Development of a multivariable risk model integrating urinary cell DNA methylation and cell-free RNA data for the detection of significant prostate cancer

Background: Prostate cancer exhibits severe clinical heterogeneity and there is a critical need for clinically implementable tools able to precisely and noninvasively identify patients that can either be safely removed from treatment pathways or those requiring further follow up. Our objectives were to develop a multivariable risk prediction model through the integration of clinical, urine-derived cell-free messenger RNA (cf-RNA) and urine cell DNA methylation data capable of noninvasively detecting significant prostate cancer in biopsy naïve patients. Methods: Post-digital rectal examination urine samples previously analyzed separately for both cellular methylation and cf-RNA expression within the Movember GAP1 urine biomarker cohort were selected for a fully integrated analysis (n = 207). A robust feature selection framework, based on bootstrap resampling and permutation, was utilized to find the optimal combination of clinical and urinary markers in a random forest model, deemed ExoMeth. Out-of-bag predictions from ExoMeth were used for diagnostic evaluation in men with a clinical suspicion of prostate cancer (PSA ≥ 4 ng/mL, adverse digital rectal examination, age, or lower urinary tract symptoms). Results: As ExoMeth risk score (range, 0-1) increased, the likelihood of high-grade disease being detected on biopsy was significantly greater (odds ratio = 2.04 per 0.1 ExoMeth increase, 95% confidence interval [CI]: 1.78-2.35). On an initial TRUS biopsy, ExoMeth accurately predicted the presence of Gleason score ≥3 + 4, area under the receiver-operator characteristic curve (AUC) = 0.89 (95% CI: 0.84-0.93) and was additionally capable of detecting any cancer on biopsy, AUC = 0.91 (95% CI: 0.87-0.95). Application of ExoMeth provided a net benefit over current standards of care and has the potential to reduce unnecessary biopsies by 66% when a risk threshold of 0.25 is accepted. Conclusion: Integration of urinary biomarkers across multiple assay methods has greater diagnostic ability than either method in isolation, providing superior predictive ability of biopsy outcomes. ExoMeth represents a more holistic view of urinary biomarkers and has the potential to result in substantial changes to how patients suspected of harboring prostate cancer are diagnosed

Machine Learning Approaches for Cancer Analysis

In addition, we propose many machine learning models that serve as contributions to solve a biological problem. First, we present Zseq, a linear time method that identifies the most informative genomic sequences and reduces the number of biased sequences, sequence duplications, and ambiguous nucleotides. Zseq finds the complexity of the sequences by counting the number of unique k-mers in each sequence as its corresponding score and also takes into the account other factors, such as ambiguous nucleotides or high GC-content percentage in k-mers. Based on a z-score threshold, Zseq sweeps through the sequences again and filters those with a z-score less than the user-defined threshold. Zseq is able to provide a better mapping rate; it reduces the number of ambiguous bases significantly in comparison with other methods. Evaluation of the filtered reads has been conducted by aligning the reads and assembling the transcripts using the reference genome as well as de novo assembly. The assembled transcripts show a better discriminative ability to separate cancer and normal samples in comparison with another state-of-the-art method. Studying the abundance of select mRNA species throughout prostate cancer progression may provide some insight into the molecular mechanisms that advance the disease. In the second contribution of this dissertation, we reveal that the combination of proper clustering, distance function and Index validation for clusters are suitable in identifying outlier transcripts, which show different trending than the majority of the transcripts, the trending of the transcript is the abundance throughout different stages of prostate cancer. We compare this model with standard hierarchical time-series clustering method based on Euclidean distance. Using time-series profile hierarchical clustering methods, we identified stage-specific mRNA species termed outlier transcripts that exhibit unique trending patterns as compared to most other transcripts during disease progression. This method is able to identify those outliers rather than finding patterns among the trending transcripts compared to the hierarchical clustering method based on Euclidean distance. A wet-lab experiment on a biomarker (CAM2G gene) confirmed the result of the computational model. Genes related to these outlier transcripts were found to be strongly associated with cancer, and in particular, prostate cancer. Further investigation of these outlier transcripts in prostate cancer may identify them as potential stage-specific biomarkers that can predict the progression of the disease. Breast cancer, on the other hand, is a widespread type of cancer in females and accounts for a lot of cancer cases and deaths in the world. Identifying the subtype of breast cancer plays a crucial role in selecting the best treatment. In the third contribution, we propose an optimized hierarchical classification model that is used to predict the breast cancer subtype. Suitable filter feature selection methods and new hybrid feature selection methods are utilized to find discriminative genes. Our proposed model achieves 100% accuracy for predicting the breast cancer subtypes using the same or even fewer genes. Studying breast cancer survivability among different patients who received various treatments may help understand the relationship between the survivability and treatment therapy based on gene expression. In the fourth contribution, we have built a classifier system that predicts whether a given breast cancer patient who underwent some form of treatment, which is either hormone therapy, radiotherapy, or surgery will survive beyond five years after the treatment therapy. Our classifier is a tree-based hierarchical approach that partitions breast cancer patients based on survivability classes; each node in the tree is associated with a treatment therapy and finds a predictive subset of genes that can best predict whether a given patient will survive after that particular treatment. We applied our tree-based method to a gene expression dataset that consists of 347 treated breast cancer patients and identified potential biomarker subsets with prediction accuracies ranging from 80.9% to 100%. We have further investigated the roles of many biomarkers through the literature. Studying gene expression through various time intervals of breast cancer survival may provide insights into the recovery of the patients. Discovery of gene indicators can be a crucial step in predicting survivability and handling of breast cancer patients. In the fifth contribution, we propose a hierarchical clustering method to separate dissimilar groups of genes in time-series data as outliers. These isolated outliers, genes that trend differently from other genes, can serve as potential biomarkers of breast cancer survivability. In the last contribution, we introduce a method that uses machine learning techniques to identify transcripts that correlate with prostate cancer development and progression. We have isolated transcripts that have the potential to serve as prognostic indicators and may have significant value in guiding treatment decisions. Our study also supports PTGFR, NREP, scaRNA22, DOCK9, FLVCR2, IK2F3, USP13, and CLASP1 as potential biomarkers to predict prostate cancer progression, especially between stage II and subsequent stages of the disease

Identification of potential tissue-specific cancer biomarkers and development of cancer versus normal genomic classifiers

Machine learning techniques for cancer prediction and biomarker discovery can hasten cancer detection and significantly improve prognosis. Recent “OMICS” studies which include a variety of cancer and normal tissue samples along with machine learning approaches have the potential to further accelerate such discovery. To demonstrate this potential, 2,175 gene expression samples from nine tissue types were obtained to identify gene sets whose expression is characteristic of each cancer class. Using random forests classification and ten-fold cross-validation, we developed nine single-tissue classifiers, two multi-tissue cancer-versus-normal classifiers, and one multi-tissue normal classifier. Given a sample of a specified tissue type, the single-tissue models classified samples as cancer or normal with a testing accuracy between 85.29% and 100%. Given a sample of non-specific tissue type, the multitissue bi-class model classified the sample as cancer versus normal with a testing accuracy of 97.89%. Given a sample of non-specific tissue type, the multi-tissue multiclass model classified the sample as cancer versus normal and as a specific tissue type with a testing accuracy of 97.43%. Given a normal sample of any of the nine tissue types, the multi-tissue normal model classified the sample as a particular tissue type with a testing accuracy of 97.35%. The machine learning classifiers developed in this study identify potential cancer biomarkers with sensitivity and specificity that exceed those of existing biomarkers and pointed to pathways that are critical to tissuespecific tumor development. This study demonstrates the feasibility of predicting the tissue origin of carcinoma in the context of multiple cancer classes

Histopathological image analysis : a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe

Absolute quantitation of DNA methylation of 28 candidate genes in prostate cancer using pyrosequencing

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Aberrant DNA methylation plays a pivotal role in carcinogenesis and its mapping is likely to provide biomarkers for improved diagnostic and risk assessment in prostate cancer (PCa). We quantified and compared absolute methylation levels among 28 candidate genes in 48 PCa and 29 benign prostate hyperplasia (BPH) samples using the pyrosequencing (PSQ) method to identify genes with diagnostic and prognostic potential. RARB, HIN1, BCL2, GSTP1, CCND2, EGFR5, APC, RASSF1A, MDR1, NKX2-5, CDH13, DPYS, PTGS2, EDNRB, MAL, PDLIM4, HLAa, ESR1 and TIG1 were highly methylated in PCa compared to BPH (p < 0.001), while SERPINB5, CDH1, TWIST1, DAPK1, THRB, MCAM, SLIT2, CDKN2a and SFN were not. RARB methylation above 21% completely distinguished PCa from BPH. Separation based on methylation level of SFN, SLIT2 and SERPINB5 distinguished low and high Gleason score cancers, e.g. SFN and SERPINB5 together correctly classified 81% and 77% of high and low Gleason score cancers respectively. Several genes including CDH1 previously reported as methylation markers in PCa were not confirmed in our study. Increasing age was positively associated with gene methylation (p < 0.0001). Accurate quantitative measurement of gene methylation in PCa appears promising and further validation of genes like RARB, HIN1, BCL2, APC and GSTP1 is warranted for diagnostic potential and SFN, SLIT2 and SERPINB5 for prognostic potential

Deep Domain Adaptation Learning Framework for Associating Image Features to Tumour Gene Profile

While medical imaging and general pathology are routine in cancer diagnosis, genetic sequencing is not always assessable due to the strong phenotypic and genetic heterogeneity of human cancers. Image-genomics integrates medical imaging and genetics to provide a complementary approach to optimise cancer diagnosis by associating tumour imaging traits with clinical data and has demonstrated its potential in identifying imaging surrogates for tumour biomarkers. However, existing image-genomics research has focused on quantifying tumour visual traits according to human understanding, which may not be optimal across different cancer types. The challenge hence lies in the extraction of optimised imaging representations in an objective data-driven manner. Such an approach requires large volumes of annotated image data that are difficult to acquire. We propose a deep domain adaptation learning framework for associating image features to tumour genetic information, exploiting the ability of domain adaptation technique to learn relevant image features from close knowledge domains. Our proposed framework leverages the current state-of-the-art in image object recognition to provide image features to encode subtle variations of tumour phenotypic characteristics with domain adaptation techniques. The proposed framework was evaluated with current state-of-the-art in: (i) tumour histopathology image classification and; (ii) image-genomics associations. The proposed framework demonstrated improved accuracy of tumour classification, as well as providing additional data-derived representations of tumour phenotypic characteristics that exhibit strong image-genomics association. This thesis advances and indicates the potential of image-genomics research to reveal additional imaging surrogates to genetic biomarkers, which has the potential to facilitate cancer diagnosis