Panoptic View of Prognostic Models for Personalized Breast Cancer Management

• Type of Material: Article
• Authors: Geetanjali Saini, Georgia State University; Karuna Mittal, Georgia State University; Padmashree Rida, Georgia State University; Emiel A. M. Janssen, Stavanger University Hospital; Keerthi Gogineni, Emory University; Ritu Aneja, Georgia State University
• Language: English
• Date: 2019-09-01
• Publisher: MDPI
• Publication Version: Final Published Version
• Copyright Statement: © 2019 by the authors. Licensee MDPI, Basel, Switzerland.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.3390/cancers11091325
• Title of Journal or Parent Work: Cancers
• ISSN: 2072-6694
• Volume: 11
• Issue: 9
• Grant/Funding Information: This research was funded by National Institutes of Health (NIH)/National Cancer Institute (NCI) BY grant number UO1 CA179671.
• Keywords: NEOADJUVANT CHEMOTHERAPY; MOLECULAR GRADE INDEX; LATE DISTANT RECURRENCE; prognosis; digital pathology; Life Sciences & Biomedicine; Science & Technology; immunohistochemistry; ESTROGEN-RECEPTOR; TUMOR-INFILTRATING LYMPHOCYTES; AMERICAN SOCIETY; 21-GENE RECURRENCE SCORE; ONCOTYPE DX; GENE-EXPRESSION; CARCINOMA IN-SITU; liquid biopsy; multigene assays; prediction; breast cancer; Oncology
• Research Categories: Health Sciences, Pathology; Health Sciences, Oncology

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.