Prognosis of treatment response (pathological complete response) in breast cancer

Jason B. Nikas, Walter C. Low, Paul A. Burgio

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


Pertaining to the female population in the USA, breast cancer is the leading cancer in terms of annual incidence rate and, in terms of mortality, the second most lethal cancer. There are currently no biomarkers available that can predict which breast cancer patients will respond to chemotherapy with both sensitivity and specificity > 80%, as mandated by the latest FDA requirements. In this study, we have developed a prognostic biomarker model (complex mathematical function) that-based on global gene expression analysis of tumor tissue collected during biopsy and prior to the commencement of chemotherapy-can identify with a high accuracy those patients with breast cancer (clinical stages I-III) who will respond to the paclitaxel-fluorouracil-doxorubicin-cyclophosphamide chemotherapy and will experience pathological complete response (Responders), as well as those breast cancer patients (clinical stages I-III) who will not do so (Non-Responders). Most importantly, both the application and the accuracy of our breast cancer prognostic biomarker model are independent of the status of the hormone receptors ER, PR, and HER2, as well as of the ethnicity and age of the subjects. We developed our prognostic biomarker model with 50 subjects [10 responders (R) and 40 non-responders (NR)], and we validated it with 43 unknown (new and different) subjects [10 responders (R) and 33 non-responders (NR)]. All 93 subjects were recruited at five different clinical centers around the world. The overall sensitivity and specificity of our prognostic biomarker model were 90.0% and 91.8%, respectively. The nine most significant genes identified, which comprise the input variables to the mathematical function, are involved in regulation of transcription; cell proliferation, invasion, and migration; oncogenesis; suppression of immune response; and drug resistance and cancer recurrence.

Original languageEnglish (US)
Pages (from-to)59-70
Number of pages12
JournalBiomarker Insights
StatePublished - May 8 2012


  • Biomarkers
  • Breast cancer
  • Global gene expression analysis
  • Prognostic biomarker models
  • Systems biology
  • Treatment response

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