Benjamin J. Bulen
- Cancer Immunotherapy and Biomarkers
- Radiomics and Machine Learning in Medical Imaging
- Colorectal and Anal Carcinomas
- Inflammatory Biomarkers in Disease Prognosis
- Esophageal Cancer Research and Treatment
- Multiple and Secondary Primary Cancers
- Lung Cancer Research Studies
- Bladder and Urothelial Cancer Treatments
- Cancer Genomics and Diagnostics
- Pancreatic and Hepatic Oncology Research
- Colorectal Cancer Treatments and Studies
- Immune Cell Function and Interaction
- Lymphoma Diagnosis and Treatment
- Lung Cancer Treatments and Mutations
- Ferroptosis and cancer prognosis
- Genetic factors in colorectal cancer
- Health Systems, Economic Evaluations, Quality of Life
- Immunotherapy and Immune Responses
- PARP inhibition in cancer therapy
- Microscopic Colitis
Abstract Background Anti-PD-1 and PD-L1 (collectively PD-[L]1) therapies are approved for many advanced solid tumors. Biomarkers beyond immunohistochemistry, microsatellite instability, tumor mutation burden (TMB) may improve benefit prediction. Methods Using treatment data genomic transcriptomic tissue profiling from an observational trial (NCT03061305), we developed Immunotherapy Response Score (IRS), a pan-tumor predictive model of PD-(L)1 benefit. IRS real-world progression free survival...
Immunotherapy response score (IRS) integrates tumor mutation burden (TMB) and quantitative expression biomarkers to predict anti-PD-1/PD-L1 [PD-(L)1] monotherapy benefit. Here, we evaluated IRS in additional cohorts. Patients from an observational trial (NCT03061305) treated with anti-PD-(L)1 were included assigned IRS-High (-H) versus -Low (-L) groups. Associations real-world progression-free survival (rwPFS) overall (OS) determined by Cox proportional hazards (CPH) modeling. Those...
<p>Supplementary Figure S8. shows overlap weighting propensity score analysis of the chemotherapy, anti-PD-(L)1, and chemotherapy + anti-PD-(L)1 validation cohort</p>
<p>Supplementary Data S1 shows the REMARK checklist for study</p>
<p>Supplementary Figure S5 shows robustness of Immunotherapy Response Score to self-reported race</p>
<p>Supplementary Figure S7. shows covariate adjusted Kaplan-Meier analysis of the chemotherapy, anti-PD-(L)1, and chemotherapy + anti-PD-(L)1 validation cohort</p>
<p>Supplementary Figure S2 shows a diagram of the overall study</p>
<p>Supplementary Figure S6 shows three group Immunotherapy Response Score classification of the anti-PD-(L)1 monotherapy validation cohort</p>
<p>Supplementary Figure S7. shows covariate adjusted Kaplan-Meier analysis of the chemotherapy, anti-PD-(L)1, and chemotherapy + anti-PD-(L)1 validation cohort</p>
<p>Supplementary Figure S8. shows overlap weighting propensity score analysis of the chemotherapy, anti-PD-(L)1, and chemotherapy + anti-PD-(L)1 validation cohort</p>
<p>Supplementary Data S1 shows the REMARK checklist for study</p>
<p>Supplementary Figure S1 shows analytical validation of the gene expression component Immunotherapy Response Score components vs. qRT-PCR</p>