Identification and validation of a prognostic proteomic signature for cervical cancer

Janet S. Rader, Amy Pan, Bradley Corbin, Marissa Iden, Yiling Lu, Christopher P. Vellano, Rehan Akbani, Gordon Mills, Pippa Simpson

    Research output: Contribution to journalArticle

    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.

    Original languageEnglish (US)
    JournalGynecologic oncology
    DOIs
    StateAccepted/In press - Jan 1 2019

    Fingerprint

    Uterine Cervical Neoplasms
    Proteomics
    Atlases
    Protein Array Analysis
    Genome
    Survival
    Antibodies
    Neoplasms
    Kaplan-Meier Estimate
    Survival Analysis
    Phosphatidylinositol 3-Kinases
    Proportional Hazards Models
    Cluster Analysis
    Biomarkers
    Hormones

    Keywords

    • Cervical cancer
    • Prognostic biomarkers
    • Reverse phase protein array
    • Survival risk
    • The Cancer Genome Atlas (TCGA)

    ASJC Scopus subject areas

    • Oncology
    • Obstetrics and Gynecology

    Cite this

    Rader, J. S., Pan, A., Corbin, B., Iden, M., Lu, Y., Vellano, C. P., ... Simpson, P. (Accepted/In press). Identification and validation of a prognostic proteomic signature for cervical cancer. Gynecologic oncology. https://doi.org/10.1016/j.ygyno.2019.08.021

    Identification and validation of a prognostic proteomic signature for cervical cancer. / Rader, Janet S.; Pan, Amy; Corbin, Bradley; Iden, Marissa; Lu, Yiling; Vellano, Christopher P.; Akbani, Rehan; Mills, Gordon; Simpson, Pippa.

    In: Gynecologic oncology, 01.01.2019.

    Research output: Contribution to journalArticle

    Rader, Janet S. ; Pan, Amy ; Corbin, Bradley ; Iden, Marissa ; Lu, Yiling ; Vellano, Christopher P. ; Akbani, Rehan ; Mills, Gordon ; Simpson, Pippa. / Identification and validation of a prognostic proteomic signature for cervical cancer. In: Gynecologic oncology. 2019.
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    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.",
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    AU - Rader, Janet S.

    AU - Pan, Amy

    AU - Corbin, Bradley

    AU - Iden, Marissa

    AU - Lu, Yiling

    AU - Vellano, Christopher P.

    AU - Akbani, Rehan

    AU - Mills, Gordon

    AU - Simpson, Pippa

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    N2 - 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.

    AB - 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.

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    KW - Survival risk

    KW - The Cancer Genome Atlas (TCGA)

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