Literature DB >> 19603265

Clinical evaluation of chemotherapy response predictors developed from breast cancer cell lines.

Cornelia Liedtke1, Jing Wang, Attila Tordai, William F Symmans, Gabriel N Hortobagyi, Ludwig Kiesel, Kenneth Hess, Keith A Baggerly, Kevin R Coombes, Lajos Pusztai.   

Abstract

The goal of this study was to develop pharmacogenomic predictors in response to standard chemotherapy drugs in breast cancer cell lines and test their predictive value in patients who received treatment with the same drugs. Nineteen human breast cancer cell lines were tested for sensitivity to paclitaxel (T), 5-fluorouracil (F), doxorubicin (A) and cyclophosphamide (C) in vitro. Baseline gene expression data were obtained for each cell line with Affymetrix U133A gene chips, and multigene predictors of sensitivity were derived for each drug separately. These predictors were applied individually and in combination to human gene expression data generated with the same Affymetrix platform from fine needle aspiration specimens of 133 stage I-III breast cancers. Tumor samples were obtained at baseline, and each patient received 6 months of preoperative TFAC chemotherapy followed by surgery. Cell line-derived prediction results were correlated with the observed pathologic response to chemotherapy. Statistically robust differentially expressed genes between sensitive and resistant cells could only be found for paclitaxel. False discovery rates associated with the informative genes were high for all other drugs. For each drug, the top 100 differentially expressed genes were combined into a drug-specific response predictor. When these cell line-based predictors were applied to patient data, there was no significant correlation between observed response and predicted response either for individual drug predictors or combined predictions. Cell line-derived predictors of response to four commonly used chemotherapy drugs did not predict response accurately in patients.

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Year:  2009        PMID: 19603265     DOI: 10.1007/s10549-009-0445-7

Source DB:  PubMed          Journal:  Breast Cancer Res Treat        ISSN: 0167-6806            Impact factor:   4.872


  23 in total

1.  Increased expression of P-glycoprotein is associated with doxorubicin chemoresistance in the metastatic 4T1 breast cancer model.

Authors:  Lili Bao; Aliyya Haque; Kamilah Jackson; Sidhartha Hazari; Krzysztof Moroz; Rachna Jetly; Srikanta Dash
Journal:  Am J Pathol       Date:  2011-02       Impact factor: 4.307

2.  Genes potentially associated with cisplatin resistance of lung cancer cells.

Authors:  K N Kashkin; E A Musatkina; A V Komelkov; E A Tonevitsky; D A Sakharov; T V Vinogradova; E P Kopantsev; M V Zinovyeva; I A Favorskaya; Ya A Kainov; V N Aushev; I B Zborovskaya; A G Tonevitsky; E D Sverdlov
Journal:  Dokl Biochem Biophys       Date:  2011-07-03       Impact factor: 0.788

3.  Genes potentially associated with resistance of lung cancer cells to paclitaxel.

Authors:  K N Kashkin; E A Musatkina; A V Komelkov; D A Sakharov; E V Trushkin; E A Tonevitsky; T V Vinogradova; E P Kopantzev; M V Zinovyeva; O V Kovaleva; K A Arkhipova; I B Zborovskaya; A G Tonevitsky; E D Sverdlov
Journal:  Dokl Biochem Biophys       Date:  2011-05-18       Impact factor: 0.788

4.  Impact of intertumoral heterogeneity on predicting chemotherapy response of BRCA1-deficient mammary tumors.

Authors:  Sven Rottenberg; Marieke A Vollebergh; Bas de Hoon; Jorma de Ronde; Philip C Schouten; Ariena Kersbergen; Serge A L Zander; Marina Pajic; Janneke E Jaspers; Martijn Jonkers; Martin Lodén; Wendy Sol; Eline van der Burg; Jelle Wesseling; Jean-Pierre Gillet; Michael M Gottesman; Joost Gribnau; Lodewyk Wessels; Sabine C Linn; Jos Jonkers; Piet Borst
Journal:  Cancer Res       Date:  2012-03-06       Impact factor: 12.701

5.  Challenges translating breast cancer gene signatures into the clinic.

Authors:  Britta Weigelt; Lajos Pusztai; Alan Ashworth; Jorge S Reis-Filho
Journal:  Nat Rev Clin Oncol       Date:  2011-08-30       Impact factor: 66.675

Review 6.  Patient-derived xenograft (PDX) models in basic and translational breast cancer research.

Authors:  Lacey E Dobrolecki; Susie D Airhart; Denis G Alferez; Samuel Aparicio; Fariba Behbod; Mohamed Bentires-Alj; Cathrin Brisken; Carol J Bult; Shirong Cai; Robert B Clarke; Heidi Dowst; Matthew J Ellis; Eva Gonzalez-Suarez; Richard D Iggo; Peter Kabos; Shunqiang Li; Geoffrey J Lindeman; Elisabetta Marangoni; Aaron McCoy; Funda Meric-Bernstam; Helen Piwnica-Worms; Marie-France Poupon; Jorge Reis-Filho; Carol A Sartorius; Valentina Scabia; George Sflomos; Yizheng Tu; François Vaillant; Jane E Visvader; Alana Welm; Max S Wicha; Michael T Lewis
Journal:  Cancer Metastasis Rev       Date:  2016-12       Impact factor: 9.264

7.  Optimally discriminative subnetwork markers predict response to chemotherapy.

Authors:  Phuong Dao; Kendric Wang; Colin Collins; Martin Ester; Anna Lapuk; S Cenk Sahinalp
Journal:  Bioinformatics       Date:  2011-07-01       Impact factor: 6.937

8.  Amplification of LAPTM4B and YWHAZ contributes to chemotherapy resistance and recurrence of breast cancer.

Authors:  Yang Li; Lihua Zou; Qiyuan Li; Benjamin Haibe-Kains; Ruiyang Tian; Yan Li; Christine Desmedt; Christos Sotiriou; Zoltan Szallasi; J Dirk Iglehart; Andrea L Richardson; Zhigang Charles Wang
Journal:  Nat Med       Date:  2010-01-24       Impact factor: 53.440

9.  A systematic evaluation of multi-gene predictors for the pathological response of breast cancer patients to chemotherapy.

Authors:  Kui Shen; Nan Song; Youngchul Kim; Chunqiao Tian; Shara D Rice; Michael J Gabrin; W Fraser Symmans; Lajos Pusztai; Jae K Lee
Journal:  PLoS One       Date:  2012-11-21       Impact factor: 3.240

10.  Cell line derived multi-gene predictor of pathologic response to neoadjuvant chemotherapy in breast cancer: a validation study on US Oncology 02-103 clinical trial.

Authors:  Kui Shen; Yuan Qi; Nan Song; Chunqiao Tian; Shara D Rice; Michael J Gabrin; Stacey L Brower; William Fraser Symmans; Joyce A O'Shaughnessy; Frankie A Holmes; Lina Asmar; Lajos Pusztai
Journal:  BMC Med Genomics       Date:  2012-11-16       Impact factor: 3.063

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