Distinct genes related to drug response identified in ER positive and ER negative breast cancer cell lines

Breast cancer patients have different responses to chemotherapeutic treatments. Genes associated with drug response can provide insight to understand the mechanisms of drug resistance, identify promising therapeutic opportunities, and facilitate personalized treatment. Estrogen receptor (ER) positive and ER negative breast cancer have distinct clinical behavior and molecular properties. However, to date, few studies have rigorously assessed drug response genes in them. In this study, our goal was to systematically identify genes associated with multidrug response in ER positive and ER negative breast cancer cell lines. We tested 27 human breast cell lines for response to seven chemotherapeutic agents (cyclophosphamide, docetaxel, doxorubicin, epirubicin, fluorouracil, gemcitabine, and paclitaxel). We integrated publicly available gene expression profiles of these cell lines with their in vitro drug response patterns, then applied meta-analysis to identify genes related to multidrug response in ER positive and ER negative cells separately. One hundred eighty-eight genes were identified as related to multidrug response in ER positive and 32 genes in ER negative breast cell lines. Of these, only three genes (DBI, TOP2A, and PMVK) were common to both cell types. TOP2A was positively associated with drug response, and DBI was negatively associated with drug response. Interestingly, PMVK was positively associated with drug response in ER positive cells and negatively in ER negative cells. Functional analysis showed that while cell cycle affects drug response in both ER positive and negative cells, most biological processes that are involved in drug response are distinct. A number of signaling pathways that are uniquely enriched in ER positive cells have complex cross talk with ER signaling, while in ER negative cells, enriched pathways are related to metabolic functions. Taken together, our analysis indicates that distinct mechanisms are involved in multidrug response in ER positive and ER negative breast cells. © 2012 Shen et al

A cost-effectiveness analysis of a chemoresponse assay for treatment of patients with recurrent epithelial ovarian cancer

AbstractObjectiveClinical validation of a chemoresponse assay was recently published, demonstrating a significant increase in overall survival in recurrent ovarian cancer patients treated with therapies to which their tumor was sensitive in the assay. The current study investigates the cost effectiveness of using the assay at the time of ovarian cancer recurrence from the payer's perspective.MethodsUsing a Markov state transition model, patient characteristics and survival data from the recent clinical study, the cumulative costs over the study horizon (71months) for both the baseline (no assay) and intervention (assay consistent, hypothetical) cohorts were evaluated.ResultsThe assay consistent cohort had an incremental cost effectiveness ratio (ICER) of $6206 per life year saved (LYS), as compared to the baseline cohort. Cost-effectiveness was further demonstrated in platinum-sensitive and platinum-resistant populations treated with assay-sensitive therapies, with ICERs of$2773 per LYS and $2736 per LYS, respectively.ConclusionsThe use of a chemoresponse assay to inform treatment decisions in recurrent ovarian cancer patients has the potential to be cost-effective in both platinum-sensitive and platinum-resistant patients

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

<div><p>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 <em>in vitro</em>-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.</p> </div

Distributional difference between prediction scores calculated by the superPC or COXEN methods from the Neve training set for responders (pCR, pathologic complete response) and non-responders (RD, residual disease), with p-value and AUC.

<p>In the top row, the prediction scores are calculated by the superPC method, and in the bottom row, the prediction scores are calculated by the COXEN method. Red lines represent median prediction scores in each group.</p

Correlation between the prediction scores calculated by the superPC and COXEN methods from the Neve training set for each test set.

<p>Correlation between the prediction scores calculated by the superPC and COXEN methods from the Neve training set for each test set.</p

Chemoresponse-derived AUD values for each cell line, labeled by ER status, for both TFAC (top) and FEC (bottom) treatments.

<p>Chemoresponse-derived AUD values for each cell line, labeled by ER status, for both TFAC (top) and FEC (bottom) treatments.</p

Prediction results for the superPC and COXEN methods in all breast cancer cell lines evaluated by AUC scores.

**<p>: P<0.05,</p>*<p>: P<0.1.</p><p>The AUC values are grouped by ER status: All (cells of both ER status), ER+ (ER− positive cells), and ER– (ER-negative cells) and are separated based on the cell line expression database used to create the cell line MGPs. Note that these five validation datasets (except Tabchy-TFAC and Iwamoto) were independent for the superPC prediction method, because this predictor was not pre-optimized or optimized using any of these data sets. For the COXEN prediction method, MAQC-training and Tabchy-FEC datasets were used for optimization, and therefore the remaining three datasets were truly independent validation sets for this method.</p

Two breast cancer cell line sets for MGP model training.

a<p>The TFAC chemoresponse test for one cell line (SW527) did not pass quality control; therefore, the AUC values for 41 cell lines were available for further analysis.</p>b<p>The FEC chemoresponse test for three cell lines (HCC1419, HCC1569, and HCC1806) did not pass quality control; therefore, the AUC values for 39 cell lines were available for further analysis.</p>c<p>The number of cell lines common to the Hoeflich data set and the 42 cell lines and whose chemoresponses were measured is 30.</p>d<p>There are 28 TFAC-treated and 27 FEC-treated cell lines common to the Neve and Hoeflich data sets.</p

Summary information for the gene expression and clinical outcome test sets for five clinical trials in the GEO database.

a<p>The Tabchy-TFAC data set (GSE20271) has 31 samples that overlap with the Iwamoto data set (GSE22093); therefore, these two data sets are not completely independent.</p

Prediction results for the COXEN model using either ER+ (ER-positive) or ER– (ER-negative) breast cancer cell lines, evaluated by area under receiver operator characteristic (AU-ROC) scores.

**<p>: P<0.05,</p>*<p>: P<0.1.</p><p>The MAQC-Validation and Tabchy-TFAC were truly independent validation sets here because the MAQC-training and Tabchy-FEC were used to optimize the COXEN model.</p