Systematic review and feasibility study on pre-analytical factors and genomic analyses on archival formalin-fixed paraffin-embedded breast cancer tissue.

Formalin-fixed paraffin-embedded (FFPE) tissue represents a valuable source for translational cancer research. However, the widespread application of various downstream methods remains challenging. Here, we aimed to assess the feasibility of a genomic and gene expression analysis workflow using FFPE breast cancer (BC) tissue. We conducted a systematic literature review for the assessment of concordance between FFPE and fresh-frozen matched tissue samples derived from patients with BC for DNA and RNA downstream applications. The analytical performance of three different nucleic acid extraction kits on FFPE BC clinical samples was compared. We also applied a newly developed targeted DNA Next-Generation Sequencing (NGS) 370-gene panel and the nCounter BC360® platform on simultaneously extracted DNA and RNA, respectively, using FFPE tissue from a phase II clinical trial. Of the 3701 initial search results, 40 articles were included in the systematic review. High degree of concordance was observed in various downstream application platforms. Moreover, the performance of simultaneous DNA/RNA extraction kit was demonstrated with targeted DNA NGS and gene expression profiling. Exclusion of variants below 5% variant allele frequency was essential to overcome FFPE-induced artefacts. Targeted genomic analyses were feasible in simultaneously extracted DNA/RNA from FFPE material, providing insights for their implementation in clinical trials/cohorts.

Unveiling the Power of Model-Agnostic Multiscale Analysis for Enhancing Artificial Intelligence Models in Breast Cancer Histopathology Images.

Developing AI models for digital pathology has traditionally relied on single-scale analysis of histopathology slides. However, a whole slide image is a rich digital representation of the tissue, captured at various magnification levels. Limiting our analysis to a single scale overlooks critical information, spanning from intricate high-resolution cellular details to broad low-resolution tissue structures. In this study, we propose a model-agnostic multiresolution feature aggregation framework tailored for the analysis of histopathology slides in the context of breast cancer, on a multicohort dataset of 2038 patient samples. We have adapted 9 state-of-the-art multiple instance learning models on our multi-scale methodology and evaluated their performance on grade prediction, TP53 mutation status prediction and survival prediction. The results prove the dominance of the multiresolution methodology, and specifically, concatenating or linearly transforming via a learnable layer the feature vectors of image patches from a high (20x) and low (10x) magnification factors achieve improved performance for all prediction tasks across domain-specific and imagenet-based features. On the contrary, the performance of uniresolution baseline models was not consistent across domain-specific and imagenet-based features. Moreover, we shed light on the inherent inconsistencies observed in models trained on whole-tissue-sections when validated against biopsy-based datasets. Despite these challenges, our findings underscore the superiority of multiresolution analysis over uniresolution methods. Finally, cross-scale analysis also benefits the explainability aspects of attention-based architectures, since one can extract attention maps at the tissue- and cell-levels, improving the interpretation of the model's decision.

Interplay between copy number alterations and immune profiles in the early breast cancer Scandinavian Breast Group 2004-1 randomized phase II trial: results from a feasibility study.

Emerging data indicate that genomic alterations can shape immune cell composition in early breast cancer. However, there is a need for complementary imaging and sequencing methods for the quantitative assessment of combined somatic copy number alteration (SCNA) and immune profiling in pathological samples. Here, we tested the feasibility of three approaches-CUTseq, for high-throughput low-input SCNA profiling, multiplexed fluorescent immunohistochemistry (mfIHC) and digital-image analysis (DIA) for quantitative immuno-profiling- in archival formalin-fixed paraffin-embedded (FFPE) tissue samples from patients enrolled in the randomized SBG-2004-1 phase II trial. CUTseq was able to reproducibly identify amplification and deletion events with a resolution of 100 kb using only 6 ng of DNA extracted from FFPE tissue and pooling together 77 samples into the same sequencing library. In the same samples, mfIHC revealed that CD4 + T-cells and CD68 + macrophages were the most abundant immune cells and they mostly expressed PD-L1 and PD-1. Combined analysis showed that the SCNA burden was inversely associated with lymphocytic infiltration. Our results set the basis for further applications of CUTseq, mfIHC and DIA to larger cohorts of early breast cancer patients.