Dataset Information

Identifying survival associated morphological features of triple negative breast cancer using multiple datasets.

ABSTRACT:

Background and objective

Biomarkers for subtyping triple negative breast cancer (TNBC) are needed given the absence of responsive therapy and relatively poor prediction of survival. Morphology of cancer tissues is widely used in clinical practice for stratifying cancer patients, while genomic data are highly effective to classify cancer patients into subgroups. Thus integration of both morphological and genomic data is a promising approach in discovering new biomarkers for cancer outcome prediction. Here we propose a workflow for analyzing histopathological images and integrate them with genomic data for discovering biomarkers for TNBC.

Materials and methods

We developed an image analysis workflow for extracting a large collection of morphological features and deployed the same on histological images from The Cancer Genome Atlas (TCGA) TNBC samples during the discovery phase (n=44). Strong correlations between salient morphological features and gene expression profiles from the same patients were identified. We then evaluated the same morphological features in predicting survival using a local TNBC cohort (n=143). We further tested the predictive power on patient prognosis of correlated gene clusters using two other public gene expression datasets.

Results and conclusion

Using TCGA data, we identified 48 pairs of significantly correlated morphological features and gene clusters; four morphological features were able to separate the local cohort with significantly different survival outcomes. Gene clusters correlated with these four morphological features further proved to be effective in predicting patient survival using multiple public gene expression datasets. These results suggest the efficacy of our workflow and demonstrate that integrative analysis holds promise for discovering biomarkers of complex diseases.

SUBMITTER: Wang C

PROVIDER: S-EPMC3721170 | biostudies-literature | 2013 Jul-Aug

REPOSITORIES: biostudies-literature

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Publications

Identifying survival associated morphological features of triple negative breast cancer using multiple datasets.

Wang Chao C Pécot Thierry T Zynger Debra L DL Machiraju Raghu R Shapiro Charles L CL Huang Kun K

Journal of the American Medical Informatics Association : JAMIA 20130412 4

<h4>Background and objective</h4>Biomarkers for subtyping triple negative breast cancer (TNBC) are needed given the absence of responsive therapy and relatively poor prediction of survival. Morphology of cancer tissues is widely used in clinical practice for stratifying cancer patients, while genomic data are highly effective to classify cancer patients into subgroups. Thus integration of both morphological and genomic data is a promising approach in discovering new biomarkers for cancer outcome ...[more]

PMID: 23585272

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Project description:ObjectiveDifferent molecular subtypes of triple-negative breast cancer (TNBC) have previously been identified through analysis of gene expression profiles. The luminal androgen receptor (LAR) subtype has been shown to have a lower rate of pathologic complete response to neoadjuvant chemotherapy than other TNBC subtypes. The purpose of this study was to determine if the imaging features of TNBCs differ by AR (androgen receptor) status, which is a surrogate immunohistochemical (IHC) marker for the chemoresistant LAR subtype of TNBC.Materials and methodsThis sub-study was part of a clinical trial in patients with stage I-III TNBC who were prospectively monitored for response while receiving neoadjuvant systemic therapy (NAST) at a single comprehensive cancer center. This interim imaging analysis included 144 patients with known AR status measured by IHC. AR-positive (AR+) tumors were defined as those in which at least 10% of tumor cells had positive nuclear AR staining. Two experienced, fellowship-trained breast radiologists who were blinded to the IHC results retrospectively reviewed and reached consensus on all imaging studies for the index lesion (i.e., mammogram, ultrasound, and breast magnetic resonance imaging). The index lesion for each patient was reviewed and described according to the fifth edition of the Breast Imaging Reporting and Data System lexicon. Logistic regression modeling was used to identify imaging features predictive of AR status. p ≤ 0.05 was considered statistically significant.ResultsUnivariate logistic regression models for AR status showed that AR+ TNBC was significantly associated with heterogeneously dense breast composition on mammography (p = 0.02), mass with calcifications (p = 0.05), irregular mass shape on mammography (p = 0.03), and irregular mass shape on sonography (p = 0.003). Multivariate logistic regression models for AR status showed that AR+ TNBC was significantly associated with heterogeneously dense breast composition on mammography (p = 0.01), high mass density on mammography (p = 0.003), and irregular mass shape on sonography (p = 0.0004).ConclusionThe imaging features of TNBCs differ by AR status. Multimodality breast imaging may help identify the LAR subtype of TNBC, which has been shown to be a subtype that is relatively resistant to neoadjuvant chemotherapy.

Dataset Information

Identifying survival associated morphological features of triple negative breast cancer using multiple datasets.

Background and objective

Materials and methods

Results and conclusion

Publications

Identifying survival associated morphological features of triple negative breast cancer using multiple datasets.

Similar Datasets

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