DNA methyltransferase inhibitors in hematological malignancies and solid tumors
Abstract Epigenetic modifications such as DNA methylation play a fundamental role in oncogenesis and the progression of neoplasms neoplasias. DNA methyltransferase inhibitors (DNMTi) constitute a family of therapeutic agents that impede the methylation at the 5‐position on cytosine nucleotides, thereby modulating the epigenetic regulation of tumor ...
Valentin Wenger +3 more
wiley +1 more source
Elevated antidrug antibodies against atezolizumab and associated clinical outcomes in advanced non-small-cell lung cancer. [PDF]
Jang M +7 more
europepmc +1 more source
Abstract The Immunoscore (IS) quantifies the immune contexture of colorectal cancer (CRC) by measuring CD3+ and CD8+ T‐cell densities in the tumour centre and invasive margin, providing superior prognostic performance compared with TNM staging and mismatch‐repair (MMR) status alone.
Matthew SS Hsu, Suet Yi Leung
wiley +1 more source
Atezolizumab Immunotherapy-Induced Encephalitis in a Patient With Triple-Negative Breast Cancer: A Case Report. [PDF]
Wahab A, Faraz A.
europepmc +1 more source
Harnessing Next‐Generation 3D Cancer Models to Elucidate Tumor‐Microbiome Crosstalk
Centralizes the microbiome within 3D tumor‐microbiome model platforms, including spheroids, organoids, 3D‐bioprinted constructs, and microfluidic chips, each enabling structured host‐tumor‐microbe studies. These systems support bacterial colonization, facilitating investigation of microbial impacts on tumor growth, immunity, and therapy. The microbiome
Marina Green Buzhor +12 more
wiley +1 more source
A network meta-analysis of first-line treatment options for patients with Child-Pugh class B functional hepatocellular carcinoma: comparison of efficacy and safety. [PDF]
Zhang YX +7 more
europepmc +1 more source
Impact of Coronavirus Disease 2019 on Unresectable Hepatocellular Carcinoma Treated with Atezolizumab/Bevacizumab [PDF]
Yun Beom Sang +6 more
openalex +1 more source
Predicting Immunotherapy Outcomes in NSCLC Using RNA and Pathology from Multicenter Clinical Trials
LIRA, a machine learning‐based model, is developed using transcriptomic data from 891 NSCLC patients in the OAK and POPLAR cohorts. Its predictive performance is validated in multiple external cohorts. Patients stratified by LIRA‐score exhibit distinct clinical characteristics and tumor microenvironment profiles.
Zhaojun Wang +32 more
wiley +1 more source

