Prognostic and predictive implications of sterile alpha motif and HD domain-containing protein 1 (SAMHD1) expression in breast cancer.

Sterile alpha motif and HD domain-containing protein 1 (SAMHD1) is a dNTP hydrolase important for intracellular dNTP homeostasis and serves as tumor suppressor and modulator of antimetabolite efficacy in cancer, though largely unexplored in breast cancer (BC). A cohort of patients with early BC (n = 564) with available gene expression data (GEP) was used. SAMHD1 protein expression was assessed by immunohistochemistry performed on tissue microarrays.

A novel clinical brain prognostic index for KRAS-mutated lung cancer and brain metastases (KRAS-BPI): Real-world evidence from two large European centers.

Introduction: Several prognostic scores were developed for non-small-cell lung cancer (NSCLC) patients with brain metastases (BM), though limited data reported for the KRAS-mutated subgroup. KRAS-targeted therapies have improved extracranial and intracranial response, highlighting the need for reliable prognostic biomarkers.

Methods: A retrospective cohort (2010-2020) comprising 220 patients with BM KRAS-mutated NSCLC from two large academic Thoracic Oncology centers (Karolinska and Heidelberg) was analyzed.

Breakthrough in RAS targeting with pan-RAS(ON) inhibitors RMC-7977 and RMC-6236.

The multi-selective tri-complex RAS(ON) inhibitors RMC-7977 and RMC-6236 signal new avenues for RAS targeting. This systematic review aims to comprehensively present the available preclinical and early clinical data on these agents. We screened Medline, Scopus, the ESMO and ASCO conference sites and ClinicalTrials.

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.

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.