Abstract
Determining which patients with early-stage breast cancer should receive chemotherapy is an important clinical issue. Chemotherapy has several adverse side effects, impacting on quality of life, along with significant economic consequences. There are a number of multi-gene prognostic signatures for breast cancer recurrence but there is less evidence that these prognostic signatures are predictive of therapy benefit. Biomarkers that can predict patient response to chemotherapy can help avoid ineffective over-treatment. The aim of this work was to assess if the OncoMasTR prognostic signature can predict pathological complete response (pCR) to neoadjuvant chemotherapy, and to compare its predictive value with other prognostic signatures: EndoPredict, Oncotype DX and Tumor Infiltrating Leukocytes. Gene expression datasets from ER-positive, HER2-negative breast cancer patients that had pre-treatment biopsies, received neoadjuvant chemotherapy and an assessment of pCR were obtained from the Gene Expression Omnibus repository. A total of 813 patients with 66 pCR events were included in the analysis. OncoMasTR, EndoPredict, Oncotype DX and Tumor Infiltrating Leukocytes numeric risk scores were approximated by applying the gene coefficients to the corresponding mean probe expression values. OncoMasTR, EndoPredict and Oncotype DX prognostic scores were moderately well correlated according to the Pearson’s correlation coefficient. Association with pCR was estimated using logistic regression. The odds ratio for a 1 standard deviation increase in risk score, adjusted for cohort, were similar in magnitude for all four signatures. Additionally, the four signatures were significant predictors of pCR. OncoMasTR added significant predictive value to Tumor Infiltrating Leukocytes signatures as determined by bivariable and trivariable analysis. In this in silico analysis, OncoMasTR, EndoPredict, Oncotype DX, and Tumor Infiltrating Leukocytes were significantly predictive of pCR to neoadjuvant chemotherapy in ER-positive and HER2-negative breast cancer patients.
Highlights
Breast cancer is the most frequently diagnosed cancer in women, with more than 2.1 million new diagnoses worldwide every year, and the second leading cause of cancer death
We evaluated the correlation between six pairs of the four signatures: OM vs. Recurrence Score (RS), OM vs. EndoPredict assay (EP), RS vs. EP, TILs vs. OM, TILs vs. EP, and TILs vs. RS for the seven datasets combined (Table 2)
Our results suggest that OM, RS, EP and TILs were significant predictors of pathological complete response (pCR) to neoadjuvant chemotherapy in ER+, HER2- breast cancer, with odds ratios ≥ 1.36
Summary
Breast cancer is the most frequently diagnosed cancer in women, with more than 2.1 million new diagnoses worldwide every year, and the second leading cause of cancer death. Accurate tools to help with optimal treatment decisions for individual patients to improve their prognosis, survival and quality of life are needed, whilst reducing associated healthcare costs [3]. Breast cancer patients who receive chemotherapy can experience several side effects and symptoms that have a negative effect on their quality of life during and after the completion of treatment [4]. Multi-gene prognostic signatures may be used to estimate risk of recurrence following surgery and endocrine treatment to make decisions about the suitability of chemotherapy. A patient who is predicted to be at low risk of breast cancer recurrence can safely forego aggressive treatment plans such as chemotherapy. The Oncotype DX Recurrence Score (RS) is a 21-gene prognostic assay that is widely used to predict risk of recurrence [10].
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