Automated image quantification of immunohistochemistry-stained immune cells in triple-negative breast cancer

Abstract

Abstract Background: Breast cancer is a molecularly heterogenous disease for which the composition of the tumour microenvironment (TME) is acknowledged with an increasing role in treatment response and prognosis. In triple-negative breast cancer (TNBC) tumour infiltrating lymphocytes (TILs), representative of a general immune response, have been associated with a favourable prognosis. With growing number of TME cell type markers being analysed by conventional IHC or other in situ methods combined with need of spatial marker relationship analysis digital image analysis tools are needed to facilitate broader in situ characterisation of the breast cancer TME. Methods: A TMA comprising 218 patients with TNBC, enrolled in the Sweden Cancerome Analysis Network – Breast (SCAN-B) study, with complementary clinicopathological, WGS, and RNA-sequencing data were used. The TMA was stained using immunohistochemistry for p53, CD3, CD4, CD8, CD20, CD68, FOXP3, and PD-L1 (SP142 antibody), with available pathology scoring for CD20, PD-L1 and TILs. An open-source digital image analysis pipeline, Tissue microarray MArker Quantification (TMArQ), for analyses of single marker IHC images was developed implementing starDist segmentation. Primary pipeline output was the number of positive cells based on IHC staining. Results: TMArQ’s cell counts for analysed immune markers were on par with results from more advanced trained machine learning algorithms and consistent with both estimates from human pathology review, different quantifications/classifications derived from RNA-sequencing as well as known prognostic patterns of immune response in TNBC. When combined with somatic genetic information (TP53-mutation and homologous recombination deficiency, HRD) the pipeline demonstrated consistency in p53 protein expression versus TP53 variant type and superior patient outcome for the combination of high CD3 counts with HRD-positivity in patients with adjuvant standard-of-care chemotherapy. Conclusions: TMArQ is an easy-to-use open-source automated pipeline for IHC-based cell detection and quantification to be used as an exploratory tool in cancer image analysis. Digital analysis tools will likely greatly facilitate further characterisation of the breast cancer TME in novel ways and allow for a more precise linking of TME features and molecular alterations detected by large-scale omics methods, thereby deepening our understanding of breast cancer.