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Combining gene mutation with gene expression data improves outcome prediction in myelodysplastic syndromes

Combining gene mutation with gene expression data improves outcome prediction in myelodysplastic syndromes
Combining gene mutation with gene expression data improves outcome prediction in myelodysplastic syndromes
Cancer is a genetic disease, but two patients rarely have identical genotypes. Similarly, patients differ in their clinicopathological parameters, but how genotypic and phenotypic heterogeneity are interconnected is not well understood. Here we build statistical models to disentangle the effect of 12 recurrently mutated genes and 4 cytogenetic alterations on gene expression, diagnostic clinical variables and outcome in 124 patients with myelodysplastic syndromes. Overall, one or more genetic lesions correlate with expression levels of ~20% of all genes, explaining 20–65% of observed expression variability. Differential expression patterns vary between mutations and reflect the underlying biology, such as aberrant polycomb repression for ?ASXL1 and ?EZH2 mutations or perturbed gene dosage for copy-number changes. In predicting survival, genomic, transcriptomic and diagnostic clinical variables all have utility, with the largest contribution from the transcriptome. Similar observations are made on the TCGA acute myeloid leukaemia cohort, confirming the general trends reported here.
biological sciences, cancer, genetics
5901
Gerstung, Moritz
35c4a83e-432b-47de-8657-cbcbcdfa8494
Pellagatti, Andrea
e96f98bb-98c5-477f-9ac5-6b9b3d238408
Malcovati, Luca
6564d132-6c02-4d8f-9741-d1d59e815e8a
Giagounidis, Aristoteles
3436d06a-78f2-44d1-97a1-bb6db04e3865
Porta, Matteo G Della
a9fcf972-b846-458c-9ce8-a683a1da6493
Jädersten, Martin
cf346c77-9849-466a-9911-577c64a2f510
Dolatshad, Hamid
0d54ba54-2956-427b-b667-6351b791f416
Verma, Amit
169f4d08-52f2-4593-bf92-478cc008202d
Cross, Nicholas C. P.
f87650da-b908-4a34-b31b-d62c5f186fe4
Vyas, Paresh
0e34bb53-e89b-42d4-ab9b-64746e0060f1
Killick, Sally
6f10a5b1-41ac-4443-90ce-17a866a4bf34
Hellström-Lindberg, Eva
7ad7d141-15bf-447c-815e-dac339d19c22
Cazzola, Mario
9b0cceec-9cda-47e9-81e6-c0ecac7b7480
Papaemmanuil, Elli
3994fc78-b2e7-4be0-a610-bb44c15977a0
Campbell, Peter J.
799a02e3-33ce-42e2-acda-2baf05fb7e6e
Boultwood, Jacqueline
653d33fa-0c0a-4a8a-b119-57a6e466b334
Gerstung, Moritz
35c4a83e-432b-47de-8657-cbcbcdfa8494
Pellagatti, Andrea
e96f98bb-98c5-477f-9ac5-6b9b3d238408
Malcovati, Luca
6564d132-6c02-4d8f-9741-d1d59e815e8a
Giagounidis, Aristoteles
3436d06a-78f2-44d1-97a1-bb6db04e3865
Porta, Matteo G Della
a9fcf972-b846-458c-9ce8-a683a1da6493
Jädersten, Martin
cf346c77-9849-466a-9911-577c64a2f510
Dolatshad, Hamid
0d54ba54-2956-427b-b667-6351b791f416
Verma, Amit
169f4d08-52f2-4593-bf92-478cc008202d
Cross, Nicholas C. P.
f87650da-b908-4a34-b31b-d62c5f186fe4
Vyas, Paresh
0e34bb53-e89b-42d4-ab9b-64746e0060f1
Killick, Sally
6f10a5b1-41ac-4443-90ce-17a866a4bf34
Hellström-Lindberg, Eva
7ad7d141-15bf-447c-815e-dac339d19c22
Cazzola, Mario
9b0cceec-9cda-47e9-81e6-c0ecac7b7480
Papaemmanuil, Elli
3994fc78-b2e7-4be0-a610-bb44c15977a0
Campbell, Peter J.
799a02e3-33ce-42e2-acda-2baf05fb7e6e
Boultwood, Jacqueline
653d33fa-0c0a-4a8a-b119-57a6e466b334

Gerstung, Moritz, Pellagatti, Andrea, Malcovati, Luca, Giagounidis, Aristoteles, Porta, Matteo G Della, Jädersten, Martin, Dolatshad, Hamid, Verma, Amit, Cross, Nicholas C. P., Vyas, Paresh, Killick, Sally, Hellström-Lindberg, Eva, Cazzola, Mario, Papaemmanuil, Elli, Campbell, Peter J. and Boultwood, Jacqueline (2015) Combining gene mutation with gene expression data improves outcome prediction in myelodysplastic syndromes. Nature Communications, 6, 5901. (doi:10.1038/ncomms6901).

Record type: Article

Abstract

Cancer is a genetic disease, but two patients rarely have identical genotypes. Similarly, patients differ in their clinicopathological parameters, but how genotypic and phenotypic heterogeneity are interconnected is not well understood. Here we build statistical models to disentangle the effect of 12 recurrently mutated genes and 4 cytogenetic alterations on gene expression, diagnostic clinical variables and outcome in 124 patients with myelodysplastic syndromes. Overall, one or more genetic lesions correlate with expression levels of ~20% of all genes, explaining 20–65% of observed expression variability. Differential expression patterns vary between mutations and reflect the underlying biology, such as aberrant polycomb repression for ?ASXL1 and ?EZH2 mutations or perturbed gene dosage for copy-number changes. In predicting survival, genomic, transcriptomic and diagnostic clinical variables all have utility, with the largest contribution from the transcriptome. Similar observations are made on the TCGA acute myeloid leukaemia cohort, confirming the general trends reported here.

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More information

Published date: 9 January 2015
Keywords: biological sciences, cancer, genetics
Organisations: Human Development & Health

Identifiers

Local EPrints ID: 373293
URI: http://eprints.soton.ac.uk/id/eprint/373293
PURE UUID: f01dd7c3-cf00-409e-8a0f-e244073f5e6a
ORCID for Nicholas C. P. Cross: ORCID iD orcid.org/0000-0001-5481-2555

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Date deposited: 14 Jan 2015 16:23
Last modified: 15 Mar 2024 03:11

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Contributors

Author: Moritz Gerstung
Author: Andrea Pellagatti
Author: Luca Malcovati
Author: Aristoteles Giagounidis
Author: Matteo G Della Porta
Author: Martin Jädersten
Author: Hamid Dolatshad
Author: Amit Verma
Author: Paresh Vyas
Author: Sally Killick
Author: Eva Hellström-Lindberg
Author: Mario Cazzola
Author: Elli Papaemmanuil
Author: Peter J. Campbell
Author: Jacqueline Boultwood

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