IntroductionAfrican American (AA) women diagnosed with breast cancer are more likely to have aggressive subtypes. Investigating differentially expressed genes between patient populations may help explain racial health disparities. Resistin, one such gene, is linked to inflammation, obesity, and breast cancer risk. Previous studies indicated that resistin expression is higher in serum and tissue of AA breast cancer patients compared to Caucasian American (CA) patients. However, resistin expression levels have not been compared between AA and CA patients in a stage- and subtype-specific context. Breast cancer prognosis and treatments vary by subtype. This work investigates differential resistin gene expression in human breast cancer tissues of specific stages, receptor subtypes, and menopause statuses in AA and CA women.MethodsDifferential gene expression analysis was performed using human breast cancer gene expression data from The Cancer Genome Atlas. We performed inter-race resistin gene expression level comparisons looking at receptor status and stage-specific data between AA and CA samples. DESeq was run to test for differentially expressed resistin values.ResultsResistin RNA was higher in AA women overall, with highest values in receptor negative subtypes. Estrogen-, progesterone-, and human epidermal growth factor receptor 2- negative groups showed statistically significant elevated resistin levels in Stage I and II AA women compared to CA women. In inter-racial comparisons, AA women had significantly higher levels of resistin regardless of menopause status. In whole population comparisons, resistin expression was higher among Stage I and III estrogen receptor negative cases. In comparisons of molecular subtypes, resistin levels were significant higher in triple negative than in luminal A breast cancer.ConclusionResistin gene expression levels were significantly higher in receptor negative subtypes, especially estrogen receptor negative cases in AA women. Resistin may serve as an early breast cancer biomarker and possible therapeutic target for AA breast cancer.

Choosing the optimal chemotherapy regimen is still an unmet medical need for breast cancer patients. In this study, we reanalyzed data from seven independent data sets with totally 1079 breast cancer patients. The patients were treated with three different types of commonly used neoadjuvant chemotherapies: anthracycline alone, anthracycline plus paclitaxel, and anthracycline plus docetaxel. We developed random forest models with variable selection using both genetic and clinical variables to predict the response of a patient using pCR (pathological complete response) as the measure of response. The models were then used to reassign an optimal regimen to each patient to maximize the chance of pCR. An independent validation was performed where each independent study was left out during model building and later used for validation. The expected pCR rates of our method are significantly higher than the rates of the best treatments for all the seven independent studies. A validation study on 21 breast cancer cell lines showed that our prediction agrees with their drug-sensitivity profiles. In conclusion, the new strategy, called PRES (Personalized REgimen Selection), may significantly increase response rates for breast cancer patients, especially those with HER2 and ER negative tumors, who will receive one of the widely-accepted chemotherapy regimens.

Bayesian networks (BNs) provide a probabilistic, graphical framework for modeling high-dimensional joint distributions with complex correlation structures. BNs have wide applications in many disciplines, including biology, social science, finance and biomedical science. Despite extensive studies in the past, network structure learning from data is still a challenging open question in BN research. In this study, we present a sequential Monte Carlo (SMC)-based three-stage approach, GRowth-based Approach with Staged Pruning (GRASP). A double filtering strategy was first used for discovering the overall skeleton of the target BN. To search for the optimal network structures we designed an adaptive SMC (adSMC) algorithm to increase the quality and diversity of sampled networks which were further improved by a third stage to reclaim edges missed in the skeleton discovery step. GRASP gave very satisfactory results when tested on benchmark networks. Finally, BN structure learning using multiple types of genomics data illustrates GRASP’s potential in discovering novel biological relationships in integrative genomic studies.

Bayesian networks (BNs) provide a probabilistic, graphical framework for modeling highdimensional joint distributions with complex dependence structures. BNs can be used to infer complex biological networks using heterogeneous data from different sources with missing values. Despite extensive studies in the past, network structure learning from data is still a challenging open question in BN research. In this study, we present a sequential Monte Carlo (SMC) based three-stage approach, GRowth-based Approach with Staged Pruning (GRASP). A double filtering strategy was first used for discovering the overall skeleton of the target BN. To search for the optimal network structures we designed an adaptive SMC (adSMC) algorithm to increase the diversity of sampled networks which were further improved by a new stage to reclaim edges missed in the skeleton discovery step. GRASP gave very satisfactory results when tested on benchmark networks. Finally, BN structure learning using multiple types of genomics data illustrates GRASP's potential in discovering novel biological relationships in integrative genomic studies.

Despite the rapid progress in personalized cancer therapy (PCT) for breast cancer, no previous studies have used genomic predictors to choose among multiple chemotherapy regimens. It is unclear that given the current regimens how much PCT can improve the response rate for patients who will receive chemotherapy. In this study, we reanalyzed data from published studies of 1111 breast cancer patients who were treated with neoadjuvant chemotherapies. Those patients were divided into three regimen groups: an anthracycline alone, anthracycline plus paclitaxel, and anthracycline plus docetaxel. We developed a new strategy called PRES (Personalized REgimen Selection) to reassign the optimal regimen to each of the patients. First, a variable selection scheme was developed to identify significant genetic predictors for chemotherapy response. The selected genetic variables were then combined with clinical variables to build random forest models to predict the response of a patient to each regimen using pCR (pathological complete response) as the measure of response. The models were used to assign an optimal regimen to each patient to maximize the chance of pCR. We found that the expected rate of pCR was improved from 21.2% to 39.6% (95% CI: 34.6% - 43.0%). We also found that 31.1% of the patients may have been overtreated and 8.2% patients undertreated. A validation study on 21 cell lines showed that our prediction agrees with their paclitaxel-sensitivity profiles. We performed additional analysis on the Cancer Genome Atlas (TCGA) data and found that 18 of the 19 genes identified are significantly differentially expressed between normal and tumor tissues, and 2 of them, TAF6L and METRN (meteorin), are associated with overall survival. In conclusion, PRES could substantially increase response rates for breast cancer patients who will receive one of the widely-accepted chemotherapy regimens at present. Citation Format: Jinfeng Zhang, Kaixian Yu, Qingxiang Amy Sang, Winston Tan, Mayassa B. Dargham, Jun S. Liu, Ty Lively, Cedric Sheffield. Personalized chemotherapy regimen selection for breast cancer. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2034.