Introduction: The heterogeneity of breast cancer requires exploring novel prognostic biomarkers as well as therapeutic targets for the treatment of the disease.
Methods: The METABRIC dataset was used to describe the gene expression of the programmed death-ligand 1 (PD-L1) and the hepatocyte growth factor receptor (MET) and their association with the tumor clinicopathologic characteristics and overall survival in breast cancer.
Results: The expression of the PD-L1 and MET genes correlated positively with the Nottingham Prognostic Index (NPI) (p=0.003 and p < 0.001, respectively). The expression of the two genes correlated inversely with luminal A and luminal B tumors (r= - 0.089, p= 0.021 and r= - 0.116, p= 0.013, respectively). The PD-L1 mRNA levels were significantly higher in hormone receptor-negative and HER2-positive tumors. MET mRNA expression levels were significantly higher in hormone receptor-negative, HER2-enriched, and non-luminal breast cancers. The PD-L1/MET double-high expression was associated with younger age of patients at diagnosis, higher NPI scores, larger tumors, advanced stage, high-grade, hormone receptor-negativity, HER2-positivity, and non-luminal tumors. None of the genes or their double expression status was significantly associated with overall survival in this analysis.
Conclusion: The expression of the PD-L1 and MET genes is remarkably associated with worse tumor clinicopathologic features and poor prognosis in patients with breast cancer. Further investigations using combination drug regimens targeting PD-L1 and MET are important, particularly in breast tumors expressing high levels of both proteins.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.2174/0115680096333231240902070108 | DOI Listing |
Eur Urol
December 2024
Department of Oncology, University of Debrecen, Debrecen, Hungary.
Future Oncol
December 2024
Department of Cancer Center, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Background: The co-occurrence of PD-L1 positivity and EGFR mutations in advanced NSCLC often limits EGFR-TKIs effectiveness, with unclear mechanisms.
Methods: We analyzed 103 treatment-naive EGFR-mutant LUAD patients from three centers, assessing PD-L1 expression and performing NGS analysis.
Results: SMO mutations and MET amplification were significantly higher in the PD-L1 ≥ 1% group versus PD-L1 < 1% group (SMO: 8% vs.
Oncoimmunology
December 2025
Department of Pulmonary Medicine, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Kyoto, Japan.
This retrospective, multicenter cohort study aimed to determine whether cancer cachexia serves as a biomarker for determining the most effective treatment for patients having non-small-cell lung cancer (NSCLC) with high programmed death ligand 1 (PD-L1) expression treated with immune checkpoint inhibitors (ICIs) alone or combined with chemotherapy (ICI/chemotherapy). We included 411 patients with advanced NSCLC with a PD-L1 tumor proportion score of ≥50%. The patients were treated with pembrolizumab monotherapy or ICI/chemotherapy.
View Article and Find Full Text PDFFront Immunol
December 2024
Department of Biochemistry, Faculty of Medicine, Medical University of Warsaw, Warsaw, Poland.
Front Oncol
November 2024
Labcorp Oncology, Durham, NC, United States.
Introduction: Matching patients to an effective targeted therapy or immunotherapy is a challenge for advanced and metastatic non-small cell lung cancer (NSCLC), especially when relying on assays that test one marker at a time. Unlike traditional single marker tests, comprehensive genomic profiling (CGP) can simultaneously assess NSCLC tumors for hundreds of genomic biomarkers and markers for immunotherapy response, leading to quicker and more precise matches to therapeutics.
Methods: In this study, we performed CGP on 7,606 patients with advanced or metastatic NSCLC using the Illumina TruSight Oncology 500 (TSO 500) CGP assay to show its coverage and utility in detecting known and novel features of NSCLC.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!