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Recent advances in the field of immuno-oncology have brought transformative changes in the management of cancer patients. The immune profile of tumours has been found to have key value in predicting disease prognosis and treatment response in various cancers. Multiplex immunohistochemistry and immunofluorescence have emerged as potent tools for the simultaneous detection of multiple protein biomarkers in a single tissue section, thereby expanding opportunities for molecular and immune profiling while preserving tissue samples. By establishing the phenotype of individual tumour cells when distributed within a mixed cell population, the identification of clinically relevant biomarkers with high-throughput multiplex immunophenotyping of tumour samples has great potential to guide appropriate treatment choices. Moreover, the emergence of novel multi-marker imaging approaches can now provide unprecedented insights into the tumour microenvironment, including the potential interplay between various cell types. However, there are significant challenges to widespread integration of these technologies in daily research and clinical practice. This review addresses the challenges and potential solutions within a structured framework of action from a regulatory and clinical trial perspective. New developments within the field of immunophenotyping using multiplexed tissue imaging platforms and associated digital pathology are also described, with a specific focus on translational implications across different subtypes of cancer. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland., (© 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.)

Regulatory CD4 + T cells (Treg) prevent tumor clearance by conventional T cells (Tconv) comprising a major obstacle of cancer immune-surveillance. Hitherto, the mechanisms of Treg repertoire formation in human cancers remain largely unclear. Here, we analyze Treg clonal origin in breast cancer patients using T-Cell Receptor and single-cell transcriptome sequencing. While Treg in peripheral blood and breast tumors are clonally distinct, Tconv clones, including tumor-antigen reactive effectors (Teff), are detected in both compartments. Tumor-infiltrating CD4 + cells accumulate into distinct transcriptome clusters, including early activated Tconv, uncommitted Teff, Th1 Teff, suppressive Treg and pro-tumorigenic Treg. Trajectory analysis suggests early activated Tconv differentiation either into Th1 Teff or into suppressive and pro-tumorigenic Treg. Importantly, Tconv, activated Tconv and Treg share highly-expanded clones contributing up to 65% of intratumoral Treg. Here we show that Treg in human breast cancer may considerably stem from antigen-experienced Tconv converting into secondary induced Treg through intratumoral activation.

Background: Breast cancer is one of the most common malignancies with increasing incidences every year and a leading cause of death among women. Although early stage breast cancer can be effectively treated, there are limited numbers of treatment options available for patients with advanced and metastatic disease. The novel breast cancer associated antigen NY-BR-1 was identified by SEREX analysis and is expressed in the majority (>70%) of breast tumors as well as metastases, in normal breast tissue, in testis and occasionally in prostate tissue. The biological function and regulation of NY-BR-1 is up to date unknown., Methods: We performed an in silico analysis on the genetic variations of the NY-BR-1 gene using data available in public SNP databases and the tools SIFT, Polyphen and Provean to find possible functional SNPs. Additionally, we considered the allele frequency of the found damaging SNPs and also analyzed data from an in-house sequencing project of 55 breast cancer samples for recurring SNPs, recorded in dbSNP., Results: Over 2800 SNPs are recorded in the dbSNP and NHLBI ESP databases for the NY-BR-1 gene. Of these, 65 (2.07%) are synonymous SNPs, 191 (6.09%) are non-synoymous SNPs, and 2430 (77.48%) are noncoding intronic SNPs. As a result, 69 non-synoymous SNPs were predicted to be damaging by at least two, and 16 SNPs were predicted as damaging by all three of the used tools. The SNPs rs200639888, rs367841401 and rs377750885 were categorized as highly damaging by all three tools. Eight damaging SNPs are located in the ankyrin repeat domain (ANK), a domain known for its frequent involvement in protein-protein interactions. No distinctive features could be observed in the allele frequency of the analyzed SNPs., Conclusion: Considering these results we expect to gain more insights into the variations of the NY-BR-1 gene and their possible impact on giving rise to splice variants and therefore influence the function of NY-BR-1 in healthy tissue as well as in breast cancer.

Breast cancer represents the second most common cancer type worldwide and has remained the leading cause of cancer-related deaths among women. The differentiation antigen NY-BR-1 appears overexpressed in invasive mammary carcinomas compared to healthy breast tissue, thus representing a promising target antigen for T cell based tumor immunotherapy approaches. Since efficient immune attack of tumors depends on the activity of tumor antigen-specific CD4(+) effector T cells, NY-BR-1 was screened for the presence of HLA-restricted CD4(+) T cell epitopes that could be included in immunological treatment approaches. Upon NY-BR-1-specific DNA immunization of HLA-transgenic mice and functional ex vivo analysis, a panel of NY-BR-1-derived library peptides was determined that specifically stimulated IFNγ secretion among splenocytes of immunized mice. Following in silico analyses, four candidate epitopes were determined which were successfully used for peptide immunization to establish NY-BR-1-specific, HLA-DRB1*0301- or HLA-DRB1*0401-restricted CD4(+) T cell lines from splenocytes of peptide immunized HLA-transgenic mice. Notably, all four CD4(+) T cell lines recognized human HLA-DR-matched dendritic cells (DC) pulsed with lysates of NY-BR-1 expressing human tumor cells, demonstrating natural processing of these epitopes also within the human system. Finally, CD4(+) T cells specific for all four CD4(+) T cell epitopes were detectable among PBMC of breast cancer patients, showing that CD4(+) T cell responses against the new epitopes are not deleted nor inactivated by self-tolerance mechanisms. Our results present the first NY-BR-1-specific HLA-DRB1*0301- and HLA-DRB1*0401-restricted T cell epitopes that could be exploited for therapeutic intervention against breast cancer., (© 2014 UICC.)

The upcoming quantification and automation in biomarker based histological tumor evaluation will require computational methods capable of automatically identifying tumor areas and differentiating them from the stroma. As no single generally applicable tumor biomarker is available, pathology routinely uses morphological criteria as a spatial reference system. We here present and evaluate a method capable of performing the classification in immunofluorescence histological slides solely using a DAPI background stain. Due to the restriction to a single color channel this is inherently challenging. We formed cell graphs based on the topological distribution of the tissue cell nuclei and extracted the corresponding graph features. By using topological, morphological and intensity based features we could systematically quantify and compare the discrimination capability individual features contribute to the overall algorithm. We here show that when classifying fluorescence tissue slides in the DAPI channel, morphological and intensity based features clearly outpace topological ones which have been used exclusively in related previous approaches. We assembled the 15 best features to train a support vector machine based on Keratin stained tumor areas. On a test set of TMAs with 210 cores of triple negative breast cancers our classifier was able to distinguish between tumor and stroma tissue with a total overall accuracy of 88%. Our method yields first results on the discrimination capability of features groups which is essential for an automated tumor diagnostics. Also, it provides an objective spatial reference system for the multiplex analysis of biomarkers in fluorescence immunohistochemistry.

Tissue microarrays (TMAs) represent an important approach for the high-throughput cellular analysis of large numbers of tissue samples on one single slide in research related to diagnostics and oncology. Whole-slide imaging now enables full scanning and subsequent image analysis of such TMAs. In contrast to automatically spotted RNA microarrays, TMAs are fabricated manually and mechanically by arranging hundreds of tissue cores in a single paraffin block. This procedure frequently results in quality problems severely hampering the later automatic image analysis of TMAs after whole-slide imaging. We therefore set out to (a) determine the extent of these quality issues in exemplary TMAs and (b) to develop a robust gridding method to identify the logical position coordinates of each TMA core on a virtual TMA slide. We present the first robust method identifying these coordinates by shifting a template grid over all cores of the TMA (template matching) and thereby measuring in how far the grid matches a predefined list of cores on the virtual TMA Slide. Analysis of 20 TMAs from Breast Cancer as well as 40 Head-and-Neck Cancer showed that frequent TMA layout issues comprise low staining, debris, core displacement, nonuniform background, missing cores, and rotated subarrays. On this highly demanding test comprising chromogen as well as fluorescence stained TMAs, our template matching method achieved an excellent position analysis. Of 8900 cores, 8864 (99.59%) were assigned properly. In all 60 slides of the test set, no localization error occurred. The automatic grid analysis of TMAs after whole-slide imaging is highly demanding and requires dedicated algorithms. We demonstrate such a method and evaluate its performance. © 2010 International Society for Advancement of Cytometry.

Sequence analysis of protein-coding human genes in human breast and colorectal cancer recently identified the KIAA1632 gene as a potential contributor to neoplastic processes in breast cancer. In the present study, we characterized the KIAA1632 gene by computational methods: detailed investigation of the genomic structure, protein prediction, identification of orthologs in other species and phylogenetic analysis. The human KIAA1632 gene was located within human genome sequence AC090355 and consists of 44 exons. KIAA1632 is located on chromosome 18q12.3-q21.1. These findings were determined by aligning the potential full-length transcript NM_020964 to the genomic sequence. The existing predicted gene model could be refined based on these alignments (using additional EST data). Protein sequence was predicted from the full-length transcript and possible orthologs in other species were identified. Using rigorous phylogenetic methods we were able to draw conclusions about the evolutionary history of these proteins. Comparison of the protein sequences of KIAA1632 and its orthologs revealed a shared 'signature motif'. This 'signature motif' was found in three well-characterized cancer antigens, raising the hypothesis of a common 'signature motif' for a certain group of cancer antigens.