BackgroundThe purpose of this study is to assess the predictive accuracy of a multi-gene predictor of response to docetaxel, 5-fluorouracil, epirubicin and cyclophosphamide combination chemotherapy on gene expression data from patients who received these drugs as neoadjuvant treatment.MethodsTumor samples were obtained from patients with stage II-III breast cancer before starting neoadjuvant chemotherapy with four cycles of 5-fluorouracil/epirubicin/cyclophosphamide (FEC) followed by four cycles of docetaxel/capecitabine (TX) on US Oncology clinical trial 02-103. Most patients with HER-2-positive cancer also received trastuzumab (H). The chemotherapy predictor (TFEC-MGP) was developed from publicly available gene expression data of 42 breast cancer cell-lines with corresponding in vitro chemotherapy sensitivity results for the four chemotherapy drugs. No predictor was developed for treatment with trastuzumab. The predictive performance of TFEC-MGP in distinguishing cases with pathologic complete response from those with residual disease was evaluated for the FEC/TX and FEC/TX plus H group separately. The area under the receiver-operating characteristic curve (AU-ROC) was used as the metric of predictive performance. Genomic predictions were performed blinded to clinical outcome.ResultsThe AU-ROC was 0.70 (95% CI: 0.57-0.82) for the FEC/TX group (n=66) and 0.43 (95% CI: 0.20-0.66) for the FEC/TX plus H group (n=25). Among the patients treated with FEC/TX, the AU-ROC was 0.69 (95% CI: 0.52-0.86) for estrogen receptor (ER)-negative (n=28) and it was 0.59 (95% CI: 0.36-0.82) for ER-positive cancers (n=37). ER status was not reported for one patient.ConclusionsOur results indicate that the cell line derived 291-probeset genomic predictor of response to FEC/TX combination chemotherapy shows good performance in a blinded validation study, particularly in ER-negative patients.

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 in vitro-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.

T o achieve maximum clinical utility, cell-based assays must produce reliable and reproducible results. To address these issues, we have developed and incorporated two automated systems into the ChemoFx assay (Precision Therapeutics, Inc., Pittsburgh, PA), a cell-based assay used to assess chemosensitivity and resistance of tumor cells to a spectrum of chemotherapeutic agents. An automated liquid-handling system plates cells and prepares and applies chemotherapeutic agents. Separate well-imaging and cell-counting systems quantify cell counts. In addition, we have developed a computerized tool to validate the accuracy of the cell quantification system. We report here that these automated systems improve the accuracy and precision of the ChemoFx assay. These systems also reduce technician time and human-induced variability. We propose that such automated systems could be incorporated into other cellbased assays and would provide increased confidence that such assays could be used to provide clinically useful information. ( JALA 2010;15:7-14)

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.

Background: Paclitaxel belongs to the taxane family of therapeutics, which have emerged as critically important drugs for breast cancer treatment. In addition to inhibiting cell growth by interfering with microtubule disassembly, its mechanism of action also includes induction of apoptosis. Recent studies suggest that besides being a key predictor for endocrine therapy response, Estrogen Receptor (ER) status also influences sensitivity of breast cancer to paclitaxel, with ER negative tumors being more responsive to the drug.Methods: The ChemoFx live cell chemoresponse assay was performed on 25 breast cancer cell lines (10 ER+ and 15 ER-). These cells were treated with a range of 10 doses of paclitaxel for 72 hours before DAPI staining of nuclei and counting. AUC (Area Under Curve) values were calculated and additional statistical analysis was performed on the resulting dose-response curves. Differential gene expression analysis was conducted to compare ER+ (n=82) and ER- (n=51) breast cancer patients using a public Microarray database. In addition, 2 of the 25 breast cancer cell lines, T47D (ER+) and SKBR3 (ER-), were treated with paclitaxel, lysed, and analyzed with Western blotting to detect cleaved caspase-3 and cleaved PARP expression, with beta-actin employed as a normal control.Results: The ChemoFx assay results revealed that none of the ER+ cells were categorized as R (responsive) to paclitaxel, with seven NR (non-responsive) and one IR (intermediate responsive). On the contrary, of the 15 ER- cell lines, three were categorized as R, only four were categorized as NR, and eight were categorized as IR. Statistical analysis suggested that paclitaxel responsiveness based on ChemoFx assay correlates with ER status (Chi-square test, p<0.05), with ER- breast tumors being more responsive to paclitaxel. Microarray analysis revealed differential expressions of genes implicated in the apoptosis pathway (q< 0.05) in ER+ and ER- breast cancers. Western blot analysis showed that paclitaxel induced cleaved caspase-3 and cleaved PARP expressions, both of which are indicators of activation of apoptosis, in SKBR3 cells (ER-), but not in T47D cells (ER+).Conclusions: ER status appears to predict in part, the response of breast cancer cells to paclitaxel as determined by the ChemoFx assay. ER-negative breast cancer cells are more likely to be responsive, which is consistent with established clinical findings. Our assay also distinguishes between NR/IR and R to paclitaxel within the ER- population. Similar ChemoFx assays are being performed on primary cultures from ER+ and ER- breast cancer patient specimens. Results from RNA microarray and Western blot analyses indicate that differences in gene expression in the apoptosis pathway, and in activation of apoptosis pathway, namely changes in expressions of cleaved PARP and cleaved caspase-3 in response to paclitaxel, may explain differences in the responsiveness of ER+ and ER- breast cancers to paclitaxel. This also suggests a potential role of cleaved PARP and cleaved caspase-3 as biomarkers in addition to ER for prediction of paclitaxel responsiveness in breast cancer. Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 2028.