Treatment Efficacy Score-continuous residual cancer burden-based metric to compare neoadjuvant chemotherapy efficacy between randomized trial arms in breast cancer trials.
breast cancer
neoadjuvant chemotherapy
residual cancer burden
Journal
Annals of oncology : official journal of the European Society for Medical Oncology
ISSN: 1569-8041
Titre abrégé: Ann Oncol
Pays: England
ID NLM: 9007735
Informations de publication
Date de publication:
08 2022
08 2022
Historique:
received:
15
12
2021
revised:
28
03
2022
accepted:
18
04
2022
pubmed:
6
5
2022
medline:
4
8
2022
entrez:
5
5
2022
Statut:
ppublish
Résumé
Difference in pathologic complete response (pCR) rate after neoadjuvant chemotherapy does not capture the impact of treatment on downstaging of residual cancer in the experimental arm. We developed a method to compare the entire distribution of residual cancer burden (RCB) values between clinical trial arms to better quantify the differences in cytotoxic efficacy of treatments. The Treatment Efficacy Score (TES) reflects the area between the weighted cumulative distribution functions of RCB values from two trial arms. TES is based on a modified Kolmogorov-Smirnov test with added weight function to capture the importance of high RCB values and uses the area under the difference between two distribution functions as a statistical metric. The higher the TES the greater the shift to lower RCB values in the experimental arm. We developed TES from the durvalumab + olaparib arm (n = 72) and corresponding controls (n = 282) of the I-SPY2 trial. The 11 other experimental arms and control cohorts (n = 947) were used as validation sets to assess the performance of TES. We compared TES to Kolmogorov-Smirnov, Mann-Whitney, and Fisher's exact tests to identify trial arms with higher cytotoxic efficacy and assessed associations with trial arm level survival differences. Significance was assessed with a permutation test. In the validation set, TES identified arms with a higher pCR rate but was more accurate to identify regimens as less effective if treatment did not reduce the frequency of high RCB values, even if the pCR rate improved. The correlation between TES and survival was higher than the correlation between the pCR rate difference and survival. TES quantifies the difference between the entire distribution of pathologic responses observed in trial arms and could serve as a better early surrogate to predict trial arm level survival differences than pCR rate difference alone.
Sections du résumé
BACKGROUND
Difference in pathologic complete response (pCR) rate after neoadjuvant chemotherapy does not capture the impact of treatment on downstaging of residual cancer in the experimental arm. We developed a method to compare the entire distribution of residual cancer burden (RCB) values between clinical trial arms to better quantify the differences in cytotoxic efficacy of treatments.
PATIENTS AND METHODS
The Treatment Efficacy Score (TES) reflects the area between the weighted cumulative distribution functions of RCB values from two trial arms. TES is based on a modified Kolmogorov-Smirnov test with added weight function to capture the importance of high RCB values and uses the area under the difference between two distribution functions as a statistical metric. The higher the TES the greater the shift to lower RCB values in the experimental arm. We developed TES from the durvalumab + olaparib arm (n = 72) and corresponding controls (n = 282) of the I-SPY2 trial. The 11 other experimental arms and control cohorts (n = 947) were used as validation sets to assess the performance of TES. We compared TES to Kolmogorov-Smirnov, Mann-Whitney, and Fisher's exact tests to identify trial arms with higher cytotoxic efficacy and assessed associations with trial arm level survival differences. Significance was assessed with a permutation test.
RESULTS
In the validation set, TES identified arms with a higher pCR rate but was more accurate to identify regimens as less effective if treatment did not reduce the frequency of high RCB values, even if the pCR rate improved. The correlation between TES and survival was higher than the correlation between the pCR rate difference and survival.
CONCLUSIONS
TES quantifies the difference between the entire distribution of pathologic responses observed in trial arms and could serve as a better early surrogate to predict trial arm level survival differences than pCR rate difference alone.
Identifiants
pubmed: 35513244
pii: S0923-7534(22)00762-1
doi: 10.1016/j.annonc.2022.04.072
pii:
doi:
Substances chimiques
Antineoplastic Agents
0
Types de publication
Journal Article
Randomized Controlled Trial
Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
814-823Informations de copyright
Copyright © 2022 European Society for Medical Oncology. Published by Elsevier Ltd. All rights reserved.
Déclaration de conflit d'intérêts
Disclosure RN: Consulting from Cardinal Health, Clovis, Fujifilm, G1 Therapeutics, Genentech, Immunomedics/Gilead, iTeos, MacroGenics, Merck, OncoSec, Pfizer, Seattle Genetics; Research Funding to Institution from Arvinas, AstraZeneca, Celgene, Corcept Therapeutics, Genentech/Roche, Immunomedics/Gilead, Merck, OBI Pharma, Odonate Therapeutics, OncoSec, Pfizer, Seattle Genetics, Taiho. BAP: Consulting from BioAtla Inc, Samumed LLC, Dare Biosciences; Stock of Merck; Research Funding to Institution from Pfizer, GlaxoSmithKline, Novartis, Genentech/Roche, Oncternal. KSA: Grants from Seattle Genetics, Daiichi Sankyo, AstraZeneca. RKM: Participation on a Data Safety Monitoring Board or Advisory Board of Genomic Health/Exact Sciences, Genentech-Roche, Seattle Genetics/Axio; Grants from Seattle Genetics, Daiichi Sankyo, AstraZeneca; Support from Quantum Leap Healthcare Collaborative, Merck, Seattle Genetics, Amgen, Genentech-Roche. JCB: Research Funding from Eli Lilly. MCL: Funding from Eisai, Exact Sciences, Genentech, Genomic Health, GRAIL, Menarini Silicon Biosystems, Merck, Novartis, Seattle Genetics, Tesaro; Support for attending meetings and/or travel from AstraZeneca, Genomic Health, Ionis; Participation on a Data Safety Monitoring Board or Advisory Board of AstraZeneca, Celgene, Roche/Genentech, Genomic Health, GRAIL, Ionis, Merck, Pfizer, Seattle Genetics, Syndax. ASC: Institutional Research Funds from Novartis. HSR: Funding from Pfizer, Merck, Novartis, Lilly, Roche, Daiichi, Seattle Genetics, MacroGenics, Sermonix, Boehringer Ingelheim, AstraZeneca, Ayala, Gilead, and Ayala; Honoraria from Puma, Merck, Samsung, NAPO. JP: Honoraria from Methods in Clinical Research; Support for attending meetings from ASCO, SABCS; Participation on a Data Safety Monitoring Board or Advisory Board of University of Wisconsin Specialized Programs of Research Excellence, VIVLI, Quantum Leap Healthcare Collaborative, Patient Centered Outcomes Institute. DAB: Employee/Leadership, Stock/Ownership, and Consulting/Advisory Board of Berry Consultants; Research Funding from Daiichi Sankyo; Travel/Accommodations/Expenses from Berry Consultants. LV: part-time employee and stockholder of Agendia NV. WFS: Stock owner in Delphi Diagnostics; Patent - “Method of measuring residual cancer and predicting patient survival” (US Patent 7711494B2). LE: Research Funding from Merck; Medical Advisory Panel member of Blue Cross Blue Shield; Website Author of UpToDate. LP: Consulting fees and honoraria from Pfizer, AstraZeneca, Merck, Novartis, Genentech, Eisai, Pieris, Immunomedics, Seattle Genetics, Almac, Biotheranostics, and Natera. All other authors have declared no conflicts of interest.