From morphologic to molecular: established and emerging molecular diagnostics for breast carcinoma

The Significance of Recognition of Human Epidermal Growth Factor 2 Low in Breast Cancer Therapy

In response to recent clinical trials that demonstrate the clinical benefit of antibody-drug conjugate drug therapy in breast cancer (BC) with human epidermal growth factor 2 (HER2) immunohistochemical scores of 1+ or 2+ and negative in situ hybridization results, a new concept of "HER2-low BC" has emerged to describe this newly relevant therapeutic category of BC. Clinical recognition of HER2-low BC has caused a paradigm shift in the therapeutic landscape and management of patients with BC and resulted in rapid changes in clinical practice guidelines. In addition the American Society of Clinical Oncology-College of American Pathologists (ASCO-CAP) recently updated their HER2 Guidelines Recommendations to specifically address HER2-low BC. A literature search in PubMed of peer-reviewed articles, regulatory communications, and relevant practice guidelines pertaining to HER2-low BC was conducted. In this review, we have summarized current published knowledge regarding the clinicopathologic and molecular features, diagnostic criteria, and most current guideline recommendations regarding HER2-low BC, and also highlight ongoing practical and diagnostic challenges when identifying HER2-low BC in routine clinical practice.

Survival analysis in breast cancer using proteomic data from four independent datasets

Breast cancer clinical treatment selection is based on the immunohistochemical determination of four protein biomarkers: ESR1, PGR, HER2, and MKI67. Our aim was to correlate immunohistochemical results to proteome-level technologies in measuring the expression of these markers. We also aimed to integrate available proteome-level breast cancer datasets to identify and validate new prognostic biomarker candidates. We searched studies involving breast cancer patient cohorts with published survival and proteomic information. Immunohistochemistry and proteomic technologies were compared using the Mann-Whitney test. Receiver operating characteristics (ROC) curves were generated to validate discriminative power. Cox regression and Kaplan-Meier survival analysis were calculated to assess prognostic power. False Discovery Rate was computed to correct for multiple hypothesis testing. We established a database integrating protein expression data and survival information from four independent cohorts for 1229 breast cancer patients. In all four studies combined, a total of 7342 unique proteins were identified, and 1417 of these were identified in at least three datasets. ESR1, PGR, and HER2 protein expression levels determined by RPPA or LC-MS/MS methods showed a significant correlation with the levels determined by immunohistochemistry (p < 0.0001). PGR and ESR1 levels showed a moderate correlation (correlation coefficient = 0.17, p = 0.0399). An additional panel of candidate proteins, including apoptosis-related proteins (BCL2,), adhesion markers (CDH1, CLDN3, CLDN7) and basal markers (cytokeratins), were validated as prognostic biomarkers. Finally, we expanded our previously established web tool designed to validate survival-associated biomarkers by including the proteomic datasets analyzed in this study ( https://kmplot.com/ ). In summary, large proteomic studies now provide sufficient data enabling the validation and ranking of potential protein biomarkers.

"Triple negative breast cancer": Translational research and the (re)assembling of diseases in post-genomic medicine

The paper examines the debate about the nature and status of "Triple-negative breast cancer", a controversial biomedical entity whose existence illustrates a number of features of post-genomic translational research. The emergence of TNBC is intimately linked to the rise of molecular oncology, and, more generally, to the changing configuration of the life sciences at the turn of the new century. An unprecedented degree of integration of biological and clinical practices has led to the proliferation of bio-clinical entities emerging from translational research. These translations take place between platforms rather than between clinical and laboratory settings. The complexity and heterogeneity of TNBC, its epistemic and technical, biological and clinical dualities, result from its multiple instantiations via different platforms, and from the uneven distribution of biological materials, techniques, and objects across clinical research settings. The fact that TNBC comes in multiple forms, some of which seem to be incompatible or, at least, only partially overlapping, appears to be less a threat to the whole endeavor, than an aspect of an ongoing translational research project. Discussions of translational research that rest on a distinction between basic research and its applications fail to capture the dynamics of this new domain of activity, insofar as application is built-in from the very beginning in the bio-clinical entities that emerge from the translational research domain.

The Discovery of Novel Biomarkers Improves Breast Cancer Intrinsic Subtype Prediction and Reconciles the Labels in the METABRIC Data Set

BACKGROUND

The prediction of breast cancer intrinsic subtypes has been introduced as a valuable strategy to determine patient diagnosis and prognosis, and therapy response. The PAM50 method, based on the expression levels of 50 genes, uses a single sample predictor model to assign subtype labels to samples. Intrinsic errors reported within this assay demonstrate the challenge of identifying and understanding the breast cancer groups. In this study, we aim to: a) identify novel biomarkers for subtype individuation by exploring the competence of a newly proposed method named CM1 score, and b) apply an ensemble learning, as opposed to the use of a single classifier, for sample subtype assignment. The overarching objective is to improve class prediction.

METHODS AND FINDINGS

The microarray transcriptome data sets used in this study are: the METABRIC breast cancer data recorded for over 2000 patients, and the public integrated source from ROCK database with 1570 samples. We first computed the CM1 score to identify the probes with highly discriminative patterns of expression across samples of each intrinsic subtype. We further assessed the ability of 42 selected probes on assigning correct subtype labels using 24 different classifiers from the Weka software suite. For comparison, the same method was applied on the list of 50 genes from the PAM50 method.

CONCLUSIONS

The CM1 score portrayed 30 novel biomarkers for predicting breast cancer subtypes, with the confirmation of the role of 12 well-established genes. Intrinsic subtypes assigned using the CM1 list and the ensemble of classifiers are more consistent and homogeneous than the original PAM50 labels. The new subtypes show accurate distributions of current clinical markers ER, PR and HER2, and survival curves in the METABRIC and ROCK data sets. Remarkably, the paradoxical attribution of the original labels reinforces the limitations of employing a single sample classifiers to predict breast cancer intrinsic subtypes.