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A Systematic Evaluation of Multi-Gene Predictors for the Pathological Response of Breast Cancer Patients to Chemotherapy

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  • Kui Shen
  • Nan Song
  • Youngchul Kim
  • Chunqiao Tian
  • Shara D Rice
  • Michael J Gabrin
  • W Fraser Symmans
  • Lajos Pusztai
  • Jae K Lee

Abstract

Previous studies have reported conflicting assessments of the ability of cell line-derived multi-gene predictors (MGPs) to forecast patient clinical outcomes in cancer patients, thereby warranting an investigation into their suitability for this task. Here, 42 breast cancer cell lines were evaluated by chemoresponse tests after treatment with either TFAC or FEC, two widely used standard combination chemotherapies for breast cancer. We used two different training cell line sets and two independent prediction methods, superPC and COXEN, to develop cell line-based MGPs, which were then validated in five patient cohorts treated with these chemotherapies. This evaluation yielded high prediction performances by these MGPs, regardless of the training set, chemotherapy, or prediction method. The MGPs were also able to predict patient clinical outcomes for the subgroup of estrogen receptor (ER)-negative patients, which has proven difficult in the past. These results demonstrated a potential of using an in vitro-based chemoresponse data as a model system in creating MGPs for stratifying patients’ therapeutic responses. Clinical utility and applications of these MGPs will need to be carefully examined with relevant clinical outcome measurements and constraints in practical use.

Suggested Citation

  • Kui Shen & Nan Song & Youngchul Kim & Chunqiao Tian & Shara D Rice & Michael J Gabrin & W Fraser Symmans & Lajos Pusztai & Jae K Lee, 2012. "A Systematic Evaluation of Multi-Gene Predictors for the Pathological Response of Breast Cancer Patients to Chemotherapy," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-9, November.
    • Handle: RePEc:plo:pone00:0049529
    DOI: 10.1371/journal.pone.0049529
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    References listed on IDEAS

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    1. Bair, Eric & Hastie, Trevor & Paul, Debashis & Tibshirani, Robert, 2006. "Prediction by Supervised Principal Components," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 119-137, March.
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    1. Silje Kjølle & Kenneth Finne & Even Birkeland & Vandana Ardawatia & Ingeborg Winge & Sura Aziz & Gøril Knutsvik & Elisabeth Wik & Joao A. Paulo & Heidrun Vethe & Dimitrios Kleftogiannis & Lars A. Aksl, 2023. "Hypoxia induced responses are reflected in the stromal proteome of breast cancer," Nature Communications, Nature, vol. 14(1), pages 1-16, December.

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