bims-tumhet 2023-11-26 papers

Issue of 2023–11–26
four papers selected by
Sergio Marchini, Humanitas Research

Histopathology. 2023 Nov 22.

Spatial transcriptomics and the anatomical pathologist: Molecular meets morphology.

Jia-Min B Pang, David J Byrne, Alice R T Bergin, Franco Caramia, Sherene Loi, Kylie L Gorringe, Stephen B Fox.

In recent years anatomical pathology has been revolutionised by the incorporation of molecular findings into routine diagnostic practice, and in some diseases the presence of specific molecular alterations are now essential for diagnosis. Spatial transcriptomics describes a group of technologies that provide up to transcriptome-wide expression profiling while preserving the spatial origin of the data, with many of these technologies able to provide these data using a single tissue section. Spatial transcriptomics allows expression profiling of highly specific areas within a tissue section potentially to subcellular resolution, and allows correlation of expression data with morphology, tissue type and location relative to other structures. While largely still research laboratory-based, several spatial transcriptomics methods have now achieved compatibility with formalin-fixed paraffin-embedded tissue (FFPE), allowing their use in diagnostic tissue samples, and with further development potentially leading to their incorporation in routine anatomical pathology practice. This mini review provides an overview of spatial transcriptomics methods, with an emphasis on platforms compatible with FFPE tissue, approaches to assess the data and potential applications in anatomical pathology practice.

Keywords: anatomical pathology; spatial transcriptomics; surgical pathology

DOI: https://doi.org/10.1111/his.15093

Life (Basel). 2023 Nov 10. pii: 2192. [Epub ahead of print]13(11):

Recurrent Somatic Copy Number Alterations and Their Association with Oncogene Expression Levels in High-Grade Ovarian Serous Carcinoma.

Hillary P Esplen, Richard K Yang, Awdhesh Kalia, Zhenya Tang, Guilin Tang, L Jeffrey Medeiros, Gokce A Toruner.

Somatic copy number alterations (SCNAs) are frequently observed in high-grade ovarian serous carcinoma (HGOSC). However, their impact on gene expression levels has not been systematically assessed. In this study, we explored the relationship between recurrent SCNA and gene expression using The Cancer Genome Atlas Pan Cancer dataset (OSC, TCGA, PanCancer Atlas) to identify cancer-related genes in HGOSC. We then investigated any association between highly correlated cancer genes and clinicopathological parameters, including age of diagnosis, disease stage, overall survival (OS), and progression-free survival (PFS). A total of 772 genes with recurrent SCNAs were observed. SCNA and mRNA expression levels were highly correlated for 274 genes; 24 genes were classified as a Tier 1 gene in the Cancer Gene Census in the Catalogue of Somatic Mutations in Cancer (CGC-COSMIC). Of these, 11 Tier 1 genes had highly correlated SCNA and mRNA expression levels: TBL1XR1, PIK3CA, UBR5, EIF3E, RAD21, EXT1, RECQL4, KRAS, PRKACA, BRD4, and TPM4. There was no association between gene amplification and disease stage or PFS. EIF3E, RAD21, and EXT1 were more frequently amplified in younger patients, specifically those under the age of 55 years. Patients with tumors carrying PRKACA, BRD4, or TPM4 amplification were associated with a significantly shorter OS. RECQL4 amplification was more frequent in younger patients, and tumors with this amplification were associated with a significantly better OS.

Keywords: gene expression; high-grade ovarian serous carcinoma; somatic copy number aberration

DOI: https://doi.org/10.3390/life13112192

Int J Mol Sci. 2023 Nov 07. pii: 16060. [Epub ahead of print]24(22):

Machine Learning Quantification of Intraepithelial Tumor-Infiltrating Lymphocytes as a Significant Prognostic Factor in High-Grade Serous Ovarian Carcinomas.

Jesús Machuca-Aguado, Antonio Félix Conde-Martín, Alejandro Alvarez-Muñoz, Enrique Rodríguez-Zarco, Alfredo Polo-Velasco, Antonio Rueda-Ramos, Rosa Rendón-García, Juan José Ríos-Martin, Miguel A Idoate.

The prognostic and predictive role of tumor-infiltrating lymphocytes (TILs) has been demonstrated in various neoplasms. The few publications that have addressed this topic in high-grade serous ovarian carcinoma (HGSOC) have approached TIL quantification from a semiquantitative standpoint. Clinical correlation studies, therefore, need to be conducted based on more accurate TIL quantification. We created a machine learning system based on H&E-stained sections using 76 molecularly and clinically well-characterized advanced HGSOC. This system enabled immune cell classification. These immune parameters were subsequently correlated with overall survival (OS) and progression-free survival (PFI). An intense colonization of the tumor cords by TILs was associated with a better prognosis. Moreover, the multivariate analysis showed that the intraephitelial (ie) TILs concentration was an independent and favorable prognostic factor both for OS (p = 0.02) and PFI (p = 0.001). A synergistic effect between complete surgical cytoreduction and high levels of ieTILs was evidenced, both in terms of OS (p = 0.0005) and PFI (p = 0.0008). We consider that digital analysis with machine learning provided a more accurate TIL quantification in HGSOC. It has been demonstrated that ieTILs quantification in H&E-stained slides is an independent prognostic parameter. It is possible that intraepithelial TIL quantification could help identify candidate patients for immunotherapy.

Keywords: algorithms; digital quantification; machine learning; ovarian cancer; tumor-infiltrating lymphocytes

DOI: https://doi.org/10.3390/ijms242216060

Genome Res. 2023 Nov 22.

Integrating SNVs and CNAs on a phylogenetic tree from single-cell DNA sequencing data.

Liting Zhang, Hank W Bass, Jerome Irianto, Xian Mallory.

Single-cell DNA sequencing enables the construction of evolutionary trees that can reveal how tumors gain mutations and grow. Different whole-genome amplification procedures render genomic materials of different characteristics, often suitable for the detection of either single-nucleotide variation or copy number aberration, but not ideally for both. Consequently, this hinders the inference of a comprehensive phylogenetic tree and limits opportunities to investigate the interplay of SNVs and CNAs. Existing methods such as SCARLET and COMPASS require that the SNVs and CNAs are detected from the same sets of cells, which is technically challenging. Here we present a novel computational tool, SCsnvcna, that places SNVs on a tree inferred from CNA signals, whereas the sets of cells rendering the SNVs and CNAs are independent, offering a more practical solution in terms of the technical challenges. SCsnvcna is a Bayesian probabilistic model using both the genotype constraints on the tree and the cellular prevalence to search the optimal solution. Comprehensive simulations and comparison with seven state-of-the-art methods show that SCsnvcna is robust and accurate in a variety of circumstances. Particularly, SCsnvcna most frequently produces the lowest error rates, with ability to scale to a wide range of numerical values for leaf nodes in the tree, SNVs, and SNV cells. The application of SCsnvcna to two published colorectal cancer data sets shows highly consistent placement of SNV cells and SNVs with the original study while also supporting a refined placement of ATP7B, illustrating SCsnvcna's value in analyzing complex multitumor samples.

DOI: https://doi.org/10.1101/gr.277249.122