Clinical and analytical validation of a combined RNA and DNA exome assay across a large tumor cohort.

Background: Combining RNA sequencing (RNA-seq) with whole exome sequencing (WES) from a single tumor sample can substantially improve the detection of clinically relevant alterations in cancer. However, routine clinical adoption of this integrated approach remains limited, especially for RNA-seq, due to the absence of standardized validation frameworks.

Methods: We developed and validated an assay that integrates RNA-seq and WES for evaluating gene expression, gene fusions, tumor microenvironment signatures, somatic single nucleotide variants (SNVs), insertions/deletions (INDELs), and copy number variations (CNVs).

The Breast Cancer Classifier refines molecular breast cancer classification to delineate the HER2-low subtype.

Current breast cancer classification methods, particularly immunohistochemistry and PAM50, face challenges in accurately characterizing the HER2-low subtype, a therapeutically relevant entity with distinct biological features. This notable gap can lead to misclassification, resulting in inappropriate treatment decisions and suboptimal patient outcomes. Leveraging RNA-seq and machine-learning algorithms, we developed the Breast Cancer Classifier (BCC), a unique transcriptomic classifier for more precise breast cancer subtyping, specifically by delineating and incorporating HER2-low as a distinct subtype.

Case report: Metastatic urothelial cancer with an exceptional response to immunotherapy and comprehensive understanding of the tumor and the tumor microenvironment.

Although immune checkpoint inhibitors (ICIs) are increasingly used as second-line treatments for urothelial cancer (UC), only a small proportion of patients respond. Therefore, understanding the mechanisms of response to ICIs is critical to improve clinical outcomes for UC patients. The tumor microenvironment (TME) is recognized as a key player in tumor progression and the response to certain anti-cancer treatments.

Fluorescence Diagnosis in Neurooncology: Retrospective Analysis of 653 Cases.

This study is to analyze fluorescence sensitivity in the diagnosis of brain and spinal cord tumors. The authors conducted a multicenter retrospective analysis of data on 653 cases in 641 patients: 553 of them had brain tumors and 88 spinal cord tumors. Brain tumor resection was performed in 523 patients, of whom 484 were adults and 39 children.