The Network of Cancer Genes (NCG): a comprehensive catalogue of known and candidate cancer genes from cancer sequencing screens

Venkata, Santhilata Kuppili; Repana, Dimitra; Nulsen, Joel; Dressler, Lisa; Bortolomeazzi, Michele; Tourna, Aikaterini; Yakovleva, Anna; Palmieri, Tommaso; Ciccarelli, Francesca D.

doi:10.1101/389858

Cited by 45 publications

(66 citation statements)

References 60 publications

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“…Known cancer genes (Repana et al , ) that are located on the same cytogenetic bands as the olfactory receptors in our metagene were retrieved. To test whether the olfactory receptor metagene can predict survival independent of the expression of neighbouring cancer genes Cox proportional hazard test was performed (Table EV9).…”

Section: Methodsmentioning

confidence: 99%

KCML : a machine‐learning framework for inference of multi‐scale gene functions from genetic perturbation screens

Sailem

Rittscher

Pelkmans

2020

Molecular Systems Biology

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Characterising context‐dependent gene functions is crucial for understanding the genetic bases of health and disease. To date, inference of gene functions from large‐scale genetic perturbation screens is based on ad hoc analysis pipelines involving unsupervised clustering and functional enrichment. We present Knowledge‐ and Context‐driven Machine Learning (KCML), a framework that systematically predicts multiple context‐specific functions for a given gene based on the similarity of its perturbation phenotype to those with known function. As a proof of concept, we test KCML on three datasets describing phenotypes at the molecular, cellular and population levels and show that it outperforms traditional analysis pipelines. In particular, KCML identified an abnormal multicellular organisation phenotype associated with the depletion of olfactory receptors, and TGFβ and WNT signalling genes in colorectal cancer cells. We validate these predictions in colorectal cancer patients and show that olfactory receptors expression is predictive of worse patient outcomes. These results highlight KCML as a systematic framework for discovering novel scale‐crossing and context‐dependent gene functions. KCML is highly generalisable and applicable to various large‐scale genetic perturbation screens.

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Section: Methodsmentioning

confidence: 99%

KCML : a machine‐learning framework for inference of multi‐scale gene functions from genetic perturbation screens

Sailem

Rittscher

Pelkmans

2020

Molecular Systems Biology

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“…Reports of cases of RCC with a constitutional chromosome rearrangement were identified through a search of PubMed using the search terms “renal cell carcinoma” or “renal cancer” or “kidney cancer/tumor” and “rearrangement/inversion/translocation or chromosome” and by searching of previously published reports (performed January 2019). When previous reports had suggested candidate genes that were either close to or disrupted by the relevant chromosomal breakpoints, evidence to suggest that the genes were implicated in human cancer was sought by reviewing curated data from the Network of Cancer Genes data portal (NCG; http://ncg.kcl.ac.uk/ version 6) (performed January 2019) where genes were classified as either “known cancer genes,” “candidate cancer genes,” or “non‐cancer genes.” Genes flagged as “false positive cancer genes” were designated as “non‐cancer genes.”…”

Section: Methodsmentioning

confidence: 99%

“…The corresponding TADs were then intersected with the genomic positions of all known gene loci (Supporting Information) to find genes contained within a given TAD and only protein‐coding genes were included. Protein‐coding genes identified within a TAD were assessed for potential function in cancer using the Network of Cancer Genes data portal (NCG; http://ncg.kcl.ac.uk/ version 6), as previously described. TAD regions were visualized using the Hi‐C data browser (http://promoter.bx.psu.edu/hi-c/index.html).…”

Section: Methodsmentioning

confidence: 99%

Characterization of renal cell carcinoma‐associated constitutional chromosome abnormalities by genome sequencing

Smith

Whitworth

West

et al. 2020

Genes Chromosomes & Cancer

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Constitutional translocations, typically involving chromosome 3, have been recognized as a rare cause of inherited predisposition to renal cell carcinoma (RCC) for four decades. However, knowledge of the molecular basis of this association is limited. We have characterized the breakpoints by genome sequencing (GS) of constitutional chromosome abnormalities in five individuals who presented with RCC. In one individual with constitutional t(10;17)(q11.21;p11.2), the translocation breakpoint disrupted two genes: the known renal tumor suppressor gene (TSG) FLCN (and clinical features of Birt‐Hogg‐Dubé syndrome were detected) and RASGEF1A. In four cases, the rearrangement breakpoints did not disrupt known inherited RCC genes. In the second case without chromosome 3 involvement, the translocation breakpoint in an individual with a constitutional t(2;17)(q21.1;q11.2) mapped 12 Kb upstream of NLK. Interestingly, NLK has been reported to interact indirectly with FBXW7 and a previously reported RCC‐associated translocation breakpoint disrupted FBXW7. In two cases of constitutional chromosome 3 translocations, no candidate TSGs were identified in the vicinity of the breakpoints. However, in an individual with a constitutional chromosome 3 inversion, the 3p breakpoint disrupted the FHIT TSG (which has been reported previously to be disrupted in two apparently unrelated families with an RCC‐associated t(3;8)(p14.2;q24.1). These findings (a) expand the range of constitutional chromosome rearrangements that may be associated with predisposition to RCC, (b) confirm that chromosome rearrangements not involving chromosome 3 can predispose to RCC, (c) suggest that a variety of molecular mechanisms are involved the pathogenesis of translocation‐associated RCC, and (d) demonstrate the utility of GS for investigating such cases.

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“…Fusion transcript calls were contrasted with genomic SV data and visually verified on DNA level using IGV. Rearranged genes implicated in cancer were identified using the Network of Cancer Genes 6.0 (NCG 6.0) and literature search.…”

Section: Methodsmentioning

confidence: 99%

Whole‐genome analysis uncovers recurrent IKZF1 inactivation and aberrant cell adhesion in blastic plasmacytoid dendritic cell neoplasm

Torres

Cats

Mei

et al. 2019

Genes Chromosomes & Cancer

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Blastic plasmacytoid dendritic cell neoplasm (BPDCN) is a rare and highly aggressive hematological malignancy with a poorly understood pathobiology and no effective therapeutic options. Despite a few recurrent genetic defects (eg, single nucleotide changes, indels, large chromosomal aberrations) have been identified in BPDCN, none are disease‐specific, and more importantly, none explain its genesis or clinical behavior. In this study, we performed the first high resolution whole‐genome analysis of BPDCN with a special focus on structural genomic alterations by using whole‐genome sequencing and RNA sequencing. Our study, the first to characterize the landscape of genomic rearrangements and copy number alterations of BPDCN at nucleotide‐level resolution, revealed that IKZF1, a gene encoding a transcription factor required for the differentiation of plasmacytoid dendritic cell precursors, is focally inactivated through recurrent structural alterations in this neoplasm. In concordance with the genomic data, transcriptome analysis revealed that conserved IKZF1 target genes display a loss‐of‐IKZF1 expression pattern. Furthermore, up‐regulation of cellular processes responsible for cell‐cell and cell‐ECM interactions, which is a hallmark of IKZF1 deficiency, was prominent in BPDCN. Our findings suggest that IKZF1 inactivation plays a central role in the pathobiology of the disease, and consequently, therapeutic approaches directed at reestablishing the function of this gene might be beneficial for patients.

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The Network of Cancer Genes (NCG): a comprehensive catalogue of known and candidate cancer genes from cancer sequencing screens

Cited by 45 publications

References 60 publications

KCML : a machine‐learning framework for inference of multi‐scale gene functions from genetic perturbation screens

KCML : a machine‐learning framework for inference of multi‐scale gene functions from genetic perturbation screens

Characterization of renal cell carcinoma‐associated constitutional chromosome abnormalities by genome sequencing

Whole‐genome analysis uncovers recurrent IKZF1 inactivation and aberrant cell adhesion in blastic plasmacytoid dendritic cell neoplasm

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