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Mass-spectrometry-based proteomic profiling of human cancers has the potential for pan-cancer analyses to identify molecular subtypes and associated pathway features that might be otherwise missed using transcriptomics. Here, we classify 532 cancers, representing six tissue-based types (breast, colon, ovarian, renal, uterine), into ten proteome-based, pan-cancer subtypes that cut across tumor lineages. The proteome-based subtypes are observable in external cancer proteomic datasets surveyed. Gene signatures of oncogenic or metabolic pathways can further distinguish between the subtypes. Two distinct subtypes both involve the immune system, one associated with the adaptive immune response and T-cell activation, and the other associated with the humoral immune response. Two additional subtypes each involve the tumor stroma, one of these including the collagen VI interacting network. Three additional proteome-based subtypes—respectively involving proteins related to Golgi apparatus, hemoglobin complex, and endoplasmic reticulum—were not reflected in previous transcriptomics analyses. A data portal is available at UALCAN website. Mass-spectrometry-based profiling can be used to stratify tumours into molecular subtypes. Here, by classifying over 500 tumours, the authors show that this approach reveals proteomic subgroups which cut across tumour types.

Mass-spectrometry-based proteomic data on human tumors—combined with corresponding multi-omics data—present opportunities for systematic and pan-cancer proteogenomic analyses. Here, we assemble a compendium dataset of proteomics data of 2002 primary tumors from 14 cancer types and 17 studies. Protein expression of genes broadly correlates with corresponding mRNA levels or copy number alterations (CNAs) across tumors, but with notable exceptions. Based on unsupervised clustering, tumors separate into 11 distinct proteome-based subtypes spanning multiple tissue-based cancer types. Two subtypes are enriched for brain tumors, one subtype associating with MYC, Wnt, and Hippo pathways and high CNA burden, and another subtype associating with metabolic pathways and low CNA burden. Somatic alteration of genes in a pathway associates with higher pathway activity as inferred by proteome or transcriptome data. A substantial fraction of cancers shows high MYC pathway activity without MYC copy gain but with mutations in genes with noncanonical roles in MYC. Our proteogenomics survey reveals the interplay between genome and proteome across tumor lineages. Pan-cancer proteomics analysis enables the analysis of protein expression across multiple cancer types. Here, the authors compare proteomics from 14 cancer types and show 11 distinct subtypes across multiple cancer types. Proteome data could link higher pathway activity levels with somatic alteration of specific genes in the pathway.

Both proteome and transcriptome data can help assess the relevance of non-coding somatic mutations in cancer. Here, we combine mass spectrometry-based proteomics data with whole genome sequencing data across 1307 human tumors spanning various tissues to determine the extent somatic structural variant (SV) breakpoint patterns impact protein expression of nearby genes. We find that about 25% of the hundreds of genes with SV-associated cis-regulatory alterations at the mRNA level are similarly associated at the protein level. SVs associated with enhancer hijacking, retrotransposon translocation, altered DNA methylation, or fusion transcripts are implicated in protein over-expression. SVs combined with altered protein levels considerably extend the numbers of patients with tumors somatically altered for critical pathways. We catalog both SV breakpoint patterns involving patient survival and genes with nearby SV breakpoints associated with increased cell dependency in cancer cell lines. Pan-cancer proteogenomics identifies targetable non-coding alterations, by virtue of the associated deregulated genes. The relevance of non-coding somatic mutations in cancer remains elusive. Here, the combination of mass spectrometry-based proteomics and whole genome sequencing data across multiple cancer types helps to assess the effects of somatic structural variant breakpoint patterns on protein expression of nearby genes.

Glioblastoma is a universally lethal form of brain cancer that exhibits an array of pathophysiological phenotypes, many of which are mediated by interactions with the neuronal microenvironment 1 , 2 . Recent studies have shown that increases in neuronal activity have an important role in the proliferation and progression of glioblastoma 3 , 4 . Whether there is reciprocal crosstalk between glioblastoma and neurons remains poorly defined, as the mechanisms that underlie how these tumours remodel the neuronal milieu towards increased activity are unknown. Here, using a native mouse model of glioblastoma, we develop a high-throughput in vivo screening platform and discover several driver variants of PIK3CA. We show that tumours driven by these variants have divergent molecular properties that manifest in selective initiation of brain hyperexcitability and remodelling of the synaptic constituency. Furthermore, secreted members of the glypican (GPC) family are selectively expressed in these tumours, and GPC3 drives gliomagenesis and hyperexcitability. Together, our studies illustrate the importance of functionally interrogating diverse tumour phenotypes driven by individual, yet related, variants and reveal how glioblastoma alters the neuronal microenvironment. Glioblastoma tumours expressing oncogenic PIK3CA variants secrete the glycan GPC3, which promotes the formation of neural synapses, brain synaptic hyperexcitability and gliomagenesis.

Structural variation heavily influences the molecular landscape of cancer, in part by impacting DNA methylation-mediated transcriptional regulation. Here, using multi-omic datasets involving >2400 pediatric brain and central nervous system tumors of diverse histologies from the Children’s Brain Tumor Network, we report hundreds of genes and associated CpG islands (CGIs) for which the nearby presence of somatic structural variant (SV) breakpoints is recurrently associated with altered expression or DNA methylation, respectively, including tumor suppressor genes ATRX and CDKN2A . Altered DNA methylation near enhancers associates with nearby somatic SV breakpoints, including MYC and MYCN . A subset of genes with SV-CGI methylation associations also have expression associations with patient survival, including BCOR , TERT , RCOR2 , and PDLIM4 . DNA methylation changes in recurrent or progressive tumors compared to the initial tumor within the same patient can predict survival in pediatric and adult cancers. Our comprehensive and pan-histology genomic analyses reveal mechanisms of noncoding alterations impacting cancer genes. Somatic structural variants (SVs) in cancer can impact DNA methylation-mediated transcriptional regulation. Here, the authors analyse multi-omics data from over 2400 samples from the Children’s Brain Tumor Network and report SVs that are associated with altered gene expression or DNA methylation, including some with prognostic relevance.

Purpose To grade the pathological response of lymph nodes (LNs) to neoadjuvant chemotherapy (NAC) in patients with locally advanced gastric cancer (LAGC) and investigate its prognostic significance. Methods This retrospective study included 196 patients who underwent NAC, followed by radical gastrectomy for LAGC between January 2010 and October 2019. Pathological responses were evaluated based on the proportion of residual tumor cells within the tumor area in the primary tumor (PT) and LNs and included the following categories: 1a (0%), 1b (< 10%), 2 (10–50%), and 3 (> 50%). Results Among 166 patients with clinically node-positive disease, 38/27/39/62 were classified as having LN regression grade (LRG) 1a/1b/2/3, respectively. Compared to LN non-responders (LRG 2 or 3), LN responders (LRG 1a or 1b) had significantly higher 5-year overall survival (72.5% vs. 19.0%, P  < 0.001) and recurrence-free survival rates (67.8% vs. 22.2%, P  < 0.001), irrespective of PT response. Furthermore, a multivariate analysis revealed that the LN response was an independent risk factor for the overall survival (hazard ratio [HR] 0.417, 95% confidence interval [CI] 0.181–0.962, P  = 0.040) and recurrence-free survival (HR 0.490, 95% CI 0.242–0.991, P  = 0.047), but not the PT response ( P  > 0.05). Conclusions The pathological LN response may be a reliable prognostic prediction tool in patients with LAGC who received NAC.

The global impact of somatic structural variants (SSVs) on gene expression in pediatric brain tumors has not been thoroughly characterised. Here, using whole-genome and RNA sequencing from 854 tumors of more than 30 different types from the Children’s Brain Tumor Tissue Consortium, we report the altered expression of hundreds of genes in association with the presence of nearby SSV breakpoints. SSV-mediated expression changes involve gene fusions, altered cis-regulation, or gene disruption. SSVs considerably extend the numbers of patients with tumors somatically altered for critical pathways, including receptor tyrosine kinases ( KRAS , MET , EGFR , NF1 ), Rb pathway ( CDK4 ), TERT , MYC family ( MYC , MYCN , MYB ), and HIPPO ( NF2 ). Compared to initial tumors, progressive or recurrent tumors involve a distinct set of SSV-gene associations. High overall SSV burden associates with TP53 mutations, histone H3.3 gene H3F3C mutations, and the transcription of DNA damage response genes. Compared to adult cancers, pediatric brain tumors would involve a different set of genes with SSV-altered cis-regulation. Our comprehensive and pan-histology genomic analyses reveal SSVs to play a major role in shaping the transcriptome of pediatric brain tumors. The global impact of somatic structural variants (SSVs) on gene expression in childhood cancers is unclear. Here, the authors analyse cancer genome and RNA sequencing data of 854 pediatric brain tumours and report a landscape of genes deregulated by SSVs.

Background The differential gene expression profile of metastatic versus primary breast tumors represents an avenue for discovering new or underappreciated pathways underscoring processes of metastasis. However, as tumor biopsy samples are a mixture of cancer and non-cancer cells, most differentially expressed genes in metastases would represent confounders involving sample biopsy site rather than cancer cell biology. Methods By paired analysis, we defined a top set of differentially expressed genes in breast cancer metastasis versus primary tumors using an RNA-sequencing dataset of 152 patients from The Breast International Group Aiming to Understand the Molecular Aberrations dataset (BIG-AURORA). To filter the genes higher in metastasis for genes essential for breast cancer proliferation, we incorporated CRISPR-based data from breast cancer cell lines. Results A significant fraction of genes with higher expression in metastasis versus paired primary were essential by CRISPR. These 264 genes represented an essential signature of breast cancer metastasis. In contrast, nonessential metastasis genes largely involved tumor biopsy site. The essential signature predicted breast cancer patient outcome based on primary tumor expression patterns. Pathways underlying the essential signature included proteasome degradation, the electron transport chain, oxidative phosphorylation, and cancer metabolic reprogramming. Transcription factors MYC, MAX, HDAC3, and HCFC1 each bound significant fractions of essential genes. Conclusions Associations involving the essential gene signature of breast cancer metastasis indicate true biological changes intrinsic to cancer cells, with important implications for applying existing therapies or developing alternate therapeutic approaches.

Background Combined whole-genome sequencing (WGS) and RNA sequencing of cancers offer the opportunity to identify genes with altered expression due to genomic rearrangements. Somatic structural variants (SVs), as identified by WGS, can involve altered gene cis -regulation, gene fusions, copy number alterations, or gene disruption. The absence of computational tools to streamline integrative analysis steps may represent a barrier in identifying genes recurrently altered by genomic rearrangement. Results Here, we introduce SVExpress, a set of tools for carrying out integrative analysis of SV and gene expression data. SVExpress enables systematic cataloging of genes that consistently show increased or decreased expression in conjunction with the presence of nearby SV breakpoints. SVExpress can evaluate breakpoints in proximity to genes for potential enhancer translocation events or disruption of topologically associated domains, two mechanisms by which SVs may deregulate genes. The output from any commonly used SV calling algorithm may be easily adapted for use with SVExpress. SVExpress can readily analyze genomic datasets involving hundreds of cancer sample profiles. Here, we used SVExpress to analyze SV and expression data across 327 cancer cell lines with combined SV and expression data in the Cancer Cell Line Encyclopedia (CCLE). In the CCLE dataset, hundreds of genes showed altered gene expression in relation to nearby SV breakpoints. Altered genes involved TAD disruption, enhancer hijacking, and gene fusions. When comparing the top set of SV-altered genes from cancer cell lines with the top SV-altered genes previously reported for human tumors from The Cancer Genome Atlas and the Pan-Cancer Analysis of Whole Genomes datasets, a significant number of genes overlapped in the same direction for both cell lines and tumors, while some genes were significant for cell lines but not for human tumors and vice versa. Conclusion Our SVExpress tools allow computational biologists with a working knowledge of R to integrate gene expression with SV breakpoint data to identify recurrently altered genes. SVExpress is freely available for academic or commercial use at https://github.com/chadcreighton/SVExpress . SVExpress is implemented as a set of Excel macros and R code. All source code (R and Visual Basic for Applications) is available.

The extent of genetic variation and its influence on gene expression across multiple tissue and cellular contexts is still being characterized, with germline Structural Variants (SVs) being historically understudied. DNA methylation also represents a component of normal germline variation across individuals. Here, we combine germline SVs (by short-read sequencing) with tumor DNA methylation across 1292 pediatric brain tumor patients. For thousands of methylation probes for CpG Islands (CGIs) or enhancers, rare and common SV breakpoints upstream or downstream associate with differential methylation in tumors spanning various histologic types, a significant subset involving genes with SV-associated differential expression. Cancer predisposition genes involving SV-associated differential methylation and expression include MSH2 , RSPA , and PALB2 . SV breakpoints falling within CGIs or histone marks H3K36me3 or H3K9me3 associate with differential CGI methylation. Genes with SVs and CGI methylation associated with patient survival include POLD4 . Our results capture a class of normal phenotypic variation having disease implications. The role of germline variation in cancer remains underexplored. Here, the authors investigate the landscape of germline structural variants on tumour DNA methylation across pediatric brain and central nervous system tumour patients and suggest disease implications.