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Transcriptomic recurrence score improves recurrence prediction for surgically treated patients with intermediate‐risk clear cell kidney cancer
BACKGROUND: Risk stratification of kidney cancer patients after nephrectomy may tailor surveillance intensity and selection for adjuvant therapy. Transcriptomic approaches are effective in predicting recurrence, but whether they add value to clinicopathologic models remains unclear. METHODS: Data fr...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10028022/ https://www.ncbi.nlm.nih.gov/pubmed/36397716 http://dx.doi.org/10.1002/cam4.5399 |
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author | Patel, Neal Hakansson, Alexander Ohtake, Shinji Muraki, Peter Proudfout, James A. Liu, Yang Webber, Lisa Ibarra, Arkaitz Liu, Vinnie Y. T. Davicioni, Elai Chamie, Karim Pantuck, Allan Shuch, Brian |
author_facet | Patel, Neal Hakansson, Alexander Ohtake, Shinji Muraki, Peter Proudfout, James A. Liu, Yang Webber, Lisa Ibarra, Arkaitz Liu, Vinnie Y. T. Davicioni, Elai Chamie, Karim Pantuck, Allan Shuch, Brian |
author_sort | Patel, Neal |
collection | PubMed |
description | BACKGROUND: Risk stratification of kidney cancer patients after nephrectomy may tailor surveillance intensity and selection for adjuvant therapy. Transcriptomic approaches are effective in predicting recurrence, but whether they add value to clinicopathologic models remains unclear. METHODS: Data from patients with clear cell renal cell carcinoma (ccRCC) was downloaded from The Cancer Genome Atlas. Clinicopathologic variables were used to calculate SSIGN (stage, size, grade, and necrosis) scores. The 16 gene recurrence score (RS) signature was generated using RNA‐seq data. Transcriptomic risk groups were calculated using the original thresholds. SSIGN groups were divided into low, intermediate, and high risk. Disease‐free status was the primary endpoint assessed. RESULTS: SSIGN and RS were calculated for 428 patients with non‐metastatic ccRCC. SSIGN low‐, intermediate‐, and high‐risk groups demonstrated 2.7%, 15.2%, and 27.5%, 3‐year recurrence risk, respectively. On multivariable analysis, the RS was associated with disease‐free status (sub‐distribution hazard ratio (sHR) 1.43 per 25 RS [95% CI (1.00–1.43)], p = 0.05). By risk groups, RS further risk stratified the SSIGN intermediate‐risk group (sHR 2.22 [95% CI 1.10–4.50], p = 0.03). SSIGN intermediate‐risk patients with low and high RS had a 3‐year recurrence rate of 8.0% and 25.2%, respectively. Within this risk group, the area under the curve (AUC) at 3 years was 0.69 for SSIGN, 0.74 for RS, and 0.78 for their combination. CONCLUSIONS: Transcriptomic recurrence scores improve risk prediction even when controlling for clinicopathologic factors. Utility may be best suited for intermediate‐risk patients who have heterogeneous outcomes and further refinement for clinical utility is warranted. |
format | Online Article Text |
id | pubmed-10028022 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-100280222023-03-22 Transcriptomic recurrence score improves recurrence prediction for surgically treated patients with intermediate‐risk clear cell kidney cancer Patel, Neal Hakansson, Alexander Ohtake, Shinji Muraki, Peter Proudfout, James A. Liu, Yang Webber, Lisa Ibarra, Arkaitz Liu, Vinnie Y. T. Davicioni, Elai Chamie, Karim Pantuck, Allan Shuch, Brian Cancer Med Research Articles BACKGROUND: Risk stratification of kidney cancer patients after nephrectomy may tailor surveillance intensity and selection for adjuvant therapy. Transcriptomic approaches are effective in predicting recurrence, but whether they add value to clinicopathologic models remains unclear. METHODS: Data from patients with clear cell renal cell carcinoma (ccRCC) was downloaded from The Cancer Genome Atlas. Clinicopathologic variables were used to calculate SSIGN (stage, size, grade, and necrosis) scores. The 16 gene recurrence score (RS) signature was generated using RNA‐seq data. Transcriptomic risk groups were calculated using the original thresholds. SSIGN groups were divided into low, intermediate, and high risk. Disease‐free status was the primary endpoint assessed. RESULTS: SSIGN and RS were calculated for 428 patients with non‐metastatic ccRCC. SSIGN low‐, intermediate‐, and high‐risk groups demonstrated 2.7%, 15.2%, and 27.5%, 3‐year recurrence risk, respectively. On multivariable analysis, the RS was associated with disease‐free status (sub‐distribution hazard ratio (sHR) 1.43 per 25 RS [95% CI (1.00–1.43)], p = 0.05). By risk groups, RS further risk stratified the SSIGN intermediate‐risk group (sHR 2.22 [95% CI 1.10–4.50], p = 0.03). SSIGN intermediate‐risk patients with low and high RS had a 3‐year recurrence rate of 8.0% and 25.2%, respectively. Within this risk group, the area under the curve (AUC) at 3 years was 0.69 for SSIGN, 0.74 for RS, and 0.78 for their combination. CONCLUSIONS: Transcriptomic recurrence scores improve risk prediction even when controlling for clinicopathologic factors. Utility may be best suited for intermediate‐risk patients who have heterogeneous outcomes and further refinement for clinical utility is warranted. John Wiley and Sons Inc. 2022-11-17 /pmc/articles/PMC10028022/ /pubmed/36397716 http://dx.doi.org/10.1002/cam4.5399 Text en © 2022 The Authors. Cancer Medicine published by John Wiley & Sons Ltd. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Articles Patel, Neal Hakansson, Alexander Ohtake, Shinji Muraki, Peter Proudfout, James A. Liu, Yang Webber, Lisa Ibarra, Arkaitz Liu, Vinnie Y. T. Davicioni, Elai Chamie, Karim Pantuck, Allan Shuch, Brian Transcriptomic recurrence score improves recurrence prediction for surgically treated patients with intermediate‐risk clear cell kidney cancer |
title | Transcriptomic recurrence score improves recurrence prediction for surgically treated patients with intermediate‐risk clear cell kidney cancer |
title_full | Transcriptomic recurrence score improves recurrence prediction for surgically treated patients with intermediate‐risk clear cell kidney cancer |
title_fullStr | Transcriptomic recurrence score improves recurrence prediction for surgically treated patients with intermediate‐risk clear cell kidney cancer |
title_full_unstemmed | Transcriptomic recurrence score improves recurrence prediction for surgically treated patients with intermediate‐risk clear cell kidney cancer |
title_short | Transcriptomic recurrence score improves recurrence prediction for surgically treated patients with intermediate‐risk clear cell kidney cancer |
title_sort | transcriptomic recurrence score improves recurrence prediction for surgically treated patients with intermediate‐risk clear cell kidney cancer |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10028022/ https://www.ncbi.nlm.nih.gov/pubmed/36397716 http://dx.doi.org/10.1002/cam4.5399 |
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