WO2022128881A1

WO2022128881A1 - Predictive biomarkers for risk of bladder cancer in diabetes patients

Info

Publication number: WO2022128881A1
Application number: PCT/EP2021/085424
Authority: WO
Inventors: Chris Watson; Stephen Peter Fitzgerald; John Lamont; Mark RUDDOCK
Original assignee: Randox Laboratories Ltd
Priority date: 2020-12-14
Filing date: 2021-12-13
Publication date: 2022-06-23
Also published as: GB202019663D0; EP4260067A1

Abstract

Herein we have identified a protein signature that is easily measured in blood and urine and highly predictive of bladder cancer (BC) risk in Type 2 Diabetes Mellitus (T2DM) patients. This biomarker model could be applied to screen T2DM patients presenting with haematuria for BC in diabetes clinics. Earlier urology referral of T2DM patients will improve outcomes for these patients.

Description

Predictive biomarkers for risk of bladder cancer in diabetes patients

Background

Diabetes mellitus (DM) has become a significant threat to human health in recent years and presents a major burden to public healthcare systems due to the degree of premature mortality and morbidity associated with the condition (1, 2). It is estimated that more than 500 million people around the world will have DM by the year 2030 (1, 2). Most of these cases will be Type 2 diabetes mellitus (T2DM). The current epidemic has been attributed to population aging, urbanization, and the increased prevalence of obesity and physical inactivity (3). T2DM has been linked to an increased risk of cancer including breast (4), colon (5) and bladder (6), and has a weak negative association with prostate cancer (7). It has also been suggested that patients with DM are more likely to develop bladder cancer (BC), or have more aggressive BC, compared to those with no history of DM (8-11).

BC is the most common malignancy of the urinary system and a leading cause of cancer-related death (11). BC occurs more commonly in men than in women and tobacco smoke is an important risk factor, accounting for amongst 50% of all cases (12). Other risk factors for BC include; age, past exposure to chemicals, drinking water contaminants (e.g. arsenic, cadmium), phenacetin-containing analgesics and some aspects of diet (13). Diabetic medications have also been associated with BC risk, with longterm insulin use being linked to increased risk of developing invasive BC (14), whereas there have been conflicting reports as to whether metformin and pioglitazone increase or decrease risk for BC (15-18). Currently, there are no screening tests for BC and so diagnosis is usually reliant on presentation of symptoms. The most common and highest-risk symptom for BC in primary care is haematuria (blood in urine) (19). Haematuria that is observed by a patient is referred to as 'macroscopic' haematuria, while haematuria that is detected by performing a urinalysis test for blood (urine dipstick) is referred to as 'microscopic' haematuria (19). The presence of microhaematuria in patients with DM is thought to be indicative of non-diabetic renal disease (NDRD) and is also considered an indication for biopsy in DM patients who also present with proteinurea (20-23).

In the UK, management of DM includes regular check-ups at designated diabetes clinics to monitor how the condition is being managed and to pre-empt possible future health problems. A simple blood or urine test to monitor risk of other associated diseases could feasibly be introduced to these nurse- led clinics. Herein we have identified a protein signature that is easily measured in blood and urine and highly predictive of BC risk in T2DM patients who present with haematuria. Although our data show that T2DM patients appear to be at no greater risk for BC than non-diabetics, they do present with many of the associated risk factors. Thus, it is important to be able to individualise patient care so that risk of BC can be accurately monitored in this sub-population.

References

1. Fang, H. et al. Diabetes Mellitus Increases the Risk of Bladder Cancer: An Updated Meta-Analysis of Observational Studies. Diabetes Technol. Ther. 15, (2013).

2. Zimmet, P., Alberti, K. G. M. . & Shaw, J. Global and societal implications of the diabetes epidemic. Nature 414, (2001).

3. Liu, X., Ji, J., Sundquist, K., Sundquist, J. & Hemminki, K. The impact of type 2 diabetes mellitus on cancer-specific survival. Cancer 118, 1353-1361 (2012).

4. Larsson, S. C., Mantzoros, C. S. & Wolk, A. Diabetes mellitus and risk of breast cancer: A meta-analysis. Int. J. Cancer 121, 856-862 (2007).

5. Yang, Y.-X., Hennessy, S. & Lewis, J. D. Insulin therapy and colorectal cancer risk among type 2 diabetes mellitus patients. Gastroenterology 127, 1044-50 (2004).

6. Khadka, R., Tian, W., Hao, X. & Koirala, R. Risk factor, early diagnosis and overall survival on outcome of association between pancreatic cancer and diabetes mellitus: Changes and advances, a review. Int. J. Surg. 52, 342-346 (2018).

7. Bonovas, S., Filioussi, K. & Tsantes, A. Diabetes mellitus and risk of prostate cancer: a meta-analysis. Diabetologia 47, 1071-8 (2004).

8. Sanchez-Vega, F. et al. Oncogenic Signaling Pathways in The Cancer Genome Atlas. Cell 173, 321- 337. elO (2018).

9. Mackenzie, T., Zens, M. S., Ferrara, A., Schned, A. & Karagas, M. R. Diabetes and Risk of Bladder Cancer: Evidence from a Case-Control Study in New England. Cancer 117, (2011).

10. Newton, C. C., Gapstur, S. M., Campbell, P. T. & Jacobs, E. J. Type 2 diabetes mellitus, insulin-use and risk of bladder cancer in a large cohort study. Int. J. Cancer 132, 2186-2191 (2013).

11. Cantiello, F. et al. Association between metabolic syndrome, obesity, diabetes mellitus and oncological outcomes of bladder cancer: A systematic review. Int. J. Urol. 22, 22-32 (2015).

12. Babjuk, M. et al. EAU Guidelines on Non-Muscle-invasive Urothelial Carcinoma of the Bladder: Update 2013. Eur. Urol. 64, 639-653 (2013).

13. Turati, F. et al. Diabetes mellitus and the risk of bladder cancer: an Italian case-control study. Br. J. Cancer 113, 127-130 (2015). 14. Currie, C. J., Poole, C. D. & Gale, E. A. M. The influence of glucose-lowering therapies on cancer risk in type 2 diabetes. Diabetologia 52, 1766-1777 (2009).

15. Currie, C. J. et al. Mortality After Incident Cancer in People With and Without Type 2 Diabetes Impact of metformin on survival. Diabetes Care 35, (2012).

16. Korhonen, P. et al. Pioglitazone use and risk of bladder cancer in patients with type 2 diabetes: retrospective cohort study using datasets from four European countries. BMJ 354, (2016).

17. Lewis, J. D. et al. Risk of bladder cancer among diabetic patients treated with pioglitazone: interim report of a longitudinal cohort study. Diabetes Care 34, 916-22 (2011).

18. Hu, J. et al. Association of metformin intake with bladder cancer risk and oncologic outcomes in type 2 diabetes mellitus patients A systematic review and meta-analysis. Medicine (Baltimore). 97, (2018).

19. Price, S. J., Shephard, E. A., Stapley, S. A., Barraclough, K. & Hamilton, W. T. Non-visible versus visible haematuria and bladder cancer risk: a study of electronic records in primary care. Br. J. Gen. Pract. (2014). doi:10.3399/bjgpl4X681409

20. Jiang, S. et al. Accuracy of hematuria for predicting non-diabetic renal disease in patients with diabetes and kidney disease: A systematic review and meta-analysis. Diabetes Res. Clin. Pract. 143, 288-300 (2018).

21. Okada, T. et al. Clinical significance of microscopic haematuria in diabetic nephropathy in type 2 diabetes patients with overt proteinuria. Nephrology 18, 563-568 (2013).

22. Mashitani, T. et al. Association between dipstick hematuria and decline in estimated glomerular filtration rate among Japanese patients with type 2 diabetes: A prospective cohort study [Diabetes Distress and Care Registry at Tenri (DDCRT 14)]. J. Diabetes Complications 31, 1079-1084 (2017).

23. Dong, Z. et al. Clinical predictors differentiating non-diabetic renal diseases from diabetic nephropathy in a large population of type 2 diabetes patients. Diabetes Res. Clin. Pract. 121, 112-118 (2016).

Summary of the invention

In a first aspect, the current invention provides a novel biomarker combination for early diagnosis of BC in T2DM patients who present with haematuria. The combination of serum MCP-l and VEGF, and urinary IL-6, CK8 and CK18 had an AUC of 0.84 for the detection of bladder carcinomas in T2DM patients, with a positive predictive value (PPV) of 63.64% and a negative predictive value (NPV) of 91.07%. A second aspect of the current invention is a method for detecting BC in an ex vivo sample taken from a T2DM patient, wherein the sample is contacted with a solid- state device onto which has been immobilized probes specific to each of the biomarkers.

A third aspect of the invention is a solid-state device comprising a substrate having an activated surface onto which is immobilized probes to MCP-l, VEGF, IL-6, and CK8/CK18 in discrete areas of said activated surface. This solid-state device not only has potential in diagnosis, but also in monitoring the progression of BC.

A fourth aspect of the invention is a method of determining the efficacy of a drug treatment for BC comprising determining the levels of MCP-l, VEGF, IL-6 and CK8/CK18 in a sample from a T2DM patient treated with the drug and comparing levels with those from a healthy control, or with levels from the same patient before treatment with the drug, wherein dependent on the biomarker, either an increase or decrease in level indicates the effectiveness of the treatment.

Brief description of the figures

Figure 1 Graphs showing the biomarker levels between the different groups and their significance

A) shows that urine IL-6 levels (pg/ml) were significantly increased in BC patients without diabetes but not those with T2DM. B) shows that urine CK8/CK18 levels (ng/ml) were significantly increased in all BC patients. C) shows that serum MCP-l levels (pg/ml) were significantly decreased in BC patients with T2DM and that they showed the opposite direction of change in BC patients without diabetes (although this increase was not found to be significant). D) shows that serum VEGF levels (pg/ml) significantly increased in all BC patients.

Detailed description

The present invention describes a biomarker-based method to aid in the early diagnosis of BC in T2DM patients who present with haematuria. Specifically, it relates to the measurement of relative levels or concentrations of biomarkers in ex vivo samples obtained from patients. In the context of the current invention, the utility for diagnosing BC has been used as way of an example. However, it is further envisaged that the invention may also be used for monitoring the progression or recurrence of BC, or to determine the effectiveness of any treatment strategy which has been implemented. The current invention can also be used to determine T2DM patients with haematuria at risk of developing BC. The term "patient" refers to any mammal to be the recipient of the diagnosis, preferably a human. Preferably the patients of the current invention are patients with diabetes and haematuria. More preferably, the patients of the current invention are T2DM patients with haematuria. The patient may be a person presenting for routine screening for disease or they may present with symptoms suggestive of cancer. The patient may also be an individual deemed at high risk for BC, due to smoking history for example. Alternatively, the patient could be an individual who has received treatment for BC and they are screened to monitor progress or detect possible recurrence.

The term "biomarker", in the context of the current invention, refers to a molecule present in a biological sample of a patient, the levels of which may be indicative of BC. Such molecules may include peptides/proteins or nucleic acids and derivatives thereof.

The preferred biomarker combination of the current invention for the detection of BC comprises monocyte chemotactic protein 1 (MCP-1), vascular endothelial growth factor (VEGF), interleukin 6 (IL- 6) and cytokeratin 8 (CK8)/cytokeratin 18 (CK18). However, it is within the scope of the invention to determine levels of additional biomarkers which could contribute to a diagnosis of BC, for example, but not limited to transforming growth factor beta 1 (TGFpi), matrix metallopeptidase 9/TIMP metallopeptidase inhibitor 1 complex (MMP9TIMP1), microalbumin, albumin/creatinine ratio (ACR), bladder tumour antigen (BTA), cystatin B, D-dimer, Fas receptor, interleukin 13 (IL-13), interleukin 1 beta (IL-lb), neuron-specific enolase (NSE) and plasminogen activator inhibitor-l/tissue plasminogen activator complex (PAI-l/tPA).

The term "MCP-1" as used herein refers to Monocyte chemotactic protein 1, also known as C-C motif chemokine 2 (UniProt: P13500).

The term "VEGF" as used herein refers to Vascular endothelial growth factor A, also known as Vascular permeability factor (UniProt: P15692).

The term "IL-6" as used herein refers to interleukin 6, also known as B-cell stimulatory factor 2 and Interferon beta-2 (UniProt: P05231).

The term "CK18" as used herein refers to cytokeratin 18, also known as keratin type I cytoskeletal 18, keratin 18 and Cell proliferation-inducing gene 46 protein (UniProt: P05783), or fragments thereof. The term "CK8" as used herein refers to cytokeratin 8, also known as keratin type II cytoskeletal 8 and keratin 8 (UniProt: P05787), or fragments thereof.

The term "CK8/CK18" as used herein refers to CK8 and/or CK18 or fragments thereof. This includes soluble oligomer complexes formed between CK8 and CK18, for example dimers. In the current invention CK8 and CK18 can be detected separately by antibodies with specificity for CK8 and CK18, or fragments thereof, or they can be detected by antibodies which have specificity for a shared epitope of CK8 and CK18. Combinations of antibodies can be used, for example, in a sandwich enzyme-linked immunosorbent assay (ELISA)-based detection method with specific capture antibodies used to initially bind CK8 and CK18 then a generic (specific to a shared epitope of CK8 and CK18) labelled detection antibody added to generate a detectable signal.

In the context of the present invention, a deviation from a control value for a biomarker may be an indication that the patient suffers from bladder cancer. Dependent on the individual biomarker this deviation may be an increase or a decrease from a control value. For example, in the patient cohort of the current invention, levels of MCP-l were lower in BC patients with T2DM when compared to those in healthy controls and controls with T2DM (Figure 1C). Levels of VEGF, IL-6 and CK8/CK18 were higher in BC patients than in controls (Figure 1A, B, and D).

The current invention provides an early stage biomarker combination (MCP-l, VEGF, IL-6 and CK8/CK18), which allows the detection of neoplastic disease at an early and still benign stage and/or early tumour stages. Early detection and removal of a BC is critical and can dramatically increase the patients' chances of survival. Additionally, the biomarker combination of the current invention allows the monitoring of BC development within an individual through serial testing of serum and urine from said individual over an extended period. For example, routine determination of the levels of the biomarkers of the preferred combination (MCP-l, VEGF, IL-6 and CK8/CK18) could detect the changes from healthy control values, which are indicative of the development of BC. A further change in levels could then be indicative of the progression of the disease to a later stage.

A further aspect of the present invention is a method of determining the efficacy of a treatment for BC comprising determining the levels of MCP-l, VEGF, IL-6 and CK8/CK18 in a sample from a patient who has had treatment for BC and, comparing levels with those from a healthy control or with levels from the same patient taken before the treatment, wherein dependent on the biomarker, either an increase or decrease in level indicates the effectiveness of the treatment. The treatment can be for example, a drug treatment, a radiotherapy-based treatment or a surgical intervention. Wherein the treatment is a drug treatment, the method of determining the efficacy of the drug treatment for BC would comprise determining the levels of biomarkers, for example MCP-l, VEGF, IL-6 and CK8/CK18 in a sample from a patient treated with the drug, and comparing biomarker levels with those from a healthy control or with levels from the same patient before treatment with the drug, wherein, dependent on the biomarker, either an increase or decrease in level indicates the effectiveness of the drug treatment. In the context of the present invention, a "control value" is understood to be the level of a particular biomarker, such as MCP-l, VEGF, IL-6 or CK8/CK18 typically found in healthy individuals. The control level of a biomarker may be determined by analysis of a sample isolated from a healthy individual or may be the level of the biomarker understood by the skilled person to be typical for a healthy individual. The control value may be a range of values considered by the skilled person to be a normal level for the biomarker in a healthy individual. The skilled person will appreciate that control values for a biomarker may be calculated by the user analysing the level of the biomarker in a sample from a healthy individual or by reference to typical values provided by the manufacturer of the assay used to determine the level of biomarker in the sample.

The "sample" of the current invention can be any ex vivo biological sample from which the levels of biomarkers can be determined. Preferably, the sample isolated from the patient is a serum or urine sample. However, the sample could be selected from, for example, whole blood, plasma, saliva or sputum. The determination of the level of biomarkers may be carried out on one or more samples obtained from the patient. For example, one or more biomarkers could be measured in a serum sample and these results combined with those for one or more biomarkers which are measured in a urine sample from the same patient. In the context of the current invention, preferably, IL-6 and CK8/CK18 are measured in urine samples and MCP-l and VEGF are measured in serum samples. The sample may be obtained from the patient by methods routinely used in the art.

The determination of the level of biomarkers in the sample may be determined by immunological methods such as an ELISA-based assay. The methods of the current invention preferably comprise the following steps; the biomarkers binding to a probe(s), adding a detector probe(s) and detecting and measuring the biomarker/probe complex signal(s), placing these values into a machine algorithm and analysing the output value, said value indicating whether the patient has or is at risk of having BC. Preferably, the methods of the present invention use a solid-state device for determining the level of biomarkers in the sample isolated from the patient.

The solid-state device comprises a substrate having a probe or multiple different probes immobilised upon it that bind specifically to a biomarker. The interactions between a biomarker and its respective probe can be monitored and quantified using various techniques that are well-known in the art. The term "probe" refers to a molecule that is capable of specifically binding to a target molecule such that the target molecule can be detected as a consequence of said specific binding. Probes that can be used in the present invention include, for example, antibodies, aptamers, phages and oligonucleotides. In a preferred embodiment of the current invention the probe is an antibody. The "level" of a biomarker refers to the amount, expression level or concentration of the biomarker within the sample. This level can be a relative level in comparison to another biomarker or a previous sample.

The term "antibody" refers to an immunoglobulin which specifically recognises an epitope on a target as determined by the binding characteristics of the immunoglobulin variable domains of the heavy and light chains (VHS and VLS), more specifically the complementarity-determining regions (CDRs). Many potential antibody forms are known in the art, which may include, but are not limited to, a plurality of intact monoclonal antibodies or polyclonal mixtures comprising intact monoclonal antibodies, antibody fragments (for example Fab, Fab', and Fv fragments, linear antibodies single chain antibodies and multi-specific antibodies comprising antibody fragments), single-chain variable fragments (scFvs), multi-specific antibodies, chimeric antibodies, humanised antibodies and fusion proteins comprising the domains necessary for the recognition of a given epitope on a target. Preferably, references to antibodies in the context of the present invention refer to polyclonal or monoclonal antibodies. Antibodies may also be conjugated to various reporter moieties for a diagnostic effect, including but not limited to radionuclides, fluorophores, dyes or enzymes including, for example, horse-radish peroxidase and alkaline phosphatase.

Such antibodies may be immobilised at discrete areas of an activated surface of the substrate. The solid-state device may perform multi-analyte assays such that the level of a biomarker in a sample isolated from the patient may be determined simultaneously with the level of a further biomarker of interest in the sample. In this embodiment, the solid-state device has a multiplicity of discrete reaction sites each bearing a desired antibody covalently bound to the substrate, and in which the surface of the substrate between the reaction sites is inert with respect to the target biomarker. The solid-state, multi-analyte device may therefore exhibit little or no non-specific binding. The combination of biomarkers may also be referred to as a panel of biomarkers.

The substrate can be any surface able to support one or more probes but is preferably a biochip. A biochip is a planar substrate that may be, for example, mineral or polymer based, but is preferably ceramic. When identifying the various biomarkers/proteins of the invention it will be apparent to the skilled person that as well as identifying the full-length protein, the identification of a fragment or several fragments of a protein is possible, provided this allows accurate identification of the protein. Similarly, although a preferred probe of the invention is a polyclonal or monoclonal antibody, other probes such as aptamers, molecular imprinted polymers, phages, short chain antibody fragments and other antibody-based probes may be used. A solid-state device that may be used in the invention may be prepared by activating the surface of a suitable substrate and applying an array of antibodies on to the discrete sites on the surface. If desired, the other active areas may be blocked. The ligands may be bound to the substrate via a linker. In particular, it is preferred that the activated surface is reacted successively with an organosilane, a bifunctional linker and the antibody. The solid-state device used in the methods of the present invention may be manufactured according to the method disclosed in, for example, GB-A-2324866 the contents of which are incorporated herein in its entirety. The solid-state device can be any substrate to which probes of the current invention can be attached for example a microtitre plate or beads. Preferably, the solid-state device used in the methods of the present invention is a biochip. The biochip may be a biochip which is incorporated into the Biochip Array Technology System (BAT) available from Randox Laboratories Limited (Crumlin, UK).

Preferably, a solid-state device may be used to determine the levels of MCP-l, VEGF, IL-6 and CK8/CK18 in the sample isolated from the patient. In a preferred embodiment the solid-state device comprises a substrate having an activated surface on to which is applied antibodies specific to each of the two or more biomarkers to discrete areas of the activated surface. Therefore, the solid-state device may perform multi-analyte assays such that the levels of biomarkers, for example MCP-l, VEGF, IL-6, and CK8/CK18 in a sample may be determined simultaneously. In this embodiment, the solid- state device has a multiplicity of discrete reaction sites each bearing a desired antibody covalently bound to the substrate, and in which the surface of the substrate between the reaction sites is inert with respect to the target biomarkers. Each probe, whether individually or in multiplex, is specific to one target analyte. For example, a probe to MCP-l will only show specific binding to this analyte and will have no significant cross-reactivity with VEGF, IL-6, CK8/CK18 or indeed any other potentially interfering substance which could compromise the assay. When two different sample types are obtained from a patient the solid-state device of the invention can consist of two identical solid-state devices with the same antibodies to the same biomarkers or it may consist of two separate solid-state devices, one for each sample type, comprising the antibodies specific to the biomarkers which are to be determined in each sample type. Conceivably, the solid- state device could be four separate devices each comprising antibodies specific to a different target biomarker. Or, for example, the solid-state device could be one device with probes to VEGF, MCP-l, CK8/CK18 and IL-6 or it could be two separate devices, one with probes to VEGF and MCP-l and another with probes to CK8/CK18 and IL-6. The solid- state device not only has potential in diagnosis but also in monitoring the progression and recurrence of BC as well as determining the success of any treatments.

In a preferred embodiment of the current invention each of the biomarker concentration values is inputted into a statistical methodology to produce an output value that correlates with the chances that the patient has or is at risk of developing BC. Preferably, the statistical methodology used is logistic regression, decision trees, support vector machines, neural networks, random forest or another machine-learning algorithm.

The performance of the results of the applied statistical methods used in accordance with the present invention can be best described by their receiver operating characteristics (ROC). The ROC curve addresses both the sensitivity (the number of true positives) and the specificity (the number of true negatives) of the test. Therefore, sensitivity and specificity values for a given combination of biomarkers are an indication of the performance of the test. For example, if a biomarker combination has a sensitivity and specificity value of 80%, out of 100 patients, 80 will be correctly identified from the determination of the presence of the particular combination of biomarkers as positive for the disease, while out of 100 patients who do not have the disease 80 will accurately test negative for the disease.

A suitable statistical classification model, such as logistic regression, can be derived for a combination of biomarkers. Moreover, the logistic regression equation can be extended to include other (clinical) variables such as age and gender of the patient as well. In the same manner as described before, the ROC curve can be used to access the performance of the discrimination between patients and controls by the logistic regression model. Therefore, the logistic regression equation can be used apart or combined with other clinical characteristics to aid clinical decision making. Although a logistic regression equation is a common statistical procedure used in such cases and is preferred in the context of the current invention, other mathematical/statistical, decision trees or machine learning procedures can also be used.

One convenient goal to quantify the diagnostic accuracy of a laboratory test is to express its performance by a single number. The most common global measure is the area under the curve (AUC) of the ROC plot. The area under the ROC curve is a measure of the probability that the perceived measurement will allow correct identification of a condition. Values typically range between 1.0 (perfect separation of the test values of the two groups) and 0.5 (no apparent distributional difference between the two groups of test values). The area does not depend only on a particular portion of the plot such as the point closest to the diagonal or the sensitivity at 90% specificity, but on the entire plot. This is a quantitative, descriptive expression of how close the ROC plot is to the perfect one (area = 1.0). In the context of the present invention, the two different conditions can be whether a patient has or does not have cancer.

EXAMPLE 1 Methods

The Haematuria Biomarker Study (HaBio) is a three-way collaboration between Queen's University Belfast, Randox Laboratories Ltd and hospitals in Northern Ireland, conducted to identify panels of serum/urine biomarkers for biochip development for cancer risk stratification in patients with haematuria (http://www.qub.ac.uk/sites/habio). Following ethical approval (ORECNI ll/NI/0164). A total of 675 patients were recruited between 17 October 2012 and 28 June 2016. A detailed description of the HaBio study methodology has been published online, and includes extensive information on study population, recruitment, clinical and biochemical measurements, biomarker analysis, and outcomes. Trial registration: http://www.isrctn.com/ISRCTN25823942. Specific to this diabetes sub-analysis of the HaBio study, all 109 T2DM patients and 218 age and sex matched nondiabetic patients were selected for analysis.

Biomarker Measurements

Urine samples (~50 ml) and serum samples (~10 ml) were collected from all patients in sterile containers. Unfiltered and uncentrifuged urine samples were immediately aliquoted and frozen at - 80°C until analyses. Urine samples were thawed on ice and then centrifuged (1200 x g, 10 minutes, 4°C) to remove any particulate matter prior to analysis. All patient samples were run in triplicate and the results are expressed as mean ± SD. Biochip Array Technology (Randox Clinical Laboratory Services, Crumlin, Northern Ireland, UK) was used for the simultaneous detection of multiple analytes from a single patient sample (e.g. urine). The technology is based on the Randox Biochip, a 9mm² solid substrate supporting an array of discrete test regions with immobilized, antigen-specific antibodies. Following antibody activation with assay buffer, standards and samples were added and incubated at 37°C for 60 minutes, then placed in a thermo-shaker at 370 rpm for 60 minutes. Antibody conjugates (HRP) were added and incubated in the thermo-shaker at 370 rpm for 60 minutes. The chemiluminescent signals formed after the addition of luminol (1:1 ratio with conjugate) were detected and measured using digital imaging technology and compared with that from a calibration curve to calculate concentration of the analytes in the samples. The following markers were detected using commercially available ELISA kits, as per manufacturer's instructions: 8OHdG (Cell Biolabs); BTA (Polymedco); CK8/CK18 (UBC® assay, IDL); Clusterin (R&D Systems); CXCL16 (R&D Systems); Cystatin B (R&D Systems); Cystatin C (Randox Daytona Rx); Fas receptor (RayBio); HAD (MyBioSource); Microalbumin (Randox Rx Daytona); Midkine (CellMid); MMP9NGAL (R&D Systems); MMP9TIMP1 (R&D Systems); PAI-l/Tpa (AssayPro); Progranulin (R&D Systems); TGFpi (R&D Systems); Thrombomodulin (R&D Systems) and TPA (Abeam). Creatinine (pmol/L) measurements were determined using a quantitative in vitro diagnostic kit from Randox Laboratories (Catalogue No CR3814), and the results were collected from a Daytona RX Series Clinical Analyser (Randox

Laboratories Ltd). The full list of measured biomarkers can be found in Table 2.

Statistical Analysis

Statistical analyses were performed using R (ref: R Core Team. R: A Language and Environment for Statistical Computing. 2018) and IBM SPSS v26. Clinical characteristics and biomarker data pertaining to each patient were analysed using either independent samples t-test for normally distributed data or Mann-Whitney Mean Rank test for non-normally distributed data. Descriptive clinical characteristics were analysed using Chi-Squared contingency test. All biomarkers were log transformed and input into Lasso regression analysis for model selection. The final model was used for ROC analysis of predictive capacity of the biomarker combination.

Results

Comparison of Patient Characteristics

Of the complete HaBio cohort of 675 patients, 111 (16.5%) have been diagnosed with diabetes mellitus (DM). Two/111 (1.8%) of these patients had Type 1 diabetes and were excluded from further analysis, as the study focused on T2DM. T2DM patients were matched with non-diabetic controls based on age and BC diagnosis in a 2:1 ratio. Patient characteristics of T2DM (n=109) and matched controls (n=218) are detailed in Table 1. Smoking and drinking habits between T2DM and non-T2DM patients were similar, however, T2DM patients had higher BMI than non-diabetic patients (31.8±6.5 vs. 28.4±4.5, p<0.001). One of the main risk factors for BC is exposure to occupational hazards such as dyes, leather, chemicals etc. Occupations were ranked as 'low, medium and high risk' according to associated risk of such exposures. Overall, the majority of patients in the full cohort of 675 patients worked in 'skilled metal, electrical and electronic trades'. T2DM patients were more likely to be in the 'high risk for BC' occupational group compared to matched non-diabetic patients, although this was not significant (56.9% vs. 48.6%, p=0.159). The T2DM group had significantly increased incidence of hypertension reported in their medical history (69.7% vs. 44.0%, p<0.001) compared to patients without T2DM. Likewise a significantly greater proportion of T2DM patients had uncontrolled blood pressure and were on blood pressure medications (86.2% vs. 60.1%, p<0.001). Based on dipstick analysis, a significantly greater proportion of T2DM patients presented with detectable glucose (44.4% vs. 6.0% p<0.001) and protein (63.0% vs. 48.6% p=0.015), respectively (Table 1). Over half (58.7%) of all patients in the matched cohort (n=327) were on statins. Other common medication classes included NSAID (40.4%), antiplatelet (38.5%), PPI (36.7%), beta blockers (31.2%) and ACE inhibitors (30.3%). Alpha blockers (27.5%) were the only medication class to be significantly associated with BC. Cause of haematuria

The majority of both T2DM and non-diabetic patients presented with macrohaematuria as opposed to microhaematuria. Haematuria caused by BPE/BPH was significantly more common in T2DM patients compared to non-diabetic patients (28.4% and 17.9%, respectively, p=0.040). Diagnosis of infection in patients presenting with haematuria was higher in the non-diabetic patients compared to T2DM patients (34.9% vs. 23.9% p=0.058), although this was not significant. BC was classified as low risk (LR) i.e. pTaGl/G2 disease with no evidence of carcinoma in situ (CIS) or variants, or high risk (HR) i.e. all other proven pathological BC including CIS. There was no significant difference in the proportion of patients classified as HR in T2DM and non-diabetic patients at time of recruitment (45.0% vs. 37.5%, p=0.553) (Table 1).

Identification of Bladder Cancer Risk Factors in T2DM and non-T2DM patients

Measurements of known clinical risk factors for BC were compared between BC and control patients within the T2DM and non-diabetic groups (Table 2). Macrohaematuria, as opposed to microhaematuria was significantly associated with BC in both T2DM and non-diabetic patients (p=0.038 and p<0.001, respectively). Smoking was significantly associated with development of BC in both T2DM and non-diabetic groups (p=0.005 and p<0.001, respectively). Although in the T2DM group, the age at which a patient quit smoking proved a significant factor, with those quitting at a younger age being less likely to be diagnosed with BC (p<0.001). In the T2DM group, diabetes control, as determined by HbAlc levels, was not significantly associated with BC (p=0.897). Similarly, duration of T2DM was not found to be significantly different between T2DM patients with and without BC (p=0.412). Diabetic medications (metformin, pioglitazone and insulin) were not significantly associated with BC (p=1.000, p=0.623 and p=0.800, respectively). However, sulphonylureas, which were administered to 31/109 (28.4%) of T2DM patients, was significantly associated with BC ((OR = 2.400 95%CI 1.023 - 5.631, p=0.050). In non-diabetics, dipstick protein levels were significantly associated with BC (p=0.005). Non-diabetic patients, who were hypertensive at recruitment, were also more likely to have BC (p=0.049), with diastolic blood pressure also being a significant risk factor in this group (p<0.001). These factors were not significantly associated with BC in the T2DM patients (Table 2). In T2DM patients, CKD was significantly associated with BC (p=0.049) and this association was not observed in non-diabetic patients (Table 2). Similarly, any other type of kidney impairment or dysfunction (collectively classified as 'Kidney Dx') was found to be significantly associated with BC in T2DM patients (p=0.006).

Bladder Cancer Outcomes in T2DM vs. non-T2DM patients BC outcomes were compared in the matched T2DM vs. non-diabetic patients. Overall, 41.3% of nondiabetic and 50.0% of T2DM patients experienced disease recurrence. Although the mean number of days before disease recurrence was greater in T2DM patients (Mean = 712.5 vs. 854.5, p=0.354), there was no significant improvement in recurrence free survival (RFS) (HR 0.97, 95% Cl 0.003 - 0.997, p=1.00). Within the non-diabetic group, 15.2% of patients experienced disease progression, compared to 13.2% patients in the T2DM group. The number of days elapsed prior to disease progression was greater in the T2DM group (Mean = 1157.30 vs. 1878.23, p=0.053), however, there was no significant improvement in progression free survival (PFS) (HR 0.52, 95% Cl -1.34 - 0.18, p= 0.17). Nine patients in the full matched cohort died from their BC (11.39% non-diabetic, 10.53% T2DM). Within the T2DM group, the duration of overall BC survival (OS) was longer than in the non- diabetic group (Mean = 1909.7 vs. 1239.52 p=0.077) however, this was not significant (HR 0.55, 95% Cl -1.109 - 0.268, p=0.26). Previous reports have suggested that patients receiving metformin have better cancer outcomes (15). In this patient cohort, >70% of T2DM patients have been or are being treated with metformin (Table 1). The mean number of days prior to disease recurrence was greater in the non-metformin treated T2DM patients (Mean = 1039.55 vs. 784.31, p=0.470), however, there was no significant improvement in RFS compared to the metformin-treated T2DM patients (HR 0.88, 95% Cl -0.271 - 0.787, p=0.79). There were also no significant differences in PFS (HR 0.84, 95% Cl - 0.194 - 0.847, p=0.85) and OS (HR 0.60, 95% Cl -0.56 - 0.576, p=0.57) between metformin and non- metformin-treated patients.

Novel Urine and Serum Biomarkers for Prediction of Bladder Cancer in diabetic patients with haematuria

A panel of 66 candidate biomarkers were measured in either serum, urine, or both, from all patients. Urine levels of CK18 and CK8 were also measured using the UBC® assay (IDL), which specifically measures soluble fragments of cytokeratin 8 and cytokeratin 18 in urine samples. Approximately half of the candidate biomarkers (34/66) were significantly associated with BC in non-diabetic patients, however only 15/66 were found to be significantly associated with BC in T2DM patients (Table 2). This suggests that the molecular signature of BC in T2DM patients differs to that of non-diabetic patients, despite having similar comorbidities and exposure to risk factors. Hence, diagnosis of BC in this patient group could be more challenging. All biomarker data were imputed into a Lasso-based regression analyses for identification of a potential predictive model for BC in T2DM patients with haematuria. This analysis identified a combination of two serum biomarkers and three urine biomarkers as an optimal model for prediction of BC in T2DM patients who present with haematuria; serum VEGF, serum MCP-1, urine CK8, urine CK18 and urine IL-6. The prior predicted probability (PPP) of this model was analysed as a single variable using receiver-operator characteristic (ROC) analysis. In the T2DM cohort, this biomarker model correctly predicted 63.6% BC cases, with a negative predictive value of 91.1%. This biomarker combination gave an AUC 0.84 for prediction of BC within T2DM patients (95% Cl 0.582 - 0.746). When applied to the non-diabetic patients, this model correctly identified 46.2% of BC cases with an AUC 0.66 (95% Cl 0.745 - 0.925). Using DeLong's test to compare both ROC curves, it was determined that this difference in performance was significant (p=0.006). For both T2DM and non-diabetic patients, the FDA approved BC biomarker, urine BTA, gave a much weaker predictive performance, with AUCs of just 0.69 and 0.64, respectively.

Table la: Demographic, clinical, and biochemical profile of patients with and without diabetes

Table lb: Demographic, clinical, and biochemical profile of patients with and without diabetes (matched)

Values are mean ± SD, n (%). Independent samples t-test or Mann Whitney Mean Rank analysis was performed to compare numerical variables between the two groups, depending on normal distribution of the variable. Chi- square contingency analysis was performed for categorical variables. P-values marked with indicate significant differences (p<0.05) between T2DM and non-diabetic groups. P-values marked with '**' indicate significant differences (p<0.001) between T2DM and non-diabetic groups. BMI = body mass index; dx = disease/disorder; hx = history; BP = blood pressure; meds = medications; BC = bladder cancer; BPE/BPH = benign prostate enlargement/benign prostate hyperplasia; neg = negative; CIS = cancer in situ; ONS = office for national statistics; path = pathology; BCH = Belfast City Hospital; CAH = Craigavon area hospital; UHD = Ulster Hospital Dundonald

Table 2 Biomarkers and significant associations (p<0.05) with T2DM and/or BC

NS = Not significant

Claims

Claims We claim:

1. A method for determining whether a type 2 diabetes mellitus (T2DM) patient has, or is at risk of developing bladder cancer, said method comprising, i) determining the level of the biomarkers monocyte chemoattractant protein 1 (MCP-l), vascular endothelial growth factor (VEGF), interleukin 6 (IL-6) and cytokeratin 8 (CK8)/cytokeratin 18 (CK18) in an ex vivo sample taken from the patient and, ii) establishing the significance of the concentration of the biomarkers by inputting each of the biomarker concentration values into a statistical methodology to produce an output value that indicates whether the patient has or is at risk of developing bladder cancer.

2. The method of claim 1 wherein the sample is a blood, serum, plasma or urine sample.

3. The method of claim 1 wherein VEGF and MCP-l are detected in a serum sample and CK8/CK18 and IL-6 are detected in a urine sample.

4. The method of any preceding claim wherein the T2DM patient is presenting with haematuria.

5. The method of claim 1 wherein the statistical methodology used is logistic regression, decision trees, support vector machines, neural networks, random forest or another machine learning algorithm.

6. The method of any preceding claim wherein the patient sample is contacted with a solid-state device comprising a substrate having an activated surface on to which is immobilized, in discrete areas of said activated surface, one or more probes specific to MCP-l, VEGF, IL-6 and CK8/CK18.

7. A solid-state device comprising a substrate having an activated surface onto which is immobilized, in discrete areas of said activated surface, one or more probes specific to MCP-l, VEGF, IL-6 and CK8/CK18, for use in the diagnosis or prognosis of bladder cancer.

8. The solid-state device of claim 7 wherein the probes are antibodies.

9. A method of determining the efficacy of a treatment for bladder cancer comprising determining the level of MCP-l, VEGF, IL-6 and CK8/CK18 in a sample taken from a treated patient and comparing levels with those from a healthy control or with levels in the same patient before treatment, wherein an increase in the level of MCP-l and/or a reduction in levels of VEGF, IL-6 and CK8/CK18 indicates the effectiveness of the treatment.

10. The method of Claim 9 wherein the treatment is a drug treatment, radiotherapy treatment or surgical intervention.