Prognostic Implications of the Residual Tumor Microenvironment after Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer Patients without Pathological Complete Response.

With a high risk of relapse and death, and a poor or absent response to therapeutics, the triple-negative breast cancer (TNBC) subtype is particularly challenging, especially in patients who cannot achieve a pathological complete response (pCR) after neoadjuvant chemotherapy (NAC). Although the tumor microenvironment (TME) is known to influence disease progression and the effectiveness of therapeutics, its predictive and prognostic potential remains uncertain. This work aimed to define the residual TME profile after NAC of a retrospective cohort with 96 TNBC patients by immunohistochemical staining (cell markers) and chromogenic in situ hybridization (genetic markers). Kaplan-Meier curves were used to estimate the influence of the selected TME markers on five-year overall survival (OS) and relapse-free survival (RFS) probabilities. The risks of each variable being associated with relapse and death were determined through univariate and multivariate Cox analyses. We describe a unique tumor-infiltrating immune profile with high levels of lymphocytes (CD4, FOXP3) and dendritic cells (CD21, CD1a and CD83) that are valuable prognostic factors in post-NAC TNBC patients. Our study also demonstrates the value of considering not only cellular but also genetic TME markers such as MUC-1 and CXCL13 in routine clinical diagnosis to refine prognosis modelling.

[Pre-analytical stage for biomarker assessment in breast cancer: 2014 update of the GEFPICS' guidelines in France]. / Recommandations du GEFPICS concernant la phase pré-analytique pour l'évaluation de HER2 et des récepteurs hormonaux dans le cancer du sein : mise à jour 2014.

Biomarker assessment of breast cancer tumor samples is part of the routine workflow of pathology laboratories. International guidelines have recently been updated, with special regards to the pre-analytical steps that are critical for the quality of immunohistochemical and in situ hybridization procedures, whatever the biomarker analyzed. Fixation and specimen handling protocols must be standardized, validated and carefully tracked. Cooperation and training of the personnel involved in the specimen workflow (e.g. radiologists, surgeons, nurses, technicians and pathologists) are of paramount importance. The GEFPICS' update of the recommendations herein details and comments the different steps of the pre-analytical process. Application of these guidelines and participation to quality insurance programs are mandatory to ensure the correct evaluation of oncotheranostic biomarkers.

[2014 update of the GEFPICS' recommendations for HER2 status determination in breast cancers in France]. / Mise à jour 2014 des recommandations du GEFPICS pour l'évaluation du statut HER2 dans les cancers du sein en France.

International guidelines on HER2 determination in breast cancer have just been updated by the American Society of Clinical Oncology (ASCO) and College of American Pathologists (CAP), on the basis of more than ten-year practice, results of clinical trials and concordance studies. The GEFPICS group, composed of expert pathologists in breast cancer, herein presents these recommendations, adapted to the French routine practice. These guidelines highlight the possible diagnosis difficulties with regards to HER2 status determination, such as intra-tumor heterogeneity, special histological subtypes and biomarker re-evaluation during metastatic relapse. Pre-analytical issues and updated scoring criteria (especially for equivocal cases) are detailed, in order to decrease the occurrence of false negative cases. In the era of personalized medicine, pathologists are more than ever involved in the quality of oncotheranostic biomarker evaluation.

A methodology to ensure and improve accuracy of Ki67 labelling index estimation by automated digital image analysis in breast cancer tissue.

INTRODUCTION: Immunohistochemical Ki67 labelling index (Ki67 LI) reflects proliferative activity and is a potential prognostic/predictive marker of breast cancer. However, its clinical utility is hindered by the lack of standardized measurement methodologies. Besides tissue heterogeneity aspects, the key element of methodology remains accurate estimation of Ki67-stained/counterstained tumour cell profiles. We aimed to develop a methodology to ensure and improve accuracy of the digital image analysis (DIA) approach. METHODS: Tissue microarrays (one 1-mm spot per patient, n = 164) from invasive ductal breast carcinoma were stained for Ki67 and scanned. Criterion standard (Ki67-Count) was obtained by counting positive and negative tumour cell profiles using a stereology grid overlaid on a spot image. DIA was performed with Aperio Genie/Nuclear algorithms. A bias was estimated by ANOVA, correlation and regression analyses. Calibration steps of the DIA by adjusting the algorithm settings were performed: first, by subjective DIA quality assessment (DIA-1), and second, to compensate the bias established (DIA-2). Visual estimate (Ki67-VE) on the same images was performed by five pathologists independently. RESULTS: ANOVA revealed significant underestimation bias (P < 0.05) for DIA-0, DIA-1 and two pathologists' VE, while DIA-2, VE-median and three other VEs were within the same range. Regression analyses revealed best accuracy for the DIA-2 (R-square = 0.90) exceeding that of VE-median, individual VEs and other DIA settings. Bidirectional bias for the DIA-2 with overestimation at low, and underestimation at high ends of the scale was detected. Measurement error correction by inverse regression was applied to improve DIA-2-based prediction of the Ki67-Count, in particularfor the clinically relevant interval of Ki67-Count < 40%. Potential clinical impact of the prediction was tested by dichotomising the cases at the cut-off values of 10, 15, and 20%. Misclassification rate of 5-7% was achieved, compared to that of 11-18% for the VE-median-based prediction. CONCLUSIONS: Our experiments provide methodology to achieve accurate Ki67-LI estimation by DIA, based on proper validation, calibration, and measurement error correction procedures, guided by quantified bias from reference values obtained by stereology grid count. This basic validation step is an important prerequisite for high-throughput automated DIA applications to investigate tissue heterogeneity and clinical utility aspects of Ki67 and other immunohistochemistry (IHC) biomarkers.

Digital image analysis in pathology: benefits and obligation.

Pathology has recently entered the era of personalized medicine. This brings new expectations for the accuracy and precision of tissue-based diagnosis, in particular, when quantification of histologic features and biomarker expression is required. While for many years traditional pathologic diagnosis has been regarded as ground truth, this concept is no longer sufficient in contemporary tissue-based biomarker research and clinical use. Another major change in pathology is brought by the advancement of virtual microscopy technology enabling digitization of microscopy slides and presenting new opportunities for digital image analysis. Computerized vision provides an immediate benefit of increased capacity (automation) and precision (reproducibility), but not necessarily the accuracy of the analysis. To achieve the benefit of accuracy, pathologists will have to assume an obligation of validation and quality assurance of the image analysis algorithms. Reference values are needed to measure and control the accuracy. Although pathologists' consensus values are commonly used to validate these tools, we argue that the ground truth can be best achieved by stereology methods, estimating the same variable as an algorithm is intended to do. Proper adoption of the new technology will require a new quantitative mentality in pathology. In order to see a complete and sharp picture of a disease, pathologists will need to learn to use both their analogue and digital eyes.