atlas (TCGA). We used a weakly-supervised approach without pixel-level annotations, and tested three different survival loss functions. The DLS was developed using 9,086 slides from 3,664 cases and evaluated using 3,009 slides from 1,216 cases. In multivariable Cox regression analysis of the combined cohort including all 10 cancers, the DLS was significantly associated with disease specific survival (hazard ratio of 1.58, 95% CI 1.28-1.70, p<0.0001) after adjusting for cancer type, stage, age, and sex. In a per-cancer adjusted subanalysis, the DLS remained a significant predictor of survival in 5 of 10 cancer types. Compared to a baseline model including stage, age, and sex, the c-index of the model demonstrated an absolute 3.7% improvement (95% CI 1.0-6.5) in the combined cohort. Additionally, our models stratified patients within individual cancer stages, particularly stage II (p=0.025) and stage III (p<0.001). By developing and evaluating prognostic models across multiple cancer types, this work represents one of the most comprehensive studies exploring the direct prediction of clinical outcomes using deep learning and histopathology images. Our analysis demonstrates the potential for this approach to provide prognostic information in multiple cancer types, and even within specific pathologic stages. However, given the relatively small number of clinical events, we observed wide confidence intervals, suggesting that future work will benefit from larger datasets

Discovery and saturation analysis of cancer genes across 21 tumour types

Although a few cancer genes are mutated in a high proportion of tumours of a given type (>20%), most are mutated at intermediate frequencies (2–20%). To explore the feasibility of creating a comprehensive catalogue of cancer genes, we analysed somatic point mutations in exome sequences from 4,742 human cancers and their matched normal-tissue samples across 21 cancer types. We found that large-scale genomic analysis can identify nearly all known cancer genes in these tumour types. Our analysis also identified 33 genes that were not previously known to be significantly mutated in cancer, including genes related to proliferation, apoptosis, genome stability, chromatin regulation, immune evasion, RNA processing and protein homeostasis. Down-sampling analysis indicates that larger sample sizes will reveal many more genes mutated at clinically important frequencies. We estimate that near-saturation may be achieved with 600–5,000 samples per tumour type, depending on background mutation frequency. The results may help to guide the next stage of cancer genomics

GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers

We describe methods with enhanced power and specificity to identify genes targeted by somatic copy-number alterations (SCNAs) that drive cancer growth. By separating SCNA profiles into underlying arm-level and focal alterations, we improve the estimation of background rates for each category. We additionally describe a probabilistic method for defining the boundaries of selected-for SCNA regions with user-defined confidence. Here we detail this revised computational approach, GISTIC2.0, and validate its performance in real and simulated datasets

Inhibitor-Sensitive FGFR1 Amplification in Human Non-Small Cell Lung Cancer

Background Squamous cell lung carcinomas account for approximately 25% of new lung carcinoma cases and 40,000 deaths per year in the United States. Although there are multiple genomically targeted therapies for lung adenocarcinoma, none has yet been reported in squamous cell lung carcinoma. Methodology/Principal Findings Using SNP array analysis, we found that a region of chromosome segment 8p11-12 containing three genes–WHSC1L1, LETM2, and FGFR1–is amplified in 3% of lung adenocarcinomas and 21% of squamous cell lung carcinomas. Furthermore, we demonstrated that a non-small cell lung carcinoma cell line harboring focal amplification of FGFR1 is dependent on FGFR1 activity for cell growth, as treatment of this cell line either with FGFR1-specific shRNAs or with FGFR small molecule enzymatic inhibitors leads to cell growth inhibition. Conclusions/Significance These studies show that FGFR1 amplification is common in squamous cell lung cancer, and that FGFR1 may represent a promising therapeutic target in non-small cell lung cancer.Novartis Pharmaceuticals CorporationAmerican Lung AssociationUniting Against Lung CancerSara Thomas Monopoli FundSeaman FoundationIndia. Dept. of BiotechnologyNational Lung Cancer Partnershi