Results 151 to 160 of about 350,370 (303)
AZD9291 has shown promise in targeted cancer therapy but is limited by resistance. In this study, we employed metabolic labeling and LC–MS/MS to profile time‐resolved nascent protein perturbations, allowing dynamic tracking of drug‐responsive proteins. We demonstrated that increased NNMT expression is associated with drug resistance, highlighting NNMT ...
Zhanwu Hou +5 more
wiley +1 more source
PARP inhibitors are used to treat a small subset of prostate cancer patients. These studies reveal that PARP1 activity and expression are different between European American and African American prostate cancer tissue samples. Additionally, different PARP inhibitors cause unique and overlapping transcriptional changes, notably, p53 pathway upregulation.
Moriah L. Cunningham +21 more
wiley +1 more source
FDA-AACR Strategies for Optimizing Dosages for Oncology Drug Products: Early-Phase Trials Using Innovative Trial Designs and Biomarkers. [PDF]
Okusanya OO +8 more
europepmc +1 more source
Can, Yerebakan, Pranava, Sinha
openaire +2 more sources
This study explores salivary RNA for breast cancer (BC) diagnosis, prognosis, and follow‐up. High‐throughput RNA sequencing identified distinct salivary RNA signatures, including novel transcripts, that differentiate BC from healthy controls, characterize histological and molecular subtypes, and indicate lymph node involvement.
Nicholas Rajan +9 more
wiley +1 more source
Development of Biosimilar Aflibercept SDZ-AFL. [PDF]
Dodeller F +6 more
europepmc +1 more source
Bridging the gap: Multi‐stakeholder perspectives of molecular diagnostics in oncology
Although molecular diagnostics is transforming cancer care, implementing novel technologies remains challenging. This study identifies unmet needs and technology requirements through a two‐step stakeholder involvement. Liquid biopsies for monitoring applications and predictive biomarker testing emerge as key unmet needs. Technology requirements vary by
Jorine Arnouts +8 more
wiley +1 more source
A Machine Learning-Based Model to Estimate the Risk of Pulmonary Hypertension in Chronic Kidney Disease Patients. [PDF]
Bashir A, Rizwan MA, Bashir M.
europepmc +1 more source

