Dataset Information

Biclustering reveals breast cancer tumour subgroups with common clinical features and improves prediction of disease recurrence.

ABSTRACT:

Background

Many studies have revealed correlations between breast tumour phenotypes, variations in gene expression, and patient survival outcomes. The molecular heterogeneity between breast tumours revealed by these studies has allowed prediction of prognosis and has underpinned stratified therapy, where groups of patients with particular tumour types receive specific treatments. The molecular tests used to predict prognosis and stratify treatment usually utilise fixed sets of genomic biomarkers, with the same biomarker sets being used to test all patients. In this paper we suggest that instead of fixed sets of genomic biomarkers, it may be more effective to use a stratified biomarker approach, where optimal biomarker sets are automatically chosen for particular patient groups, analogous to the choice of optimal treatments for groups of similar patients in stratified therapy. We illustrate the effectiveness of a biclustering approach to select optimal gene sets for determining the prognosis of specific strata of patients, based on potentially overlapping, non-discrete molecular characteristics of tumours.

Results

Biclustering identified tightly co-expressed gene sets in the tumours of restricted subgroups of breast cancer patients. The co-expressed genes in these biclusters were significantly enriched for particular biological annotations and gene regulatory modules associated with breast cancer biology. Tumours identified within the same bicluster were more likely to present with similar clinical features. Bicluster membership combined with clinical information could predict patient prognosis in conditional inference tree and ridge regression class prediction models.

Conclusions

The increasing clinical use of genomic profiling demands identification of more effective methods to segregate patients into prognostic and treatment groups. We have shown that biclustering can be used to select optimal gene sets for determining the prognosis of specific strata of patients.

SUBMITTER: Wang YK

PROVIDER: S-EPMC3598775 | biostudies-literature | 2013 Feb

REPOSITORIES: biostudies-literature

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Publications

Biclustering reveals breast cancer tumour subgroups with common clinical features and improves prediction of disease recurrence.

Wang Yi Kan YK Print Cristin G CG Crampin Edmund J EJ

BMC genomics 20130213

<h4>Background</h4>Many studies have revealed correlations between breast tumour phenotypes, variations in gene expression, and patient survival outcomes. The molecular heterogeneity between breast tumours revealed by these studies has allowed prediction of prognosis and has underpinned stratified therapy, where groups of patients with particular tumour types receive specific treatments. The molecular tests used to predict prognosis and stratify treatment usually utilise fixed sets of genomic bi ...[more]

PMID: 23405961

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Project description:Tumour-infiltrating lymphocytes (TILs) have been demonstrated to significantly influence prognosis and response to therapy of invasive breast cancer (IBC). Thus, it has been suggested that TIL density or/and immunophenotype could serve as biomarkers for selection of IBC patients for immunotherapy. However, much less is known about significance of TILs in breast ductal carcinoma in situ (DCIS).We retrospectively investigated TIL density and immunophenotype in 96 pure DCIS and 35 microinvasive carcinomas (miCa). TIL density was assessed on H&E-stained breast biopsy sections as the percentage of tumour stromal area occupied by TILs, and classified into 4 grades: 0 (0%-9%), 1 (10-29%), 2 (30-49%) and 3 (50%-100%). TIL immunophenotype was assessed by immunohistochemistry for CD8, CD4, FoxP3, CD38 or CD20.Compared to pure DCIS, miCa contained significantly more cases with TIL density grade 3 (p?=?0.028). Concordantly, CD8+, CD4+ and CD38+ cells were more numerous in miCa than in pure DCIS. In the pure DCIS subgroup with TIL density grades 2 and 3, all TIL subpopulations were more numerous than in the pure DCIS with TIL density grades 0 and 1, however the ratio between T-lymphocytes (CD8+ and CD4+) and B-lymphocytes (CD20+) was significantly lower (p?=?0.029). On the other side, this ratio was significantly higher in miCa, in comparison with pure DCIS having TIL density grades 2 and 3 (p?=?0.017). By cluster analysis of tumour cell pathobiological features we demonstrated similarity between miCa and the pure DCIS with TIL density grades 2 and 3. The only significant difference between those two categories was in the ratio of T- to B-TILs, higher in miCa.Results indicate that TIL density level can distinguish 2 biologically different DCIS subgroups, one of which (DCIS with ?30% TILs, the TIL-rich DCIS) is like miCa. Similarity of TIL-rich pure DCIS and miCa as well as the role of B-lymphocytes in DCIS invasiveness are worth further investigating with regards to the potential development of immunotherapy-based prevention of DCIS progression.

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Dataset Information

Biclustering reveals breast cancer tumour subgroups with common clinical features and improves prediction of disease recurrence.

Background

Results

Conclusions

Publications

Biclustering reveals breast cancer tumour subgroups with common clinical features and improves prediction of disease recurrence.

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