Panoptic View of Prognostic Models for Personalized Breast Cancer Management

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

Development and validation of a hypoxia‑related prognostic signature for breast cancer

“…Therapeutic modalities and clinical prognosis mainly rely on classical pathological risk and prognostic factors, such as age, stage, tumor grade and tumor type ( 3 , 4 ). However, advancements in genetic profiling provide a precise molecular complement of each patient's tumor in order to better understand the heterogeneity of breast cancer ( 5 ).…”

Success Factors and Barriers in Combining Personalized Medicine and Patient Centered Care in Breast Cancer. Results from a Systematic Review and Proposal of Conceptual Framework

“…Nine [25,26,29,31,[38][39][40]47,48] out of twenty-five articles (36%) reported information on both categories (PM and PC).…”

“…In particular, two [40,47] out of nine articles (22%) referred to the molecular board; eight [25,26,29,31,38,39,47,48] (89%) referred to evidence-based genetic testing.…”

Prognostic and clinical significance of focal adhesion kinase expression in breast cancer: A systematic review and meta-analysis

“…Worldwide, it is predicted that approximately 2.1 million cases of breast cancer among females will have been diagnosed in 2018 [ 1 ]. Furthermore, despite the reduced mortality of breast cancer due to individualized treatment encompassing early diagnosis, surgery, chemotherapy, radiotherapy, endocrine, and targeted therapy [ 2 ], distant metastasis remains one of the major challenges in the treatment of breast cancer cases. It is recognized that the mechanism(s) mediating metastasis are complex.…”