Method for the detection of cancer

Abstract

The invention relates to uses and methods of distinguishing benign from malignant nodules comprising measuring cell free nucleosomes or components thereof.

Description

[0001] METHOD FOR THE DETECTION OF CANCER

[0002] FIELD OF THE INVENTION

[0003] The invention relates to a body fluid test for the detection of cancer, particularly but not exclusively lung cancer patents, using a biomarker. The invention finds particular use in distinguishing between benign and cancerous nodules found during imaging.

[0004] BACKGROUND OF THE INVENTION

[0005] Lung cancer is a significant worldwide public health issue. Lung cancer has the highest mortality rate in many advanced countries including in Taiwan, Europe and the USA. The WHO reported that in 2020 lung cancer was the second most common cancer worldwide with 2.21 million cases (in terms of new cases). Cancer mortality is reduced when cases are detected and treated early. However, the majority of lung cancer patients present with inoperable, advanced disease entailing a poor prognosis. Advances in surgical, radiotherapeutic and chemotherapeutic approaches have been made, but the long-term survival rate remains at a low level in part due to late diagnosis.

[0006] To reduce the high mortality rate of lung cancer, many experts promote lung cancer screenings based on the results of several trials (e.g., NELSON trial and the US National Lung Screening Trial). Such screening programmes employ low-dose computed tomography (LDCT) to reduce the radiation administered to the person screened. However, despite its high sensitivity, the specificity of CT in lung cancer diagnosis is poor. Notably, it has been reported that 24% of the LDCT screening exams produced a positive result, which required follow-up, but 96% of these findings were false positives (i.e., negative for lung cancer). This often leads to harmful and / or costly unintended consequences, such as repeated follow-up scans and invasive biopsies) (Park et al. Cancer (2013) 119:1306013; Thalanayer et al. Ann Am Thorc Soc (2015) 12:1193-6],

[0007] Asymptomatic solitary pulmonary nodules revealed incidentally through a scan that was carried out for other reasons have also become a serious medical problem given the relatively large number of false positives. Again the potentially unnecessary invasive diagnostics in the case of benign pulmonary nodules reduces quality of life and increases anxiety.

[0008] Therefore, it is critical to improve the discrimination of benign from malignant screen-detected lung nodules. The present invention meets a key unmet clinical need for the management of pulmonary nodules using a non-invasive diagnostic test that discriminates between malignant and benign indeterminate pulmonary nodules in patients. The test will be helpful in making correct clinical decisions and reduce the risk of unnecessary interventions. The test of the present invention may result in lowering healthcare costs which may allow screening programmes to be implemented more widely.

[0009] STATEMENTS OF THE INVENTION

[0010] According to a first aspect of the invention, there is provided a use of a cell free nucleosome as a biomarker in a body fluid sample, for determining that a nodule in a subject is benign.

[0011] According to a further aspect of the invention, there is provided a use of a cell free nucleosome as a biomarker in a body fluid sample, for distinguishing benign nodules from malignant nodules in a subject.

[0012] According to a further aspect of the invention, there is provided a method for determining that a nodule in a subject is benign, which comprises the steps of:

[0013] (i) contacting a body fluid sample obtained from the subject with a binding agent to detect or measure cell free nucleosomes; and

[0014] (ii) using the level of cell free nucleosomes detected to determine the nodule is benign.

[0015] According to a further aspect of the invention, there is provided a method of distinguishing benign from malignant nodules in a subject with indeterminate nodules, which comprises the steps of:

[0016] (i) contacting a body fluid sample obtained from the subject with a binding agent to detect or measure cell free nucleosomes; and

[0017] (ii) using the level of cell free nucleosomes detected to determine the nodule is benign.

[0018] According to a further aspect of the invention, there is provided a method of determining the likelihood that a nodule in a subject is not malignant, comprising:

[0019] (i) contacting a body fluid sample obtained from the subject with a binding agent to detect or measure cell free nucleosomes; and

[0020] (ii) using the level of cell free nucleosomes detected to determine whether the nodule is not malignant.

[0021] According to a further aspect of the invention, there is provided a method of determining the likelihood that a nodule in a subject is not malignant, comprising:

[0022] (i) measuring the presence or levels of cell free nucleosomes present in a body fluid sample obtained from the subject; (ii) calculating a probability of cancer score based on the presence or levels of the cell free nucleosomes measured in step (a); and

[0023] (iii) ruling out cancer for the subject if the score in step (b) is lower than a predetermined score.

[0024] According to a further aspect of the invention, there is provided a method of distinguishing benign from malignant pulmonary nodules in a subject, comprising:

[0025] (a) conducting imaging on the subject, such as a CT or LDCT scan on a subject;

[0026] (b) obtaining a body fluid sample from the subject if the imaging identifies an indeterminate pulmonary nodule;

[0027] (c) measuring the presence or levels of one or more biomarkers selected from the group consisting of: cell free nucleosomes containing H3.1 , a H3K9Me3 core histone modification, a H3K27Me3 core histone modification and a H3K36Me3 core histone modification; wherein the amount of one or more of the biomarkers measured in step (c) distinguishes benign pulmonary nodules from malignant pulmonary nodules.

[0028] According to a further aspect of the invention, there is provided a kit comprising one or more reagents for carrying out the method as defined herein.

[0029] BRIEF DESCRIPTION OF THE FIGURES

[0030] Figure 1 : Results for panel comprising H3.1 + rH3K9Me3. (A) Representative box plots across the diagnostic groups within the first group of samples (the training set). (B) ROC curve using this panel to differentiate between cancerous and benign nodules.

[0031] Figure 2: Results for panel comprising H3.1 + rH3K27Me3. (A) Representative box plots across the diagnostic groups within the first group of samples. (B) ROC curve using this panel to differentiate between cancerous and benign nodules.

[0032] Figure 3: Results for panel comprising rH3K27Me3. (A) Representative box plots across the diagnostic groups within the first group of samples. (B) ROC curve using this panel to differentiate between cancerous and benign nodules.

[0033] Figure 4: Results for panel comprising rH3K9Me3. (A) Representative box plots across the diagnostic groups within the first group of samples. (B) ROC curve using this panel to differentiate between cancerous and benign nodules. Figure 5: Results for panel comprising H3.1 + rH3K9Me3 + rH3K27Me3 + rH3K36Me3.

[0034] (A) Representative box plots across the diagnostic groups within the first group of samples.

[0035] (B) ROC curve using this panel to differentiate between cancerous and benign nodules.

[0036] Figure 6: Results for panel comprising H3.1 + rH3K9Me3 + rH3K36Me3.

[0037] (A) Representative box plots across the diagnostic groups within the first group of samples.

[0038] (B) ROC curve using this panel to differentiate between cancerous and benign nodules.

[0039] (C) Representative box plots across the diagnostic groups within the first group of samples when including the Stage IA sub-stages.

[0040] Figure ?: Results for panel comprising CEA + H3.1 + rH3K9Me3 + rH3K27Me3 + rH3K36Me3. Representative box plots across the diagnostic groups within the first group of samples including the Stage IA sub-stages.

[0041] Figure 8: Results for panel comprising H3.1 + rH3K9Me3 + rH3K36Me3. Representative box plots across the diagnostic groups within the first group of samples including the different benign sub-categories.

[0042] Figure 9: Results for panel comprising CEA + H3.1 + rH3K9Me3 + rH3K27Me3 + rH3K36Me3. Representative box plots across the diagnostic groups within the first group of samples including the different benign sub-categories.

[0043] Figure 10: Results for panel comprising H3.1 + H3K9Me3 + H3K27Me3 + H3K36Me3.

[0044] (A) Representative box plots across the diagnostic groups within the first group of samples.

[0045] (B) ROC curve using this panel to differentiate between cancerous and benign nodules.

[0046] Figure 11 : Results for panel comprising H3.1 + H3K9Me3 + H3K27Me3 + H3K36Me3. Representative box plots across the diagnostic groups within the first group of samples including the different benign nodule sizes.

[0047] Figure 12: Results for panel comprising H3.1 + H3K9Me3 + H3K27Me3 + H3K36Me3. Representative box plots across the diagnostic groups within the first group of samples including the different sub-categories of Stage IA.

[0048] Figure 13: Results for panel comprising H3.1 + H3K9Me3 + H3K27Me3 + H3K36Me3. Representative box plots across the diagnostic groups within the first group of samples including the different types of cancer. Figure 14: Results for panel comprising H3.1 + H3K9Me3 + H3K27Me3 + H3K36Me3. Representative box plots across the diagnostic groups within the first group of samples including the different benign sub-categories.

[0049] Figure 15: Results for panel comprising H3.1 + H3K9Me3 + H3K27Me3 + H3K36Me3. Representative box plots across the diagnostic groups within the first group of samples including the different types of cancer.

[0050] Figure 16: Results for panel comprising H3.1 + H3K27Me3 + H3K36Me3.

[0051] (A) Representative box plots across the diagnostic groups within the first group of samples.

[0052] (B) ROC curve using this panel to differentiate between cancerous and benign nodules.

[0053] Figure 17: Results for panel comprising H3.1 + H3K27Me3 + H3K36Me3. Representative box plots across the diagnostic groups within the first group of samples including the different benign nodule sizes.

[0054] Figure 18: Results for panel comprising H3.1 + H3K27Me3 + H3K36Me3. Representative box plots across the diagnostic groups within the first group of samples including the different sub-categories of Stage IA.

[0055] Figure 19: Results for panel comprising H3.1 + H3K27Me3 + H3K36Me3. Representative box plots across the diagnostic groups within the first group of samples including the different types of cancer.

[0056] Figure 20: Results for panel comprising H3.1 + H3K27Me3 + H3K36Me3. Representative box plots across the diagnostic groups within the first group of samples including the different benign sub-categories.

[0057] Figure 21 : Results for panel comprising H3.1 + H3K27Me3 + H3K36Me3. Representative box plots across the diagnostic groups within the first group of samples including the different types of cancer.

[0058] Figure 22: Results for panel comprising H3.1 + H3K27Me3. ROC curve using this panel to differentiate between cancerous and benign nodules. DETAILED DESCRIPTION OF THE INVENTION

[0059] The present invention derives from the surprising discovery, that in patients presenting with pulmonary nodule(s), biomarkers in the blood exist that specifically identify and classify lung cancer. Accordingly the invention provides unique advantages to the patient associated with early detection of lung cancer, including increased life span, decreased morbidity and mortality, decreased exposure to radiation during screening and repeat screenings and a minimally invasive diagnostic model. Importantly, the methods of the invention allow for a subject to avoid invasive procedures where the pulmonary nodule is not malignant.

[0060] The routine clinical use of chest computed tomography (CT) scans identifies millions of pulmonary nodules annually, of which only a small minority are malignant but contribute to the dismal five-year survival rate for patients diagnosed with non-small cell lung cancer (NSCLC). The early diagnosis of lung cancer in patients with pulmonary nodules is a top priority, as decision-making based on clinical presentation, in conjunction with current non-invasive diagnostic options such as chest CT and positron emission tomography (PET) scans, and other invasive alternatives, has not altered the clinical outcomes of patients with Stage I NSCLC. The subgroup of pulmonary nodules between 8mm and 20mm in size is increasingly recognized as being "intermediate" relative to the lower rate of malignancies below 8mm and the higher rate of malignancies above 20mm. Invasive sampling of the lung nodule by biopsy using transthoracic needle aspiration or bronchoscopy may provide a cytopathologic diagnosis of NSCLC, but are also associated with both false-negative and non-diagnostic results. In summary, a key unmet clinical need for the management of pulmonary nodules is a non- invasive diagnostic test that discriminates between malignant and benign processes in patients with indeterminate pulmonary nodules (IPNs), especially between 8mm and 20mm in size.

[0061] The clinical decision to be more or less aggressive in treatment is based on risk factors, primarily nodule size, smoking history and age in addition to imaging. As these are not conclusive, there is a great need for a blood test that would be both non-invasive and provide complementary information to risk factors and imaging.

[0062] Accordingly, these and related embodiments will find uses in screening methods for lung conditions, and particularly lung cancer diagnostics. More importantly, the invention finds use in determining the clinical management of a patient. That is, the method of invention is useful in ruling in or ruling out a particular treatment protocol for an individual subject. According to one aspect of the invention there is provided use of a cell free nucleosome as a biomarker in a body fluid sample, for determining that a nodule in a subject is benign. The invention may therefore be used to determine whether a nodule in a subject is benign or malignant.

[0063] According to another aspect of the invention there is provided use of a cell free nucleosome as a biomarker in a body fluid sample, for distinguishing a benign nodule from a malignant nodule in a subject.

[0064] Uses of the invention may distinguish a benign nodule by comparing to one or more control samples, such as a control sample from a malignant nodule or a control sample from a healthy subject.

[0065] Nodules are growths or lumps that may be malignant or benign. References herein to “benign nodule” refer to non-cancerous nodules, whereas “malignant nodule” as used herein refer to cancerous nodules. Typically, benign nodules will not spread to other parts of the body, they do not grow or grow slowly and they are usually not life threatening which means they may not be removed or otherwise treated. In contrast, malignant nodules are cancerous and therefore need to be treated, ideally immediately, to prevent growth or spread of the cancerous cells in the malignant nodule.

[0066] In a particular embodiment the nodule is a pulmonary nodule.

[0067] Presented is a body fluid test, or classifier, for identifying benign nodules.

[0068] The term "classifier" as used herein refers to an algorithm that discriminates between disease states with a predetermined level of statistical significance. A two-class classifier is an algorithm that uses data points from measurements from a sample and classifies the data into one of two groups. In certain embodiments, the data used in the classifier is the relative levels of biomarkers in a biological sample. Levels of the biomarker in a subject can be compared to levels in patients previously diagnosed with a specified condition. In cases where the classifier can distinguish between a benign nodule and a malignant nodule, but cannot distinguish between a benign nodule and disease free, imaging can be used to distinguish between a subject which has a nodule (be it benign or malignant) and a healthy subject, i.e. a subject without a nodule. In many circumstances a test which was not able to distinguish between a malignant nodule and a healthy subject would not be useful; however, in the present case when a test is used in combination with an imaging technique this issue does not arise as only patients who are found to have an IPN after screening will need to be tested to determine the nature of the IPN.

[0069] The "classifier" maximizes the probability of distinguishing a randomly selected cancer sample from a randomly selected benign sample, i.e. , the AUC of ROC curve.

[0070] Importantly we have found that the present invention allows for all stages of cancer (including pre-cancer, minimal invasive cancer, stage I, stage II, stage III and stage IV) to be distinguished from benign nodules.

[0071] The panel worked well in patients with non-malignant nodules having an early stage cancer (0, I and II). One key finding is that the present invention was able to distinguish between benign nodules and nodules which were pre-cancer, minimally invasive cancer, stage Ia1 , stage IA2, stage IA3, stage IB and stage II cancer, along with stage III and stage IV cancer. Early stage cancers are more likely to present with smaller nodules therefore a rule-out test for early stage cancers is highly useful in the context of screening programs because scanning methods such as LDCT have poor specificity for differentiation of non-malignant nodules leading to unnecessary biopsy or repeat scans. Thus, the results of other clinical parameters may be used with the panel to further increase accuracy and utility.

[0072] One key finding is that the invention is applicable irrespective of the size of the nodule. Thus, whilst clinically it may be appropriate for an indeterminate nodule of a particular size, such as greater than 3cm in diameter, to be biopsied, the present invention will assist a clinician to distinguish between a benign and malignant lesion, whatever the size of nodule is being investigated.

[0073] In one embodiment the pulmonary nodule has a diameter of less than or equal to about 3 cm or about 2cm. In one embodiment the pulmonary nodule has a diameter of equal to or greater than 0.8 cm.

[0074] One key finding is that the invention is applicable to a subject irrespective of their risk of developing a malignant nodule, e.g. the invention is equally applicable to people who are assessed as being at higher risk of developing a malignant nodule, such as smokers or previous smokers, as it is to people who are assessed as being of lower risk of developing a malignant nodule, such as non-smokers. This is an important finding as there is an increasing trend for indeterminate nodules to be found in non-smokers, particularly women. Currently such nodules are commonly found incidentally following investigations for another condition. The present invention can therefore assist a clinician in distinguishing between a benign and a malignant nodule in such a patient group. Therefore a blood test would be useful to help to choose a treatment (i.e. whether a biopsy or surgery is required or not); and to help to eliminate the frequency of radiation exposure from repeat / follow-up scans.

[0075] Furthermore, population screening is to-date only offered to patients who are deemed to be at risk of developing a malignant tumour. This is in part due to the clinical difficulties in distinguishing between indeterminate nodules, particularly where on-going exposure to radiation is required through follow-up scans, but also the economic cost of carrying out any regular follow-up appointments may deter a health authority from introducing a more widespread screening programme. The present invention may therefore encourage wider screening programmes.

[0076] One key finding is that the invention is capable of distinguishing between benign nodules and malignant nodules irrespective of the cause of the benign pulmonary nodule. For example the invention is capable of distinguishing between malignant nodules and lung nodules associated with anthracosis, hamartoma, fibrosis or an infection such a cryptococcosis or granulomatous inflammation.

[0077] Biomarker

[0078] The nucleosome is the basic unit of chromatin structure and consists of a protein complex of eight highly conserved core histones (comprising of a pair of each of the histones H2A, H2B, H3, and H4). Around this complex is wrapped approximately 146 base pairs of DNA. Another histone, H1 or H5, acts as a linker and is involved in chromatin compaction. The DNA is wound around consecutive nucleosomes in a structure often said to resemble “beads on a string” and this forms the basic structure of open or euchromatin. In compacted or heterochromatin this string is coiled and super coiled into a closed and complex structure (Herranz and Esteller (2007) Methods Mol. Biol. 361 : 25-62).

[0079] References to “nucleosome” may refer to “cell free nucleosome” when detected in body fluid samples. It will be appreciated that the term cell free nucleosome throughout this document is intended to include any cell free chromatin fragment that includes one or more nucleosomes. “Epigenetic features”, “epigenetic signal features” or “epigenetic signal structures” of a cell free nucleosome as referred herein may comprise, without limitation, one or more histone post-translational modifications, histone isoforms, modified nucleotides and / or proteins bound to a nucleosome in a nucleosome-protein adduct. It will be understood that the cell free nucleosome may be detected by binding to a component thereof. The term “component thereof” as used herein refers to a part of the nucleosome, i.e. the whole nucleosome does not need to be detected. The component of the cell free nucleosomes may be selected from the group consisting of: a histone protein {i.e. histone H1 , H2A, H2B, H3 or H4), a histone post-translational modification, a histone variant or isoform, a protein bound to the nucleosome i.e. a nucleosome-protein adduct), a DNA fragment associated with the nucleosome and / or a modified nucleotide associated with the nucleosome. For example, the component thereof may be histone (isoform) H3.1 or histone H1 or DNA.

[0080] Methods and uses of the invention may measure the level of (cell free) nucleosomes per se. References to “nucleosomes per se” refers to the total nucleosome level or concentration present in the sample, regardless of any epigenetic features the nucleosomes may or may not include. Detection of the total nucleosome level typically involves detecting a histone protein common to all nucleosomes, such as histone H4. Therefore, nucleosomes per se may be measured by detecting a core histone protein, such as histone H4. As described herein, histone proteins form structural units known as nucleosomes which are used to package DNA in eukaryotic cells. As previously reported in WO 2016 / 067029 (incorporated herein by reference), particular histone variants, such as histone H3.1 , H3.2 or H3t, may be used to isolate cell free nucleosomes originating from tumour cells. Therefore, the total level of cell free nucleosomes of tumour origin may be detected.

[0081] Normal cell turnover in adult humans involves the creation by cell division of some 1011cells daily and the death of a similar number, mainly by apoptosis. During the process of apoptosis chromatin is broken down into mononucleosomes and oligonucleosomes which are released from the cells. Under normal conditions the levels of circulating nucleosomes found in healthy subjects is reported to be low. Elevated levels are found in subjects with a variety of conditions including many cancers, auto-immune diseases, inflammatory conditions, stroke and myocardial infarction (Holdenrieder & Stieber (2009) Grit Rev Clin Lab Sci, 46(1): 1-24).

[0082] Current nucleosome ELISA methods are used primarily in cell culture, usually as a method to detect apoptosis (Salgame et al. (1997) Nucleic Acids Res, 25(3): 680-681 ; Holdenrieder et al. (2001) supra, van Nieuwenhuijze et al. (2003) Ann Rheum Dis, 62: 10-14), but are also used for the measurement of circulating cell free nucleosomes in serum and plasma (Holdenrieder et al. (2001)). Cell free serum and plasma nucleosome levels released into the circulation by dying cells have been measured by ELISA methods in studies of a number of different cancers to evaluate their use as a potential biomarker. Mean circulating nucleosome levels are reported to be high in most, but not all, cancers studied. However, patients with malignant tumours are reported to have serum nucleosome concentrations that varied considerably and some patients with advanced tumour disease were found to have low circulating nucleosome levels, within the range measured for healthy subjects (Holdenrieder et al. (2001)).

[0083] The cell free nucleosome may be a mononucleosome or oligonucleosome, or a mixture thereof.

[0084] Mononucleosomes and oligonucleosomes can be detected by Enzyme-Linked ImmunoSorbant Assay (ELISA) and several methods have been reported (e.g. Salgame et al. (1997); Holdenrieder et al. (2001); van Nieuwenhuijze et al. (2003)). These assays typically employ an anti-histone antibody (for example anti-H2B, anti-H3 or anti-H 1 , H2A, H2B, H3 and H4) as capture antibody and an anti-DNA or anti-H2A-H2B-DNA complex antibody as detection antibody.

[0085] Circulating nucleosomes are not a homogeneous group of protein-nucleic acid complexes. Rather, they are a heterogeneous group of chromatin fragments originating from the digestion of chromatin on cell death and include an immense variety of epigenetic structures including particular histone isoforms (or variants), post-translational histone modifications, nucleotides or modified nucleotides, and protein adducts. It will be clear to those skilled in the art that an elevation in nucleosome levels will be associated with elevations in some circulating nucleosome subsets containing particular epigenetic signals including nucleosomes comprising particular histone isoforms (or variants), comprising particular post-translational histone modifications, comprising particular nucleotides or modified nucleotides and comprising particular protein adducts. Assays for these types of chromatin fragments are known in the art (for example, see WO 2005 / 019826, WO 2013 / 030579, WO 2013 / 030578, WO 2013 / 084002 which are herein incorporated by reference).

[0086] The biomarker used in the uses and methods of the invention may be the level of cell free nucleosomes per se and / or an epigenetic feature of a cell free nucleosome. It will be understood that the terms “epigenetic signal structure” and “epigenetic feature” are used interchangeably herein. They refer to particular features of the nucleosome that may be detected. In one embodiment, the epigenetic feature of the nucleosome is selected from the group consisting of: a post-translational histone modification, a histone variant, a particular nucleotide and a protein adduct. In one embodiment, the epigenetic feature of the nucleosome comprises one or more histone variants or isoforms. The epigenetic feature of the cell free nucleosome may be a histone isoform, such as a histone isoform of a core nucleosome, in particular a histone H3 isoform. The term “histone variant” and “histone isoform” may be used interchangeably herein. The structure of the nucleosome can also vary by the inclusion of alternative histone isoforms or variants which are different gene or splice products and have different amino acid sequences. Many histone isoforms are known in the art. Histone variants can be classed into a number of families which are subdivided into individual types. The nucleotide sequences of a large number of histone variants are known and publicly available for example in the National Human Genome Research Institute NHGRI Histone Database (Marino-Ramirez et al. The Histone Database: an integrated resource for histones and histone fold-containing proteins. Database Vol.2011. and http: / / genome.nhgri.nih.gov / histones / complete.shtml), the GenBank (NIH genetic sequence) Database, the EMBL Nucleotide Sequence Database and the DNA Data Bank of Japan (DDBJ). For example, variants of histone H2 include H2A1 , H2A2, mH2A1 , mH2A2, H2AX and H2AZ. In another example, histone isoforms of H3 include H3.1 , H3.2 and H3t.

[0087] In one embodiment, the histone isoform is H3.1.

[0088] The structure of nucleosomes can vary by post translational modification (PTM) of histone proteins. PTM of histone proteins typically occurs on the tails of the core histones and common modifications include acetylation, methylation or ubiquitination of lysine residues as well as methylation of arginine residues and phosphorylation of serine residues and many others. Many histone modifications are known in the art and the number is increasing as new modifications are identified (Zhao and Garcia, 2015 Cold Spring Harb Perspect Biol, 7: a025064). Therefore, in one embodiment, the epigenetic feature of the cell free nucleosome may be a histone post translational modification (PTM). The histone PTM may be present on a core nucleosome histone (e.g. H2A, H2B, H3 or H4), or a linker histone (e.g. H1 or H5). The histone PTM may be a histone PTM of a core nucleosome, e.g. H3, H2A, H2B or H4, in particular H3, H2A or H2B. In particular, the histone PTM is a histone H3 PTM. Examples of such PTMs are described in WO 2005 / 019826 and WO 2017 / 068359.

[0089] For example, the post translational modification may include acetylation, methylation, which may be mono-, di-or tri-methylation, phosphorylation, ribosylation, citrullination, ubiquitination, hydroxylation, glycosylation, nitrosylation, glutamination and / or isomerisation (see Ausio (2001) Biochem Cell Bio 79: 693). In one embodiment, the histone PTM is methylation and in particular methylation of a lysine residue. In a further embodiment, the methylation is of a histone 3 lysine residue. In a yet further embodiment, the histone PTM is selected from H3K4Me, H3K4Me2, H3K9Me, H3K9Me3, H3K27Me3 or H3K36Me3. In particularly preferred embodiment the histone PTM is selected from H3K9Me3, H3K27Me3 or H3K36Me. In a yet further preferred embodiment, the histone PTM is H3K27Me3.

[0090] In one embodiment, the histone PTM is acetylation of a lysine residue. In a further embodiment, the acetylation is of a histone 3 lysine residue. In a yet further embodiment, the histone PTM is selected from H3K9Ac, H3K14AC, H3K18AC or H3K27AC. In another embodiment, the histone PTM is H4PanAc. In one embodiment, the histone PTM is phosphorylation of a serine residue. In a further embodiment, the phosphorylation is of an isoform X of histone 2A (H2AX) serine residue or phosphorylation of a histone 3 serine residue. In a yet further embodiment, the histone PTM is selected from pH2AX or H3S10Ph. In one embodiment, the histone PTM is selected from citrullination or ribosylation. In a further embodiment, the histone PTM is citrullinated H3 (H3cit) or citrullinated H4 (H4cit). In a further embodiment, the histone PTM is citrullination of a histone 3 arginine residue. In a yet further embodiment, the histone PTM is H3R8Cit. In one embodiment, the histone PTM is selected from the group consisting of: H3K4Me, H3K4Me2, H3K9Me, H3K9Me3, H3K27Me3, H3K36Me3, H3K9Ac, H3K14Ac, H3K18Ac, H3K27Ac, H4PanAc, pH2AX, H3S10Ph and H3R8Cit.

[0091] A group or class of related histone post translational modifications (rather than a single modification) may also be detected. A typical example, without limitation, would involve a 2- site immunoassay employing one antibody or other selective binder directed to bind to nucleosomes and one antibody or other selective binder directed to bind the group of histone modifications in question. Examples of such antibodies directed to bind to a group of histone modifications would include, for illustrative purposes without limitation, anti-pan-acetylation antibodies (e.g. a Pan-acetyl H4 antibody [H4panAc]), anti-citrullination antibodies or antiubiquitin antibodies. In one embodiment, the histone PTM is selected from citrullination or ribosylation, in particular citrullination. In a further embodiment, the histone PTM is H3 citrulline (H3cit) or H4 citrulline (H4cit). In a yet further embodiment, the histone PTM is H3cit.

[0092] In one embodiment, the epigenetic feature of the nucleosome comprises one or more DNA modifications. In addition to the epigenetic signalling mediated by nucleosome histone isoform and PTM composition, nucleosomes also differ in their nucleotide and modified nucleotide composition. Global DNA hypomethylation is a hallmark of cancer cells and some nucleosomes may comprise more 5-methylcytosine residues (or 5-hydroxymethylcytosine residues or other nucleotides or modified nucleotides) than other nucleosomes. In one embodiment, the DNA modification is selected from 5-methylcytosine or 5- hydroxymethylcytosine.

[0093] In one embodiment, the epigenetic feature of the nucleosome comprises one or more proteinnucleosome adducts or complexes. A further type of circulating nucleosome subset is nucleosome protein adducts. It has been known for many years that chromatin comprises a large number of non-histone proteins bound to its constituent DNA and / or histones. These chromatin associated proteins are of a wide variety of types and have a variety of functions including transcription factors, transcription enhancement factors, transcription repression factors, histone modifying enzymes, DNA damage repair proteins and many more. These chromatin fragments including nucleosomes and other non-histone chromatin proteins or DNA and other non-histone chromatin proteins are described in the art.

[0094] In one embodiment, the protein adducted to the nucleosome (and which therefore may be used as a biomarker) is selected from: a transcription factor, a High Mobility Group Protein or chromatin modifying enzyme. References to “transcription factor” refer to proteins that bind to DNA and regulate gene expression by promoting ( / .e. activators) or suppressing ( / .e. repressors) transcription. Transcription factors contain one or more DNA-binding domains (DBDs), which attach to specific sequences of DNA adjacent to the genes that they regulate.

[0095] All of the circulating nucleosomes and nucleosome moieties, types or subgroups described herein may be useful in the present invention.

[0096] Another way the structure of nucleosomes may vary is by mutation. Therefore, in one embodiment, the epigenetic feature is a mutated histone. In a further embodiment, the mutation is in histone 3 (H3). In a yet further embodiment, the mutation in H3 is when lysine 27 is replaced by a methionine (H3K27M).

[0097] It will be understood that more than one epigenetic feature of cell free nucleosomes may be detected in methods and uses of the invention. Multiple biomarkers may be used as a combined biomarker. Therefore, in one embodiment, the use comprises more than one epigenetic feature of cell free nucleosomes as a combined biomarker. The epigenetic features may be the same type (e.g. PTMs, histone isoforms, nucleotides or protein adducts) or different types (e.g. a PTM in combination with a histone isoform). For example, a post- translational histone modification and a histone variant may be detected ( / .e. more than one type of epigenetic feature is detected). Alternatively, or additionally, more than one type of post-translational histone modification is detected, or more than one type of histone isoform is detected. In one aspect, the use comprises a post-translational histone modification and a histone isoform as a combined biomarker in a body fluid sample, for the diagnosis or detection of a nodule (in particular, to determine whether the nodule is benign or malignant). In one embodiment, the use comprises methylation is of a histone 3 lysine residue and a histone 3 isoform as the combined biomarker. In a further embodiment, the combined biomarker is H3.1 and H3K9Me3, H3K27Me3 and / or H3K36Me3. In a yet further embodiment, the combined biomarker is H3.1 and H3K27Me3.

[0098] The variables in this analysis may be reported as absolute levels of cell free nucleosomes, cell free nucleosomes containing H3.1 or cell free nucleosomes containing a histone PTM or mutation.

[0099] As described herein, the level of one or more of the histone biomarkers also may be normalised against the level of cell free nucleosomes in the sample, i.e. to determine the proportion of cell free nucleosomes containing the histone biomarkers of interest. Therefore, the level of biomarkers H3K9Me3, H3K27Me3 and H3K36Me3 may be measured as a ratio of the level of cell free nucleosomes or a component thereof in the sample. In particular, H3K9Me3, H3K27Me3 and H3K36Me3 may be measured as a ratio of the level of cell free nucleosomes containing a histone H3 variant, such as histone H3.1 , in the sample.

[0100] In terms of nomenclature, by way of example the proportion of nucleosomes containing a histone PTM measured as a ratio of the level of cell free nucleosomes containing a histone H3.1 variant is represented as: rH3K9Me3 = H3K9Me31 H3.1 rH3K27Me3 = H3K27Me31 H3.1 rH3K36Me3 = H3K36Me31 H3.1

[0101] The combination of markers described herein may be used to prepare a kit or panel test, in particular for use in the diagnosis of lung cancer and / or monitoring of patients with lung cancer or suspected lung cancer.

[0102] This panel may also be combined with detection protein markers such as CEA, CYFRA and CRP.

[0103] As used herein, the term “CEA” refers to carcinoembryonic antigen. As used herein, the term “CYFRA” or “CYFRA21-1 ,” also known as Cyfra 21-1, refers to cytokeratin fragment 19, also known as cytokeratin-19 fragment.

[0104] As used herein, the term “CRP” refers to C-reactive protein.

[0105] Whilst the additional of such proteins to the panel of biomarkers provided useful results, biomarkers such as CEA are often associated with the later stages of cancer. Therefore, it was notable that results were achieved in the absence of protein biomarkers such as CEA, CRFRA and CRP.

[0106] Therefore, according to a further aspect of the invention, there is provided a kit comprising reagents to detect the level of the biomarkers of the present invention. The kits described herein may be for use in the diagnosis of lung cancer.

[0107] According to a further aspect of the invention there is provided the use of the kit as defined herein to identify a patient in need of treatment for lung cancer.

[0108] According to a further aspect of the invention there is provided the use of the kit as defined herein to monitor a patient for progression of lung cancer (e.g. further growth of the tumour, or advancement to a different stage of cancer). Embodiments of this aspect include use to detect disease progression in watchful waiting, active surveillance and monitoring postsurgery or other treatment for relapse.

[0109] Examples of combinations of biomarkers and panels which can be employed in the present invention include those set out in Tables 1 and 2.

[0110] Table 1: AUC of cancer vs. benign nodules for models using different biomarkers and combinations.

[0111] Table 2: Further biomarker combinations within the scope of the invention

[0112] Examples of particularly useful combinations include:

[0113] H3.1 and H3K27Me3;

[0114] H3.1 , rH3K9Me3 and rH3K36Me3;

[0115] H3.1 , H3K9Me3, H3K36Me3 and CEA;

[0116] H3.1 , rH3K9Me3, rH3K27Me3 and rH3K36Me3;

[0117] H3.1 , rH3K9Me3, rH3K27Me3, rH3K36Me3 and CEA; rH3K9Me3 and rH3K36Me3; rH3K9Me3, rH3K36Me3 and CEA;

[0118] H3.1 and rH3K9Me3;

[0119] H3.1 and rH3K27Me3;

[0120] H3.1 , H3K27Me3 and H3K36Me3;

[0121] H3K27Me3 and H3K36Me3;

[0122] HeK27Me3, H3K36Me3 and CEA;

[0123] H3.1 , H3K9Me3, H3K27Me3 and H3K36Me3;

[0124] H3K9Me3, H3K27Me3 and H3K36Me3; or

[0125] H3K9Me3, H3K27Me3, H3K36Me3 and CEA

[0126] The term “biomarker” means a distinctive biological or biologically derived indicator of a process, event, or condition. Biomarkers can be used in methods of diagnosis, e.g. clinical screening, and prognosis assessment and in monitoring the results of therapy, identifying patients most likely to respond to a particular therapeutic treatment, drug screening and development. Biomarkers and uses thereof are valuable for identification of new drug treatments and for discovery of new targets for drug treatment.

[0127] The sample may be any biological fluid (or body fluid) sample taken from a subject including, without limitation, cerebrospinal fluid (CSF), whole blood, blood serum, plasma, menstrual blood, endometrial fluid, urine, saliva, or other bodily fluid (stool, tear fluid, synovial fluid, sputum), breath, e.g. as condensed breath, or an extract or purification therefrom, or dilution thereof. In a preferred embodiment, the body fluid sample is selected from blood, serum or plasma. Biological samples also include specimens from a live subject, or taken post-mortem. The samples can be prepared, for example where appropriate diluted or concentrated, and stored in the usual manner. It will be understood that methods and uses of the present invention find particular use in blood, serum or plasma samples obtained from a patient. In one embodiment, the sample is a blood or plasma sample. In a further embodiment, the sample is a serum sample. In a further embodiment both serum and plasma samples are used for the measurement of different members of an assay panel.

[0128] Preferably, plasma samples are used. Plasma samples may be collected in collection tubes containing one or more anticoagulants such as ethylenediamine tetraacetic acid (EDTA), heparin, or sodium citrate, in particular EDTA.

[0129] In one embodiment of the present invention whole blood samples are collected into EDTA plasma tubes and tested against chosen biomarkers. Levels of CEA, CYFRA and CRP were measured using commercially available immunoassay methods. H3K9Me3, H3K27Me3 and H3K36Me3 were measured using a sandwich immunoassay method employing one antibody directed to bind to the selected histone modification and one antibody directed to bind to an epitope present in intact nucleosomes. H3.1 was also measured using a sandwich immunoassay method employing one antibody directed to bind to the selected histone isoform and one antibody directed to bind to an epitope present in intact nucleosomes.

[0130] Assay results were modelled by Logistic Regression analysis to train for the model or algorithm with the highest AUC for a comparison of patients with lung cancer vs patients with non- malignant nodules (benign). For preparing an appropriate diagnostic test, it is recommended that the most specific test is used to confirm (i.e. rule-in) a diagnosis and the most sensitive test is used to establish that a disease is unlikely (i.e. rule-out). The panels tested were able to provide suitable rule-in and rule-out tests for lung cancer. Therefore the biomarkers are useful to confirm cancer cases in need of further investigation and treatment.

[0131] The results presented in the figures may be summarised as follows:

[0132] The panel comprising H3.1 + rH3K9Me3 for detecting lung cancer showed a diagnostic sensitivity of 90.73 % and a specificity of 80.21 % using a logistic regression algorithm. Representative box plots across the diagnostic groups within the first group of samples (the training set) is presented in Figure 1(A). The AUC for differentiating between cancerous and benign nodules was 89.44% (Figure 1 (B)). When the same panel and logistic regression algorithm was applied to the samples in the second group of samples (the validation set), the panel showed a diagnostic sensitivity of 58.68 % and a specificity of 90.62 %, and an AUC for differentiating between cancerous and benign nodules of 77.26 %.

[0133] The panel comprising H3.1 + rH3K27Me3 for detecting lung cancer showed a diagnostic sensitivity of 90.98 % and a specificity of 81.91 % using a logistic regression algorithm. Representative box plots across the diagnostic groups within the first group of samples is presented in Figure 2(A). The AUC for differentiating between cancerous and benign nodules was 89.14 % (Figure 2(B)). When the same panel and logistic regression algorithm was applied to the samples in the second group of samples, the panel showed a diagnostic sensitivity of 64.67 % and a specificity of 89.06 %, and an AUC for differentiating between cancerous and benign nodules of 82.46 %.

[0134] The panel comprising rH3K27Me3 for detecting lung cancer showed a diagnostic sensitivity of 88.78 % and a specificity of 82.98 % using a logistic regression algorithm. Representative box plots across the diagnostic groups within the first group of samples is presented in Figure 3(A). The AUC for differentiating between cancerous and benign nodules was 88.10% (Figure 3(B)). When the same panel and logistic regression algorithm was applied to the samples in the second group of samples, the panel showed a diagnostic sensitivity of 66.47 % and a specificity of 85.94 %, and an AUC for differentiating between cancerous and benign nodules of 81.00 %.

[0135] The panel comprising rH3K9Me3 for detecting lung cancer showed a diagnostic sensitivity of 88.54 % and a specificity of 81.25 % using a logistic regression algorithm. Representative box plots across the diagnostic groups within the first group of samples is presented in Figure 4(A). The AUC for differentiating between cancerous and benign nodules was 87.68 % (Figure 4(B)). When the same panel and logistic regression algorithm was applied to the samples in the second group of samples, the panel showed a diagnostic sensitivity of 61.68 % and a specificity of 87.50 %, and an AUC for differentiating between cancerous and benign nodules of 75.81 %.

[0136] The panel comprising H3.1 + rH3K9Me3 + rH3K27Me3 + rH3K36Me3 for detecting lung cancer showed a diagnostic sensitivity of 88.02 % and a specificity of 87.23 % using a logistic regression algorithm. Representative box plots across the diagnostic groups within the first group of samples is presented in Figure 5(A). The AUC for differentiating between cancerous and benign nodules was 90.08 % (Figure 5(B)). When the same panel and logistic regression algorithm was applied to the samples in the second group of samples, the panel showed a diagnostic sensitivity of 71.26 % and a specificity of 84.38 %, and an AUC for differentiating between cancerous and benign nodules of 81.64 %.

[0137] The panel comprising H3.1 + rH3K9Me3 + rH3K36Me3 for detecting lung cancer showed a diagnostic sensitivity of 89.73 % and a specificity of 82.29 % using a logistic regression algorithm. Representative box plots across the diagnostic groups within the first group of samples is presented in Figure 6(A). The AUC for differentiating between cancerous and benign nodules was 90.18 % (Figure 6(B)). When the same panel and logistic regression algorithm was applied to the samples in the second group of samples, the panel showed a diagnostic sensitivity of 71.26 % and a specificity of 84.38 %, and an AUC for differentiating between cancerous and benign nodules of 81 .64%. When the cancer stage I samples were split into the further sub-stages of stage IA cancer (i.e. IA1 , IA2 and IA3) the panel showed a diagnostic sensitivity of 89.73 % and a specificity of 82.65 % using a logistic regression algorithm, and the AUC for differentiating between cancerous and benign nodules was 90.34%. Representative box plots across the diagnostic groups within the first group of samples (when including the Stage IA sub-stages) is presented in Figure 6(C).

[0138] When the cancer stage I samples were split into the further sub-stages of stage IA cancer (i.e. IA1 , IA2 and IA3), the panel comprising CEA + H3.1 + rH3K9Me3 + rH3K27Me3 + rH3K36Me3 for detecting lung cancer showed a diagnostic sensitivity of 89.98 % and a specificity of 87.50 % using a logistic regression algorithm. Representative box plots across the diagnostic groups within the first group of samples (including the Stage IA sub-stages) is presented in Figure 7. The AUC for differentiating between cancerous and benign nodules was 92.08 %.

[0139] The results also show the test is able to distinguish between benign nodules from a range of sources and lung cancer: When the benign samples were split into the different types of benign lesions (i.e. nodules which subsequently disappear, anthracosis, cryptococcosis, hamartoma, fibrosis, granulomatous, other infections, uncategorised benign lesions (“benign various”) and other benign lesions), the panel comprising H3.1 + rH3K9Me3 + rH3K36Me3 for detecting lung cancer showed a diagnostic sensitivity of 89.73 % and a specificity of 82.29 % using a logistic regression algorithm. Representative box plots across the diagnostic groups within the first group of samples (including the different benign sub-categories) is presented in Figure 8. The AUC for differentiating between cancerous and benign nodules was 90.18 %.

[0140] When the benign samples were split into the different types of benign lesions, the panel comprising CEA + H3.1 + rH3K9Me3 + rH3K27Me3 + rH3K36Me3 for detecting lung cancer showed a diagnostic sensitivity of 89.98 % and a specificity of 87.23 % using a logistic regression algorithm. Representative box plots across the diagnostic groups within the first group of samples (including the different benign sub-categories) is presented in Figure 9. The AUC for differentiating between cancerous and benign nodules was 92.12 %.

[0141] The panel comprising H3.1 + H3K9Me3 + H3K27Me3 + H3K36Me3 for detecting lung cancer showed a diagnostic sensitivity of 87.29 % and a specificity of 86.17 % using a logistic regression algorithm. Representative box plots across the diagnostic groups within the first group of samples is presented in Figure 10(A). The AUC for differentiating between cancerous and benign nodules was 88.53 % (Figure 10(B)).

[0142] Looking at nodule size in more detail, the panel comprising H3.1 + H3K9Me3 + H3K27Me3 + H3K36Me3 for detecting lung cancer still showed a diagnostic sensitivity of 87.29 % and a specificity of 86.17 % using a logistic regression algorithm. Representative box plots across the diagnostic groups within the first group of samples (including the different benign nodule sizes) is presented in Figure 11. The AUC for differentiating between cancerous and benign nodules was maintained at 88.53 %.

[0143] Looking at early cancer stage in more detail, the panel comprising H3.1 + H3K9Me3 + H3K27Me3 + H3K36Me3 for detecting lung cancer still showed a diagnostic sensitivity of 87.29 % and a specificity of 86.17 % using a logistic regression algorithm. Representative box plots across the diagnostic groups within the first group of samples (including the different subcategories of Stage I A) is presented in Figure 12. The AUC for differentiating between cancerous and benign nodules was maintained at 88.53 %. Looking at the types of cancer in more detail, the panel comprising H3.1 + H3K9Me3 + H3K27Me3 + H3K36Me3 for detecting lung cancer still showed a diagnostic sensitivity of 87.29 % and a specificity of 86.17 % using a logistic regression algorithm. Representative box plots across the diagnostic groups within the first group of samples (including the different types of cancer, i.e. adenocarcinoma, adenosquamous carcinoma, pleomorphic carcinoma, squamous cell carcinoma and other various cancers) is presented in Figure 13. The AUC for differentiating between cancerous and benign nodules was maintained at 88.53 %.

[0144] Looking at the origins of the benign nodules in more detail, the panel comprising H3.1 + H3K9Me3 + H3K27Me3 + H3K36Me3 for detecting lung cancer still showed a diagnostic sensitivity of 87.29 % and a specificity of 86.17 % using a logistic regression algorithm. Representative box plots across the diagnostic groups within the first group of samples (including the different benign sub-categories) is presented in Figure 14. The AUC for differentiating between cancerous and benign nodules was maintained at 88.53 %.

[0145] Looking at the types of cancer in more detail, the panel comprising H3.1 + H3K9Me3 + H3K27Me3 + H3K36Me3 for detecting lung cancer still showed a diagnostic sensitivity of 87.29 % and a specificity of 86.17 % using a logistic regression algorithm. Representative box plots across the diagnostic groups within the first group of samples (including the different types of cancer, i.e. lepidic predominant, acinar predominant, papillary predominant, micropapillary predominant, solid predominant, mixed, solid or micropapillary, and squamous cell carcinoma) is presented in Figure 15. The AUC for differentiating between cancerous and benign nodules was 88.53 %.

[0146] The panel comprising H3.1 + H3K27Me3 + H3K36Me3 for detecting lung cancer showed a diagnostic sensitivity of 91.93 % and a specificity of 81.91 % using a logistic regression algorithm. Representative box plots across the diagnostic groups within the first group of samples is presented in Figure 16(A). The AUC for differentiating between cancerous and benign nodules was 88.20 % (Figure 16(B)). When the same panel and logistic regression algorithm was applied to the samples in the second group of samples, the panel showed a diagnostic sensitivity of 59.51 % and a specificity of 84.38 %, and an AUC for differentiating between cancerous and benign nodules of 76.87%.

[0147] Looking at nodule size in more detail, the panel comprising H3.1 + H3K27Me3 + H3K36Me3 for detecting lung cancer still showed a diagnostic sensitivity of 91.93 % and a specificity of 81.91 % using a logistic regression algorithm. Representative box plots across the diagnostic groups within the first group of samples (including the different benign nodule sizes) is presented in Figure 17. The AUC for differentiating between cancerous and benign nodules was maintained at 88.20 %.

[0148] Looking at early cancer stage in more detail, the panel comprising H3.1 + H3K27Me3 + H3K36Me3 for detecting lung cancer still showed a diagnostic sensitivity of 91.93 % and a specificity of 81.91 % using a logistic regression algorithm. Representative box plots across the diagnostic groups within the first group of samples (including the different sub-categories of Stage IA) is presented in Figure 18. The AUC for differentiating between cancerous and benign nodules was maintained at 88.20 %.

[0149] Looking at types of cancer in more detail, the panel comprising H3.1 + H3K27Me3 + H3K36Me3 for detecting lung cancer still showed a diagnostic sensitivity of 91.93 % and a specificity of 81.91 % using a logistic regression algorithm. Representative box plots across the diagnostic groups within the first group of samples (including the different types of cancer, i.e. adenocarcinoma, adenosquamous carcinoma, pleomorphic carcinoma, squamous cell carcinoma and other various cancers) is presented in Figure 19. The AUC for differentiating between cancerous and benign nodules was maintained at 88.20 %.

[0150] Looking at the origins of the benign nodules in more detail, the panel comprising H3.1 + H3K27Me3 + H3K36Me3 for detecting lung cancer still showed a diagnostic sensitivity of 91.93 % and a specificity of 81.91 % using a simple regression algorithm. Representative box plots across the diagnostic groups within the first group of samples (including the different benign sub-categories) is presented in Figure 20. The AUC for differentiating between cancerous and benign nodules was maintained at 88.20 %.

[0151] Looking at types of cancer in more detail, the panel comprising H3.1 + H3K27Me3 + H3K36Me3 for detecting lung cancer still showed a diagnostic sensitivity of 91.93 % and a specificity of 81.91 % using a simple regression algorithm. Representative box plots across the diagnostic groups within the first group of samples (including the different types of cancer, i.e. lepidic predominant, acinar predominant, papillary predominant, micropapillary predominant, solid predominant, mixed, solid or micropapillary, and squamous cell carcinoma) is presented in Figure 21. The AUC for differentiating between cancerous and benign nodules was maintained at 88.20 %.

[0152] The panel comprising H3.1 + H3K27Me3 for detecting lung cancer showed a diagnostic sensitivity of 92.63 % and a specificity of 51.28 % using a logistic regression algorithm. The AUC for differentiating between cancerous and benign nodules was 80 % (Figure 22). In one embodiment the biomarkers are for use in diagnosing the stage of cancer.

[0153] Lung Cancer

[0154] The present invention finds particular utility in relation to the diagnosis of lung cancer, and in particular the diagnosis of pulmonary nodules associated with lung cancer. One key finding of the invention is its ability to distinguish between benign and malignant pulmonary nodules from a range of types of lung cancers.

[0155] In one embodiment, the biomarkers are for use in diagnosing at all stages of cancer. Cancer may be assigned as stage 0, stage I, stage II, stage III and stage IV. Stage definition varies with different cancer diseases and is known in the art. Typically, stage I is classified as when the cancer is small and confined locally to the tissue of origin. Stage II is classified as when the cancer has grown larger and beyond its origin into nearby tissues within the organ or to nearby lymph nodes. Stage III is classified as when the cancer has grown into nearby tissues beyond the organ of origin but has not spread to other more distant parts of the body. Stage IV is classified as when the cancer has spread to one or more distant parts of the body, such as the liver or lungs. Early stage cancer generally includes stages 0, 1 and 11. Late stage cancer generally includes stages III and IV.

[0156] A key finding is the present invention is able to distinguish between benign nodules and even pre-cancers, minimally invasive cancers and Stage I cancers. The present invention is able to distinguish between benign nodules and nodules from cancer sage IA1 , IA2, IA3 or stage IB.

[0157] Precancerous lung tissue may also be called carcinoma in situ, CIS, in situ carcinoma, preinvasive lesions, or precancer. Doctors may diagnose precancer as stage 0 NSCLC.

[0158] There are two primary subtypes of precancerous lung tissue: atypical adenomatous hyperplasia (AAH) and squamous cell carcinoma in situ (CIS). Precancers of the lung are categorized by which type of lung cell is affected.

[0159] Minimally invasive adenocarcinoma (MIA) of the lung is a relatively new category in the classification of adenocarcinoma of the lung. Lesions that fall into this category refer to small solitary adenocarcinomas <3 cm (i.e. <30 mm) with either pure lepidic growth or predominant lepidic growth with <5 mm of stromal invasion. The present invention finds particular utility in the lung cancer, and non small cell lung cancer (NSCLC) or small cell lung cancer (SCLC). This classification is based upon the microscopic appearance of the tumour cells. SCLC is the most aggressive and rapidly growing of all the types. In a particular embodiment the lung cancer is NSCLC. NSCLC is the most common lung cancer. Examples of NSCLC include adenocarcinoma, squamous cell cancer, large cell carcinoma, adenosquamous carcinoma, pleomorphic carcinoma and sarcomatoid carcinoma.

[0160] The present invention also finds utility in distinguishing benign nodules from those of lepidic- predominant adenocarcinoma, acinar predominant adenocarcinoma, papillary predominant adenocarcinoma, micropapillary predominant carcinoma, solid adenocarcinoma, and mixed histology lung cancer.

[0161] The term “nodule” or "pulmonary nodules" (PNs) refers to lesions or lung lesions that can be visualized by radiographic techniques. Generally a pulmonary nodule is any nodule less than or equal to 3.0 centimetres in diameter. In one example a pulmonary nodule has a diameter of about 0.8 cm to 2 cm. An IPN is a nodule that cannot be definitively defined as benign or malignant based on imaging. An indeterminate pulmonary nodule may be a small, focal opacity in the lung measuring up to 3 cm that does not have features strongly suggestive of a benign etiology.

[0162] The invention is capable of distinguishing between benign nodules and malignant nodules irrespective of the cause of the benign pulmonary nodule. For example the invention is capable of distinguishing between malignant nodules and benign nodules associated with anthracosis, hamartoma, fibrosis or an infection such a cryptococcosis or granulomatous inflammation, or a nodule which subsequently disappears on a follow-up scan.

[0163] Detection and diagnosis methods

[0164] The invention provides methods which can be used in the detection or diagnosis of patients with a nodule, particularly a pulmonary nodule, of indeterminate status.

[0165] Therefore, according to a further aspect, there is provided a method for determining that a nodule in a subject is benign, which comprises the steps of:

[0166] (i) contacting a body fluid sample obtained from the subject with a binding agent to detect or measure cell free nucleosomes; and

[0167] (ii) using the level of cell free nucleosomes detected to determine the nodule is benign. According to a further aspect there is provided a method of distinguishing benign from malignant nodules in a subject with indeterminate nodules, which comprises the steps of:

[0168] (i) contacting a body fluid sample obtained from the subject with a binding agent to detect or measure cell free nucleosomes; and

[0169] (ii) using the level of cell free nucleosomes detected to determine the nodule is benign.

[0170] According to a further aspect there is provided a method of determining the likelihood that a nodule in a subject is not malignant, comprising:

[0171] (i) contacting a body fluid sample obtained from the subject with a binding agent to detect or measure cell free nucleosomes; and

[0172] (ii) using the level of cell free nucleosomes detected to determine the nodule is benign.

[0173] According to a further aspect of there is provided a method of determining the likelihood that a nodule in a subject is not malignant, comprising:

[0174] (i) measuring the presence or levels of cell free nucleosomes present in a body fluid sample obtained from the subject;

[0175] (ii) calculating a cancer score based on the presence or levels of the cell free nucleosomes measured in step (a); and

[0176] (iii) determining that the nodule in the subject is not malignant by ruling out cancer for the subject if the score in step (b) is lower than a predetermined score.

[0177] In addition to their uses as stand-alone tests, cancer rule-in or rule-out blood tests may be useful as adjunct methods to other screening modalities. In a particular embodiment the determination is conducted in combination with an imaging technique. In a particular embodiment the method nodule may have initially be identified by an imaging technique, for example, by an LDCT scan. The present invention finds particular utility when used in combination with an imaging technique such as Low-Dose Computed tomography (LDCT). LDCT has several limitations including the high prevalence of non-malignant nodules detected leading to overdiagnosis, the potential harms of cumulative radiation dose and poor adherence to recommended follow-up. Therefore, a novel blood-based test could offer a simple follow-up confirmation approach to help to discriminate between lung cancer and non-malignant nodules. Patients would benefit from either or both of a blood-test to rule-in cancer or rule-out cancer.

[0178] In one embodiment, the subject has a pulmonary nodule. According to a further aspect, there is provided a method of distinguishing benign from malignant pulmonary nodules in a subject, comprising:

[0179] (a) conducting imaging on a subject such as a CT or LDCT scan on a subject;

[0180] (b) obtaining a body fluid sample from the subject if the imaging identifies an indeterminate pulmonary nodule;

[0181] (c) measuring the presence or levels of one or more of the following panel of measurements: cell free nucleosomes containing H3.1 , a H3K9Me3 core histone modification, a H3K27Me3 core histone modification and a H3K36Me3 core histone modification;

[0182] (d) wherein the amount of one or more of the measurements in (c) distinguishes benign pulmonary nodules from malignant pulmonary nodules.

[0183] According to a further aspect, there is provided a method of distinguishing benign from malignant pulmonary nodules in a subject, comprising:

[0184] (a) conducting imaging on a subject such as a CT or LDCT scan on a subject;

[0185] (b) obtaining a body fluid sample from the subject if the imaging identifies an indeterminate pulmonary nodule;

[0186] (c) measuring the presence or level of cell free nucleosomes containing H3.1 and a H3K27Me3 core histone modification;

[0187] (d) wherein the amount measured in (c) distinguishes benign pulmonary nodules from malignant pulmonary nodules.

[0188] In another aspect, the method of the invention is performed to identify a subject at high risk of having lung cancer, for example due to a history of smoking, and therefore in need of further testing ( / .e. further lung cancer investigations). The further testing may involve biopsy and / or a PET scan.

[0189] In one embodiment, the method additionally comprises determining at least one clinical parameter for the patient. This parameter can be used in the interpretation of results. Clinical parameters may include any relevant clinical information for example, without limitation, gender, weight, Body Mass Index (BMI), smoking status and dietary habits. Therefore, in one embodiment, the clinical parameter is selected from the group consisting of: smoking status, family history of lung cancer, age, sex and body mass index (BMI). In a further embodiment, the clinical parameter is selected from the group consisting of: smoking status and family history of lung cancer. For example, the risk of lung cancer increases with the number of cigarettes smoked over time; doctors refer to this risk in terms of pack-years of smoking history (the number of packs of cigarettes smoked per day multiplied by the number of years smoked). In one embodiment individual assay cut-off levels are used and the patient is considered positive in the panel test if individual panel assay results are above (or below if applicable) the assay cut-off level for all or a minimum number of the panel assays (for example, one of two, two of two, two of three etc). In one embodiment of the invention a decision tree model or algorithm is employed for analysis of the results.

[0190] It will be clear to those skilled in the art, that any combination of the biomarkers disclosed herein may be used in panels and algorithms for the detection of malignant nodules and that further markers may be added to a panel including these markers.

[0191] The present invention provides a method of determining the likelihood that a lung condition in a subject is cancer by measuring at least one biomarker and in general a panel of biomarkers in a body fluid sample obtained from the subject; calculating a cancer score based on the biomarker measurements and ruling out cancer for the subject if the score is lower than a predetermined score. When cancer is ruled out, the subject does not receive a treatment protocol or it is deemed appropriate to extend the period between scans. Treatment protocols include for example pulmonary function test (PFT), pulmonary imaging, a biopsy, a surgery, a chemotherapy, a radiotherapy, or any combination thereof. In some embodiments, the imaging is an x-ray, a chest computed tomography (CT) scan, or a positron emission tomography (PET) scan.

[0192] The present invention further provides a method of ruling in the likelihood of cancer for a subject by measuring an abundance of panel of biomarkers in a sample obtained from the subject, calculating a probability of cancer score based on the biomarker measurements and ruling in the likelihood of cancer for the subject if the score is higher than a pre-determined score.

[0193] In another aspect, the invention further provides a method of determining the likelihood of the presence of a lung condition in a subject by measuring an abundance of panel of biomarkers in a sample obtained from the subject, calculating a probability of cancer score based on the biomarker measurements and concluding the presence of said lung condition if the score is equal or greater than a pre-determined score. In one embodiment the lung condition is lung cancer such as for example, NSCLC.

[0194] In another aspect, the invention provides a method of determining the likelihood that a pulmonary nodule in a subject is not lung cancer, comprising: (a) measuring the levels of a panel of the biomarkers present in a body fluid sample obtained from the subject; (b) calculating a probability of lung cancer score based on the levels of the panel of biomarkers of step (a); and (c) ruling out lung cancer for the subject if the score in step (b) is lower than a pre-determined score.

[0195] To evaluate the diagnostic performance of a particular set of biomarkers, a ROC curve may be generated.

[0196] A "ROC curve" refers to a plot of the true positive rate (sensitivity) against the false positive rate (specificity) for a binary classifier system as its discrimination threshold is varied. A ROC curve can be represented equivalently by plotting the fraction of true positives out of the positives (TPR=true positive rate) versus the fraction of false positives out of the negatives (FPR=false positive rate). Each point on the ROC curve represents a sensitivity / specificity pair corresponding to a particular decision threshold.

[0197] AUC represents the “area under the ROC curve”. The AUC is an overall indication of the diagnostic accuracy of 1) a biomarker or a panel of biomarkers and 2) a ROC curve. AUC is determined by the "trapezoidal rule." For a given curve, the data points are connected by straight line segments, perpendiculars are erected from the abscissa to each data point, and the sum of the areas of the triangles and trapezoids so constructed is computed.

[0198] The term "score" or "scoring" used herein refers to calculating a probability likelihood for a sample determined by the logistic regression algorithm. For the present invention, values closer to 1.0 are used to represent the likelihood that a sample is cancer, values closer to 0.0 represent the likelihood that a sample is benign.

[0199] A rule-in test provides a result identifying those patients in whom there is a high probability of cancer. In the context of a patient in whom a pulmonary nodule of unknown aetiology is found by LDCT this would identify patients in whom the nodule is malignant. The characteristics of a good rule-in test therefore include a low false positive rate to provide confidence that a nodule is cancerous in nature. In terms of Receiver Operating Characteristic (ROC) curves, this corresponds to those patients identified as positive for cancer at very high clinical specificity at the bottom-left of the ROC curve (i.e. those patients identified as having cancer whilst not falsely diagnosing a cancer in any patients who do not have a cancer).

[0200] A rule-out test provides a result identifying those patients in whom there is a low probability of cancer. In the context of a patient in whom a pulmonary nodule of unknown aetiology is found by LDCT this would identify patients with nodules that are highly unlikely to be a cancer and who therefore do not need treatment or aggressive invasive testing. These patients may therefore be followed up less intensively or discharged until the next scheduled screening test. The characteristics of a good rule-out test therefore include a low false negative rate to provide confidence that a nodule is not cancerous in nature. In terms of ROC curves, this corresponds to those patients identified as negative for cancer at very high clinical sensitivity at the topright of the ROC curve (i.e. those patients identified as not having cancer whilst missing nobody with a cancer).

[0201] More broadly, it is demonstrated that there are many variations on this invention that are also diagnostic tests for the likelihood that a nodule is benign or malignant. These are variations include the panel of biomarkers, biomarker standards, measurement methodology and / or classification algorithm.

[0202] As disclosed herein, archival samples from subjects presenting with pulmonary nodules (PNs) were analysed for differential nucleosome levels including nucleosome isoforms and histone modifications. The results were used to identify biomarker nucleosomes including epigenetically modified nucleosomes that have differential level in plasma in conjunction with various lung conditions (cancer vs. non-cancer). The panels identified include those listed in the Tables above.

[0203] In some embodiments, the method further comprises comparing the biomarker(s) with a cutoff value comprising an AUC of equal to or greater than 70%, 75%, 80%, or 85% or 88%.

[0204] The methods provided herein are minimally invasive and pose little or no risk of adverse effects. As such, they may be used to diagnose, monitor and provide clinical management of subjects who do not exhibit any symptoms of a lung condition and subjects classified as low risk for developing a lung condition. For example, the present invention may be used to diagnose lung cancer in a subject who presents with a PN following imaging but who nonetheless is classified as low risk for developing lung cancer.

[0205] The present invention may make use of best team players. The term "best team players" refers to the biomarkers that rank the best in the random panel selection algorithm, i.e., perform well on panels. When combined into a classifier these biomarkers can segregate cancer from benign samples. "Best team player" biomarkers is synonymous with "cooperative biomarkers". The term "cooperative biomarkers" refers biomarkers that appear more frequently on high performing panels of proteins than expected by chance. This gives rise to a biomarker’s cooperative score which measures how (in)frequently it appears on high performing panels.

[0206] The subject has or is suspected of having a pulmonary nodule. In one embodiment, the pulmonary nodule has a diameter of less than or equal to 3 cm. In one embodiment, the pulmonary nodule has a diameter of about 0.8cm to 2.0cm. The subject may have stage IA lung cancer (i.e., the tumour is smaller than 3 cm).

[0207] In one embodiment the score is calculated from a logistic regression model applied to the biomarker measurements.

[0208] In various embodiments, the method of the present invention further comprises normalizing the measurements.

[0209] In one aspect, the determining the likelihood of cancer is determined by the sensitivity, specificity, negative predictive value or positive predictive value associated with the score. The score determined has a negative predictive value (NPV) is at least about 60%, at least 70% or at least 80%.

[0210] As stated above, bioinformatic and biostatistical analyses were used first to identify individual biomarkers with statistically significant differential levels, and then using these biomarkers to derive one or more combinations of biomarkers or panels of biomarkers, which collectively demonstrated superior discriminatory performance compared to any individual biomarker.

[0211] Bioinformatic and biostatistical methods are used to derive coefficients (C) for each individual (protein) biomarker in the panel that reflects its relative expression level, i.e. increased or decreased, and its weight or importance with respect to the panel's net discriminatory ability, relative to the other proteins. The quantitative discriminatory ability of the panel can be expressed as a mathematical algorithm with a term for each of its constituent biomarker being the product of its coefficient and the biomarker’s plasma level (P) (as measured by immunoassay), e.g. C x P, with an algorithm consisting of n proteins described as: Cl x PI + C2 x P2 + C3 x P3 + ... + Cn x Pn. An algorithm that discriminates between disease states with a predetermined level of statistical significance may be refers to a "disease classifier". The development of the disease classifier, and the selection of markers (both blood and clinical parameters) may be based on a combination of accuracy, area under the curve (AUC), sensitivity, specificity values, and / or Youden index (Sensitivity+Specificity-1) that provide a measure of the performance of the disease classifier. Detecting and / or quantifying may be compared to a cut-off level (also referred to as a threshold). Cut-off values can be predetermined by analysing results from multiple patients and controls, and determining a suitable value for classifying a subject as with or without the disease. For example, for diseases where the level of biomarker is higher in patients suffering from the disease, then if the level detected is higher than the cut-off, the patient is indicated to suffer from the disease. Alternatively, for diseases where the level of biomarker is lower in patients suffering from the disease, then if the level detected is lower than the cut-off, the patient is indicated to suffer from the disease. The advantages of using simple cut-off values include the ease with which clinicians are able to understand the test and the elimination of any need for software or other aids in the interpretation of the test results. Cut-off levels can be determined using methods in the art.

[0212] In one embodiment, the level of biomarker (e.g. cell free nucleosomes and / or post- translational modifications as described herein) is higher in patients suffering from cancer. The level of cell free nucleosomes measured in the plasma sample may be compared to a cut-off level. In one embodiment, the cut off level is greater than about 30 ng / ml, 40 ng / ml, 50 ng / ml or 60 ng / ml, such as greater than about 65 ng / ml.

[0213] Detecting and / or quantifying may also be compared to a control. It will be clear to those skilled in the art that the control subjects may be selected on a variety of basis which may include, for example, subjects known to be free of the disease or may be subjects with a different disease (for example, for the investigation of differential diagnosis). The “control” may comprise a healthy subject, a non-diseased subject and / or a subject without a nodule. Comparison with a control is well known in the field of diagnostics.

[0214] Therefore, in one embodiment, the method additionally comprises comparing the level of said cell free nucleosomes in said body fluid sample with one or more controls. For example, the method may comprise comparing the level of cell free nucleosomes present in a body fluid sample obtained from the subject with the level of cell free nucleosomes present in a body fluid sample obtained from a normal subject. The control may be a healthy subject. Alternatively, the control may be a diseased subject, such as subject with an infection.

[0215] In one embodiment, the level of cell free nucleosomes is elevated compared to the control.

[0216] It will be understood that it is not necessary to measure controls levels for comparative purposes on every occasion. For example, for healthy / non-diseased controls, once the ‘normal range’ is established it can be used as a benchmark for all subsequent tests. A normal range can be established by obtaining samples from multiple control subjects without a nodule or a benign nodule and testing for the level of biomarker. Results ( / .e. biomarker levels) for subjects with a nodule can then be examined to see if they fall within, or outside of, the respective normal range (i.e. to determine if the nodule is malignant). Use of a ‘normal range’ is standard practice for the detection of disease.

[0217] As used herein, “healthy” refers to an individual in whom no evidence of cancer is found, i.e., the individual does not have a malignant nodule. Such an individual may be classified as “lung cancer-negative” or as having healthy lungs, or normal, non-compromised lung function. A healthy patient or subject has no symptoms of lung cancer or other lung disease. In some embodiments, a healthy patient or subject may be used as a reference patient for comparison to diseased or suspected diseased samples for determination of lung cancer in a patient or a group of patients.

[0218] As used herein, a “reference patient,” “reference subject,” or “reference group” refers to a group of patients or subjects to which a test sample from a patient or subject suspected of having or being at risk of harbouring cancer (e.g. a malignant nodule) may be compared. In some embodiments, such a comparison may be used to determine whether the test subject has a malignant nodule (e.g. lung cancer). A reference patient or group may serve as a control for testing or diagnostic purposes. As described herein, a reference patient or group may be a sample obtained from a single patient, or may represent a group of samples, such as a pooled group of samples.

[0219] In certain embodiments, the diagnostic methods disclosed herein can be used in combination with other clinical assessment methods, including for example various radiographic and / or invasive methods. Similarly, in certain embodiments, the diagnostic methods disclosed herein can be used to identify candidates for other clinical assessment methods, or to assess the likelihood that a subject will benefit from other clinical assessment methods.

[0220] Detecting and / or quantifying may be performed directly on the purified or enriched nucleosome sample, or indirectly on an extract therefrom, or on a dilution thereof. Quantifying the amount of the biomarker present in a sample may include determining the concentration of the biomarker present in the sample. Uses and methods of detecting, monitoring and of diagnosis according to the invention described herein are useful to confirm the existence of a disease, to monitor development of the disease by assessing onset and progression, or to assess amelioration or regression of the disease. Uses and methods of detecting, monitoring and of diagnosis are also useful in methods for assessment of clinical screening, prognosis, choice of therapy, evaluation of therapeutic benefit, i.e. for drug screening and drug development.

[0221] The detection or measurement may comprise an immunoassay, immunochemical, mass spectroscopy, chromatographic, chromatin immunoprecipitation or biosensor method. In particular, detection and / or measurement may comprise a 2-site immunoassay method for nucleosome moieties. Such a method is preferred for the measurement of nucleosomes or nucleosome incorporated epigenetic features in situ employing two anti-nucleosome binding agents or an anti-nucleosome binding agent in combination with an anti-histone modification or anti-histone variant or anti-DNA modification or anti-adducted protein detection binding agent. Also, detection and / or measurement may comprise a 2-site immunoassay employing a labelled anti-nucleosome detection binding agent in combination with an immobilized anti- histone modification or anti-histone variant or anti-DNA modification or anti-adducted protein binding agent.

[0222] The inventors herein used a 2-site immunoassay for H3.1 -nucleosomes employing an immobilized anti-histone H3.1 antibody directed to bind to an epitope around amino acids SO- 33 of the histone H3.1 protein to capture clipped and non-clipped nucleosomes, together with a labelled anti-nucleosome antibody directed to bind to an epitope present in intact nucleosomes but not present on isolated (free) histone or DNA nucleosome components. This type of epitope may be referred to as a “conformational nucleosome epitope” herein because it requires the native three-dimensional configuration of the target nucleosome to be intact.

[0223] In one embodiment, the method of detection or measurement comprises contacting the body fluid sample with a solid phase comprising a binding agent that detects cell free nucleosomes or a component thereof, and detecting binding to said binding agent.

[0224] In one embodiment, the method of detection or measurement comprises: (i) contacting the sample with a first binding agent which binds to an epigenetic feature of a cell free nucleosome; (ii) contacting the sample bound by the first binding agent in step (i) with a second binding agent which binds to cell free nucleosomes; and (iii) detecting or quantifying the binding of the second binding agent in the sample.

[0225] In another embodiment, the method of detection or measurement comprises: (i) contacting the sample with a first binding agent which binds to cell free nucleosomes; (ii) contacting the sample bound by the first binding agent in step (i) with a second binding agent which binds to an epigenetic feature of the cell free nucleosome; and (iii) detecting or quantifying the binding of the second binding agent in the sample.

[0226] In one embodiment, the method of detection or measurement comprises: (i) contacting the sample with a first binding agent which binds to particular histone isoform of a cell free nucleosome; (ii) contacting the sample bound by the first binding agent in step (i) with a second binding agent which binds to cell free nucleosomes; and (iii) detecting or quantifying the binding of the second binding agent in the sample.

[0227] In another embodiment, the method of detection or measurement comprises: (i) contacting the sample with a first binding agent which binds to cell free nucleosomes; (ii) contacting the sample bound by the first binding agent in step (i) with a second binding agent which binds to a particular histone isoform of the cell free nucleosome; and (iii) detecting or quantifying the binding of the second binding agent in the sample.

[0228] Detecting or measuring the level of the biomarker(s) may be performed using one or more reagents, such as a suitable binding agent. For example, the one or more binding agents may comprise a ligand or binder specific for the desired biomarker, e.g. nucleosomes or component part thereof, an epigenetic feature of a nucleosome, a structural / shape mimic of the nucleosome or component part thereof, optionally in combination with one or more interleukins.

[0229] It will be clear to those skilled in the art that the terms “antibody”, “binder” or “ligand” as used herein are not limiting but are intended to include any binder capable of binding to particular molecules or entities and that any suitable binder can be used in the method of the invention. It will also be clear that the term “nucleosomes” is intended to include mononucleosomes and oligonucleosomes and any protein-DNA chromatin fragments that can be analysed in fluid media. In one embodiment, the binding agent, such as the antibody, specifically binds to the target biomarker. The specificity of an antibody is the ability of the antibody to recognize a particular antigen as a unique molecular entity and distinguish it from another. An antibody that “specifically binds” to an antigen or an epitope is a term well understood in the art. A molecule is said to exhibit “specific binding” if it reacts more frequently, more rapidly, with greater duration and / or with greater affinity with a particular target antigen or epitope, than it does with alternative targets. An antibody “specifically binds” to a target antigen or epitope if it binds with greater affinity, avidity, more readily, and / or with greater duration than it binds to other substances. Methods of detecting biomarkers are known in the art. The reagents may comprise one or more ligands or binders, for example, naturally occurring or chemically synthesised compounds, capable of specific binding to the desired target. A ligand or binder may comprise a peptide, an antibody or a fragment thereof, or a synthetic ligand such as a plastic antibody, or an aptamer or oligonucleotide, capable of specific binding to the desired target. The antibody can be a monoclonal antibody or a fragment thereof. It will be understood that if an antibody fragment is used then it retains the ability to bind the biomarker so that the biomarker may be detected (in accordance with the present invention). A ligand / binder may be labelled with a detectable marker, such as a luminescent, fluorescent, enzyme or radioactive marker; alternatively or additionally a ligand according to the invention may be labelled with an affinity tag, e.g. a biotin, avidin, streptavidin or His (e.g. hexa-His) tag. Alternatively, ligand binding may be determined using a label-free technology for example that of ForteBio Inc.

[0230] The term “detecting” or “diagnosing” as used herein encompasses identification, confirmation, and / or characterisation of a disease state. Methods of detecting, monitoring and of diagnosis according to the invention are useful to confirm the existence of a disease, to monitor development of the disease by assessing onset and progression, or to assess amelioration or regression of the disease. Methods of detecting, monitoring and of diagnosis are also useful in methods for assessment of clinical screening, prognosis, choice of therapy, evaluation of therapeutic benefit, i.e. for drug screening and drug development.

[0231] In one embodiment, the method described herein is repeated on multiple occasions. This embodiment provides the advantage of allowing the detection results to be monitored over a time period. Such an arrangement will provide the benefit of monitoring or assessing the efficacy of treatment of a disease state. Such monitoring methods of the invention can be used to monitor onset, progression, stabilisation, amelioration, relapse and / or remission.

[0232] In monitoring methods, test samples may be taken on two or more occasions. The method may further comprise comparing the level of the biomarker(s) present in the test sample with one or more control(s) and / or with one or more previous test sample(s) taken earlier from the same test subject, e.g. prior to commencement of therapy, and / or from the same test subject at an earlier stage of therapy. The method may comprise detecting a change in the nature or amount of the biomarker(s) in test samples taken on different occasions.

[0233] A change in the level of the biomarker in the test sample relative to the level in a previous test sample taken earlier from the same test subject may be indicative of a beneficial effect, e.g. stabilisation or improvement, of said therapy on the disorder or suspected disorder. Furthermore, once treatment has been completed, the method of the invention may be periodically repeated in order to monitor for the recurrence of a disease.

[0234] Methods for monitoring efficacy of a therapy can be used to monitor the therapeutic effectiveness of existing therapies and new therapies in human subjects and in non-human animals {e.g. in animal models). These monitoring methods can be incorporated into screens for new drug substances and combinations of substances.

[0235] According to a further aspect, there is provided a method for monitoring the efficacy of a therapy in a subject having, suspected of having, or being predisposed to a malignant nodule, which comprises the steps of:

[0236] (i) contacting a body fluid sample obtained from the subject with a binding agent to detect or measure cell free nucleosomes; and

[0237] (ii) comparing the level of cell free nucleosomes detected with an earlier body fluid sample taken from said subject to determine the efficacy of said therapy.

[0238] In a further embodiment the monitoring of more rapid changes due to fast acting therapies may be conducted at shorter intervals of hours or days.

[0239] According to a further aspect, there is provided a kit comprising one or more reagents for carrying out the method as defined herein.

[0240] According to a further aspect, there is provide the use of a kit comprising one or more reagents to detect or measure the level of cell free nucleosomes or a component thereof, such as H3, 1 , to detect, monitor or diagnose a malignant nodule.

[0241] According to a further aspect of the invention, there is provided a kit to detect, monitor or diagnose a nodule in a subject, wherein said kit comprises a first binding agent which specifically binds to an epigenetic feature of a cell free nucleosome (e.g. H3K9Me3, H3K27Me3, H3K36Me3) and a second binding agent which specifically binds to cell free nucleosomes.

[0242] Diagnostic or monitoring kits (or panels) are provided for performing methods of the invention. Such kits will suitably comprise one or more ligands for detection and / or quantification of the biomarker according to the invention, and / or a biosensor, and / or an array as described herein, optionally together with instructions for use of the kit. A further aspect of the invention is a kit for detecting the presence of a disease state, comprising a biosensor capable of detecting and / or quantifying one or more of the biomarkers as defined herein. As used herein, the term “biosensor” means anything capable of detecting the presence of the biomarker. Examples of biosensors are described herein. Biosensors may comprise a ligand binder or ligands, as described herein, capable of specific binding to the biomarker. Such biosensors are useful in detecting and / or quantifying a biomarker of the invention.

[0243] Suitably, biosensors for detection of one or more biomarkers combine biomolecular recognition with appropriate means to convert detection of the presence, or quantitation, of the biomarker in the sample into a signal. Biosensors can be adapted for "alternate site" diagnostic testing, e.g. in the ward, outpatients’ department, surgery, home, field and workplace. Biosensors to detect one or more biomarkers of the invention include acoustic, plasmon resonance, holographic, Bio-Layer Interferometry (BLI) and microengineered sensors. Imprinted recognition elements, thin film transistor technology, magnetic acoustic resonator devices and other novel acousto-electrical systems may be employed in biosensors for detection of the one or more biomarkers.

[0244] Biomarkers for detecting the presence of a disease are essential targets for discovery of novel targets and drug molecules that retard or halt progression of the disorder. As the level of the biomarker is indicative of disorder and of drug response, the biomarker is useful for identification of novel therapeutic compounds in in vitro and / or in vivo assays. Biomarkers described herein can be employed in methods for screening for compounds that modulate the activity of the biomarker.

[0245] Thus, in a further aspect of the invention, there is provided the use of a binder or ligand, as described, which can be a peptide, antibody or fragment thereof or aptamer or oligonucleotide directed to a biomarker according to the invention; or the use of a biosensor, or an array, or a kit according to the invention, to identify a substance capable of promoting and / or of suppressing the generation of the biomarker.

[0246] The immunoassays described herein include any method employing one or more antibodies or other specific binders directed to bind to the biomarkers defined herein. Immunoassays include 2-site immunoassays or immunometric assays employing enzyme detection methods (for example ELISA), fluorescence labelled immunometric assays, time-resolved fluorescence labelled immunometric assays, chemiluminescent immunometric assays, immunoturbidimetric assays, particulate labelled immunometric assays and immunoradiometric assays as well as single-site immunoassays, reagent limited immunoassays, competitive immunoassay methods including labelled antigen and labelled antibody single antibody immunoassay methods with a variety of label types including radioactive, enzyme, fluorescent, time-resolved fluorescent and particulate labels. All of said immunoassay methods are well known in the art, see for example Salgame et al. (1997) and van Nieuwenhuijze et al. (2003).

[0247] Identifying, detecting and / or quantifying can be performed by any method suitable to identify the presence and / or amount of a specific protein in a biological sample from a subject or a purification or extract of a biological sample or a dilution thereof. In particular, quantifying may be performed by measuring the concentration of the target in the sample or samples. Biological samples that may be tested in a method of the invention include those as defined hereinbefore. The samples can be prepared, for example where appropriate diluted or concentrated, and stored in the usual manner. The present invention finds particular use in plasma samples which may be obtained from the subject.

[0248] Identification, detection and / or quantification of biomarkers may be performed by detection of the biomarker or of a fragment thereof, e.g. a fragment with C-terminal truncation, or with N- terminal truncation. Fragments are suitably greater than 4 amino acids in length, for example 5, 6, 7, 8, 9, 10, 11 , 12, 13, 14, 15, 16, 17, 18, 19, or 20 amino acids in length. It is noted in particular that peptides of the same or related sequence to that of histone tails are particularly useful fragments of histone proteins.

[0249] For example, detecting and / or quantifying can be performed by one or more method(s) selected from the group consisting of: SELDI (-TOF), MALDI (-TOF), a 1-D gel-based analysis, a 2-D gel-based analysis, Mass spec (MS), reverse phase (RP) LC, size permeation (gel filtration), ion exchange, affinity, HPLC, LIPLC and other LC or LC MS-based techniques. Appropriate LC MS techniques include ICAT® (Applied Biosystems, CA, USA), or iTRAQ® (Applied Biosystems, CA, USA). Liquid chromatography (e.g. high pressure liquid chromatography (HPLC) or low pressure liquid chromatography (LPLC)), thin-layer chromatography, NMR (nuclear magnetic resonance) spectroscopy could also be used.

[0250] Methods involving detection and / or quantification of one or more biomarkers of the invention can be performed on bench-top instruments, or can be incorporated onto disposable, diagnostic or monitoring platforms that can be used in a non-laboratory environment, e.g. in the physician’s office or at the subject’s bedside. Suitable biosensors for performing methods of the invention include “credit” cards with optical or acoustic readers. Biosensors can be configured to allow the data collected to be electronically transmitted to the physician for interpretation and thus can form the basis for e-medicine.

[0251] Biomarker-based tests provide a first line assessment of ‘new’ subjects, and provide objective measures for accurate and rapid diagnosis, not achievable using the current measures.

[0252] Biomarker monitoring methods, biosensors and kits are also vital as subject monitoring tools, to enable the physician to determine whether relapse is due to worsening of the disorder. If pharmacological treatment is assessed to be inadequate, then therapy can be reinstated or increased; a change in therapy can be given if appropriate. As the biomarkers are sensitive to the state of the disorder, they provide an indication of the impact of drug therapy.

[0253] References to “subject”, “individual” or “patient” are used interchangeably herein. The subject may be a human or an animal subject. In one embodiment, the subject is a human. In one embodiment, the subject is a (non-human) animal. In some embodiments the invention encompasses animal subjects (wild or domesticated). In some embodiments, the invention relates to veterinary uses including for livestock and companion animals such as cats, dogs, horses, donkeys, rats, rabbits, mice, guinea pigs, sheep, goats, pigs, deer, llamas, cows and cattle. In one embodiment, the subject is a non-human mammal, such as a dog, mouse, rat or horse, in particular a dog.

[0254] The use, panels and methods described herein may be performed in vitro, in vivo or ex vivo. The methods described herein are preferably performed in vitro. References to acts carried out on a body fluid sample “obtained” from a subject are intended to encompass acts carried only a body fluid sample already obtained of “obtainable” from a subject and vice versa.

[0255] The methods described herein are preferably performed in vitro. References to acts carried out on a body fluid sample “obtained” from a subject are intended to encompass acts carried only a body fluid sample already obtained of “obtainable” from a subject and vice versa.

[0256] Methods and biomarkers described herein may be used to identify if a patient is in need of a biopsy. Therefore, according to a further aspect of the invention there is provided a method of identifying a patient in need of a biopsy comprising obtaining a body fluid sample from said patient, detecting the level of cell free nucleosomes in the body fluid sample, and using the results obtained to identify whether the patient is in need of a biopsy. According to a further aspect of the invention there is provided a method of identifying a patient in need of a biopsy comprising obtaining a body fluid sample from said patient, applying the sample to a panel test as defined herein, and using the results obtained from the panel test to identify whether the patient is in need of a biopsy.

[0257] Additional biomarkers

[0258] The level of cell free nucleosomes may be detected or measured as one of a panel of measurements. The panel may comprise different epigenetic features of the nucleosome as described hereinbefore (e.g. a histone isoform and a PTM).

[0259] In one embodiment, the panel comprises one or more cytokines, such as one or more interleukins.

[0260] Interleukins (ILs) are a group of cytokines, usually secreted by leukocytes, that act as signal molecules. They have key roles in stimulating immune responses and inflammation. They were first identified in the 1970s and have been designated numerically as more interleukin types have been discovered. Examples of interleukins include, but are not limited to: IL-1 , IL- 2, IL-3, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL-10, IL-11 , IL-12, IL-13, IL-14 and IL-15.

[0261] In one embodiment, the one or more interleukins is selected from the group consisting of: lnterleukin-6 (IL-6), Interleukin-10 (IL-10) and lnterleukin-1 p (IL-1 P).

[0262] The interleukin may be IL-6, lnterleukin-6 (IL-6) is a cytokine with a wide variety of biological functions. It is a potent inducer of fever and the acute phase response. The sequence of human IL-6 is known in the art and is described at UniProt Accession No. P05231. In one particular embodiment, the interleukin may be IL-6 and the panel of measurements may comprise measurement of histone isoform H3.1 and IL-6.

[0263] Alternatively, or additionally, the interleukin may be IL-10. Interleukin-10 (IL-10) is an antiinflammatory cytokine with a wide variety of biological functions. The sequence of human IL- 10 is known in the art and is described at UniProt Accession No. P22301. In one particular embodiment, the interleukin may be IL-10 and the panel of measurements may comprise measurement of histone post-translational modification H3cit and IL-10.

[0264] Alternatively, or additionally, the interleukin may be IL-i p. lnterleukin-1 p (IL-1P) is a pro- inflammatory cytokine and is involved in a variety of cellular activities, including cell proliferation, differentiation, and apoptosis. In one particular embodiment, the interleukin may be IL-1 p and the panel of measurements may comprise measurement of histone isoform H3.1 and IL-ip.

[0265] In one embodiment, the panel comprises an epigenetic feature of a cell free nucleosome and an interleukin. In another embodiment, the panel comprises an epigenetic feature of a cell free nucleosome and two interleukins. For example, the cell free nucleosome measurement can be combined with more than one interleukin measurement, such as IL-6 and IL-1 or IL-10 and IL-1 p or IL-6 and IL-10. In a further embodiment, the epigenetic feature of a cell free nucleosome is selected from a histone isoform, such as H3.1 , and a post translationally modified histone. In a yet further embodiment, the panel of measurements is H3.1 , IL-6 and IL-ip.

[0266] It will be clear to those skilled in the art, that any combination of the biomarkers disclosed herein may be used in panels and algorithms for the detection of a benign or malignant nodule, and that further markers may be added to a panel including these markers.

[0267] According to an aspect of the invention there is provided the use of a panel test to assess a patient with a nodule, wherein the panel test comprises reagents to detect measurements of nucleosomes or a component thereof and one or more interleukins and / or epigenetic feature(s), in a body fluid sample obtained from the patient.

[0268] Methods of Treatment

[0269] According to another aspect of the invention there is provided a method of treatment for lung cancer comprising identifying a patient in need of treatment for lung cancer using a panel test of the invention and providing said treatment in a body fluid sample obtained from the patient.

[0270] In one embodiment, the method additionally comprises performing one or more scanning methods on the subject. For example, the scanning method may be LDCT.

[0271] Treatments available for lung cancer include surgery (including biopsy), radiotherapy (including brachytherapy), hormone therapy, immunotherapy, as well as a variety of drug treatments for use in chemotherapy. In one embodiment, the treatment(s) administered are selected from: surgery, radiotherapy, hormone therapy, immunotherapy and / or chemotherapy.

[0272] Methods of the invention are able to distinguish between subjects with cancer and subjects with a non-malignant nodule. Therefore, in one aspect the diagnosis comprises differential diagnosis of a patient with lung cancer from a patient with a non-malignant (i.e. benign) nodule. Methods of patient assessment

[0273] The invention finds particular use in assessing whether a patient requires further investigation for the cancer (e.g. for diagnosis and / or identification of organ location). Such procedures, including PET scans, other scans and biopsies are invasive or potentially hazardous and are relatively costly to healthcare providers. Therefore there is a need to reduce the number of patients sent for unnecessary investigations. For example, this aspect of the invention will be useful for assessing persons with a nodule in need of a biopsy. Therefore, according to a further aspect of the invention, there is provided a method of assessing if a patient requires further testing for cancer, comprising: detecting or measuring the level of biomarkers of the invention in a body fluid sample obtained from the patient; and using the level detected in the body fluid sample to determine if the patient requires further testing for cancer.

[0274] In one embodiment, the cancer is lung cancer (in particular if the nodule is a pulmonary nodule). In one embodiment, the further test for lung cancer is a lung biopsy.

[0275] In one embodiment, the patient has a pulmonary nodule. This may have been identified, for example, by an LDCT scan.

[0276] According to a further aspect of the invention there is provided a method of identifying a patient in need of a LDCT scan comprising applying a body fluid sample obtained from the patient to a panel test as defined herein, and using the results obtained from the panel test to identify whether the patient is in need of a scan.

[0277] In one embodiment, the method described herein is repeated on multiple occasions. This embodiment provides the advantage of allowing the detection results to be monitored over a time period. Such an arrangement will provide the benefit of monitoring or assessing the efficacy of treatment of a disease state. Such monitoring methods of the invention can be used to monitor onset, progression, stabilisation, amelioration, relapse and / or remission.

[0278] Thus, the invention also provides a method of monitoring efficacy of a therapy for a disease state in a subject, suspected of having such a disease, comprising detecting and / or quantifying the biomarker (e.g. biomarker panel described herein) present in a biological sample from said subject. In monitoring methods, test samples may be taken on two or more occasions. The method may further comprise comparing the level of the biomarker(s) present in the test sample with one or more control(s) and / or with one or more previous test sample(s) taken earlier from the same test subject, e.g. prior to commencement of therapy, and / or from the same test subject at an earlier stage of therapy. The method may comprise detecting a change in the nature or amount of the biomarker(s) in test samples taken on different occasions.

[0279] Thus, according to a further aspect of the invention, there is provided a method for monitoring efficacy of therapy for a disease state in a human or animal subject, comprising:

[0280] (a) quantifying the panel biomarkers as defined herein; and

[0281] (b) comparing the panel result in a test sample with that of one or more control(s) and / or one or more previous test sample(s) taken at an earlier time from the same test subject.

[0282] A change in the biomarker result in the test sample relative to the level in a previous test sample taken earlier from the same test subject may be indicative of a beneficial effect, e.g. stabilisation or improvement, of said therapy on the disorder or suspected disorder. Furthermore, once treatment has been completed, the method of the invention may be periodically repeated in order to monitor for the recurrence of a disease.

[0283] Methods for monitoring efficacy of a therapy can be used to monitor the therapeutic effectiveness of existing therapies and new therapies in human subjects and in non-human animals (e.g. in animal models). These monitoring methods can be incorporated into screens for new drug substances and combinations of substances.

[0284] In a further embodiment, the monitoring of more rapid changes due to fast acting therapies may be conducted at shorter intervals of hours or days.

[0285] Kits and Panel tests

[0286] According to a further aspect of the invention there is provided a kit comprising one or more reagents for carrying out a method as described herein.

[0287] According to a further aspect of the invention there is provided the use of the kit as defined herein to identify a patient in need of treatment for cancer. In particular, the kit may be used for identifying a patient in need of treatment for lung cancer, if the nodule is a pulmonary nodule and is determined to be malignant according to methods of the invention. According to a further aspect of the invention there is provided the use of the kit as defined herein to monitor a patient for progression of cancer (e.g. further growth of the nodule, or advancement to a different stage of cancer). Embodiments of this aspect include use to detect disease progression in watchful waiting, active surveillance and monitoring post-surgery or other treatment for relapse.

[0288] According to a further aspect of the invention there is provided the use of the kit as defined herein to evaluate the effectiveness of a cancer treatment in a patient.

[0289] According to a further aspect of the invention there is provided the use of the kit as defined herein to select a treatment for a patient with a malignant nodule.

[0290] In other embodiments the kit may include reagents to detect total nucleosome levels and / or epigenetic features of a nucleosome (e.g. post-translational modification-levels, such as H3K9Me3, H3K27Me3 and H3K36Me3).

[0291] The kit may comprise reagents for one or more additional biomarkers as described herein. For example, in one embodiment, the kit additionally comprises a reagent to detect the level of CRP, CYFRA (e.g. CYFRA21-1) or CEA.

[0292] The invention will now be illustrated with reference to the following non-limiting examples.

[0293] EXAMPLES

[0294] EXAMPLE 1 :

[0295] Methods

[0296] A total of 806 individuals who had a positive result from LDCT screening and underwent surgery or biopsy were included in the study. Plasma samples from these participants were analyzed for nucleosomes containing histone modifications including H3K27Me3, H3K36Me3, or the H3.1 histone isoform by quantitative immunoassay (Nu.Q®, Belgian Volition SRL). Among these individuals, 648 were diagnosed with either lung cancer or a pre-cancerous lesion, while 158 were diagnosed with a benign lesion based on pathological reports. Logistic regression was used to analyze the assay data, and a simple algorithm was developed to predict whether a nodule was benign or malignant. The algorithm was created using samples from a first group (n=561) as a training set and validated using samples from a second group (n=245). Table 3: Diagnostic groups within sample groups

[0297] Results

[0298] The plasma epigenetic nucleosome assay for detecting lung cancer showed a diagnostic sensitivity of 92% and a specificity of 82% using a simple regression algorithm, trained on data from the first group. The AUG for differentiating between cancerous and benign nodules was 88%. When the assay was applied to data from the second group, it achieved an AUG of 77% for distinguishing between cancer and benign nodules, with a sensitivity of 60% and a specificity of 84%. The validation test revealed that the algorithm correctly identified patients with stage I cancer and carcinoma in situ in 58% and 61 % of cases, respectively.

[0299] Conclusions

[0300] This large validation study indicates that the epigenetic nucleosome assay has predictive, diagnostic, and prognostic value and could reduce the false-positive rate of LDCT.

[0301] EXAMPLE 2:

[0302] Plasma samples from the participants (n=806) discussed in Example 1 were analyzed for nucleosomes containing histone modification H3K27Me3 and the H3.1 histone isoform by guantitative immunoassay (Nu.Q®, Belgian Volition SRL). Logistic regression was used to analyze the assay data, and an algorithm was developed to predict whether a nodule was benign or malignant. The algorithm was created using samples from a first group (n=483) as a training set and validated using samples from a second group (n=121). The algorithm was then tested and parameters were reported using an independent validation and test data set (n=202). Table 4: Model parameters for H3.1 + H3K27Me3 panel

[0303] The results obtained from the test dataset are summarised in Table 5. A threshold was calculated on the validation dataset by finding the best fit to Youden’s index at more than 80% sensitivity. Patients with a score above the threshold are determined as having a malignant nodule using this model. Different thresholds could be chosen using the Youden index, depending on the desired sensitivity. The AUG for differentiating between cancerous and benign nodules using this model was 80%, as shown in Figure 22. Table 5: Results obtained using H3.1 + H3K27Me3 panel

Claims

CLAIMS1. Use of a cell free nucleosome as a biomarker in a body fluid sample, for determining that a nodule in a subject is benign.

2. Use of a cell free nucleosome as a biomarker in a body fluid sample, for distinguishing benign nodules from malignant nodules in a subject.

3. The use as defined in any preceding claim, wherein the nodule is a pulmonary nodule.

4. The use as defined in any preceding claim, wherein the nodule is an indeterminate nodule.

5. The use as defined in any preceding claim, wherein the nodule has a diameter of less than or equal to about 2cm or less than or equal to about 3 cm.

6. The use as defined in claim 5, wherein the pulmonary nodule has a diameter of about 0.8 cm or about 1 cm to about 2 cm or about 3 cm.

7. The use as defined in any preceding claim, wherein the nodule is a malignant lung cancer nodule.

8. The use as defined in any preceding claim, wherein the nodule is a malignant nodule selected from squamous cell carcinoma, adenocarcinoma, adenosquamous carcinoma, pleomorphic carcinoma or small cell carcinoma.

9. The use as defined in either of claims 7 or 8, wherein the malignant nodule is or is part of a cancer which is solid, micropapillary, lepidic, acinar or papillary.

10. The use as defined in any preceding claim, wherein the nodule is pre-cancer, minimally invasive cancer, Stage I (including Stage IA1, Stage IA2, Stage IA3 or Stage IB), Stage II, Stage III or Stage IV cancer.

11. The use as defined in any preceding claim, wherein the determination or distinction is conducted in conjunction with an imaging technique.

12. The use as defined in claim 11 , wherein the imaging technique is a computed tomography (CT) scan, an X-ray or a positron emission tomography (PET) scan.

13. The use as defined in claim 12, wherein the CT scan is a low dose computed tomography (LDCT) scan.

14. The use as defined in any one of claims 11 to 13, wherein the use is subsequent to the subject undergoing an imaging technique.

15. The use as defined in any preceding claim, wherein the subject is a smoker, an exsmoker or a non-smoker.

16. The use as defined in any preceding claim, wherein the cell free nucleosome is a mononucleosome or oligonucleosome.

17. The use as defined in any preceding claim, wherein the biomarker is the presence or level of cell free nucleosomes and / or an epigenetic feature of a cell free nucleosome.

18. The use as defined in claim 17, wherein the epigenetic feature of the cell free nucleosome is a histone isoform, such as a histone isoform of a core nucleosome, in particular a histone H3 isoform.

19. The use as defined in claim 18, wherein the histone isoform is H3.1.

20. The use as defined in claim 17, wherein the epigenetic feature of the cell free nucleosome is a histone post translational modification (PTM), such as a histone PTM of a core nucleosome, in particular a histone H3 PTM.21 . The use as defined in claim 20, wherein the histone PTM is methylation.

22. The use as defined in claim 21 , wherein the histone PTM is H3K9Me3, H3K27Me3 or H3K36Me3, in particular H3K27Me3.

23. The use as defined in any preceding claim, which comprises a panel of two or more biomarkers, such as the level or presence of two or more of H3.1 , H3K9Me3, H3K27Me3 and H3K36Me3.

24. The use as defined in any preceding claim, which comprises a panel comprising the cell free nucleosome and one of more of carcinoembryonic antigen (CEA), cytokeratin fragments (CYFRA21-1) and C-reactive protein (CRP).

25. The use as defined in claim 23 or claim 24, wherein the panel is:H3.1 and H3K27Me3;H3.1 , rH3K9Me3 and rH3K36Me3;H3.1 , H3K9Me3, H3K36Me3 and CEA;H3.1 , rH3K9Me3, rH3K27Me3 and rH3K36Me3;H3.1 , rH3K9Me3, rH3K27Me3, rH3K36Me3 and CEA; rH3K9Me3 and rH3K36Me3; rH3K9Me3, rH3K36Me3 and CEA;H3.1 and rH3K9Me3;H3.1 and rH3K27Me3;H3.1 , H3K27Me3 and H3K36Me3;H3K27Me3 and H3K36Me3;HeK27Me3, H3K36Me3 and CEA;H3.1 , H3K9Me3, H3K27Me3 and H3K36Me3;H3K9Me3, H3K27Me3 and H3K36Me3; orH3K9Me3, H3K27Me3, H3K36Me3 and CEA; wherein “r” denotes that the level of histone PTM is reported as a proportion of cell free nucleosomes present in the sample.

26. The use as defined in any preceding claim, wherein the benign nodule is associated with anthracosis, hamartoma, fibrosis or an infection such a cryptococcosis or granulomatous inflammation.

27. A method for determining that a nodule in a subject is benign, which comprises the steps of:(i) contacting a body fluid sample obtained from the subject with a binding agent to detect or measure cell free nucleosomes; and(ii) using the level of cell free nucleosomes detected to determine the nodule is benign.

28. A method of distinguishing benign from malignant nodules in a subject with indeterminate nodules, which comprises the steps of:(i) contacting a body fluid sample obtained from the subject with a binding agent to detect or measure cell free nucleosomes; and(ii) using the level of cell free nucleosomes detected to determine the nodule is benign.

29. A method of determining the likelihood that a nodule in a subject is not malignant, comprising:(i) contacting a body fluid sample obtained from the subject with a binding agent to detect or measure cell free nucleosomes; and(ii) using the level of cell free nucleosomes detected to determine whether the nodule is not malignant.

30. A method of determining the likelihood that a nodule in a subject is not malignant, comprising:(i) measuring the presence or levels of cell free nucleosomes present in a body fluid sample obtained from the subject;(ii) calculating a probability of cancer score based on the presence or levels of the cell free nucleosomes measured in step (a); and(iii) ruling out cancer for the subject if the score in step (b) is lower than a predetermined score.

31. The method as defined in any one of claims 27 to 30, wherein the nodule is a pulmonary nodule.

32. The method as defined in any one of claims 27 to 31, wherein the nodule has a diameter of less than or equal to about 2cm or less than or equal to about 3 cm.

33. The method as defined in claim 32, wherein the pulmonary nodule has a diameter of about 0.8 cm or about 1 cm to about 2 cm or about 3 cm.

34. The method as defined in any one of claims 27 to 33, wherein the benign nodule is associated with anthracosis, hamartoma, fibrosis or an infection such a cryptococcosis or granulomatous inflammation.

35. The method as defined in any one of claims 27 to 34, wherein the nodule is a malignant lung cancer nodule.

36. The method as defined in any one of claims 27 to 35, wherein the nodule is a malignant nodule selected from squamous cell carcinoma, adenocarcinoma, adenosquamous carcinoma, pleomorphic carcinoma or small cell carcinoma.

37. The method as defined in either of claims 35 or 36, wherein the malignant nodule is or is part of a cancer which is solid, micropapillary, lepidic, acinar or papillary.

38. The method as defined in any one of claims 27 to 37, wherein the malignant nodule is pre-cancer, minimally invasive cancer, Stage I (including Stage IA1 , Stage IA2, Stage IA3 or Stage IB), Stage II, Stage III or Stage IV.

39. The method as defined in any one of claims 27 to 38, wherein the determination is conducted in combination with an imaging technique.

40. The method as defined in claim 39, wherein the imaging technique is a computed tomography (CT) scan, an X-ray or a positron emission tomography (PET) scan.

41. The method as defined in claim 40, wherein the CT scan is a low does computed tomography (LDCT) scan.

42. The method as defined in any one of claims 27 to 41 , wherein the presence or level of cell free nucleosomes measured is an epigenetic feature of a cell free nucleosome.

43. The method as defined in claim 42, wherein the epigenetic feature of the cell free nucleosome is a histone isoform, such as a histone isoform of a core nucleosome, in particular a histone H3 isoform.

44. The method as defined in claim 43, wherein the histone isoform is H3.1 .

45. The method as defined in claim 42, wherein the epigenetic feature of the cell free nucleosome is a histone post translational modification (PTM), such as a histone PTM of a core nucleosome, in particular a histone H3 PTM.

46. The method as defined in claim 45, wherein the histone PTM is methylation.

47. The method as defined in claim 46, wherein the histone PTM is H3K9Me3, H3K27Me3 or H3K36Me3, in particular H3K27Me3.

48. The method as defined in any one of claims 27 to 47, which comprises a panel of two or more biomarkers, such as the level or presence of two or more of H3.1, H3K9Me3, H3K27Me3 and H3K36Me3.

49. The method as defined in any one of claims 27 to 48, which comprises a panel comprising the cell free nucleosome and one of more of carcinoembryonic antigen (CEA), cytokeratin fragments (CYFRA21-1) and C-reactive protein (CRP).

50. The method as defined in claim 48 or claim 49, wherein the panel is:H3.1 and H3K27Me3;H3.1 , rH3K9Me3 and rH3K36Me3;H3.1 , H3K9Me3, H3K36Me3 and CEA;H3.1 , rH3K9Me3, rH3K27Me3 and rH3K36Me3;H3.1 , rH3K9Me3, rH3K27Me3, rH3K36Me3 and CEA; rH3K9Me3 and rH3K36Me3; rH3K9Me3, rH3K36Me3 and CEA;H3.1 and rH3K9Me3;H3.1 and rH3K27Me3;H3.1 , H3K27Me3 and H3K36Me3;H3K27Me3 and H3K36Me3;HeK27Me3, H3K36Me3 and CEA;H3.1 , H3K9Me3, H3K27Me3 and H3K36Me3;H3K9Me3, H3K27Me3 and H3K36Me3; orH3K9Me3, H3K27Me3, H3K36Me3 and CEA; wherein “r” denotes that the level of histone PTM is reported as a proportion of cell free nucleosomes present in the sample.

51. A method of distinguishing benign from malignant pulmonary nodules in a subject, comprising:(a) conducting imaging on the subject, such as a CT or LDCT scan on a subject;(b) obtaining a body fluid sample from the subject if the imaging identifies an indeterminate pulmonary nodule;(c) measuring the presence or levels of one or more biomarkers selected from the group consisting of: cell free nucleosomes containing H3.1, a H3K9Me3 core histone modification, a H3K27Me3 core histone modification and a H3K36Me3 core histone modification;wherein the amount of one or more of the biomarkers measured in step (c) distinguishes benign pulmonary nodules from malignant pulmonary nodules.

52. The method as defined in any one of claims 27 to 51 , wherein the level of cell free nucleosomes or component thereof is detected or measured using an immunoassay, immunochemical, mass spectroscopy, chromatographic, chromatin immunoprecipitation or biosensor method.

53. The method as defined in claim 52, wherein the detection or measurement employs a single binding agent.

54. The method as defined in claim 52, wherein the detection or measurement is a 2-site immunometric assay employing two binding agents.

55. The method as defined in claim 53 or claim 54, wherein the binding agent is an antibody.

56. The method as defined in any one of claims 27 to 55, wherein the method of detection or measurement step comprises contacting the sample with a solid phase comprising a binding agent that detects cell free nucleosomes or a component thereof, and detecting binding to said binding agent.

57. The method as defined in any one of claims 27 to 56, wherein the method of detection or measurement comprises: (a) contacting the sample with a first binding agent which binds to an epigenetic feature of a cell free nucleosome; (b) contacting the sample bound by the first binding agent in step (a) with a second binding agent which binds to cell free nucleosomes; and (c) detecting or quantifying the binding of the second binding agent in the sample.

58. The method as defined in any one of claims 27 to 57, wherein the subject is a human or an animal subject.

59. The method as defined in any one of claims 27 to 58, wherein the subject is at risk of developing lung cancer.

60. The method as defined in any one of claims 27 to 59, wherein the subject is a smoker, an ex-smoker or a non-smoker.

61. The method as defined in any one of claims 27 to 60, additionally comprising comparing the level of said cell free nucleosomes in said body fluid sample with one or more controls.

62. The method as defined in claim 61 , wherein the control is a healthy subject, optionally a healthy subject comprising a benign nodule.

63. The method as defined in any one of claims 61 to 62, wherein the level of cell free nucleosomes is elevated compared to the control.

64. The method as defined in any one of claims 27 to 63, wherein the body fluid sample is a blood, serum, plasma or cerebrospinal fluid (CSF) sample.

65. A kit comprising one or more reagents for carrying out the method as defined in any one of claims 27 to 64.

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