Analyses of non-coding somatic drivers in 2,658 cancer whole genomes

Drivers, Pcawg

doi:10.1038/s41586-020-1965-x

Cited by 497 publications

(626 citation statements)

References 72 publications

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“…Stromal contamination was excluded by the relative abundance of the fibroblast marker FAP compared to the luminal markers ESR1, EPCAM, GATA3, and KRT8. C, Venn diagram intersecting the UVABC RHEG set with a pan-cancer set of driver genes (23,37). The intersection was updated to include proximal RHEGs as described in Supplementary Table ST2.…”

Section: Discussionmentioning

confidence: 99%

Pan-cancer Drivers are Recurrent Transcriptional Regulatory Heterogeneities in Early-stage Luminal Breast Cancer

Singh

Sutcliffe

Repich

et al. 2020

Preprint

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The authors declare no potential conflicts of interest. Running title: Stochastic profiling of luminal breast cancer by 10cRNA-seqThe heterogeneous composition of solid tumors is known to impact disease progression and response to therapy. Malignant cells coexist in different regulatory states that can be accessed transcriptomically by single-cell RNA sequencing, but these methods have many caveats related to sensitivity, noise, and sample handling. We revised a statistical fluctuation analysis called stochastic profiling to combine with 10-cell RNA sequencing, which was designed for laser-capture microdissection (LCM) and extended here for immuno-LCM. When applied to a cohort of late-onset, early-stage luminal breast cancers, the integrated approach identified thousands of candidate regulatory heterogeneities. Intersecting the candidates from different tumors yielded a relatively stable set of 710 recurrent heterogeneously expressed genes (RHEGs) that were significantly variable in >50% of patients. RHEGs were not confounded by dissociation artifacts, cell cycle oscillations, or driving mutations for breast cancer. Rather, we detected RHEG enrichments for epithelial-to-mesenchymal transition genes and, unexpectedly, the latest pan-cancer assembly of driver genes across cancer types other than breast.Heterogeneous transcriptional regulation conceivably provides a faster, reversible mechanism for malignant cells to sample the effects of potential oncogenes or tumor suppressors on cancer hallmarks. Statement of significanceProfiling intratumor heterogeneity of luminal breast carcinoma cells identifies a recurrent set of genes suggesting sporadic activation of pathways known to drive other types of cancer.

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Section: Discussionmentioning

confidence: 99%

Pan-cancer Drivers are Recurrent Transcriptional Regulatory Heterogeneities in Early-stage Luminal Breast Cancer

Singh

Sutcliffe

Repich

et al. 2020

Preprint

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“…Beyond somatic substitution events, cancer genomes also suffer other types of mutations, including small insertions and deletions and regional copy-number changes 4 . Such events are fewer in number than SNVs but can have more profound functional consequences.…”

Section: Discussionmentioning

confidence: 99%

“…The Pan-Cancer Analysis of Whole Genomes (PCAWG) study has further undertaken the analysis of 2,658 whole cancer genomes from the ICGC and TCGA to characterize regions unobserved via exome sequencing studies 1 . This resource has led to studies of the processes underlying somatic events 2 , complex structural variation 3 , driver genes 4 , and timing 5 . However, these analyses rely on variants that can be positioned uniquely in the genome and thus the repertoire of variation in non-unique regions still remains unexplored.…”

Section: Introductionmentioning

confidence: 99%

A pan-cancer landscape of somatic substitutions in non-unique regions of the human genome

Tarabichi

Demeulemeester

Verfaillie

et al. 2020

Preprint

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Around 13% of the human genome displays high sequence similarity with at least one other chromosomal position and thereby poses challenges for computational analyses such as detection of somatic events in cancer. We here extract features of sequencing data from across non-unique regions and employ a machine learning pipeline to describe a landscape of somatic substitutions in 2,658 cancers from the PCAWG cohort. We show mutations in non-unique regions are consistent with mutations in unique regions in terms of mutation load and substitution profiles, and can be validated with linked-read sequencing. This uncovers hidden mutations in ~1,700 coding sequences and thousands of regulatory elements, including known cancer genes, immunoglobulins, and highly mutated gene families.

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“…Transcriptional profiling of normal and tumor samples has revealed numerous tissue-specific lncRNAs [1, 15, 16], indicating further potential for discovery and development of cancer biomarkers based on the noncoding transcriptome. Some lncRNAs are also frequently mutated in cancer genomes and recent studies have identified candidate driver mutations by surveying whole-genome sequencing data in multiple cancer types [17, 18]. Projects such as The Cancer Genome Atlas (TCGA) [19], International Cancer Genome Consortium (ICGC) [20], METABRIC [21] and others have accumulated multi-omics datasets and patient clinical profiles for thousands of cancer samples.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive analysis of lncRNAs reveals candidate prognostic biomarkers in multiple cancer types

Isaev

Jiang

Lee

et al. 2019

Preprint

Self Cite

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12Long non-coding RNAs (lncRNAs) are increasingly recognized as functional units in can-13 cer pathways and powerful molecular biomarkers, however most lncRNAs remain un-14 characterized. Here we performed a systematic discovery of prognostic lncRNAs in 9,326 15 patient tumors of 29 types using a proportional-hazards elastic net machine-learning 16 framework. lncRNAs showed highly tissue-specific transcript abundance patterns. We 17 identified 179 prognostic lncRNAs whose abundance correlated with patient risk and im-18 proved the performance of common clinical variables and molecular tumor subtypes. 19 Pathway analysis revealed a large diversity of the high-risk tumors stratified by lncRNAs 20and suggested their functional associations. In lower-grade gliomas, discrete activation 21 of HOXA10-AS indicated poor patient prognosis, neurodevelopmental pathway activation 22 and a transcriptomic similarity to glioblastomas. HOXA10-AS knockdown in patient-de-23 rived glioblastoma cells caused decreased cell proliferation and deregulation of glioma 24 driver genes and proliferation pathways. Our study underlines the pan-cancer potential 25 of the non-coding transcriptome for developing molecular biomarkers and innovative 26 therapeutic strategies. 27 28 cancer patient prognosis with transcript abundance of protein-coding genes and their genetic 61 and epigenetic alterations [22][23][24][25]. However, associations of lncRNAs with cancer patient sur-62 vival and biological function remain largely unexplored. A recent study characterized recurrent 63 hypomethylation patterns affecting a thousand lncRNAs in the TCGA PanCanAtlas cohort and 64 identified the EPIC1 lncRNA as a marker of poor prognosis in a subset of breast cancers [26]. 65Another TCGA study associated mutations and transcript abundance profiles of lncRNAs with 66 regulatory networks and molecular pathways and nominated candidate oncogenic and tumor 67 suppressive lncRNAs, some of which were functionally validated in cancer cell lines [27]. Analy-68 sis of cell-cycle correlated lncRNAs revealed a subset of S-phase enriched lncRNAs whose 69 transcript abundance profiles correlated with patient survival in multiple TCGA cohorts [28]. 70However, those studies did not analyze robust prognostic performance of lncRNAs using ma-71 chine-learning and cross-validation approaches, indicating further potential to systematically dis-72 cover lncRNAs as candidate prognostic biomarkers of multiple cancer types. 73Here we evaluated the transcript abundance profiles of nearly 6,000 lncRNAs as prognostic bi-74 omarkers in human cancers. Using a comprehensive machine-learning analysis, we compiled a 75 robust catalogue of prognostic lncRNAs across nearly 10,000 tumors of 29 types from the 76 TCGA PanCanAtlas project [22, 29]. The majority of our candidate lncRNAs showed improved 77 prognostic potential compared to standard clinical features and molecular tumor subtypes. We 78 associated prognostic lncRNAs with large-scale deregulation of hallmark cancer pathways, re-79 vealing e...

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Analyses of non-coding somatic drivers in 2,658 cancer whole genomes

Cited by 497 publications

References 72 publications

Pan-cancer Drivers are Recurrent Transcriptional Regulatory Heterogeneities in Early-stage Luminal Breast Cancer

Pan-cancer Drivers are Recurrent Transcriptional Regulatory Heterogeneities in Early-stage Luminal Breast Cancer

A pan-cancer landscape of somatic substitutions in non-unique regions of the human genome

Comprehensive analysis of lncRNAs reveals candidate prognostic biomarkers in multiple cancer types

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