Janet S Rader1, Amy Pan2, Bradley Corbin3, Marissa Iden3, Yiling Lu4, Christopher P Vellano5, Rehan Akbani6, Gordon B Mills7, Pippa Simpson2. 1. Department of Obstetrics and Gynecology, Medical College of Wisconsin, United States of America. Electronic address: jrader@mcw.edu. 2. Department of Pediatrics, Medical College of Wisconsin, United States of America. 3. Department of Obstetrics and Gynecology, Medical College of Wisconsin, United States of America. 4. Department of Systems Biology, The University of Texas MD Anderson Cancer Center, United States of America. 5. Translational Research to Advance Therapeutics and Innovation in Oncology Platform, The University of Texas MD Anderson Cancer Center, United States of America. 6. Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, United States of America. 7. Cell, Development and Cancer Biology, Knight Cancer Institute, Oregon Health & Science University, United States of America.
Abstract
OBJECTIVE: To date, The Cancer Genome Atlas (TCGA) has provided the most extensive molecular characterization of invasive cervical cancer (ICC). Analysis of reverse phase protein array (RPPA) data from TCGA samples showed that cervical cancers could be stratified into 3 clusters exhibiting significant differences in survival outcome: hormone, EMT, and PI3K/AKT. The goals of the current study were to: 1) validate the TCGA RPPA results in an independent cohort of ICC patients and 2) to develop and validate an algorithm encompassing a small antibody set for clinical utility. METHODS: Subjects consisted of 2 ICC patient cohorts with accompanying RPPA and clinical-pathologic data: 155 samples from TCGA (TCGA-155) and 61 additional, unique samples (MCW-61). Using data from 173 common RPPA antibodies, we replicated Silhouette clustering analysis in both ICC cohorts. Further, an index score for each patient was calculated from the survival-associated antibodies (SAAs) identified using Random survival forests (RSF) and the Cox proportional hazard regression model. Kaplan-Meier survival analysis and the log-rank test were performed to assess and compare cluster or risk group survival outcome. RESULTS: In addition to validating the prognostic ability of the proteomic clusters reported by TCGA, we developed an algorithm based on 22 unique antibodies (SAAs) that stratified women with ICC into low-, medium-, or high-risk survival groups. CONCLUSIONS: We provide a signature of 22 antibodies which accurately predicted survival outcome in 2 separate groups of ICC patients. Future studies examining these candidate biomarkers in additional ICC cohorts is warranted to fully determine their clinical potential.
OBJECTIVE: To date, The Cancer Genome Atlas (TCGA) has provided the most extensive molecular characterization of invasive cervical cancer (ICC). Analysis of reverse phase protein array (RPPA) data from TCGA samples showed that cervical cancers could be stratified into 3 clusters exhibiting significant differences in survival outcome: hormone, EMT, and PI3K/AKT. The goals of the current study were to: 1) validate the TCGA RPPA results in an independent cohort of ICC patients and 2) to develop and validate an algorithm encompassing a small antibody set for clinical utility. METHODS: Subjects consisted of 2 ICC patient cohorts with accompanying RPPA and clinical-pathologic data: 155 samples from TCGA (TCGA-155) and 61 additional, unique samples (MCW-61). Using data from 173 common RPPA antibodies, we replicated Silhouette clustering analysis in both ICC cohorts. Further, an index score for each patient was calculated from the survival-associated antibodies (SAAs) identified using Random survival forests (RSF) and the Cox proportional hazard regression model. Kaplan-Meier survival analysis and the log-rank test were performed to assess and compare cluster or risk group survival outcome. RESULTS: In addition to validating the prognostic ability of the proteomic clusters reported by TCGA, we developed an algorithm based on 22 unique antibodies (SAAs) that stratified women with ICC into low-, medium-, or high-risk survival groups. CONCLUSIONS: We provide a signature of 22 antibodies which accurately predicted survival outcome in 2 separate groups of ICC patients. Future studies examining these candidate biomarkers in additional ICC cohorts is warranted to fully determine their clinical potential.
Authors: A Daneri-Navarro; G Macias-Lopez; A Oceguera-Villanueva; S Del Toro-Arreola; A Bravo-Cuellar; R Perez-Montfort; S Orbach-Arbouys Journal: Eur J Cancer Date: 1998-03 Impact factor: 9.162
Authors: John J Wallbillich; Paul Mh Tran; Shan Bai; Lynn Kh Tran; Ashok K Sharma; Sharad A Ghamande; Jin-Xiong She Journal: Am J Cancer Res Date: 2020-05-01 Impact factor: 6.166