Hormone receptor and ERBB2 status in gene expression profiles of human breast tumor samples

MetaKTSP: a meta-analytic top scoring pair method for robust cross-study validation of omics prediction analysis

MOTIVATION

Supervised machine learning is widely applied to transcriptomic data to predict disease diagnosis, prognosis or survival. Robust and interpretable classifiers with high accuracy are usually favored for their clinical and translational potential. The top scoring pair (TSP) algorithm is an example that applies a simple rank-based algorithm to identify rank-altered gene pairs for classifier construction. Although many classification methods perform well in cross-validation of single expression profile, the performance usually greatly reduces in cross-study validation (i.e. the prediction model is established in the training study and applied to an independent test study) for all machine learning methods, including TSP. The failure of cross-study validation has largely diminished the potential translational and clinical values of the models. The purpose of this article is to develop a meta-analytic top scoring pair (MetaKTSP) framework that combines multiple transcriptomic studies and generates a robust prediction model applicable to independent test studies.

RESULTS

We proposed two frameworks, by averaging TSP scores or by combining P-values from individual studies, to select the top gene pairs for model construction. We applied the proposed methods in simulated data sets and three large-scale real applications in breast cancer, idiopathic pulmonary fibrosis and pan-cancer methylation. The result showed superior performance of cross-study validation accuracy and biomarker selection for the new meta-analytic framework. In conclusion, combining multiple omics data sets in the public domain increases robustness and accuracy of the classification model that will ultimately improve disease understanding and clinical treatment decisions to benefit patients.

AVAILABILITY AND IMPLEMENTATION

An R package MetaKTSP is available online. (http://tsenglab.biostat.pitt.edu/software.htm).

CONTACT

ctseng@pitt.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Identification of a novel luminal molecular subtype of breast cancer

The molecular classification of human breast tumors has afforded insights into subtype specific biological processes, patient prognosis and response to therapies. However, using current methods roughly one quarter of breast tumors cannot be classified into one or another molecular subtype. To explore the possibility that the unclassifiable samples might comprise one or more novel subtypes we employed a collection of publically available breast tumor datasets with accompanying clinical information to assemble 1,593 transcript profiles: 25% of these samples could not be assigned to one of the current molecular subtypes of breast cancer. All of the unclassifiable samples could be grouped into a new molecular subtype, which we termed “luminal-like”. We also identified the luminal-like subtype in an independent collection of tumor samples (NKI295). We found that patients harboring tumors of the luminal-like subtype have a better prognosis than those with basal-like breast cancer, a similar prognosis to those with ERBB2+, luminal B or claudin-low tumors, but a worse prognosis than patients with luminal A or normal-like breast tumors. Our findings suggest the occurrence of another molecular subtype of breast cancer that accounts for the vast majority of previously unclassifiable breast tumors.

Expression of SCUBE2 gene declines in high grade endometrial cancer and associates with expression of steroid hormone receptors and tumor suppressor PTEN

SCUBE2 (Signal peptide-CUB-epidermal growth factor-like domain-containing 2) gene codes for a cell-surface glycoprotein. In breast cancer, SCUBE2 transcript levels are part of important prognostic and predictive gene signatures and are linked to expression of estrogen receptor α (ERα). To elucidate the role of this gene in endometrial cancer, we compared SCUBE2 expression in malignant and normal endometrial tissue specimens. We then examined its correlation with steroid hormone receptors and PTEN and compared it to SCUBE2 expression in breast cancer samples. Expression of SCUBE2 was found to be decreased in G3 endometrial cancer when compared to postmenopausal endometrium or to G1 tumors (p < 0.05). In postmenopausal endometrium, SCUBE2 transcript levels were more than twice as high as in premenopausal women. In breast cancer, SCUBE2 expression was found to be notably reduced particularly in ERα-negative G3 tumors. Both in endometrial and breast cancer we observed a significant positive correlation of SCUBE2 transcript levels with expression of ERα, PR and PTEN. Our data suggest that SCUBE2, like in breast cancer, associates with ERα and might have a potential as prognostic or predictive marker in endometrial cancer.

HER2 status for prognosis and prediction of treatment efficacy in adenocarcinomas: a review

The past few years have seen flourish new biologic parameters for cancer prognosis that are revolutionizing therapeutic strategies. HER-2 is in this perspective a striking example, as it is now a key element for the care of 15-20% of breast cancer. HER-2 overexpression has first been reported as a prognostic factor before its consideration as a main parameter to predict treatment efficacy. However, although HER-2 status is now also used as a prognostic factor for many cancers, its ability to predict the action of trastuzumab in these new contexts is much lower than in breast cancer. In this literature review, we aimed to discuss HER-2 overexpression as a prognostic factor and as a predictive parameter of treatment response in selected solid tumors with a focus on adenocarcinomas.

"Omics" of HER2-positive breast cancer

HER2/neu amplification/overexpression is the only somatic mutation widely considered to be a marker of disease outcome and response to treatment in breast cancer. Pathologists have made large efforts to achieve accuracy in characterizing HER2/neu status. The introduction of transtuzumab contributed to development of additional measures to identify sensitive and resistant subclasses of HER2/neu-positive tumors. In this article, we describe the latest advances in HER2/neu status diagnostic assessment and the most relevant research emerging from "Omics" (genomics, epigenetics, transcriptomics, and proteomics) studies on HER2/neu-positive breast cancer. A large quantity of biomarkers from different studies highlighted HER2/neu-positive specific proliferation, cell cycle arrest, and apoptosis mechanisms, as well as immunological and metabolic behavior. Major driver genes of tumor progression have had a candidate status (GRB7, MYC, CCND1, EGFR, etc.), even though the main role for HER2/neu is largely recognized. Nonetheless, existing omics data and HER2/neu-positive molecular profiles seem to suggest that few proteogenomic alterations in HER2, EGFR, and PI3K networks could significantly affect the effectiveness of transtuzumab. The systematic search of molecular alterations in and across these pathways can help to select the most appropriate drug for a given patient based on in-depth understanding of complexity in tumor biology.