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A retrospective validation of CanAssist Breast in European early-stage breast cancer patient cohort

CanAssist Breast (CAB), a prognostic test uses immunohistochemistry (IHC) approach coupled with artificial intelligence-based machine learning algorithm for prognosis of early-stage hormone-receptor positive, HER2/neu negative breast cancer patients. It was developed and validated in an Indian cohor...

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Autores principales: Gunda, Aparna, Basavaraj, Chetana, Serkad V, Chandra Prakash, Adinarayan, Manjula, Kolli, Ramu, Siraganahalli Eshwaraiah, Mallikarjuna, Saura, Cristina, Ruiz, Fiorella, Gomez, Patricia, Peg, Vicente, Jimenez, Jose, Sprung, Susanne, Fiegl, Heidi, Brunner, Christine, Egle, Daniel, Bhattacharyya, GS, Bakre, Manjiri M
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8892025/
https://www.ncbi.nlm.nih.gov/pubmed/35245746
http://dx.doi.org/10.1016/j.breast.2022.02.010
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author Gunda, Aparna
Basavaraj, Chetana
Serkad V, Chandra Prakash
Adinarayan, Manjula
Kolli, Ramu
Siraganahalli Eshwaraiah, Mallikarjuna
Saura, Cristina
Ruiz, Fiorella
Gomez, Patricia
Peg, Vicente
Jimenez, Jose
Sprung, Susanne
Fiegl, Heidi
Brunner, Christine
Egle, Daniel
Bhattacharyya, GS
Bakre, Manjiri M
author_facet Gunda, Aparna
Basavaraj, Chetana
Serkad V, Chandra Prakash
Adinarayan, Manjula
Kolli, Ramu
Siraganahalli Eshwaraiah, Mallikarjuna
Saura, Cristina
Ruiz, Fiorella
Gomez, Patricia
Peg, Vicente
Jimenez, Jose
Sprung, Susanne
Fiegl, Heidi
Brunner, Christine
Egle, Daniel
Bhattacharyya, GS
Bakre, Manjiri M
author_sort Gunda, Aparna
collection PubMed
description CanAssist Breast (CAB), a prognostic test uses immunohistochemistry (IHC) approach coupled with artificial intelligence-based machine learning algorithm for prognosis of early-stage hormone-receptor positive, HER2/neu negative breast cancer patients. It was developed and validated in an Indian cohort. Here we report the first blinded validation of CAB in a multi-country European patient cohort. FFPE tumor samples from 864 patients were obtained from-Spain, Italy, Austria, and Germany. IHC was performed on these samples, followed by recurrence risk score prediction. The outcomes were obtained from medical records. The performance of CAB was analyzed by hazard ratios (HR) and Kaplan Meier curves. CAB stratified European cohort (n = 864) into distinct low- and high-risk groups for recurrence (P < 0.0001) with HR of 3.32 (1.85–5.93) like that of mixed (India, USA, and Europe) (n = 1974), 3.43 (2.34–4.93) and Indian cohort (n = 925), 3.09 (1.83–5.21). CAB provided significant prognostic information (P < 0.0001) in women aged ≤ 50 (HR: 4.42 (1.58–12.3), P < 0.0001) and >50 years (HR: 2.93 (1.44–5.96), P = 0.0002). CAB had an HR of 2.57 (1.26–5.26), P = 0.01) in women with N1 disease. CAB stratified significantly higher proportions (77%) as low-risk over IHC4 (55%) (P < 0.0001). Additionally, 82% of IHC4 intermediate-risk patients were stratified as low-risk by CAB. Accurate risk stratification of European patients by CAB coupled with its similar performance inIndian patients shows that CAB is robust and functions independent of ethnic differences. CAB can potentially prevent overtreatment in a greater number of patients compared to IHC4 demonstrating its usefulness for adjuvant systemic therapy planning in European breast cancer patients.
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spelling pubmed-88920252022-03-04 A retrospective validation of CanAssist Breast in European early-stage breast cancer patient cohort Gunda, Aparna Basavaraj, Chetana Serkad V, Chandra Prakash Adinarayan, Manjula Kolli, Ramu Siraganahalli Eshwaraiah, Mallikarjuna Saura, Cristina Ruiz, Fiorella Gomez, Patricia Peg, Vicente Jimenez, Jose Sprung, Susanne Fiegl, Heidi Brunner, Christine Egle, Daniel Bhattacharyya, GS Bakre, Manjiri M Breast Original Article CanAssist Breast (CAB), a prognostic test uses immunohistochemistry (IHC) approach coupled with artificial intelligence-based machine learning algorithm for prognosis of early-stage hormone-receptor positive, HER2/neu negative breast cancer patients. It was developed and validated in an Indian cohort. Here we report the first blinded validation of CAB in a multi-country European patient cohort. FFPE tumor samples from 864 patients were obtained from-Spain, Italy, Austria, and Germany. IHC was performed on these samples, followed by recurrence risk score prediction. The outcomes were obtained from medical records. The performance of CAB was analyzed by hazard ratios (HR) and Kaplan Meier curves. CAB stratified European cohort (n = 864) into distinct low- and high-risk groups for recurrence (P < 0.0001) with HR of 3.32 (1.85–5.93) like that of mixed (India, USA, and Europe) (n = 1974), 3.43 (2.34–4.93) and Indian cohort (n = 925), 3.09 (1.83–5.21). CAB provided significant prognostic information (P < 0.0001) in women aged ≤ 50 (HR: 4.42 (1.58–12.3), P < 0.0001) and >50 years (HR: 2.93 (1.44–5.96), P = 0.0002). CAB had an HR of 2.57 (1.26–5.26), P = 0.01) in women with N1 disease. CAB stratified significantly higher proportions (77%) as low-risk over IHC4 (55%) (P < 0.0001). Additionally, 82% of IHC4 intermediate-risk patients were stratified as low-risk by CAB. Accurate risk stratification of European patients by CAB coupled with its similar performance inIndian patients shows that CAB is robust and functions independent of ethnic differences. CAB can potentially prevent overtreatment in a greater number of patients compared to IHC4 demonstrating its usefulness for adjuvant systemic therapy planning in European breast cancer patients. Elsevier 2022-02-22 /pmc/articles/PMC8892025/ /pubmed/35245746 http://dx.doi.org/10.1016/j.breast.2022.02.010 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Original Article
Gunda, Aparna
Basavaraj, Chetana
Serkad V, Chandra Prakash
Adinarayan, Manjula
Kolli, Ramu
Siraganahalli Eshwaraiah, Mallikarjuna
Saura, Cristina
Ruiz, Fiorella
Gomez, Patricia
Peg, Vicente
Jimenez, Jose
Sprung, Susanne
Fiegl, Heidi
Brunner, Christine
Egle, Daniel
Bhattacharyya, GS
Bakre, Manjiri M
A retrospective validation of CanAssist Breast in European early-stage breast cancer patient cohort
title A retrospective validation of CanAssist Breast in European early-stage breast cancer patient cohort
title_full A retrospective validation of CanAssist Breast in European early-stage breast cancer patient cohort
title_fullStr A retrospective validation of CanAssist Breast in European early-stage breast cancer patient cohort
title_full_unstemmed A retrospective validation of CanAssist Breast in European early-stage breast cancer patient cohort
title_short A retrospective validation of CanAssist Breast in European early-stage breast cancer patient cohort
title_sort retrospective validation of canassist breast in european early-stage breast cancer patient cohort
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8892025/
https://www.ncbi.nlm.nih.gov/pubmed/35245746
http://dx.doi.org/10.1016/j.breast.2022.02.010
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