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Single drug biomarker prediction for ER- breast cancer outcome from chemotherapy.

ABSTRACT: ER-negative breast cancer includes most aggressive subtypes of breast cancer such as triple negative (TN) breast cancer. Excluded from hormonal and targeted therapies effectively used for other subtypes of breast cancer, standard chemotherapy is one of the primary treatment options for these patients. However, as ER- patients have shown highly heterogeneous responses to different chemotherapies, it has been difficult to select most beneficial chemotherapy treatments for them. In this study, we have simultaneously developed single drug biomarker models for four standard chemotherapy agents: paclitaxel (T), 5-fluorouracil (F), doxorubicin (A) and cyclophosphamide (C) to predict responses and survival of ER- breast cancer patients treated with combination chemotherapies. We then flexibly combined these individual drug biomarkers for predicting patient outcomes of two independent cohorts of ER- breast cancer patients who were treated with different drug combinations of neoadjuvant chemotherapy. These individual and combined drug biomarker models significantly predicted chemotherapy response for 197 ER- patients in the Hatzis cohort (AUC?=?0.637, P?=?0.002) and 69 ER- patients in the Hess cohort (AUC?=?0.635, P?=?0.056). The prediction was also significant for the TN subgroup of both cohorts (AUC?=?0.60, 0.72, P?=?0.043, 0.009). In survival analysis, our predicted responder patients showed significantly improved survival with a >17 months longer median PFS than the predicted non-responder patients for both ER- and TN subgroups (log-rank test P-value?=?0.018 and 0.044). This flexible prediction capability based on single drug biomarkers may allow us to even select new drug combinations most beneficial to individual patients with ER- breast cancer.

SUBMITTER: Chen YZ

PROVIDER: S-EPMC5920016 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Single drug biomarker prediction for ER- breast cancer outcome from chemotherapy.

Chen Yong-Zi YZ Kim Youngchul Y Soliman Hatem H HH Ying GuoGuang G Lee Jae K JK

Endocrine-related cancer 20180329 6

ER-negative breast cancer includes most aggressive subtypes of breast cancer such as triple negative (TN) breast cancer. Excluded from hormonal and targeted therapies effectively used for other subtypes of breast cancer, standard chemotherapy is one of the primary treatment options for these patients. However, as ER- patients have shown highly heterogeneous responses to different chemotherapies, it has been difficult to select most beneficial chemotherapy treatments for them. In this study, we h ...[more]

PMID: 29599124

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Project description:Background: We have established a statistical approach on building a transcriptional regulatory network in silico at a global transcriptome scale. Here, we characterize clinical relevant gene activities regulated by a transcription factor-estrogen-related receptor gamma (ESRRG) via such approach. Methods: We measured gene expression relationship between ESRRG and its target gene via univariate coefficient of intrinsic dependence and Galton Pearson’s correlation coefficient. A few more bioinformatics tools were used for further investigation on ESRRG. Results: ESRRG is a negative determinant of lymphovascular invasion, lymph nodal category, stage, histological grade, nuclear pleomorphism and tubule formation in ER(+) infiltrating ductal carcinoma of breast. Interestingly, ESRRG was the only one in the final intersection of Venn Diagram analyses for determinants of those clinical indices. We established an ESRRG core network consisting of 118 probes. Both MYB and ARNT2 were up-regulated by ESRRG and in two ESRRG subnetworks (29 probes, 89 probes), respectively. We observed genes for tumor suppressive activities and responsiveness to anticancer treatments within ESRRG core network and a conserved protective role of ESRRG. Conclusions: ESRRG is a tumor suppressor typically for ER(+) breast cancer and up-regulates tumor suppressor genes mostly within ERα transcriptional regulatory network. An ESRRG core network reveals ESRRG to be a favorable marker and MYB to be the main partner for its cancer protective role during early tumor promotion. But, ARNT2 is an additional partner of ESRRG during early tumor progression. Impact: A core ESRRG transcriptional regulatory network indicates the major clinical impact of ESRRG. 181 surgical specimens of primary infiltrating ductal carcinoma (IDC) were obtained from patients who underwent surgery at National Taiwan University Hospital (NTUH) between 1998 and 2007. They include 90 ER(+) IDCs and 91 ER(-) IDCs. Tissue samples were excised, snap frozen in liquid nitrogen, and stored at -80℃. Breast cancer samples containing relatively pure cancer as defined by greater than 50% tumor cells per high-power field examined in an adjacent section of tumor sample were for this study (Lien HC, et al. Oncogene 2007; 26: 7859-71). Twenty five non-tumor samples were surgically taken from breast cancer patients in 181 IDC patients to generate 25 gene expression profiles as a control in this study. All patients had given informed consent according to the guidelines approved by the Institutional Review Board at NTUH. The matrix file linked to the bottom of this Series includes reanalyzed data from the 119 Samples of this Series and 87 Samples from Series GSE9309 and GSE17040.

Project description: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 EndoPredict, Oncotype DX and 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.

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Single drug biomarker prediction for ER- breast cancer outcome from chemotherapy.

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Single drug biomarker prediction for ER- breast cancer outcome from chemotherapy.

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