Multi-gene expression-based statistical approaches to predicting patients' clinical outcomes and responses

Intra- and interspecies gene expression models for predicting drug response in canine osteosarcoma

Background

Genomics-based predictors of drug response have the potential to improve outcomes associated with cancer therapy. Osteosarcoma (OS), the most common primary bone cancer in dogs, is commonly treated with adjuvant doxorubicin or carboplatin following amputation of the affected limb. We evaluated the use of gene-expression based models built in an intra- or interspecies manner to predict chemosensitivity and treatment outcome in canine OS. Models were built and evaluated using microarray gene expression and drug sensitivity data from human and canine cancer cell lines, and canine OS tumor datasets. The “COXEN” method was utilized to filter gene signatures between human and dog datasets based on strong co-expression patterns. Models were built using linear discriminant analysis via the misclassification penalized posterior algorithm.

Results

The best doxorubicin model involved genes identified in human lines that were co-expressed and trained on canine OS tumor data, which accurately predicted clinical outcome in 73 % of dogs (p = 0.0262, binomial). The best carboplatin model utilized canine lines for gene identification and model training, with canine OS tumor data for co-expression. Dogs whose treatment matched our predictions had significantly better clinical outcomes than those that didn’t (p = 0.0006, Log Rank), and this predictor significantly associated with longer disease free intervals in a Cox multivariate analysis (hazard ratio = 0.3102, p = 0.0124).

Conclusions

Our data show that intra- and interspecies gene expression models can successfully predict response in canine OS, which may improve outcome in dogs and serve as pre-clinical validation for similar methods in human cancer research.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-0942-8) contains supplementary material, which is available to authorized users.

Personalized medicine in breast cancer: a systematic review

The recent advent of "-omics" technologies have heralded a new era of personalized medicine. Personalized medicine is referred to as the ability to segment heterogeneous subsets of patients whose response to a therapeutic intervention within each subset is homogeneous. This new paradigm in healthcare is beginning to affect both research and clinical practice. The key to success in personalized medicine is to uncover molecular biomarkers that drive individual variability in clinical outcomes or drug responses. In this review, we begin with an overview of personalized medicine in breast cancer and illustrate the most encountered statistical approaches in the recent literature tailored for uncovering gene signatures.

Development of commercial gene-expression-based signatures: review of the scientific strategies

Many scientific articles have been published that use gene-expression-based technologies to discriminate a trait of interest, typically a disease subgroup, within a patient population. However, few gene-expression-based signatures have at present reached the market and become a financially and clinically successful product. The technological, scientific and medical challenges, the regulatory environment and the financial considerations are all essential parts of the development process. Here we discuss the scientific aspects of successfully developing a gene-expression-based signature and review the global strategy of six products that made it to the market. We also present a point-to-point guide that should help researchers to successfully develop genomic signatures, thus paving the way towards personalized medicine.