Treatment Efficacy Score-continuous residual cancer burden-based metric to compare neoadjuvant chemotherapy efficacy between randomized trial arms in breast cancer trials

M Marczyk; A Mrukwa; C Yau; D Wolf; Y-Y Chen; R Balassanian; R Nanda; B A Parker; G Krings; H Sattar; J C Zeck; K S Albain; J C Boughey; M C Liu; A D Elias; A S Clark; S J Venters; S Shad; A Basu; S M Asare; M Buxton; A L Asare; H S Rugo; J Perlmutter; A M DeMichele; D Yee; D A Berry; L Van't Veer; W F Symmans; L Esserman; L Pusztai; I-SPY Consortium

doi:10.1016/j.annonc.2022.04.072

Treatment Efficacy Score-continuous residual cancer burden-based metric to compare neoadjuvant chemotherapy efficacy between randomized trial arms in breast cancer trials

Ann Oncol. 2022 Aug;33(8):814-823. doi: 10.1016/j.annonc.2022.04.072. Epub 2022 May 2.

Authors

M Marczyk¹, A Mrukwa², C Yau³, D Wolf⁴, Y-Y Chen⁵, R Balassanian⁶, R Nanda⁷, B A Parker⁸, G Krings³, H Sattar⁹, J C Zeck¹⁰, K S Albain¹¹, J C Boughey¹², M C Liu¹³, A D Elias¹⁴, A S Clark¹⁵, S J Venters¹⁶, S Shad³, A Basu³, S M Asare¹⁷, M Buxton³, A L Asare¹⁷, H S Rugo¹⁸, J Perlmutter¹⁹, A M DeMichele¹⁵, D Yee²⁰, D A Berry²¹, L Van't Veer¹⁶, W F Symmans²², L Esserman³, L Pusztai²³; I-SPY Consortium

Affiliations

¹ Department of Data Mining and Engineering, Silesian University of Technology, Gliwice, Poland; Department of Breast Medical Oncology, Yale School of Medicine, New Haven, USA.
² Department of Data Mining and Engineering, Silesian University of Technology, Gliwice, Poland.
³ Departments of Surgery, University of California, San Francisco, USA.
⁴ Departments of Surgery, University of California, San Francisco, USA; Departments of Laboratory Medicine, University of California, San Francisco, USA.
⁵ Departments of Pathology, University of California, San Francisco, USA.
⁶ Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, USA.
⁷ Department of Medicine, Section of Hematology/Oncology, University of Chicago, Chicago, USA.
⁸ Department of Medicine, Division of Hematology-Oncology, University of California San Diego, La Jolla, USA.
⁹ Department of Pathology, University of Chicago, Chicago, USA.
¹⁰ Department of Pathology, Georgetown University, Washington, USA.
¹¹ Department of Medicine, Division of Hematology-Oncology, Loyola University Chicago Stritch School of Medicine, Maywood, USA.
¹² Departments of Surgery, Mayo Clinic, Rochester, USA.
¹³ Departments of Oncology, Mayo Clinic, Rochester, USA.
¹⁴ Department of Medicine, Division of Medical Oncology, University of Colorado Anschutz Medical Center, Aurora, USA.
¹⁵ Department of Medicine, Division of Hematology-Oncology, University of Pennsylvania, Philadelphia, USA.
¹⁶ Departments of Laboratory Medicine, University of California, San Francisco, USA.
¹⁷ Quantum Leap Healthcare Collaborative, San Francisco, USA.
¹⁸ Department of Medicine, Division of Hematology-Oncology, University of California, San Francisco, USA.
¹⁹ Gemini Group, Ann Arbor, USA.
²⁰ Department of Medicine, Division of Hematology, Oncology, and Transplantation, University of Minnesota, Minneapolis, USA.
²¹ Berry Consultants, LLC, Houston, USA.
²² Departments of Pathology and Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, USA.
²³ Department of Breast Medical Oncology, Yale School of Medicine, New Haven, USA. Electronic address: [email protected].

PMID: 35513244
DOI: 10.1016/j.annonc.2022.04.072

Abstract

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.

Keywords: breast cancer; neoadjuvant chemotherapy; residual cancer burden.

Publication types

Randomized Controlled Trial
Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural

MeSH terms

Antineoplastic Agents* / therapeutic use
Antineoplastic Combined Chemotherapy Protocols / therapeutic use
Breast Neoplasms* / pathology
Female
Humans
Neoadjuvant Therapy
Neoplasm, Residual / drug therapy
Neoplasm, Residual / pathology
Treatment Outcome

Substances

Antineoplastic Agents