Novel functional assay for spindle-assembly checkpoint by cyclin-dependent kinase activity to predict taxane chemosensitivity in breast tumor patient

A four gene signature of chromosome instability (CIN4) predicts for benefit from taxanes in the NCIC-CTG MA21 clinical trial

Recent evidence demonstrated CIN4 as a predictive marker of anthracycline benefit in early breast cancer. An analysis of the NCIC CTG MA.21 clinical trial was performed to test the role of existing CIN gene expression signatures as prognostic and predictive markers in the context of taxane based chemotherapy.

RNA was extracted from patients in cyclophosphamide, epirubicin and fluorouracil (CEF) and epirubicin, cyclophosphamide and paclitaxel (EC/T) arms of the NCIC CTG MA.21 trial and analysed using NanoString technology.

After multivariate analysis both high CIN25 and CIN70 score was significantly associated with an increased in RFS (HR 1.76, 95%CI 1.07-2.86, p=0.0018 and HR 1.59, 95%CI 1.12-2.25, p=0.0096 respectively). Patients whose tumours had low CIN4 gene expression scores were associated with an increase in RFS (HR: 0.64, 95% CI 0.39-1.03, p=0.06) when treated with EC/T compared to patients treated with CEF.

In conclusion we have demonstrated CIN25 and CIN70 as prognostic markers in breast cancer and that CIN4 is a potential predictive maker of benefit from taxane treatment.

The Discovery of Novel Biomarkers Improves Breast Cancer Intrinsic Subtype Prediction and Reconciles the Labels in the METABRIC Data Set

BACKGROUND

The prediction of breast cancer intrinsic subtypes has been introduced as a valuable strategy to determine patient diagnosis and prognosis, and therapy response. The PAM50 method, based on the expression levels of 50 genes, uses a single sample predictor model to assign subtype labels to samples. Intrinsic errors reported within this assay demonstrate the challenge of identifying and understanding the breast cancer groups. In this study, we aim to: a) identify novel biomarkers for subtype individuation by exploring the competence of a newly proposed method named CM1 score, and b) apply an ensemble learning, as opposed to the use of a single classifier, for sample subtype assignment. The overarching objective is to improve class prediction.

METHODS AND FINDINGS

The microarray transcriptome data sets used in this study are: the METABRIC breast cancer data recorded for over 2000 patients, and the public integrated source from ROCK database with 1570 samples. We first computed the CM1 score to identify the probes with highly discriminative patterns of expression across samples of each intrinsic subtype. We further assessed the ability of 42 selected probes on assigning correct subtype labels using 24 different classifiers from the Weka software suite. For comparison, the same method was applied on the list of 50 genes from the PAM50 method.

CONCLUSIONS

The CM1 score portrayed 30 novel biomarkers for predicting breast cancer subtypes, with the confirmation of the role of 12 well-established genes. Intrinsic subtypes assigned using the CM1 list and the ensemble of classifiers are more consistent and homogeneous than the original PAM50 labels. The new subtypes show accurate distributions of current clinical markers ER, PR and HER2, and survival curves in the METABRIC and ROCK data sets. Remarkably, the paradoxical attribution of the original labels reinforces the limitations of employing a single sample classifiers to predict breast cancer intrinsic subtypes.

Correlating transcriptional networks with pathological complete response following neoadjuvant chemotherapy for breast cancer

We aimed to investigate the association between gene co-expression modules and responses to neoadjuvant chemotherapy in breast cancer by using a systematic biological approach. The gene expression profiles and clinico-pathological data of 508 (discovery set) and 740 (validation set) patients with breast cancer who received neoadjuvant chemotherapy were analyzed. Weighted gene co-expression network analysis was performed and identified seven co-regulated gene modules. Each module and gene signature were evaluated with logistic regression models for pathological complete response (pCR). The association between modules and pCR in each intrinsic molecular subtype was also investigated. Two transcriptional modules were correlated with tumor grade, estrogen receptor status, progesterone receptor status, and chemotherapy response in breast cancer. One module that constitutes upregulated cell proliferation genes was associated with a high probability for pCR in the whole (odds ratio (OR) = 5.20 and 3.45 in the discovery and validation datasets, respectively), luminal B, and basal-like subtypes. The prognostic potentials of novel genes, such as MELK, and pCR-related genes, such as ESR1 and TOP2A, were identified. The upregulation of another gene co-expression module was associated with weak chemotherapy responses (OR = 0.19 and 0.33 in the discovery and validation datasets, respectively). The novel gene CA12 was identified as a potential prognostic indicator in this module. A systems biology network-based approach may facilitate the discovery of biomarkers for predicting chemotherapy responses in breast cancer and contribute in developing personalized medicines.