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1. The 41-gene classifier TRAR predicts response of HER2 positive breast cancer patients in the NeoALTTO study

Author

Di Cosimo, Serena, Triulzi, Tiziana, Pizzamiglio, Sara, De Cecco, L., de Azambuja, Evandro, Fumagalli, Debora, Putzai, L., Harbeck, Nadia, Izquierdo, Miguel, Peña, L., Daidone, Maria Grazia, Huober, J., Gori, Stefania, Cinieri, Saverio, Torri, Valter, Baselga, José, Piccart-Gebhart, Martine, De Braud, Filippo De, Apolone, Giovanni, Verderio, Paolo, Tagliabue, Elda, Di Cosimo, Serena, Triulzi, Tiziana, Pizzamiglio, Sara, De Cecco, L., de Azambuja, Evandro, Fumagalli, Debora, Putzai, L., Harbeck, Nadia, Izquierdo, Miguel, Peña, L., Daidone, Maria Grazia, Huober, J., Gori, Stefania, Cinieri, Saverio, Torri, Valter, Baselga, José, Piccart-Gebhart, Martine, De Braud, Filippo De, Apolone, Giovanni, Verderio, Paolo, and Tagliabue, Elda

Abstract

Background: Dual HER2-inhibition combined with neoadjuvant chemotherapy allows increased pathological complete response (pCR) rate. However, with the addition of new agents, there is a growing need to select patients to minimise overtreatment. Herein, we evaluated the 41-gene classifier TRAR to predict pCR to anti-HER2 therapies in the NeoALTTO trial. Patients and methods: Gene expression data were obtained using RNA from 226 pretreatment tumour biopsies. Logistic regression analysis and the area under the receiver operating characteristic (ROC) curve (AUC) were used to evaluate TRAR predictive and discriminatory capabilities. Results: TRAR levels were associated with pCR (odds ratio, OR: 0.25, 95% confidence interval, CI: 0.15–0.42). The ROC analysis showed AUC values of 0.73 (95% CI: 0.67–0.80) overall; 0.70 (0.59–0.81) and 0.71 (0.62–0.80) for positive and negative oestrogen receptor cases and 0.74 (0.60–0.88), 0.76 (0.65–0.87) and 0.71 (0.59–0.83) for trastuzumab, lapatinib and combined treatment arms, respectively. TRAR provided reliable predictive information beyond established clinicopathological variables (OR: 0.26, 95% CI: 0.14–0.47). Furthermore, addition of TRAR to these variables provided greater predictive capability than the addition of PAM50: AUC 0.78 (0.72–0.84) versus 0.74 (0.67–0.81), p = 0.04. Conclusion: TRAR represents a promising tool to refine the ability to identify patients sensitive to anti-HER2 (including trastuzumab-only)-based therapy and eligible for de-escalated treatment strategies., SCOPUS: ar.j, info:eu-repo/semantics/published

Published
2019

2. The 41-gene classifier TRAR predicts response of HER2 positive breast cancer patients in the NeoALTTO study.

Author

Di Cosimo S, Triulzi T, Pizzamiglio S, De Cecco L, de Azambuja E, Fumagalli D, Putzai L, Harbeck N, Izquierdo M, Peña L, Daidone MG, Huober J, Gori S, Cinieri S, Torri V, Baselga J, Piccart M, de Braud FG, Apolone G, Verderio P, and Tagliabue E

Subjects
Antineoplastic Agents, Immunological adverse effects, Antineoplastic Combined Chemotherapy Protocols adverse effects, Breast Neoplasms enzymology, Breast Neoplasms pathology, Chemotherapy, Adjuvant, Clinical Decision-Making, Clinical Trials, Phase III as Topic, Female, Humans, Lapatinib adverse effects, Multicenter Studies as Topic, Neoadjuvant Therapy, Patient Selection, Precision Medicine, Predictive Value of Tests, Protein Kinase Inhibitors adverse effects, Randomized Controlled Trials as Topic, Receptor, ErbB-2 metabolism, Retrospective Studies, Risk Factors, Time Factors, Trastuzumab adverse effects, Treatment Outcome, Antineoplastic Agents, Immunological therapeutic use, Antineoplastic Combined Chemotherapy Protocols therapeutic use, Biomarkers, Tumor genetics, Breast Neoplasms drug therapy, Breast Neoplasms genetics, Gene Expression Profiling, Lapatinib therapeutic use, Protein Kinase Inhibitors therapeutic use, Receptor, ErbB-2 antagonists & inhibitors, Transcriptome, Trastuzumab therapeutic use
Abstract

Background: Dual HER2-inhibition combined with neoadjuvant chemotherapy allows increased pathological complete response (pCR) rate. However, with the addition of new agents, there is a growing need to select patients to minimise overtreatment. Herein, we evaluated the 41-gene classifier TRAR to predict pCR to anti-HER2 therapies in the NeoALTTO trial., Patients and Methods: Gene expression data were obtained using RNA from 226 pretreatment tumour biopsies. Logistic regression analysis and the area under the receiver operating characteristic (ROC) curve (AUC) were used to evaluate TRAR predictive and discriminatory capabilities., Results: TRAR levels were associated with pCR (odds ratio, OR: 0.25, 95% confidence interval, CI: 0.15-0.42). The ROC analysis showed AUC values of 0.73 (95% CI: 0.67-0.80) overall; 0.70 (0.59-0.81) and 0.71 (0.62-0.80) for positive and negative oestrogen receptor cases and 0.74 (0.60-0.88), 0.76 (0.65-0.87) and 0.71 (0.59-0.83) for trastuzumab, lapatinib and combined treatment arms, respectively. TRAR provided reliable predictive information beyond established clinicopathological variables (OR: 0.26, 95% CI: 0.14-0.47). Furthermore, addition of TRAR to these variables provided greater predictive capability than the addition of PAM50: AUC 0.78 (0.72-0.84) versus 0.74 (0.67-0.81), p = 0.04., Conclusion: TRAR represents a promising tool to refine the ability to identify patients sensitive to anti-HER2 (including trastuzumab-only)-based therapy and eligible for de-escalated treatment strategies., (Copyright © 2019 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Published
2019
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