AACR 2023 - Transcriptomic Analysis Identifying Novel Expression Biomarkers of Response in Pancreatic Cancer
AACR 2023 - Transcriptomic Analysis Identifying Novel Expression Biomarkers of Response in Pancreatic Cancer
AACR 2023 - Transcriptomic Analysis Identifying Novel Expression Biomarkers of Response in Pancreatic Cancer
AACR 2023 - Transcriptomic Analysis Identifying Novel Expression Biomarkers of Response in Pancreatic Cancer
Resulted in a robust candidate set of 5 genes (predictors) from a large PDAC, FFX-treated cohort.
Each predictor is individually statistically significantly associated with response to treatment.
The separation of the survival curves is larger in several of these genes, compared with nENT1 which is a previous biomarker of Gemcitabine (GA) effectiveness in PDAC.
All 5 candidate genes together are more predictive of response to FFX in this cohort compared to the all-gene model.
Additional biological validation and additional computational variations of the study designs are required to confirm the predictors.
Resulted in a robust candidate set of 5 genes (predictors) from a large PDAC, FFX-treated cohort.
Each predictor is individually statistically significantly associated with response to treatment.
The separation of the survival curves is larger in several of these genes, compared with nENT1 which is a previous biomarker of Gemcitabine (GA) effectiveness in PDAC.
All 5 candidate genes together are more predictive of response to FFX in this cohort compared to the all-gene model.
Additional biological validation and additional computational variations of the study designs are required to confirm the predictors.
Resulted in a robust candidate set of 5 genes (predictors) from a large PDAC, FFX-treated cohort.
Each predictor is individually statistically significantly associated with response to treatment.
The separation of the survival curves is larger in several of these genes, compared with nENT1 which is a previous biomarker of Gemcitabine (GA) effectiveness in PDAC.
All 5 candidate genes together are more predictive of response to FFX in this cohort compared to the all-gene model.
Additional biological validation and additional computational variations of the study designs are required to confirm the predictors.
Resulted in a robust candidate set of 5 genes (predictors) from a large PDAC, FFX-treated cohort.
Each predictor is individually statistically significantly associated with response to treatment.
The separation of the survival curves is larger in several of these genes, compared with nENT1 which is a previous biomarker of Gemcitabine (GA) effectiveness in PDAC.
All 5 candidate genes together are more predictive of response to FFX in this cohort compared to the all-gene model.
Additional biological validation and additional computational variations of the study designs are required to confirm the predictors.
