Patent Publication Number: US-2022213550-A1

Title: A method for diagnosing cancers of the genitourinary tract

Description:
The present invention relates to a method for diagnosing cancers of the genitourinary tract, as well as methods of treatment of patients diagnosed using the method. It further comprises the development of treatment regimes for selected patients, based upon the determination, kits for carrying out the determination and computers and devices programmed to carry out the determination. 
     BACKGROUND TO THE INVENTION 
     Genitourinary tract cancers including prostate, bladder and kidney cancers represent a major cause of morbidity and mortality worldwide. Bladder cancer is the 5th most common cancer in Europe with over 123,000 new cases every year resulting in 40,000 deaths. In Europe prostate cancer is ranked first among the most frequently diagnosed cancer among men, with around 345,000 new cases a year. There are around 88 400 new cases of kidney cancer per year making it the 10th most common cancer. 
     Haematuria is one of the most common findings on urinalysis in patients encountered by general practitioners with an incidence of 4 per 1000 patients per year; and it represents about 6% of new patients seen by urologists. Haematuria or irritative voiding are both symptoms of early transitional cell carcinoma. However, fewer than 1 in 10 people with haematuria actually have a genitourinary tract cancer. Haematuria or irritative voiding is more often related to less serious diseases such as urinary tract infections or benign prostatic hyperplasia. However, patients with these nonspecific symptoms may undergo extensive urological investigations, even though only a small percentage of them actually have malignancies. In this connection, the workup and screening of haematuria patients often requires cystoscopy. Cystoscopy is the gold standard diagnostic test for bladder cancer because it allows direct visualization and biopsy of the bladder urothelium. 
     As cystoscopy is invasive and also costly, both patients and clinicians would benefit greatly from the development of a cost-effective non-invasive test platform for the detection of genitourinary tract cancers. Moreover the diagnosis of genitourinary tract cancers at an early stage greatly improves outcomes following treatment interventions such as surgery, radiotherapy, chemotherapy and molecular targeted therapies/immunotherapies. To date, urine tests for detecting bladder cancer have focused on the detection of one or more protein biomarkers using immunoassay techniques (see for example Kelly et al. PLoS ONE 7 (7): e40305). A particular biomarker detected is the MCM5. 
     Mcm2-7 protein expression in normal epithelium is restricted to the basal stem/transit amplifying compartments and is absent from surface layers as cells adopt a fully differentiated phenotype. Superficial cells obtained either through exfoliation or by surface sampling should therefore be negative for Mcm2-7 proteins. In premalignant (dysplastic) epithelial lesions and in malignancy there is an expansion of the proliferative compartment coupled to arrested differentiation, resulting in the appearance of proliferating, MCM-positive cells in superficial layers. Immunodetection of Mcm2-7 protein in exfoliated or surface-sampled cells is thus indicative of an underlying premalignant/dysplastic lesion or malignancy. 
     One of these cell cycle regulated proteins is Mcm5 which represents a component of the DNA helicase and, therefore, is a potential biomarker for cancer detection. It has been shown by immunocytochemical methods for detection of Mcm5 or alternatively application of a 12A7-4B4 sandwich Mcm5 immunoassay that this approach can be used for the detection of a wide range of cancers including cervical, oesophageal, prostate, and bladder, renal and pancreatic cancers including cholangiocarcinoma. 
     However, the detection of one or more protein biomarkers of the this type using immunoassay can result in erroneous results, and in particular, false positives. 
     Urine tests can be used in such immunoassays as disruption of the normal process of differentiation during the formation of cancer results in tumour cells being locked into the proliferative cell cycle. This results in the elevation of a range of cell cycle regulated proteins in the tumour cells shed into body fluids such as urine. 
     SUMMARY OF THE INVENTION 
     According to the present invention there is provided a method for detecting or monitoring a malignancy of the genitourinary tract, said method comprising analysing nucleic acid obtained from a sample from a subject, detecting the presence of at least three biomarkers which are each an aberration in a coding sequence of a proliferation-linked gene indicative of a malignancy therein and/or the tumour mutational burden, and relating the presence of one or more of said biomarkers to the presence of malignancy. An illustration of the method is shown in  FIG. 1 . 
     The method of the present invention can be performed by extracting DNA/RNA from, for example, a urine sample or liquid biopsy. The DNA/RNA samples are then run through Next Generation Sequencing equipment, for example, a ThermoFisher Ion Torrent using the method of the present invention which is known as OncoUro DX™. The results thereof can be used in cancer detection, cancer screening, analysis of haematuria, monitoring therapeutic responses, surveillance, precision oncology therapy selection. 
     Conveniently, the sample is a urine sample, a liquid biopsy or bodily fluid taken from the kidney or other suitable source of liquid in the genito-urinary tract. The bodily fluid can also be blood, plasma, ascites, pleural effusion, cerebral spinal fluid or peritoneal washing. If the method of the invention is carried out using the analysis of a urine sample then it is non-invasive and the sample can be obtained by the patient themselves. 
     Nucleic acid, specifically RNA and DNA are extracted from the sample, and conveniently, this extraction forms a preliminary step in the method of the invention. The sample may be a fresh sample, but the method may also be carried out using stored samples, for example, on formalin fixed samples, or samples which have been preserved in either 10% neutral buffered formalin, formal saline solution, or alcohol and formalin based buffered solutions, e.g. Cytolyt®, preservCyt or ColliPee. This means that samples may be prepared in one environment, such as a clinic or hospital, and then readily transferred to an appropriate laboratory for analysis in accordance with the method of the present invention. 
     As used herein, the term “biomarker” refers to any molecule, gene, sequence mutation or characteristic such as increased or decreased gene expression from a coding sequence of proliferative-linked genes. Examples of such genes are identified in Table 1. These may include mutations in the gene sequence, in particular ‘hotspot’ mutations which are known to give rise to oncological outcomes, copy number variations of genes, aberrant gene fusions or increased or decreased RNA expression as well as the tumour mutational burden. 
     By determining a range, in particular a wide range of biomarkers, associated with a wide spectrum of oncogenes, driver gene mutations, genomic instability as measured by tumour mutational burden, proliferation state and immune checkpoint regulation, a reliable diagnosis or monitoring of malignancy, such as a cancer, can be made with a high degree of sensitivity and also specificity (i.e with a low frequency of fake positives). 
     Biomarkers detected in the method of the invention may be selected from cell cycle regulated genes, actionable genetic variants, biomarkers associated with signalling networks and cell cycle checkpoints. 
     Particular examples of cell cycle regulated genes are proliferation markers and particular examples of these are set out in Table 1. The detection of increased levels of such markers may be indicative of the presence to malignancies or cancers. In general, these biomarkers are detected by analysis of RNA found in the sample, as would be understood in the art. 
     Actionable genetic variants which may be used as biomarkers in accordance with the invention include hotspot mutations such as those set out in Tables 2 and 7 hereinafter, aberrant gene fusions such as those set out in Tables 3 and 5 hereinafter, and copy number variants, such as those set out in Tables 4 and 6 hereinafter. 
     Conveniently, the method of the invention will involve the determination of at at least 5 biomarkers in any of Tables 1 to 7. Preferably, at least 8 biomarkers in any of Tables 1 to 7 will be determined. Conveniently, at least 10, 20, 30 or 40 of the biomarkers in Tables 1 to 7 will be determined. Preferably all of the biomarkers in Tables 1 to 7 as well as the tumour mutational burden will be determined. The larger the number of biomarkers used, the greater the probability that dysregulated genes will be identified, so that false negatives are avoided. 
     Conveniently, at least one of the biomarkers detected is taken from each of Tables 1 to 7 as well as the tumour mutational burden. This maximises the range of indicators and thus types of malignancy that may be detected. 
     Preferably, at least some of the biomarkers detected are associated with signalling networks, in particular those associated with the PD-L1 immune checkpoint. Such biomarkers are listed in Tables 5, 6 and 7 below. By analysing these biomarkers in particular, the susceptibility of a particular patient to anti-PD-1/PD-L1 directed therapy or-immunotherapy can be assessed. 
     Anti-PD-1/PD-L1 directed immunotherapies have become one of the most important group of agents used in immunotherapy. The PD-1/PD-L1 pathway is normally involved in promoting tolerance and preventing tissue damage in the setting of chronic inflammation. Programmed death 1 (PD-1) and its ligands, PD-L1 and PD-L2, deliver inhibitory signals that regulate the balance between T cell activation, tolerance, and immunopathology. The PD-L1 is a transmembrane protein that binds to the PD-1 receptor during immune system modulation. This PD-1/PD-L1 interaction protects normal cells from immune recognition by inhibiting the action of T-cells thereby preventing immune-mediated tissue damage. 
     Harnessing the immune system in the fight against cancer has become a major topic of interest. Immunotherapy for the treatment of cancer is a rapidly evolving field from therapies that globally and non-specifically stimulate the immune system to more targeted approaches. The PD-1/PD-L1 pathway has emerged as a powerful target for immunotherapy. A range of cancer types have been shown to express PD-L1 which binds to PD-1 expressed by immune cells resulting in imrnunosupressive effects that allows these cancers to evade tumour destruction, The PD-1/PD-L1 interaction inhibits T-cell activation and augments the proliferation of T-regulatory cells (T-regs) which further suppresses the effector immune response against the tumour. This mimics the approach used by normal cells to avoid immune recognition. Targeting PD-1/PD-L1 has therefore emerged as a new and powerful approach for immunotherapy directed therapies. 
     Targeting the PD-1/PD-L1 pathway with therapeutic antibodies directed at PD-1 and PD-L1 has emerged as a powerful therapy in those cancer types displaying features of immune evasion. Disrupting the PD-1/PD-L1 pathway with therapeutic antibodies directed against either PD-1 or PD-L1 (anti-PD-L1 or anti-PD-1 agents) results in restoration of effector immune responses with preferential activation of T-cells directed against the tumour. 
     There are many drugs in development targeting the PD-1/PD-L1 pathway including Pembrolizumab, atezolizumab, avelumab, nivolumab, durvalumab, PDR-001, BGB-A317, REG W2810, SHR-1210 
     Treatment with such agents or using a suitable immunotherapy approach may be indicated for patients determined to be susceptible to this type of therapy as a result of the presence of one or more of the biomarkers listed in Tables 5, 6 and 7 hereinafter. 
     Preferably, at least some of the biomarkers detected are directly associated with the PD-1/PD-L1 pathway, and so is a biomarker of a gene selected from CD279 (PD1), CD274 (PD1) or CD273 (PD2). In a particular embodiment, both PD-L1 and PD-1 are assessed together providing a much more powerful assessment of the PD-1/PD-L1 signalling axis. 
     Conveniently, the method measures both PD-L1 and PD-L2 gene amplification (copy number variant; CNV) which has been linked to mRNA overexpression and may represent a much more reliable parameter to predict response to PD-1/PD-L1 inhibitors. 
     Preferably, a range of biomarkers associated with different functions are selected. In this connection, it has been identified that choosing a range of biomarkers provides a better indication of susceptibility to treatment which targets an immune pathway and in particular, the PD-1/PD-L1. Mutations in other genes, in particular oncogenic mutations, are likely to give rise to so-called ‘neo-antigens’. Neo-antigens are newly formed antigens that have not previously been recognised by the immune system. When the neo-antigens are cancer-specific antigens, this can result in T-cell activation against cancer cells if the immune system is effective and not subject to suppression. Therefore, where neo-antigens are present, patients may show a more efficient and durable response to agents which act on immune pathways such as the PD-1/PD-L1 pathway. 
     Malignancies of the genito-urinary tract that may be detected or monitored in accordance with the method of the invention includes renal, bladder and prostate cancer. Preferably, these may be detected using method of the invention, and thus the method of the invention may be utilised in screening methods. Accordingly, the subjects may be apparently healthy individuals, and the screening may take place on large numbers of subjects. When a urine sample is used the non-invasive nature of the method of the invention makes it particularly suitable for this type of large-scale screening process. This may be advantageous in allowing the early stage detection of cancers of the genito-urinary tract in subjects who have not demonstrated any symptoms. 
     However, the method of the invention may also be utilised to monitor the progress of a malignancy in a subject who has been previously diagnosed with a cancer of the genito-urinary tract. In such cases, the method may be carried out repeatedly over a period of time, for example, over a period of weeks, months or even years, whilst a particular treatment or therapy, such as chemotherapy, immunotherapy or radiotherapy, is being administered, and the results used to monitor the efficacy of a particular treatment or therapy on the progress of the disease. In particular, the method of the invention can be used to measure a therapeutic response, for example by detecting a decrease in particular biomarkers such as cell cycle regulated proteins, or a decrease in mutations associated with oncogenes or tumours. 
     The results may then be used to direct the continued treatment, for example by modifying or expanding on the existing treatment. Preferably, the method of the invention, by identifying specific biornarkers, which include actionable mutations and immune checkpoint regulators, allows for the development of targeted therapies and immunotherapies that are specific for a subject&#39;s particular cancer, such as anti PD-1 or PD-L1 therapies as discussed above. 
     Conveniently, the subject is suffering from haematuria. Using the method of the invention, it would be possible to identify the very low percentage of haematuria patients who harbour a cancer of the genito-urinary tract. This will circumvent the costly and invasive procedure of cystoscopy for patients with benign haematuria. 
     Preferably, one of the biomarkers measured in accordance with the method of the invention is the tumour mutational burden (TMB). Unlike protein-based biomarkers, TMB is a quantitative measure of the total number of mutations per coding area of a tumor genome. 
     Since only a fraction of somatic mutations gives rise to neo-antigens, measuring the total number of somatic mutations (or TMB) within a particular coding area acts as a proxy for neo-antigen burden. The TMB may be measured using exome sequencing using Next Generation Sequence equipment in particular of 409 genes covering a 1.7 Mb coding region (Table 8). From all variants detected including non-synonymous somatic mutations (SNVs and indels), all likely germline polymorphisms and predicted oncogenic drivers are removed from the analysis. The latter is performed to prevent ascertainment bias of sequencing known cancer genes. Tumour Mutational Burden is then calculated as mutations per Mb of DNA sequenced (mut/Mb). Analysis of TMB is both quantitative and qualitative and is reported as a metric (mut/Mb) as well as status. Status of High is classified as &gt;10 mut/Mb, and Low&lt;10 mut/Mb. An example of measurement of TMB is shown in  FIG. 2 . 
     It has been recognised that TMB is a predictor of response to, for example, anti-PD-1/PD-L1/PD-L2 checkpoint inhibitors, and therefore it will provide a useful possible biomarker in the method of the invention. 
     In accordance with the invention, biomarkers are identified using DNA or RNA analysis or a combination thereof. Nucleic acids (DNA and/or RNA) extracted from a sample as described above is used to construct a library, using conventional methods, for example as outlined below. The library is enriched as necessary and then used as a template for enrichment, again using conventional methods as outlined below. Analysis of this is then carried out using semiconductor Next Generation Sequencing equipment such as sold by ThermoFisher under the trade mark Ion Torrent using the method set out in the present invention. 
     Preferably the method of the invention uses targeted semiconductor sequencing to cover the entire coding regions of the biomarkers of interest. Amplicons can be designed to overlap for sequence coverage redundancy and to be able to amplify fragmented DNA templates obtained from, for example, routine diagnostic Paraffin Wax Embedded Tissue (PWET) samples. The method is therefore able to identify a wide variety of actionable genetic variants including point mutations, deletions, duplications, and insertions. 
     Thus, conveniently, the method of the invention utilises Next Generation Sequencing technology to quantitate biomarkers present in a sample. In this way, the method of the invention can be carried out using only a small amount of sample, such as may be found in a urine sample, and it may be optimised for analysis of degraded DNA/RNA. Further, a quantitative test gives a much more accurate method than immunohistochemistry 
     Some biomarkers may be most readily identified by an analysis of gene expression, for example, using quantitative measurement of RNA transcripts. Thus in a particular embodiment, the method includes the step of analysing levels of RNA, wherein a change in the expression level as compared to wild type is indicative of malignancy. Biomarkers which may be identified in this way are listed in Table 1 and 5 below. Conveniently, in tumours where the PD-L1 checkpoint is implicated, elevated levels of RNA from CD273 (PD-L2), CD274 (PD-L1) and CD279 (PD-1) may be detected. 
     Preferably, gene expression at multiple exon-intron loci across mRNAs such as PD-L1 and PD-1 mRNAs is carried out, which is then coupled to a bioinformatics program that normalises gene expression across the whole gene allowing very accurate quantitative measurement of RNA expression levels. The analytical validation of PD-L1 mRNA expression is shown in  FIG. 3 . 
     In addition, elevated levels of RNA of NFATC1 (Nuclear Factor Of Activated T-Cells 1), PIK3CA (Phosphatidylinositol-4,5-Bisphosphate 3-Kinase Catalytic Subunit Alpha), PIK3CD (Phosphatidylinositol-4,5-Bisphosphate 3-Kinase Catalytic Subunit Delta), PRDM1 (PR domain zinc finger protein 1), PTEN, (Phosphatase and tensin homology, PTPN11 (Tyrosine-protein phosphatase non-receptor type 11), MTOR (mechanistic target of rapamycin), HIF1a (Hypoxia-inducible factor 1-alpha) and FOXO1m (forkhead box class 01 mutant) may be quantified. In addition, the method of the invention may detect loss of gene expression of the mismatch repair genes MLH1, PMS2, MSH6 and MLH2. Loss of function of one of these genes results in genomic instability leading to increased expression of tumour surface neo-antigens and thereby increases response rates to anti-cancer directed immunotherapies. 
     Levels of any specific RNA which would be considered to be elevated as compared to normal and therefore indicative of a positive biomarker are shown in Table 5, where expression cut-off values are listed. 
     Conveniently, DNA from said sample is analysed and a mutation in a gene encoding a biomarker is detected that impacts on expression or function of the gene or gene product. Particular examples where actionable mutation, for example via an SNV hotspot mutation occurs, are found biomarkers listed in Tables 2 and 7 below. An example of SNV detection is shown in  FIG. 4 . In particular, such biomarkers may be found in genes selected from the group consisting of ALK (anaplastic lymphoma kinase gene), BRAF (B-Raf gene), CD274 (PD-L1 gene), EGFR (epidermal growth factor receptor gene), ERBB2 (Receptor tyrosine-protein kinase erbB-2 or human epidermal growth factor receptor 2 gene), FGFR (fibroblast growth factor receptor gene), KIT (KIT proto-oncogene receptor tyrosine kinase gene), KRAS (K-ras gtpase gene), MET (MET proto-oncogene, receptor tyrosine kinase) or N RAS (N-ras protooncogene, GTPase gene). Specific mutations of these genes which are indicative of an oncogenic mutation are set out in Tables 2 and 7 below. These mutations may be detected using conventional techniques, as illustrated hereinafter. 
     Preferably, biomarkers resulting from gene rearrangement leading to aberrant gene fusions may be detected, and these are listed in Tables 3 and 5 below. An example of detection of a gene fusion is shown in  FIG. 5 . For example, it is recognised that the ALK gene may be fused with portions of the echinoderm microtubule-associated protein-like 4 (EML4) gene in some cancers, that FGFR genes may form fusions for instance with kinases in others, and that MET gene may become fused with other genes including for example TFG, CLIP2 or PTRZ1. Detection of any such fusion genes/proteins will provide a positive biomarker indication. 
     Conveniently, the analysis identifies the presence of copy number variants which may lead to increased expression. DNA from said sample can be analysed and the presence of a variation in copy number of a gene encoding a biomarker can be detected. Suitable biomarkers in this case are listed in Tables 4 and 6 below and include for example, ERBB2, FGFR, FGFR1, FCFR2, FGFR3, FGFR4, CD273 (PD-L2 gene) or CD273. In such cases, for example, an increase in copy number may result in amplification or increased expression and is indicative of malignancy. An example of CNV detection is shown in  FIG. 6 . In such cases, the greater the increase in copy number the higher the susceptibility to therapies which target such genes may be indicated. 
     The various analytical methods and techniques for the determination of the biomarkers are suitably carried out in a high-throughput assay platform as far as possible. 
     Conveniently, an algorithm indicative of the presence and/or level of malignancy is applied to the results obtained as described above. In particular, a score of ‘0’ is applied to results which show no or minimal changes over wild type or normal expression profiles of the various biomarkers (e.g. 0 to 500 normalised Reads per Million Reads (nRPM) in the case of RNA expression), whereas a score of 1 is applied to any mutations or variations noted. In the case of multiple copies of a particular gene being detected, a higher score may be allocated depending upon the number of copies detected. For example, a score of 1 may be applied to an increase of just 2 or 3 copies, a score of 2 may be applied to from 4 to 8 copies and a score of of 3 for &gt;8 copies. A higher score may also be applied in the case of high or very high RNA expression level changes (e.g. 2 for 500-1500 nRPM and 3 for &gt;1500 nRPM). 
     The TMB score discussed above may be included in such an algorithm. In this case, a score of 1 may be allocated for a ‘low’ TMB of &lt;10 mut/Mb and a score of 2 may be allocated for a high TMB of &gt;10 mut/Mb. 
     A particular example of such an algorithm is shown in  FIG. 7 . In that case, a score 0 is indicative of no malignancy being present, a score of 1-2 is indicative of a malignancy, with intermediate specificity and high sensitivity, a score of 3-5 is indicative of a malignancy, with high specificity and high sensitivity and a score in excess of 6 is indicative of a malignancy, with very high specificity and very high sensitivity. Scores for A to E may not be present depending upon the analysis which has been undertaken. For example, analysis of SNV+CNV+RNA expression may take place so A+B+E would lead to the polygene detection score. 
     Suitably, the algorithm is integrated into the high throughput system used to derive the biomarker data, so that the results are generated. Such systems will comprise a processor and a memory storing instructions to receive the data obtained using the method of the invention, analyse and transform it to produce a ‘score’ indicative of the presence and specificity of malignancy using the algorithm described above. These results may then be suitably displayed on a graphic interface. In some cases, the memory will comprise a non-transitory computer-readable medium. Such systems and mediums form an aspect of the invention. 
     Genetic variants detected using the method of the invention may be linked via a suitable bioinformatics platform to a wide range of potential inhibitors of components of the DNA replication initiation pathway including Cdc7 inhibitors from those in clinical trials through to FDA/EMA approved therapies. 
     Once identified in this way, subjects who are or have been identified as having a malignancy of the genitourinary tract may be classified depending upon their apparent susceptibility to a certain type of therapy, and may be treated accordingly, using suitable agents. These will be administered in line with normal clinical practice. Thus, conveniently, the method of the invention further comprises generating a customised recommendation for treatment, based upon the results obtained. 
     The method of the invention addresses the problems and severe limitations of current ELISA based diagnostics. In particular, the method is amenable for full automation. It may be quantitative in particular when using the algorithm described above, and does not require subjective human interpretation by a pathologist. In particular embodiments, the method of the invention does not require the input of a pathologist for manual assessment of biomarkers. The whole test is fully automated and therefore is not subject to inter-observer or intra-observer variability. 
     Conveniently, the method of the invention provides a comprehensive and integrated readout of all biomarkers linked to genito-urinary malignancy, The algorithm, as described above, can be used to integrate all these predictive biomarkers into a Polygenic Detection Score (PDS) as illustrated schematically in  FIG. 7  hereinafter. 
     This quantitative nature of the assay means precise cut points may be identified that predict response to specific therapies such as anti-PD-1/PD-L1/PD-L2 directed immunotherapies. By providing a clear quantitative result, any subjectivity and inter-observer variability associated interpretation of conventional assays is avoided. 
     The method of the invention is able, for example, to assess the complete PD-1/PD-L1 signalling axis in an integrated approach which cannot be achieved using a single IHC biomarker. Using the method of the invention, activation/increased expression of many elements in the PD-1/PD-L1 signalling axis including PD-1, PD-L1, PD-L2, NFATC1, PIK3CA, PIK3CD, PRDM1, PTEN, PTPN11, MTOR, HIF1A, IFN-gamma and FOXO1 may be assessed. 
     Preferably, the method of the invention includes assessment of oncogenic mutations that are linked to response to anti-PD-1/PD-L1/PD-L2 directed immunotherapies. A broad range of oncogenes are assessed for gene aberration, mutations, fusions and amplification (Tables 5, 6 and 7) with linkage to anti-PD-1/PD-L1 directed immunotherapies. Many of these genes are components of growth signalling networks. Oncogenic activation of these growth signalling networks leads to induction of the PD-1 ligands, PD-L1 and PD-L2. 
     Thus conveniently, the method of the invention may provide a fully automated test that has been designed to analyse all components involved in the PD-1/PD-L1 immune regulatory anti-cancer response including tumour cells, T immune cells and antigen presenting cells (APSs). This provides a quantitative integrated picture of all components involved in the PD-1/PD-L1/PD-L2 immune regulatory cancer response in terms of all cell types (tumour cell and immune cells) and at all levels of the PD-1/PD-L1 signalling axis. 
     In addition to a comprehensive analysis such as of the PD-1/PD-L1/PD-L2 signalling axis, the method of the invention has been designed to detect all oncogenic mutations that are indicative of genito-urinary cancers. Integrating information derived from such mutations allows for a customised recommendation for therapy to be prepared. In particular, the association of the PD-1/PD-L1/PD-L2 signalling axis with oncogenic predictors provides the most powerful predictor of response to anti-PD-1/PD-L1/PD-L2 directed immunotherapies. For example detection of (i) mutated and (ii) amplified PD-L1 is also linked to (iii) increased expression of PD-L1. This provides three independent but linked predictors of response to anti-PD-1/PD-L1/PD-L2 directed immunotherapies. By integrating information from the PD-1/PD-L1/PD-L2 signalling axis with oncogenic activation of growth signalling networks, the algorithm described herein is able to precisely identify those patients most likely to respond to anti-PD-1/PD-L1/PD-L2 directed immunotherapies. 
     The method of the present invention can use high throughput analytical platforms to match patient&#39;s tumours to specific targeted therapies, from FDA/EMA approved, ESMO/NCCN guideline references and in all phases of clinical trials worldwide. Examples of linkages between genetic variant and targeted therapies are shown in  FIGS. 2, 4, 5 and 6 . 
     The Thermo Fisher Ion Torrent™ platform has been utilised to develop the assay of the invention. The aim was, for the reasons explained above, to provide a means for not only detecting genito-urinary cancers, but also providing a comprehensive picture of the tumours, to allow appropriate therapies to be prescribed. 
     Use of a single integrated test as described herein, and using sets of primers (see Example 1, 1.1) spanning the exon/intron boundaries of both immune regulatory genes at multiple loci across the both genes and has been designed in such a way that they are able to amplify the degraded RNA material extracted from routine formalin fixed urine samples. The accurate and precise measurement of these multiple components, such as those of the PD-1/PD-L1/PD-L2 signalling axis allows for the provision of a quantitative integrated profile of this immune regulatory pathway. The output from this assay platform can then be used to provide precise cut offs for these immune regulatory biomarkers which are predictive of therapeutic response to different therapies including in particular anti-PD-1/PD-L1/PD-L2 directed therapies. 
     Amplicons have also been designed to overlap for sequence coverage redundancy to optimise amplification of fragmented DNA templates obtained from routine diagnostic PWET samples. The DNA analysis is designed to detect oncogenic mutations and gene copy aberrations which have been identified not only as indicators of malignancy but also predictors of response to specific therapies such as anti-PD-1/PD-L1/PD-L2 directed immunotherapies. In addition to RNA expression analysis of PD-1/PD-L1/PD-L2 signalling molecules, RNA expression analysis can be performed to detect oncogenic fusion transcripts identified as predictors of response to anti-PD-1/PD-L1/PD-L2 directed immunotherapies. 
     Kits suitable for carrying out the method of the invention are novel and form a further aspect of the invention. These may comprise combinations of amplification primers required to detect 3 or more of the biomarkers listed in Tables 1 to 7 and/or the TMB. 
     Furthermore, apparatus arranged to carry out the method described above are also novel and form a further aspect of the invention. In particular, apparatus will comprise means for carrying out DNA and/or RNA analyses as described above, linked to a computer programmed to implement the algorithm as described above. A computer or a machine-readable cassette programmed in this way forms yet a further aspect of the invention. 
    
    
     
       DETAILED DESCRIPTION OF THE INVENTION 
       The invention will now be described by way of example with reference to the accompanying diagrammatic drawings in which: 
         FIG. 1  shows a schematic diagram illustrating the method of the invention. Tumour cells and/or cell free DNA/RNA are isolated from urine, a liquid biopsy or bodily fluid such as blood. DNA and RNA is then analysed using next generation sequencing profiling specifically covering cancer genes linked to targeted therapies and immunotherapies. These cancer genes represent companion diagnostics (CDx) to the corresponding targeted therapies/immunotherapies. In addition to providing personalized medicine with regard to treatment selection integrated multiparameter analysis of these companion diagnostic, actionable genetic mutations can also be utilized in cancer detection, screening, analysis of haematuira patients, surveillance and monitoring of therapeutic response. 
         FIG. 2  shows an example of measurement of Tumour Mutational Burden (TMB). Panel A: shows the TMB readout and Panel B shows bioinformatics linkage with relevant evidence based cognate anti-PD-L1/PD-1 immunotherapies. 
         FIG. 3  shows analytical validation of the quantative measurements of mRNA levels by NGS in FFPE samples using PD-L1 expression as an example. Reads per million (RPM) counts is used as a surrogate measure of mRNA gene expression. Four cell line controls stably expressing variable levels of PD-L1 assessed by PD-L1 protein were selected representing tumour proportions score of 0%, 25%, 75% and 100% as assessed at the protein level by immunocytochemistry. The RPM counts are shown for two primer sets spanning exon/intron boundaries for the PD-L1 gene. 
       A) shows RPM counts from the two different amplicons targeting the PD-L1 gene. 
       B) shows PD-L1 RPM counts (mRNA) generated by the method of the present invention compared to PD-L1 protein expression assessed by IHC. 
       C) shows photomicrographs of four cell line controls immunohistochemically stained with an antibody against PD-L1 and expressing different levels of PD-L1 protein. The data shows that the method of the present invention provides an accurate quantative assessment of mRNA expression when applied to routine formalin fixed paraffin wax embedded samples. Notably the RPM shows a rapid increase in parallel with protein expression as measured by ICC across cut point values of 1%, 10%, 25% and 50%. These are the clinically important cut points defined by a number of approved ICC Cdx PD-L1 assays for the identification of responders to anti-PD-L1/PD-1 directed immunotherapies (eg VENTANA PD-L1 (SP263) Assay, VENTANA PD-L1 (SP142) Assay, Dako PD-L1 IHC 28-8 pharmDx, Dako PD-L1 IHC 22C3 pharmDx). Application of this method using multiple primer sets spanning exon/intron boundaries across all genes subject to expression analysis (see Tables 1, 3 and 5 is incorporated into the method of the present invention. 
         FIG. 4  shows detection of Single Nucleotide Variants (SNVs) and linked cognate targeted therapies. In this tumour sample two actionable SNVs were detected in the PIK3CA oncogene and the p53 tumour suppressor gene (Panel A). Panel B shows identified variant details and the bioinformatics linked cognate targeted therapies are shown in Panel C. 
         FIG. 5  shows detection of oncogenic fusion genes and linked cognate targeted therapies. In this tumour sample an oncogenic fusion involving the BRAF gene and the mitochondrial ribosomal gene MRPS33 were detected (Panel A). The bioinformatics linked cognate targeted therapies are shown in Panel B (same Key as  FIG. 4 ). 
         FIG. 6  shows detection of gene amplification variants and linked cognate targeted therapies. In this tumour sample gene amplification was detected in relation to two cancer driver genes AKT2 and CCNE1 (Panel A and B). The bioinformatics linked cognate targeted therapies are shown in Panel C (same Key as  FIG. 4 ). 
         FIG. 7  is is a schematic diagram illustrating a method embodying the invention including a polygenic prediction score algorithm used to interpret the results and provide an indication of malignancy (same Key as  FIG. 4 ). 
     
    
    
     EXAMPLE 1 
     Detection of Genetic Biomarkers 
     1.1 Overview of Primer Design 
     Primers for detecting each of the biomarkers listed in Tables 1 to 7 were designed in accordance with conventional practice using techniques known to those skilled in the art. In general, primer of 18-30 nucleotides in length are optimal with a melting temperature (T m ) between 65° C.-75° C. The GC content of the primers should be between 40-60%, with the 3′ of the primer ending in a C or G to promote binding. The formation of secondary structures within the primer itself is minimised by ensuring a balanced distribution of GC-rich and AT-rich domains. Intra/inter-primer homology should be avoided for optimal primer performance. 
     1.1.1 Primers for Copy Number Detection 
     Primers were designed, as discussed in 1.1, to span regions in the genes listed in Tables 4 and 6. Several amplicons per gene were designed. The depth of coverage is measured for each of these amplicons. The copy number amplification and deletion algorithm is based on a hidden Markov model (HMM). Prior to copy number determination, read coverage is corrected for GC bias and compared to a preconfigured baseline. 
     1.1.2 Primer for Hotspot Detection 
     Primers were designed, as discussed in 1.1, to target specific regions prone to oncogenic somatic mutations as listed in Table 2 and in consideration with the general points discussed above. 
     1.1.3 Primers for RNA Expression Analysis 
     Extracted RNA is processed via RT-PCR to create complementary DNA (Cdna) which is then amplified using primers designed, as discussed in 1.1. Multiple primer sets were designed to span the exon/intron boundaries across all genes subject to expression analysis as listed Table 1 and 5 
     1.1.4 Primers for RNA Fusion Detection 
     A pair of targeted exon-exon breakpoint assay primers were designed, as discussed in 1.1, for each fusion listed in Tables 3 and 5. Primers flanking the fusion breakpoint generate specific fusion amplicons which are aligned to the reference sequence allowing for identification of fusion genes. Expression imbalance assays enable the equivalent expression levels to be monitored in normal samples, with an imbalance between the 5′ and 3′ assays indicating samples have a fusion breakpoint. 
     1.2 DNA and RNA Extraction 
     DNA and RNA was extracted from a formalin fixed urine sample. Two xylene washes were performed by mixing 1 ml of xylene with the sample. The samples were centrifuged and xylene removed. This was followed by 2 washes with 1 ml of pure ethyl alcohol. After the samples were air-dried, 25 μl of digestion buffer, 75 μl of nuclease free water and 4 μl of protease were added to each sample. Samples were then digested at 55° C. for 3 hours followed by 1 hour digestion at 90° C. 
     120 μl of Isolation additive was mixed with each sample and the samples added to filter cartridges in collection tubes and centrifuged. The filters were moved to new collection tubes and kept in the fridge for DNA extraction at a later stage. The flow-through was kept for RNA extraction and 275 μl of pure ethyl alcohol was added and the sample moved to a new filter in a collection tube and centrifuged. After a wash of 700 μl of Wash 1 buffer the RNA was treated with DNase as follows; a DNase mastermix was prepared using 6 μl of 10× DNase buffer, 50 μl of nuclease free water and 4 μl of DNase per sample. This was added to the centre of each filter and incubated at room temperature for 30 minutes. 
     After the incubation 3 washes were performed using Wash 1, then Wash 2/3 removing the wash buffer from the collection tubes after each centrifugation. The filters were moved to a new collection tube and the elution solution (heated to 95° C.) was added to each filter and incubated for 1 minute. After centrifuging the sample, the filter was discarded and the RNA collected in the flow through moved to a new low bind tube. 
     The DNA in the filters were washed with Wash 1 buffer, centrifuged and flow through discarded. The DNA was treated with RNase (50 μl nuclease water and 10 μl RNase) and incubated at room temperature for 30 minutes. As above with the RNA, three washes were completed and the samples eluted in elution solution heated at 95° C. 
     1.3 DNA and RNA Measurement 
     The quantity of DNA and RNA from the extracted samples were measured using the Qubit® 3.0 fluorometer and the Qubit® RNA High Sensitivity Assay kit (CAT: Q32855) and Qubit® dsDNA High Sensitivity Assay kit (Cat: Q32854). 1 μl of RNA/DNA combined with 199 μl of combined HS buffer and reagent were used in Qubit® assay tubes for measurement. 10 μl of standard 1 or 2 were combined with 190 μl of the buffer and reagent solution for the controls. 
     1.4 Library Preparation 
     RNA samples were diluted to 5 ng/μl if necessary and reverse transcribed to cDNA in a 96 well plate using the SuperScript VILO cDNA synthesis kit (CAT 11754250). A mastermix of 2 μl of VILO, 1 μl of 10× SuperScript III Enzyme mix and 5 μl of nuclease free water was made for all of the samples. 8 μl of the MasterMix was used along with 2 μl of the RNA in each well of a 96 well plate. The following program was run: 
     
       
         
           
               
               
               
             
               
                   
                   
               
               
                   
                 Temperature 
                 Time 
               
               
                   
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
            
               
                   
                 42° C. 
                 30 
                 min 
               
               
                   
                 85° C. 
                 5 
                 min 
               
            
           
           
               
               
               
            
               
                   
                 10° C. 
                 Hold 
               
               
                   
                   
               
            
           
         
       
     
     Amplification of the cDNA was then performed using 4 μl of 6 RNA primers covering multiple exon-intron loci across the gene, 4 μl of AmpliSeq HiFi* 1  and 2 μl of nuclease free water into each sample well. The plate was run on the thermal cycler for 30 cycles using the following program: 
     
       
         
           
               
               
               
               
             
               
                   
               
               
                 Stage 
                 Step 
                 Temperature 
                 Time 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
            
               
                 Hold 
                 Activate the enzyme 
                 99° C. 
                 2 
                 min 
               
               
                 Cycle (30 cycles) 
                 Denature 
                 99° C. 
                 15 
                 sec 
               
               
                   
                 Anneal and extend 
                 60° C. 
                 4 
                 min 
               
            
           
           
               
               
               
               
            
               
                 Hold 
                 — 
                 10° C. 
                 Hold 
               
               
                   
               
            
           
         
       
     
     DNA samples were diluted to 5 ng/μl and added to AmpliSeq Hifi* 1 , nuclease free water and set up using two DNA primer pools (5 μl of pool 1 and 5 μl of pool 2) in a 96 well plate. The following program was run on the thermal cycler: 
     
       
         
           
               
               
               
               
             
               
                   
               
               
                 Stage 
                 Step 
                 Temperature 
                 Time 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
            
               
                 Hold 
                 Activate the enzyme 
                 99° C. 
                 2 
                 min 
               
               
                 Cycle (18 cycles) 
                 Denature 
                 99° C. 
                 15 
                 sec 
               
               
                   
                 Anneal and extend 
                 60° C. 
                 4 
                 min 
               
            
           
           
               
               
               
               
            
               
                 Hold 
                 — 
                 10° C. 
                 Hold (up to 
               
               
                   
                   
                   
                 16 hours) 
               
               
                   
               
            
           
         
       
     
     Following amplification, the amplicons were partially digested using 2 μl of LIB Fupa* 1 , mixed well and placed on the thermal cycler on the following program: 
     
       
         
           
               
               
               
             
               
                   
                   
               
               
                   
                 Temperature 
                 Time 
               
               
                   
                   
               
             
            
               
                   
                 50° C. 
                 10 min 
               
               
                   
                 55° C. 
                 10 min 
               
               
                   
                 60° C. 
                 20 min 
               
               
                   
                 10° C. 
                 Hold (for up to 1 hour) 
               
               
                   
                   
               
            
           
         
       
     
     4 μl of switch solution* 1 , 2 μl of diluted Ion XPRESS Barcodes 1-16 (CAT: 4471250) and 2 μl of LIB DNA ligase* 1  were added to each sample, mixing thoroughly in between addition of each component. The following program was run on the thermal cycler: 
     
       
         
           
               
               
               
             
               
                   
                   
               
               
                   
                 Temperature 
                 Time 
               
               
                   
                   
               
             
            
               
                   
                 22° C. 
                 30 min 
               
               
                   
                 72° C. 
                 10 min 
               
               
                   
                 10° C. 
                 Hold (for up to 1 hour) 
               
               
                   
                   
               
            
           
         
       
     
     The libraries were then purified using 30 μl of Agencourt AMPure XP (Biomeck Coulter cat: A63881) and incubated for 5 minutes. Using a plate magnet, 2 washes using 70% ethanol were performed. The samples were then eluted in 50 μl TE. 
     1.5 qPCR 
     The quantity of library was measured using the Ion Library TaqMan quantitation kit (cat: 4468802). Four 10-fold serial dilutions of the  E. coli  DH10B Ion control library were used (6.8 pmol, 0.68 pmol, 0.068 pmol and 0.0068 pmol) to create the standard curve. Each sample was diluted 1/2000, and each sample, standard and negative control were tested in duplicate. 10 μl of the 2× TaqMan mastermix and 1 μl of the 20× TaqMan assay were combined in a well of a 96 well fast thermal cycling plate for each sample. 9 μl of the 1/2000 diluted sample, standard or nuclease free water (negative control) were added to the plate and the qPCR was run on the ABI StepOnePlus™ machine (Cat: 4376600) using the following program: 
     
       
         
           
               
               
               
               
             
               
                   
                   
               
               
                   
                 Stage 
                 Temperature 
                 Time 
               
               
                   
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
            
               
                   
                 Hold (UDG incubation) 
                 50° C. 
                 2 
                 min 
               
               
                   
                 Hold (polymerase activation) 
                 95° C. 
                 20 
                 sec 
               
               
                   
                 Cycle (40 cycles) 
                 95° C. 
                 1 
                 sec 
               
               
                   
                   
                 60° C. 
                 20 
                 sec 
               
               
                   
                   
               
            
           
         
       
     
     Samples were diluted to 100 pmol using TE and 10 μl of each sample pooled to either a DNA tube or RNA tube. To combine the DNA and RNA samples, a ratio of 80:20 DNA:RNA was used. 
     1.6 Template Preparation 
     The Ion One Touch™ 2 was initialized using the Ion S5 OT2 solutions and supplies* 2  and 150 μl of breaking solution* 2  was added to each recovery tube. The pooled RNA samples were diluted further in nuclease free water (8 μl of pooled sample with 92 μl of water) and an amplification mastermix was made using the Ion S5 reagent mix* 2  along with nuclease free water, ION S5 enzyme mix* 2 , Ion sphere particles (ISPs)* 2  and the diluted library. The mastermix was loaded into the adapter along with the reaction oil* 2 . The instrument was loaded with the amplification plate, recovery tubes, router and amplification adapter loaded with sample and amplification mastermix. 
     1.7 Enrichment 
     For the enrichment process, melt off was made using 280 μl of Tween* 2  and 40 μl of 1M Sodium Hydroxide. Dynabeads® MyOne™ Streptavidin C1 (CAT:65001) were washed with the OneTouch wash solution* 2  using a magnet. The beads were suspended in 130 μl of MyOne bead capture solution* 2 . The ISPs were recovered by removing the supernatant, transferring to a new low bind tube and subsequently washed in 800 μl of nuclease free water. After centrifuging the sample and removing the supernatant of water, 20 μl of template positive ISPs remained. 80 μl of ISP resuspension solution* 2  was added for a final volume of 100 μl. 
     A new tip, 0.2 ml tube and an 8 well strip was loaded on the OneTouch™ ES machine with the following: 
     Well 1: 100 μl of template positive ISPs 
     Well 2: 130 μl of washed Dynabeads® MyOne™ streptavidin C1 beads, resuspended in MyOne bead capture 
     Well 3: 300 μl of Ion OneTouch ES Wash solution* 2    
     Well 4: 300 μl of Ion OneTouch ES Wash solution 
     Well 5: 300 μl of Ion OneTouch ES Wash solution 
     Well 6: Empty 
     Well 7: 300 μl of melt off 
     Well 8: Empty 
     Following the run which takes approximately 35 minutes, the enriched ISPs were centrifuged, the supernatant removed and washed with 200 μl of nuclease free water. Following a further centrifuge step and supernatant removal, 10 μl of ISPs remained. 90 μl of nuclease free water was added and the beads were resuspended. 
     1.8 Sequencing 
     The Ion S5 system™ (Cat: A27212) was initialized using the Ion S5 reagent cartridge, Ion S5 cleaning solution and Ion S5 wash solutions* 2 . 
     5 μl of Control ISPs* 2  were added to the enriched sample and mixed well. The tube was centrifuged and the supernatant removed to leave the sample and control ISPs. 15 μl of Ion S5 annealing buffer* 2  and 20 μl of sequencing primer* 2  were added to the sample. The sample was loaded on the thermal cycler for primer annealing at 95° C. for 2 minutes and 37° C. for 2 minutes. Following thermal cycling, 10 μl of Ion S5 loading buffer* 2  was added and the sample mixed. 
     50% annealing buffer was made using 500 μl of Ion S5 annealing buffer* 2  and 500 μl of nuclease free water* 2 . 
     The entire sample was then loaded into the loading port of an Ion 540™ chip (Cat: A27766) and centrifuged in a chip centrifuge for 10 minutes. 
     Following this, 100 μl of foam (made using 49 μl of 50% annealing buffer and 1 μl of foaming solution* 2 ) was injected into the port followed by 55 μl of 50% annealing buffer into the chip well, removing the excess liquid from the exit well. The chip was centrifuged for 30 seconds with the chip notch facing out. This foaming step was repeated. 
     The chip was flushed twice using 100 μl of flushing solution (made using 250 μl of isopropanol and 250 μl of Ion S5 annealing buffer) into the loading port, and excess liquid removed from the exit well. 3 flushes with 50% annealing buffer into the loading port were then performed. 60 μl of 50% annealing buffer was combined with 6 μl of Ion S5 sequencing polymerase* 2 . 65 μl of the polymerase mix was then loaded into the port, incubated for 5 minutes and loaded on the S5 instrument for sequencing which takes approximately 3 hours and 16 hours for data transfer. *1 From the Ion Ampliseg™ library 2.0 (Cat: 4480441)*2 From the Ion 540™ OT2 kit (Cat: A27753) 
     1.9 Data Analysis 
     1.9.1 DNA CNV Analysis 
     Copy number variations (CNVs) represent a class of variation in which segments of the genome have been duplicated (gains) or deleted (losses). Large, genomic copy number imbalances can range from sub-chromosomal regions to entire chromosomes. 
     Raw data were processed on the Ion S5 System and transferred to the Torrent Server for primary data analysis performed using the Oncomine Comprehensive Assay Baseline v2.0. This plug-in is included in Torrent Suite Software, which comes with each Ion Torrent™ sequencer. Copy number amplification and deletion detection was performed using an algorithm based on a hidden Markov model (HMM). The algorithm uses read coverage across the genome to predict the copy-number. Prior to copy number determination, read coverage is corrected for GC bias and compared to a preconfigured baseline. 
     The median of the absolute values of all pairwise differences (MAPD) score is reported per sample and is used to assess sample variability and define whether the data are useful for copy number analysis. MAPD is a per-sequencing run estimate of copy number variability, like standard deviation (SD). If one assumes the log 2 ratios are distributed normally with mean 0 against a reference a constant SD, then MAPD/0.67 is equal to SD. However, unlike SD, using MAPD is robust against high biological variability in log 2 ratios induced by known conditions such as cancer. Samples with an MAPD score above 0.5 should be carefully reviewed before validating CNV call. 
     The results from copy number analysis after normalisation can be visualised from the raw data. 
     Somatic CNV detection provides Confidence bounds for each Copy Number Segment. The Confidence is the estimated percent probability that Copy Number is less than the given Copy Number bound. A lower and upper percent and the respective Copy Number value bound are given for each CNV. Confidence intervals for each CNV are also stated, and amplifications of a copy number&gt;6 with the 5% confidence value of ≥4 after normalization and deletions with 95% Cl≤1 are classified as present. 
     1.9.2 DNA Hotspot Analysis 
     Raw data were processed on the Ion S5 System and transferred to the Torrent Server for primary data analysis performed using the custom workflow. Mapping and alignment of the raw data to a reference genome is performed and then hotspot variants are annotated in accordance with the BED file. Coverage statistics and other related QC criteria are defined in a vcf file which includes annotation using a rich set of public sources. Filtering parameters can be applied to identify those variants passing QC thresholds and these variants can be visualised on IGV. In general, the rule of classifying variants with &gt;10% alternate allele reads, and in &gt;10 unique reads are classified as ‘detected’. 
     Several in-silico tools are utilised to assess the pathogenicity of identified variants these include PhyloP, SIFT, Grantham, COSMIC and PolyPhen-2. 
     1.9.3 RNA Expression Analysis 
     Raw data were processed on the Ion S5 System and transferred to the Torrent Server for primary data analysis performed using the AnnpliSeqRNA plug-in. This plug-in is included in Torrent Suite Software, which comes with each Ion Torrent™ sequencer. The AnnpliSeqRNA plugin uses the Torrent Mapping Alignment Program (TMAP). TMAP is optimized for Ion Torrent™ sequencing data for aligning the raw sequencing reads against a custom reference sequence set containing all transcripts targeted by the AmpliSeq kit. The assay specific information is contained within a bespoke BED file. To maintain specificity and sensitivity, TMAP implements a two-stage mapping approach. First, four alignment algorithms, BWA-short, BWA-long, SSAHA, and Super-maximal Exact Matching we employed to identify a list of Candidate Mapping Locations (CMLs). A further aligning process is performed using the Smith Waterman algorithm to find the final best mapping. As part of the annpliSeqRNA plugin, raw read counts of the targeted genes is performed using samtools (samtools view -c -F -4 -L bed_file bam_file). Ion AmpliSeq RNA normalization for a given sample is automatically calculated by the plug-in as the number of reads mapped per gene per million reads mapped or RPM. This figure is then log 2 -transformed normalized reads per million, (nRPM). 
     The bespoke BED file is a formatted to contain the nucleotide positions of each amplicon per transcript in the mapping reference. Reads aligning to the expected amplicon locations and meeting filtering criteria such as minimum alignment length are reported as percent “valid” reads. “Targets Detected” is defined as the number of amplicons detected (≥10 read counts) as a percentage of the total number of targets. 
     After mapping, alignment and normalization, The AnnpliSeqRNA plug-in provides data on QC metrics, visualization plots, and normalized counts per gene that corresponds to gene expression information that includes a link to a downloadable file detailing the read counts per gene in a tab-delimited text file. The number of reads aligning to a given gene target represents an expression value referred to as “counts”. This Additional plug-in analyses include output for each barcode of the number of genes (amplicons) with at least 1, 10, 100, 1,000, and 10,000 counts to enable determination of the dynamic range and sensitivity per sample. 
     A summary table of the above information, including mapping statistics per barcode of total mapped reads, percentage on target, and percentage of panel genes detected (“Targets Detected”) is viewable in Torrent Suite Software to quickly evaluate run and library performance. 
     EXAMPLE 2 
     Analysis of Tumour Mutational Burden 
     2.0 DNA Measurement 
     DNA from a urine sample was quantified post extraction following the protocol in section 1.3 above. 
     2.1 Library Preparation 
     DNA samples were diluted to 5 ng/μl and added to 5× Ion AmpliSeq Hifi (from the From the Ion AmpliSeg™ library kit plus (4488990), nuclease free water and set up using two DNA primer pools (5 μl of pool 1 and 5 μl of pool 2) in a 96 well plate. The following program was run on the thermal cycler: 
     
       
         
           
               
               
               
               
               
             
               
                   
                   
               
               
                   
                 Stage 
                 Step 
                 Temperature 
                 Time 
               
               
                   
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
               
            
               
                   
                 Hold 
                 Activate the enzyme 
                 99° C. 
                 2 
                 min 
               
               
                   
                 Cycle (15) 
                 Denature 
                 99° C. 
                 15 
                 sec 
               
               
                   
                   
                 Anneal and extend 
                 60° C. 
                 16 
                 min 
               
            
           
           
               
               
               
               
               
            
               
                   
                 Hold 
                 — 
                 10° C. 
                 Hold 
               
               
                   
                   
               
            
           
         
       
     
     Following amplification, the amplicons were partially digested using 2 μl of LIB FuPa (From the Ion 540™ OT2 kit (Cat: A27753)), mixed well and placed on the thermal cycler on the following program: 
     
       
         
           
               
               
               
             
               
                   
                   
               
               
                   
                 Temperature 
                 Time 
               
               
                   
                   
               
             
            
               
                   
                 50° C. 
                 20 min 
               
               
                   
                 55° C. 
                 20 min 
               
               
                   
                 60° C. 
                 20 min 
               
               
                   
                 10° C. 
                 Hold (for up to 1 hour) 
               
               
                   
                   
               
            
           
         
       
     
     4 μl of switch solution* 3 , 2 μl of diluted Ion XPRESS Barcodes 1-16 (Cat: 4471250) and 2 μl of LIB DNA ligase (From the Ion Ampliseg™ library kit plus (4488990)) were added to each sample, mixing thoroughly in between addition of each component. The following program was run on the thermal cycler: 
     
       
         
           
               
               
               
             
               
                   
                   
               
               
                   
                 Temperature 
                 Time 
               
               
                   
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
            
               
                   
                 22° C. 
                 30 
                 min 
               
               
                   
                 68° C. 
                 5 
                 min 
               
               
                   
                 72° C. 
                 5 
                 min 
               
            
           
           
               
               
               
            
               
                   
                 10° C. 
                 Hold (for up to 24 hours) 
               
               
                   
                   
               
            
           
         
       
     
     2.3 Purification 
     Libraries were purified as in section 1.3 using 45 μl of Agencourt AMPure XP (Biomeck Coulter cat: A63881). 
     2.4 q-PCR 
     The quantity of library was measured using the Ion Library TaqMan quantitation kit (cat: 4468802). Three 10-fold serial dilutions of the  E. coli  DH1OB Ion control library were used (6.8 pmol, 0.68 pmol and 0.068 pmol) to create the standard curve. Each sample was diluted 1/500 and each sample, standard and negative control were tested in duplicate. 10 μl of the 2× TaqMan mastermix and 1 μl of the 20×TaqMan assay were combined in a well of a 96 well fast thermal cycling plate for each sample. 9 μl of the 1/500 diluted sample, standard or nuclease free water (negative control) were added to the plate and the qPCR was run on the ABI StepOnePlus™ machine (Cat: 4376600) using the program listed in section 1.5. 
     Samples were diluted to 100 pMol using the results from the q-PCR and pooled ready for template preparation. Following this, template preparation, enrichment of the sample and sequencing were performed as written in sections 1.6, 1.7 and 1.8 respectively. 
     Example 3 
     Application of Polygenic Detection Score (PDS) Algorithm to Results 
     Case 1. Results obtained from a sample from a patient with macroscopic haematuria. 
     Assay results:
         A. SNV Hotspot mutation: BRAF (PDS=1)   B. CNV: PD-L1 amplificationT, JAK2 amplification (PDS=2+2=4)   C. TMB=5 mut/MB (PDS=1)   D. Gene fusions: no fusion detected (PDS=0)   E. Gene expression
           a. High PD-L1 expression (PDS=3)   b. Proliferation signature: high MCM2-3-5, high GEMININ, High PLK1, High FOXM1, High BUB1 (PDS=2)   
               

     PDS Algorithm score=11 
     Indicative of malignancy and potential response to anti-PD-L1 inhibitors (e.g Durvalumab, pembrolizumab) and BRAF inhibitors. 
     
       
         
           
               
             
               
                 TABLE 1 
               
               
                   
               
               
                 Cell cycle regulated genes 
               
               
                 Detection of increased expression of these proliferation linked genes 
               
               
                 is indicative of the presence of genito-urinary tract cancer 
               
               
                 Proliferation markers 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                   
                 BUB1 
                 MELK 
               
               
                   
                 CCNB2 
                 MKI67 
               
               
                   
                 CD3D 
                 TOP2A 
               
               
                   
                 CD3E 
                 MCM2 
               
               
                   
                 CD3G 
                 MCM3 
               
               
                   
                 CDK1 
                 MCM5 
               
               
                   
                 CDKN3 
                 GEMININ 
               
               
                   
                 FOXM1 
                 PLK1 
               
               
                   
                 KIAA0101 
                 MRE11A 
               
               
                   
                 MAD2L1 
               
               
                   
                   
               
            
           
         
       
     
     Actionable Mutations in Oncogenes and Tumour Suppressors Including Fusion Genes (Table 2, 3 and 4) 
       
     
       
         
           
               
             
               
                 TABLE 2 
               
               
                   
               
               
                 Actionable genetic variants - point mutations in targeted Oncogenic 
               
               
                 hotspots and full length tumour suppressor gene sequencing. 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                 ABL1 mutation 
                 ERBB2 L869R mutation 
                 KRAS A59 mutation 
               
               
                 ABL1-BCR E255K mutation 
                 ERBB2 mutation 
                 KRAS aberration 
               
               
                 ABL1-BCR E255V mutation 
                 ERBB2 mutation status 
                 KRAS activating mutation 
               
               
                 ABL1-BCR F317C mutation 
                 ERBB2 positive 
                 KRAS exon 2 mutation 
               
               
                 ABL1-BCR F317I mutation 
                 ERBB2 S310F mutation 
                 KRAS exon 3 mutation 
               
               
                 ABL1-BCR F317L mutation 
                 ERBB2 status 
                 KRAS exon 4 mutation 
               
               
                 ABL1-BCR F317V mutation 
                 ERBB2 V659E mutation 
                 KRAS G12 mutation 
               
               
                 ABL1-BCR F359C mutation 
                 ERBB2 V777L mutation 
                 KRAS G12V mutation 
               
               
                 ABL1-BCR F359I mutation 
                 ERBB3 mutation 
                 KRAS G13 mutation 
               
               
                 ABL1-BCR F359V mutation 
                 ERBB4 mutation 
                 KRAS K117 mutation 
               
               
                 ABL1-BCR KD mutation 
                 ERCC1 mutation 
                 KRAS mutation 
               
               
                 ABL1-BCR T315A mutation 
                 ERCC2 mutation 
                 KRAS mutation status 
               
               
                 ABL1-BCR T315I mutation 
                 FANC mutation 
                 KRAS positive 
               
               
                 ABL1-BCR V299L mutation 
                 FANCA mutation 
                 KRAS Q61 mutation 
               
               
                 ABL1-BCR Y253H mutation 
                 FANCC mutation 
                 MET activating mutation 
               
               
                 AKT mutation 
                 FANCD2 mutation 
                 MET mutation 
               
               
                 AKT1 activating mutation 
                 FANCF mutation 
                 MLH1 mutation 
               
               
                 AKT1 mutation 
                 FANCG mutation 
                 MMR pathway 
               
               
                 AKT2 aberration 
                 FBXW7 mutation 
                 MRE11 mutation 
               
               
                 AKT2 activating mutation 
                 FGF aberration 
                 MSH2 mutation 
               
               
                 AKT2 E17K mutation 
                 FGF19 aberration 
                 MSH6 mutation 
               
               
                 AKT2 mutation 
                 FGFR aberration 
                 MTOR aberration 
               
               
                 AKT3 activating mutation 
                 FGFR activating mutation 
                 MTOR activating mutation 
               
               
                 AKT3 E17K mutation 
                 FGFR mutation 
                 MTOR mutation 
               
               
                 AKT3 mutation 
                 FGFR pathway 
                 NBN mutation 
               
               
                 ALK aberration 
                 FGFR1 aberration 
                 NF1 aberration 
               
               
                 ALK mutation 
                 FGFR1 K656E mutation 
                 NF1 mutation 
               
               
                 ATM aberration 
                 FGFR1 mutation 
                 NF2 aberration 
               
               
                 ATM mutation 
                 FGFR1 N546K mutation 
                 NF2 mutation 
               
               
                 ATR mutation 
                 FGFR1 T141A mutation 
                 NRAS A146 mutation 
               
               
                 AXL mutation 
                 FGFR2 aberration 
                 NRAS A59 mutation 
               
               
                 BAP1 mutation 
                 FGFR2 C382R mutation 
                 NRAS aberration 
               
               
                 BLM mutation 
                 FGFR2 G380R mutation 
                 NRAS activating mutation 
               
               
                 BRAF aberration 
                 FGFR2 K659E mutation 
                 NRAS exon 2 mutation 
               
               
                 BRAF activating mutation 
                 FGFR2 mutation 
                 NRAS exon 3 mutation 
               
               
                 BRAF mutation 
                 FGFR2 N549K mutation 
                 NRAS exon 4 mutation 
               
               
                 BRAF mutation status 
                 FGFR2 P253R mutation 
                 NRAS G12 mutation 
               
               
                 BRAF V600 mutation 
                 FGFR2 positive 
                 NRAS G12V mutation 
               
               
                 BRAF V600D mutation 
                 FGFR2 S252W mutation 
                 NRAS G13 mutation 
               
               
                 BRAF V600E mutation 
                 FGFR2 Y375C mutation 
                 NRAS K117 mutation 
               
               
                 BRAF V600E mutation status 
                 FGFR3 A391E mutation 
                 NRAS mutation 
               
               
                 BRAF V600K mutation 
                 FGFR3 aberration 
                 NRAS mutation status 
               
               
                 BRAF V600R mutation 
                 FGFR3 activating 
                 NRAS Q61 mutation 
               
               
                   
                 mutation 
               
               
                 BRCA mutation 
                 FGFR3 F384L mutation 
                 NTRK aberration 
               
               
                 BRCA1 aberration 
                 FGFR3 F386L mutation 
                 NTRK activating mutation 
               
               
                 BRCA1 mutation 
                 FGFR3 G370C mutation 
                 NTRK mutation 
               
               
                 BRCA2 aberration 
                 FGFR3 G372C mutation 
                 NTRK1 aberration 
               
               
                 BRCA2 mutation 
                 FGFR3 G380R mutation 
                 NTRK1 positive 
               
               
                 BRIP1 mutation 
                 FGFR3 G382R mutation 
                 NTRK2 aberration 
               
               
                 CBL mutation 
                 FGFR3 G697C mutation 
                 NTRK2 positive 
               
               
                 CD274 positive 
                 FGFR3 K650E mutation 
                 NTRK3 aberration 
               
               
                 CDK12 mutation 
                 FGFR3 K650M mutation 
                 NTRK3 positive 
               
               
                 CDK4 mutation 
                 FGFR3 K650Q mutation 
                 PALB2 mutation 
               
               
                 DDR2 S768R mutation 
                 FGFR3 K650T mutation 
                 PDGFR aberration 
               
               
                 CDK6 mutation 
                 FGFR3 K652E mutation 
                 PDGFR mutation 
               
               
                 EGFR A289V mutation 
                 FGFR3 K652M mutation 
                 PDGFRA aberration 
               
               
                 CDKN2A mutation 
                 FGFR3 K652T mutation 
                 PDGFRA D842 mutation 
               
               
                 CDKN2A negative 
                 FGFR3 mutation 
                 PDGFRA D842V mutation 
               
               
                 CDKN2A status 
                 FGFR3 R248C mutation 
                 PDGFRA mutation 
               
               
                 CDKN2B mutation 
                 FGFR3 S249C mutation 
                 PDGFRB aberration 
               
               
                 CHEK1 mutation 
                 FGFR3 S371C mutation 
                 PDGFRB mutation 
               
               
                 CHEK2 mutation 
                 FGFR3 S373C mutation 
                 PI3K activating mutation 
               
               
                 CSF1R aberration 
                 FGFR3 Y373C mutation 
                 PI3K/AKT/MTOR mutation 
               
               
                 CSF1R mutation 
                 FGFR3 Y375C mutation 
                 PI3K/AKT/MTOR pathway 
               
               
                 DDR2 I638F mutation 
                 FGFR4 aberration 
                 PIK3CA aberration 
               
               
                 DDR2 L239R mutation 
                 FGFR4 mutation 
                 PIK3CA activating mutation 
               
               
                 DDR2 mutation 
                 FLT1 mutation 
                 PIK3CA exon 20 mutation 
               
               
                 EGFR activating mutation 
                 FLT3 aberration 
                 PIK3CA exon 9 mutation 
               
               
                 EGFR exon 18 activating mutation 
                 FLT3 activating mutation 
                 PIK3CA mutation 
               
               
                 EGFR exon 18 mutation 
                 FLT3 D835 mutation 
                 PIK3CA mutation status 
               
               
                 EGFR exon 19 activating mutation 
                 FLT3 I836 mutation 
                 PIK3CB mutation 
               
               
                 EGFR exon 19 deletion 
                 FLT3 ITD mutation 
                 PIK3CG mutation 
               
               
                 EGFR exon 19 insertion 
                 FLT3 mutation 
                 PIK3R1 aberration 
               
               
                 EGFR exon 19 mutation 
                 FLT3 positive 
                 PIK3R1 mutation 
               
               
                 EGFR exon 19 sensitizing mutation 
                 FLT3 TKD mutation 
                 PIK3R2 activating mutation 
               
               
                 EGFR exon 20 activating mutation 
                 FLT4 mutation 
                 PMS2 mutation 
               
               
                 EGFR exon 20 deletion 
                 G1/S cell cycle pathway 
                 POLE aberration 
               
               
                 EGFR exon 20 insertion 
                 GNA11 mutation 
                 POLE codons 268-471 mutation 
               
               
                 EGFR exon 20 mutation 
                 GNAQ mutation 
                 POLE mutation 
               
               
                 EGFR exon 20 resistance mutation 
                 HRAS activating mutation 
                 PTCH1 mutation 
               
               
                 EGFR exon 21 activating mutation 
                 HRAS G12 mutation 
                 PTEN aberration 
               
               
                 EGFR exon 21 mutation 
                 HRAS G12V mutation 
                 PTEN mutation 
               
               
                 EGFR exon 21 sensitizing mutation 
                 HRAS G13 mutation 
                 RAD50 mutation 
               
               
                 EGFR G598V mutation 
                 HRAS mutation 
                 RAD51 mutation 
               
               
                 EGFR G719 mutation 
                 HRAS Q61 mutation 
                 RAD51B mutation 
               
               
                 EGFR G719A mutation 
                 HRR mutation 
                 RAD51C mutation 
               
               
                 EGFR G719C mutation 
                 HRR pathway 
                 RAD51D mutation 
               
               
                 EGFR G719S mutation 
                 IDH1 mutation 
                 RAF aberration 
               
               
                 EGFR L62R mutation 
                 IDH1 R132 mutation 
                 RAF1 mutation 
               
               
                 EGFR L858 mutation 
                 IDH1 R132H mutation 
                 RARA-PML fusion 
               
               
                 EGFR L858R mutation 
                 IDH2 mutation 
                 RAS activating mutation 
               
               
                 EGFR L861 mutation 
                 IDH2 R140 mutation 
                 RAS mutation 
               
               
                 EGFR L861Q mutation 
                 IDH2 R140G mutation 
                 RAS/RAF/MEK/ERK mutation 
               
               
                 EGFR L861R mutation 
                 IDH2 R140L mutation 
                 RAS/RAF/MEK/ERK pathway 
               
               
                 EGFR mutation 
                 IDH2 R140Q mutation 
                 RB1 mutation 
               
               
                 EGFR mutation status 
                 IDH2 R140W mutation 
                 RB1 status 
               
               
                 EGFR P596L mutation 
                 IDH2 R172 mutation 
                 RET aberration 
               
               
                 EGFR positive 
                 IDH2 R172G mutation 
                 RET activating mutation 
               
               
                 EGFR R108K mutation 
                 IDH2 R172K mutation 
                 RET mutation 
               
               
                 EGFR R222C mutation 
                 IDH2 R172M mutation 
                 RET mutation status 
               
               
                 EGFR resistance mutation 
                 IDH2 R172S mutation 
                 RET positive 
               
               
                 EGFR S768I mutation 
                 IDH2 R172W mutation 
                 RHEB aberration 
               
               
                 EGFR T790M mutation 
                 JAK2 V617F mutation 
                 ROS1 aberration 
               
               
                 EGFR T790M mutation status 
                 KDR aberration 
                 ROS1 mutation 
               
               
                 EGFR V769L mutation 
                 KDR mutation 
                 ROS1 positive 
               
               
                 EGFR V774M mutation 
                 KDR positive 
                 SMARCB1 mutation 
               
               
                 EGFRi sensitizing mutation 
                 KIT aberration 
                 SMO activating mutation 
               
               
                 ERBB aberration 
                 KIT activating mutation 
                 SMO mutation 
               
               
                 ERBB2 aberration 
                 KIT D816V mutation 
                 SRC mutation 
               
               
                 ERBB2 activating mutation 
                 KIT exon 11 mutation 
                 STK11 aberration 
               
               
                 ERBB2 D769H mutation 
                 KIT exon 13 mutation 
                 STK11 mutation 
               
               
                 ERBB2 E757A mutation 
                 KIT exon 14 mutation 
                 TP53 mutation 
               
               
                 ERBB2 exon 19 deletion 
                 KIT exon 17 mutation 
                 TSC mutation 
               
               
                 ERBB2 exon 20 deletion 
                 KIT exon 18 mutation 
                 TSC1 aberration 
               
               
                 ERBB2 exon 20 insertion 
                 KIT exon 8 mutation 
                 TSC1 mutation 
               
               
                 ERBB2 G776V mutation 
                 KIT exon 9 mutation 
                 TSC2 aberration 
               
               
                 ERBB2 L755A mutation 
                 KIT mutation 
                 TSC2 mutation 
               
               
                 ERBB2 L755S mutation 
                 KIT resistance mutation 
               
               
                 ERBB2 L841V mutation 
                 KRAS A146 mutation 
               
               
                   
               
            
           
         
       
     
     
       
         
           
               
             
               
                 TABLE 3 
               
               
                   
               
               
                 Actionable genetic variants - Fusions. 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                   
                 ABL1 fusion 
                 FGFR2-PLPP4 fusion 
               
               
                   
                 ABL1-BCR fusion 
                 FGFR2-SLC45A3 fusion 
               
               
                   
                 ALK fusion 
                 FGFR2-TACC2 fusion 
               
               
                   
                 ALK-CLIP4 fusion 
                 FGFR2-UBQLN1 fusion 
               
               
                   
                 ALK-CLTC fusion 
                 FGFR2-VCL fusion 
               
               
                   
                 ALK-EML4 fusion 
                 FGFR3 fusion 
               
               
                   
                 ALK-KIF5B fusion 
                 FGFR3-BAIAP2L1 fusion 
               
               
                   
                 ALK-NPM1 fusion 
                 FGFR3-NSD2 fusion 
               
               
                   
                 ALK-RANBP2 fusion 
                 FGFR3-TACC3 fusion 
               
               
                   
                 ALK-STRN fusion 
                 FLT3 amplification 
               
               
                   
                 ALK-TFG fusion 
                 FLT3 fusion 
               
               
                   
                 AXL fusion 
                 KRAS fusion 
               
               
                   
                 BRAF fusion 
                 MET exon 14 mutation 
               
               
                   
                 BRCA1 fusion 
                 MET exon 14 skipping 
               
               
                   
                   
                 mutation 
               
               
                   
                 BRCA2 fusion 
                 MET fusion 
               
               
                   
                 EGFRvIII 
                 NTRK fusion 
               
               
                   
                 FGFR fusion 
                 NTRK1 fusion 
               
               
                   
                 FGFR1 fusion 
                 NTRK2 fusion 
               
               
                   
                 FGFR1-TACC1 fusion 
                 NTRK3 fusion 
               
               
                   
                 FGFR2 fusion 
                 NUTM1 fusion 
               
               
                   
                 FGFR2-AFF3 fusion 
                 PDGFRA fusion 
               
               
                   
                 FGFR2-BICC1 fusion 
                 PDGFRB fusion 
               
               
                   
                 FGFR2-CASP7 fusion 
                 RAFI fusion 
               
               
                   
                 FGFR2-CCAR1 fusion 
                 RET fusion 
               
               
                   
                 FGFR2-CCDC6 fusion 
                 RET-KIF5B fusion 
               
               
                   
                 FGFR2-CFAP58 fusion 
                 ROS1 fusion 
               
               
                   
                 FGFR2-MCU fusion 
                 RSPO3 fusion 
               
               
                   
                 FGFR2-OFD1 fusion 
               
               
                   
                   
               
            
           
         
       
     
     
       
         
           
               
             
               
                 TABLE 4 
               
               
                   
               
               
                 Actionable genetic variants - copy number changes. 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                   
                 AKT1 amplification 
                 FGFR2 amplification 
               
               
                   
                 AKT2 amplification 
                 FGFR3 amplification 
               
               
                   
                 ALK amplification 
                 FGFR4 amplification 
               
               
                   
                 ATM deletion 
                 KIT amplification 
               
               
                   
                 AXL amplification 
                 KRAS amplification 
               
               
                   
                 BRAF amplification 
                 MET amplification 
               
               
                   
                 BRCA1 deletion 
                 MYC amplification 
               
               
                   
                 BRCA2 deletion 
                 MYCL amplification 
               
               
                   
                 CCND1 amplification 
                 MYCN amplification 
               
               
                   
                 CCND2 amplification 
                 NTRK amplification 
               
               
                   
                 CCND3 amplification 
                 PDGFRA amplification 
               
               
                   
                 CCNE1 amplification 
                 PDGFRB amplification 
               
               
                   
                 CD274 amplification 
                 PIK3CA amplification 
               
               
                   
                 CDK4 amplification 
                 PIK3CB amplification 
               
               
                   
                 CDK6 amplification 
                 PTCH1 deletion 
               
               
                   
                 CDKN2A deletion 
                 PTEN deletion 
               
               
                   
                 EGFR amplification 
                 RAS amplification 
               
               
                   
                 ERBB2 amplification 
                 RB1 deletion 
               
               
                   
                 ERBB2 overexpression 
                 SMARCB1 deletion 
               
               
                   
                 FGF amplification 
                 TP53 deletion 
               
               
                   
                 FGF19 amplification 
                 TSC1 deletion 
               
               
                   
                 FGFR amplification 
                 TSC2 deletion 
               
               
                   
                 FGFR1 amplification 
               
               
                   
                   
               
            
           
         
       
     
     Signalling Networks Linked to the PD-L1 Immune Checkpoint Table 5, 6 and 7 
       
     
       
         
           
               
             
               
                 TABLE 5 
               
               
                   
               
               
                 RNA analysis - expression and fusion targets 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
            
               
                 CD274 (PDL1) 
                 NKG7 
                 CDKN3 
                 &gt;500 nRPM 
               
               
                 PDCD1LG2 (PDL2, 
                 PTPRC 
                 FOXM1 
                 &gt;500 nRPM 
               
               
                 CD273) 
               
               
                 PDCD1 (PD1, 
                 SRGN 
                 KIAA0101 
                 &gt;500 nRPM 
               
               
                 CD279) 
               
               
                 CXCL9 
                 AIF1 
                 MAD2L1 
                 &gt;500 nRPM 
               
               
                 FOXO1 
                 CD14 
                 MELK 
                 &gt;500 nRPM 
               
               
                 HIF1A 
                 CD16 
                 MKI67 
                 &gt;500 nRPM 
               
               
                 MTOR 
                 CD163 
                 TOP2A 
                 &gt;500 nRPM 
               
               
                 NFATC1 
                 CD68 
                 MCM2 
                 &gt;500 nRPM 
               
               
                 PIK3CA 
                 HLA-DQB1 
                 MCM3 
                 &gt;500 nRPM 
               
               
                 PIK3CD 
                 HLA-DRB1 
                 MCM5 
                 &gt;500 nRPM 
               
               
                 PRDM1 (Blimp1) 
                 HLA-DPA1 
                 GEMININ 
                 &gt;500 nRPM 
               
               
                 PTEN 
                 HLA-DRA 
                 PLK1 
                 &gt;500 nRPM 
               
               
                 PTPN11 
                 CTLA4 
                 MRE11A 
                 &gt;500 nRPM 
               
               
                 CD19 
                 IDO1 
                 COL1A1 
                 &gt;500 nRPM 
               
               
                 CD38 
                 CD3E 
                 COL1A2 
                 &gt;500 nRPM 
               
               
                 CD79A 
                 CD40 (TNFRSF5) 
                 COL3A1 
                 &gt;500 nRPM 
               
               
                 NTN3 
                 TNFRSF9 
                 COL5A1 
                 &gt;500 nRPM 
               
               
                   
                 (CD137) 
               
               
                 POU2AF1 
                 TNFRSF17 
                 COL5A2 
                 &gt;500 nRPM 
               
               
                 SKAP2 
                 LAG3 
                 COL6A3 
                 &gt;500 nRPM 
               
               
                 MZB1 
                 HAVCR2 (TIM3) 
                 MMP2 
                 &gt;500 nRPM 
               
               
                 TNFRSF17 
                 CD47 
                 POSTN 
                 &gt;500 nRPM 
               
               
                 CD3 
                 TNFRSF4 (OX40) 
                 SPARC 
                 &gt;500 nRPM 
               
               
                 CD4 
                 TNFRSF18 (GITR) 
                 VCAN 
                 &gt;500 nRPM 
               
               
                 CD8 
                 CD276 (B7-H3) 
                 ALK 
                 &gt;500 nRPM 
               
               
                 CD56 
                 TIGIT 
                 FGFR1 
                 &gt;500 nRPM 
               
               
                 CD15 
                 BUB1 
                 FGFR2 
                 &gt;500 nRPM 
               
               
                 CCL5 
                 CCNB2 
                 FGFR3 
                 &gt;500 nRPM 
               
               
                 CD37 
                 CD3D 
                 MET 
                 &gt;500 nRPM 
               
               
                 CD53 
                 CD3E 
                 ROS1 
                 &gt;500 nRPM 
               
               
                 CTSS 
                 CD3G 
                 NTRK1-3 
                 &gt;500 nRPM 
               
               
                 FYB 
                 CDK1 
                 RET 
                 &gt;500 nRPM 
               
               
                   
               
            
           
         
       
     
     
       
         
           
               
             
               
                 TABLE 6 
               
               
                   
               
               
                 Copy number gene targets 
               
               
                   
               
             
            
               
                 CD274 (PD-L1) 
               
               
                 PDCD1LG2 (PD-L2; 
               
               
                 CD273) 
               
               
                 PDCD1 (PD1; CD279) 
               
               
                 ERBB2 
               
               
                 FGFR1 
               
               
                 FGFR2 
               
               
                 FGFR3 
               
               
                 FGFR4 
               
               
                 MET 
               
               
                   
               
            
           
         
       
     
     
       
         
           
               
             
               
                 TABLE 7 
               
             
            
               
                   
               
               
                 DNA hotspot, and full gene sequencing targets 
               
            
           
           
               
               
               
            
               
                   
                 Hotspot genes 
                 Full length gene targets 
               
               
                   
                   
               
               
                   
                 ALK 
                 JAK1 
               
               
                   
                 BRAF exon 15 
                 JAK2 
               
               
                   
                 EGFR exon 18 
                 TP53 
               
               
                   
                 EGFR exon 19 activating 
                 MLH1 
               
               
                   
                 mutation 
               
               
                   
                 EGFR exon 19 deletion 
                 MSH2 
               
               
                   
                 EGFR exon 19 insertion 
                 MSH6 
               
               
                   
                 EGFR exon 19 sensitizing 
                 PMS2 + promoter 
               
               
                   
                 mutation 
               
               
                   
                 EGFR exon 20 activating 
                 POLE 
               
               
                   
                 mutation 
               
               
                   
                 EGFR exon 20 insertion 
                 POLD1 
               
               
                   
                 EGFR exon 20 mutation 
               
               
                   
                 EGFR exon 21 activating 
               
               
                   
                 mutation 
               
               
                   
                 EGFR exon 21 sensitizing 
               
               
                   
                 mutation 
               
               
                   
                 EGFR G719 mutation 
               
               
                   
                 EGFR L858R mutation 
               
               
                   
                 EGFR L861 mutation 
               
               
                   
                 EGFR S768 mutation 
               
               
                   
                 EGFR T790M mutation 
               
               
                   
                 KIT activating mutation 
               
               
                   
                 KIT resistance mutation 
               
               
                   
                 KRAS activating mutation 
               
               
                   
                 MET activating mutation 
               
               
                   
                 NRAS activating mutation 
               
               
                   
                 PMS2 promoter mutations 
               
               
                   
                   
               
            
           
         
       
     
     
       
         
           
               
             
               
                 TABLE 8 
               
               
                   
               
               
                 Genes targeted got analysis of Tumour Mutational Burden (TMB). 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
               
               
               
            
               
                 ABL1 
                 CCND1 
                 EP400 
                 GATA2 
                 LAMP1 
                 MYH11 
                 PLAG1 
                 SMARCA4 
               
               
                 ABL2 
                 CCND2 
                 EPHA3 
                 GATA3 
                 LCK 
                 MYH9 
                 PLCG1 
                 SMARCB1 
               
               
                 ACVR2A 
                 CCNE1 
                 EPHA7 
                 GDNF 
                 LIFR 
                 NBN 
                 PLEKHG5 
                 SMO 
               
               
                 ADAMTS20 
                 CD79A 
                 EPHB1 
                 GNA11 
                 LPHN3 
                 NCOA1 
                 PML 
                 SMUG1 
               
               
                 AFF1 
                 CD79B 
                 EPHB4 
                 GNAQ 
                 LPP 
                 NCOA2 
                 PMS1 
                 SOCS1 
               
               
                 AFF3 
                 CDC73 
                 EPHB6 
                 GNAS 
                 LRP1B 
                 NCOA4 
                 PMS2 
                 SOX11 
               
               
                 AKAP9 
                 CDH1 
                 ERBB2 
                 GPR124 
                 LTF 
                 NF1 
                 POT1 
                 SOX2 
               
               
                 AKT1 
                 CDH11 
                 ERBB3 
                 GRM8 
                 LTK 
                 NF2 
                 POU5F1 
                 SRC 
               
               
                 AKT2 
                 CDH2 
                 ERBB4 
                 GUCY1A2 
                 MAF 
                 NFE2L2 
                 PPARG 
                 SSX1 
               
               
                 AKT3 
                 CDH20 
                 ERCC1 
                 HCAR1 
                 MAFB 
                 NFKB1 
                 PPP2R1A 
                 STK11 
               
               
                 ALK 
                 CDH5 
                 ERCC2 
                 HIF1A 
                 MAGEA1 
                 NFKB2 
                 PRDM1 
                 STK36 
               
               
                 APC 
                 CDK12 
                 ERCC3 
                 HLF 
                 MAGI1 
                 NIN 
                 PRKAR1A 
                 SUFU 
               
               
                 AR 
                 CDK4 
                 ERCC4 
                 HNF1A 
                 MALT1 
                 NKX2-1 
                 PRKDC 
                 SYK 
               
               
                 ARID1A 
                 CDK6 
                 ERCC5 
                 HOOK3 
                 MAML2 
                 NLRP1 
                 PSIP1 
                 SYNE1 
               
               
                 ARID2 
                 CDK8 
                 ERG 
                 HRAS 
                 MAP2K1 
                 NOTCH1 
                 PTCH1 
                 TAF1 
               
               
                 ARNT 
                 CDKN2A 
                 ESR1 
                 HSP90AA1 
                 MAP2K2 
                 NOTCH2 
                 PTEN 
                 TAF1L 
               
               
                 ASXL1 
                 CDKN2B 
                 ETS1 
                 HSP90AB1 
                 MAP2K4 
                 NOTCH4 
                 PTGS2 
                 TAL1 
               
               
                 ATF1 
                 CDKN2C 
                 ETV1 
                 ICK 
                 MAP3K7 
                 NPM1 
                 PTPN11 
                 TBX22 
               
               
                 ATM 
                 CEBPA 
                 ETV4 
                 IDH1 
                 MAPK1 
                 NRAS 
                 PTPRD 
                 TCF12 
               
               
                 ATR 
                 CHEK1 
                 EXT1 
                 IDH2 
                 MAPK8 
                 NSD1 
                 PTPRT 
                 TCF3 
               
               
                 ATRX 
                 CHEK2 
                 EXT2 
                 IGF1R 
                 MARK1 
                 NTRK1 
                 RAD50 
                 TCF7L1 
               
               
                 AURKA 
                 CIC 
                 EZH2 
                 IGF2 
                 MARK4 
                 NTRK3 
                 RAF1 
                 TCF7L2 
               
               
                 AURKB 
                 CKS1B 
                 FAM123B 
                 IGF2R 
                 MBD1 
                 NUMA1 
                 RALGDS 
                 TCL1A 
               
               
                 AURKC 
                 CMPK1 
                 FANCA 
                 IKBKB 
                 MCL1 
                 NUP214 
                 RARA 
                 TET1 
               
               
                 AXL 
                 COL1A1 
                 FANCC 
                 IKBKE 
                 MDM2 
                 NUP98 
                 RB1 
                 TET2 
               
               
                 BAI3 
                 CRBN 
                 FANCD2 
                 IKZF1 
                 MDM4 
                 PAK3 
                 RECQL4 
                 TFE3 
               
               
                 BAP1 
                 CREB1 
                 FANCF 
                 IL2 
                 MEN1 
                 PALB2 
                 REL 
                 TGFBR2 
               
               
                 BCL10 
                 CREBBP 
                 FANCG 
                 IL21R 
                 MET 
                 PARP1 
                 RET 
                 TGM7 
               
               
                 BCL11A 
                 CRKL 
                 FANCJ 
                 IL6ST 
                 MITF 
                 PAX3 
                 RHOH 
                 THBS1 
               
               
                 BCL11B 
                 CRTC1 
                 FAS 
                 IL7R 
                 MLH1 
                 PAX5 
                 RNASEL 
                 TIMP3 
               
               
                 BCL2 
                 CSF1R 
                 FBXW7 
                 ING4 
                 MLL 
                 PAX7 
                 RNF2 
                 TLR4 
               
               
                 BCL2L1 
                 CSMD3 
                 FGFR1 
                 IRF4 
                 MLL2 
                 PAX8 
                 RNF213 
                 TLX1 
               
               
                 BCL2L2 
                 CTNNA1 
                 FGFR2 
                 IRS2 
                 MLL3 
                 PBRM1 
                 ROS1 
                 TNFAIP3 
               
               
                 BCL3 
                 CTNNB1 
                 FGFR3 
                 ITGA10 
                 MLLT10 
                 PBX1 
                 RPS6KA2 
                 TNFRSF14 
               
               
                 BCL6 
                 CYLD 
                 FGFR4 
                 ITGA9 
                 MMP2 
                 PDE4DIP 
                 RRM1 
                 TNK2 
               
               
                 BCL9 
                 CYP2C19 
                 FH 
                 ITGB2 
                 MN1 
                 PDGFB 
                 RUNX1 
                 TOP1 
               
               
                 BCR 
                 CYP2D6 
                 FLCN 
                 ITGB3 
                 MPL 
                 PDGFRA 
                 RUNX1T1 
                 TP53 
               
               
                 BIRC2 
                 DAXX 
                 FLI1 
                 JAK1 
                 MRE11A 
                 PDGFRB 
                 SAMD9 
                 TPR 
               
               
                 BIRC3 
                 DCC 
                 FLT1 
                 JAK2 
                 MSH2 
                 PER1 
                 SBDS 
                 TRIM24 
               
               
                 BIRC5 
                 DDB2 
                 FLT3 
                 JAK3 
                 MSH6 
                 PGAP3 
                 SDHA 
                 TRIM33 
               
               
                 BLM 
                 DDIT3 
                 FLT4 
                 JUN 
                 MTOR 
                 PHOX2B 
                 SDHB 
                 TRIP11 
               
               
                 BLNK 
                 DDR2 
                 FN1 
                 KAT6A 
                 MTR 
                 PIK3C2B 
                 SDHC 
                 TRRAP 
               
               
                 BMPR1A 
                 DEK 
                 FOXL2 
                 KAT6B 
                 MTRR 
                 PIK3CA 
                 SDHD 
                 TSC1 
               
               
                 BRAF 
                 DICER1 
                 FOXO1 
                 KDM5C 
                 MUC1 
                 PIK3CB 
                 Sep-09 
                 TSC2 
               
               
                 BRD3 
                 DNMT3A 
                 FOXO3 
                 KDM6A 
                 MUTYH 
                 PIK3CD 
                 SETD2 
                 TSHR 
               
               
                 BTK 
                 DPYD 
                 FOXP1 
                 KDR 
                 MYB 
                 PIK3CG 
                 SF3B1 
                 UBR5 
               
               
                 BUB1B 
                 DST 
                 FOXP4 
                 KEAP1 
                 MYC 
                 PIK3R1 
                 SGK1 
                 UGT1A1 
               
               
                 CARD11 
                 EGFR 
                 FZR1 
                 KIT 
                 MYCL1 
                 PIK3R2 
                 SH2D1A 
                 USP9X 
               
               
                 CASC5 
                 EML4 
                 G6PD 
                 KLF6 
                 MYCN 
                 PIM1 
                 SMAD2 
                 VHL 
               
               
                 CBL 
                 EP300 
                 GATA1 
                 KRAS 
                 MYD88 
                 PKHD1 
                 SMAD4 
                 WAS 
               
               
                   
                   
                   
                   
                   
                   
                   
                 WHSC1 
               
               
                   
                   
                   
                   
                   
                   
                   
                 WRN 
               
               
                   
                   
                   
                   
                   
                   
                   
                 WT1 
               
               
                   
                   
                   
                   
                   
                   
                   
                 XPA 
               
               
                   
                   
                   
                   
                   
                   
                   
                 XPC 
               
               
                   
                   
                   
                   
                   
                   
                   
                 XPO1 
               
               
                   
                   
                   
                   
                   
                   
                   
                 XRCC2 
               
               
                   
                   
                   
                   
                   
                   
                   
                 ZNF384 
               
               
                   
                   
                   
                   
                   
                   
                   
                 ZNF521