Identification of potential new treatment response markers and therapeutic targets using a Gaussian process-based method in lapatinib insensitive breast cancer models

Data-driven information extraction and enrichment of molecular profiling data for cancer cell lines

Motivation

With the proliferation of research means and computational methodologies, published biomedical literature is growing exponentially in numbers and volume. Cancer cell lines are frequently used models in biological and medical research that are currently applied for a wide range of purposes, from studies of cellular mechanisms to drug development, which has led to a wealth of related data and publications. Sifting through large quantities of text to gather relevant information on cell lines of interest is tedious and extremely slow when performed by humans. Hence, novel computational information extraction and correlation mechanisms are required to boost meaningful knowledge extraction.

Results

In this work, we present the design, implementation, and application of a novel data extraction and exploration system. This system extracts deep semantic relations between textual entities from scientific literature to enrich existing structured clinical data concerning cancer cell lines. We introduce a new public data exploration portal, which enables automatic linking of genomic copy number variants plots with ranked, related entities such as affected genes. Each relation is accompanied by literature-derived evidences, allowing for deep, yet rapid, literature search, using existing structured data as a springboard.

Availability and implementation

Our system is publicly available on the web at https://cancercelllines.org.

Vitamin D pathway related polymorphisms and vitamin D receptor expression in breast cancer

Vitamin D deficiency is an emerging risk factor for breast cancer suggesting its role in breast cancer pathogenesis. Recent evidence suggests vitamin D receptor (VDR) expression is a prognosis predictor in breast cancer. We set out to determine the status of VDR expression in histologically characterized breast cancers, and whether common genetic variants modify VDR expression in breast cancer. One-hundred and twenty Kuwaiti female breast cancer fixed tissues were assessed for VDR expression to identify the level and location of its expression by immunohistochemistry. VDR variants (rs731236, rs2228570), and vitamin D binding protein (VDBP) variants (rs4588, rs7041) genotypes were ascertained in breast cancer specimens using Taqman genotyping assays. VDR nuclear expression correlated with low grade tumors (p = 0.01), whereas cytoplasmic expression correlated with lymph node positive tumors (p = 0.03). Absence of VDR expression was a marker for high-grade dedifferentiated tumors (p = 0.01). VDBP rs7041 associated with breast cancer risk (OR 1.92, 95% CI: 1.34 - 2.73; p = 0.0004), and VDR rs2228570 correlated with increased VDR cytoplasmic expression (p < 0.0001). In conclusion, VDR expression is altered in breast cancer confirming its involvement in breast cancer progression. Genetic factors appear to play a role in breast cancer risk, and may modify tumor sensitization to vitamin D.

Anti-cancer Drug Response Prediction Using Neighbor-Based Collaborative Filtering with Global Effect Removal

Patients of the same cancer may differ in their responses to a specific medical therapy. Identification of predictive molecular features for drug sensitivity holds the key in the era of precision medicine. Human cell lines have harbored most of the same genetic changes found in patients’ tumors and thus are widely used in the research of drug response. In this work, we formulated drug-response prediction as a recommender system problem and then adopted a neighbor-based collaborative filtering with global effect removal (NCFGER) method to estimate anti-cancer drug responses of cell lines by integrating cell-line similarity networks and drug similarity networks based on the fact that similar cell lines and similar drugs exhibit similar responses. Specifically, we removed the global effect in the available responses and shrunk the similarity score for each cell line pair as well as each drug pair. We then used the K most similar neighbors (hybrid of cell-line-oriented and drug-oriented) in the available responses to predict the unknown ones. Through 10-fold cross-validation, this approach was shown to reach accurate and reproducible outcomes of drug sensitivity. We also discussed the biological outcomes based on the newly predicted response values.