Methods for diagnosing, treating and staging lung cancer

Abstract

The present invention provides means and methods for the diagnosis of lung cancer, regardless of sub-type in a subject In particular a panel of miRNA biomarkers is provided that is able to diagnose the presence of lung cancer, including early stage lung cancer via a blood sample.

Description

[0001] METHODS

[0002] Field of the invention

[0003] The invention is in the field of diagnostics.

[0004] Background

[0005] An improved understanding of lung cancer genomics has led to continuous advancement in chemotherapy and immunotherapy treatment for lung cancer (Howlader et al., 2020; Herbst et al., 2020). Despite higher survival rates, lung cancer remains the leading cause of cancer death worldwide in 2020; accounting for 11.4% of cancer diagnosis and 18% of cancer death (Sung et al., 2021).

[0006] Lung cancer mortality is almost twice that of all other major cancer types as most lung cancer cases are diagnosed at advanced stages (stage III / IV) (https: / / seer.cancer.gov / statistics-network / explorer / application.html). There are effective cancer screening programmes for breast and colorectal cancer, ensuring that a large proportion of these cancer types are diagnosed at an early stage. The 5-year survival rate of patients with distant lung disease is 9%, a huge reduction when compared to patients with localized disease whom 5-year survival can be as high as 65% (https: / / seer.cancer.gov / statfacts / html / lungb.html). As patients with early-stage lung cancer are usually asymptomatic, it is imperative that an effective lung cancer screening protocol is developed to reduce lung cancer mortality.

[0007] Lung cancer screening trials utilizing low dose computed tomography scans (LDCT) of the thorax in smokers, have showed up to a 24% relative risk reduction in lung cancer mortality. However, CT imaging has a high false positive rate (56%) for the diagnosis of lung cancer (which can lead to undue psychological stress, additional diagnostic and surgical procedures, and a rise in health cost for patients (Jonas et al., 2021)) and is not available in the primary healthcare settings. (National Lung Screening Trial Research Team et al., 2011; de Koning et al., 2020). Further, the implementation of a lung cancer screening program incorporating LDCT scan of the thorax at a population level is filled with numerous challenges (Wait etal., 2022; van Meerkbeek etal., 2021).

[0008] The lack of a clearly defined target population that will benefit from LDCT screening is a factor that has precluded nationwide establishment of lung cancer screening program. Lung cancer epidemiology and the proportion of lung cancer associated with smoking varies greatly worldwide (Sung et al., 2020). Certain populations may have additional risk factors for lung cancer, including air pollution (Harris 2023; Gourd 2022). As LDCT trials only demonstrated mortality benefit and cost-effectiveness in heavy smokers, it is unclear if similar benefits would be achieved in a non-smokers or population with low smoking prevalence.

[0009] A successful lung cancer screening program with protocolised scheduling of LDCT, CT scan reporting and management of nodule requires the support of a holistic healthcare system. Some of the minimum requisites include adequate expertise manpower, accessible and up-to-date infrastructure, and strong clinical governance, to solid fuels (Cheng et al., 2022).

[0010] The present invention aims to address these problems and provide a simple, non- invasive means to screen for the presence of lung cancer, particularly early-stage lung cancer.

[0011] Brief summary of the invention

[0012] Liquid biopsy involves the sampling of blood serum or bodily fluids for circulating tumour cells (CTCs) or tumour biomarkers, such as proteins, circulating DNA (ctDNA), messenger RIMA (mRNA) and micro-RNA (miRNA). The process of liquid biopsy can be fast, minimally invasive, and more readily accessible as compared to radio-imaging methods for cancer detection. Biomarkers that are dysregulated in pre-clinical or early stages of cancer would have a highly promising role in cancer screening (Freitas et al., 2021). ctDNA is released into the blood stream from cancer cells that undergo apoptosis. In early stages of cancer, ctDNA may not be detectable or are present in very low quantities. As a result, the sensitivity of ctDNA is relatively lower in diagnosis of early- stage cancers (Li and Liang, 2020; Bettegowda et al., 2014). ctDNA assays also require DNA sequencing, which is costly and time consuming, requiring up to weeks for results to be processed. Hence, ctDNA is an unsuitable biomarker for the purpose of cancer screening.

[0013] The inventors have surprisingly identified a signature of miRNA biomarkers that can be detected in a blood or serum sample that is able to diagnose the presence of lung cancer, regardless of lung cancer subtypes. The miRNA panel shows great potential in changing the landscape for lung cancer screening and diagnostic algorithm of lung nodules picked up on LDCT. The inventors have further shown that nodule size can be integrated into a predictive model to improve the accuracy, selectivity and specificity. Nodule size alone can be a useful predictor. The inventors have also shown that a biomarker panel either alone or in combination with nodule size characterisation can be used to distinguish between benign and malignant lung nodules.

[0014] Detailed description of the invention

[0015] In a first aspect the invention provides a method for determining the presence of lung cancer in a subject wherein the method comprises determining the level of any one or more of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16, or all of the following miRNAs in a sample obtained from the subject:

[0016] Biomarker 9 [SEQ ID NO: 9] mir-196a, Biomarker 5 [SEQ ID NO: 5] mir-1268, Biomarker 10 [SEQ ID NO: 10] mir-130b, Biomarker 2 [SEQ ID NO: 2] miR-1290, Biomarker 6 [SEQ ID NO: 6] mir-106b, Biomarker 1 [SEQ ID NO: 1] miR-1246; Biomarker 3 [SEQ ID NO: 3] mir-221; Biomarker 4 [SEQ ID NO: 4] mir-223; Biomarker 7 [SEQ ID NO: 7] mir-26; Biomarker 8 [SEQ ID NO: 8] mir-21; Biomarker 11 [SEQ ID NO: 11] mir-155; Biomarker 12 [SEQ ID NO: 12] mir-23a; Biomarker 13 [SEQ ID NO: 13] mir-16; Biomarker 14 [SEQ ID NO: 14] mir-222; Biomarker 15 [SEQ ID NO: 15] mir-451; Biomarker 16 [SEQ ID NO: 16] mir-486; wherein the relevant miRNA biomarkers are:

[0017] The skilled person will appreciate that it is reasonable to expect there to be some divergence in these sequences across individuals. Accordingly, in some embodiments of any aspect described herein, determining the level of any one or more of the miRNAs described herein includes determining the level of one or more miRNAs that have at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% identical to the relevant SEQ ID NO, for example to any of SEQ ID NOs: 1-16.

[0018] The term "identical to" is known to the person skilled in the art, and relates to the percentage identity between two or more nucleic acid sequences or two or more amino acid sequences. The terms "identical to" and "sequence identity" are interchangeable - i.e., where a first sequence is X% (where X is an integer between 0 and 100) "identical to" a second sequence, the first sequence shares X% "sequence identity" with the second sequence.

[0019] To determine the percentage identity of two amino acid sequences or of two nucleic acid sequences, the sequences are aligned for optimal comparison purposes (e.g., gaps can be introduced in the sequence of a first amino acid or nucleic acid sequence for optimal alignment with a second amino acid or nucleic acid sequence). The amino acid residues or nucleotides at corresponding amino acid positions or nucleotide positions are then compared. When a position in the first sequence is occupied by the same amino acid residue or nucleotide as the corresponding position in the second sequence, then the molecules are identical at that position. The percent identity between the two sequences is a function of the number of identical positions shared by the sequences ( / .e., % identity=number of identical overlapping positions / total number of positionsxl00%). In one embodiment, the two sequences are the same length.

[0020] Percentage identity between two sequences can also be identified using a mathematical algorithm. One example of a mathematical algorithm for the comparison of two sequences is the algorithm of Karlin and Altschul, 1990, Proc. Natl. Acad. Sci. U.S.A. 87:2264 2268 (herein incorporated by reference in its entirety), modified as in Karlin and Altschul, 1993, Proc. Natl. Acad. Sci. U.S.A. 90:5873 5877 (herein incorporated by reference in its entirety). Such an algorithm is incorporated into the NBLAST and XBLAST programs of Altschul et al., 1990, J. Mol. Biol. 215:403 (herein incorporated by reference in its entirety). To obtain gapped alignments for comparison purposes, Gapped BLAST can be utilized as described in Altschul et al., 1997, Nucleic Acids Res. 25:3389 3402 (herein incorporated by reference in its entirety). Alternatively, PSI BLAST can be used to perform an iterated search which detects distant relationships between molecules (Id.). When utilizing BLAST, Gapped BLAST, and PSI Blast programs, the default parameters of the respective programs (e.g., of XBLAST and NBLAST) can be used (see, e.g., National Center for Biotechnology Information (NCBI) on the worldwide web, ncbi.nlm.nih.gov). Another example of a mathematical algorithm for the comparison of sequences is the algorithm of Myers and Miller, 1988, CABIOS 4: 11 17 (herein incorporated by reference in its entirety). Such an algorithm is incorporated in the ALIGN program (version 2.0) which is part of the GCG sequence alignment software package. Another example of a mathematical algorithm for the comparison of two sequences is the algorithm of Thompson et al., 1994, Nucleic Acids Res. 22(22) :4673-80 (herein incorporated by reference in its entirety), incorporated into the ClustalW program.

[0021] The percentage identity between two sequences can be determined using techniques similar to those described above, with or without allowing gaps. In calculating percentage identity, typically only exact matches are counted.

[0022] Each of these biomarkers has been found to have diagnostic power in the absence of being used in combination with the other biomarkers. For example, each of the 16 biomarkers described herein is considered to be useful in the diagnosis or determination of the presence of lung cancer when the level of said biomarker is determined from a blood or plasma sample. See for example the table below and Figure 1.

[0023] Some of these markers (amongst many others) have been associated with lung cancer previously. However, the inventors have surprisingly found that only a subset of these (i.e. the 16 biomarkers described herein) are able to demonstrate the necessary specificity and sensitivity, when the level of those markers are determined from a blood or plasma sample, to be useful. Furthermore, some of these biomarkers were considered previously to be downregulated in the lung cancer condition. Surprisingly, all of the 16 biomarkers described herein were found to be upregulated, i.e. demonstrate a higher level of expression in the lung cancer state than the healthy state, contradicting the state of the art.

[0024] The inventors have found that a diagnostic panel comprising determining the level of any combination of any 6 or 7 or 8 of the above 16 biomarkers is particularly useful in predicting the presence of lung cancer. For example, analysis of the patient cohort of Example 3 provides the following exemplary combinations and the sensitivity, specificity and accuracy are as follows, and hold for all permutations of 8 miRNAs from the above 16:

[0025] Accordingly in one embodiment, the method comprises determining the level of any 6 or 7 or 8 or more of the following miRNAs:

[0026] Biomarker 9 [SEQ ID NO: 9] mir-196a, Biomarker 5 [SEQ ID NO: 5] mir-1268, Biomarker 10 [SEQ ID NO: 10] mir-130b, Biomarker 2 [SEQ ID NO: 2] miR-1290, Biomarker 6 [SEQ ID NO: 6] mir-106b, Biomarker 1 [SEQ ID NO: 1] miR-1246; Biomarker 3 [SEQ ID NO: 3] mir-221; Biomarker 4 [SEQ ID NO: 4] mir-223; Biomarker 7 [SEQ ID NO: 7] mir-26; Biomarker 8[SEQ ID NO: 8] mir-21; Biomarker 11 [SEQ ID NO: 11] mir-155; Biomarker 12 [SEQ ID NO: 12] mir-23a; Biomarker 13 [SEQ ID NO: 13] mir-16; Biomarker 14 [SEQ ID NO: 14] mir-222; Biomarker 15 [SEQ ID NO: 15] mir-451; Biomarker 16 [SEQ ID NO: 16] mir-486.

[0027] The inventors have found that 8 of the miRNA biomarkers are particularly useful. Accordingly in another embodiment, the method comprises determining the level of any 1, 2, 3, 4, 5, 6, 7, 8 or all of the following miRNAs:

[0028] Biomarker 1 [SEQ ID NO: 1] miR-1246; Biomarker 5 [ SEQ ID NO: 5] mir-1269;

[0029] Biomarker 10 [SEQ ID NO: 10] mir-130b; Biomarker 6 [ SEQ ID NO: 6] mir-106b;

[0030] Biomarker 12 [SEQ ID NO: 12] mir-23a; Biomarker 9 [SEQ ID NO: 9] mir-196a,

[0031] Biomarker 2 [ SEQ ID NO: 2] mir-1290; Biomarker 7 [SEQ ID NO: 7] mir-26 or that has at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% identical to any of the above sequences.

[0032] When data from a larger cohort was interrogated (see Example 5) the inventors have found a particular selection of 6 miRNAs to be particularly useful. This combination of 6 affords an AUC of 78-86%, sensitivity of 70-79% and specificity of 73-85%. Accordingly, in another preferred embodiment, the method comprises determining the level of all of the following miRNAs:

[0033] Biomarker 9 [SEQ ID NO: 9] mir-196a, Biomarker 5 [SEQ ID NO: 5] mir-1268, Biomarker 10 [SEQ ID NO: 10] mir-130b, Biomarker 2 [SEQ ID NO: 2] miR-1290, Biomarker 6 [SEQ ID NO: 6] mir-106b, Biomarker 1 [SEQ ID NO: 1] miR-1246.

[0034] The skilled person will appreciate that the levels of biomarkers in addition to those set out above can be determined and used in the analysis. Accordingly in some embodiments the method for determining the presence of lung cancer in a subject comprises determining the level of at least all of the following miRNAs in a sample obtained from the subject:

[0035] Biomarker 9 [SEQ ID NO: 9] mir-196a, Biomarker 5 [SEQ ID NO: 5] mir-1268, Biomarker 10 [SEQ ID NO: 10] mir-130b, Biomarker 2 [SEQ ID NO: 2] miR-1290, Biomarker 6 [SEQ ID NO: 6] mir-106b, Biomarker 1 [SEQ ID NO: 1] miR-1246.

[0036] In other embodiments, only the levels of the 6 specific biomarkers are determined and used in the analysis, i.e. the level of any of the other 16 biomarkers set out herein is not used, and / or the level of any other biomarker is not used. Accordingly in some embodiments the method for determining the presence of lung cancer in a subject comprises determining the level of only the following miRNAs in a sample obtained from the subject:

[0037] Biomarker 9 [SEQ ID NO: 9] mir-196a, Biomarker 5 [SEQ ID NO: 5] mir-1268, Biomarker 10 [SEQ ID NO: 10] mir-130b, Biomarker 2 [SEQ ID NO: 2] miR-1290, Biomarker 6 [SEQ ID NO: 6] mir-106b, Biomarker 1 [SEQ ID NO: 1] miR-1246.

[0038] This applies similarly to all panels of biomarkers described herein. In some instances, the method involves determining the level of all biomarkers in a given panel, but also allows for the determination of the levels of additional biomarkers, whether they are any of the remaining 16 miRNA biomarkers set out here, or other biomarkers. In some instances, only the levels of the specified miRNA biomarkers are determined and used in subsequent analysis, i.e. no other biomarkers for example miRNA biomarkers are analysed. Additionally, data from a smaller cohort (Example 3) describes another panel of 6 miRNAs that are particularly useful. Accordingly, in another embodiment, the method comprises determining the level of all of the following miRNAs:

[0039] Biomarker 5 [ SEQ ID NO: 5] mir-1269; Biomarker 3 [ SEQ ID NO: 3] mir-221; Biomarker 6 [ SEQ ID NO: 6] mir-106b; Biomarker 13 [ SEQ ID NO: 13] mir-16; Biomarker 9 [SEQ ID NO: 9] mir-196a, Biomarker 2 [ SEQ ID NO: 2] mir-1290 or that has at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% identical to any of the above sequences.

[0040] Biomarkers 2 [SEQ ID NO: 2], 5 [SEQ ID NO: 5], 6 [SEQ ID NO: 6] and 9 [SEQ ID NO: 9] are common to all of the preferred biomarker panels.

[0041] Accordingly in some embodiments the method for determining the presence of lung cancer in a subject comprises determining the level of all of Biomarker 2 [SEQ ID NO: 2], Biomarker 5 [SEQ ID NO: 5], Biomarker 6 [SEQ ID NO: 6] and Biomarker 9 [SEQ ID NO: 9] and either: a) Biomarker 1 [SEQ ID NO: 1], and Biomarker 10 [SEQ ID NO: 10]; or b) Biomarker 3 [SEQ ID NO: 3] and Biomarker 13 [SEQ ID NO: 13].

[0042] In some embodiments the method for determining the presence of lung cancer in a subject comprises determining the level of all of Biomarker 2 [SEQ ID NO: 2], Biomarker 5 [SEQ ID NO: 5], Biomarker 6 [SEQ ID NO: 6] and Biomarker 9 [SEQ ID NO: 9] and either: a) Biomarker 1 [SEQ ID NO: 1], and Biomarker 10 [SEQ ID NO: 10]; b) Biomarker 3 [SEQ ID NO: 3] and Biomarker 13 [SEQ ID NO: 13]; or c) Biomarker 1 [SEQ ID NO: 1], and Biomarker 10 [SEQ ID NO: 10]; Biomarker

[0043] 7 [SEQ ID NO: 7] and Biomarker 12 [SEQ ID NO: 12].

[0044] The particular combinations of miRNAs that can be used to determine the presence of cancer can be termed a biomarker signature. Accordingly reference herein to a biomarker signature refers to a combination of miRNAs which can be used to determine the presence of lung cancer. In some embodiments, the present invention makes use of several statistical models to determine AUC, sensitivity, and specificity. These models are briefly described below:

[0045] • RF - The Random Forest algorithm is a machine learning algorithm that creates an ensemble of decision trees to reach a singular, more accurate prediction or result.

[0046] • KNN - The k-nearest neighbours (KNN) algorithm is a non-parametric, supervised learning classifier, which uses proximity to make classifications or predictions about the grouping on an individual data point.

[0047] • NNET - A neural network model consisting of interconnected nodes, organised into layers.

[0048] • SVM - Support vector machines are a set of supervised learning methods used in classification, regression and outliers detection.

[0049] • GBM - Gradient boosting is a machine learning technique based on one or more models in which subsequent models aim to rectify errors present in the first model.

[0050] • NB - Naive Bayes Model is a probabilistic model which operates under the assumption that features are conditionally independent, given the target class.

[0051] • LR - Logistic regression is a statistical method based on fitting binary variables to a logistic function. In the context of the present invention, the model can be used as a baseline comparison.

[0052] The skilled person would appreciate the strengths and limitation of each algorithm and understand the most appropriate method of implementation. The skilled person would also appreciate the use of multiple statistical methods which can serve as a multi-fold cross validation tool. For example, one, two, three, four or five-fold cross validation can be conducted with any one or more statistical model mentioned above.

[0053] These models can be used with data obtained from any one or more of or combinations of markers selected from the 16 biomarkers herein.

[0054] Using these models, a diagnostic panel comprising or consisting of all of the above 16 biomarkers has been shown to be particularly useful in predicting the presence of lung cancer see Example 5. For example, exemplary combinations and the sensitivity, specificity and accuracy are as follows for the 16 miRNA biomarker panel: Model Diagnostic parameters 16 miRNA biomarker panel

[0055] AUC 1.000

[0056] RF Sensitivity 1.000

[0057] Specificity 1.000

[0058] AUC 0.788

[0059] KNN Sensitivity 0.725

[0060] Specificity 0.701

[0061] AUC 0.975

[0062] NNET Sensitivity 0.912

[0063] Specificity 0.913

[0064] AUC 0.872

[0065] SVM Sensitivity 0.837

[0066] Specificity 0.779

[0067] AUC 0.958

[0068] GBM Sensitivity 0.875

[0069] Specificity 0.913

[0070] AUC 0.775

[0071] NB Sensitivity 0.712

[0072] Specificity 0.748

[0073] By "sample obtained from a subject" we include any sample from a subject that comprises miRNAs. Preferably the sample to be tested is provided from an individual that is a mammal. In some embodiments, the individual may be a primate (for example, a human; a monkey; an ape); a rodent (for example, a mouse, a rat, a hamster, a guinea pig, a gerbil, a rabbit); a canine (for example, a dog); a feline (for example, a cat); an equine (for example, a horse); a bovine (for example, a cow); or a porcine (for example, a pig). Most preferably, the mammal is human. The sample to be tested in the methods of the invention may comprise or consist of: a cell; tissue; fluid sample (or derivative thereof); and may preferably comprise or consist of blood (fractionated or unfractionated), plasma, plasma cells, serum, tissue cells, pleural fluid, pleural cells. In one embodiment, the sample is a lung tissue sample. In an alternative or additional embodiment, the sample is a sample comprising or consisting of lung cells, for example epithelial cells or alveolar cells or pleural cells. In a preferred embodiment, the sample comprises one or more lung cancer cells. In some embodiments, the sample comprises one or more lung cancer cells and / or is selected from the group comprising: a biopsy (such as a core needle biopsy; fine needle biopsy; bronchoscopy sample); a tissue sample (such as a lung tissue or lung cancer tissue); an organ sample; and / or a bodily fluid sample (such as blood, saliva, sputum, urine, or pleural fluid).

[0074] Preferably the same is a blood sample or a serum / plasma sample.

[0075] A blood sample may be obtained from a subject using methods known to the person skilled in the art. Exemplary sampling methods may be selected from the group comprising or consisting of: venipuncture; fingerprick; arteriopuncture; capillary sampling; heel-prick; scalp vein sampling; and ear lobe puncture.

[0076] By "lung cancer" we include any type of cancer that forms in tissues in the lungs, usually in the cells lining air passages. The two main types are small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC).

[0077] The methods of the invention are able to determine the presence of lung cancer. The methods of the present invention are not able to determine the subtype of lung cancer. Once the presence of lung cancer has been determined, other tests are then advised to determine the type and the stage of the lung cancer. To determine cancer subtype, further diagnostic procedures would be necessary - for example, histological analysis of a lung tissue biopsy.

[0078] The methods of the invention permit the diagnosis of both early lung cancer and later stage lung cancer. The methods described herein are particularly useful in determining the presence of early stage lung cancer. In one embodiment, the method is for diagnosis of early-stage lung cancer.

[0079] By "diagnosis" we include determining the presence or absence of a disease state in an individual (e.g., determining whether an individual is or is not suffering from lung cancer).

[0080] In one embodiment, where the subject has been diagnosed with lung cancer based on the level of the one or more miRNAs, the subject is further analysed to determine the stage and / or subtype of lung cancer. Suitable methods for further analysing the subject to determine the stage and / or subtype of lung cancer are known to the person skilled in the art, and generally include providing a sample from a subject and analysing said sample using techniques such as cytopathology or karyotyping. In some embodiments the sample for further analysis is a lung biopsy. Accordingly, in some embodiments where the method comprises determining the stage and / or subtype of lung cancer, said determining comprises providing a lung tissue biopsy from the subject and analysing said lung tissue biopsy.

[0081] In some embodiments, the lung cancer is Non-Small Cell Lung Cancer (NSCLC). Therefore, in some embodiments, the method is for the diagnosis of NSCLC in an individual, and comprises the method of determining the level of the miRNA markers as set out herein, with additional tests to determine the type of lung cancer as NSCLC. Suitable methods of determining the type of lung cancer as NSCLC are provided herein and include the analysis of a lung biopsy from the subject.

[0082] In an additional or alternative embodiments, the NSCLC is selected from the group comprising: adenocarcinoma; squamous cell carcinoma; adenosquamous carcinoma; large cell carcinoma; or large cell neuroendocrine cancer. In some preferred embodiments, the NSCLC is adenocarcinoma.

[0083] In additional or alternative embodiments, the NSCLC in the individual is early-stage NSCLC (i.e., Stage 0, 1 or Stage II) or is late-stage NSCLC (i.e. Stage III or Stage IV).

[0084] By "classifying the NSCLC in the individual", we include assigning NSCLC in an individual into a particular group. The NSCLC within these groups may have similar physical properties or pathologies, they may be expected to behave similarly, or the individuals with these NSCLC groups may be expected to have similar prognoses. In a preferred embodiment, individuals with NSCLC in the same group or subtype have a similar or the same prognosis.

[0085] In some embodiments, the lung cancer is Small Cell Lung Cancer (SCLC). Therefore, in some embodiments, the method is for the diagnosis of SCLC in an individual, and comprises the method of determining the level of the miRNA markers as set out herein, with additional tests to determine the type of lung cancer as SCLC. Suitable methods of determining the type of lung cancer as SCLC are provided herein and include further analysis of a lung biopsy.

[0086] By "Small Cell Lung Cancer (SCLC)" we include any type of lung cancer that is not NonSmall Cell Lung Cancer (NSCLC). By "Non-Small Cell Lung Cancer (NSCLC)" we include any type of lung cancer that is not Small Cell Lung Cancer (SCLC). For example, the NSCLC may be adenocarcinoma; squamous cell carcinoma; adenosquamous carcinoma; large cell carcinoma; or large cell neuroendocrine cancer.

[0087] In one embodiment, the invention provides a method for lung cancer screening wherein the method comprises performing the method for determining the presence of lung cancer of the invention

[0088] The methods of this invention are suitable for testing a sample from any individual who has, or is suspected of having, lung cancer, whether that is NSCLC or SCLC. The method is considered to be particularly useful as part of a screening programme for the detection of early stage cancers in subjects that have no symptoms of cancer, or may have some symptoms of lung cancer but who have not yet received a diagnosis of cancer.

[0089] In one embodiment, the subject a) has not had a diagnosis of lung cancer; b) is considered to be a healthy individual; c) has not undergone treatment for lung cancer; and / or d) has any one or more symptoms of lung cancer, but has not had a diagnosis of lung cancer. in some embodiments the method is considered to be useful in the context of determining metastatic cancers that have spread to the lung tissue. Accordingly in some embodiments the subject has received a diagnosis of a cancer that is not a lung cancer. In some embodiments the subject has received a diagnosis of a cancer that is not a lung cancer but which has a high probably of metastasis to the lung.

[0090] In some embodiments, the methods described herein are able to diagnose lung cancer at an early stage, as the methods can be carried out on a sample from an individual either when they are showing no symptoms (i.e., asymptomatic) or when they are displaying minor symptoms. Therefore, one advantage of the invention described herein is that it can allow diagnosis of lung cancer at a much earlier stage than traditional diagnostic methods, allowing patients to seek prompt treatment that is key to long term survival. Symptoms of lung cancer can be found in Wilkinson et al. (2017) Oxford Handbook of Clinical Medicine (10th Ed), Oxford University Press, and Cassidy et al. (2015) Oxford Handbook of Oncology (4thEd), Oxford University Press (each herein incorporated by reference in its entirety). Exemplary symptoms of lung cancer include those selected from the group comprising or consisting of: persistent cough; breathlessness; coughing up blood; chest pain; shoulder pain; chest ache; shoulder ache; recurrent chest infection; loss of appetite; fatigue; weight loss (optionally unexplained weight loss); changes in the shape of fingers and / or fingernails; finger clubbing; hypertrophic pulmonary osteoarthropathy; nausea and vomiting; headaches; confusion; weakness; restlessness; irritability; muscle weakness, spasms, cramps, or aches; seizures; dry eyes; blurred vision; dysphagia; dizziness; dry mouth; constipation; erectile dysfunction.

[0091] It will be clear to the skilled person that when determining whether a level of one or more miRNAs indicates the presence of lung cancer or not, the level of the one or more miRNAs in the sample from the test subject can be compared to a control value.

[0092] The control value can be a negative control, and / or can be a positive control. The skilled person is able to determine adequate control samples. It will be appreciated that the test and any control samples should be from the same species and preferably the same sample type.

[0093] For example, in some embodiments the level of the one or more miRNAs in the test sample is compared to the level of the same one or more miRNAs in one or more negative control samples. A negative control sample may be, for example, the average level of the miRNA in a test sample obtained from a number of subjects that are known to not have lung cancer. In some embodiments, the control sample has been obtained from a healthy individual; or a population of healthy individuals. In some embodiments where the level of the one or more miRNAs in the sample from the test subject is higher or lower than the level in the control samples, the test subject is confirmed as having lung cancer. In some embodiments the level of the miRNAs in the test subject has to be above, below or within pre-determined threshold or range for a determination of lung cancer to be made. In some embodiments, the pre-determined threshold is a universal threshold, a constant value for the Ct value. In some embodiments, the predetermined threshold level is a level of the miRNA associated with a population of healthy individuals, i.e., individuals known to not have lung cancer. Determining appropriate thresholds is within the skilled person's abilities. Conversely, if the level of the one or more miRNAs in the test sample is the same as, or similar to the level of the miRNAs in the negative control samples, for example within a predetermined range, above or below a predetermined threshold, the subject is determined to not have lung cancer.

[0094] As will be appreciated, the level of one or more miRNAs in the test sample may be alternatively or additionally compared to the level of miRNAs in one or more positive control samples. Accordingly, in some embodiments the control sample is a positive control sample, for example is the average level of the miRNA in a test sample obtained from a number of subjects that are known to have lung cancer.

[0095] In some embodiments where the level of the one or more miRNAs in the sample from the test subject is the same as or higher than the level in the positive control samples or is within a pre-determined range, the test subject is confirmed as having lung cancer. In some embodiments the level of the miRNAs in the test subject has to be above, below or within pre-determined threshold or range in order for a determination of lung cancer to be made. Determining appropriate thresholds is within the skilled person's abilities.

[0096] Conversely, if the level of the one or more miRNAs in the test sample is above or below the level of the miRNAs in the positive control samples, or above / below a predetermined threshold value or range, the subject is determined to not to have lung cancer.

[0097] The control samples may be physical samples that are processed at the same time as the test sample. However, it is generally more practical if the control samples are levels of each particular miRNA that have been predetermined, for example when developing the initial signature. In this way, the value of a given miRNA in the test sample can be compared to a known level of the same miRNA in a positive and / or negative control sample, and a determination made as to the presence or absence of lung cancer.

[0098] In one embodiment, the method further comprises the step of determining that the subject has lung cancer if the level of the one or more miRNAs in the sample from the test subject is: a) at least 1.25, or at least 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, or at least 3.0, 3.5, 4.0, 4.5, or at least 5.0, 6.0, 7.0, 8.0, 9.0, or at least 10, 20, 30, 40, 50, 60, 70, 80, 90, or at least 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, or at least 1280 times higher than the level of the one or more miRNAs in the negative control sample, the population of healthy individuals, or the predetermined threshold level; and / or b) above a predetermined range or within a pre-determined threshold level.

[0099] In one embodiment, the method further comprises method the step of determining that the subject does not have or is not at high risk of having lung cancer, where the level of the one or more miRNAs in the test sample is: a) the same as or substantially similar to the level of the same one or more miRNAs in the negative control sample the population of healthy individuals, or the predetermined threshold level; b) is not at least 1.25, or at least 1.5, 1.75, 2.0, 2.25, 2.5, 2.75 or at least 3.0, 3.5, 4.0, 4.5, or at least 5.0, 6.0, 7.0, 8.0, 9.0, or at least 10, 20, 30, 40, 50, 60, 70, 80, 90, or at least 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, or at least 1280 times higher than the level of the one or more miRNAs in the negative control sample, the population of healthy individuals, or the predetermined threshold level; and / or c) within a predetermined range or below a predetermined threshold.

[0100] The above is described in the context of the miRNAs that provide a signature that correlates with disease being over expressed in the diseased phenotype vs the nondiseased phenotype. The skilled person will however appreciate that there may be some additional miRNAs that are repressed in the diseased phenotype vs the nondiseased phenotype, and the skilled person will understand how to apply that signature, for example will know which controls to use and what comparisons to make. For the avoidance of doubt, the level of the miRNAs set out in SEQ IDs 1-16 are upregulated or overexpressed in the lung-cancer condition state and their overexpression relative to a control sample from a healthy individual / population of healthy individuals indicates the presence of lung cancer.

[0101] In some embodiments the level of the one or more miRNAs determined in a test sample from the test subject is compared to both a positive and a negative control.

[0102] By "determining the level of" we include the meaning of determining the expression level of. The skilled person is readily able to determine the level of or the expression level of a particular miRNA in a particular sample. Routine methods include PCR based amplification methods such as qPCR or real-time PCR or digital PCR. In some embodiments the miRNA is converted to cDNA and then amplified. In some embodiments the miRNA is converted to cDNA in such a way as to allow amplification of all cDNAs using a common set of primers. Commercially available kits can be used to isolate the RNA and to perform the necessary reverse transcription step. In some instances the miRNA is extended at each end prior to reverse transcription. This allows the cDNA to be amplified by universal primers.

[0103] The amount of each miRNA present in the sample from the patient can be determined using quantitative methods such as real time PCR or digital PCR, such as using TaqMan primers and probes or using SYBRGreen detection methods. As the skilled person is aware, SYBRGreen is a cyanine dye that may be used to dye nucleic acids including DNA and RNA, for example miRNA. In some embodiments the threshold cycle value (the Ct value) is used to determine the level of the about of a given miRNA present in the sample.

[0104] Accordingly in some embodiments said determining is carried out using quantitative PCR.

[0105] The level of the one or more miRNAs in the sample can be determined using any suitable means. For example, in one embodiment the levels of the miRNAs are determined using reverse transcription followed by qPCR. In one embodiment, the qPCR is TaqMan PCR.

[0106] The skilled person is able to design appropriate primers to amplify the required miRNA(s). For example, miRNAs may suitably be amplified using universal primers that bind to the poly(A) tail of the miRNA. The primers may be DNA primers that driver the generation of cDNA as part of a reverse transcription step. The end of the miRNA - for example, the 3' end - may be extended prior to the reverse transcription step. Additional primers for use in the qPCR step may be targeted to the known sequence of the cDNA to be detected, or may be universal primers.

[0107] In some other embodiments, determination of the level of the one or more miRNAs is via hybridisation of miRNA, cDNA, or DNA amplicon generated during the qPCR reaction to a detection probe. The detection probe may be an oligonucleotide, for example. The oligonucleotide may be conjugated to a moiety that generates a visible read-out, for example horseradish peroxidase or a fluorescent moiety. Where the oligonucleotide probe is conjugates to a fluorescent moiety, it may also be conjugated to a quencher moiety. Such arrangements include, for example, TaqMan probes, the functioning of which is known to the person skilled in the art.

[0108] Preferably the method of determining the level of the one or more miRNAs produces a visible readout, i.e., visible to the naked eye, so that the method can be performed in the absence of sophisticated laboratory equipment. Alternatively, the visible read out may be a fluorescence read out as described herein. As will be appreciated, there are a variety of different fluorescent moieties that may be conjugated to oligonucleotide probes for the detection of nucleic acids, each of which may emit fluorescent light of a different wavelength. Accordingly, although such read-outs may require the use of sophisticated laboratory equipment to be detected, the use of a fluorescent moiety- conjugated probe for the detection the miRNA, cDNA, or DNA amplicon generated during a qPCR reaction may advantageously allow the detection of more than one miRNA, cDNA, or DNA amplicon in a single reaction. Accordingly, in some embodiments, the method of determining the level of the one or more miRNA is a multiplex method, wherein the levels multiple different miRNAs are detected in one reaction.

[0109] In some embodiments, determination of the level of the one or more miRNAs is by using a nucleic acid dye that binds to an miRNA, cDNA, or DNA amplicon generated during the qPCR reaction. The nucleic acid dye may be selected from the group comprising or consisting of: ethidium bromide, SYBRSafe, and SYBRGreen. Preferably, the nucleic acid dye is SYBRGreen. Binding of the nucleic acid dye to the miRNA, cDNA, or DNA generates a visible read-out when exposed to an excitation wavelength. Excitation wavelengths for different nucleic acid dyes are known to the person skilled in the art. Methods of using and detecting visible read out from nucleic acid dyes are known to the person skilled in the art.

[0110] Any suitable method can be used to obtain miRNA from the sample.

[0111] In one embodiment, the miRNAs are isolated using column-based extraction or magnetic-bead based extraction.

[0112] In one embodiment, the one or more miRNAs are produced by the subject, optionally produced by the lung tissue of the subject, optionally produced at higher levels by a lung tumour cell or host immune cells. The host immune cells may be present in the lung tumour; may be present in tissues that are adjacent to or proximal to the lung tumour; and / or may disseminated through the body of the subject, for example be circulating in the blood, lymph, or interstitial fluid of the subject.

[0113] In one embodiment, the method further comprises performing a low dose computed tomography scan (LDCT) on the patient, or comprises incorporating data from a LDCT along with the levels of the specified miRNAs to make the determination of the presence, or not, of lung cancer.

[0114] The skilled person will appreciate that the present invention has distinct advantages since the panels of biomarkers are powerful and are able, in the absence of other information such as from scans, to allow the determination of the presence or absence of lung cancer Since the levels of the biomarkers are able to be determined from a simple blood test, determination of the presence or absence of lung cancer can be a low cost, simply, non-invasive process.

[0115] However, as described in Example 5, combining the levels of the specified biomarkers in a sample from a patient with data about lung nodule size obtained via a scan, is particularly powerful.

[0116] Accordingly, in a further embodiment, the level of the one or more miRNAs is considered in conjunction with data obtained from an image, such as obtained from a scan such as an LDCT scan. A diagnosis of the presence or absence of lung cancer may be based on the level of the one or more miRNAs and information obtained from the LDCT scan.

[0117] In one embodiment, wherein the subject has been diagnosed with a lung nodule following a computerised tomography (CT) scan, MRI scan, LDCT scan or X-ray scan.

[0118] In some preferred embodiments the method for determining the presence of lung cancer in a subject of the invention comprising combining information about one or more lung nodules with the determined level of any one or more of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16, or all of the following miRNAs in a sample obtained from the subject.

[0119] Information about lung nodules includes information about the lung nodule size. In some embodiments, the lung nodule size can be determined by determining: a) the diameter of one or more lung nodule(s) across the longest axis or section of the nodule, optionally wherein information is also obtained for the remaining axes; and / or b) the volume of one or more lung nodule.

[0120] Methods of determining the diameter of a lung nodule, determining the diameter of a lung nodule along the longest axis or section of the nodule, and determining the volume of a lung nodule are known to the person skilled in the art. The size of a lung nodule may suitably be determined from a histological sample comprising at least one lung nodule provided from a subject; or may be determined by analysing data obtained from an image, such as obtained from a scan such as an LDCT scan.

[0121] Furthermore, nodule size may be calculated from either: a) the largest nodule present in, identified in, or obtained from the lung; b) the largest nodule present in, identified in, or obtained from a particular part of the lung; c) an average from a plurality of similarly sized nodules present in, identified in, or obtained from the lung; d) an average from a plurality of all nodules present in, identified in, or obtained from the lung.

[0122] The inventors have also identified a suitable biomarker panel that may be used alone or in combination with lung nodule characterisation to distinguish between benign and malignant lung nodules (Example 6). In particular, the inventors have shown the effectiveness of this method in characterising lung nodules of sizes less than or equal to 30 mm.

[0123] Accordingly, in one embodiment, the method comprises determining the level of all of the following miRNAs:

[0124] Biomarker 5 [SEQ ID NO: 5] mir-1268; Biomarker 10 [SEQ ID NO: 10] mir-130b; Biomarker 6 [SEQ ID NO: 6] mir-106b; Biomarker 3 [SEQ ID NO: 3] mir-221; Biomarker 8 [SEQ ID NO: 8] mir-21; and Biomarker 12 [SEQ ID NO: 12] mir-23a.

[0125] In some embodiments, the method further comprises characterising lung nodule size and applying a statistic approach (as outlined above) to distinguish between benign and malignant lung nodules. In some embodiments, the method comprises determining the level of all of the following miRNAs: Biomarker 5 [SEQ ID NO: 5] mir-1268; Biomarker 10 [SEQ ID NO: 10] mir-130b; Biomarker 6 [SEQ ID NO: 6] mir-106b; Biomarker 3 [SEQ ID NO: 3] mir-221; Biomarker 8 [SEQ ID NO: 8] mir-21; Biomarker 12 [SEQ ID NO: 12] mir-23a;

[0126] In a lung nodule, lung nodules, or a plurality of nodules with a size of < 30 mm.

[0127] In another embodiment, wherein in the event that the individual is diagnosed with lung cancer, the method further comprises a step of providing the individual with a lung cancer therapy, optionally wherein the lung cancer therapy is selected from the group comprising: surgery, chemotherapy, radiotherapy, immunotherapy, chemoimmunotherapy, thermochemotherapy and combinations thereof, adoptive cell therapies, gene therapies, cancer vaccines, and oncolytic virus therapies.

[0128] The invention also provides a method of determining whether a lung nodule is a lung cancer tumour or is benign, wherein the method comprises performing the method of determining the presence of lung cancer according to any of the aforementioned methods, on a subject identified as having a lung nodule, optionally identified as having a lung nodule based on a CT scan.

[0129] The inventors have assessed patient variables including age, gender, race, smoking, nodule number, nodule size, nodule location, emphysema, speculation. Nodule size was found to be an especially important variable in predicting the presence of cancer.

[0130] Accordingly, the invention provides a method for determining the presence of lung cancer in a subject wherein the method comprises: a) obtaining data about nodule size, nodule number, and / or nodule location of one or more nodules present in a subject; and

[0131] (b) applying the data to a machine learning model to determine the presence of lung cancer wherein the model has been validated using one or more model or models selected from the group comprising or consisting of:

[0132] RF;

[0133] KNN;

[0134] NNET;

[0135] SVM;

[0136] GBM; and / or

[0137] NB. In some prefered embodiments, the model is GBM. In some embodiments, the initial model was trained using RF analysis. In some optional embodiments, the method further comprises comparing the data to a baseline analysis performed using logistic regression (LR).

[0138] In some embodiments the data is obtained from a low-dose computed tomography scan (LDCT) or other suitable scan.

[0139] In some embodiments the method comprises performing a low-dose computed tomography (LDCT) scan on the patient to obtain the data about: nodule size; nodule number; and / or nodule location.

[0140] In further embodiments, the method can comprise cross-validation with one or more statistical methods selected from:

[0141] RF;

[0142] KNN;

[0143] NNET;

[0144] SVM;

[0145] GBM; and / or

[0146] NB.

[0147] The inventors have found that the accuracy, selectivity and / or specificity of the invention can be improved by combining a selection of miRNA biomarkers described in the first aspect of the invention with nodule characterisation data attained from a scan such as an LDCT scan. The combination of two minimally invasive diagnostic methods affords advantages for the patient.

[0148] Accordingly, the invention provides a method for determining the presence of lung cancer in a subject wherein the method comprises: a) determining the level of one or more miRNAs in a sample from the subject; b) obtaining data about one or more nodules present in a subject, for example nodule size, nodule number, and / or nodule location of one or more nodules present in a subject; and c) applying the level of the one or more miRNAs and the data about one or more nodules present in a subject to a statistical model to determine the presence of lung cancer wherein the model can comprise one or more of the following: d) RF; e) KNN; f) NNET; g) SVM; h) GBM; and / or i) NB.

[0149] Preferably the model is GBM.

[0150] Preferences for the one or more or panels of biomarkers are set out elsewhere herein. In one embodiment, the miRNAs selected for use in combination with a LDCT scan is 6 or more of the following:

[0151] Biomarker 9 [SEQ ID NO: 9] mir-196a, Biomarker 5 [SEQ ID NO: 5] mir-1268, Biomarker 10 [SEQ ID NO: 10] mir-130b, Biomarker 2 [SEQ ID NO: 2] miR-1290, Biomarker 6 [SEQ ID NO: 6] mir-106b, Biomarker 1 [SEQ ID NO: 1] miR-1246; Biomarker 3 [SEQ ID NO: 3] mir-221; Biomarker 4 [SEQ ID NO: 4] mir-223; Biomarker 7 [SEQ ID NO: 7] mir-26; Biomarker 8[SEQ ID NO: 8] mir-21; Biomarker 11 [SEQ ID NO: 11] mir-155; Biomarker 12 [SEQ ID NO: 12] mir-23a; Biomarker 13 [SEQ ID NO: 13] mir-16; Biomarker 14 [SEQ ID NO: 14] mir-222; Biomarker 15 [SEQ ID NO: 15] mir-451; Biomarker 16 [SEQ ID NO: 16] mir-486.

[0152] As described above, the inventors have found that smaller biomarker panels can be effective. Accordingly, a smaller selection of miRNA biomarkers can be combined with information about one or more nodules in a subject, for example information obtained via an LDCT. In some embodiments, the biomarker panel can comprise all of the following miRNAs

[0153] Biomarker 2 [SEQ ID NO: 2], Biomarker 5 [SEQ ID NO: 5], Biomarker 6 [SEQ ID NO: 6] and Biomarker 9 [SEQ ID NO: 9] and either: i) Biomarker 1 [SEQ ID NO: 1], and Biomarker 10 [SEQ ID NO: 10]; or ii) Biomarker 3 [SEQ ID NO: 3] and Biomarker 13 [SEQ ID NO: 13].

[0154] In some embodiments, the biomarker panel can comprise all of the following miRNAs: Biomarker 9 [SEQ ID NO: 9] mir-196a, Biomarker 5 [SEQ ID NO: 5] mir-1268, Biomarker 10 [SEQ ID NO: 10] mir-130b, Biomarker 2 [SEQ ID NO: 2] miR-1290, Biomarker 6 [SEQ ID NO: 6] mir-106b, Biomarker 1 [SEQ ID NO: 1] miR-1246.

[0155] Alternatively, in another embodiment, the biomarker panel can comprise all of the following miRNAs:

[0156] Biomarker 1 [SEQ ID NO: 1] miR-1246; Biomarker 5 [ SEQ ID NO: 5] mir-1269;

[0157] Biomarker 10 [SEQ ID NO: 10] mir-130b; Biomarker 6 [ SEQ ID NO: 6] mir-106b;

[0158] Biomarker 12 [SEQ ID NO: 12] mir-23a; Biomarker 9 [SEQ ID NO: 9] mir-196a,

[0159] Biomarker 2 [ SEQ ID NO: 2] mir-1290; Biomarker 7 [SEQ ID NO: 7] mir-26 or that has at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% identical to any of the above sequences.

[0160] The inventors have assessed the efficacy of measuring nodule size; a combination of nodule size and all of the 16 miRNA biomarkers in accordance with the first aspect of the invention; and nodule size and the top 6 miRNA biomarkers which are Biomarker 9 [SEQ ID NO: 9] mir-196a, Biomarker 5 [SEQ ID NO: 5] mir-1268, Biomarker 10 [SEQ ID NO: 10] mir-130b, Biomarker 2 [SEQ ID NO: 2] miR-1290, Biomarker 6 [SEQ ID NO: 6] mir-106b, Biomarker 1 [SEQ ID NO: 1] miR-1246.

[0161] The results shown below demonstrate that each method, in combination with each machine learning algorithm, has a high degree of accuracy, selectivity and specificity.

[0162] Diagnostic test

[0163] 16 miRNA Top 6

[0164] Diagnostic Nodule biomarkers biomarkers Model parameters size and nodule and nodule size size

[0165] AUC 0.989 1.000 1.000

[0166] RF Sensitivity 0.962 1.000 1.000

[0167] Specificity 0.945 1.000 1.000

[0168] AUC 0.975 0.985 0.989

[0169] KNN Sensitivity 0.975 0.937 0.921

[0170] Specificity 0.890 0.929 0.975

[0171] AUC 0.9553 0.964 0.961

[0172] NNET Sensitivity 0.975 0.975 0.937

[0173] Specificity 0.898 0.9370 0.929

[0174] AUC 0.944 0.975 0.983

[0175] SVM Sensitivity 0.975 0.975 0.975

[0176] Specificity 0.898 0.929 0.937

[0177] AUC 0.979 0.985 1.000

[0178] GBM Sensitivity 0.95 0.962 1.000

[0179] Specificity 0.929 0.953 1.000

[0180] AUC 0.984 0.959 0.978

[0181] NB Sensitivity 0.987 0.862 0.912

[0182] Specificity 0.984 0.937 0.976

[0183] The invention also provides a method for determining the efficacy of a treatment against lung cancer in a subject wherein the method comprises: a) determining the level of one or more miRNAs in a sample from the subject; b) treating said subject with an appropriate therapeutic to treat lung cancer; c) determining the level of the same one or more miRNAs as in (a) in a sample from the subject. The method may further comprise making a determination that the treatment is effective if the level of the one or more miRNAs in (c) i.e. post-treatment is lower than the level of the same miRNAs in (a) i.e. prior to treatment. The method may further comprise making a determination that the treatment is ineffective if the level of the one or more miRNAs in (c) i.e. post-treatment is the same as or higher than the level of the same miRNAs in (a) i.e. prior to treatment.

[0184] Preferences for features of this method are as described elsewhere herein, for example preferences for the miRNAs and panels thereof are as described elsewhere herein, as are preferences for combination with information about one or more lung nodules in a subject.

[0185] The invention also provides a method for lung nodule management comprising a) determining the level of one or more miRNAs in a sample from a subject that has been identified as having a lung nodule; b) determining whether the lung nodule is likely to be a tumour based upon the level of the one or more miRNAs determined in (a), for example in accordance with the method of determining the presence of lung cancer of the invention.

[0186] The method may further comprise treating the subject for a non-cancerous lung nodule when the subject is determined to not have lung cancer based on the level of the one or more miRNAs, for example for example in accordance with the method of determining the presence of lung cancer of the invention.

[0187] Preferences for features of this method are as described elsewhere herein, for example preferences for the miRNAs are as described elsewhere herein.

[0188] As will be understood, any method for determining the efficacy of a treatment against lung cancer in a subject; or the method for lung nodule management may comprise additional diagnostic steps comprising performing diagnostic tests other than determining the level of one or more miRNA. Such methods may include, for example, cytopathology, karyotyping.

[0189] The invention also provides a kit for performing any of the methods described herein.

[0190] The invention also provides a kit comprising one or more or all of the following: a) Means to determine the level of at least one of the following;

[0191] Biomarker 1 [SEQ ID NO: 1] miR-1246; Biomarker 2 [SEQ ID NO: 2] miR-1290; Biomarker 3 [SEQ ID NO: 3] mir-221; Biomarker 4 [SEQ ID NO: 4] mir-223; Biomarker 5 [SEQ ID NO: 5] mir-1268; Biomarker 6 [SEQ ID NO: 6] mir-106b; Biomarker 7 [SEQ ID NO: 7] mir-26; Biomarker 8[SEQ ID NO: 8] mir-21; Biomarker 9 [SEQ ID NO: 9] mir-196a; Biomarker 10[SEQ ID NO: 10] mir-130b; Biomarker 11 [SEQ ID NO: 11] mir-155; Biomarker 12 [SEQ ID NO: 12] mir-23a; Biomarker 13 [SEQ ID NO: 13] mir- 16; Biomarker 14 [SEQ ID NO: 14] mir-222; Biomarker 15 [SEQ ID NO: 15] mir-451; Biomarker 16 [SEQ ID NO: 16] mir-486; b) means for the extraction and purification of RNA from a sample; and / or c) a control sample of RNA prepared from a sample from subjects, optionally wherein the means to determine the level of at least one of the miRNAs comprises: i) at least one pair of primers are arranged so as to amplify at least one or more of the following miRNAs:

[0192] Biomarker 1 [SEQ ID NO: 1] miR-1246; Biomarker 2 [SEQ ID NO: 2] miR-1290; Biomarker 3 [SEQ ID NO: 3] mir-221; Biomarker 4 [SEQ ID NO: 4] mir-223; Biomarker 5 [SEQ ID NO: 5] mir-1268; Biomarker 6 [SEQ ID NO: 6] mir-106b; Biomarker 7 [SEQ ID NO: 7] mir-26; Biomarker 8[SEQ ID NO: 8] mir-21; Biomarker 9 [SEQ ID NO: 9] mir-196a; Biomarker 10[SEQ ID NO: 10] mir- 130b; Biomarker 11 [SEQ ID NO: 11] mir-155; Biomarker 12 [SEQ ID NO: 12] mir-23a; Biomarker 13 [SEQ ID NO: 13] mir-16; Biomarker 14 [SEQ ID NO: 14] mir-222; Biomarker 15 [SEQ ID NO: 15] mir-451; Biomarker 16 [SEQ ID NO: 16] mir-486; or miRNAs with a sequence that has at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% identical to SEQ ID NOs: 1- 16; ii) at least one or more probe arranged so as to hybridise to at least one or more of the following miRNAs:

[0193] Biomarker 1 [SEQ ID NO: 1] miR-1246; Biomarker 2 [SEQ ID NO: 2] miR-1290; Biomarker 3 [SEQ ID NO: 3] mir-221; Biomarker 4 [SEQ ID NO: 4] mir-223; Biomarker 5 [SEQ ID NO: 5] mir-1268; Biomarker 6 [SEQ ID NO: 6] mir-106b; Biomarker 7 [SEQ ID NO: 7] mir-26; Biomarker 8[SEQ ID NO: 8] mir-21; Biomarker 9 [SEQ ID NO: 9] mir-196a; Biomarker 10[SEQ ID NO: 10] mir- 130b; Biomarker 11 [SEQ ID NO: 11] mir-155; Biomarker 12 [SEQ ID NO: 12] mir-23a; Biomarker 13 [SEQ ID NO: 13] mir-16; Biomarker 14 [SEQ ID NO: 14] mir-222; Biomarker 15 [SEQ ID NO: 15] mir-451; Biomarker 16 [SEQ ID NO: 16] mir-486; or miRNAs with a sequence that has at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% identical to SEQ ID NOs: 1- 16; iii) reverse transcriptase and / or DNA polymerase; iv) a microarray; and / or vi) an array card.

[0194] In another embodiment, b) comprises microRNA extraction columns or magnetic beads.

[0195] The kit may comprise means to determine the level of any one or more biomarker panels set out above, for example in some embodiments the kit comprises means to determine the level of all of Biomarker 9 [SEQ ID NO: 9] mir-196a, Biomarker 5 [SEQ ID NO: 5] mir-1268, Biomarker 10 [SEQ ID NO: 10] mir-130b, Biomarker 2 [SEQ ID NO: 2] miR-1290, Biomarker 6 [SEQ ID NO: 6] mir-106b, Biomarker 1 [SEQ ID NO: 1] miR-1246.

[0196] By "miRNA extraction columns", we include the meaning columns used to isolate miRNAs from the blood sample in a process called extraction. This step is critical to concentrate and purify microRNAs for subsequent analysis. Extraction columns may contain resins or filters that selectively bind miRNAs while removing other contaminants.

[0197] In some embodiments, the at least one or more probe(s) in are TaqMan probes. In some embodiments, the means to determine the level of at least one of the miRNA comprise PCR buffers.

[0198] By "Taqman probes", we include the meaning specialized DNA probes that are labelled with a fluorescent dye (or moiety) and a quencher molecule. The probes are designed to be complementary to the target miRNA sequence to be detected. During the analysis, the probe binds to the target miRNA, and when the DNA polymerase replicates the DNA strand, the quencher is cleaved, leading to the release of fluorescence by the fluorescent dye (or moiety). This fluorescence signal indicates the presence and quantity of the target miRNA.

[0199] By "buffers", we include the meaning solutions used to create and maintain a suitable chemical environment for the various reactions in the liquid biopsy process. They help stabilize the microRNAs, enzymes, and other components involved. Different steps of the process might require different buffer compositions and pH levels. Buffers may also comprise co-factors required by enzymes for their function. For example, a buffer for use with a reverse transcriptase and / or DNA polymerase may comprise a magnesium salt that provides Mg2+ions required for polynucleotide extension by the polymerases; and / or ATP.

[0200] By "enzymes", we include the meaning biological catalysts that facilitate biochemical reactions. In the context of a liquid biopsy test, enzymes like reverse transcriptase and DNA polymerase are used. Reverse transcriptase converts miRNAs into cDNA, and DNA polymerase amplifies the cDNA for detection. As will be appreciated by the skilled person, TaqMan assays require the presence of a polymerase with exonuclease activity. Accordingly, in some embodiments the polymerase has exonuclease activity, optionally 3'-5' exonuclease activity or 5'-3' exonuclease activity. These enzymes are crucial for the accurate and sensitive detection of miRNAs.

[0201] In one embodiment the kit comprises: a) Means to determine the level of at least one of the miRNAs associated with the presence of said lung cancer; b) means for the extraction and purification of RNA from blood, other body fluids or a lung tissue sample; c) a control sample of RNA prepared from blood, other body fluids or a lung tissue sample from subjects that do not have lung cancer.

[0202] In some embodiments the means to determine the level of at least one of the miRNAs comprises: a) at least one pair of primers are arranged so as to amplify at least one or more or all of the following miRNAs: Biomarker 1 [SEQ ID NO: 1] miR-1246; Biomarker 2 [SEQ ID NO: 2] miR-1290; Biomarker 3 [SEQ ID NO: 3] mir-221; Biomarker 4 [SEQ ID NO: 4] mir-223; Biomarker 5 [SEQ ID NO: 5] mir-1268; Biomarker 6 [SEQ ID NO: 6] mir-106b; Biomarker 7 [SEQ ID NO: 7] mir-26; Biomarker 8[SEQ ID NO: 8] mir- 21; Biomarker 9 [SEQ ID NO: 9] mir-196a; Biomarker 10[SEQ ID NO: 10] mir-130b; Biomarker 11 [SEQ ID NO: 11] mir-155; Biomarker 12 [SEQ ID NO: 12] mir-23a; Biomarker 13 [SEQ ID NO: 13] mir-16; Biomarker 14 [SEQ ID NO: 14] mir-222; Biomarker 15 [SEQ ID NO: 15] mir-451; Biomarker 16 [SEQ ID NO: 16] mir-486; or miRNAs with a sequence that has at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% identical to SEQ ID NOs: 1-16; b) one or more hybridisation probes able to specifically hybridise to one or more of the following miRNAs: at least one pair of primers are arranged so as to amplify at least one or more or all of the following miRNAs: Biomarker 1 [SEQ ID NO: 1] miR-1246; Biomarker 2 [SEQ ID NO: 2] miR-1290; Biomarker 3 [SEQ ID NO: 3] mir-221; Biomarker 4 [SEQ ID NO: 4] mir-223; Biomarker 5 [SEQ ID NO: 5] mir-1268; Biomarker 6 [SEQ ID NO: 6] mir-106b; Biomarker 7 [SEQ ID NO: 7] mir-26; Biomarker 8[SEQ ID NO: 8] mir- 21; Biomarker 9 [SEQ ID NO: 9] mir-196a; Biomarker 10[SEQ ID NO: 10] mir-130b; Biomarker 11 [SEQ ID NO: 11] mir-155; Biomarker 12 [SEQ ID NO: 12] mir-23a; Biomarker 13 [SEQ ID NO: 13] mir-16; Biomarker 14 [SEQ ID NO: 14] mir-222; Biomarker 15 [SEQ ID NO: 15] mir-451; Biomarker 16 [SEQ ID NO: 16] mir-486; or miRNAs with a sequence that has at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% identical to SEQ ID NOs: 1-16;.

[0203] In some embodiment the kit comprises means to determine the level of (for example primers and / or probes):

[0204] All of Biomarker 9 [SEQ ID NO: 9] mir-196a, Biomarker 5 [SEQ ID NO: 5] mir- 1268, Biomarker 10 [SEQ ID NO: 10] mir-130b, Biomarker 2 [SEQ ID NO: 2] miR- 1290, Biomarker 6 [SEQ ID NO: 6] mir-106b, Biomarker 1 [SEQ ID NO: 1] miR-1246; any 1, 2, 3, 4, 5, 6, 7, or 8, or all of the following miRNAs:

[0205] Biomarker 1 [SEQ ID NO: 1] miR-1246; Biomarker 2 [SEQ ID NO: 2] miR-1290; Biomarker 3 [SEQ ID NO: 3] mir-221; Biomarker 5 [SEQ ID NO: 5] mir-1268; Biomarker 9 [SEQ ID NO: 9] mir-196a; Biomarker 10[SEQ ID NO: 10] mir-130b; Biomarker 12 [SEQ ID NO: 12] mir-23a; and Biomarker 16 [SEQ ID NO: 16] mir-486; or determining the level of any 1, 2, 3, 4, 5, 6, 7, or 8 or all of miRNAs with a sequence that has at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% identical to SEQ ID NOs: 1-3, 5, 9, 10, 12, and / or 16; or all of the following miRNAs:

[0206] Biomarker 1 [SEQ ID NO: 1] miR-1246; Biomarker 2 [SEQ ID NO: 2] miR-1290; Biomarker 3 [SEQ ID NO: 3] mir-221; Biomarker 5 [SEQ ID NO: 5] mir-1268; Biomarker 9 [SEQ ID NO: 9] mir-196a; Biomarker 10[SEQ ID NO: 10] mir-130b; Biomarker 12 [SEQ ID NO: 12] mir-23a; and Biomarker 16 [SEQ ID NO: 16] mir-486; or determining the level of miRNAs with a sequence that has at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% identical to SEQ ID NOs: 1-3, 5, 9, 10, 12, and 16.

[0207] In some embodiment the kit comprises means to determine the level of (for example primers and / or probes) all of:

[0208] Biomarker 9 [SEQ ID NO: 9] mir-196a, Biomarker 5 [SEQ ID NO: 5] mir-1268,

[0209] Biomarker 10 [SEQ ID NO: 10] mir-130b, Biomarker 2 [SEQ ID NO: 2] miR-1290,

[0210] Biomarker 6 [SEQ ID NO: 6] mir-106b, Biomarker 1 [SEQ ID NO: 1] miR-1246; or Biomarker 5 [ SEQ ID NO: 5] mir-1269; Biomarker 3 [ SEQ ID NO: 3] mir-221;

[0211] Biomarker 6 [ SEQ ID NO: 6] mir-106b; Biomarker 13 [ SEQ ID NO: 13] mir-16;

[0212] Biomarker 9 [SEQ ID NO: 9] mir-196a, Biomarker 2 [ SEQ ID NO: 2] mir-1290 or that has at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% identical to any of the above sequences.

[0213] The invention further provides a method of treating lung cancer in an individual comprising the steps of:

[0214] (i) diagnosing lung cancer according to any of the aforementioned methods; and

[0215] (ii) providing the individual with lung cancer therapy.

[0216] The invention further provides, a method, use, array, or kit for determining the presence of lung cancer in an individual substantially as described herein.

[0217] Preferences and options for a given aspect, feature or parameter of the invention should, unless the context indicates otherwise, be regarded as having been disclosed in combination with any and all preferences and options for all other aspects, features and parameters of the invention. For example, the invention provides: a method for determining the presence of lung cancer in a subject wherein the method comprises determining the level of any one or more of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16, or all of the following miRNAs in a sample obtained from the subject:

[0218] Biomarker 1 [SEQ ID NO: 1] miR-1246; Biomarker 2 [SEQ ID NO: 2] miR-1290; Biomarker 3 [SEQ ID NO: 3] mir-221; Biomarker 4 [SEQ ID NO: 4] mir-223; Biomarker 5 [SEQ ID NO: 5] mir-1268; Biomarker 6 [SEQ ID NO: 6] mir-106b; Biomarker 7 [SEQ ID NO: 7] mir-26; Biomarker 8[SEQ ID NO: 8] mir-21; Biomarker 9 [SEQ ID NO: 9] mir-196a; Biomarker 10[SEQ ID NO: 10] mir-130b; Biomarker 11 [SEQ ID NO: 11] mir-155; Biomarker 12 [SEQ ID NO: 12] mir-23a; Biomarker 13 [SEQ ID NO: 13] mir- 16; Biomarker 14 [SEQ ID NO: 14] mir-222; Biomarker 15 [SEQ ID NO: 15] mir-451; Biomarker 16 [SEQ ID NO: 16] mir-486; or miRNAs with a sequence that has at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% identical to SEQ ID NOs: 1-16. and the invention also provides: a method of determining whether a lung nodule is a lung cancer tumour or is benign, wherein the method comprises performing the method of determining the presence of lung cancer as provided herein on a subject identified as having a lung nodule, optionally identified as having a lung nodule based on a CT scan.

[0219] The invention also provides:

[0220] A method of treating lung cancer in an individual comprising the steps of:

[0221] (i) diagnosing lung cancer according to the as provided herein; and

[0222] (ii) providing the individual with lung cancer therapy.

[0223] The invention also provides the use of the miRNA signatures described, to determine the presence of lung cancer in a subject. Preferences for features of this aspect of the invention are as described elsewhere herein.

[0224] The invention also provides a method, use, array, or kit for determining the presence of lung cancer in an individual substantially as described herein.

[0225] The listing or discussion of an apparently prior-published document in this specification should not necessarily be taken as an acknowledgement that the document is part of the state of the art or is common general knowledge. The contents of all prior- published documents listed or discussed in this specification are herein incorporated by reference in their entirety.

[0226] The invention is also further defined by reference to the following numbered paragraphs: 1. A method for determining the presence of lung cancer in a subject wherein the method comprises determining the level of any one or more of or 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16, or all of the following miRNAs in a sample obtained from the subject:

[0227] Biomarker 1 [SEQ ID NO: 1] miR-1246; Biomarker 2 [SEQ ID NO: 2] miR-1290; Biomarker 3 [SEQ ID NO: 3] mir-221; Biomarker 4 [SEQ ID NO: 4] mir-223; Biomarker 5 [SEQ ID NO: 5] mir-1268; Biomarker 6 [SEQ ID NO: 6] mir-106b; Biomarker 7 [SEQ ID NO: 7] mir-26; Biomarker 8[SEQ ID NO: 8] mir-21; Biomarker 9 [SEQ ID NO: 9] mir-196a; Biomarker 10[SEQ ID NO: 10] mir-130b; Biomarker 11 [SEQ ID NO: 11] mir-155; Biomarker 12 [SEQ ID NO: 12] mir-23a; Biomarker 13 [SEQ ID NO: 13] mir- 16; Biomarker 14 [SEQ ID NO: 14] mir-222; Biomarker 15 [SEQ ID NO: 15] mir-451; Biomarker 16 [SEQ ID NO: 16] mir-486; or determining the level of any one or more of or 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16, or all of the miRNAs with a sequence that has at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% identical to SEQ ID NOs: 1-16.

[0228] 2. The method according to paragraph 1, wherein the method comprises: a) determining the level of all of the following miRNAs

[0229] Biomarker 2 [SEQ ID NO: 2], Biomarker 5 [SEQ ID NO: 5], Biomarker 6 [SEQ ID NO: 6] and Biomarker 9 [SEQ ID NO: 9] and either: i) Biomarker 1 [SEQ ID NO: 1], and Biomarker 10 [SEQ ID NO: 10]; or ii) Biomarker 3 [SEQ ID NO: 3] and Biomarker 13 [SEQ ID NO: 13]; b) determining the level of all of Biomarker 2 [SEQ ID NO: 2], Biomarker 5 [SEQ ID NO: 5], Biomarker 6 [SEQ ID NO: 6] and Biomarker 9 [SEQ ID NO: 9], Biomarker 1 [SEQ ID NO: 1], and Biomarker 10 [SEQ ID NO: 10]; c) determining the level of any 6 or more of the following miRNAs:

[0230] Biomarker 1 [SEQ ID NO: 1] miR-1246; Biomarker 2 [SEQ ID NO: 2] miR-1290; Biomarker 3 [SEQ ID NO: 3] mir-221; Biomarker 4 [SEQ ID NO: 4] mir-223; Biomarker 5 [SEQ ID NO: 5] mir-1268; Biomarker 6 [SEQ ID NO: 6] mir-106b; Biomarker 7 [SEQ ID NO: 7] mir-26; Biomarker 8[SEQ ID NO: 8] mir-21; Biomarker 9 [SEQ ID NO: 9] mir-196a; Biomarker 10[SEQ ID NO: 10] mir-130b; Biomarker 11 [SEQ ID NO: 11] mir-155; Biomarker 12 [SEQ ID NO: 12] mir-23a; Biomarker 13 [SEQ ID NO: 13] mir- 16; Biomarker 14 [SEQ ID NO: 14] mir-222; Biomarker 15 [SEQ ID NO: 15] mir-451; Biomarker 16 [SEQ ID NO: 16] mir-486; or determining the level of any 6 or more of the miRNAs with a sequence that has at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% identical to SEQ ID NOs: 1-16

[0231] 3. The method according to paragraph 1, wherein the method comprises determining the level of any 1, 2, 3, 4, 5, 6, 7, or 8, or all of the following miRNAs: Biomarker 1 [SEQ ID NO: 1] miR-1246; Biomarker 2 [SEQ ID NO: 2] miR-1290; Biomarker 3 [SEQ ID NO: 3] mir-221; Biomarker 5 [SEQ ID NO: 5] mir-1268; Biomarker 9 [SEQ ID NO: 9] mir-196a; Biomarker 10[SEQ ID NO: 10] mir-130b; Biomarker 12 [SEQ ID NO: 12] mir-23a; and Biomarker 16 [SEQ ID NO: 16] mir-486; or determining the level of any 1, 2, 3, 4, 5, 6, 7, or 8 or all of miRNAs with a sequence that has at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% identical to SEQ ID NOs: 1-3, 5, 9, 10, 12, and / or 16.

[0232] 4a. The method according to any one of paragraphs 1-3, wherein the method comprises determining the level of all of the following miRNAs:

[0233] Biomarker 1 [SEQ ID NO: 1] miR-1246; Biomarker 2 [SEQ ID NO: 2] miR-1290; Biomarker 3 [SEQ ID NO: 3] mir-221; Biomarker 5 [SEQ ID NO: 5] mir-1268; Biomarker 9 [SEQ ID NO: 9] mir-196a; Biomarker 10[SEQ ID NO: 10] mir-130b; Biomarker 12 [SEQ ID NO: 12] mir-23a; and Biomarker 16 [SEQ ID NO: 16] mir-486; or determining the level of miRNAs with a sequence that has at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% identical to SEQ ID NOs: 1-3, 5, 9, 10, 12, and 16.

[0234] 4b. The method according to any one of paragraphs l-4a, wherein the method comprises determining the level of all of the following miRNAs:

[0235] Biomarker 5 [ SEQ ID NO: 5] mir-1269; Biomarker 3 [ SEQ ID NO: 3] mir-221; Biomarker 6 [ SEQ ID NO: 6] mir-106b; Biomarker 13 [ SEQ ID NO: 13] mir-16; Biomarker 9 [SEQ ID NO: 9] mir-196a, Biomarker 2 [ SEQ ID NO: 2] mir-1290 or that has at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% identical to any of the above sequences.

[0236] 4c. The method according to any one of paragraphs l-4a, wherein the method comprises determining the level of all of the following miRNAs:

[0237] Biomarker 5 [SEQ ID NO: 5] mir-1268; Biomarker 10 [SEQ ID NO: 10] mir-130b; Biomarker 6 [SEQ ID NO: 6] mir-106b; Biomarker 3 [SEQ ID NO: 3] mir-221; Biomarker 8 [SEQ ID NO: 8] mir-21; Biomarker 12 [SEQ ID NO: 12] mir-23a or that has at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% identical to any of the above sequences.

[0238] 5. The method according to any one of paragraphs l-4b, wherein the one or more miRNAs are produced by the subject, optionally produced by the lung tissue of the subject, optionally produced at higher levels by a lung tumour cell or immune cell.

[0239] 6. The method of any of paragraphs 1-5, wherein the sample is a liquid biopsy, optionally is a blood sample or a plasma sample.

[0240] 7. The method of any of paragraphs 1-6 wherein the sample comprises one or more lung cancer cells; and / or is selected from the group comprising: a biopsy (such as a core needle biopsy; fine needle biopsy; bronchoscopy sample); a tissue sample (such as a lung tissue or lung cancer tissue); an organ sample; and / or a bodily fluid sample (such as blood, saliva, sputum, urine, or pleural fluid).

[0241] 8. The method according to any of the preceding paragraphs wherein the method further comprises comparing the level of the one or more miRNAs from the subject to the level of the same one or more miRNAs in one or more control samples.

[0242] 9. The method according to paragraph 8, wherein the control sample is a negative control sample, for example a sample that has been obtained from a healthy individual; a population of healthy individuals; an individual that does not have lung cancer; or a population of individuals wherein each individual in the population does not have lung cancer.

[0243] 10. The method of any of the preceding paragraphs wherein the method further comprises comparing the level of the one or more miRNAs from the subject to a predetermined threshold level, optionally wherein the predetermined threshold level is a level of the miRNA associated with a population of healthy individuals, i.e., individuals known to not have lung cancer, optionally where the predetermined threshold level is the Ct value for the level of the miRNA as determined by qPCR.

[0244] 11. The method according to paragraph 9 or 10 further comprising the step of determining that the subject has lung cancer if the level of the one or more miRNAs in the sample from the test subject is: a) at least 1.25, or at least 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, or at least 3.0, 3.5, 4.0, 4.5, or at least 5.0, 6.0, 7.0, 8.0, 9.0, or at least 10, 20, 30, 40, 50, 60, 70, 80, 90, or at least 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, or at least 1280 times higher than the level of the one or more miRNAs in the negative control sample, the population of healthy individuals, or the predetermined threshold level; and / or b) above a predetermined range or within a predetermined threshold level.

[0245] 12. The method according to paragraph 9 or 10, further comprising the step of determining that the subject does not have or is not at high risk of having lung cancer, where the level of the one or more miRNAs in the test sample is: a) the same as or substantially similar to the level of the same one or more miRNAs in the negative control sample the population of healthy individuals, or the predetermined threshold level; b) is not at least 1.25, or at least 1.5, 1.75, 2.0, 2.25, 2.5, 2.75 or at least 3.0, 3.5, 4.0, 4.5, or at least 5.0, 6.0, 7.0, 8.0, 9.0, or at least 10, 20, 30, 40, 50, 60, 70, 80, 90, or at least 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, or at least 1280 times higher than the level of the one or more miRNAs in the negative control sample, the population of healthy individuals, or the predetermined threshold level; and / or c) within a predetermined range or below a predetermined threshold.

[0246] 13. The method according to any of the preceding paragraphs wherein the method further comprises performing a low-dose computed tomography (LDCT) scan on the patient.

[0247] 14. The method according to any of the preceding paragraphs wherein the level of the one or more miRNAs is considered in conjunction with an LDCT scan, optionally wherein a diagnosis of the presence or absence of lung cancer is based on the level of the one or more miRNAs and information obtained from the LDCT scan.

[0248] 19. The method according to any one of the preceding paragraphs, wherein where the subject has been diagnosed with lung cancer based on the level of the one or more miRNAs, the subject is further analysed to determine the stage and / or subtype of lung cancer. 20. The method of paragraph 19, wherein determining the stage and / or subtype of lung cancer comprises providing a lung tissue biopsy from the subject and analysing said lung tissue biopsy.

[0249] 21. The method according to any one of the preceding paragraphs wherein the lung cancer is selected from the group comprising or consisting of: NSCLC or SCLC.

[0250] 22. The method according to any one of the preceding paragraphs, wherein the lung cancer is Non-Small Cell Lung Cancer (NSCLC).

[0251] 23. The method according to paragraph 22, wherein the NSCLC is selected from the group comprising: adenocarcinoma; squamous cell carcinoma; adenosquamous carcinoma; large cell carcinoma; or large cell neuroendocrine cancer, preferably wherein the NSCLC is adenocarcinoma.

[0252] 24. The method according to paragraph 22 or 24, wherein the NSCLC in the individual is early-stage NSCLC (i.e., Stage 0, I or Stage II) or is late-stage NSCLC (i.e., Stage III or Stage IV).

[0253] 25. The method according to any one of paragraphs 1-21, wherein the lung cancer is Small Cell Lung Cancer (SCLC).

[0254] 26. A method for lung nodule management comprising a) determining the level of one or more miRNAs as described in any of paragraphs 1-25 in a sample from a subject that has been identified as having a lung nodule; b) determining whether the lung nodule is likely to be a tumour based upon the level of the one or more miRNAs determined in (a), for example in accordance with the method of determining the presence of lung cancer of the invention.

[0255] 27. The method according to any one of the preceding paragraphs wherein the subject: a) has not had a diagnosis of lung cancer; b) is considered to be a healthy individual; c) has not undergone treatment for lung cancer; and / or d) has any one or more of the following symptoms, but has not had a diagnosis of lung cancer: persistent cough; breathlessness; coughing up blood; chest pain; shoulder pain; chest ache; shoulder ache; recurrent chest infection; loss of appetite; fatigue; weight loss (optionally unexplained weight loss); changes in the shape of fingers and / or fingernails; finger clubbing; hypertrophic pulmonary osteoarthropathy; nausea and vomiting; headaches; confusion; weakness; restlessness; irritability; muscle weakness, spasms, cramps, or aches; seizures; dry eyes; blurred vision; dysphagia; dizziness; dry mouth; constipation; erectile dysfunction.

[0256] 28. The method according to any one of the preceding paragraphs wherein the method is for diagnosis of early-stage lung cancer.

[0257] 29. The method according to paragraph 28, wherein the early-stage lung cancer is: a) early-stage NSCLC (i.e., Stage 0, I or Stage II NSCLC); or b) early-stage SCLC (i.e., limited SCLC).

[0258] 30. The method according to any one of the preceding paragraphs wherein the subject has been diagnosed with a lung nodule following a computerised tomography (CT) scan, MRI scan, LDCT scan or X-ray scan.

[0259] 31. The method according to any of the preceding paragraphs wherein in the event that the individual is diagnosed with lung cancer, the method further comprises a step of providing the individual with a lung cancer therapy, optionally wherein the lung cancer therapy is selected from the group comprising: surgery, chemotherapy, radiotherapy, immunotherapy, chemoimmunotherapy, thermochemotherapy and combinations thereof, adoptive cell therapies, gene therapies, cancer vaccines, and oncolytic virus therapies.

[0260] 32. A method of determining whether a lung nodule is a lung cancer tumour or is benign, wherein the method comprises performing the method of determining the presence of lung cancer according to any of paragraphs 1-31 on a subject identified as having a lung nodule, optionally identified as having a lung nodule based on a CT scan.

[0261] 33. A kit comprising one or more or all of the following: a) means to determine the level of at least one of the following;

[0262] Biomarker 1 [SEQ ID NO: 1] miR-1246; Biomarker 2 [SEQ ID NO: 2] miR-1290; Biomarker 3 [SEQ ID NO: 3] mir-221; Biomarker 4 [SEQ ID NO: 4] mir-223; Biomarker 5 [SEQ ID NO: 5] mir-1268; Biomarker 6 [SEQ ID NO: 6] mir-106b; Biomarker 7 [SEQ ID NO: 7] mir-26; Biomarker 8[SEQ ID NO: 8] mir-21; Biomarker 9 [SEQ ID NO: 9] mir-196a; Biomarker 10[SEQ ID NO: 10] mir-130b; Biomarker 11 [SEQ ID NO: 11] mir-155; Biomarker 12 [SEQ ID NO: 12] mir-23a; Biomarker 13 [SEQ ID NO: 13] mir- 16; Biomarker 14 [SEQ ID NO: 14] mir-222; Biomarker 15 [SEQ ID NO: 15] mir-451; Biomarker 16 [SEQ ID NO: 16] mir-486; b) means for the extraction and purification of RNA from a sample; and / or c) a control sample of RNA prepared from a sample from subjects, optionally wherein the means to determine the level of at least one of the miRNAs comprises: i) at least one pair of primers are arranged so as to amplify at least one or more of the following miRNAs;

[0263] Biomarker 1 [SEQ ID NO: 1] miR-1246; Biomarker 2 [SEQ ID NO: 2] miR-1290; Biomarker 3 [SEQ ID NO: 3] mir-221; Biomarker 4 [SEQ ID NO: 4] mir-223; Biomarker 5 [SEQ ID NO: 5] mir-1268; Biomarker 6 [SEQ ID NO: 6] mir-106b; Biomarker 7 [SEQ ID NO: 7] mir-26; Biomarker 8[SEQ ID NO: 8] mir-21; Biomarker 9 [SEQ ID NO: 9] mir-196a; Biomarker 10[SEQ ID NO: 10] mir- 130b; Biomarker 11 [SEQ ID NO: 11] mir-155; Biomarker 12 [SEQ ID NO: 12] mir-23a; Biomarker 13 [SEQ ID NO: 13] mir-16; Biomarker 14 [SEQ ID NO: 14] mir-222; Biomarker 15 [SEQ ID NO: 15] mir-451; Biomarker 16 [SEQ ID NO: 16] mir-486; or at least one or more miRNAs with a sequence that has at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% identical to SEQ ID NOs: 1-16; ii) at least one or more probe arranged so as to hybridise to at least one or more of the following miRNAs:

[0264] Biomarker 1 [SEQ ID NO: 1] miR-1246; Biomarker 2 [SEQ ID NO: 2] miR-1290; Biomarker 3 [SEQ ID NO: 3] mir-221; Biomarker 4 [SEQ ID NO: 4] mir-223; Biomarker 5 [SEQ ID NO: 5] mir-1268; Biomarker 6 [SEQ ID NO: 6] mir-106b; Biomarker 7 [SEQ ID NO: 7] mir-26; Biomarker 8[SEQ ID NO: 8] mir-21; Biomarker 9 [SEQ ID NO: 9] mir-196a; Biomarker 10[SEQ ID NO: 10] mir- 130b; Biomarker 11 [SEQ ID NO: 11] mir-155; Biomarker 12 [SEQ ID NO: 12] mir-23a; Biomarker 13 [SEQ ID NO: 13] mir-16; Biomarker 14 [SEQ ID NO: 14] mir-222; Biomarker 15 [SEQ ID NO: 15] mir-451; Biomarker 16 [SEQ ID NO: 16] mir-486; or at least one or more miRNAs with a sequence that has at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% identical to SEQ ID NOs: 1-16; iii) at least one or more probe arranged so as to hybridise all of the following miRNAs (i.e. there is at least one probe that can hybridise to each of the miRNAs, rather than a single probe that can bind to all of the miRNAs - however this embodiment is also considered within the scope of the invention] : Biomarker 5 [ SEQ ID NO: 5] mir-1269; Biomarker 3 [ SEQ ID NO: 3] mir-221; Biomarker 6 [ SEQ ID NO: 6] mir-106b; Biomarker 13 [ SEQ ID NO: 13] mir- 16; Biomarker 9 [SEQ ID NO: 9] mir-196a, Biomarker 2 [ SEQ ID NO: 2] mir- 1290 or that has at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% identical to any of the above sequences. optionally wherein the at least one or more probe(s) are TaqMan probes; iii) reverse transcriptase and / or DNA polymerase; iv) a microarray; and / or vi) an array card.

[0265] 35. The method according to paragraph 33, wherein b) means for the extraction and purification of RNA from a sample comprises microRNA extraction columns or magnetic beads.

[0266] 36. A method of treating lung cancer in an individual comprising the steps of:

[0267] (i) diagnosing lung cancer according to the method defined in any one of Paragraphs 1-35; and

[0268] (ii) providing the individual with lung cancer therapy.

[0269] 37. A method, use, array, or kit for determining the presence of lung cancer in an individual substantially as described herein.

[0270] 38. A method, use, array, or kit for managing one or more lung nodule(s) in an individual substantially as described herein, wherein the lung nodule is assessed with respect to one or more of the following: nodule size; nodule number; and / or nodule location.

[0271] 39. The method of paragraph 38, wherein the method comprises the method set out in any one of paragraphs 26-31.

[0272] 40. A method, use, array, or kit for determining the efficacy of lung cancer treatment in an individual substantially as described herein. 41. A method, use, array, or kit for the purpose of companion diagnostics in an individual undergoing cancer treatment.

[0273] References

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[0308] Figure legends

[0309] Figure 1 : Diagnostic accuracy of individual biomarkers. The accuracy of each individual biomarker is determined by taking the average of its sensitivity and specificity and it ranges from 58%-72%. A) Importance ranking of each individual biomarker. B) Area under the curve for each biomarker.

[0310] Figure 2: The biomarkers can pick up lung cancer at early stages better than late stages. The performance in terms of sensitivity of the combination of 8 biomarkers is compared between the early-stage and late-stage lung cancer patients. The lung cancer patients includes both NSCLC and SCLC patients. The combination of 8 biomarkers is more superior in detection in early stages than late stages.

[0311] Figure 3: Ranking of potential lung cancer risk factors using Random Forest (RF) analysis. The factors include patient demographics (Age, Gender, Race, Smoking Status, Presence of Emphysema / COPD) and lung nodule characteristics (Number of Nodules, Size of the Largest Nodule, Location of Nodule, and Nodule Spiculation). The analysis identified the size of the largest nodule as the highest-ranked clinical risk predictor for lung cancer, followed by the number of nodules location (upper vs. lower lobe), nodule number, age, spiculation, smoking status, race, presence of emphysema / COPD and gender. This ranking underscored the importance of lung nodule features and demographic factors in assessing lung cancer risk.

[0312] Figure 4: Ranking of miRNA biomarker importance using Random Forest (RF) analysis. The miRNA biomarkers are listed in order of importance, with the top six biomarkers (9, 5, 10, 2, 6, and 1) identified as having importance scores greater than the mean. This hierarchy illustrates the relative contributions of each miRNA to the predictive model, and establishes a set of biomarkers for use in determining the presence of lung cancer. The complete ranking includes biomarkers 7, 3, 13, 8, 11, 4, 12, 16, 15, and 14. Figure 5: Ranking of miRNA biomarker importance using Random Forest (RF) analysis. The miRNA biomarkers are listed in order of importance, with the top six biomarkers (10, 5, 6, 8, 3, and 12) identified as having importance scores greater than the mean. This hierarchy illustrates the relative contributions of each miRNA to the predictive model, and establishes a set of biomarkers for use in determining the presence of lung cancer. The complete ranking includes biomarkers 9, 4, 11, 7, 2, 16, 14, 15, 13, and 1.

[0313] Examples

[0314] Example 1 - Lung cancer diagnosis study design

[0315] This is an age and gender matched case-control study that recruit subjects from a tertiary teaching hospital in Singapore (Sengkang General Hospital). The inclusion criteria for cases are subjects with a new diagnosis of lung cancer made from positive histopathology. Inclusion criteria for cohort are subjects with no radiographic imaging evidence of lung nodules or mass and subjects who have benign lung condition following biopsy of lung nodules. All subjects are aged between 18 and 85 years of age and mentally competent to give informed consent for participation in the study. Patients with a known history of malignancy or active malignancy undergoing treatment will be excluded from the study.

[0316] Example 2 - Materials and methods

[0317] All subjects underwent venipuncture and have 5ml of peripheral blood samples drawn in plain serum tubes (BD vacutainer plus plastic serum tube). Blood samples were left to clot for 45-60min and centrifuged at 2000g for 15mins at room temperature. Serum was aliquoted and stored in cryotubes at -80 °C for long term storage. Haemolyzed samples were not analysed due to potential contamination by miRNAs released from red blood cells (Kirschner et al., 2011). miRNA extraction, isolation and quantification

[0318] The expression for each of the miRNA was quantified and compared between the cases and healthy controls. The experimental details are as follow. Total RNA isolation (including miRNA) from 200 pL of blood serum of each patient was extracted. cDNA was synthesized by extending the miRNA at each end prior to RT. The cDNA was then pre-amplified using universal primers and a master mix to uniformly increase the amount of cDNA for each target, maintaining the relative differential levels. Unlike traditional preamplification, these primers recognize the universal sequences added to every miRNA at the 5 ' and 3 ' ends, helping to ensure there is no amplification bias. TaqMan primers and probes were used to quantitate each miRNA target via real time qPCR. The primer and probe set for each miRNA were either preformulated or customized. The reactions were incubated in a 96-well plate at 95°C for 30s, followed by 40 cycles of 95°C for 3s and 60°C for 30s. The qPCR reaction can be carried out in either Thermoscientific Quantstudio 3 or Applied Biosystems HT7500. Statistical analysis

[0319] Ct values are generated from the qPCR results for each of the miRNA biomarkers for all the patients. The Ct value of each miRNA is normalized with a threshold Ct value to determine the fold change. A fold change of greater than 2 is considered a positive expression of the miRNA in the blood sample.

[0320] Example 3 - miRNA biomarker validation results

[0321] Patient characteristics miRNA levels were analysed in a case-control cohort consisting of 45 cases and 55 controls. The patients were between 24 to 82 years old. The demographics and baseline characteristics of the study cohort is as summarised.

[0322] Among the cases, 41 were diagnosed with non-small cell lung cancer (NSCLC), while 4 had small cell carcinomas. The distribution of lung cancer stages at the time of diagnosis was as follows: 18 patients at Stage I, 3 patients at Stage II, 4 patients at Stage III, and 16 patients at Stage IV with metastatic disease. Among the small cell carcinomas, 3 patients were diagnosed at the limited stage, while 1 patient was at the extensive stage (Table 1).

[0323] Table 1: Demographics and baseline characteristics of the study cohort miRNA biomarkers for early lung cancer detection

[0324] Through an extensive manual curation of published literature, including PubMed, Embase, Web of Science and ScienceDirect and published systematic reviews, we meticulously compiled a comprehensive list of 48 potential biomarkers for lung cancer (18-20). This rigorous process involved thorough examination of numerous studies, analysing their findings, and identifying markers that showed promising correlations with the disease. Subsequently, we conducted detailed expression analysis on these 48 biomarkers on early-stage lung cancer samples. Out of the 48 biomarkers, 16 biomarkers demonstrated consistent and significant expression patterns within our samples, exhibiting at least a 2-fold increase compared to healthy controls. This suggests their potential relevance in the context of lung cancer.

[0325] Diagnostic accuracy of miRNA panel for lung cancer

[0326] To assess the performance of each biomarker, we evaluated their sensitivity and specificity. These measures allow us to determine their ability to accurately identify lung cancer cases and distinguish them from healthy individuals. Additionally, we calculated the overall accuracy, which represents an average of the sensitivity and specificity values. The accuracy of each biomarker ranged from 58% to 72%, as depicted in Figure 1. These findings highlight the potential of each these selected biomarkers as diagnostic tools for early-stage lung cancer with varied performance.

[0327] # of ton Validation Testing

[0328] Bio markers - biomarkersAUC AUC Sensit Specif PPV NPV

[0329] 5 1 0.724 0.731 0.818 0.643 0.643 0.833

[0330] 5,3 2 0.768 0.757 0.818 0.643 0.643 0.828

[0331] 5,3,6 3 0.775 0.744 0.818 0.643 0.643 0.818

[0332] 5,3,6,13 4 0.774 0.750 0.818 0.643 0.643 0.833

[0333] 5,3,6,13,9 5 0.780 0.763 0.909 0.571 0.625 0.900

[0334] 5,3,6,13,9,2 6 0.781 0.796 1.000 0.571 0.647 1.000

[0335] 5,3,6,13,9,2,15 7 0.783 0.763 0.909 0.571 0.625 0.889

[0336] 5,3,6,13,9,2,15,7 8 0.781 0.770 0.909 0.571 0.625 0.894 # of ton Validation Testing

[0337] Bio markers - biomarkersAUC AUC Sensit Specif PPV NPV

[0338] 5,3,6,13,9,2,15,7,8 9 0.778 0.731 0.818 0.642 0.642 0.818

[0339] 5,3,6,13,9,2,15,7,8,1 10 0.783 0.757 0.909 0.571 0.625 0.900

[0340] 5,3,6,13,9,2,15,7,8,1 ,16 11 0.788 0.753 0.909 0.571 0.625 0.889

[0341] 5,3,6,13,9,2,15,7,8,1 ,16,11 12 0.782 0.740 0.909 0.571 0.625 0.900

[0342] 5,3,6,13,9,2,15,7,8,1 ,16,11 ,12 13 0.786 0.737 0.909 0.643 0.667 0.909

[0343] 5,3,6,13,9,2,15,7,8,1 ,16,11 ,12,10 14 0.785 0.718 0.909 0.571 0.625 0.900

[0344] 5,3,6,13,9,2,15,7,8,1 ,16,11 ,12,10,4 15 0.786 0.718 0.636 0.786 0.700 0.733

[0345] All 16 0.793 0.708 0.727 0.714 0.667 0.769

[0346] To further optimize the diagnostic accuracy of these biomarkers in lung cancer detection, we employed machine learning techniques to vary the combination between six to twelve biomarkers. Leveraging the power of computational algorithms, we developed a robust model capable of assessing the combined predictive value of multiple biomarkers. The objective was to identify the most effective combination that would yield the highest accuracy, sensitivity, and specificity in diagnosing lung cancer. After an in-depth analysis using the machine learning approach, we successfully identified the optimal combination of eight biomarkers. This carefully chosen set demonstrated exceptional performance in accurately distinguishing lung cancer cases from healthy individuals. Our lung cancer biomarkers have an Area Under the Curve (AUC) value of 0.83 and this indicates a high level of sensitivity and specificity in distinguishing between lung cancer cases and healthy individuals. Comparing early- stage vs late-stage patients, we can achieve a high sensitivity of 83% and 71% respectively (Table 2).

[0347] Table 2: Comparison of early-stage vs late-stage patients for lung cancer biomarkers

[0348] The higher sensitivity for early-stage patients suggested that our biomarkers perform better in picking up early stage than late-stage cancer and this embodies its application for early cancer screening. Additionally, we attained a specificity of 75% which is superior to the standard of care using low-dose CT scan to reduce the likelihood of false positives (Figure 2). The discovery of this refined panel of 8 biomarkers underscores the potential for significant advancements in the early detection for lung cancer screening.

[0349] Diagnostic accuracy of miRNA panel for lung cancer (Including nodule / CT features')

[0350] With the positive outcomes of several lung cancer screening trials demonstrating the effectiveness of lung cancer screening in reducing mortality, the implementation of CT screening programs targeting high-risk individuals is expected to lead to an increase in the incidence of incidental lung nodules. While the majority of these incidental pulmonary nodules (IPNs) are benign, the current predictive tools such as Brock's calculators, for distinguishing between benign and malignant nodules are not optimal. This has resulted in a significant number of follow-up CT scans, unnecessary invasive biopsies with associated risks, and, though rare, even mortality, causing heightened patient anxiety and contributing to excessive healthcare expenditures.

[0351] Addressing these challenges is crucial, and there is an urgent need for more accurate and efficient diagnostic methods to differentiate between benign and potentially cancerous nodules. Our approach of incorporating miRNA data with LDCT scan results and patient clinical information shows great promise in addressing the diagnostic challenge posed by indeterminate pulmonary lung nodules, especially those measuring less than 6mm.

[0352] By utilizing advanced machine learning techniques, specifically logistic regression, we have successfully distinguished between benign and malignant lung nodules with remarkable efficacy. Our comprehensive multi-modal approach combines miRNA data with LDCT scan data, including nodule size, shape, position, and patient clinical information, such as patient history and smoking status. The integration of miRNA biomarkers with LDCT scans has resulted in a significant enhancement of the sensitivity of our miRNA test, increasing it from 71-83% to an impressive 95%. Moreover, we have also improved the specificity from 75%-91%. This promising advancement holds potential to revolutionize lung cancer screening and diagnosis, providing better patient outcomes and optimizing healthcare resources.

[0353] Example 4 - miRNA biomarker validation discussion

[0354] Potential application as a lung cancer screening tool (Stand-alone)

[0355] Our lung cancer biomarkers have an Area Under the Curve (AUC) value of 0.83 and this indicates a high level of sensitivity and specificity in distinguishing between lung cancer cases and healthy individuals. Comparing early-stage vs late-stage patients, we can achieve a high sensitivity of 83% and 71% respectively. The higher sensitivity for early-stage patients suggested that our biomarkers perform better in picking up early stage than late-stage cancer and this embodies its application for early cancer screening. Additionally, we attained a specificity of 75% which is superior to the standard of care using low-dose CT scan to reduce the likelihood of false positives (Figure 2). The discovery of this refined panel of 8 biomarkers underscores the potential for significant advancements in the early detection for lung cancer screening.

[0356] Potential application as a lung cancer screening tool in conjunction with LDCT (Reducing false positives)

[0357] Lung nodule management remains a challenge to clinicians, especially in Asia which is endemic tuberculosis areas. Different guidelines are available with various recommendations; however, the suitability of these guidelines for the Asian population is still unclear especially for lung nodules that are smaller than 30 mm. With the use of low-dose CT scans, there is a concern regarding the false positive rate, which can lead to unnecessary anxiety, pain, and invasive biopsies for patients. Therefore, there is a critical need for a reliable and accurate diagnostic test that can effectively identify the right patients who require further invasive procedures.

[0358] In our pursuit to address this diagnostic challenge, we incorporated miRNA data with the LDCT scan results and patient clinical information. By utilizing machine learning logistics regression techniques, we were able to effectively distinguish benign lung nodules from malignant ones. From our study, using this multi-modal approach, we can enhance the sensitivity of our miRNA test from 71-83% to an impressive 95%, and also improving the specificity from 75%- 91%. The integration of our biomarkers with LDCT scans has demonstrated significant potential in improving the overall diagnostic accuracy for identifying lung cancer cases. Our findings indicate that incorporating miRNA data into the existing LDCT screening process can effectively improve the poor specificity of LDCT scan. This advancement has the potential to minimize false positives, reducing unnecessary patient distress and invasive procedures.

[0359] Strength and limitation

[0360] Our study is one of the first to validate a panel of miRNA biomarkers in detection of lung cancer, regardless of lung cancer subtypes. Multiple previous studies have previously validated miRNA biomarkers for NSCLS alone (Geng et al., 2014; Sozzi et al., 2014; Ying et al., 2020).

[0361] The limitation of our study is our relatively small sample size, limiting the power of statistical analysis and strength of association between the miRNA biomarkers and lung cancer. This study also only involved a cohort of patients from South-east Asia ethnicity. Different racial and ethnic groups may have different baseline miRNA profiles, potentially altering the sensitivity and specificity of our propriety miRNA panel for lung cancer testing in different populations.

[0362] Conclusion

[0363] A panel utilising 8 miRNA biomarkers in peripheral blood specimen performed very well in detection of lung cancer, regardless of subtypes. The miRNA panel shows great potential in changing the landscape for lung cancer screening and diagnostic algorithm of lung nodules picked up on LDCT.

[0364] Example 5

[0365] This Example relates to a study on a larger cohort, using various machine learning algorithms, and investigates the use of combining a panel of miRNA biomarkers with lung nodule size as determined by CT scan.

[0366] Method

[0367] This is a case-control study. Subjects with a new diagnosis of lung cancer confirmed by tissue biopsy were included as cases. Controls included subjects with either no radiographic imaging evidence of lung nodules or masses or subjects who were diagnosed with benign lung condition following a lung biopsy. All subjects were between 21 and 87 years of age and mentally competent so as to give informed consent for participation in the study. Patients with a known history of malignancy or active malignancy undergoing treatment were excluded from the study.

[0368] All subjects underwent venepuncture and had 5ml of peripheral blood samples drawn in plain serum tubes (BD vacutainer plus plastic serum tube). Blood samples were left to clot for 45-60min and centrifuged at 2000g for 15mins at room temperature. The resultant serum samples were then aliquoted and stored in cryotubes at -80 °C for long term storage. If a blood sample was haemolyzed, the specimen was not analysed due to potential contamination by miRNAs released from red blood cells.

[0369] The expression for each of the 16 miRNAs were quantified and compared between the known cases and healthy controls. Total RNA (including miRNA) from 200 pL of blood serum of each patient was extracted. Reverse transcription (RT) was carried out using Averywell's miRNA cDNA Synthesis Kit. cDNA was synthesized by extending the miRNA at each end prior to RT. The cDNA was subsequently pre-amplified using universal primers and a master mix to uniformly increase the amount of cDNA for each target, maintaining the relative differential levels. Unlike traditional preamplification, these primers recognize the universal sequences added to every miRNA at the 5 ' and 3 ' ends, therefore preventing amplification bias. TaqMan primers and probes were then used to quantitate each miRNA target via real time qPCR. The reactions were incubated in a 96-well plate at 95°C for 30s, followed by 40 cycles of 95°C for 3s and 60°C for 30s. qPCR reactions were carried out in either Thermoscientific Quantstudio 3 or Applied Biosystems HT7500. Results

[0370] From our miRNA biomarker screening, we have shortlisted 16 biomarkers and evaluated their expression in 204 patients, excluding patients with hemolyzed blood. Using Random Forest (RF) analysis, the importance of each of the biomarkers was ranked. The top six miRNA biomarkers— specifically, biomarkers 9, 5, 10, 2, 6, and 1— were found to have importance scores exceeding the mean. This ranking underscores the contributions of each miRNA to the predictive model, indicating their potential relevance in lung cancer risk assessment. The full ranking of miRNA biomarkers, in order of importance, includes 7, 3, 13, 8, 11, 4, 12, 16, 15, and 14.

[0371] We also aimed to develop a multivariate risk predictor for lung cancer by evaluating various features, including lung nodule characteristics, and patient demographics. A total of 122 controls and 82 cases were included in this study. Mean age was 60 years old, and most patients were male (61%) and Chinese (75%). A significant proportional of the cohort were older and had a history of smoking and emphysema / COPD (Table 3). Adenocarcinoma was the most common histological subtype (70%). This study investigated the characteristics of lung nodules— focusing on the number of nodules, size of the largest nodule, location (upper vs. lower lobe), spiculation, and patient demographics (age, smoking status, race, presence of emphysema / COPD, and gender)— by comparing lung cancer cases with matched controls. Results indicated that the malignant nodules were significantly larger (130mm versus 15mm), more numerous, and tended to be situated in the upper lobes, with spiculations / lobulations (Table 3). Adenocarcinoma was the most common histological subtype (70%). Using Random Forest (RF) analysis to train the dataset, we showed that the size of the largest nodule was the most significant predictor of lung cancer risk, followed by the number of nodules, nodule location, age, spiculation, smoking status, race, presence of emphysema / COPD, and gender. These findings highlight the critical importance of both nodule characteristics and demographic factors in assessing lung cancer risk and can be used in conjunction with miRNA for lung cancer risk prediction.

[0372] To further explore predictive capabilities, we employed several machine learning methods, including K-Nearest Neighbors (KNN), Neural Networks (NNET), Support Vector Machines (SVM), and Naive Bayes (NB), utilizing 5-fold cross-validation. The top six miRNA biomarkers alone achieved an area under the curve (AUC) ranging from 0.78 to 0.80, with sensitivity between 70% and 78% and specificity from 73% to 85%. Notably, incorporating the size of the largest nodule into the model with the top six biomarkers significantly enhanced predictive performance. This combined model yielded an AUC of 0.96 to 0.99, with sensitivity ranging from 92% to 98% and specificity from 93% to 98%. These results demonstrate the substantial improvement in risk prediction accuracy when combining miRNA biomarkers with lung nodule size characteristics.

[0373] The data is shown in Table 3, Figure 3 and 4.

[0374] Table 3. Clinical / demographics and nodule characteristics:

[0375] Abbreviations: COPD (chronic obstructive pulmonary disease), N.A. (not applicable), SD (standard deviation).aother malignancies included: mucoepidermoid (n=l), mesothelioma (n=2) and neuroendocrine carcinoma (n=3)

[0376] Conclusion

[0377] Overall, the study enabled the generation of 5 distinct models for detecting the presence of lung cancer. The five models delineated by the study include:

[0378] 1. Model 1 : Biomarkers only: all 16 (first aspect of the invention);

[0379] 2. Model 2: Top six-biomarkers (according to importance score) : BM9, BM5, BM10, BM2, BM6, BM1;

[0380] 3. Model 3: Nodule size;

[0381] 4. Model 4: All biomarkers and Nodule size; and

[0382] 5. Model 5: top six-biomarkers and Nodule size.

[0383] The comparison for each model is shown in the cross-validation table shown below. The results demonstrate that models incorporating nodule size gave superior statistical scores, with respect to AUC, sensitivity, and specificity, in comparison to models with only biomarker data.

[0384] ML results using full data: all data training with 5-fold cross validation

[0385] Model 1 Model 2 Model 3 Model 4 Model 5

[0386] KNN AUC 0.788 0.781 0.975 0.985 0.989

[0387] Sensitivity 0.725 0.700 0.975 0.937 0.921

[0388] Specificity 0.701 0.732 0.890 0.929 0.975

[0389] NNET AUC 0.975 0.863 0.9553 0.964 0.961

[0390] Sensitivity 0.912 0.775 0.975 0.975 0.937

[0391] Specificity 0.913 0.850 0.898 0.9370 0.929

[0392] SVM AUC 0.872 0.802 0.944 0.975 0.983

[0393] Sensitivity 0.837 0.762 0.975 0.975 0.975

[0394] Specificity 0.779 0.732 0.898 0.929 0.937

[0395] NB AUC 0.775 0.790 0.984 0.959 0.978

[0396] Sensitivity 0.712 0.712 0.987 0.862 0.912

[0397] Specificity 0.748 0.787 0.984 0.937 0.976 Example 6

[0398] This Example relates to a study on a similarly sized cohort to that in Example 5, using various machine learning algorithms to investigate the use of combining a panel of miRNA biomarkers with lung nodule characterisation to differentiate between malignant and benign lung nodules. Specifically, the study focused on lung nodule sizes equal to or less than 30 mm.

[0399] Method

[0400] This is a case control study which examined a patient cohort comprising 141 patients with 110 healthy controls (defined in accordance with the previous examples) and 31 cases.

[0401] Results

[0402] Using Random Forest (RF) analysis, the importance of each of the biomarkers was ranked. The top six miRNA biomarkers— specifically, biomarkers 10, 5, 6, 8, 3, and 12 —were found to have importance scores exceeding the mean. This ranking underscores the contributions of each miRNA to the predictive model, indicating their potential relevance in lung cancer risk assessment. The full ranking of miRNA biomarkers, in order of importance, includes 9, 4, 11, 7, 2, 16, 14, 15, 13, and 1.

[0403] To further explore predictive capabilities, we employed several machine learning methods, including K-Nearest Neighbors (KNN), Neural Networks (NNET), Support Vector Machines (SVM), and Naive Bayes (NB), utilizing 5-fold cross-validation. The top six miRNA biomarkers alone achieved an area under the curve (AUC) ranging from 0.697 to 0.904, with sensitivity between 68% and 87% and specificity from 69% to 87%. Notably, incorporating the size of the largest nodule into the model with the top six biomarkers significantly enhanced predictive performance. This combined model yielded an AUC of 0.967 to 0.994, with sensitivity ranging from 94% to 100% and specificity from 93% to 96%. These results demonstrate the substantial improvement in risk prediction accuracy when combining miRNA biomarkers with lung nodule size characteristics.

[0404] The data is shown in Figure 5.

[0405] Conclusion Overall, the study enabled the generation of 5 distinct models for distinguishing the difference between benign and malignant lung nodules. The five models delineated by the study include:

[0406] 1. Model 1 : Biomarkers only: all 16 (first aspect of the invention);

[0407] 2. Model 2: Top six-biomarkers (according to importance score) : BM10, BM5, BM6, BM8, BM3, BM12;

[0408] 3. Model 3: Nodule size;

[0409] 4. Model 4: All biomarkers and Nodule size; and

[0410] 5. Model 5: top six-biomarkers and Nodule size.

[0411] The comparison for each model is shown in the cross-validation table shown below. The results demonstrate that models incorporating nodule size gave superior statistical scores, with respect to AUC, sensitivity, and specificity, in comparison to models with only biomarker data.

[0412] ML results using full data: all data training with 5-fold cross validation

[0413] Model Model Model 3 Model Model

[0414] 1 2 4 5

[0415] KNN AUC 0.806 0.823 0.981 0.975 0.975

[0416] Sensitivity 0.774 0.677 0.935 1.000 0.935

[0417] Specificity 0.664 0.736 0.936 0.900 0.927

[0418] NNET AUC 0.961 0.890 0.964 1.000 0.967

[0419] Sensitivity 0.871 0.839 0.968 1.000 1.000

[0420] Specificity 0.864 0.828 0.909 1.000 0.936

[0421] SVM AUC 0.913 0.904 0.939 0.985 0.983

[0422] Sensitivity 0.871 0.871 0.968 0.968 0.968

[0423] Specificity 0.918 0.873 0.909 0.927 0.936

[0424] NB AUC 0.872 0.697 0.964 0.980 0.994

[0425] Sensitivity 0.839 0.709 0.968 0.903 0.968

[0426] Specificity 0.845 0.691 0.909 0.945 0.964

Claims

Claims1. A method for determining the presence of lung cancer in a subject wherein the method comprises determining the level of any 6 or more of the following miRNAs: Biomarker 9 [SEQ ID NO: 9] mir-196a, Biomarker 5 [SEQ ID NO: 5] mir-1268, Biomarker 10 [SEQ ID NO: 10] mir-130b, Biomarker 2 [SEQ ID NO: 2] miR-1290, Biomarker 6 [SEQ ID NO: 6] mir-106b, Biomarker 1 [SEQ ID NO: 1] miR-1246; Biomarker 3 [SEQ ID NO: 3] mir-221; Biomarker 4 [SEQ ID NO: 4] mir-223; Biomarker 7 [SEQ ID NO: 7] mir-26; Biomarker 8[SEQ ID NO: 8] mir-21; Biomarker 11 [SEQ ID NO: 11] mir-155; Biomarker 12 [SEQ ID NO: 12] mir-23a; Biomarker 13 [SEQ ID NO: 13] mir-16; Biomarker 14 [SEQ ID NO: 14] mir-222; Biomarker 15 [SEQ ID NO: 15] mir-451; Biomarker 16 [SEQ ID NO: 16] mir-486.

2. The method according to claim 1, wherein the method comprises determining the level of all of the following miRNAsBiomarker 2 [SEQ ID NO: 2], Biomarker 5 [SEQ ID NO: 5], Biomarker 6 [SEQ ID NO: 6] and Biomarker 9 [SEQ ID NO: 9] and either: i) Biomarker 1 [SEQ ID NO: 1], and Biomarker 10 [SEQ ID NO: 10]; or ii) Biomarker 3 [SEQ ID NO: 3] and Biomarker 13 [SEQ ID NO: 13].

3. The method according to any of claims 1 or 2 wherein the method comprises determining the level of a) all of the following miRNAs:Biomarker 9 [SEQ ID NO: 9] mir-196a, Biomarker 5 [SEQ ID NO: 5] mir-1268, Biomarker 10 [SEQ ID NO: 10] mir-130b, Biomarker 2 [SEQ ID NO: 2] miR-1290, Biomarker 6 [SEQ ID NO: 6] mir-106b, Biomarker 1 [SEQ ID NO: 1] miR-1246; or b) all of the following miRNA:Biomarker 9 [SEQ ID NO: 9] mir-196a, Biomarker 5 [SEQ ID NO: 5] mir-1268, Biomarker 10 [SEQ ID NO: 10] mir-130b, Biomarker 2 [SEQ ID NO: 2] miR-1290, Biomarker 6 [SEQ ID NO: 6] mir-106b, Biomarker 1 [SEQ ID NO: 1] miR-1246; Biomarker 7 [SEQ ID NO: 7] mir-26; Biomarker 12 [SEQ ID NO: 12] mir-23a; or c) all of the following miRNA:Biomarker 5 [ SEQ ID NO: 5] mir-1269; Biomarker 3 [ SEQ ID NO: 3] mir-221; Biomarker 6 [ SEQ ID NO: 6] mir-106b; Biomarker 13 [ SEQ ID NO: 13] mir-16; Biomarker 9 [SEQ ID NO: 9] mir-196a, Biomarker 2 [ SEQ ID NO: 2] mir-1290 or determining the level of miRNAs with a sequence that has at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% identical to SEQ ID NOs: 1-3, 5, 9, 10, 12, and 16; or d) all of the following miRNA:Biomarker 1 [SEQ ID NO: 1] miR-1246; Biomarker 2 [SEQ ID NO: 2] miR-1290;Biomarker 3 [SEQ ID NO: 3] mir-221; Biomarker 5 [SEQ ID NO: 5] mir-1268;Biomarker 9 [SEQ ID NO: 9] mir-196a; Biomarker 10 [SEQ ID NO: 10] mir-130b;Biomarker 12 [SEQ ID NO: 12] mir-23a; and Biomarker 16 [SEQ ID NO: 16] mir-486; or e) all of the following mRNA:Biomarker 5 [SEQ ID NO: 5] mir-1268; Biomarker 10 [SEQ ID NO: 10] mir-130b; Biomarker 6 [SEQ ID NO: 6] mir-106b; Biomarker 3 [SEQ ID NO: 3] mir-221; Biomarker 8 [SEQ ID NO: 8] mir-21; Biomarker 12 [SEQ ID NO: 12] mir-23a or that has at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% identical to any of the above sequences.

4. The method according to any of claims 1-3 wherein the sample is a blood sample or a plasma sample.

5. The method according to any of claim 1-4 wherein the sample: a) is a liquid biopsy, optionally is a blood sample or a plasma sample; and / or b) comprises one or more lung cancer cells; and / or is selected from the group comprising: a biopsy (such as a core needle biopsy; fine needle biopsy; bronchoscopy sample); a tissue sample (such as a lung tissue or lung cancer tissue); an organ sample; and / or a bodily fluid sample (such as blood, saliva, sputum, urine, or pleural fluid).

6. The method according to any one of claims 1-5, wherein the one or more miRNAs are produced by the subject, optionally produced by the lung tissue of the subject, optionally produced at higher levels by a lung tumour cell or immune cell.

7. The method according to any of the preceding claims wherein the method further comprises comparing the level of the one or more miRNAs from the subject to the level of the same one or more miRNAs in one or more control samples.

8. The method according to claim 7, wherein the control sample is a negative control sample.

9. The method of claim 8 wherein the negative control sample is a sample that has been obtained from a healthy individual; or a population of healthy individuals.

10. The method of any of the preceding claims wherein the method further comprises comparing the level of the one or more miRNAs from the subject to a predetermined threshold level, optionally wherein the predetermined threshold level is a level of the miRNA associated with a population of healthy individuals, i.e., individuals known to not have lung cancer, optionally where the predetermined threshold level is the Ct value for the level of the miRNA as determined by qPCR.

11. The method according to claim 9 or 10 further comprising the step of determining that the subject has lung cancer if the level of the one or more miRNAs in the sample from the test subject is: i) a) at least 1.25, or at least 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, or at least 3.0, 3.5, 4.0, 4.5, or at least 5.0, 6.0, 7.0, 8.0, 9.0, or at least 10, 20, 30, 40, 50, 60, 70, 80, 90, or at least 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, or at least 1280 times higher than the level of the one or more miRNAs in the negative control sample, the population of healthy individuals, or the predetermined threshold level; and / or b) above a predetermined range or within a predetermined threshold level; or ii) a) the same as or substantially similar to the level of the same one or more miRNAs in the negative control sample the population of healthy individuals, or the predetermined threshold level; b) is not at least 1.25, or at least 1.5, 1.75, 2.0, 2.25, 2.5, 2.75 or at least 3.0, 3.5, 4.0, 4.5, or at least 5.0, 6.0, 7.0, 8.0, 9.0, or at least 10, 20, 30, 40, 50, 60, 70, 80, 90, or at least 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, or at least 1280 times higher than the level of the one or more miRNAs in the negative control sample, the population of healthy individuals, or the predetermined threshold level; and / orc) within a predetermined range or below a predetermined threshold.

12. The method according to any of the preceding claims wherein the method comprises considering data about one or more lung nodules found in a subject with the level of the one or more miRNAs.

13. The method of claim 12 wherein the level of the one or more miRNAs and the data about the one or more lung nodules found in a subject is analysed with a statistical model to determine the presence of lung cancer wherein the model can comprise one or more of the following:RF;KNN;NNET;SVM;GBM; and / orNB.

14. The method according to any of claims 12 or 13 wherein the data about one or more lung nodules in a subject is the lung nodule size.

15. The method according to any of the preceding claims wherein the level of the one or more miRNAs is analysed with a statistical model to determine the presence of lung cancer wherein the model can comprise one or more of the following:RF;KNN;NNET;SVM;GBM; and / orNB.

16. The method according to any one of the preceding claims, wherein where the subject has been diagnosed with lung cancer based on the level of the one or more miRNAs, the subject is further analysed to determine the stage and / or subtype of lung cancer; optionally wherein determining the stage and / or subtype of lung cancer comprises providing a lung tissue biopsy from the subject and analysing said lung tissue biopsy.

17. The method according to any one of the preceding claims wherein the lung cancer is selected from the group comprising or consisting of: NSCLC or SCLC.

18. The method according to any one of the preceding claims, wherein the lung cancer is Non-Small Cell Lung Cancer (NSCLC); optionally wherein: a) the NSCLC is selected from the group comprising: adenocarcinoma; squamous cell carcinoma; adenosquamous carcinoma; large cell carcinoma; or large cell neuroendocrine cancer, preferably wherein the NSCLC is adenocarcinoma; and / or b) the NSCLC in the individual is early-stage NSCLC (i.e., Stage 0, I or Stage II) or is late-stage NSCLC (i.e., Stage III or Stage IV).

19. The method according to any one of claims 1-17, wherein the lung cancer is Small Cell Lung Cancer (SCLC).

20. The method according to any one of the preceding claims, wherein the method comprises lung cancer screening, lung nodule management and treatment response monitoring and companion diagnostics.

21. The method according to any one of the preceding claims wherein the subject: a) has not had a diagnosis of lung cancer; b) is considered to be a healthy individual; c) has not undergone treatment for lung cancer; and / or d) has any one or more of the following symptoms, but has not had a diagnosis of lung cancer: persistent cough; breathlessness; coughing up blood; chest pain; shoulder pain; chest ache; shoulder ache; recurrent chest infection; loss of appetite; fatigue; weight loss (optionally unexplained weight loss); changes in the shape of fingers and / or fingernails; finger clubbing; hypertrophic pulmonary osteoarthropathy; nausea and vomiting; headaches; confusion; weakness; restlessness; irritability; muscle weakness, spasms, cramps, or aches; seizures; dry eyes; blurred vision; dysphagia; dizziness; dry mouth; constipation; erectile dysfunction; ....

22. The method according to any one of the preceding claims wherein the method is for diagnosis of early-stage lung cancer; optionally wherein the early-stage lung cancer is: a) early-stage NSCLC (i.e., Stage 0, I or Stage II NSCLC); or b) early-stage SCLC (i.e., limited or extensive SCLC).

23. The method according to any one of the preceding claims wherein the subject has been diagnosed with a lung nodule following a computerised tomography (CT) scan, MRI scan, LDCT scan or X-ray scan.

24. The method according to any of the preceding claims wherein in the event that the individual is diagnosed with lung cancer, the method further comprises a step of providing the individual with a lung cancer therapy, optionally wherein the lung cancer therapy is selected from the group comprising: surgery, chemotherapy, radiotherapy, immunotherapy, chemoimmunotherapy, thermochemotherapy and combinations thereof, adoptive cell therapies, gene therapies, cancer vaccines, and oncolytic virus therapies.

25. A method of determining whether a lung nodule is a lung cancer tumour or is benign, wherein the method comprises performing the method of determining the presence of lung cancer according to any of claims 1-24 on a subject identified as having a lung nodule, optionally identified as having a lung nodule based on a CT scan.

26. A kit comprising one or more or all of the following: a) means to determine the level of at least one of the following;Biomarker 1 [SEQ ID NO: 1] miR-1246; Biomarker 2 [SEQ ID NO: 2] miR-1290; Biomarker 3 [SEQ ID NO: 3] mir-221; Biomarker 4 [SEQ ID NO: 4] mir-223; Biomarker [SEQ ID NO: 5] mir-1268; Biomarker 6 [SEQ ID NO: 6] mir-106b; Biomarker 7 [SEQ ID NO: 7] mir-26; Biomarker 8 [SEQ ID NO: 8] mir-21; Biomarker 9 [SEQ ID NO: 9] mir-196a; Biomarker 10[SEQ ID NO: 10] mir-130b; Biomarker 11 [SEQ ID NO: 11] mir-155; Biomarker 12 [SEQ ID NO: 12] mir-23a; Biomarker 13 [SEQ ID NO: 13] mir- 16; Biomarker 14 [SEQ ID NO: 14] mir-222; Biomarker 15 [SEQ ID NO: 15] mir-451; Biomarker 16 [SEQ ID NO: 16] mir-486; b) means for the extraction and purification of RNA from a sample; and / or c) a control sample of RNA prepared from a sample from subjects, optionally wherein the means to determine the level of at least one of the miRNAs comprises: i) at least one pair of primers are arranged so as to amplify at least one or more of the following miRNAs;Biomarker 1 [SEQ ID NO: 1] miR-1246; Biomarker 2 [SEQ ID NO: 2] miR-1290; Biomarker 3 [SEQ ID NO: 3] mir-221; Biomarker 4 [SEQ ID NO: 4] mir-223; Biomarker 5 [SEQ ID NO: 5] mir-1268; Biomarker 6 [SEQ ID NO: 6] mir-106b; Biomarker 7 [SEQ ID NO: 7] mir-26; Biomarker 8 [SEQ ID NO: 8] mir-21; Biomarker 9 [SEQ ID NO: 9] mir-196a; Biomarker 10[SEQ ID NO: 10] mir-130b; Biomarker 11 [SEQ ID NO: 11] mir-155; Biomarker 12 [SEQ ID NO: 12] mir-23a; Biomarker 13 [SEQ ID NO: 13] mir-16; Biomarker 14 [SEQ ID NO: 14] mir-222; Biomarker 15 [SEQ ID NO: 15] mir-451; Biomarker 16 [SEQ ID NO: 16] mir-486; or at least one or more miRNAs with a sequence that has at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% identical to SEQ ID NOs: 1-16; ii) at least one or more probe arranged so as to hybridise to at least one or more of the following miRNAs:Biomarker 1 [SEQ ID NO: 1] miR-1246; Biomarker 2 [SEQ ID NO: 2] miR-1290; Biomarker 3 [SEQ ID NO: 3] mir-221; Biomarker 4 [SEQ ID NO: 4] mir-223; Biomarker 5 [SEQ ID NO: 5] mir-1268; Biomarker 6 [SEQ ID NO: 6] mir-106b; Biomarker 7 [SEQ ID NO: 7] mir-26; Biomarker 8 [SEQ ID NO: 8] mir-21; Biomarker 9 [SEQ ID NO: 9] mir-196a; Biomarker 10[SEQ ID NO: 10] mir- 130b; Biomarker 11 [SEQ ID NO: 11] mir-155; Biomarker 12 [SEQ ID NO: 12] mir-23a; Biomarker 13 [SEQ ID NO: 13] mir-16; Biomarker 14 [SEQ ID NO: 14] mir-222; Biomarker 15 [SEQ ID NO: 15] mir-451; Biomarker 16 [SEQ ID NO: 16] mir-486; or at least one or more miRNAs with a sequence that has at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% identical to SEQ ID NOs: 1-16; optionally wherein the at least one or more probe(s) are TaqMan probes; iii) reverse transcriptase and / or DNA polymerase; iv) a microarray; and / or vi) an array card.

27. The kit according to claim 26, wherein b) means for the extraction and purification of RNA from a sample comprises microRNA extraction columns or magnetic beads.

28. A method of treating lung cancer in an individual comprising the steps of:(i) diagnosing lung cancer according to the method of determining the presence of lung cancer in a subject defined in any one of Claims 1-24; and(ii) providing the individual with lung cancer therapy.

29. A method of determining the presence of lung cancer in a subject, diagnosing lung cancer, or post treatment monitoring in a subject comprisinga) determining the level of all of the following miRNAs Biomarker 2 [SEQ ID NO: 2], Biomarker 5 [SEQ ID NO: 5], Biomarker 6 [SEQ ID NO: 6] and Biomarker 9 [SEQ ID NO: 9], Biomarker 1 [SEQ ID NO: 1], and Biomarker 10 [SEQ ID NO: 10]; and b) providing data about the size of one or more lung nodules in a subject; and performing a statistical analysis on both of (a) and (b) using a machine learning algorithm selected from one or more of the following:GBM;RF;KNN;NNET;SVM; and / orNB;Optionally wherein the model is GBM.

30. A method, use, array, or kit for determining the presence of lung cancer in an individual substantially as described herein.

31. A method for determining the presence of lung cancer in a subject wherein the method comprises obtaining data about the nodule size of one or more nodules present in the lung(s) of a subject, optionally data obtained from a low-dose computed tomography (LDCT) scan on the patient and applying the data to a statistical model to determine the presence of lung cancer; optionally wherein the model comprises one or more model selected from the group comprising or consisting of:RF;KNN;NNET;SVM;GBM; and / orNB; optionally wherein the model further comprises cross-validation with one or more of the following:RF;KNN;NNET;SVM;GBM; and / orNB.

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