Integrated proteogenomic characterization of clear cell renal cell carcinoma

To elucidate the deregulated functional modules that drive clear cell renal cell carcinoma (ccRCC), we performed comprehensive genomic, epigenomic, transcriptomic, proteomic, and phosphoproteomic characterization of treatment-naive ccRCC and paired normal adjacent tissue samples. Genomic analyses identified a distinct molecular subgroup associated with genomic instability. Integration of proteogenomic measurements uniquely identified protein dysregulation of cellular mechanisms impacted by genomic alterations, including oxidative phosphorylation-related metabolism, protein translation processes, and phospho-signaling modules. To assess the degree of immune infiltration in individual tumors, we identified microenvironment cell signatures that delineated four immune-based ccRCC subtypes characterized by distinct cellular pathways. This study reports a large-scale proteogenomic analysis of ccRCC to discern the functional impact of genomic alterations and provides evidence for rational treatment selection stemming from ccRCC pathobiology

Proteogenomic characterization of endometrial carcinoma

We undertook a comprehensive proteogenomic characterization of 95 prospectively collected endometrial carcinomas, comprising 83 endometrioid and 12 serous tumors. This analysis revealed possible new consequences of perturbations to the p53 and Wnt/β-catenin pathways, identified a potential role for circRNAs in the epithelial-mesenchymal transition, and provided new information about proteomic markers of clinical and genomic tumor subgroups, including relationships to known druggable pathways. An extensive genome-wide acetylation survey yielded insights into regulatory mechanisms linking Wnt signaling and histone acetylation. We also characterized aspects of the tumor immune landscape, including immunogenic alterations, neoantigens, common cancer/testis antigens, and the immune microenvironment, all of which can inform immunotherapy decisions. Collectively, our multi-omic analyses provide a valuable resource for researchers and clinicians, identify new molecular associations of potential mechanistic significance in the development of endometrial cancers, and suggest novel approaches for identifying potential therapeutic targets

Integrative multi-omic cancer profiling reveals DNA methylation patterns associated with therapeutic vulnerability and cell-of-origin

DNA methylation plays a critical role in establishing and maintaining cellular identity. However, it is frequently dysregulated during tumor development and is closely intertwined with other genetic alterations. Here, we leveraged multi-omic profiling of 687 tumors and matched non-involved adjacent tissues from the kidney, brain, pancreas, lung, head and neck, and endometrium to identify aberrant methylation associated with RNA and protein abundance changes and build a Pan-Cancer catalog. We uncovered lineage-specific epigenetic drivers including hypomethylated FGFR2 in endometrial cancer. We showed that hypermethylated STAT5A is associated with pervasive regulon downregulation and immune cell depletion, suggesting that epigenetic regulation of STAT5A expression constitutes a molecular switch for immunosuppression in squamous tumors. We further demonstrated that methylation subtype-enrichment information can explain cell-of-origin, intra-tumor heterogeneity, and tumor phenotypes. Overall, we identified cis-acting DNA methylation events that drive transcriptional and translational changes, shedding light on the tumor\u27s epigenetic landscape and the role of its cell-of-origin

microRNA dependent and independent deregulation of long non-coding RNAs by an oncogenic herpesvirus

<div><p>Kaposi’s sarcoma (KS) is a highly prevalent cancer in AIDS patients, especially in sub-Saharan Africa. Kaposi’s sarcoma-associated herpesvirus (KSHV) is the etiological agent of KS and other cancers like Primary Effusion Lymphoma (PEL). In KS and PEL, all tumors harbor latent KSHV episomes and express latency-associated viral proteins and microRNAs (miRNAs). The exact molecular mechanisms by which latent KSHV drives tumorigenesis are not completely understood. Recent developments have highlighted the importance of aberrant long non-coding RNA (lncRNA) expression in cancer. Deregulation of lncRNAs by miRNAs is a newly described phenomenon. We hypothesized that KSHV-encoded miRNAs deregulate human lncRNAs to drive tumorigenesis. We performed lncRNA expression profiling of endothelial cells infected with wt and miRNA-deleted KSHV and identified 126 lncRNAs as putative viral miRNA targets. Here we show that KSHV deregulates host lncRNAs in both a miRNA-dependent fashion by direct interaction and in a miRNA-independent fashion through latency-associated proteins. Several lncRNAs that were previously implicated in cancer, including MEG3, ANRIL and UCA1, are deregulated by KSHV. Our results also demonstrate that KSHV-mediated UCA1 deregulation contributes to increased proliferation and migration of endothelial cells.</p></div

KSHV miRNAs directly bind to and downregulate host lncRNAs.

<p><b>(A)</b> Uninfected TIVE cells were transfected with 5 nM final concentration of miRNA mimic pools (<i>Loc541472</i>: miR-K12-1, K12-6-5p; <i>CD27-AS1</i>: miR-K12-1*, K12-11*; <i>RP11-438-N16</i>.<i>1</i>: miR-K12-1*, K12-8*, K12-11*; <i>Linc00607</i>: miR-K12-2*, K12-11*). Relative expression levels of target lncRNAs were analyzed 48 h post-transfection using qRT-PCR. The bar graphs show the mean values ± SEM after normalization to GAPDH (n = 3). <b>(B)</b> Biotinylated miRNA mimics of miR-K12-6-5p and miR-K12-11* were transfected into uninfected TIVE-ExLTC cells (5 nM final concentration) and were pulled down 24 h later. Target lncRNAs were analyzed using qRT-PCR. siGLO pulldown was used a negative control. The bar graphs show the mean values ± SEM after normalization to input (n = 3). p-values: * < 0.01; ** < 0.005; *** < 0.0005; and **** <0.0001.</p

KSHV miRNAs and Ago2 are partially localized in the nuclei of latently infected cells.

<p><b>(A)</b> qRT-PCR analysis of mature KSHV miRNA distribution in the cytoplasmic and nuclear fractions of PEL cells. Percentage distribution was calculated by normalizing to expression in whole PEL cells, assuming no loss during fractionation. RNU48 was used as a nuclear control for fractionation. The bar graphs show the mean values (n = 3) ± SEM. p-values: * < 0.05; ** < 0.01; *** < 0.005. <b>(B)</b> Subcellular distribution of Ago2 proteins in PEL cells analyzed using Western blotting. Tubulin was probed as positive control for cytoplasm, Sm and Lamin A/C are positive controls for nuclei and Calnexin is the negative control for Endoplasmic Reticulum <b>(C)</b> Localization of Ago2 in PEL nuclei analyzed using IFA and confocal microscopy. Ago2 is shown in green and DAPI in blue. DAPI is shown at half the original intensity.</p

Examples of oncogenic and tumor-suppressor lncRNAs deregulated by KSHV.

<p>Examples of oncogenic and tumor-suppressor lncRNAs deregulated by KSHV.</p

Expression profiling of wt-KSHV and Δcluster-KSHV infected endothelial cells.

<p><b>(A)</b> Latency associated region of wt-KSHV in a Bac16 backbone. The region deleted in the Δcluster-KSHV virus is highlighted. <b>(B)</b> Heatmap of unsupervised hierarchical clustering of the microarray samples in the ‘rescued’ category of genes (n = 3 technical replicates).</p

LncRNA ANRIL is targeted by both KSHV miRNAs and latency proteins.

<p>All bar graphs show the mean values ± SEM after normalization to GAPDH (n = 3), unless specified otherwise. <b>(A)</b> ANRIL expression in Uninfected, wt-KSHV-infected and Δcluster-KSHV-infected cells measured by qRT-PCR. <b>(B)</b> Uninfected and wt-KSHV-infected TIVE cells were transfected with pcDNA3.1-ANRIL and relative over-expression of ANRIL was measured using qRT-PCR. LSD-1 was used a control to verify comparable transfection efficiencies of uninfected and infected cells. Y-axis is calculated as the ratio of fold-overexpression observed in wt-KSHV infected cells to the fold-overexpression observed in uninfected cells. Overexpressions were normalized to any expression changes observed by transfecting empty vector, which is thus set at one. <b>(C)</b> Uninfected TIVE cells were transfected with 5 nM final concentration of miRNA mimic pool (miR-K12-1*, K12-6-5p, K12-2* and K12-11*). Relative expression level of ANRIL was analyzed 48 h post-transfection using qRT-PCR. (<b>D</b>) Biotinylated miRNA mimics of miR-K12-6-5p and miR-K12-11* were transfected into uninfected TIVE cells (5 nM final concentration) and were pulled down 24 h later. ANRIL expression was analyzed using qRT-PCR. siGLO pulldown was used as a negative control. The data were normalized to input. <b>(E)</b> ANRIL expression in Uninfected, wt-KSHV-infected and Δall-KSHV-infected cells measured by qRT-PCR (n = 2). <b>(F)</b> HeLa cells were transfected with latency gene(s) (LANA, vCyclin, vFLIP, Kaposin or vCyclin + Kaposin) expressed from pcDNA3.2 vector. ANRIL expression was analyzed 72 h post-transfection using qRT-PCR. p-values: * < 0.05; ** < 0.01; *** < 0.005; **** < 0.0005; ***** < 10⁻⁴ and n.s. = not significant.</p