Publications by authors named "J D Wagstaff"
Precision oncology matches tumors to targeted therapies based on the presence of actionable molecular alterations. However, most tumors lack actionable alterations, restricting treatment options to cytotoxic chemotherapies for which few data-driven prioritization strategies currently exist. Here, we report an integrated computational/experimental treatment selection approach applicable for both chemotherapies and targeted agents irrespective of actionable alterations.
View Article and Find Full Text PDFPurpose: To investigate the predictive value of RECIST response within 3, 6, or 12 months on long-term survival, and explore differences between nivolumab+ipilimumab and nivolumab monotherapy, we analyzed pooled 5-year data of 935 responder and non-responder patients at various time points after treatment initiation in CheckMate 069, 066, and 067 studies.
Patients And Methods: Treatment-naive advanced melanoma patients received nivolumab+ipilimumab or nivolumab monotherapy. To decrease immortal time bias, 3-, 6-, or 12-month overall survival (OS) and progression-free survival (PFS) landmark analyses were performed.
Article Synopsis
- Nivolumab (NIVO) combined with ipilimumab (IPI) shows better long-term overall survival (OS) in patients with unresectable/metastatic melanoma than NIVO alone, based on pooled data from major trials.
- Patients treated with the combination therapy had a median follow-up OS of 45.0 months, with 6-year survival rates at 52%, compared to 41% for NIVO monotherapy after a median follow-up of 35.8 months.
- Clinical factors affecting survival include elevated lactate dehydrogenase (LDH) levels, age over 65 with the combination therapy, and presence of liver metastases with NIVO alone.
Article Synopsis
- The GA4GH Phenopacket Schema, released in 2022 and approved as a standard by ISO, allows the sharing of clinical and genomic data, including phenotypic descriptions and genetic information, to aid in genomic diagnostics.
- Phenopacket Store Version 0.1.19 offers a collection of 6668 phenopackets linked to various diseases and genes, making it a crucial resource for testing algorithms and software in genomic research.
- This collection represents the first extensive case-level, standardized phenotypic information sourced from medical literature, supporting advancements in diagnostic genomics and machine learning applications.