👤 Evie Wren

Formalin-fixed paraffin-embedded (FFPE) tissue specimens represent precious resources for clinical genomic profiling studies, especially when coupled with comprehensive medical records. Even though next-generation sequencing (NGS) is an effective tool to detect somatic mutations and somatic copy number alterations (sCNA), the biggest challenges in unlocking clinically translatable genomic information from FFPE tissue are low DNA yields and degraded DNA, affected by variable formalin fixation. Another issue is that the proportion of carcinoma and other noncarcinoma cells is variable and can be confounded by intratumoral heterogeneity. To explore these challenges, we isolated pure carcinoma and stromal cells using the DEPArray™ NxT system, a microchip-based digital sorter that allows isolation of pure, homogeneous subpopulations of cells from FFPE samples. We isolated pure carcinoma and stromal cell populations from 12 FFPE tissues, including tissues from nine primary and metastatic breast cancer and three primary ovarian high-grade serous carcinomas. This was followed by downstream shallow whole-genome sequencing (WGS) for copy number landscape profiling (10 samples) and/or a targeted panel for somatic mutation and sCNA analysis (seven samples), subject to cell availability. Seven out of 10 samples (even some with low tumour content or of old age) produced good-quality genomic data, detecting sCNA in all carcinoma population samples but not in the stromal populations. Mutation analysis was performed successfully in 6/7 samples and somatic mutations were detected in all of them. Our workflow enabled the identification of clinically actionable targets, including PIK3CA, ERBB2, FGFR1/2, CDK6, CCNE1, KRAS amplifications and RB, BRCA1/2 losses in patients that would direct therapy. © 2025 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland. Show less

Glioblastoma multiforme (GBM) is a high-grade glioma with poor prognosis. Identification of new biomarkers specific to GBM could help in disease diagnosis. We have developed and validated a bioinformatics method to predict proteins likely to be suitable as glioma biomarkers via a global microarray meta-analysis to identify uncharacterized genes consistently coexpressed with known glioma-associated genes. A novel bioinformatics method was implemented called global microarray meta-analysis, using approximately 16,000 microarray experiments to identify uncharacterized genes consistently coexpressed with known glioma-associated genes. These novel biomarkers were validated as proteins highly expressed in human gliomas varying in tumor grades using immunohistochemistry. Glioma gene databases were used to assess delineation of expression of these markers in varying glioma grades and subtypes of GBM. We have identified 5 potential biomarkers-spondin1, Plexin-B2, SLIT3, fibulin-1, and LINGO1-that were validated as proteins highly expressed on the surface of human gliomas using immunohistochemistry. Expression of spondin1, Plexin-B2, and SLIT3 was significantly higher (P < .01) in high-grade gliomas than in low-grade gliomas. These biomarkers were significant discriminators in grade IV gliomas compared with either grade III or II tumors and also distinguished between GBM subclasses. This study strongly suggests that this type of bioinformatics approach has high translational potential to rapidly discern which poorly characterized proteins may be of clinical relevance. Show less