Joint analysis of histopathology image features and gene expression in breast cancer

BACKGROUND Genomics and proteomics are nowadays the dominant techniques for novel biomarker discovery. However, histopathology images contain a wealth of information related to the tumor histology, morphology and tumor-host interactions that is not accessible through these techniques. Thus, integrating the histopathology images in the biomarker discovery workflow could potentially lead to the identification of new image-based biomarkers and the refinement or even replacement of the existing genomic and proteomic signatures. However, extracting meaningful and robust image features to be mined jointly with genomic (and clinical, etc.) data represents a real challenge due to the complexity of the images. RESULTS We developed a framework for integrating the histopathology images in the biomarker discovery workflow based on the bag-of-features approach - a method that has the advantage of being assumption-free and data-driven. The images were reduced to a set of salient patterns and additional measurements of their spatial distribution, with the resulting features being directly used in a standard biomarker discovery application. We demonstrated this framework in a search for prognostic biomarkers in breast cancer which resulted in the identification of several prognostic image features and a promising multimodal (imaging and genomic) prognostic signature. The source code for the image analysis procedures is freely available. CONCLUSIONS The framework proposed allows for a joint analysis of images and gene expression data. Its application to a set of breast cancer cases resulted in image-based and combined (image and genomic) prognostic scores for relapse-free survival

Scoping for RNA biomarkers in colorectal cancer

Colorectal cancer (CRC) is a major health problem worldwide and a significant issue in New Zealand. Treatment for patients with CRC is morbid and costly, involving a combination of surgery, radiotherapy and chemotherapy. Although most patients will benefit from these forms of treatment, a significant proportion will suffer recurrence(s) and eventual death. Despite increased understanding of the molecular events underlying CRC development, established molecular techniques have only produced a limited number of biomarkers suitable for use in routine clinical practice to predict risk, prognosis and response to treatment. Recent rapid technological developments, however, have made genomic sequencing of CRC more economical and efficient, creating the potential to discover genetic biomarkers that have greater diagnostic, prognostic and therapeutic capabilities for the management of CRC. Translating potential gene biomarkers from genome-wide expression studies into clinical utility has typically relied on PCR-based technology and immunohistochemistry. These methods have technical limitations associated with them that are exacerbated by tumour heterogeneity. This makes validation and translation of biomarkers into clinical use difficult. This thesis utilised a novel RNA in-situ hybridisation assay, RNAscope, to investigate the RNA expression of two candidate prognostic gene markers in CRC patients. To circumvent tumour heterogeneity issues, and to improve reproducibility amongst gene expression studies, I adopted a gene selection process using copy number alterations as a criterion. Results showed RNAscope was able to measure the intra-tumoural gene expression of two potential candidate gene markers (GFI1 and TNFRSF11A) in archival formalin-fixed paraffin embedded CRC samples. Reduced gene expression levels was significantly associated with poor prognostic clinicopathological features that was similar to results shown previously by The Cancer Genome Atlas (TCGA) Network. RNAscope has the capability to produce quantitative gene expression levels at a cell-specific level. To test this feature, RNAscope was combined with an image analysis platform (ImageJ) to quantify GFI1 and TNFRSF11A mRNA expression levels. Results showed cell-specific data could be produced allowing cell-type determination of gene expression levels. Compatibility of a variety of image analysis platforms with RNAscope was further investigated with histological and cell monolayer preparations, showing all image analysis platforms were suitable for the RNAscope assay. The limited literature available on the potential candidate gene biomarker, TNFRSF11A, in CRC prompted the investigation of the functional role of TNFRSF11A in an in vitro model. Reduced TNFRSF11A mRNA expression levels were hypothesized to increase proliferation and migration of CRC cells. Transfection of CRC cells with siRNA achieved a reduction in gene expression levels, however, results from the cell based functional assays did not conclusively support the initial hypothesis. An alternative hypothesis is that the results were representative of the molecular subtype for that cell line. Further work will be required to determine the functional role of TNFRSF11A in colorectal tumorigenesis, which may involve replicating the heterogeneous nature of CRC with an array of cell lines representing various molecular subtypes. Results from this thesis demonstrate the utility of RNAscope for assessing potential RNA biomarkers and investigating their role in tumorigenesis. Incorporating RNAscope with image analysis methods provides quantified data which could be clinically useful for setting diagnostic thresholds in companion diagnostics, particularly for the administration of immunotherapies. Furthermore, performing RNAscope on specimens that can be processed through whole slide scanners with or without computational modelling will allow spatio-temporal investigations of RNA within tissue at a single cell level. Such studies will lead to a better understanding of colorectal cancer development to more effectively discover and translate new gene biomarkers into clinical practice