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Background Bladder and kidney cancer are among the ten most commonly diagnosed cancers in the UK every year. Delayed diagnosis is associated with poorer survival and patient reported outcomes. Bladder and kidney cancers pose diagnostic challenges as presenting symptoms such as haematuria and lower urinary tract symptoms are common, and can be due to benign causes. Women with bladder cancer in particular, are more likely to experience diagnostic delay and worse survival than men. Kidney cancer is expected to be among the cancers with the fastest increasing incidence over the next 20 years. It is therefore imperative to understand how and why delays, and sub-quality care occurs during the diagnostic process to improve outcomes. Prior work has indicated that missed diagnostic opportunities (MDOs) exist for a range of diseases, including cancer, and may contribute to diagnostic delay. How often and why MDOs might occur in bladder and kidney cancer is unknown. My PhD seeks to address this evidence gap. Aim In this thesis, I combined frameworks from the early cancer diagnosis (the Pathways to Treatment model) and diagnostic research (the SaferDx model) fields to underpin the design, analysis and interpretation of five core studies (a systematic review, three quantitative studies and one mixed-methods study) with the aim to better understand diagnostic delay and MDOs in bladder and kidney cancer. Methods First, I performed a systematic review to examine the factors associated with the quality of the diagnostic process in patients with bladder and kidney cancer. This review highlighted evidence gaps in the definition of timeliness, the quality of assessment of lower risk urological symptoms (other than haematuria), and the clinical and system factors that might contribute to suboptimal quality of the diagnostic process. Next , I performed three quantitative studies (1 to 3) using linked primary care, secondary care imaging data and cancer registry data with 5,322 patients diagnosed with bladder and kidney cancer between January 2012 to December 2015. In Studies 1 and 2, I examined the potential 'diagnostic window' of bladder and kidney cancer following relevant blood or imaging tests, using a novel form of Poisson regression modelling extending Joinpoint regression analysis advised by a medical statistician. Next, I examined for signals of MDOs in bladder and kidney cancer patients who met the 2005 National Institute for Health and Care Excellence (NICE) guidelines for fast-track referral. I operationalised four NICE-qualifying presentation scenarios (visible haematuria, non-visible haematuria, recurrent UTIs and abdominal mass), and examined the predictors of a prolonged interval from qualifying for NICE-referral to cancer diagnosis (NICE-DI) using multivariable logistic regression (Study 3). Lastly, I performed a prospective, mixed-methods study (Study 4) in 940 symptomatic patients from 9 general practices in East of England. Study 4 consisted of a case note review (n=940) and qualitative patient interviews (n=15). I performed descriptive statistics, and logistic regression to examine predictors of having a referral. I then analysed the qualitative interviews using thematic analysis and used mixed-methods synthesis to provide an overarching understanding of the factors contributing to MDOs. Results The diagnostic window of bladder and kidney cancer was about 6-8 months following an abnormal blood test, or a relevant imaging test. This window represents the time frame during which a potential cancer diagnosis may be achieved, and that more timely diagnosis is possible in at least some patients. My subsequent findings demonstrated that the following aspects of the diagnostic process are likely to harbour MDOs: i. Test delays, highlighting the need to improve access and reduce time-to-testing interval : clinical and disease factors were the most likely to contribute to pre-analytical test delays, including having kidney cancer (due to presentation with less specific symptoms) compared to bladder cancer, having stages other than 4 at diagnosis, presenting with UTI symptoms and not having haematuria pre-diagnosis. Post-analytical test delays might be due to the lack of or poor communication of test results, resulting in the failure to close a diagnostic loop. ii. Variations in time to referral and diagnosis leading to inequality in diagnostic timeliness in patient subgroups: Women, and patients with recurrent UTIs were particularly at risk of no referrals, and prolonged primary care interval, than men and patients with other NICE-qualifying clinical presentations (any type of haematuria or abdominal mass). Patients with lower risk urological symptoms (non-visible haematuria and recurrent UTIs) had 1.5 and 3 times higher odds respectively of experiencing diagnostic delay, compared to patients with visible haematuria. The mechanisms for MDOs involve process breakdowns during the initial patient-doctor consultation, and the follow-up of patients which might contribute to diagnostic delays and MDOs. These include inadequate history taking and examination, clinicians assuming urological symptoms to be UTIs, tendency for GPs to treat recurrent and persistent UTIs without reviews, lack of communication of test results and suboptimal follow-up activities due to results being given by receptionists or not at all. Conclusions My thesis demonstrates that MDOs in primary care exist in the evaluation and diagnostic process of bladder and kidney cancer. In particular, women, and patients with recurrent UTIs are the most at risk of experiencing suboptimal care in the primary care diagnostic process. MDOs secondary to process breakdowns during initial diagnostic assessment, and the follow-up of tests, most likely contribute to the observed diagnostic delays. System factors such as rigid consultation norms may influence these process breakdowns. Targeting patients groups with non-alarm urological symptoms, and improving system and clinician related factors such as access to and availability of tests and clinician willingness to test may reduce MDOs in patients with bladder and kidney cancer.

The incidence of renal cell carcinoma (RCC) and small renal masses (SRMs), defined as < 4cm in diameter, is increasing dramatically. SRMs encompass a variety of potential diagnoses, including benign and malignant tumours, the most common of which is clear cell RCC (ccRCC). Current methods are unable to confidently distinguish pathological subtypes of SRMs, meaning patients with benign tumours are undergoing unnecessary invasive surgery. In addition, there are difficulties risk stratifying patients with ccRCC and predicting outcomes. Genomic alterations, such as mutational analysis, may have a role in RCC diagnosis and prognostication, but are unlikely to be sufficient alone due to low recurrence rates and significant intra-tumoral heterogeneity (ITH), which limit detection. Changes in DNA methylation are abundant and often early events in tumorigenesis, which may overcome these challenges as potential tumour markers both in tissue and liquid biopsies. To address the aforementioned diagnostic challenge, I characterised DNA methylation and gene expression in tissue from patients with benign and malignant renal tumours to elucidate similarities and differences between tumour subtypes. Subsequently, DNA methylation data were combined on over 1200 tissue samples and these were used to train and test MethylBoostER (Methylation and XGBoost for Evaluation of Renal tumours), a machine learning model to predict common pathological subtypes of renal tumours. MethylBoostER was externally validated on four independent publicly available datasets (N=518), demonstrating a high accuracy (receiver operating characteristic area under the curve; AUC >0.90). MethylBoostER predicted consistent classification of multi-region samples from the same patient in 90% of individuals, suggesting ITH does not limit model applicability in a biopsy setting. Subsequently, I undertook a systematic evaluation of methylation heterogeneity in ccRCC, exploring associations with clinical/prognostic parameters and highlighting implications for biomarker selection. I evaluated multi-region tissue samples (N=135) from ccRCC patients (N=18) and assessed heterogeneity between patients, within a patient and within a sample. Inter-patient heterogeneity dominated over intra-tumoural heterogeneity. My analysis represents the first evaluation of epipolymorphism, a measure of methylation heterogeneity within a sample, in ccRCC. Significant differential epipolymorphism was noted in ccRCC versus normal kidney at the promoter region of genes known to be implicated in kidney cancer and this finding was externally validated in an independent cohort (N=71). Although changes in epipolymorphism are believed to be a stochastic process, my results suggest that disordered methylation may accumulate in functionally relevant loci which are known to contribute to ccRCC tumorigenesis. Circulating tumour DNA (ctDNA) represents a promising target for non-invasive liquid biopsy in both diagnostic and prognostic applications. Mutational analyses of ctDNA have produced disappointing detection rates in ccRCC, possibly hampered by low ctDNA levels and high mutational ITH. I therefore performed targeted methylation analysis of ctDNA using a novel method- Nimbus (Non-destructive Integration of Methylation to Boost Underlying Signals). Targeted analysis of hypomethylated regions in plasma ctDNA distinguished ccRCC from cancer-free controls with an AUC of 0.96 and produced superior detection rates compared to mutational analysis (93% vs 50%). My results suggest that tumour signal may be enriched in post-biopsy fluid (proximal sample) compared to plasma (distal sample), a strategy that could be useful in patients with SRMs to complement the current diagnostic pathway and overcome low concentrations of plasma ctDNA. In summary, I comprehensively characterise DNA methylation in tissue and liquid samples derived from patients with renal tumours. In the future, DNA methylation analysis of renal tumour biopsy tissue and/or liquid biopsy samples could enable improved diagnosis of patients with SRMs as well as facilitating prognostic stratification.

The cornerstone of treatment for metastatic clear cell renal cell carcinoma (ccRCC) are anti-VEGF and/or immune checkpoint inhibitors (CPI). However, there are no clinically implemented predictive biomarkers in any therapeutic settings. Intratumour heterogeneity (ITH) is a pervasive phenomenon of ccRCC. Studies to date examining putative biomarkers have used single-region biopsies and therefore not accounted for ITH. Moreover, genomic and tumour immune microenvironment (TME) changes under therapy have not been examined, which could inform potential mechanisms of anti-VEGF/CPI synergy and renal dynamic correlates of therapy response and resistance. I examined the determinants of anti-PD-1 monotherapy response in multiregion tumour samples collected pre- and post-treatment within a prospective phase II clinical trial. I discovered maintenance of previously expanded T cell clones underpin nivolumab response. Within another prospective clinical study which examined effects of axitinib in patients with metastatic ccRCC, I show preliminary data from driver mutation and gene expression profiling of longitudinal, multiregion tumour samples. Separately, using a tumour-informed, phylogenetic approach to profile circulating tumour DNA, I showed clonal phylogeny of ccRCC can be recapitulated by peripheral sampling as a potential means of circumnavigating the shortcomings of tissue-based biopsies. From March 2020, the SARS-CoV-2 pandemic halted routine laboratory operations for 18 months. During this time and as an emergency response, my doctoral studies pivoted towards uncovering the immunological basis of poor outcomes to SARS-CoV-2 infection and functional immunity against SARS-CoV-2 variants of concern (VOC) and following COVID-19 vaccination in cancer patients. I report the rapid establishment of a prospective, pan-cancer study (CAPTURE) for longitudinal immune profiling of cancer patients in the context of SARS-CoV-2 and COVID-19 vaccines. CAPTURE provided a large dataset (n=784 patients enrolled to date), within which I describe a case of cytokine release syndrome following BNT162b2 vaccination in a patient under anti-PD-1 therapy; and show patients with hematological malignancies had impaired neutralising antibody responses against VOC that was disease lineage and anti-CD20 treatment-specific, in the setting of both SARS-CoV-2 infection (n=118) and COVID-19 vaccines (n=585).

Lineage-specific transcription factors have emerged as a promising class of essential genes in cancer. The best examples of leveraging this phenomenon in the clinic is targeting the androgen receptor and the oestrogen receptor in prostate and breast cancer respectively. Despite the success of these therapies, the mechanisms that maintain lineage fidelity in advanced cancer clones, and whether lineage factor pathways could be exploited in other cancer types remain poorly understood. Using clear cell renal cell carcinoma (ccRCC) as a model, I characterise mechanisms that underlie lineage factor dependence in cancer. Using CRISPR/Cas9 loss-of-function screening coupled with in vitro and in vivo validation I show that the loss of SMARCB1, a member of the SWI/SNF chromatin remodelling complex, confers an advantage to ccRCC cells upon inhibition of the essential renal lineage factor PAX8. SMARCB1 knockout (KO) leads to large-scale loss of a kidney-specific enhancer program, conversion to a cellular state resembling that of rhabdoid tumours, and the re-activation of proliferative pathways. Using a second CRISPR/Cas9 screen, I show that these proliferative pathways are underpinned by the acquisition of new transcriptional dependencies. These dependencies represent rare essential genes across different lineage-specific and oncogenic pathways, a principle validated in a large-scale CRISPR/Cas9 screening data set comprising hundreds of cancer cell lines. In summary, dependence on tissue-specific lineage factors in cancer can be modulated via epigenetic remodelling.

Fourier transform infrared spectroscopy (FTIR) and microscopy (FT-IRMS) are well-established as tools for the study of biological cells. Studies of chemically-fixed cells using infrared (IR) have been able to yield significant information regarding cell morphology, disease development and drug-cell interactions. However, such studies are limited by fixation effects which introduce a range of spectral variations, limiting the interpretation of IR spectra of fixed cells. It is therefore desirable to study living cells in real time, particularly as cells are undergoing response to an external stimulus. This does, however, present a number of challenges. Maintaining live cells requires an aqueous sample environment. Water is, however, a very strong absorber of IR light, producing a spectrum which dwarfs the biochemical signals in the sample and makes the extraction of good-quality spectra with high signal-to-noise (S/N) extremely challenging. The brilliance of synchrotron radiation (SR), combined with development of an in-house water correction procedure performing a least-squares fit with a (Matrigel) reference spectrum, has been able to generate high-quality spectra while retaining a sufficient level of spectral detail to analyse relative subtle biochemical changes. Wildtype and drug-resistant strains of renal cell carcinoma (RCC) have been treated with different chemotherapy agents and formalin-fixed. Analysis of fixed, dried samples using both single-point SR IR microscopy and bench-top focal plane array (FPA) imaging has identified spectral features linked to efflux or similar drug-resistance mechanisms, opening the possibility of being able to identify drug resistance in the early stages of treatment. These results were able to be replicated using fixed cells suspended in phosphate-buffered saline (PBS), providing validation of the water correction algorithm. Two live cell studies were performed in a static aqueous sample holder. K562 acute myeloid leukaemia (AML) cells were treated with two novel chemotherapy agents, and differences were identified in the resulting spectra that could be directly linked to the mode of action of the two compounds. This analysis made use of DNA bands that are only accessible when measuring hydrated cells. The second study used a wildtype and drug-resistant strain of PEO1 ovarian cancer cells, and analysed their response to a novel agent, showing differences in the measured spectra between wildtype and resistance samples. Static-mode live cell studies have been able to provide significant information, but are limited by the survival time of the samples. A customised commercial liquid sample holder has undergone simple and cost-effective modifications to produce a dynamic flow system for live cell measurements, potentially up to and beyond 24 hours. This includes the novel implementation of a hydrophobic barrier to direct and control flow. Trypan Blue staining has confirmed cell viability up to at least 24 hours, and two biological studies have been performed to demonstrate different properties of the system. Measuring samples at different temperatures has proved that the system is capable of inducing and simultaneously measuring cellular changes, while a study of deuterated palmitic acid uptake has shown that a sample can be maintained for long enough to acquire spectra of a large number of cells.

Aim: To investigates the molecular changes that occur in response to VEGFr TKI therapy to better understand the acquired resistance process. A further goal is to investigate the potential of SRC inhibitors to slow or prevent VEGFr TKI resistance. Methods: Work was conducted in in vitro assays (MTS assays, scratch and transwell migration assays), a preclinical in vivo model of resistance (786-O xenografts) and IHC was conducted in sequential RCC patient tissue taken before and after 12-16 weeks of VEGFr TKI therapy. Findings: 100% (n=15) of 786-O xenografts developed a resistant phenotype when continually exposed to VEGFr-TKI treatment. PCR with species-specific probes showed VEGFr-TKI induced significant up-regulation of several pro-angiogenic factors in both the tumour and host compartments. These factors included VEGF ligand, FGF-2, HGF, and MET receptor. In addition, genes associated with epithelial mesenchymal transition were up-regulated in treated xenografts. Interestingly, the pro-angiogenic factor PGF and the pro-metastatic gene s100a4 were up-regulated with time, independently of treatment. Gene pathway analysis suggested VEGFr-TKI treatment induced a process resembling fibrosis or wound healing. Furthermore, collagen was increased in treated xenografts. IHC in RCC patient tissue verified that some of the above pathways were affected in the clinical setting. VEGFr-TKI treatment caused a significant reduction in vessel density (CD31), and up-regulation of FGF-2 ligand and vessel-bound MET receptor. Collagen was also increased in VEGFr-TKI treated clinical samples. In vitro assays demonstrated that VHL gene mutation promoted resistance to SRC TKIs. Adding a SRC TKI to VEGFr-TKI therapy had a synergistic anti-tumour effect on 786-O xenografts. However, the combination could not prevent growth in tumours that had acquired a VEGFr TKI resistant phenotype. There was no evidence that the addition of a SRC TKI affected genes implicated in the resistance process. Interpretation: VEGFr-TKI treatment is associated with dynamic molecular changes to several relevant biomarkers. Targeting any one pathway in isolation may have an incremental anti-tumour effect, but because multiple pathways are affected, it is perhaps unlikely to result in a sustained improvement in tumour response. Heterogeneity of protein expression adds further complication to a targeted approach. Collagen deposition increases with VEGFr TKI therapy. Collagen has been shown to promote angiogenesis and metastasis. Further investigation is warranted to understand whether the addition of anti-fibrotic agents to anti-angiogenic therapy could have an incremental benefit on patient outcome.

MicroRNAs (miRNAs) are short, non-coding RNA sequences which regulate gene expression. Regulation is mediated primarily through binding to complementary sites in their un-translated regions which leads to mRNA degradation or translational repression. Hypoxia is a known feature of many tumours, and increased hypoxia is associated with poor prognosis. Hypoxia leads to the up-regulation of many genes involved in a variety of functions including angiogenesis, a shift to glycolytic metabolism, and cell proliferation. This is mediated by the heterodimeric transcription factor HIF (hypoxia inducible factor), which is stabilised in hypoxia. In normoxia, the von-Hippel Lindau protein (VHL) targets HIF for degradation. Mutation in the VHL gene, as is frequently seen in clear cell renal cell cancer (CCRCC), results in constitutive over-expression of HIF and its gene targets, leading to a pro-angiogenic and pro-tumourigenic state. This thesis examined the expression of hypoxia-regulated miRNAs in cancer. The principal aims were to determine gene targets of miR-210, and to explore the effects of its over-expression and knock-down, both in vitro and in vivo. The expression of hypoxia-regulated miRNAs was examined in clinical renal tumour samples with matched normal tissue controls, and correlated with VHL mutation status. It was found that miR-210 targeted the iron sulphur cluster homologue (ISCU) gene, and was responsible for much of its down-regulation in hypoxia. Knock-down of ISCU had consequences on cell metabolism, in particular involving mitochondrial function and iron metabolism. miR-210 was found to be highly over-expressed in clear cell renal tumours (CCRCC), with greater expression seen in tumours with VHL mutations. miR-210 over-expression was also observed in papillary renal tumours, but to a lesser extent than in CCRCCs. miR-210 expression appeared to be correlated with reduced stage and grade, and improved survival. ISCU protein expression in CCRCCs was determined by immunohistochemistry, which showed that its expression correlated negatively with miR-210 expression, suggesting a functional role of miR-210 in vivo.

Hypoxia regulates many hundreds of genes with important roles in ischemic and neoplastic disorders. Central to this response are the hypoxia inducible transcription factors (HIF). This work aimed to better understand the direct transcriptional response to HIF by mapping HIF-binding sites across the genome using chromatin immunoprecipitation coupled to high-throughput sequencing (ChIP-seq). ChIP-seq for HIF in MCF-7 breast cancer cells under hypoxic conditions revealed more than 400 high-stringency HIF-binding sites genome-wide. Each member of the HIF heterodimer was present with near complete concordance. Binding of the two principle isoforms revealed a high degree of overlap with no differences in the DNA-binding motif. HIF-binding was associated with upregulation, but not downregulation of genes indicating that it functions as a transcriptional activator but not as a repressor. HIF-binding occurred preferentially at gene promoters, but was also present at promoter-distant sites, which were also associated with gene regulation, implicating long-range interactions in hypoxic gene activation. HIF-binding was associated with markers of open chromatin and active enhancers that were present in normoxia, indicating that HIF-binding sites are already “prepared” to bind HIF before the hypoxic stimulus. Analysis of normoxic and hypoxic RNA pol2 and H3K4me3 signals revealed distinctive hypoxia-inducible changes unique to HIF-binding genes. Comparable numbers of HIF-binding sites were observed in a second cell line (von Hippel-Lindau defective 786-O renal cancer cells) as in MCF-7 breast cancer cells, although approximately 65% were unique to 786-O cells. These unique sites were more frequently promoter-distant. Correlation with expression analyses from renal tumours indicated that many HIF-binding genes were upregulated in renal cancer. One such RCC unique promoter-distant HIF-binding site was identified at an intergenic locus on chromosome 11q13.3 that has been associated with renal cancer in Genome-Wide Association Studies. The HIF-binding site was in high linkage disequilibrium with the disease associated SNP and had the epigenetic hallmarks of an enhancer. Analysis of pan-genomic expression analyses identified the cell-cycle regulator cyclin D1 as highly HIF-regulated, and a physical association between the HIF-binding site and the CCND1 promoter could be determined. Furthermore, in a renal cancer cell line heterozygous at this locus, the RCC-protective allele disrupted HIF-binding leading to an allelic imbalance in cyclin D1 expression.

Machine learning (ML) and deep learning (DL) approaches have been used as indispensable tools in modern artificial intelligence-based computer-aided diagnostic (AIbased CAD) systems that can provide non-invasive, early, and accurate diagnosis of a given medical condition. These AI-based CAD systems have proven themselves to be reproducible and have the generalization ability to diagnose new unseen cases with several diseases and medical conditions in different organs (e.g., kidneys, prostate, brain, liver, lung, breast, and bladder). In this dissertation, we will focus on the role of such AI-based CAD systems in early diagnosis of two kidney diseases, namely: acute rejection (AR) post kidney transplantation and renal cancer (RC). A new renal computer-assisted diagnostic (Renal-CAD) system was developed to precisely diagnose AR post kidney transplantation at an early stage. The developed Renal-CAD system perform the following main steps: (1) auto-segmentation of the renal allograft from surrounding tissues from diffusion weighted magnetic resonance imaging (DW-MRI) and blood oxygen level-dependent MRI (BOLD-MRI), (2) extraction of image markers, namely: voxel-wise apparent diffusion coefficients (ADCs) are calculated from DW-MRI scans at 11 different low and high b-values and then represented as cumulative distribution functions (CDFs) and extraction of the transverse relaxation rate (R2*) values from the segmented kidneys using BOLD-MRI scans at different echotimes, (3) integration of multimodal image markers with the associated clinical biomarkers, serum creatinine (SCr) and creatinine clearance (CrCl), and (4) diagnosing renal allograft status as nonrejection (NR) or AR by utilizing these integrated biomarkers and the developed deep learning classification model built on stacked auto-encoders (SAEs). Using a leaveone- subject-out cross-validation approach along with SAEs on a total of 30 patients with transplanted kidney (AR = 10 and NR = 20), the Renal-CAD system demonstrated 93.3% accuracy, 90.0% sensitivity, and 95.0% specificity in differentiating AR from NR. Robustness of the Renal-CAD system was also confirmed by the area under the curve value of 0.92. Using a stratified 10-fold cross-validation approach, the Renal-CAD system demonstrated its reproduciblity and robustness with a diagnostic accuracy of 86.7%, sensitivity of 80.0%, specificity of 90.0%, and AUC of 0.88. In addition, a new renal cancer CAD (RC-CAD) system for precise diagnosis of RC at an early stage was developed, which incorporates the following main steps: (1) estimating the morphological features by applying a new parametric spherical harmonic technique, (2) extracting appearance-based features, namely: first order textural features are calculated and second order textural features are extracted after constructing the graylevel co-occurrence matrix (GLCM), (3) estimating the functional features by constructing wash-in/wash-out slopes to quantify the enhancement variations across different contrast enhanced computed tomography (CE-CT) phases, (4) integrating all the aforementioned features and modeling a two-stage multilayer perceptron artificial neural network (MLPANN) classifier to classify the renal tumor as benign or malignant and identify the malignancy subtype. On a total of 140 RC patients (malignant = 70 patients (ccRCC = 40 and nccRCC = 30) and benign angiomyolipoma tumors = 70), the developed RC-CAD system was validated using a leave-one-subject-out cross-validation approach. The developed RC-CAD system achieved a sensitivity of 95.3% ± 2.0%, a specificity of 99.9% ± 0.4%, and Dice similarity coefficient of 0.98 ± 0.01 in differentiating malignant from benign renal tumors, as well as an overall accuracy of 89.6% ± 5.0% in the sub-typing of RCC. The diagnostic abilities of the developed RC-CAD system were further validated using a randomly stratified 10-fold cross-validation approach. The results obtained using the proposed MLP-ANN classification model outperformed other machine learning classifiers (e.g., support vector machine, random forests, and relational functional gradient boosting) as well as other different approaches from the literature. In summary, machine and deep learning approaches have shown potential abilities to be utilized to build AI-based CAD systems. This is evidenced by the promising diagnostic performance obtained by both Renal-CAD and RC-CAD systems. For the Renal- CAD, the integration of functional markers extracted from multimodal MRIs with clinical biomarkers using SAEs classification model, potentially improved the final diagnostic results evidenced by high accuracy, sensitivity, and specificity. The developed Renal-CAD demonstrated high feasibility and efficacy for early, accurate, and non-invasive identification of AR. For the RC-CAD, integrating morphological, textural, and functional features extracted from CE-CT images using a MLP-ANN classification model eventually enhanced the final results in terms of accuracy, sensitivity, and specificity, making the proposed RC-CAD a reliable noninvasive diagnostic tool for RC. The early and accurate diagnosis of AR or RC will help physicians to provide early intervention with the appropriate treatment plan to prolong the life span of the diseased kidney, increase the survival chance of the patient, and thus improve the healthcare outcome in the U.S. and worldwide.

Precision Medicine seeks to tailor treatments to the individual and thus aims to find the “right drug,for the right person, in the right dose”. This dissertation deals with computational approaches fordrug discovery and repurposing that combine cheminformatics and predictive omics signatures forapplications of precision medicine in brain disorders and renal cancer. Brain disorders and renalcancer represent two disease classes that are in dire need of new drugs and further advances inpersonalized medicine treatments. Brain disorders such as Alzheimer disease, schizophrenia, orParkinson disease while representing a growing prevalence of disability worldwide have nosuccessful treatments despite years of research. Another difficult to treat disease is renal cancer.Metastatic renal cancer exhibits a mere 12% 5-year survival rate, however a durable, completeresponse to treatments is rarely achieved. In this dissertation, chemical and omics domains arecombined to develop a new computational framework for drug discovery and repurposing for braindisorders and renal cancer. The chemical domain includes cheminformatics and metabolomics,and the omics domain encompasses transcriptomics and proteomics. The approaches that areintegrated into this dissertation include transcriptional signature connectivity analysis, structureactivity relationship, as well as analysis of transcriptomic, metabolomic, and proteomic data. Theresults are disseminated through publicly available tools and software repositories to furtheradvance research in precision medicine.

Renal cell carcinoma is a high risk and high mortality cancer. While the VHL pathway is frequently altered in renal cell carcinoma, emerging evidences points towards involvement of multiple complex pathways in the progression of renal cell carcinoma. In this present work, we aimed to investigate the role of newly identified protein, Major Intrinsically disordered NOTCH2-Associated Receptor 1 (MINAR1), in renal cell carcinoma. We used immunohistochemistry and demonstrated that MINAR1 is highly expressed in normal kidney epithelium. Furthermore, MINAR1 was expressed at variable levels in human renal cell carcinoma cell lines. More importantly, we found that MINAR1 was significantly downregulated in human samples of renal cell carcinoma. Although further studies are needed, our data suggests a potential role for MINAR1 in renal biology. Given that MINAR1 expression appears to be downregulated in renal cancer patients, it is suggestive that MINAR1 could function as a tumor suppressor.

Targeted therapy represents a class of cancer treatments that interferes with specific cancer-associated molecules and aims to specifically attack reprogrammed cancer cells, leaving normal cells unaffected. In this dissertation, we investigate novel approaches for targeted therapy in Clear Cell-Renal Cell Carcinoma (CC-RCC) and Triple Negative Breast Cancer (TNBC).In CC-RCC, we uncovered an interaction of the tumor suppressor gene VHL with the Rho/ROCK signaling pathway, identifying this pathway as a potential therapeutic target. We found that ROCK inhibition preferentially induces massive mitotic errors in VHL-deficient cells and suggest that the induction of mitotic catastrophe represents a novel therapeutic strategy for VHL-deficient cancers. We further demonstrate that Cyclin-dependent kinase (CDK) inhibitors show significant cytotoxic activity in VHL-deficient CC-RCC in vitro and in a patient-derived xenograft (PDX) orthotopic mouse model. Importantly, we show that these inhibitors do not induce apoptosis in non-transformed cells, or CC-RCC cells engineered to re-express VHL. In TNBC, we obtained clinical evidence that the oncogene CUB Domain Containing Protein 1 (CDCP1) is co-activated with the oncogenic kinase c-Src and its downstream target Protein Kinase C δ (PKC-δ) in a subset of patient specimens, and that this class of patients may benefit from the emerging therapeutics targeting this pathway.Together, these studies contribute to expansion of the targeted therapy library in CC-RCC and TNBC and help to set the stage for more focused preclinical studies to evaluate the efficacy of targeting these pathways.

The von Hippel-Lindau (VHL) tumor suppressor pVHL is lost in 90% of clear-cell renal-cell carcinomas (ccRCCs). Jade-1 is a renal tumor suppressor that is normally stabilized by pVHL. MutS Homolog2 (MSH2) is a key initiator in DNA mismatch repair (MMR). Defects in MMR are associated with genome-wide instability and predisposition to certain types of cancer. Mass spectrometry data of immunoprecipitated Flag-tagged Jade-1 lysates showed signal for MSH2, suggesting Jade-1 may participate in MMR. Here, we confirmed an interaction between endogenous MSH2 and endogenous Jade-1 by coimmunoprecipitation. Using cell fractionation, we found that MSH2 and Jade-1 translocated to the nucleus in response to alkylating agent MNNG in kidney proximal tubule cells. We also visualized the translocation of Jade-1 by immunofluorescence. Silencing JADE1 also influenced the kinetics of MSH2 translocation. In addition, by colony forming assay, JADE1-silenced cells were resistant to mismatch damage induced by MNNG, which is a feature of cells with an MMR defect. Furthermore, reintroducing pVHL into renal cancer cells also changed the amount of translocated MSH2 and Jade-1. In contrast to wild-type mice, Jade1 heterozygous mice got spontaneous tumors, and those tumors continued to show heterozygosity for Jade1. Taken together, our results identify a mechanism for Jade-1 regulation of MMR through its nuclear translocation. pVHL may also contribute to MSH2 and Jade-1 translocation by increasing Jade-1 abundance. These findings establish an early role for Jade-1 in MMR, provide further indication that Jade-1 helps maintain genomic stability in the kidney and support that Jade-1 is a haploinsufficient renal tumor suppressor.