Predicting Disease Recurrence, Early Progression, and Overall Survival Following Surgical Resection for High-risk Localized and Locally Advanced Renal Cell Carcinoma

Andres F. Correa; Opeyemi A. Jegede; Naomi B. Haas; Keith T. Flaherty; Michael R. Pins; Adebowale Adeniran; Edward M. Messing; Judith Manola; Christopher G. Wood; Christopher J. Kane; Michael A.S. Jewett; Janice P. Dutcher; Robert S. DiPaola; Michael A. Carducci; Robert G. Uzzo

doi:10.1016/j.eururo.2021.02.025

Predicting Disease Recurrence, Early Progression, and Overall Survival Following Surgical Resection for High-risk Localized and Locally Advanced Renal Cell Carcinoma

Andres F. Correa, Opeyemi A. Jegede, Naomi B. Haas, Keith T. Flaherty, Michael R. Pins, Adebowale Adeniran, Edward M. Messing, Judith Manola, Christopher G. Wood, Christopher J. Kane, Michael A.S. Jewett, Janice P. Dutcher, Robert S. DiPaola, Michael A. Carducci, Robert G. Uzzo

Research output: Contribution to journal › Article › peer-review

34 Scopus citations

Abstract

Background: Risk stratification for localized renal cell carcinoma (RCC) relies heavily on retrospective models, limiting their generalizability to contemporary cohorts. Objective: To introduce a contemporary RCC prognostic model, developed using prospective, highly annotated data from a phase III adjuvant trial. Design, setting, and participants: The model utilizes outcome data from the ECOG-ACRIN 2805 (ASSURE) RCC trial. Outcome measurements and statistical analysis: The primary outcome for the model is disease-free survival (DFS), with overall survival (OS) and early disease progression (EDP) as secondary outcomes. Model performance was assessed using discrimination and calibration tests. Results and limitations: A total of 1735 patients were included in the analysis, with 887 DFS events occurring over a median follow-up of 9.6 yr. Five common tumor variables (histology, size, grade, tumor necrosis, and nodal involvement) were included in each model. Tumor histology was the single most powerful predictor for each model outcome. The C-statistics at 1 yr were 78.4% and 81.9% for DFS and OS, respectively. Degradation of the DFS, DFS validation set, and OS model's discriminatory ability was seen over time, with a global c-index of 68.0% (95% confidence interval or CI [65.5, 70.4]), 68.6% [65.1%, 72.2%], and 69.4% (95% CI [66.9%, 71.9%], respectively. The EDP model had a c-index of 75.1% (95% CI [71.3, 79.0]). Conclusions: We introduce a contemporary RCC recurrence model built and internally validated using prospective and highly annotated data from a clinical trial. Performance characteristics of the current model exceed available prognostic models with the added benefit of being histology inclusive and TNM agnostic. Patient summary: Important decisions, including treatment protocols, clinical trial eligibility, and life planning, rest on our ability to predict cancer outcomes accurately. Here, we introduce a contemporary renal cell carcinoma prognostic model leveraging high-quality data from a clinical trial. The current model predicts three outcome measures commonly utilized in clinical practice and exceeds the predictive ability of available prognostic models.

Original language	English
Pages (from-to)	20-31
Number of pages	12
Journal	European Urology
Volume	80
Issue number	1
DOIs	https://doi.org/10.1016/j.eururo.2021.02.025
State	Published - Jul 2021

Bibliographical note

Publisher Copyright:
© 2021 European Association of Urology

Funding

Other: None. Financial disclosures: Andres F. Correa certifies that all conflicts of interest, including specific financial interests and relationships and affiliations relevant to the subject matter or materials discussed in the manuscript (eg, employment/affiliation, grants or funding, consultancies, honoraria, stock ownership or options, expert testimony, royalties, or patents filed, received, or pending), are the following: Andres F. Correa, MD: no conflicts of interest. Opeyemi Jegede, MPH: no conflicts of interest. Naomi B. Haas, MD: study chair for the ECOG-ACRIN E2508/ASSURE. Keith T. Flaherty, MD: no conflicts of interest. Michael R. Pins, MD: no conflicts of interest. Adebowale Adeniran, MD: no conflicts of interest. Edward M. Messing, MD: no conflicts of interest. Judith Manola, MS: no conflicts of interest. Christopher G. Wood, MD: no conflicts of interest. Christopher J. Kane, MD: no conflicts of interest. Michael A.S. Jewett, MD: no conflicts of interest. Janice P. Dutcher, MD: consultant for Prometheus and a Data Safety and Monitoring Committee member for trials of advanced renal cell cancer conducted by Tracon, BMS, Merck, and Eisai; Renal Task Force Med Onc Co-chair for Cancer Therapy Evaluation Program. Michael A. Carducci, MD: consultant and advisor for Astellas Pharma, AbbVie, Genentech, Pfizer, and Foundation Medicine; research funding from Bristol-Myers Squibb (Inst), Pfizer (Inst), AstraZeneca (Inst), Gilead Sciences (Inst), EMD Serono (Inst), and eFFECTOR Therapeutics (Inst). Robert S. DiPaola, MD: no conflicts of interest. Robert G. Uzzo, MD: financial interest and/or other relationship with Pfizer, Novartis, Janssen, and Argos. Funding/Support and role of the sponsor: This study was coordinated by the ECOG-ACRIN Cancer Research Group (Peter J. O'Dwyer, MD and Mitchell D. Schnall, MD, PhD, Group Co-Chairs), and supported by the National Cancer Institute of the National Institutes of Health (under the following award numbers: CA180820, CA180794, CA180867, CA180858, CA180888, CA180821, CA180863) and the Canadian Cancer Society (#704970). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. government.

Funders	Funder number
U.S. Government
eFFECTOR Therapeutics
National Institutes of Health (NIH)	CA180888, CA180821, CA180867, CA180820, CA180794, CA180858
National Childhood Cancer Registry – National Cancer Institute	U10CA180863
Bristol-Myers Squibb
Pfizer
Astellas Pharma Inc.
AstraZeneca
Genentech Incorporated
Merck
Novartis
EMD Serono Research Institute
Gilead Sciences
AbbVie
Janssen Pharmaceuticals
Canadian Cancer Society Research Institute	704970
Eisai

Keywords

ASSURE trial
Disease-free survival
Prognostic model
Renal cell carcinoma

ASJC Scopus subject areas

Urology

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1016/j.eururo.2021.02.025

Cite this

Correa, A. F., Jegede, O. A., Haas, N. B., Flaherty, K. T., Pins, M. R., Adeniran, A., Messing, E. M., Manola, J., Wood, C. G., Kane, C. J., Jewett, M. A. S., Dutcher, J. P., DiPaola, R. S., Carducci, M. A., & Uzzo, R. G. (2021). Predicting Disease Recurrence, Early Progression, and Overall Survival Following Surgical Resection for High-risk Localized and Locally Advanced Renal Cell Carcinoma. European Urology, 80(1), 20-31. https://doi.org/10.1016/j.eururo.2021.02.025

@article{71abad727b5749809890201b2156d389,

title = "Predicting Disease Recurrence, Early Progression, and Overall Survival Following Surgical Resection for High-risk Localized and Locally Advanced Renal Cell Carcinoma",

abstract = "Background: Risk stratification for localized renal cell carcinoma (RCC) relies heavily on retrospective models, limiting their generalizability to contemporary cohorts. Objective: To introduce a contemporary RCC prognostic model, developed using prospective, highly annotated data from a phase III adjuvant trial. Design, setting, and participants: The model utilizes outcome data from the ECOG-ACRIN 2805 (ASSURE) RCC trial. Outcome measurements and statistical analysis: The primary outcome for the model is disease-free survival (DFS), with overall survival (OS) and early disease progression (EDP) as secondary outcomes. Model performance was assessed using discrimination and calibration tests. Results and limitations: A total of 1735 patients were included in the analysis, with 887 DFS events occurring over a median follow-up of 9.6 yr. Five common tumor variables (histology, size, grade, tumor necrosis, and nodal involvement) were included in each model. Tumor histology was the single most powerful predictor for each model outcome. The C-statistics at 1 yr were 78.4% and 81.9% for DFS and OS, respectively. Degradation of the DFS, DFS validation set, and OS model's discriminatory ability was seen over time, with a global c-index of 68.0% (95% confidence interval or CI [65.5, 70.4]), 68.6% [65.1%, 72.2%], and 69.4% (95% CI [66.9%, 71.9%], respectively. The EDP model had a c-index of 75.1% (95% CI [71.3, 79.0]). Conclusions: We introduce a contemporary RCC recurrence model built and internally validated using prospective and highly annotated data from a clinical trial. Performance characteristics of the current model exceed available prognostic models with the added benefit of being histology inclusive and TNM agnostic. Patient summary: Important decisions, including treatment protocols, clinical trial eligibility, and life planning, rest on our ability to predict cancer outcomes accurately. Here, we introduce a contemporary renal cell carcinoma prognostic model leveraging high-quality data from a clinical trial. The current model predicts three outcome measures commonly utilized in clinical practice and exceeds the predictive ability of available prognostic models.",

keywords = "ASSURE trial, Disease-free survival, Prognostic model, Renal cell carcinoma",

author = "Correa, {Andres F.} and Jegede, {Opeyemi A.} and Haas, {Naomi B.} and Flaherty, {Keith T.} and Pins, {Michael R.} and Adebowale Adeniran and Messing, {Edward M.} and Judith Manola and Wood, {Christopher G.} and Kane, {Christopher J.} and Jewett, {Michael A.S.} and Dutcher, {Janice P.} and DiPaola, {Robert S.} and Carducci, {Michael A.} and Uzzo, {Robert G.}",

note = "Publisher Copyright: {\textcopyright} 2021 European Association of Urology",

year = "2021",

month = jul,

doi = "10.1016/j.eururo.2021.02.025",

language = "English",

volume = "80",

pages = "20--31",

number = "1",

}

Correa, AF, Jegede, OA, Haas, NB, Flaherty, KT, Pins, MR, Adeniran, A, Messing, EM, Manola, J, Wood, CG, Kane, CJ, Jewett, MAS, Dutcher, JP, DiPaola, RS, Carducci, MA & Uzzo, RG 2021, 'Predicting Disease Recurrence, Early Progression, and Overall Survival Following Surgical Resection for High-risk Localized and Locally Advanced Renal Cell Carcinoma', European Urology, vol. 80, no. 1, pp. 20-31. https://doi.org/10.1016/j.eururo.2021.02.025

TY - JOUR

T1 - Predicting Disease Recurrence, Early Progression, and Overall Survival Following Surgical Resection for High-risk Localized and Locally Advanced Renal Cell Carcinoma

AU - Correa, Andres F.

AU - Jegede, Opeyemi A.

AU - Haas, Naomi B.

AU - Flaherty, Keith T.

AU - Pins, Michael R.

AU - Adeniran, Adebowale

AU - Messing, Edward M.

AU - Manola, Judith

AU - Wood, Christopher G.

AU - Kane, Christopher J.

AU - Jewett, Michael A.S.

AU - Dutcher, Janice P.

AU - DiPaola, Robert S.

AU - Carducci, Michael A.

AU - Uzzo, Robert G.

PY - 2021/7

Y1 - 2021/7

N2 - Background: Risk stratification for localized renal cell carcinoma (RCC) relies heavily on retrospective models, limiting their generalizability to contemporary cohorts. Objective: To introduce a contemporary RCC prognostic model, developed using prospective, highly annotated data from a phase III adjuvant trial. Design, setting, and participants: The model utilizes outcome data from the ECOG-ACRIN 2805 (ASSURE) RCC trial. Outcome measurements and statistical analysis: The primary outcome for the model is disease-free survival (DFS), with overall survival (OS) and early disease progression (EDP) as secondary outcomes. Model performance was assessed using discrimination and calibration tests. Results and limitations: A total of 1735 patients were included in the analysis, with 887 DFS events occurring over a median follow-up of 9.6 yr. Five common tumor variables (histology, size, grade, tumor necrosis, and nodal involvement) were included in each model. Tumor histology was the single most powerful predictor for each model outcome. The C-statistics at 1 yr were 78.4% and 81.9% for DFS and OS, respectively. Degradation of the DFS, DFS validation set, and OS model's discriminatory ability was seen over time, with a global c-index of 68.0% (95% confidence interval or CI [65.5, 70.4]), 68.6% [65.1%, 72.2%], and 69.4% (95% CI [66.9%, 71.9%], respectively. The EDP model had a c-index of 75.1% (95% CI [71.3, 79.0]). Conclusions: We introduce a contemporary RCC recurrence model built and internally validated using prospective and highly annotated data from a clinical trial. Performance characteristics of the current model exceed available prognostic models with the added benefit of being histology inclusive and TNM agnostic. Patient summary: Important decisions, including treatment protocols, clinical trial eligibility, and life planning, rest on our ability to predict cancer outcomes accurately. Here, we introduce a contemporary renal cell carcinoma prognostic model leveraging high-quality data from a clinical trial. The current model predicts three outcome measures commonly utilized in clinical practice and exceeds the predictive ability of available prognostic models.

AB - Background: Risk stratification for localized renal cell carcinoma (RCC) relies heavily on retrospective models, limiting their generalizability to contemporary cohorts. Objective: To introduce a contemporary RCC prognostic model, developed using prospective, highly annotated data from a phase III adjuvant trial. Design, setting, and participants: The model utilizes outcome data from the ECOG-ACRIN 2805 (ASSURE) RCC trial. Outcome measurements and statistical analysis: The primary outcome for the model is disease-free survival (DFS), with overall survival (OS) and early disease progression (EDP) as secondary outcomes. Model performance was assessed using discrimination and calibration tests. Results and limitations: A total of 1735 patients were included in the analysis, with 887 DFS events occurring over a median follow-up of 9.6 yr. Five common tumor variables (histology, size, grade, tumor necrosis, and nodal involvement) were included in each model. Tumor histology was the single most powerful predictor for each model outcome. The C-statistics at 1 yr were 78.4% and 81.9% for DFS and OS, respectively. Degradation of the DFS, DFS validation set, and OS model's discriminatory ability was seen over time, with a global c-index of 68.0% (95% confidence interval or CI [65.5, 70.4]), 68.6% [65.1%, 72.2%], and 69.4% (95% CI [66.9%, 71.9%], respectively. The EDP model had a c-index of 75.1% (95% CI [71.3, 79.0]). Conclusions: We introduce a contemporary RCC recurrence model built and internally validated using prospective and highly annotated data from a clinical trial. Performance characteristics of the current model exceed available prognostic models with the added benefit of being histology inclusive and TNM agnostic. Patient summary: Important decisions, including treatment protocols, clinical trial eligibility, and life planning, rest on our ability to predict cancer outcomes accurately. Here, we introduce a contemporary renal cell carcinoma prognostic model leveraging high-quality data from a clinical trial. The current model predicts three outcome measures commonly utilized in clinical practice and exceeds the predictive ability of available prognostic models.

KW - ASSURE trial

KW - Disease-free survival

KW - Prognostic model

KW - Renal cell carcinoma

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U2 - 10.1016/j.eururo.2021.02.025

DO - 10.1016/j.eururo.2021.02.025

M3 - Article

C2 - 33707112

AN - SCOPUS:85102248582

SN - 0302-2838

VL - 80

SP - 20

EP - 31

JO - European Urology

JF - European Urology

IS - 1

ER -

Predicting Disease Recurrence, Early Progression, and Overall Survival Following Surgical Resection for High-risk Localized and Locally Advanced Renal Cell Carcinoma

Abstract

Bibliographical note

Funding

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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