Review
. 2019 Sep;11(9).
doi: 10.3390/cancers11091325.

Panoptic View of Prognostic Models for Personalized Breast Cancer Management

Geetanjali Saini 1 Karuna Mittal 1 Padmashree Rida 1 Emiel A M Janssen 2 Keerthi Gogineni 3 Ritu Aneja 4 
Affiliations
  • PMID: 31500225
  •     151 References
  •     3 citations

Abstract

The efforts to personalize treatment for patients with breast cancer have led to a focus on the deeper characterization of genotypic and phenotypic heterogeneity among breast cancers. Traditional pathology utilizes microscopy to profile the morphologic features and organizational architecture of tumor tissue for predicting the course of disease, and is the first-line set of guiding tools for customizing treatment decision-making. Currently, clinicians use this information, combined with the disease stage, to predict patient prognosis to some extent. However, tumoral heterogeneity stubbornly persists among patient subgroups delineated by these clinicopathologic characteristics, as currently used methodologies in diagnostic pathology lack the capability to discern deeper genotypic and subtler phenotypic differences among individual patients. Recent advancements in molecular pathology, however, are poised to change this by joining forces with multiple-omics technologies (genomics, transcriptomics, epigenomics, proteomics, and metabolomics) that provide a wealth of data about the precise molecular complement of each patient's tumor. In addition, these technologies inform the drivers of disease aggressiveness, the determinants of therapeutic response, and new treatment targets in the individual patient. The tumor architecture information can be integrated with the knowledge of the detailed mutational, transcriptional, and proteomic phenotypes of cancer cells within individual tumors to derive a new level of biologic insight that enables powerful, data-driven patient stratification and customization of treatment for each patient, at each stage of the disease. This review summarizes the prognostic and predictive insights provided by commercially available gene expression-based tests and other multivariate or clinical -omics-based prognostic/predictive models currently under development, and proposes a more inclusive multiplatform approach to tackling the challenging heterogeneity of breast cancer to individualize its management. "The future is already here-it's just not very evenly distributed."-William Ford Gibson.

Keywords: breast cancer; digital pathology; immunohistochemistry; liquid biopsy; multigene assays; prediction; prognosis.

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Curr Opin Genet Dev, 2014 Mar 25; 24. PMID: 24657539
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J Proteome Res, 2009 Dec 10; 9(2). PMID: 19994911
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J Clin Oncol, 2011 Apr 13; 29(15). PMID: 21483002
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EPISPOT assay: detection of viable DTCs/CTCs in solid tumor patients.
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Ryan A Denu, Lauren M Zasadil, +3 authors, Mark E Burkard.
BMC Cancer, 2016 Feb 03; 16. PMID: 26832928    Free PMC article.
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A whole slide image-based machine learning approach to predict ductal carcinoma in situ (DCIS) recurrence risk.
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Are online prediction tools a valid alternative to genomic profiling in the context of systemic treatment of ER-positive breast cancer?
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Development and validation of a hypoxia-related prognostic signature for breast cancer.
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