1p36 deletion is a marker for tumour dissemination in microsatellite stable stage II-III colon cancer

Mayrhofer, Markus; Kultima, Hanna Göransson; Birgisson, Helgi; Sundström, Magnus; Mathot, Lucy; Edlund, Karolina; Viklund, Björn; Sjöblom, Tobias; Botling, Johan; Micke, Patrick; Påhlman, Lars; Glimelius, Bengt; Isaksson, Anders

doi:10.1186/1471-2407-14-872

Cited by 13 publications

(10 citation statements)

References 24 publications

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“…Of the 37 ICs, 20 correlated with normal/cancer status (P < 0.05/37 ∼ 0.001), with 4 of these capturing correlations between CNV and gene expression (table.S1 in SI). All 4 ICs were strongly enriched for specific chromosomal bands (table.S1 in SI), in line with those reported in the literature [1,30], and one of these (IC-35) also exhibited concomitant correlation between DNAm and gene expression (table.S1 in SI). Plotting the weights of IC-35 along the CNV, DNAm and mRNA axes confirmed the ability of tWFOBI to identify patterns of mRNA expression variation which are driven by local CNV and which also associate with local variation in DNAm (Fig.7A).…”

Section: Resultssupporting

confidence: 89%

Tensorial blind source separation for improved analysis of multi-omic data

Teschendorff

Han

Paul

et al. 2018

Preprint

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There is an increased need for integrative analyses of multi-omic data. Although several algorithms for analysing multi-omic data exist, no study has yet performed a detailed comparison of these methods in biologically relevant contexts. Here we benchmark a novel tensorial independent component analysis (tICA) algorithm against current state-of-the-art methods. Using simulated and real multi-omic data, we find that tICA outperforms established methods in identifying biological sources of data variation at a significantly reduced computational cost. Using two independent multi cell-type EWAS, we further demonstrate how tICA can identify, in the absence of genotype information, mQTLs at a higher sensitivity than competing multi-way algorithms. We validate mQTLs found with tICA in an independent set, and demonstrate that approximately 75% of mQTLs are independent of blood cell subtype. In an application to multi-omic cancer data, tICA identifies many gene modules whose expression variation across tumors is driven by copy number or DNA methylation changes, but whose deregulation relative to normal tissue is independent of copy-number or DNA methylation, an important finding that we confirm by direct analysis of individual data-types. In summary, tICA is a powerful novel algorithm for decomposing multi-omic data, which will be of great value to the research community.

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Section: Resultssupporting

confidence: 89%

Tensorial blind source separation for improved analysis of multi-omic data

Teschendorff

Han

Paul

et al. 2018

Preprint

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show abstract

“…Of the 37 ICs, 20 correlated with normal/cancer status ( P <0.05/37∼0.001), with four of these capturing correlations between CNV and gene expression (Additional file 1 : Table S1). All four ICs were strongly enriched for specific chromosomal bands (Additional file 1 : Table S1), in line with those reported in the literature [ 1 , 30 ], and one of these (IC-35) also exhibited concomitant correlation between DNAm and gene expression (Additional file 1 : Table S1). Plotting the weights of IC-35 along the CNV, DNAm and mRNA axes confirmed the ability of tWFOBI to identify patterns of mRNA expression variation, which are driven by local CNV and which also associate with local variation in DNAm (Fig.…”

Section: Resultssupporting

confidence: 86%

Tensorial blind source separation for improved analysis of multi-omic data

et al. 2018

View full text Add to dashboard Cite

There is an increased need for integrative analyses of multi-omic data. We present and benchmark a novel tensorial independent component analysis (tICA) algorithm against current state-of-the-art methods. We find that tICA outperforms competing methods in identifying biological sources of data variation at a reduced computational cost. On epigenetic data, tICA can identify methylation quantitative trait loci at high sensitivity. In the cancer context, tICA identifies gene modules whose expression variation across tumours is driven by copy-number or DNA methylation changes, but whose deregulation relative to normal tissue is independent of such alterations, a result we validate by direct analysis of individual data types.Electronic supplementary materialThe online version of this article (10.1186/s13059-018-1455-8) contains supplementary material, which is available to authorized users.

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“…We aimed to collect 20 to 25 cases each of stages II and III colorectal cancers with and without distant metastases, as well as 25 stage IV cases (6,10). Fresh frozen tissue and whole blood samples from 112 colorectal cancer patients [224 matched tumor/ normal (T/N) paired samples] were collected (Table 1).…”

Section: Methodsmentioning

confidence: 99%

“…For example, loss of 1p36 has consistently been associated with metastatic disease (5,6) and mutations in the tumor suppressor FBXW7 did not occur with distant metastases (7,8). If metastasis-causing mutations arise late in tumor development, they may be subclonal and therefore present at a low frequency in the primary tumor (9).…”

Section: Introductionmentioning

confidence: 99%

Somatic Ephrin Receptor Mutations Are Associated with Metastasis in Primary Colorectal Cancer

et al. 2017

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The contribution of somatic mutations to metastasis of colorectal cancers is currently unknown. To find mutations involved in the colorectal cancer metastatic process, we performed deep mutational analysis of 676 genes in 107 stages II to IV primary colorectal cancer, of which half had metastasized. The mutation prevalence in the ephrin (EPH) family of tyrosine kinase receptors was 10-fold higher in primary tumors of metastatic colorectal than in nonmetastatic cases and preferentially occurred in stage III and IV tumors. Mutational analyses in situ confirmed expression of mutant EPH receptors. To enable functional studies of EPHB1 mutations, we demonstrated that DLD-1 colorectal cancer cells expressing EPHB1 form aggregates upon coculture with ephrin B1 expressing cells. When mutations in the fibronectin type III and kinase domains of EPHB1 were compared with wild-type EPHB1 in DLD-1 colorectal cancer cells, they decreased ephrin B1-induced compartmentalization. These observations provide a mechanistic link between EPHB receptor mutations and metastasis in colorectal cancer.

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1p36 deletion is a marker for tumour dissemination in microsatellite stable stage II-III colon cancer

Cited by 13 publications

References 24 publications

Tensorial blind source separation for improved analysis of multi-omic data

Tensorial blind source separation for improved analysis of multi-omic data

Tensorial blind source separation for improved analysis of multi-omic data

Somatic Ephrin Receptor Mutations Are Associated with Metastasis in Primary Colorectal Cancer

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