Literature DB >> 17634532

Thirty-gene pharmacogenomic test correlates with residual cancer burden after preoperative chemotherapy for breast cancer.

Florentia Peintinger1, Keith Anderson, Chafika Mazouni, Henry M Kuerer, Christos Hatzis, Feng Lin, Gabriel N Hortobagyi, W Fraser Symmans, Lajos Pusztai.   

Abstract

PURPOSE: We examined whether the response predicted by a 30-gene pharmacogenomic test correlated with the residual cancer burden (RCB) after preoperative chemotherapy with paclitaxel, 5-fluorouracil, doxorubicin, and cyclophosphamide (T/FAC). EXPERIMENTAL
DESIGN: Gene expression profiling was done at diagnosis in 74 patients with stages I to III breast cancer and was used to calculate a pharmacogenomic score and predict response to chemotherapy [pathologic complete response (pCR) or residual disease (RD)]. All patients received 6 months of preoperative T/FAC. Following pathologic review, a RCB score was calculated based on residual tumor and lymph node features. Four RCB classes were assigned; RCB-0 (pCR), RCB-I (near-PCR), RCB-II (moderate RD), and RCB-III (extensive RD). The correlations between the pharmacogenomic score, predicted pathologic response, RCB score, and RCB class were examined.
RESULTS: Thirty-three patients were predicted to have pCR, and 40 were predicted to have RD. Observed responses were RCB-0: n=20 (27%); RCB-I: n=5 (7%); RCB-II: n=36 (49%); and RCB-III: n=13 (16%) patients. Pharmacogenomic and RCB scores were correlated (Pearson's R=-0.501, P<0.0001). There was no difference between the mean genomic predictor scores for RCB-0/I groups (P=0.94), but these were different from the mean scores of the RCB-II/III groups (P<0.001). Among the 25 patients with RCB-0/I response, 19 (76%) were predicted to achieve pCR. The pharmacogenomic test correctly predicted RD in 92% of the patients with RCB-III, which corresponds to chemotherapy-resistant disease.
CONCLUSIONS: The 30-gene pharmacogenomic test showed good correlation with the extent of residual invasive cancer burden measured as both continuous and categorical variables.

Entities:  

Mesh:

Year:  2007        PMID: 17634532     DOI: 10.1158/1078-0432.CCR-06-2600

Source DB:  PubMed          Journal:  Clin Cancer Res        ISSN: 1078-0432            Impact factor:   12.531


  8 in total

1.  Molecular subtyping predicts pathologic tumor response in early-stage breast cancer treated with neoadjuvant docetaxel plus capecitabine with or without trastuzumab chemotherapy.

Authors:  Soley Bayraktar; Melanie Royce; Lisette Stork-Sloots; Femke de Snoo; Stefan Glück
Journal:  Med Oncol       Date:  2014-09-04       Impact factor: 3.064

2.  Tau expression correlated with breast cancer sensitivity to taxanes-based neoadjuvant chemotherapy.

Authors:  Kun Wang; Quan-Tong Deng; Ning Liao; Guo-Chun Zhang; Yan-Hui Liu; Fang-Ping Xu; Jian Zu; Xue-Rui Li; Yi-Long Wu
Journal:  Tumour Biol       Date:  2012-09-14

3.  Evaluation of microtubule-associated protein-Tau expression as a prognostic and predictive marker in the NSABP-B 28 randomized clinical trial.

Authors:  Lajos Pusztai; Jong-Hyeon Jeong; Yun Gong; Jeffrey S Ross; Chungyeul Kim; Soonmyung Paik; Roman Rouzier; Fabrice Andre; Gabriel N Hortobagyi; Norman Wolmark; W Fraser Symmans
Journal:  J Clin Oncol       Date:  2009-08-10       Impact factor: 44.544

4.  Evaluation of a 30-gene paclitaxel, fluorouracil, doxorubicin, and cyclophosphamide chemotherapy response predictor in a multicenter randomized trial in breast cancer.

Authors:  Adel Tabchy; Vicente Valero; Tatiana Vidaurre; Ana Lluch; Henry Gomez; Miguel Martin; Yuan Qi; Luis Javier Barajas-Figueroa; Eduardo Souchon; Charles Coutant; Franco D Doimi; Nuhad K Ibrahim; Yun Gong; Gabriel N Hortobagyi; Kenneth R Hess; W Fraser Symmans; Lajos Pusztai
Journal:  Clin Cancer Res       Date:  2010-09-09       Impact factor: 12.531

5.  Effect of training-sample size and classification difficulty on the accuracy of genomic predictors.

Authors:  Vlad Popovici; Weijie Chen; Brandon G Gallas; Christos Hatzis; Weiwei Shi; Frank W Samuelson; Yuri Nikolsky; Marina Tsyganova; Alex Ishkin; Tatiana Nikolskaya; Kenneth R Hess; Vicente Valero; Daniel Booser; Mauro Delorenzi; Gabriel N Hortobagyi; Leming Shi; W Fraser Symmans; Lajos Pusztai
Journal:  Breast Cancer Res       Date:  2010-01-11       Impact factor: 6.466

Review 6.  Microarrays in the 2010s: the contribution of microarray-based gene expression profiling to breast cancer classification, prognostication and prediction.

Authors:  Pierre-Emmanuel Colombo; Fernanda Milanezi; Britta Weigelt; Jorge S Reis-Filho
Journal:  Breast Cancer Res       Date:  2011-06-27       Impact factor: 6.466

7.  Investigating a multigene prognostic assay based on significant pathways for Luminal A breast cancer through gene expression profile analysis.

Authors:  Haiyan Gao; Mei Yang; Xiaolan Zhang
Journal:  Oncol Lett       Date:  2018-02-02       Impact factor: 2.967

8.  Analysis of Tau Protein Expression in Predicting Pathological Complete Response to Neoadjuvant Chemotherapy in Different Molecular Subtypes of Breast Cancer.

Authors:  Chuqian Lei; Ciqiu Yang; Bin Xia; Fei Ji; Yi Zhang; Hongfei Gao; Qianqian Xiong; Yufeng Lin; Xiaosheng Zhuang; Liulu Zhang; Teng Zhu; Minyi Cheng; Mei Yang; Kun Wang
Journal:  J Breast Cancer       Date:  2020-01-22       Impact factor: 3.588

  8 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.