Genomic evidence for distinct carbon substrate preferences and ecological niches of Bathyarchaeota in estuarine sediments

Investigations of the biogeochemical roles of benthic Archaea in marine sediments are hampered by the scarcity of cultured representatives. In order to determine their metabolic capacity, we reconstructed the genomic content of four widespread uncultured benthic Archaea recovered from estuary sediments at 48% to 95% completeness. Four genomic bins were found to belong to different subgroups of the former Miscellaneous Crenarcheota Group (MCG) now called Bathyarchaeota: MCG-6, MCG-1, MCG-7/17 and MCG-15. Metabolic predictions based on gene content of the different genome bins indicate that subgroup 6 has the ability to hydrolyse extracellular plant-derived carbohydrates, and that all four subgroups can degrade detrital proteins. Genes encoding enzymes involved in acetate production as well as in the reductive acetyl-CoA pathway were detected in all four genomes inferring that these Archaea are organo-heterotrophic and autotrophic acetogens. Genes involved in nitrite reduction were detected in all Bathyarchaeota subgroups and indicate a potential for dissimilatory nitrite reduction to ammonium. Comparing the genome content of the different Bathyarchaeota subgroups indicated preferences for distinct types of carbohydrate substrates and implicitly, for different niches within the sedimentary environment

Relative importance of methylotrophic methanogenesis in sediments of the Western Mediterranean Sea

Microbial production of methane is an important terminal metabolic process during organic matter degradation in marine sediments. It is generally acknowledged that hydrogenotrophic and acetoclastic methanogenesis constitute the dominant pathways of methane production; the importance of methanogenesis from methylated compounds remains poorly understood. We conducted various biogeochemical and molecular genetic analyses to characterize substrate availability, rates of methanogenesis, and methanogen community composition, and further evaluated the contribution of different substrates and pathways for methane production in deltaic surface and subsurface sediments of the Western Mediterranean Sea. Major substrates representing three methanogenic pathways, including H2, acetate, and methanol, trimethylamine (TMA), and dimethylsulfide (DMS), were detected in the pore waters and sediments, and exhibited variability over depth and between sites. In accompanying incubation experiments, methanogenesis rates from various 14C labeled substrates varied as well, suggesting that environmental factors, such as sulfate concentration and organic matter quality, could significantly influence the relative importance of individual pathway. In particular, methylotrophic and hydrogenotrophic methanogenesis contributed to the presence of micromolar methane concentrations in the sulfate reduction zone, with methanogenesis from methanol accounting for up to 98% of the total methane production in the topmost surface sediment. In the sulfate-depleted zone, hydrogenotrophic methanogenesis was the dominant methanogenic pathway (67–98%), and enhanced methane production from acetate was observed in organic-rich sediment (up to 31%). Methyl coenzyme M reductase gene (mcrA) analysis revealed that the composition of methanogenic communities was generally consistent with the distribution of methanogenic activity from different substrates. This study provides the first quantitative assessment of methylotrophic methanogenesis in marine sediments and has important implications for marine methane cycling. The occurrence of methylotrophic methanogenesis in surface sediments could fuel the anaerobic oxidation of methane (AOM) in the shallow sulfate reduction zone. Release of methane produced from methylotrophic methanogenesis could be a source of methane efflux to the water column, thus influencing the benthic methane budgets

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

Application of a risk-management framework for integration of stromal tumor-infiltrating lymphocytes in clinical trials

Stromal tumor-infiltrating lymphocytes (sTILs) are a potential predictive biomarker for immunotherapy response in metastatic triple-negative breast cancer (TNBC). To incorporate sTILs into clinical trials and diagnostics, reliable assessment is essential. In this review, we propose a new concept, namely the implementation of a risk-management framework that enables the use of sTILs as a stratification factor in clinical trials. We present the design of a biomarker risk-mitigation workflow that can be applied to any biomarker incorporation in clinical trials. We demonstrate the implementation of this concept using sTILs as an integral biomarker in a single-center phase II immunotherapy trial for metastatic TNBC (TONIC trial, NCT02499367), using this workflow to mitigate risks of suboptimal inclusion of sTILs in this specific trial. In this review, we demonstrate that a web-based scoring platform can mitigate potential risk factors when including sTILs in clinical trials, and we argue that this framework can be applied for any future biomarker-driven clinical trial setting

Driver Fusions and Their Implications in the Development and Treatment of Human Cancers.

Gene fusions represent an important class of somatic alterations in cancer. We systematically investigated fusions in 9,624 tumors across 33 cancer types using multiple fusion calling tools. We identified a total of 25,664 fusions, with a 63% validation rate. Integration of gene expression, copy number, and fusion annotation data revealed that fusions involving oncogenes tend to exhibit increased expression, whereas fusions involving tumor suppressors have the opposite effect. For fusions involving kinases, we found 1,275 with an intact kinase domain, the proportion of which varied significantly across cancer types. Our study suggests that fusions drive the development of 16.5% of cancer cases and function as the sole driver in more than 1% of them. Finally, we identified druggable fusions involving genes such as TMPRSS2, RET, FGFR3, ALK, and ESR1 in 6.0% of cases, and we predicted immunogenic peptides, suggesting that fusions may provide leads for targeted drug and immune therapy

The immune landscape of cancer

We performed an extensive immunogenomic analysis of more than 10,000 tumors comprising 33 diverse cancer types by utilizing data compiled by TCGA. Across cancer types, we identified six immune subtypes—wound healing, IFN-γ dominant, inflammatory, lymphocyte depleted, immunologically quiet, and TGF-β dominant—characterized by differences in macrophage or lymphocyte signatures, Th1:Th2 cell ratio, extent of intratumoral heterogeneity, aneuploidy, extent of neoantigen load, overall cell proliferation, expression of immunomodulatory genes, and prognosis. Specific driver mutations correlated with lower (CTNNB1, NRAS, or IDH1) or higher (BRAF, TP53, or CASP8) leukocyte levels across all cancers. Multiple control modalities of the intracellular and extracellular networks (transcription, microRNAs, copy number, and epigenetic processes) were involved in tumor-immune cell interactions, both across and within immune subtypes. Our immunogenomics pipeline to characterize these heterogeneous tumors and the resulting data are intended to serve as a resource for future targeted studies to further advance the field

Comparative Molecular Analysis of Gastrointestinal Adenocarcinomas

We analyzed 921 adenocarcinomas of the esophagus, stomach, colon, and rectum to examine shared and distinguishing molecular characteristics of gastrointestinal tract adenocarcinomas (GIACs). Hypermutated tumors were distinct regardless of cancer type and comprised those enriched for insertions/deletions, representing microsatellite instability cases with epigenetic silencing of MLH1 in the context of CpG island methylator phenotype, plus tumors with elevated single-nucleotide variants associated with mutations in POLE. Tumors with chromosomal instability were diverse, with gastroesophageal adenocarcinomas harboring fragmented genomes associated with genomic doubling and distinct mutational signatures. We identified a group of tumors in the colon and rectum lacking hypermutation and aneuploidy termed genome stable and enriched in DNA hypermethylation and mutations in KRAS, SOX9, and PCBP1. Liu et al. analyze 921 gastrointestinal (GI) tract adenocarcinomas and find that hypermutated tumors are enriched for insertions/deletions, upper GI tumors with chromosomal instability harbor fragmented genomes, and a group of genome-stable colorectal tumors are enriched in mutations in SOX9 and PCBP1

The Immune Landscape of Cancer

We performed an extensive immunogenomic anal-ysis of more than 10,000 tumors comprising 33diverse cancer types by utilizing data compiled byTCGA. Across cancer types, we identified six im-mune subtypes\u2014wound healing, IFN-gdominant,inflammatory, lymphocyte depleted, immunologi-cally quiet, and TGF-bdominant\u2014characterized bydifferences in macrophage or lymphocyte signa-tures, Th1:Th2 cell ratio, extent of intratumoral het-erogeneity, aneuploidy, extent of neoantigen load,overall cell proliferation, expression of immunomod-ulatory genes, and prognosis. Specific drivermutations correlated with lower (CTNNB1,NRAS,orIDH1) or higher (BRAF,TP53,orCASP8) leukocytelevels across all cancers. Multiple control modalitiesof the intracellular and extracellular networks (tran-scription, microRNAs, copy number, and epigeneticprocesses) were involved in tumor-immune cell inter-actions, both across and within immune subtypes.Our immunogenomics pipeline to characterize theseheterogeneous tumors and the resulting data areintended to serve as a resource for future targetedstudies to further advance the field

A Comprehensive Pan-Cancer Molecular Study of Gynecologic and Breast Cancers

We analyzed molecular data on 2,579 tumors from The Cancer Genome Atlas (TCGA) of four gynecological types plus breast. Our aims were to identify shared and unique molecular features, clinically significant subtypes, and potential therapeutic targets. We found 61 somatic copy-number alterations (SCNAs) and 46 significantly mutated genes (SMGs). Eleven SCNAs and 11 SMGs had not been identified in previous TCGA studies of the individual tumor types. We found functionally significant estrogen receptor-regulated long non-coding RNAs (lncRNAs) and gene/lncRNA interaction networks. Pathway analysis identified subtypes with high leukocyte infiltration, raising potential implications for immunotherapy. Using 16 key molecular features, we identified five prognostic subtypes and developed a decision tree that classified patients into the subtypes based on just six features that are assessable in clinical laboratories. By performing molecular analyses of 2,579 TCGA gynecological (OV, UCEC, CESC, and UCS) and breast tumors, Berger et al. identify five prognostic subtypes using 16 key molecular features and propose a decision tree based on six clinically assessable features that classifies patients into the subtypes