Literature DB >> 23104719

Correlation between response to neoadjuvant chemotherapy and survival in locally advanced breast cancer patients.

A Romero1, J A García-Sáenz, M Fuentes-Ferrer, J A López Garcia-Asenjo, V Furió, J M Román, A Moreno, M de la Hoya, E Díaz-Rubio, M Martín, T Caldés.   

Abstract

BACKGROUND: Measurement of residual disease following neoadjuvant chemotherapy that accurately predicts long-term survival in locally advanced breast cancer (LABC) is an essential requirement for clinical trials development. Several methods to assess tumor response have been described. However, the agreement between methods and correlation with survival in independent cohorts has not been reported. PATIENTS AND METHODS: We report survival and tumor response according to the measurement of residual breast cancer burden (RCB), the Miller and Payne classification and the Response Evaluation Criteria in Solid Tumors (RECIST) criteria, in 151 LABC patients. Kappa Cohen's coefficient (К) was used to test the agreement between methods. We assessed the correlation between the treatment outcome and overall survival (OS) and relapse-free survival (RFS) by calculating Harrell's C-statistic (c).
RESULTS: The agreement between Miller and Payne classification and RCB classes was very high (К = 0.82). In contrast, we found a moderate-to-fair agreement between the Miller and Payne classification and RECIST criteria (К = 0.52) and RCB classes and RECIST criteria (К = 0.38). The adjusted C-statistic to predict OS for RCB index (0.77) and RCB classes (0.75) was superior to that of RECIST criteria (0.69) (P = 0.007 and P = 0.035, respectively). Also, RCB index (c = 0.71), RCB classes (c = 0.71) and Miller and Payne classification (c = 0.67) predicted better RFS than RECIST criteria (c = 0.61) (P = 0.005, P = 0.006 and P = 0.028, respectively).
CONCLUSIONS: The pathological assessment of tumor response might provide stronger prognostic information in LABC patients.

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Year:  2012        PMID: 23104719     DOI: 10.1093/annonc/mds493

Source DB:  PubMed          Journal:  Ann Oncol        ISSN: 0923-7534            Impact factor:   32.976


  20 in total

1.  Dynamic contrast-enhanced MRI-based biomarkers of therapeutic response in triple-negative breast cancer.

Authors:  Daniel I Golden; Jafi A Lipson; Melinda L Telli; James M Ford; Daniel L Rubin
Journal:  J Am Med Inform Assoc       Date:  2013-06-19       Impact factor: 4.497

2.  Long-Term Prognostic Risk After Neoadjuvant Chemotherapy Associated With Residual Cancer Burden and Breast Cancer Subtype.

Authors:  W Fraser Symmans; Caimiao Wei; Rebekah Gould; Xian Yu; Ya Zhang; Mei Liu; Andrew Walls; Alex Bousamra; Maheshwari Ramineni; Bruno Sinn; Kelly Hunt; Thomas A Buchholz; Vicente Valero; Aman U Buzdar; Wei Yang; Abenaa M Brewster; Stacy Moulder; Lajos Pusztai; Christos Hatzis; Gabriel N Hortobagyi
Journal:  J Clin Oncol       Date:  2017-01-30       Impact factor: 44.544

3.  Assignment of tumor subtype by genomic testing and pathologic-based approximations: implications on patient's management and therapy selection.

Authors:  A Romero; A Prat; J A García-Sáenz; N Del Prado; A Pelayo; V Furió; J M Román; M de la Hoya; E Díaz-Rubio; C M Perou; T Cladés; M Martín
Journal:  Clin Transl Oncol       Date:  2013-08-02       Impact factor: 3.405

4.  Standardization of pathologic evaluation and reporting of postneoadjuvant specimens in clinical trials of breast cancer: recommendations from an international working group.

Authors:  Elena Provenzano; Veerle Bossuyt; Giuseppe Viale; David Cameron; Sunil Badve; Carsten Denkert; Gaëtan MacGrogan; Frédérique Penault-Llorca; Judy Boughey; Giuseppe Curigliano; J Michael Dixon; Laura Esserman; Gerd Fastner; Thorsten Kuehn; Florentia Peintinger; Gunter von Minckwitz; Julia White; Wei Yang; W Fraser Symmans
Journal:  Mod Pathol       Date:  2015-07-24       Impact factor: 7.842

5.  Clinical and pathological response to neoadjuvant chemotherapy with different chemotherapy regimens predicts the outcome of locally advanced breast cancer.

Authors:  Shicong Tang; Ke Wang; Kai Zheng; Jiadong Liu; Hengyu Zhang; Mingjian Tan; Hongwan Li; Huimeng Li; Xin Tan; Dequan Liu; Rong Guo
Journal:  Gland Surg       Date:  2020-10

6.  Circulating tumor cells may serve as a supplement to RECIST in neoadjuvant chemotherapy of patients with locally advanced breast cancer.

Authors:  Ji Wang; Xinyang Wang; Rui Chen; Mengdi Liang; Minghui Li; Ge Ma; Tiansong Xia; Shui Wang
Journal:  Int J Clin Oncol       Date:  2022-02-05       Impact factor: 3.402

7.  Metabolic Syndrome Predicts Response to Neoadjuvant Chemotherapy in Breast Cancer.

Authors:  Ying Lu; Pinxiu Wang; Ning Lan; Fei Kong; Awaguli Abdumijit; Shiyan Tu; Yanting Li; Wenzhen Yuan
Journal:  Front Oncol       Date:  2022-07-01       Impact factor: 5.738

8.  Quantitative digital histopathology and machine learning to predict pathological complete response to chemotherapy in breast cancer patients using pre-treatment tumor biopsies.

Authors:  Khadijeh Saednia; Andrew Lagree; Marie A Alera; Lauren Fleshner; Audrey Shiner; Ethan Law; Brianna Law; David W Dodington; Fang-I Lu; William T Tran; Ali Sadeghi-Naini
Journal:  Sci Rep       Date:  2022-06-11       Impact factor: 4.996

9.  Impact of biomarker changes during neoadjuvant chemotherapy for clinical response in patients with residual breast cancers.

Authors:  Yukie Enomoto; Takashi Morimoto; Arisa Nishimukai; Tomoko Higuchi; Ayako Yanai; Yoshimasa Miyagawa; Keiko Murase; Michiko Imamura; Yuichi Takatsuka; Takashi Nomura; Masashi Takeda; Takahiro Watanabe; Seiichi Hirota; Yasuo Miyoshi
Journal:  Int J Clin Oncol       Date:  2015-09-04       Impact factor: 3.402

10.  BRCA1 Alternative splicing landscape in breast tissue samples.

Authors:  Atocha Romero; Francisco García-García; Irene López-Perolio; Gorka Ruiz de Garibay; José A García-Sáenz; Pilar Garre; Patricia Ayllón; Esperanza Benito; Joaquín Dopazo; Eduardo Díaz-Rubio; Trinidad Caldés; Miguel de la Hoya
Journal:  BMC Cancer       Date:  2015-04-03       Impact factor: 4.430

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