Clonal evolution of a tumor ecosystem depends on different selection pressures that are principally immune and treatment mediated. We integrate RNA-seq, DNA sequencing, TCRseq and SNP array data across multiple regions of liver cancer specimens to map spatio-temporal interactions between cancer and immune cells. We investigate how these interactions reflect intra-tumor heterogeneity (ITH) by correlating regional neo-epitope and viral antigen burden with the regional adaptive immune response. Regional expression of passenger mutations dominantly recruits adaptive responses as opposed to hepatitis B virus and cancer-testis antigens. We detect different clonal expansion of the adaptive immune system in distant regions of the same tumor. An ITH-based gene signature improves singlebiopsy patient survival predictions and an expression survey of 38,553 single cells across 7 regions of 2 patients further reveals heterogeneity in liver cancer. These data quantify transcriptomic ITH and how the different components of the HCC ecosystem interact during cancer evolution.

Human natural killer (NK) cells in peripheral blood perform many functions, and classification of specific subsets has been a longstanding goal. We report single-cell RNA sequencing of NK cells, comparing gene expression in unstimulated and interleukin (IL)-2–activated cells from healthy cytomegalovirus (CMV)-negative donors. Three NK cell subsets resembled well-described populations; CD56brightCD16−, CD56dimCD16+CD57−, and CD56dimCD16+CD57+. CD56dimCD16+CD57− cells subdivided to include a population with higher chemokine mRNA and increased frequency of killer-cell immunoglobulin-like receptor expression. Three novel human blood NK cell populations were identified: a population of type I interferon–responding NK cells that were CD56neg; a population exhibiting a cytokine-induced memory-like phenotype, including increased granzyme B mRNA in response to IL-2; and finally, a small population, with low ribosomal expression, downregulation of oxidative phosphorylation, and high levels of immediate early response genes indicative of cellular activation. Analysis of CMV+ donors established that CMV altered the proportion of NK cells in each subset, especially an increase in adaptive NK cells, as well as gene regulation within each subset. Together, these data establish an unexpected diversity in blood NK cells and provide a new framework for analyzing NK cell responses in health and disease.

Viral infection perturbs host cells and can be used to uncover regulatory mechanisms controlling cellular responses and susceptibility to infections. Using cell biological, biochemical, and genetic tools, we reveal that influenza A virus (IAV) infection induces global transcriptional defects at the 3' ends of active host genes and RNA polymerase II (RNAPII) run-through into extragenic regions. Deregulated RNAPII leads to expression of aberrant RNAs (3' extensions and host-gene fusions) that ultimately cause global transcriptional downregulation of physiological transcripts, an effect influencing antiviral response and virulence. This phenomenon occurs with multiple strains of IAV, is dependent on influenza NS1 protein, and can be modulated by SUMOylation of an intrinsically disordered region (IDR) of NS1 expressed by the 1918 pandemic IAV strain. Our data identify a strategy used by IAV to suppress host gene expression and indicate that polymorphisms in IDRs of viral proteins can affect the outcome of an infection.

The fragile X-related disorders result from expansion of a CGG/CCG microsatellite in the 5’ UTR of the FMR1 gene. We have previously demonstrated that the MSH2/MSH3 complex, MutSβ, that is important for mismatch repair, is essential for almost all expansions in a mouse model of these disorders. Here we show that the MSH2/MSH6 complex, MutSα also contributes to the production of both germ line and somatic expansions as evidenced by the reduction in the number of expansions observed in Msh6-/- mice. This effect is not mediated via an indirect effect of the loss of MSH6 on the level of MSH3. However, since MutSβ is required for 98% of germ line expansions and almost all somatic ones, MutSα is apparently not able to efficiently substitute for MutSβ in the expansion process. Using purified human proteins we demonstrate that MutSα, like MutSβ, binds to substrates with loop-outs of the repeats and increases the thermal stability of the structures that they form. We also show that MutSα facilitates binding of MutSβ to these loop-outs. These data suggest possible models for the contribution of MutSα to repeat expansion. In addition, we show that unlike MutSβ, MutSα may also act to protect against repeat contractions in the Fmr1 gene.

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Background: Patient stratification based on molecular subtypes is an important strategy for cancer precision medicine. Deriving clinically informative cancer molecular subtypes from transcriptomic data generated on whole tumor tissue samples is a non-trivial task, especially given the various non-cancer cellular elements intertwined with cancer cells in the tumor microenvironment. Methods: We developed a computational deconvolution method, DeClust, that stratifies patients into subtypes based on cancer cell-intrinsic signals identified by distinguishing cancer-type-specific signals from non-cancer signals in bulk tumor transcriptomic data. DeClust differs from most existing methods by directly incorporating molecular subtyping of solid tumors into the deconvolution process and outputting molecular subtype-specific tumor reference profiles for the cohort rather than individual tumor profiles. In addition, DeClust does not require reference expression profiles or signature matrices as inputs and estimates cancer-type-specific microenvironment signals from bulk tumor transcriptomic data. Results: DeClust was evaluated on both simulated data and 13 solid tumor datasets from The Cancer Genome Atlas (TCGA). DeClust performed among the best, relative to existing methods, for estimation of cellular composition. Compared to molecular subtypes reported by TCGA or other similar approaches, the subtypes generated by DeClust had higher correlations with cancer-intrinsic genomic alterations (e.g., somatic mutations and copy number variations) and lower correlations with tumor purity. While DeClust-identified subtypes were not more significantly associated with survival in general, DeClust identified a poor prognosis subtype of clear cell renal cancer, papillary renal cancer, and lung adenocarcinoma, all of which were characterized by CDKN2A deletions. As a reference profile-free deconvolution method, the tumor-type-specific stromal profiles and cancer cell-intrinsic subtypes generated by DeClust were supported by single-cell RNA sequencing data. Conclusions: DeClust is a useful tool for cancer cell-intrinsic molecular subtyping of solid tumors. DeClust subtypes, together with the tumor-type-specific stromal profiles generated by this pan-cancer study, may lead to mechanistic and clinical insights across multiple tumor types.

ObjectiveSurveillance tools for early cancer detection are suboptimal, including hepatocellular carcinoma (HCC), and biomarkers are urgently needed. Extracellular vesicles (EVs) have gained increasing scientific interest due to their involvement in tumour initiation and metastasis; however, most extracellular RNA (exRNA) blood-based biomarker studies are limited to annotated genomic regions.DesignEVs were isolated with differential ultracentrifugation and integrated nanoscale deterministic lateral displacement arrays (nanoDLD) and quality assessed by electron microscopy, immunoblotting, nanoparticle tracking and deconvolution analysis. Genome-wide sequencing of the largely unexplored small exRNA landscape, including unannotated transcripts, identified and reproducibly quantified small RNA clusters (smRCs). Their key genomic features were delineated across biospecimens and EV isolation techniques in prostate cancer and HCC. Three independent exRNA cancer datasets with a total of 479 samples from 375 patients, including longitudinal samples, were used for this study.ResultsExRNA smRCs were dominated by uncharacterised, unannotated small RNA with a consensus sequence of 20 nt. An unannotated 3-smRC signature was significantly overexpressed in plasma exRNA of patients with HCC (p<0.01, n=157). An independent validation in a phase 2 biomarker case–control study revealed 86% sensitivity and 91% specificity for the detection of early HCC from controls at risk (n=209) (area under the receiver operating curve (AUC): 0.87). The 3-smRC signature was independent of alpha-fetoprotein (p<0.0001) and a composite model yielded an increased AUC of 0.93.ConclusionThese findings directly lead to the prospect of a minimally invasive, blood-only, operator-independent clinical tool for HCC surveillance, thus highlighting the potential of unannotated smRCs for biomarker research in cancer.

BackgroundExosomes and other extracellular vesicles (EVs) have emerged as an important mechanism of cell-to-cell communication. However, previous studies either did not fully resolve what genetic materials were shuttled by exosomes or only focused on a specific set of miRNAs and mRNAs. A more systematic method is required to identify the genetic materials that are potentially transferred during cell-to-cell communication through EVs in an unbiased manner.ResultsIn this work, we present a novel next generation of sequencing (NGS) based approach to identify EV mediated mRNA exchanges between co-cultured adipocyte and macrophage cells. We performed molecular and genomic profiling and jointly considered data from RNA sequencing (RNA-seq) and genotyping to track the “sequence varying mRNAs” transferred between cells. We identified 8 mRNAs being transferred from macrophages to adipocytes and 21 mRNAs being transferred in the opposite direction. These mRNAs represented biological functions including extracellular matrix, cell adhesion, glycoprotein, and signal peptides.ConclusionsOur study sheds new light on EV mediated RNA communications between adipocyte and macrophage cells, which may play a significant role in developing insulin resistance in diabetic patients. This work establishes a new method that is applicable to examining genetic material exchanges in many cellular systems and has the potential to be extended to in vivo studies as well.Electronic supplementary materialThe online version of this article (10.1186/s12864-017-4359-1) contains supplementary material, which is available to authorized users.