Detection of methylated lung cancer tumor DNA

Abstract

Disclosed are methods and a kit for monitoring disease state of a lung cancer, including non-small cell lung cancer. The methods include determination of methylation status of cell-free DNA, with methylation being representative of tumor burden. The method can also be used to monitor the efficacy of a cancer treatment regime.

Description

[0001] Attorney Docket No. P74736

[0002] DETECTION OF METHYLATED LUNG CANCER TUMOR DNA

[0003] The instant application contains a Sequence Listing which has been submitted electronically in XML file format and is hereby incorporated by reference in its entirety. Said XML copy, created on November 8, 2024, is named “V69765_SL.xml” and is 171,654 bytes in size.

[0004] RELATED APPLICATIONS

[0005] The present application claims priority to U. S. provisional application no. 63 / 719,487, filed November 12, 2024, the entire contents of which are hereby incorporated by reference.

[0006] FIELD OF INVENTION

[0007] This disclosure relates to detection of lung cancer tumors and methylated lung cancer tumor DNA, including in applications such as disease monitoring, assessment of treatment progression or relapse or tumor burden, detection of disease or disease risk, and / or diagnosis.

[0008] BACKGROUND

[0009] Lung cancer is responsible for the highest number of cancer-related deaths worldwide. One reason for this is that there continue to be detrimental delays in diagnosis and treatment initiation and treatment response practices due to the lack of an accurate and consistent detection methods, thus leading to poor prognosis. Moreover, there is a need for the development of a test with the ability to continually estimate and assess response to treatments and facilitate long-term monitoring of tumor burden.

[0010] Lung cancer is a disease resulting from a series of mutations within cells of the lungs that result in uncontrolled mitotic divisions or issues with apoptosis mechanisms, subsequently forming cancerous tumors (Clinic, 2022).

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[0012] There are two main types of lung cancer: small cell lung cancer (SCLC) and nonsmall cell lung cancer (NSCLC), both originally named based on their microscopic morphological characteristics (Clinic, 2022; Public Health Agency of Canada, 2019). The former, SCLC, grows quickly, typically presents as a relatively small tumor with small, round cells, and accounts for 10-15% of all lung cancer cases (Clinic, 2022). The latter, NSCLC, is the most common type of lung cancer, accounting for 80-85% of all lung cancer diagnoses and primarily consists of carcinomas.

[0013] Currently, most plasma-based liquid biopsies primarily detect mutations in cell-free DNA (cfDNA) and circulating tumor DNA (ctDNA) to diagnose the presence and characteristics of NSCLC tumors (Ansari et al.., 2016; Nooreldeen & Bach, 2021), rather than methylation. The release of ctDNA into the blood occurs through a combination of necrosis, apoptosis, and secretion and once assayed and identified, the typical genetic alterations being analyzed in ctDNA include point mutations, chromosomal rearrangements, and copy number variations (Nooreldeen & Bach, 2021). Since cfDNA may also be released by normal cells, specific genomic alterations (e.g. mutations in oncogenes and tumor suppressor genes, epigenetic changes, gene amplifications) enable the discrimination between ctDNA and cfDNA as these alterations are commonly found in the genome of cancer cells (Hie & Hofman, 2016; Santarpia et al., 2018). In this capacity, researchers typically look at the ctDNA for mutations in biomarkers commonly associated with NSCLC such as EGFR, ALK, and BRAF (Liu et al., 2020). The use of quantitative PCR (qPCR) assays has shown promise in the diagnostic capabilities of cfDNA measurements such as in the study conducted by Sozzi et al. (2003) in which they were able to discriminate between NSCLC patients and healthy subjects based on the higher levels of cfDNA in the NSCLC patients. Moreover, since the half-life of ctDNA in plasma ranges from 16 minutes to 2.5 hours, ctDNA harbors the

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[0015] potential for monitoring treatment response using real-time information from the tumor (Elazezy & Joosse, 2018).

[0016] Additionally, the measurement of cfDNA levels in plasma has shown clear promise for prognostic applications, along with recurrence predictions (Santarpia et al., 2018). For instance, a study consisting of 1035 heavy smokers found that a higher amount of cfDNA at the time of diagnosis indicated a more aggressive disease and individuals were associated with a poorer 5-year survival prognosis (Santarpia et al., 2018). Additionally, another study demonstrated that lung cancer patients who relapsed post-operatively demonstrated significantly higher cfDNA concentrations than individuals who remained disease-free (Santarpia et al., 2018).

[0017] It has been shown that 50% of individuals with Stage I NSCLC disease may have detectable levels of cfDNA and 100% of patients with Stages II-IV NSCLC have detectable quantities (Santarpia et al., 2018). ctDNA can be detected at a tumor burden of 5 x 106cells via liquid biopsies compared to the burden of 109tumor cells required for detection by PET scans, one of the conventional imaging methods for tumor monitoring (Hie & Hofrnan, 2016; Ma et al., 2019; Nooreldeen & Bach, 2021). Thus ctDNA detection may, unlike CT or MRI scans which present with three to six-month delays, provide patients more timely information regarding tumor status (Ma et al., 2019). The use of liquid biopsy methods may also enable the differentiation between pseudoprogression and progressive disease through tumor burden analysis, a task which cannot be adequately completed through conventional CT or MRI scans (Ma et al., 2019).

[0018] Prior methods of detection of NSCLC and corresponding treatment response are undesirable due to various reasons such as high cost, radiation exposure, and invasiveness. As such, a non-invasive, low cost procedure is desired for monitoring and / or diagnosing lung cancer, including, e.g., NSCLC.

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[0020] SUMMARY

[0021] The methylation of CpG islands within circulating tumor DNA (ctDNA) act as distinct biomarkers of molecular response. The methods disclosed herein have been demonstrated to be sensitive and specific in the detection and monitoring of lung cancer, including NSCLC and SCLC. This assay was developed and validated using NSCLC cell lines to ensure specificity. In an embodiment, assays as described herein may employ a next generation sequencing approach to identify tumor specific methylated CpG residues. The assays described herein can be used in a clinical setting to monitor disease state, assess treatment progression or relapse or tumor burden, detection of disease or disease risk, and / or diagnosis, for example.

[0022] In a first embodiment, the disclosure includes a method of determining methylation status in a lung cancer patient, the method comprising amplifying a plurality of target regions of DNA extracted from a cell -free sample obtained from the lung cancer patient, generating sequencing reads from each of the plurality of amplified target regions; determining a methylation status of two or more adjacent CpG residues within each of the plurality of target regions for each of the sequencing reads; wherein, with reference to Human Genome version 19, the plurality of target regions is a plurality of target regions found within the group consisting of

[0023] ADCY5 chr3:123167226-123167572,

[0024] CD200 chr3: 112051941-112052455,

[0025] CHST8 chrl9: 34112683-34113010,

[0026] CPLX2 chr5: 175298564-175299035,

[0027] GATA3 chrlO: 8095484-8095687,

[0028] GEM chr8: 95246514-95246814,

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[0030] H0XA7 chr7: 27195602-27196153,

[0031] KCNS2 chr8: 99439441-99439685,

[0032] KLF14 chr7: 130418549-130419057,

[0033] LINC014 chrlO: 101279944-101280256,

[0034] LOC101 chr5: 77268452-77268888,

[0035] MDFI chr6: 41606439-41606770,

[0036] MIR129-2 chrll: 43602795-43602929,

[0037] PAX5 chr9: 37037526-37038053,

[0038] PAX6 chr 11: 31837379-31838724,

[0039] PITX2 chr4: 111544016-111544299,

[0040] PRKC8 chrl6: 23847325-23847675,

[0041] SLC30A10 chrl: 220101728-220101962,

[0042] TSHZ3 chrl9: 31841391-31841663, and

[0043] TWIST1 chr7: 19156621-19157193,

[0044] wherein, with reference to Human Genome version 19, at least one of the plurality of target regions is found within the group consisting of

[0045] ADCY5 chr3:123167226-123167572

[0046] CD200 chr3: 112051941-112052455

[0047] CHST8 chrl9: 34112683-34113010

[0048] CPLX2 chr5: 175298564-175299035

[0049] GATA3 chrlO: 8095484-8095687,

[0050] GEM chr8: 95246514-95246814,

[0051] KCNS2 chr8: 99439441-99439685,

[0052] KLF14 chr7: 130418549-130419057,

[0053] LINC014 chrlO: 101279944-101280256,

[0054] LOC101 chr5: 77268452-77268888,

[0055] MDFI chr6: 41606439-41606770,

[0056] PAX5 chr9: 37037526-37038053,

[0057] PITX2 chr4: 111544016-111544299,

[0058] SLC30A10 chrl: 220101728-220101962,

[0059] TSHZ3 chrl9: 31841391-31841663, and

[0060] TWIST1 chr7: 19156621-19157193.

[0061] In another embodiment, the first embodiment includes the plurality of target regions being a plurality of target regions selected from the group consisting of

[0062] the ADCY5 target region amplified by SEQ ID NOS: 1 and 2,

[0063] the CD200 target region amplified by SEQ ID NOS: 3 and 4,

[0064] the CHST8 target region amplified by SEQ ID NOS: 5 and 6,

[0065] the CPLX2 target region amplified by SEQ ID NOS: 7 and 8,

[0066] the CPLX2 target region amplified by SEQ ID NOS: 9 and 10,

[0067] the GATA3 target region amplified by SEQ ID NOS: 11 and 12,

[0068] the GEM target region amplified by SEQ ID NOS: 13 and 14,

[0069] the GEM target region amplified by SEQ ID NOS: 15 and 16,

[0070] the HOXA7 target region amplified by SEQ ID NOS: 17 and 18,

[0071] the HOXA7 target region amplified by SEQ ID NOS: 19 and 20,

[0072] the KCNS2 target region amplified by SEQ ID NOS: 21 and 22,

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[0074] the KLF14 target region amplified by SEQ ID NOS: 23 and 24,

[0075] the LINC014 target region amplified by SEQ ID NOS: 25 and 26,

[0076] the LOC101 target region amplified by SEQ ID NOS: 27 and 28,

[0077] the MDFI target region amplified by SEQ ID NOS: 29 and 30,

[0078] the MDFI target region amplified by SEQ ID NOS: 31 and 32,

[0079] the MIR129-2 target region amplified by SEQ ID NOS: 33 and 34,

[0080] the PAX5 target region amplified by SEQ ID NOS: 35 and 36,

[0081] the PAX6 target region amplified by SEQ ID NOS: 37 and 38,

[0082] the PITX2 target region amplified by SEQ ID NOS: 39 and 40,

[0083] the PRKC8 target region amplified by SEQ ID NOS: 41 and 42,

[0084] the SLC30A10 target region amplified by SEQ ID NOS: 43 and 44,

[0085] the TSHZ3 target region amplified by SEQ ID NOS: 45 and 46, and

[0086] the TWIST1 target region amplified by SEQ ID NOS: 47 and 48.

[0087] In another embodiment of the first embodiment, wherein prior to amplifying the plurality of target regions of DNA extracted from the cell-free sample obtained from the lung cancer patient, the DNA is subjected to bisulfite conversion.

[0088] In another embodiment of the first embodiment, prior to amplifying the plurality of target regions of DNA extracted from the cell-free sample obtained from the lung cancer patient, the DNA is subjected to enzymatic modification.

[0089] In another embodiment of the first embodiment, with reference to Human Genome version 19, the plurality of target regions is at least 10 regions found within the group consisting of

[0090] ADCY5 chr3:123167226-123167572,

[0091] CD200 chr3: 112051941-112052455,

[0092] CHST8 chrl9: 34112683-34113010,

[0093] CPLX2 chr5: 175298564-175299035,

[0094] GATA3 chrlO: 8095484-8095687,

[0095] GEM chr8: 95246514-95246814,

[0096] HOXA7 chr7: 27195602-27196153,

[0097] KCNS2 chr8: 99439441-99439685,

[0098] KLF14 chr7: 130418549-130419057,

[0099] LINC014 chrlO: 101279944-101280256,

[0100] LOC101 chr5: 77268452-77268888,

[0101] MDFI chr6: 41606439-41606770,

[0102] MIR129-2 chrll: 43602795-43602929,

[0103] PAX5 chr9: 37037526-37038053,

[0104] PAX6 chr 11: 31837379-31838724,

[0105] PITX2 chr4: 111544016-111544299,

[0106] PRKC8 chrl6: 23847325-23847675,

[0107] SLC30A10 chrl: 220101728-220101962,

[0108] {P7473606844378. DOCX} 6 Attorney Docket No. P74736

[0109] TSHZ3 chrl9: 31841391-31841663, and

[0110] TWIST1 chr7: 19156621-19157193.

[0111] In a second embodiment, the disclosure includes a method of monitoring a disease state of a lung cancer patient, the method comprising amplifying a plurality of target regions of DNA extracted from a cell -free sample obtained from the lung cancer patient, the patient having previously been administered a treatment for lung cancer; generating sequencing reads from each of the plurality of amplified target regions; determining a methylation status of two or more adjacent CpG residues within each of the plurality of target regions for each of the sequencing reads; assigning a methylation score to each of the plurality of target regions based on the methylation status of the two or more adjacent CpG residues; and calculating a total methylation score which is the sum of the methylation scores of each of the plurality of target regions, wherein, with reference to Human Genome version 19, the plurality of target regions is a plurality of regions found within the group consisting of ADCY5 chr3:123167226-123167572,

[0112] CD200 chr3: 112051941-112052455,

[0113] CHST8 chrl9: 34112683-34113010,

[0114] CPLX2 chr5: 175298564-175299035,

[0115] GATA3 chrlO: 8095484-8095687,

[0116] GEM chr8: 95246514-95246814,

[0117] H0XA7 chr7: 27195602-27196153,

[0118] KCNS2 chr8: 99439441-99439685,

[0119] KLF14 chr7: 130418549-130419057,

[0120] LINC014 chrlO: 101279944-101280256,

[0121] LOC101 chr5: 77268452-77268888,

[0122] MDFI chr6: 41606439-41606770,

[0123] MIR129-2 chrll: 43602795-43602929,

[0124] PAX5 chr9: 37037526-37038053,

[0125] PAX6 chr 11: 31837379-31838724,

[0126] PITX2 chr4: 111544016-111544299,

[0127] PRKC8 chrl6: 23847325-23847675,

[0128] SLC30A10 chrl: 220101728-220101962,

[0129] TSHZ3 chrl9: 31841391-31841663, and

[0130] TWIST1 chr7: 19156621-19157193,

[0131] wherein, with reference to Human Genome version 19, at least one of the plurality of target regions is found within the group consisting of

[0132] {P7473606844378. DOCX} 7 Attorney Docket No. P74736

[0133] ADCY5 chr3:123167226-123167572,

[0134] CD200 chr3: 112051941-112052455,

[0135] CHST8 chrl9: 34112683-34113010,

[0136] CPLX2 chr5: 175298564-175299035,

[0137] GATA3 chrlO: 8095484-8095687,

[0138] GEM chr8: 95246514-95246814,

[0139] KCNS2 chr8: 99439441-99439685,

[0140] KLF14 chr7: 130418549-130419057,

[0141] LINC014 chrlO: 101279944-101280256,

[0142] LOC101 chr5: 77268452-77268888,

[0143] MDFI chr6: 41606439-41606770,

[0144] PAX5 chr9: 37037526-37038053,

[0145] PITX2 chr4: 111544016-111544299,

[0146] SLC30A10 chrl: 220101728-220101962,

[0147] TSHZ3 chrl9: 31841391-31841663, and

[0148] TWIST1 chr7: 19156621-19157193.

[0149] In another embodiment of the second embodiment, prior to amplifying the plurality of target regions of DNA extracted from the cell-free sample obtained from the lung cancer patient, the DNA is subjected to bisulfite conversion.

[0150] In another embodiment of the second embodiment, prior to amplifying the plurality of target regions of DNA extracted from the cell-free sample obtained from the lung cancer patient, the DNA is subjected to enzymatic modification.

[0151] In another embodiment of the second embodiment, the methylation score for each target region is generated by determining a number of sequencing reads of the target region where two or more adjacent CpG residues within the target region are methylated, calculating a percentage of sequencing reads of the target region where two or more adjacent CpG residues within the target region are methylated relative to the total number of sequencing reads of the region, and assigning the methylation score based on the calculation.

[0152] In another embodiment of the second embodiment, the total methylation score is plotted over time, and a slope of the methylation score over time is determined, a slope of greater than -0.2 being indicative of a need to administer an increased dose of the treatment or to administer an alternative treatment.

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[0154] In another embodiment of the second embodiment, when greater than or equal to 10% of the sequencing reads of the target region contain methylation of two more adjacent CpG residues in the target region, the methylation score of the target region is 1, and when less than 10% of the sequencing reads of the target region contain methylation of two more adjacent CpG residues in the target region, the methylation score of the target region is 0.

[0155] In another embodiment of the second embodiment, the total methylation score is plotted over time, and a slope of the methylation score over time is determined, a slope of greater than -0.2 being indicative of a need to administer an increased dose of the treatment or to administer an alternative treatment.

[0156] In another embodiment of the second embodiment, a percentage of sequencing reads where two more adjacent CpG residues within the target region are methylated relative to the total number of sequencing reads of the target region is calculated, the percentage being the methylation score of the target region.

[0157] In another embodiment of the second embodiment, the total methylation score is plotted over time, and a slope of the methylation score over time is determined, a slope of greater than -0.2 being indicative of a need to administer an increased dose of the treatment or to administer an alternative treatment.

[0158] In a third embodiment, the disclosure includes a method of treatment, the method comprising administering a lung cancer medicament to a patient in need thereof, amplifying a plurality of target regions of DNA extracted from a cell-free sample obtained from the lung cancer patient; generating sequencing reads from each of the plurality of amplified target regions; determining a methylation status of two more adjacent CpG residues within each of the plurality of target regions for each of the sequencing reads; assigning a methylation score to each of the plurality of target regions based on the methylation status of the two more adjacent CpG residues; and calculating a total methylation score which is the sum of the

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[0160] methylation scores of each of the plurality of target regions, wherein, with reference to Human Genome version 19, the plurality of target regions is a plurality of target regions found within the group consisting of

[0161] ADCY5 chr3:123167226-123167572,

[0162] CD200 chr3: 112051941-112052455,

[0163] CHST8 chrl9: 34112683-34113010,

[0164] CPLX2 chr5: 175298564-175299035,

[0165] GATA3 chrlO: 8095484-8095687,

[0166] GEM chr8: 95246514-95246814,

[0167] H0XA7 chr7: 27195602-27196153,

[0168] KCNS2 chr8: 99439441-99439685,

[0169] KLF14 chr7: 130418549-130419057,

[0170] LINC014 chrlO: 101279944-101280256,

[0171] LOC101 chr5: 77268452-77268888,

[0172] MDFI chr6: 41606439-41606770,

[0173] MIR129-2 chrll: 43602795-43602929,

[0174] PAX5 chr9: 37037526-37038053,

[0175] PAX6 chr 11: 31837379-31838724,

[0176] PITX2 chr4: 111544016-111544299,

[0177] PRKC8 chrl6: 23847325-23847675,

[0178] SLC30A10 chrl: 220101728-220101962,

[0179] TSHZ3 chrl9: 31841391-31841663, and

[0180] TWIST1 chr7: 19156621-19157193,

[0181] wherein, with reference to Human Genome version 19, at least one of the plurality of target regions is found within the group consisting of

[0182] ADCY5 chr3:123167226-123167572,

[0183] CD200 chr3: 112051941-112052455,

[0184] CHST8 chrl9: 34112683-34113010,

[0185] CPLX2 chr5: 175298564-175299035,

[0186] GATA3 chrlO: 8095484-8095687,

[0187] GEM chr8: 95246514-95246814,

[0188] KCNS2 chr8: 99439441-99439685,

[0189] KLF14 chr7: 130418549-130419057,

[0190] LINC014 chrlO: 101279944-101280256,

[0191] LOC101 chr5: 77268452-77268888,

[0192] MDFI chr6: 41606439-41606770,

[0193] PAX5 chr9: 37037526-37038053,

[0194] PITX2 chr4: 111544016-111544299,

[0195] SLC30A10 chrl: 220101728-220101962,

[0196] TSHZ3 chrl9: 31841391-31841663, and

[0197] TWIST1 chr7: 19156621-19157193.

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[0199] In another embodiment of the third embodiment, prior to amplifying the plurality of target regions of DNA extracted from the cell-free sample obtained from the lung cancer patient, the DNA is subjected to bisulfite conversion.

[0200] In another embodiment of the third embodiment, prior to amplifying the plurality of target regions of DNA extracted from the cell-free sample obtained from the lung cancer patient, the DNA is subjected to enzymatic modification.

[0201] In another embodiment of the third embodiment, the methylation score for each target region is generated by determining a number of sequencing reads of the target region where two or more adjacent CpG residues within the target region are methylated, calculating a percentage of sequencing reads of the target region where two or more adjacent CpG residues within the target region are methylated relative to the total number of sequencing reads of the region, and assigning the methylation score based on the calculation.

[0202] In another embodiment of the third embodiment, the total methylation score is plotted over time, and a slope of the methylation score over time is determined, a slope of greater than -0.2 being indicative of a need to administer an increased dose of the treatment or to administer an alternative treatment.

[0203] In another embodiment of the third embodiment, when greater than or equal to 10% of the sequencing reads of the target region contain methylation of two or more adjacent CpG residues in the target region, the methylation score of the target region is 1, and wherein when less than 10% of the sequencing reads of the target region contain methylation of two or more adjacent CpG residues in the target region, the methylation score of the target region is 0.

[0204] In another embodiment of the third embodiment, the total methylation score is plotted over time, and a slope of the methylation score over time is determined, a slope of greater than -0.2 being is indicative of a need to administer an increased dose of the treatment or to administer an alternative treatment.

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[0206] In another embodiment of the third embodiment, a percentage of sequencing reads where two or more adjacent CpG residues within the target region are methylated relative to the total number of sequencing reads of the target region is calculated, the percentage being the methylation score of the target region.

[0207] In another embodiment of the third embodiment, the total methylation score is plotted over time, and a slope of the methylation score over time is determined, a slope of greater than -0.2 being is indicative of a need to administer an increased dose of the treatment or to administer an alternative treatment.

[0208] In a fourth embodiment, the disclosure includes a method of monitoring progression or stability of disease in a lung cancer patient, the method comprising (I) a first methylation analysis, comprising (a) amplifying a plurality of target regions of tumor DNA extracted from a first cell-free sample obtained from the lung cancer patient; (b) generating sequencing reads from each of the plurality of amplified target regions; (c) determining whether each CpG residue within each of the plurality of target regions for each of the sequencing reads is methylated; (d) assigning a methylation score to each of the plurality of target regions based on the methylation status of two or more adjacent CpG residues; (e) calculating a first total methylation score which is the sum of the methylation scores of each of the plurality of target regions, (II) a second methylation analysis, comprising (f) amplifying the plurality of target regions of tumor DNA extracted from a second cell -free sample obtained from the lung cancer patient, the second cell-free sample being obtained chronologically after the first cell-free sample; (g) generating sequencing reads from each of the plurality of amplified target regions; (h) determining whether each CpG residue within each of the plurality of target regions for each of the sequencing reads is methylated; (i) assigning a methylation score to each of the plurality of target regions based on the methylation status of the two or more adjacent CpG residues; (j) calculating a second total methylation score which is the sum of

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[0210] the methylation scores of each of the plurality of target regions, wherein the second total methylation score in analysis (II) higher than the first total methylation score in analysis (I) is indicative of disease progression, wherein the second total methylation score in analysis (II) substantially equal to the first total methylation score in analysis (I) is indicative of stable disease, wherein the second total methylation score in analysis (II) lower than the first total methylation score in analysis (I) is indicative of improving disease, wherein, with reference to Human Genome version 19, the plurality of target regions is a plurality of target regions found within the group consisting of

[0211] ADCY5 chr3:123167226-123167572,

[0212] CD200 chr3: 112051941-112052455,

[0213] CHST8 chrl9: 34112683-34113010,

[0214] CPLX2 chr5: 175298564-175299035,

[0215] GATA3 chrlO: 8095484-8095687,

[0216] GEM chr8: 95246514-95246814,

[0217] H0XA7 chr7: 27195602-27196153,

[0218] KCNS2 chr8: 99439441-99439685,

[0219] KLF14 chr7: 130418549-130419057,

[0220] LINC014 chrlO: 101279944-101280256,

[0221] LOC101 chr5: 77268452-77268888,

[0222] MDFI chr6: 41606439-41606770,

[0223] MIR129-2 chrll: 43602795-43602929,

[0224] PAX5 chr9: 37037526-37038053,

[0225] PAX6 chr 11: 31837379-31838724,

[0226] PITX2 chr4: 111544016-111544299,

[0227] PRKC8 chrl6: 23847325-23847675,

[0228] SLC30A10 chrl: 220101728-220101962,

[0229] TSHZ3 chrl9: 31841391-31841663, and

[0230] TWIST1 chr7: 19156621-19157193,

[0231] wherein, with reference to Human Genome version 19, at least one of the plurality of target regions is found within the group consisting of

[0232] ADCY5 chr3:123167226-123167572,

[0233] CD200 chr3: 112051941-112052455,

[0234] CHST8 chrl9: 34112683-34113010,

[0235] CPLX2 chr5: 175298564-175299035,

[0236] GATA3 chrlO: 8095484-8095687,

[0237] GEM chr8: 95246514-95246814,

[0238] KCNS2 chr8: 99439441-99439685,

[0239] KLF14 chr7: 130418549-130419057,

[0240] LINC014 chrlO: 101279944-101280256,

[0241] {P7473606844378. DOCX} 13 Attorney Docket No. P74736

[0242] LOCIOI chr5: 77268452-77268888,

[0243] MDFI chr6: 41606439-41606770,

[0244] PAX5 chr9: 37037526-37038053,

[0245] PITX2 chr4: 111544016-111544299,

[0246] SLC30A10 chrl: 220101728-220101962,

[0247] TSHZ3 chrl9: 31841391-31841663, and

[0248] TWIST1 chr7: 19156621-19157193.

[0249] In another embodiment of the fourth embodiment, prior to amplifying the plurality of target regions of DNA extracted from the cell-free sample obtained from the lung cancer patient, the DNA is subjected to bisulfite conversion.

[0250] In another embodiment of the fourth embodiment, prior to amplifying the plurality of target regions of DNA extracted from the cell-free sample obtained from the lung cancer patient, the DNA is subjected to enzymatic modification.

[0251] In another embodiment of the fourth embodiment, the methylation score for each target region is generated by determining a number of sequencing reads of the target region where two or more adjacent CpG residues within the target region are methylated, calculating a percentage of sequencing reads of the target region where two or more adjacent CpG residues within the target region are methylated relative to the total number of sequencing reads of the region, and assigning the methylation score based on the calculation.

[0252] In another embodiment of the fourth embodiment, the total methylation score is plotted over time, and a slope of the methylation score over time is determined, a slope of greater than -0.2 being indicative of a need to administer an increased dose of the treatment or to administer an alternative treatment.

[0253] In another embodiment of the fourth embodiment, when greater than or equal to 10% of the sequencing reads of the target region contain methylation of two or more adjacent CpG residues in the target region, the methylation score of the target region is 1, and when less than 10% of the sequencing reads of the target region contain methylation of two or more adjacent CpG residues in the target region, the methylation score of the target region is 0.

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[0255] In another embodiment of the fourth embodiment, the total methylation score is plotted over time, and a slope of the methylation score over time is determined, a slope of greater than -0.2 being indicative of a need to administer an increased dose of the treatment or to administer an alternative treatment.

[0256] In another embodiment of the fourth embodiment, a percentage of sequencing reads where two or more adjacent CpG residues within the target region are methylated relative to the total number of sequencing reads of the target region is calculated, the percentage being the methylation score of the target region.

[0257] In another embodiment of the fourth embodiment, the total methylation score is plotted over time, and a slope of the methylation score over time is determined, a slope of greater than -0.2 being indicative of a need to administer an increased dose of the treatment or to administer an alternative treatment.

[0258] In a fifth embodiment, the disclosure includes a kit comprising primers for amplifying a plurality of DNA target regions, wherein, with reference to Human Genome version 19, the plurality of target regions is a plurality of target regions found within the group consisting of ADCY5 chr3:123167226-123167572,

[0259] CD200 chr3: 112051941-112052455,

[0260] CHST8 chrl9: 34112683-34113010,

[0261] CPLX2 chr5: 175298564-175299035,

[0262] GATA3 chrlO: 8095484-8095687,

[0263] GEM chr8: 95246514-95246814,

[0264] H0XA7 chr7: 27195602-27196153,

[0265] KCNS2 chr8: 99439441-99439685,

[0266] KLF14 chr7: 130418549-130419057,

[0267] LINC014 chrlO: 101279944-101280256,

[0268] LOC101 chr5: 77268452-77268888,

[0269] MDFI chr6: 41606439-41606770,

[0270] MIR129-2 chrll: 43602795-43602929,

[0271] PAX5 chr9: 37037526-37038053,

[0272] PAX6 chr 11: 31837379-31838724,

[0273] PITX2 chr4: 111544016-111544299,

[0274] PRKC8 chrl6: 23847325-23847675,

[0275] SLC30A10 chrl: 220101728-220101962,

[0276] TSHZ3 chrl9: 31841391-31841663, and

[0277] TWIST1 chr7: 19156621-19157193,

[0278] {P7473606844378. DOCX} 15 Attorney Docket No. P74736

[0279] wherein, with reference to Human Genome version 19, at least one of the plurality of target regions is found within the group consisting of

[0280] ADCY5 chr3:123167226-123167572,

[0281] CD200 chr3: 112051941-112052455,

[0282] CHST8 chrl9: 34112683-34113010,

[0283] CPLX2 chr5: 175298564-175299035,

[0284] GATA3 chrlO: 8095484-8095687,

[0285] GEM chr8: 95246514-95246814,

[0286] KCNS2 chr8: 99439441-99439685,

[0287] KLF14 chr7: 130418549-130419057,

[0288] LINC014 chrlO: 101279944-101280256,

[0289] LOC101 chr5: 77268452-77268888,

[0290] MDFI chr6: 41606439-41606770,

[0291] PAX5 chr9: 37037526-37038053,

[0292] PITX2 chr4: 111544016-111544299,

[0293] SLC30A10 chrl: 220101728-220101962,

[0294] TSHZ3 chrl9: 31841391-31841663, and

[0295] TWIST1 chr7: 19156621-19157193.

[0296] In another embodiment of the fifth embodiment, the primers are configured to amplify DNA which was subjected to bisulfite conversion.

[0297] In another embodiment of the fifth embodiment, the primers are configured to amplify DNA which was subjected to enzymatic modification.

[0298] In a sixth embodiment, the disclosure includes a method of determining methylation status in a lung cancer patient, the method comprising amplifying a plurality of target regions of DNA extracted from a cell -free sample obtained from the lung cancer patient, generating sequencing reads from each of the plurality of amplified target regions; determining a methylation status of two or more adjacent CpG residues within each of the plurality of target regions for each of the sequencing reads; wherein the plurality of target regions each satisfy all of the following: (i) the target region comprises at least one methylation site having a methylation value at or below -0.3 in normal tissue; (ii) a difference between the average methylation value in lung cancer and in normal tissue is greater than 0.3, and (iii) the target

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[0300] region being methylated in at least 50% of the tumors in a population of samples for lung cancer and are not methylated in tissues and blood of subjects without lung cancer tumors.

[0301] In a seventh embodiment, the disclosure includes a method of diagnosing lung cancer, the method comprising amplifying a plurality of target regions of DNA extracted from a cell-free sample obtained from a subject, generating sequencing reads from each of the plurality of amplified target regions; determining a methylation status of two or more adjacent CpG residues within each of the plurality of target regions for each of the sequencing reads; and determining that the subject has lung cancer when a least two of the plurality of target regions have a fraction of methylation of greater than that of a population of individuals who do not have lung cancer, wherein, with reference to Human Genome version 19, the plurality of target regions is a plurality of target regions found within the group consisting of ADCY5 chr3:123167226-123167572,

[0302] CD200 chr3: 112051941-112052455,

[0303] CHST8 chrl9: 34112683-34113010,

[0304] CPLX2 chr5: 175298564-175299035,

[0305] GATA3 chrlO: 8095484-8095687,

[0306] GEM chr8: 95246514-95246814,

[0307] H0XA7 chr7: 27195602-27196153,

[0308] KCNS2 chr8: 99439441-99439685,

[0309] KLF14 chr7: 130418549-130419057,

[0310] LINC014 chrlO: 101279944-101280256,

[0311] LOC101 chr5: 77268452-77268888,

[0312] MDFI chr6: 41606439-41606770,

[0313] MIR129-2 chrll: 43602795-43602929,

[0314] PAX5 chr9: 37037526-37038053,

[0315] PAX6 chr 11: 31837379-31838724,

[0316] PITX2 chr4: 111544016-111544299,

[0317] PRKC8 chrl6: 23847325-23847675,

[0318] SLC30A10 chrl: 220101728-220101962,

[0319] TSHZ3 chrl9: 31841391-31841663, and

[0320] TWIST1 chr7: 19156621-19157193,

[0321] wherein, with reference to Human Genome version 19, at least one of the plurality of target regions is found within the group consisting of

[0322] ADCY5 chr3:123167226-123167572,

[0323] CD200 chr3: 112051941-112052455,

[0324] CHST8 chrl9: 34112683-34113010,

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[0326] CPLX2 chr5: 175298564-175299035,

[0327] GATA3 chrlO: 8095484-8095687,

[0328] GEM chr8: 95246514-95246814,

[0329] KCNS2 chr8: 99439441-99439685,

[0330] KLF14 chr7: 130418549-130419057,

[0331] LINC014 chrlO: 101279944-101280256,

[0332] LOC101 chr5: 77268452-77268888,

[0333] MDFI chr6: 41606439-41606770,

[0334] PAX5 chr9: 37037526-37038053,

[0335] PITX2 chr4: 111544016-111544299,

[0336] SLC30A10 chrl: 220101728-220101962,

[0337] TSHZ3 chrl9: 31841391-31841663, and

[0338] TWIST1 chr7: 19156621-19157193.

[0339] In an eighth embodiment, the disclosure includes a method of determining methylation status of two or more adjacent CpG residues in a subject having or at risk of having lung cancer, the method comprising generating sequencing reads of a plurality of target regions of DNA extracted from a cell-free sample obtained from the subject, determining a methylation status of two or more adjacent CpG residues within each of the plurality of target regions for each of the sequencing reads; wherein, with reference to Human Genome version 19, the plurality of target regions is a plurality of target regions found within the group consisting of

[0340] ADCY5 chr3:123167226-123167572,

[0341] CD200 chr3: 112051941-112052455,

[0342] CHST8 chrl9: 34112683-34113010,

[0343] CPLX2 chr5: 175298564-175299035,

[0344] GATA3 chrlO: 8095484-8095687,

[0345] GEM chr8: 95246514-95246814,

[0346] HOXA7 chr7: 27195602-27196153,

[0347] KCNS2 chr8: 99439441-99439685,

[0348] KLF14 chr7: 130418549-130419057,

[0349] LINC014 chrlO: 101279944-101280256,

[0350] LOC101 chr5: 77268452-77268888,

[0351] MDFI chr6: 41606439-41606770,

[0352] MIR129-2 chrll: 43602795-43602929,

[0353] PAX5 chr9: 37037526-37038053,

[0354] PAX6 chrl 1: 31837379-31838724,

[0355] PITX2 chr4: 111544016-111544299,

[0356] PRKC8 chrl6: 23847325-23847675,

[0357] SLC30A10 chrl: 220101728-220101962,

[0358] TSHZ3 chrl9: 31841391-31841663, and

[0359] TWIST1 chr7: 19156621-19157193,

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[0361] wherein, with reference to Human Genome version 19, at least one of the plurality of target regions is found within the group consisting of

[0362] ADCY5 chr3:123167226-123167572

[0363] CD200 chr3: 112051941-112052455

[0364] CHST8 chrl9: 34112683-34113010

[0365] CPLX2 chr5: 175298564-175299035

[0366] GATA3 chrlO: 8095484-8095687,

[0367] GEM chr8: 95246514-95246814,

[0368] KCNS2 chr8: 99439441-99439685,

[0369] KLF14 chr7: 130418549-130419057,

[0370] LINC014 chrlO: 101279944-101280256,

[0371] LOC101 chr5: 77268452-77268888,

[0372] MDFI chr6: 41606439-41606770,

[0373] PAX5 chr9: 37037526-37038053,

[0374] PITX2 chr4: 111544016-111544299,

[0375] SLC30A10 chrl: 220101728-220101962,

[0376] TSHZ3 chrl9: 31841391-31841663, and

[0377] TWIST1 chr7: 19156621-19157193.

[0378] In another of any of the above first through eighth embodiments, each of the target regions is 75 to 150 bp in length, and wherein each of the target regions comprises 2 to 12 CpG methylation sites.

[0379] BRIEF DESCRIPTION OF THE DRAWINGS

[0380] Fig. 1 depicts a flowchart showing how, in an embodiment, a methylation signature for a biological trait may be identified.

[0381] Fig. 2 illustrates an embodiment of how methylation may be assessed at 3 individual CpG sites in one target region (ADCY5-1) of circulating tumor DNA at a given time point. Here, sequencing read data is provided for a subject having lung cancer at “Week 0” before starting treatment with Pembrolizumab in the form of a pattern map. The locations of the illustrated CpG sites are as follows, with reference to Human Genome Version 19: CpGl (first column) = chr3: 123, 167,462, CpG2 (second column) = chr3: 123, 167,466, and CpG3 (third column) = 123,167,470. Each row, better viewed in the zoom section to the left,

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[0383] represents a sequencing read. The dark shaded individual reads represent methylation and the light shaded individual reads represent a lack of methylation. Thus, in this case the sequence read provides information with regard to the methylation status at 3 CpG sites.

[0384] Figures 3A-3G show methylation results in the ADCY5-1 target region. Figs. 3A-3E show the change in methylation in the ADCY5-1 target region at each CpG of a lung cancer subject undergoing treatment with Pembrolizumab at weeks 0-7. “Fraction CpGs Methylated %” refers to the number of “Methylated Reads” divided by the “Total Reads” as a percentage. As further described herein the “Fraction CpGs Methylated %” at the CpG sites tested and tumor burden both decreased over time with treatment. Fig. 3F is a table showing, for ADCY5-1, the number of methylated reads and total reads, at each timepoint, where a sequencing read is considered a methylated read if at least two adjacent CpGs are methylated. Fig. 3G represents, for ADCY5-1, the fraction of methylated reads (Y-axis) over 5 different time points in weeks (X axis).

[0385] Figs. 4A-4G are similar to Figures 3A-3G, except they relate to the target region CD200-1.

[0386] Figs. 5A-5G are similar to Figures 3A-3G, except they relate to the target region CHST8-1.

[0387] Figs. 6A-6G are similar to Figures 3A-3G, except they relate to the target region CPLX2-1.

[0388] Figs. 7A-7G are similar to Figures 3A-3G, except they relate to the target region CPLX2-3.

[0389] Figs. 8A-8G are similar to Figures 3A-3G, except they relate to the target region GATA3-1.

[0390] Figs. 9A-9G are similar to Figures 3A-3G, except they relate to the target region GEM-1.

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[0392] Figs. 10A-10G are similar to Figures 3A-3G, except they relate to the target region GEM-2.

[0393] Figs. 11A-11G are similar to Figures 3A-3G, except they relate to the target region H0XA7-1.

[0394] Figs. 12A-12G are similar to Figures 3A-3G, except they relate to the target region HOXA7-2.

[0395] Figs. 13A-13G are similar to Figures 3A-3G, except they relate to the target region KCNS2-1.

[0396] Figs. 14A-14G are similar to Figures 3A-3G, except they relate to the target region KLF14-1.

[0397] Figs. 15A-15G are similar to Figures 3A-3G, except they relate to the target region LINC014-1.

[0398] Figs. 16A-16G are similar to Figures 3A-3G, except they relate to the target region LOC101-2.

[0399] Figs. 17A-17G are similar to Figures 3A-3G, except they relate to the target region MDFI-1.

[0400] Figs. 18A-18G are similar to Figures 3A-3G, except they relate to the target region MDFI-2.

[0401] Figs. 19A-19G are similar to Figures 3A-3G, except they relate to the target region MIR129-1.

[0402] Figs. 20A-20G are similar to Figures 3 A-3G, except they relate to the target region PAX5-2.

[0403] Figs. 21 A-21G are similar to Figures 3A-3G, except they relate to the target region PAX6-2.

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[0405] Figs. 22A-22G are similar to Figures 3 A-3G, except they relate to the target region PITX2-1.

[0406] Figs. 23A-23G are similar to Figures 3A-3G, except they relate to the target region PRKC8-1.

[0407] Figs. 24A-24G are similar to Figures 3 A-3G, except they relate to the target region SLC30A10-1.

[0408] Figs. 25A-25G are similar to Figures 3 A-3G, except they relate to the target region TSHZ3-1.

[0409] Figs. 26A-26G are similar to Figures 3 A-3G, except they relate to the target region TWIST1-1.

[0410] Figures 27A-27W progressively show cumulative fractional methylation scores from each of Figures 3G-26G.

[0411] Figures 28A-28K show normalized ctDNA fraction of methylation in patients in the PIPEN study, including dates of CT scans, methylation score progression, and tumor progression.

[0412] Figures 29 and 30 compare the normalized ctDNA fraction of methylation of those patients who responded poorly (Fig. 29) to Pembrolizumab and those who responded well (Fig. 30) to Pembrolizumab based on CT imaging around 3 months. Date of tumor progression and patient death (if applicable) are also shown.

[0413] Figures 31 and 32 are Kaplan-Meier curves which plot survival (Figure 31) or progression free survival (Figure 32) in patients dichotomized based on the slope of ctDNA fraction of methylation at zero weeks through to approximately 3 months.

[0414] Figure 33 is a swimmer plot that was created to represent the compiled response and outcome data of all patients.

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[0416] Figure 34 is an Area Under the Curve (AUC) analysis using TCGA data as an indication of the test performance.

[0417] Figure 35 is an analysis of hypermethylated regions in various NSCLC lung cancer and in normal tissues. Average methylation of lung cancer Cluster 1 tumour and normal tissue samples was calculated in each of 5 candidate target regions flanking the identified hypermethylated region. An ~200bp hypermethylated region was selected as the candidate target region location. Forward and reverse primers can be specifically designed for this region.

[0418] Figure 36 shows target region sequences assessed with UCSC genome browser for candidacy as ideal target regions for assay based on the presence of hypomethylated areas in multiple normal blood cell. This ensures both no methylation of this region in normal peripheral blood cells, and the location of CpG islands in relation to sequences.

[0419] Figure 37 is a Venn diagram showing regions of hypermethylation identified within each Cluster.

[0420] Figure 38 illustrates a methylation analysis (BiQ analysis) in multiple lung cancer cell lines and in normal and synthetically methylated Peripheral Blood Mononuclear Cells, which is a heatmap for methylation analysis of the indicated amplicons. The level of methylation is indicated using shading, where increased methylation is denoted by a darker color.

[0421] Figure 39 shows methylation levels for each candidate target region assessed on a panel of nine normal tissues. This data is derived from TCGA data and a separate Myelodysplastic Syndrome (MDS) dataset. The number of samples positive for methylation (>-0.3) for each candidate target region was calculated for each normal tissue. The candidate target regions are on the x-axis, the various shadings each represent a unique normal tissue. The candidate target regions that were eliminated are indicated with arrows. The set of normal tissues includes bladder (BLCA), colon (COAD), kidney (KIRC), liver (LHC),

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[0423] pancreas (PAAD), prostate (PRAD), breast (BRCA), lung, and bone marrow samples from patients with Myelodysplastic Syndrome (MDS).

[0424] Figure 40 shows methylation status of 50 NSCLC amplicons from PIPEN2 and PIPEN4 for patient plasma samples from sequencing results. Next Generation Sequencing results for each candidate target region were analyzed using BiQ Analyzer HiMod.

[0425] Methylation patterns for 50 target regions in six PIPEN2 for patient plasma samples (top 6 rows) and six PIPEN4 sample (bottom 6 rows) are displayed. The level of methylation is indicated using shading, where increased methylation is denoted by a darker color.

[0426] Figure 41 shows the sum of the fraction of methylation for patient plasma samples during immunotherapy treatment for patients from Fig 40. The fraction of reads methylated at 2 or more CpG residues compared to all reads for that region are summed for each sample and plotted verses time from the start of therapy.

[0427] Figure 42 shows, for the PIPEN1 patient plasma samples, the fraction of methylated reads for each of the listed target regions as various time points. The “Sample ID” refers to the patient and sample number. The time in weeks from the start of therapy is indicated. A positive target region refers to one where the fraction of methylated reads is greater than or equal to 0.10.

[0428] Figure 43 shows a heatmap of the methylation fraction of the 24 target regions for the 19 PIPEN patient plasma samples, as compared with 19 plasma samples from healthy control subjects.

[0429] DETAILED DESCRIPTION OF THE INVENTION

[0430] Generally, this disclosure provides a method for detecting a lung cancer tumor or monitoring the state of tumor burden in a subject that can be applied to cell-free samples, e.g., to detect cell-free circulating tumor DNA, and related methods. The methods utilize detection

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[0432] of adjacent methylation signals within a single sequencing read as a basic “positive” tumor signal.

[0433] 1. Definitions

[0434] By “cell-free DNA (cfDNA)” is meant DNA that is not present in a cell. cfDNA may circulate freely in a bodily fluid, such as in the bloodstream.

[0435] “Circulating tumor DNA (ctDNA)”, as used herein, refers to cfDNA originating from a tumor and which may be present in a subject having a tumor or obtained from a biological sample of subject having a tumor.

[0436] By “cell-free sample”, as used herein, is meant a biological sample that is substantially devoid of intact cells. This may be a derived from a biological sample that is itself substantially devoid of cells, or may be derived from a sample from which cells have been removed. Example cell-free samples include those derived from blood, such as serum or plasma; urine; or samples derived from other sources, such as semen, sputum, feces, ductal exudate, lymph, recovered lavage, bronchial washings, biopsy or bronchial brushings.

[0437] By “region methylated in cancer” is meant a contiguous segment of the genome containing sites capable of being methylated (for example, CpG dinucleotides), wherein methylation of at least two adjacent CpG sites methylation of which is associated with a malignant cellular state. Methylation of a region may be associated with more than one different type of cancer, or with one type of cancer specifically. Within this, methylation of a “region methylated in cancer” may be associated with more than one subtype, or with one subtype specifically. In some cases, a region methylated in cancer is insufficient in length to qualify as a “CpG island” (defined below), but still has a high CpG density.

[0438] The terms cancer “type” and “subtype” are used relatively herein, such that one “type” of cancer, such as lung cancer, may be “subtypes” based on e.g., stage, morphology,

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[0440] histology, gene expression, receptor profile, mutation profile, aggressiveness, prognosis, malignant characteristics, etc. Likewise, “type” and “subtype” may be applied at a finer level, e.g., to differentiate one histological “type” into “subtypes”, e.g., defined according to mutation profile or gene expression.

[0441] By “adjacent methylated CpG sites,” “adjacent CpG sites” and the like, is meant two methylated CpG dinucleotide sites that are, sequentially, next to each other in the genome and / or corresponding genomic DNA. It will be understood that this term does not necessarily require the sites to actually be directly beside each other in the physical DNA structure.

[0442] Rather, in a sequence of DNA including spaced apart CpG sites A, B, and C in the context A-(n)n-B-(n)n-C, wherein (n)n refers to the number of base pairs (bp) (e.g., up to 300 bp), sites A and B would be recognized as “adjacent” as would sites B and C. Sites A and C, however, would not be considered to be adjacent methylated sites.

[0443] “CpG sites” or “CpG residues” are regions in a DNA sequence which comprise cytosine followed by guanine in the 5’ to 3’ direction. The cytosine in a CpG site may be methylated or unmethylated. A CpG site is also sometimes referred to as a CG site or a CpG dinucleotide.

[0444] “CpG islands” are regions of the genome having a high frequency of CpG sites. CpG islands have a length greater than 200 bp, and have a GC content of at least 50%. In a CpG island, the ratio of the observed number of CG dinucleotides to the expected number of CG nucleotides expected based on the number of guanine and cytosine bases in the region is greater than 0.6.

[0445] “CpG shores” are regions extending short distances from CpG islands in which methylation may also occur. CpG shores may be found in the region 0 to 2 kb upstream and downstream of a CpG island.

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[0447] “CpG shelves” are regions extending short distances from CpG shores in which methylation may also occur. CpG shelves may generally be found in the region between 2 kb and 4 kb upstream and downstream of a CpG island (i.e., extending a further 2 kb out from a CpG shore).

[0448] “Primer”, “Primer pair” and the like, refer to synthetic oligonucleotides that allow for the PCR amplification of a particular target region of DNA.

[0449] A “target region” refers to a segment of DNA which is to be amplified by a “primer pair” or “primer set.” A target region in the disclosed methods will contain two or more CpG sites, and may correspond to a portion of a CpG island or an entirety of a CpG island. In some cases, the name of a target region corresponds to the name of a forward primer in a primer pair.

[0450] An “amplified product of a target region” refers a segment of DNA which has been amplified by a “primer pair” or “primer set”. The sequence of the amplified product of a target region corresponds to the sequence of the target region prior to amplification.

[0451] A “sequencing read” or “read” refers to a nucleotide sequence of an amplified PCR product or native DNA that provides sequence information regarding an individual DNA molecule in a plurality of DNA molecules. Thus, multiple sequencing reads from the same target region may represent different original molecules of DNA, each with a potentially different sequence and / or methylation status. Reads may be generated from cfDNA generally or ctDNA specifically. Thus, sequencing read data is indicative of a population of original DNA molecules.

[0452] By “threshold,” as used herein, is meant a value of a slope of a graph of the sum of a fraction of methylated reads over time, which is selected to discriminate between a patient likely to have tumor progression or death and a patient who is not likely to have such

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[0454] outcomes. Thresholds can be set according to the disease in question, and may be based on earlier analysis, e.g., of a training set.

[0455] The term “threshold” is also used in the context of a percentage of sequencing reads of a target region which contain methylation of at least two adjacent CpG residues in that target region, in order to establish whether the CpG site is considered methylated. Thresholds may also be set for a site according to the predictive value of methylation at a particular site. Thresholds may be different for each methylation site, and data from multiple sites can be combined in the end analysis.

[0456] “Distribution,” as used herein in this context, is meant to indicate the number and location of tumor signals across the regions. Statistical analysis may be used to compare the observed distribution with, e.g., pre-established patterns (data) associated with a form of cancer. In other embodiments, the distribution may be compared to multiple pre-established patterns. In one embodiment, the method further comprises determining a distribution of tumor signals across the regions, and comparing the distribution to a plurality of patterns, each one associated with a cancer type, wherein similarity between the distribution and one of the plurality of patterns is indicative of the associated cancer type.

[0457] By “concordant” or “concordance,” as used herein, is meant methylation status that is consistent by location and / or by repeated observation. In some embodiments, concordant results provide additional confidence in a positive tumor signal. As has already been stated, the basic “tumor signal” defined herein comprises at least two adjacent methylated sites within a single sequencing read. However, additional layers of concordance can be used to increase confidence for tumor detection, in some embodiments, and not all of these need be derived from the same sequencing read. Layers of concordance that may provide confidence in tumor detection may include, for example:

[0458] (a) detection of methylation of at least two adjacent methylation sites;

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[0460] (b) detection of methylation of more than two adjacent methylation sites;

[0461] (c) detection of methylation at adjacent sites within the same section of a target region amplified by one primer pair;

[0462] (d) detection of methylation at non-adjacent sites within the same section of a region amplified by one primer pair;

[0463] (e) detection of methylation at adjacent sites within the same target region;

[0464] (f) detection of methylation at non-adjacent sites within the same target region;

[0465] (g) any one of (a) to (f) in the same sequencing read;

[0466] (h) any one of (a) to (f) in at least two sequencing reads;

[0467] (i) any one of (a) to (f) in a plurality of sequencing reads;

[0468] (j) repeated observation of any one of (a) to (i); or

[0469] (k) any combination or subset of the above.

[0470] The term “methylation score” refers to a number representing the methylation status of the target region or regions. The methylation score may be calculated in several ways, as discussed herein.

[0471] The term “methylation value” refers to data derived from TCGA level 3 methylation data which ranges from -0.5 to 0.5, which is in turn derived from the beta-value of 0 to 1.

[0472] The term “bisulfite conversion” includes any type of bisulfite conversion, including oxidative bisulfite conversion. Examples of bisulfite conversion as contemplated herein are not particularly limited and include those disclosed here: www.neb.com / en / applications / dna-amplification-pcr-and-qpcr / specialty-pcr / bisulfite-sequencing.

[0473] The term “enzymatic modification” includes any type of enzymatic modification, including APOB EC-coupled modification, TET-assisted 5 -methyl cytosine modification, and procedures using enzymatic protection. Examples of enzymatic modification are provided at the following:

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[0475] www.neb.com / en-us / tools-and-resources / feature-articles / enzymatic-methyl-seq-the-next-generation-of-methylome-analysis

[0476] and

[0477] biomodal.com / blog / a-novel-method-for-simultaneous-sequencing-of-genetic-and-epigenetic-bases /

[0478] 2. Identification of regions methylated in cancer

[0479] Various design parameters may be used to identify the regions methylated in cancer, within which target regions subject to amplification are identified. In one embodiment, the regions methylated in cancer are not methylated in healthy tissue, which is understood to be non-malignant. Healthy tissue is often selected based on the origin of the corresponding tumor.

[0480] Regions methylated in cancer may be identified based on desired aims or required specificity, in some embodiments. For instance, it may be desirable to screen for more than one cancer type. Thus, in one embodiment, the regions methylated in cancer are collectively methylated in more than one tumor type. It may be desirable to include regions methylated generally in a group of cancers, and regions methylated in specific cancers in order to provide different tiers of information. Thus, in one embodiment, the regions methylated in cancer comprise regions methylated in cancer that are specifically methylated in specific tumors, and regions methylated in cancer that are methylated in more than one tumor type. Likewise, it may be desirable to include a second tier of regions methylated in cancer that can differentiate between tumor types. In one embodiment, the regions specifically methylated in specific tumors comprise a plurality of groups, each specific to one tumor type. However, it may be desirable in some contexts to have a test that is focused on one type of cancer. Thus,

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[0482] in one embodiment, the regions methylated in cancer are methylated specifically in one tumor type.

[0483] More specifically, in some embodiments, regions methylated in cancer may be identified that are methylated in particular subtypes of a cancer exhibiting particular histology, karyotype, gene expression (or profile thereof), gene mutation (or profile thereof), general methylation subtypes (or profile thereof), staging, etc. Accordingly, the target regions to be amplified may comprise one or more groups of target regions, each being established to be methylated in one particular cancer subtype. In one embodiment, the target regions to be amplified may be methylated in a cancer subtype bearing particular mutations.

[0484] Within the context of a test of some embodiments, information about not only the presence, but also the pattern and distribution of tumor signals both within specific target regions and between different target regions may help to detect or validate the presence of a form of cancer. In one embodiment, the method further comprises determining a distribution of tumor signals across the target regions, and comparing the distribution to at least one pattern associated with a cancer, wherein similarity between the distribution and the pattern is indicative of the cancer.

[0485] A number of design parameters may be considered in the identification of regions methylated in cancer, according to some embodiments. Data for this identification process may be from a variety of sources such as, e.g., The Cancer Genome Atlas (TCGA) (cancergenome.nih.gov / ), derived by the use of, e.g., Illumina Infinium HumanMethylation450 BeadChip (www.illumina.com / products / methylation_450_beadchip _kits.html) for a wide range of cancers, or from other sources based on, e.g., whole genome bisulphite sequencing (WBGS), or other methodologies.

[0486] For instance, “methylation value” may be used to identify TGCA-identified CpG islands. In one embodiment, the step of amplification is carried out with primer sets designed

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[0488] to amplify at least one target region having an average methylation value of below -0.3 in normal tissues. This can be established in a plurality of normal tissue samples, for example 4. The methylation value may be at or below -0.1, -0.2, -0.3, -0.4, or -0.5. In one embodiment, the primer sets are designed to amplify at least one target region having a difference between the average methylation value in the cancer and the average normal tissue of greater than 0.3. The difference may be greater than 0.1, 0.2, 0.3, 0.4, or 0.5. It is noted that the methylation values herein are derived from TCGA level 3 methylation data which ranges from -0.5 to 0.5, which is in turn derived from the beta-value of 0 to 1. Proximity of other target regions that meet this requirement may also play a role in identifying regions, in some embodiments. In one embodiment, the primer sets include primer pairs amplifying at least one target region having at least one other target region within 200 bp that also has a methylation value of below -0.3 in normal issue, and a difference between the average methylation value in the cancer and the normal tissue of greater than 0.3. In another embodiment the adjacent target region having these features may be 300 bp. The adjacent site may be within 100, 200, 300, 400, or 500bp.

[0489] In some embodiments, target regions may be identified for amplification based on the number of tumors in the validation set having methylation at that site. For example, a target region may be identified if it is methylated in at least 40%, 50%, 55%, 60%, 65%, 70%, 75%, 80, 85%, 90, or 95% of tumors tested. For example, target regions may be identified if they are methylated in at least 75% of tumors tested, including within specific subtypes. For some validations, it will be appreciated that tumor-derived cell lines may be used for the testing.

[0490] In another embodiment, the method further comprises oxidative bisulphite conversion. In addition to the analysis of regions methylated in cancer, additional information that may be of clinical significance may be derived from the analysis of hydroxymethylation. Bisulphite sequencing results in the conversion of unmethylated

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[0492] cytosine residues into uracil / thymidine residues, while both methylated and hydroxymethylated cytosines remain unconverted. However, oxidative bisulphite treatment allows for the conversion of hydroxymethyl ated cytosines to uracil / thymidine, allowing for the differential analysis of both types of modifications. By comparison of bisulphite to oxidative bisulphite treatments, the presence of hydroxymethylation can be deduced. This information may be of significance as its presence or absence may be correlated with clinical features of the tumor which may be clinically useful either as a predictive or prognostic factor. Accordingly, in some embodiments, information about hydroxymethylation could additionally be used in the above-described embodiments.

[0493] In one aspect, the presence of specific patterns of methylation is linked to underlying characteristics of particular tumors. In these cases, the methylation patterns detected by the method are indicative of clinically relevant aspects of the tumors such as aggressiveness, likelihood of recurrence, and response to various therapies. Detection of these patterns in the blood may thus provide both prognostic and predictive information related to a patient's tumor.

[0494] In one aspect, there is provided a method for identifying a methylation signature indicative of a biological characteristic, the method comprising: obtaining data for a population comprising a plurality of genomic methylation data sets, each of said genomic methylation data sets associated with biological information for a corresponding sample, segregating the methylation data sets into a first group corresponding to one tissue or cell type possessing the biological characteristic and a second group corresponding to a plurality of tissue or cell types not possessing the biological characteristic, matching methylation data from the first group to methylation data from the second group on a site-by-site basis across the genome, identifying a set of regions methylated in cancer that meet a predetermined threshold for establishing differential methylation between the first and second groups,

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[0496] identifying, using the set of region methylated in cancer, target regions comprising at least two differentially methylated CpGs with 3OObp that meet said predetermined criteria, and extending the target regions to encompass at least one adjacent differentially methylated CpG site that does not meet the predetermined criteria, where the extended target regions provide the methylation signature indicative of the biological trait.

[0497] In one embodiment, validating of the extended target regions is performed by testing for differential methylation within the extended target regions using DNA from at least one independent sample possessing the biological trait and DNA from at least one independent sample not possessing the biological sample.

[0498] In one embodiment, the set of regions methylated in cancer is limited to regions methylated in cancer that further exhibit differential methylation with peripheral blood mononuclear cells from a control sample.

[0499] Fig. 1 depicts a flowchart showing how a methylation signature for a biological trait may be determined. One or more steps of this method may be implemented on a computer. Accordingly, another aspect of this disclosure relates to a non-transitory computer-readable medium comprising instructions that direct a processor to carry out steps of this method.

[0500] 2.1 Protocol for the Identification of Differentially Methylated Regions

[0501] 2.1.1 Use of TCGA data for identifying target regions specifically methylated in NSCLC Level 3 (processed) Illumina Infinium HumanMethylation450 BeadChip array data (www.illumina.com / techniques / microarrays / methylation-arrays.html) was downloaded from The Cancer Genome Atlas (TCGA) site (www.cancer.gov / ccg / research / genome-sequencing / tcga) for the appropriate human tumor types. Tumor and normal samples were separated and the methylation values (from -0.5 to + 0.5) for each group were averaged. The individual candidate differentially methylated genomic location data was mapped. After

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[0503] having divided by methylation subtypes, candidate differentially methylated genomic locations that fulfilled the following example criteria were then identified when the following criteria were satisfied:

[0504] The average methylation values for the normal tissues all below -0.3;

[0505] 1. The difference between the average lung tumor and average normal tissues values greater than 0.3, or at least 50% methylation in the tumor group; and

[0506] 2. Two CpG sites within 300 bp of each other fulfill criteria 1 and 2.

[0507] The resulting locations were then reviewed for their distribution across methylation subtypes. These criteria establish that the particular genomic locations are not methylated in normal tissues, that the difference between the tumor and normal tissues is significant, and that multiple CpG sites in a relatively small area are co-ordinately methylated. Genomic locations which had multiple consecutive methylated TCGA-identified CpG sites (i.e., 2 or more) were prioritized for further analysis. It is noted that Illumina 450k TCGA CpG sites only include a subset of CpG sites which are used as landmarks, and that there may be many additional CpG sites between consecutive TCGA-identified CpG sites. Average values for approximately 10 other TCGA defined genomic locations to either side of the identified genomic location were plotted for all tumor and normal tissue samples to define a region exhibiting differential methylation. Thus, ‘regions methylated in cancer’ exhibiting concerted differential methylation between tumor and normal tissues for single or multiple tumor types were identified.

[0508] A secondary screen for a lack of methylation of these regions in blood was carried out by examining the methylation status of the defined regions in multiple tissues using nucleotide level genome wide bisulphite sequencing data. Specifically, the UCSC Genome Browser (genome.ucsc.edu / ) was used to examine methylation data from multiple sources.

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[0510] Data was processed by the method described in Song Q, et al., A reference methylome database and analysis pipeline to facilitate integrative and comparative epigenomics. PLOS ONE 2013 8(12): e81148

[0511] (journals. plos.org / plosone / article?id=10.1371 / journal.pone.0081148) for use in the UCSC Browser and to identify hypo-methylated regions (above blue lines).

[0512] 2,1,2 Protocol for the Design of Region-Specific Primers for PCR Amplification and Next Generation Sequencing

[0513] Within each region methylated in cancer, target regions spanning two or more TGCA-identified CpG sites were identified. For target regions identified as being differentially methylated in tumors, PCR primers were designed that are able to recognize bisulphite converted DNA which is methylated.

[0514] Using METHYL PRIMER EXPRESS software (Thermo Fisher Scientific Inc., Waltham, MA) or PYROMARK software (Qiagen Sciences Inc., Germantown, MD), or other web-based programs, the DNA sequence of the target region was converted to the sequence obtained when fully methylated DNA is bisulphite converted (i.e., C residues in a CpG dinucleotide remain Cs, while all other C residues are converted to T residues). Since bisulfite conversion changes all cytosine (C) residues not in a methylated CpG site to thymine (T), this conversion significantly reduces complexity since, in regions other than a methylated CpG site, there are now only three bases (adenine (A), guanine (G) and thymine (T)). The converted DNA was then analysed using Primer-Blast (www.ncbi.nlm.nih.gov / tools / primer-blast / ) to generate optimal primers. Regions of low complexity (i.e. including long runs of T residues) are not amenable to primer design as primers would not be specific. Primers were then prioritized based on number of CpG residues between the primers with a preference for

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[0516] more CpGs. Additionally, primer pairs were sometimes selected based on their location to give a uniform distribution.

[0517] Primers were not expressly designed to contain CpG residues, but due to the nature of the regions, generally CpG islands, most had at least 1 to 3 CpGs within them. This renders the primers biased towards the amplification of methylated DNA, but in many cases the primers recognize and amplify non-methylated DNA as well. The target region between the primers includes 2 or more CpG residues. Primers were chosen to amplify target regions from 75 to 150 base pairs in size with melting temperatures in the range of 52°C to 68°C. Multiple primers were designed for each region methylated in cancer to provide increased sensitivity by providing multiple opportunities to detect that region methylated in cancer. Adapter sequences (CS 1 and CS2) were included at the 5’ end of the primers to allow for barcoding and for sequencing on multiple sequencing platforms by the use of adaptor primers for secondary PCR.

[0518] Primers were characterized by PCR amplification of NSCLC cancer cell line DNA and DNA from various primary tumors. PCR amplification was done with individual sets of primers and Next Generation Sequencing carried out to characterize the methylation status of specific target regions. Primer sets exhibiting appropriate tumor specific methylation were then combined into a multiplex PCR reaction containing many primers.

[0519] 3, Sample collection and data analysis

[0520] Blood was obtained from patients having cancer. Plasma or serum was then isolated from the blood to give a cell free samples. Cell-free DNA (cfDNA) was then isolated from the cell-free samples. The cfDNA was then amplified using the primers designed to amplify particular target regions. Individual reads for a given target region were then assessed for their methylation status through sequencing. When using the bisulfite sequencing based

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[0522] approach, methylated CpG residues remain as CpG, while unmethylated CpG residues are seen as TpG sequences.

[0523] The test can be carried out using a Multi-Singleplex process. In the Multi-Singleplex process, a linear amplification (as no primer pairs are present) is carried out using the reverse primers only on the samples. Typically, 50 cycles are performed followed by purification of the linearly amplified product. This is to avoid dilution of the target regions when the individual exponential PCR amplification is carried out. The linear products are spread across singleplex PCR reactions for each of the target regions. Exponential amplification is then carried out using the forward and reverse primers to yield amplimers from each target region. These are then combined into one tube and an aliquot is sequenced. Typically, 2 million reads per sample are generated with the aim of producing in the area of 20,000 reads per amplicon, but this can vary greatly. Due to the methylation bias of the amplification (due to the primers corresponding to the methylated sequence) there can be a reduced or even minimal level of product produced if a particular region is not methylated in that sample. The proportion of “methylated” reads will also vary depending on the presence of tumor and normal DNA in the sample.

[0524] First of all, reads that did not meet certain quality standards were not counted (i.e. reads must have 90% sequence match across the region between the primers and all potential CpG residues in that region are identifiable). For each target region, the number of reads where at least two adjacent CpG residues between the primers (i.e. not including primer sequences themselves) are methylated are counted as a “methylated read.” Additionally, in some embodiments and for some target regions, reads where all CpG residues are methylated are counted, and are described as “100% methylated reads” or “fully methylated reads” and are used in subsequent calculations.

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[0526] Furthermore, in some embodiments and for some target regions, a read is considered “methylated” if at least a particular threshold, such as 80%, of CpGs are methylated, without discrimination to the order of such CpGs. This is most commonly applied to target regions with a larger number (i.e., 4 to 12) CpGs are present, and as such, there will be multiple adjacent CpGs in any case. Furthermore, when this scheme is used, the methylated reads are checked against normal samples to ensure minimal background is present.

[0527] Two methods of analyzing the data by obtaining a methylation score are disclosed. First, the methylation score can be calculated in a binary manner, such that each target region is given a score of 0 or 1. A ratio of methylated reads to total reads is determined. A minimum of 100 total reads per target region are required for that target region to be considered. According to this scheme, when greater than or equal to a certain percentage (for example, 10%) of the sequencing reads of the target region contain methylation of at least two adjacent CpG residues in that target region, the methylation score of the target region in question is 1. When less than a certain percentage (for example, 10%) of the sequencing reads of the target region contain methylation of at least two adjacent CpG residues in the target region, the methylation score of the target region is 0. These binary values of 0 or 1 are summed for all target regions which are analyzed, to give a total methylation score. This relates to the amount of ctDNA present in a roughly exponential manner, based on mutated allele frequency in clinical samples, as well as based on dilution series with known quantities ofDNA.

[0528] According to another scheme for calculating the methylation score, a fractional value is obtained for each target region. In particular, for each target region, the fraction of sequencing reads where at least two adjacent CpG residues are methylated is calculated relative to the total number of sequencing reads of the target region. This fraction is the methylation score of the target region. For example, if 20 sequencing reads have methylation

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[0530] of at least two adjacent CpG residues in the target region, and 80 sequencing reads do not have methylation of at least two adjacent CpG residues in the target region, the fractional methylation score for that target region would be 0.2. These fractional values are summed for all target regions which are analyzed, to give a total fractional methylation score. This yields a ‘continuous variable’, thereby producing a more nuanced and accurate assessment of methylation.

[0531] As a simplified hypothetical example, consider an embodiment where 5 target regions are tested. In a first target region, 14% of the reads have at least two adjacent methylated CpG residues. In a second target region, 25% of the reads have at least two adjacent methylated CpG residues. In a third target region, 7% of the reads have at least two adjacent methylated CpG residues. In the fourth and fifth target regions, 0% of the reads have at least two adjacent methylated CpG residues. Using the first method described above, the methylation score is 2 out of 5. However, using the second method described above, the methylation score is 0.46 (0.14+0.25+0.07+0+0) out of 5. Thus, the ‘continuous variable’ calculation allows for a more precise assessment of methylation.

[0532] Next, whichever scheme for calculating methylation score is used, this information can be evaluated over time (for example, over several weeks or months) to determine whether a patient is responding to a treatment regime. In particular, the methylation score at a given timepoint is compared to the methylation score at a previous timepoint, or preferably, is compared to the methylation score at the onset of the treatment regime. A reduced methylation score compared to a previous timepoint or the onset of the treatment regime is indicative of the patient having increased likelihood of a decreasing tumor burden, while a methylation score which is unchanged or increased is indicative the patient having an unchanged or increasing tumor burden. A reduction in methylation score of greater than 50% is considered an excellent response.

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[0534] More particularly, the patient’s methylation score can be evaluated at a plurality of time points, by plotting these methylation score against time points. In such a case, a negative slope is indicative of a decreasing tumor burden, and a zero or positive slope is indicative of a patient being an unchanged or increasing tumor burden.

[0535] It is further noted that obtaining sequencing reads can be performed using bisulfite conversion or enzymatic modification, followed by amplification using PCR. However, other embodiments are possible which do not require PCR amplification. For instance, sequencing reads may be obtained via Oxford Nanopore sequencing (nanoporetech.com / platform / technology) and the like. This technique involves use of a device comprising flow cells containing nanopores embedded in an electro-resistant membrane. As a nucleic acid molecule such as DNA or RNA molecule passes through a nanopore, current is disrupted to produce a characteristic pattern or “squiggle”. The pattern is then decoded (e.g., using a basecalling algorithm) to determine the nucleic acid sequence. Furthermore, this technique is able to distinguish methylated from un-methylated C residues during this sequencing process. In this manner, the technique can directly output a sequence including methylation status, without prior amplification.

[0536] 4, Applications of the assay

[0537] The assay disclosed herein may be applied for various uses. First, the assay disclosed herein may be used in a clinical setting as an early detection test for lung cancer, particularly small cell lung cancer (SCLC) or non-small cell lung cancer (NSCLC), and more particularly for NSCLC.

[0538] Second, the assay disclosed herein may be used for monitoring remission of cancer in a patient, such as a patient having previously diagnosed with NSCLC, particularly metastasized NSCLC. For instance, after having entered remission for a cancer, a patient

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[0540] may opt to undergo the disclosed assay on a regular basis, such as every month. At each point of assay, it could be determined whether the patient remains in remission, or if an early signal of recurrence of the cancer is detected. In the case of recurrence of the cancer, the clinician may then opt for an appropriate course of drug therapy, chemotherapy, or radiotherapy.

[0541] Third, the assay disclosed herein may be used for monitoring a patient’s response to administration of a drug or treatment to a patient known to have a cancer, such as NSCLC, particularly metastasized NSCLC. As an example, which will be described in greater detail below, the assay disclosed herein may be performed alongside administration of a drug such as Pembrolizumab. By monitoring an increase, decrease or non-change of a methylation score (using either of the two calculation methods noted above), it may be determined whether the patient is likely to be responding to the drug, such as Pembrolizumab.

[0542] In the case of a reduced methylation score, the clinician may consider continuing administration of the drug, since the patient is likely responding well to the drug. On the other hand, in the case of an increase or non-change of a methylation score, the clinician may consider increasing the dosage of the drug, or changing to a different drug, since the patient is likely not responding well to the drug. Using this method, tumor progression may be detected up to 4 months in advance of its identification through typical CT imaging methods.

[0543] Being that the assay described herein is looking at tumor burden, the assay is thus agnostic as to the particular treatment regimen. In some embodiments, the assay can be used to monitor the success of treatment with an immunotherapy drug, while in other embodiments, the assay can be used to monitor the success of treatment with a cytotoxic drug or radiotherapy. Examples of an immunotherapy drug include, but are not limited to, Pembrolizumab (Keytruda), nivolumab (Opdivo), durvalumab (Imfinzi), atezolizumab (Tecentriq), ipilimumab (Yervoy), and cemiplimab (Libtayo). Examples of a cytotoxic drug

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[0545] include, but are not limited to cisplatin (or carboplatin) and gemcitabine, docetaxel (Taxotere), etoposide (Vepesid), paclitaxel, pemetrexed (Alimta), irinotecan and vinorelbine.

[0546] 5, Examples

[0547] 5,1. Identification of target regions for non-small cell lung cancer (NSCLC)

[0548] Identification of target regions for NSCLC was performed as follows. First, the TCGAlung adenocarcinoma (LUAD) database consisting of 492 lung NSCLC samples with Illumina 450k array methylation data was analyzed. Hierarchical clustering of the LUAD methylation data indicated that there are 3 subgroups present (Clusters), though subsequent analysis suggests that Cluster 3 was simply samples of poor quality. Lung methylation levels (beta values) were expressed as 0 to 1. The average TCGA methylation data from normal tissue samples from the lung, breast, prostate and colon were also obtained and had values from -0.5 to +0.5. For each LUAD cluster, the average methylation value for each CpG was calculated, and 0.5 was subtracted from the average to normalize the methylation value to the data from normal tissues. All data was sorted according to chromosomal location on the hg 18 genome version. CpG residues were identified where at least 2 sequential sites had methylation levels below -0.3 in all 4 normal tissues and above -0.1 in the tumor cluster and where the 2 CpGs were within 300 base pairs of each other. See Figure 35, for example, which demonstrates this for CPLX2.

[0549] These regions were further characterized using the UCSC genome browser to examine the methylation of these regions in a variety of normal blood cells to determine if there would be a risk of background due to the presence of blood cell DNA in cfDNA. See Figure 36, which demonstrates this for CPLX2.

[0550] A total of 98 candidate target regions corresponding to 62 regions were identified with significant overlap between Clusters, notably Clusters 1 and 2. See Figure 37. The sequence

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[0552] of DNA across these regions and flanking either side was obtained. Optimal PCR primers were then designed to the methylated version of DNA as if the DNA had been bisulfite converted. Target regions were designed so that at least 2 CpG residues were located between each primer and the primers themselves typically had multiple CpGs present in them. The primers were used to amplify bisulfite converted DNA from the A549 and CALU6 NSCLC adenocarcinoma cell lines, the CALU1 squamous carcinoma cell line, peripheral blood mononuclear cells (PBMC) from a healthy individual, and the same synthetically methylated PBMC DNA. See Figure 38. Sequencing of these regions revealed methylation status, and regions methylated in normal PBMCs were rejected. A total of 85 candidate target regions were taken through for further analysis.

[0553] These 85 candidate regions were re-screened bioinformatically using a total of 9 different normal tissues, as well as 4 bone marrow samples from patients with myelodysplastic disease (MDS) which is suspected of producing background in mutation based assays. Regions with a high level of methylation in these normal tissues were eliminated (arrows) and a total of 50 target regions were carried forward for further analysis. See Figure 39.

[0554] As discussed below in section 5.2, PIPEN cohort plasma patient samples were analyzed using the 50 target regions that had been carried through to this point. See Figure 40. As assessed by CT scans, PIPEN2 was a poor responder to Pembrolizumab, while PIPEN4 was a good responder to Pembrolizumab. Changes in the sum of fraction of methylation illustrate the trend in ctDNA level. See Figure 41.

[0555] Additionally, the number of positive candidate target regions in patient samples during immunotherapy treatment was determined. A candidate target region was considered positive based on a threshold of 10 reads methylated at any CpG residue. Only the positive

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[0557] candidate target regions during entry (Week 0) are included. Methylation of PIPEN2 and PIPEN4 are shown.

[0558] Based on the performance of the target regions, both in terms of yield of reads and positivity, a subset of 24 target regions were chosen for further use with the PIPEN cohort. This selection was performed using rank order, in groups of 8. Thus, the top three groups of eight yielded the 24 target regions employed in the assay. The next highest ranked groups of eight, for example three groups of eight, can also be employed as supplemental target regions.

[0559] It is further noted that having a multiple CpG residues within each target region (minimum 2 to 12) produces an assay with high specificity, as this increases the likelihood that a read is derived from a tumor molecule and unlikely to arise from PCR or NGS errors. The sensitivity of the assay depends on the number of target regions making up the test. At concentrations of ctDNA where there is less than a complete genome equivalent present in a sample (i.e. unique regions are present at less than 1 copy) having more regions means that lower levels of DNA can be detected. For example, having ten target regions means that one tenth of a genome can be detected, if one positive target region is identified. This is an elastic property of the test, i.e. more regions result in higher sensitivity and fewer regions result in lower sensitivity.

[0560] Based on the protocol described above and in section 2.1.1, with reference to Human Genome Version 19, the following were identified as regions differentially methylated in NSCLC:

[0561] ADCY5 chr3:123167226-123167572,

[0562] CD200 chr3: 112051941-112052455,

[0563] CHST8 chrl9: 34112683-34113010,

[0564] CPLX2 chr5: 175298564-175299035,

[0565] GATA3 chrlO: 8095484-8095687,

[0566] GEM chr8: 95246514-95246814,

[0567] H0XA7 chr7: 27195602-27196153,

[0568] KCNS2 chr8: 99439441-99439685,

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[0570] KLF14 chr7: 130418549-130419057,

[0571] LINC014 chrlO: 101279944-101280256,

[0572] LOC101 chr5: 77268452-77268888,

[0573] MDFI chr6: 41606439-41606770,

[0574] MIR129-2 chrll: 43602795-43602929,

[0575] PAX5 chr9: 37037526-37038053,

[0576] PAX6 chr 11: 31837379-31838724,

[0577] PITX2 chr4: 111544016-111544299,

[0578] PRKC8 chrl6: 23847325-23847675,

[0579] SLC30A10 chrl: 220101728-220101962,

[0580] TSHZ3 chrl9: 31841391-31841663, and

[0581] TWIST1 chr7: 19156621-19157193.

[0582] Additionally, with reference to Human Genome Version 19, the following have been identified as supplemental regions methylated in lung cancer:

[0583] HAND2 chr4:174451343-174451568,

[0584] HOXB4 chrl7: 46655394-46655603,

[0585] LNC01143 chr2: 71115680-71115994,

[0586] MSC chr8: 72756058-72756341,

[0587] QRFPRchr4: 122301816-122302226,

[0588] TALI chrl: 47697898-47698176,

[0589] VWC2 chr7: 49813031-49813486, and

[0590] ZNF781 chrl9: 38183080-38183312

[0591] The data for Human Genome Version 19 is available at, for example www.ncbi.nlm.nih.gov / datasets / genome / GCF_000001405.13 / and https: / / genome.ucsc.edu / cgi-bin / hgGateway.

[0592] Within each region methylated in cancer, there are multiple candidate target regions. For example, in the gene CPLX2, three primer pairs, each defining a target region, were developed as shown below.

[0593] Normal genomic DNA:

[0594] CGGCAGCCGCCCGCGGCAGAAGCCGCGGCTCCAGCTCGCGTGGCGGAAGG GCACGCGGCGGCGGGAGCGGGAAGAGCAGAAGGAACCACCTCGTGGAGTC GGGCCGGAGCCCTGCAGGGGCGCAGACGGTAGCAGGGACCGCCAGGTCGG TGCTGGCCGAGGGCCGGGAGGGCGGAGCTGGGCTGTGGGCAACAGGGTCC CGGCTCTCGCCTCGAGGCCTTGGGGCAGCTCGGGAAGCTGGGGAGCACGG CTTTCGGGGGGACAGCTCCCCAAGGCTGCCTGGGCACCGGATGGGGACTG AGAGGCGGTAAAGGGGACACTCCCGGGTGCGCTCTCCGTGGTGCTGAAAG GACAGCGCCTAGCGAGGGGCCGCGAGCAGGCAGCTAGCTGCGAACGAAGA

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[0596] GGAAGCGT GGGCCCTAGCCGGAGAC T CCGGGCAGAGCCCGAACGGCCGGG GTCCCGGAGCCAGGACCGCCCCG

[0597] Primer 1

[0598] FWD: AGAAGTCGCGGTTTTAGTTCGTTTGGCG

[0599] RVS: TTCGTGGAGTCGGGTCGGAGTTTTGTAG

[0600] R Primer CTACAAAACTCCGACCCGACTCCACGAA

[0601] Bisul fite converted Sequence:

[0602] GAAT T GTACGCGGCGGCGGGAGT T GGAATAG TAG

[0603] Primer 2

[0604] FWD: GTAGGGATCGTTAGGTCGGTGTTGGTCG

[0605] RVS: TTTTGGGGTAGTTCGGGAAGTTGGGGAG

[0606] R Primer CTCCCCAACTTCCCGAACTACCCCAAAA

[0607] Bisul fite converted Sequence:

[0608] AGGGTCGGGAGGGCGGAGTTGGGTTGTGGGTAATAGGGTTTCGGTTTTCGTTTCGAGG

[0609] Primer 3

[0610] FWD: TTTGGGTATCGGATGGGGATTGAGAGGC

[0611] RVS: GAGGGGTCGCGAGTAGGTAGTTAGTTGC

[0612] R Primer GCAACTAACTACCTACTCGCGACCCCTC

[0613] Bisul fite converted Sequence:

[0614] GGTAAAGGGGATATTTTCGGGTGCGTTTTTCGTGGTGTTGAAAGGATAGTTTTTTGC

[0615] Based on the protocol described above in section 2.1.2, using cell lines, a total of 24 target regions were identified. These target regions are found within the following regions methylated in cancer: ADCY5, CD200, CHST8, CPLX2, GATA3, GEM, H0XA7, KCNS2, KLF14, LINC014, LOC101, MDFI, MIR129-2, PAX5, PAX6, PITX2, PRKC8, SLC30A10, TSHZ3, and TWIST1. These target regions may be used alone or in combination to differentially determine whether lung cancer is present in a human subject. The number of target regions used is not particularly limited, but sensitivity of the assay is increased as the number of target regions used is increased. This is because each target region is identified based on a frequency of being positive in 50% of patients. If a single target region is used in to test a particular patient, there is a 50% probability that the patient will be positive for the target region. However, if two target regions are used for a particular patient, there would be a 75% probability that the patient will be positive for at least one target region, based on the

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[0617] following calculation: l-(0.5x0.5) = 0.75. Using a similar calculation, if seven target regions are used for a particular patient, there is a 99% probability that the patient will be positive for at least one target region, and if 10 target regions are used for a particular patient, there is a 99.9% probability that the patient will be positive for at least one target region.

[0618] Primers were designed for target regions to be amplified. The primers and corresponding target regions are identified in table below.

[0619] Target Region Name Target Region Forward Primer Reverse Primer SEQ ID NO: SEQ ID NO: SEQ ID NO:

[0620] (Bisulfite

[0621] DNA)

[0622] ADCY5-1 97 1 2

[0623] CD200-1 98 3 4

[0624] CHST8-1 99 5 6

[0625] CPLX2-1 100 7 8

[0626] CPLX2-3 101 9 10

[0627] GATA3-1 102 11 12

[0628] GEM-1 103 13 14

[0629] GEM-2 104 15 16

[0630] H0XA7-1 105 17 18

[0631] HOXA7-2 106 19 20

[0632] KCNS2-1 107 21 22

[0633] KLF14-2 108 23 24

[0634] LINC014-1 109 25 26

[0635] LOC101-1 110 27 28

[0636] MDFI-1 111 29 30

[0637] MDF1-2 112 31 32

[0638] MIR129-2-1 113 33 34

[0639] PAX5-2 114 35 36

[0640] PAX6-2 115 37 38

[0641] PITX2-1 116 39 40

[0642] PRKC8-1 117 41 42

[0643] SLC3010-1 118 43 44

[0644] TSHZ3-1 119 45 46

[0645]

[0646] TWIST 1-1 120 47 48

[0647] Additionally, a total of 24 supplemental target regions were identified for inclusion in the disclosed lung cancer test. These target regions are found within the following regions methylated in cancer: ADCY5, CD200, CHST8, HAND2, HOXB4, KCNS2, KLF14,

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[0649] LINC001143, LOCIOI, MSC, PAX5, PAX6, QRFPR, SLC30A10, TALI, TSHZ3, VWC2, WT1, andZNF781.

[0650] Based on the above, primers were designed for the supplemental target regions to be amplified. The primers and corresponding supplemental target regions are identified in table below.

[0651] Supplemental Target Target Region Forward Primer Reverse Primer Region Name SEQ ID NO: SEQ ID NO: SEQ ID NO:

[0652] (Bisulfite

[0653] DNA)

[0654] ADCY5-2 121 49 50

[0655] CD200-2 122 51 52

[0656] CHST8-2 123 53 54

[0657] HAND2-I 124 55 56

[0658] HOXB4-1 125 57 58

[0659] HOXB4-2 126 59 60

[0660] KCNS2-2 127 61 62

[0661] KLF14-1 128 63 64

[0662] LINC01143-1 129 65 66

[0663] LOC101-1 130 67 68

[0664] MSC-1 131 69 70

[0665] MSC-2 132 71 72

[0666] PAX5-1 133 73 74

[0667] PAX6-1 134 75 76

[0668] QRFPR- 1 135 77 78

[0669] QRFPR-2 136 79 80

[0670] SLC30A10-2 137 81 82

[0671] TAL1-1 138 83 84

[0672] TSHZ3-2 139 85 86

[0673] VWC2-1 140 87 88

[0674] VWC2-2 141 89 90

[0675] WT1-1 142 91 92

[0676] ZNF781-1 143 93 94

[0677] ZNF781-2 144 95 96

[0678] CPLX2-2 177 145 146

[0679] ADCY5-3 178 147 148

[0680] CD200-3 179 149 150

[0681] HOXA7-3 180 151 152

[0682] KLF14-3 181 153 154

[0683] KLF14-4 182 155 156

[0684] KLF14-5 183 157 158

[0685] PAX5-3 184 159 160

[0686]

[0687] PAX5-4 185 161 162

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[0689] PAX6-7 186 163 164

[0690] PAX6-4 187 165 166

[0691] PAX6-9 188 167 168

[0692] PAX6-5 189 169 170

[0693] PRKC8-5 190 171 172

[0694] PRKC8-2 191 173 174

[0695]

[0696] ZNF781-2 192 175 176

[0697] 5,2, Evaluation of methylation status of target regions in Pembrolizumab Immunotherapy Prediction of Efficacy in NSCLC (PIPEN) study

[0698] The Pembrolizumab Immunotherapy Prediction of Efficacy (PIPEN) study consists of a cohort of 19 metastatic carcinoma and squamous NSCLC patients who underwent first-line immunotherapy treatment at the Cancer Center of Southeast Ontario (CCSEO). These individuals were recruited and both primary tumor biopsy material and sequential blood samples were retrieved to measure various biomarkers. The recruited patients all displayed high PD-L1 expression in their tumors but the response rate in this population of patients is still below 30%. As per the standard of care, lung biopsies were obtained and the samples were tested for PD-L1 and ALK expression, along with EGFR / KRAS mutation status.

[0699] Eligibility for Pembrolizumab immunotherapy was determined by a few distinct criteria: patients must be EGFR mutation-negative and must not have an ALK translocation, patients must demonstrate high PD-L1 expression within their tumor cells and must not have received any prior systemic chemotherapy treatment. The Pembrolizumab treatment was administered intravenously every three weeks. Blood draws of about 40 mL were performed at frequent intervals. The following corresponds to one patient among 19 in the PIPEN study. This patient is referred to as “PIPEN1”.

[0700] Plasma samples from the patient were analyzed using for methylation of following target regions: ADCYT5-1, CD200-1, CHST8-1, CPLX2-1, CPLX2-3, GATA3, GEM-1, GEM-2, H0XA7-1, HOXA7-2, KCNS2-1, KLF14-1, LINCO14-1, LOC101-2, MDFI-1,

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[0702] MDFI-2, MIR129-2-1, PAX5-2, PAX6-2, PITX2-1, PRKC8-1, SLC30A10-1, TSHZ3-1, and TWIST1-1.

[0703] Samples from before the initiation of Pembroluzimab immunotherapy (week 0) and at weeks 2, 3, 4 and 7 after the initiation of therapy were analyzed. The data was divided into the individual target regions (from a total of 24). First, as an explanatory diagram, Figure 2 illustrates the results for ADCY5-1. Figure 2 illustrates sequencing read data for week 0 as a pattern map where each column represents an individual CpG and each row represents a sequencing read. Shaded portions represent methylated CpGs while the unshaded portions represent unmethylated CpGs. Because of the large number of reads analyzed, it is difficult to discern individual reads. Thus, the ‘transition’ from methylated to unmethylated reads is magnified in greater detail to the left.

[0704] In Figure 2, there are 7543 total reads, which includes both methylated and unmethylated reads. When any 2 or more adjacent CpG residues are methylated, that read is considered to be a methylated read and is considered to originate from a fragment of tumor DNA. In Figure 2, there are 3 CpGs that are analyzed and a total of 896 reads where 2 or more adjacent CpG residues are methylated. In this specific example, all 3 CpGs are methylated in the 896 reads.

[0705] The fraction of methylation for individual CpGs can be determined by dividing all the methylated CpGs in each column against the total number of reads in the column and is shown in Figures 3 A to 3E as a percentage (Y-axis). As illustrated in Figure 2, the majority of the methylated reads are methylated at all 3 CpGs, i.e., fully methylated. In Figures 3A-3E, the horizontal line represents the lowest fraction of methylation value where all 3 CpGs are methylated. Note that the distance (in base pairs) between each CpG is indicated between the bars in the graph.

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[0707] In Figure 3F, the number of methylated reads is indicated in the second row. The third row is the total reads, including both methylated and unmethylated reads. The fourth row is the “Fraction Methylated %,” which is the number of methylated reads divided by the total reads as a percentage. The graph in Figure 3G represents the fraction of methylated reads (Y-axis) over 5 different time points in weeks (X axis).

[0708] In the other regions analyzed, there are anywhere from 2 (PAX5-2) to 8 (TWIST-1) CpGs analyzed, and in each case at least 2 adjacent CpGs must be methylated in a given read for the read to be considered a methylated read of tumor origin.

[0709] Data similar to that of Figures 3A-3G is provided with respect to CD200-1, CHST8-1, CPLX2-1, CPLX2-3, GATA3-1, GEM-1, GEM-2, H0XA7-1, HOXA7-2, KCNS2-1, KLF14-1, LINC014-1, LOC101-2, MDFI-1, MDFI-2, MIR129-2-1, PAX5-2, PAX6-2, PITX2-1, PRKC8-1, SLC30A10-1, TSHZ3-1, and TWIST1-1 in Figures 4A-4F to 26A-26F. It is noted that the data of Figures 3 A-3G to 26A-26G uses the fractional value calculation method described above to obtain the methylation score.

[0710] Figures 27A-27W progressively show cumulative fractional methylation scores from each of Figures 3G-26G where the methylation fraction from each of the regions at each time point is added together. More specifically, Figure 27A shows the cumulative fractional methylation scores for ADCY5-1 and CD200-1. Figure 27B shows the cumulative fractional methylation scores for ADCY5-1, CD200-1, and CHST8-1. Figure 27C shows the cumulative fractional methylation scores for ADCY5-1, CD200-1, CHST8-1, and CPLX2-1. Figure 27D shows the cumulative fractional methylation scores for ADCY5-1, CD200-1, CHST8-1, CPLX2-1, and CPLX2-3. Figure 27E shows the cumulative fractional methylation scores for ADCY5-1, CD200-1, CHST8-1, CPLX2-1, CPLX2-3, and GATA3-1. Figure 27F shows the cumulative fractional methylation scores for ADCY5-1, CD200-1, CHST8-1, CPLX2-1, CPLX2-3, GATA3-1, and GEM-1. Figure 27G shows the cumulative fractional methylation

[0711] {P7473606844378. DOCX} 52 Attorney Docket No. P74736 scores for ADCY5-1, CD200-1, CHST8-1, CPLX2-1, CPLX2-3, GATA3-1, GEM-1, and GEM-2. Figure 27H shows the cumulative fractional methylation scores for ADCY5-1, CD200-1, CHST8-1, CPLX2-1, CPLX2-3, GATA3-1, GEM-1, GEM-2, and H0XA7-1. Figure 27I shows the cumulative fractional methylation scores for ADCY5-1, CD200-1, CHST8-1, CPLX2-1, CPLX2-3, GATA3-1, GEM-1, GEM-2, H0XA7-1, and HOXA7-2. Figure 27J shows the cumulative fractional methylation scores for ADCY5-1, CD200-1, CHST8-1, CPLX2-1, CPLX2-3, GATA3-1, GEM-1, GEM-2, H0XA7-1, HOXA7-2, and KCNS2-1. Figure 27K shows the cumulative fractional methylation scores for ADCY5-1, CD200-1, CHST8-1, CPLX2-1, CPLX2-3, GATA3-1, GEM-1, GEM-2, H0XA7-1, H0XA7-2, KCNS2-1, and KLF14-1. Figure 27L shows the cumulative fractional methylation scores for ADCY5-1, CD200-1, CHST8-1, CPLX2-1, CPLX2-3, GATA3-1, GEM-1, GEM-2, H0XA7-1, HOXA7-2, KCNS2-1, KLF14-1, and LINC014-1. Figure 27M shows the cumulative fractional methylation scores for ADCY5-1, CD200-1, CHST8-1, CPLX2-1, CPLX2-3, GATA3-1, GEM-1, GEM-2, H0XA7-1, HOXA7-2, KCNS2-1, KLF14-1, LINC014-1, and LOC101-2. Figure 27N shows the cumulative fractional methylation scores for ADCY5-1, CD200-1, CHST8-1, CPLX2-1, CPLX2-3, GATA3-1, GEM-1, GEM-2, H0XA7-1, HOXA7-2, KCNS2-1, KLF14-1, LINC014-1, LOC101-2, and MDFI-1. Figure 270 shows the cumulative fractional methylation scores for ADCY5-1, CD200-1, CHST8-1, CPLX2-1, CPLX2-3, GATA3-1, GEM-1, GEM-2, H0XA7-1, HOXA7-2, KCNS2-1, KLF14-1, LINC014-1, LOC101-2, MDFI-1, and MDFI-2. Figure 27P shows the cumulative fractional methylation scores for ADCY5-1, CD200-1, CHST8-1, CPLX2-1, CPLX2-3, GATA3-1, GEM-1, GEM-2, H0XA7-1, HOXA7-2, KCNS2-1, KLF14-1, LINC014-1, LOC101-2, MDFI-1, MDFI-2, and MIR129-2-1. Figure 27Q shows the cumulative fractional methylation scores for ADCY5-1, CD200-1, CHST8-1, CPLX2-1, CPLX2-3, GATA3-1, GEM-1, GEM-2, H0XA7-1, HOXA7-2, KCNS2-1, KLF14-1, LINC014-1, LOC101-2,

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[0713] MDFI-1, MDFI-2, MIR129-2-1, and PAX5-2. Figure 27R shows the cumulative fractional methylation scores for ADCY5-1, CD200-1, CHST8-1, CPLX2-1, CPLX2-3, GATA3-1, GEM-1, GEM-2, H0XA7-1, HOXA7-2, KCNS2-1, KLF14-1, LINC014-1, LOC101-2, MDFI-1, MDFI-2, MIR129-2-1, PAX5-2, and PAX6-2. Figure 27S shows the cumulative fractional methylation scores for ADCY5-1, CD200-1, CHST8-1, CPLX2-1, CPLX2-3, GATA3-1, GEM-1, GEM-2, H0XA7-1, HOXA7-2, KCNS2-1, KLF14-1, LINC014-1, LOC101-2, MDFI-1, MDFI-2, MIR129-2-1, PAX5-2, PAX6-2, and PITX2-1. Figure 27T shows the cumulative fractional methylation scores for ADCY5-1, CD200-1, CHST8-1, CPLX2-1, CPLX2-3, GATA3-1, GEM-1, GEM-2, H0XA7-1, HOXA7-2, KCNS2-1, KLF14- 1, LINC014-1, LOC101-2, MDFI-1, MDFI-2, MIR129-2-1, PAX5-2, PAX6-2, PITX2-1 and PRKC8-1. Figure 27U shows the cumulative fractional methylation scores for ADCY5-1, CD200-1, CHST8-1, CPLX2-1, CPLX2-3, GATA3-1, GEM-1, GEM-2, H0XA7-1, H0XA7- 2, KCNS2-1, KLF14-1, LINC014-1, LOC101-2, MDFI-1, MDFI-2, MIR129-2-1, PAX5-2, PAX6-2, PITX2-1, PRKC8-1, and SLC30A10-1. Figure 27V shows the cumulative fractional methylation scores for ADCY5-1, CD200-1, CHST8-1, CPLX2-1, CPLX2-3, GATA3-1, GEM-1, GEM-2, H0XA7-1, HOXA7-2, KCNS2-1, KLF14-1, LINC014-1, LOC101-2, MDFI-1, MDFI-2, MIR129-2-1, PAX5-2, PAX6-2, PITX2-1, PRKC8-1, SLC30A10-1, and TSHZ3-1. Figure 27W shows the cumulative fractional methylation scores for ADCY5-1, CD200-1, CHST8-1, CPLX2-1, CPLX2-3, GATA3-1, GEM-1, GEM-2, H0XA7-1, H0XA7-2, KCNS2-1, KLF14-1, LINC014-1, LOC101-2, MDFI-1, MDFI-2, MIR129-2-1, PAX5-2, PAX6-2, PITX2-1, PRKC8-1, SLC30A10-1, TSHZ3-1, and TWIST1-1.

[0714] As can be seen from Figures 27A-27W, when more target regions are combined in a test collectively, the quantitative aspect of the assay is strengthened. This cumulative analysis helps to ‘smooth’ random variation which is inherent in such an assay. Although 24 target

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[0716] regions are shown, additional target regions could be added to increase the sensitivity of the assay.

[0717] Based on the sum of the fractional methylation scores for all 24 target regions, it likely that in PIPEN1, tumor burden decreased in week 4, as evidenced by the decreasing methylation levels. By week 7, the methylation levels decreased to minimum values.

[0718] 5,3, Confirmation of correlation between methylation values and tumor progression

[0719] In order to confirm that the assay described in section 5.2 is correlated with treatment response, the methylation data was compared to the tumor status of the PIPEN patients, using CT scan analysis.

[0720] Although not illustrated, with respect to each PIPEN patient, their total fractional methylation score using all 24 target regions was determined at week 0, in a similar manner as represented by Figure 27W noted above. Then, in subsequent weeks, the patient’s total fraction methylation score using all 24 target regions was again determined. Using the patient’s week 0 total fractional methylation score as the denominator, a normalized ratio showing a relative change in fractional methylation score was determined.

[0721] For instance, in Figure 27W, with respect to PIPEN1, the approximate total fractional methylation scores at weeks 0, 2, 3, 4 and 7 were 6.4, 7.4, 7.5, 3.8, and 0.2, respectively. The patient’s normalized ctDNA fractions of methylation at weeks 0, 2, 3, 4, and 7 were thus 1.0, 1.16, 1.17, 0.6, and 0.03, respectively. This decrease in normalized ctDNA fraction of methylation suggests a decrease in tumor burden, and thus a positive response to the Pembrolizumab treatment regimen. As discussed below with respect to Figures 31 and 32, the slope of Figure 27W can be used as a threshold to predict whether the patient will have tumor progression or death.

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[0723] In order to confirm this, the normalized ctDNA fraction of methylation was plotted over time, as shown in Figure 28A. In Figure 28A, upward-pointing arrows below the X axis indicate dates of CT scans, a downward-pointing triangle near the plotted line indicates a “methylation score progression.” A star indicates a “tumor progression” as observed by a CT scan.

[0724] As to a methylation score progression, this is defined as an increase in methylation score of 20% or more compared to the previous sample. In Figure 28 A, this is denoted by a triangle.

[0725] As to a tumor progression, this was determined by an oncologist studying CT scans. A progression was typically determined relative to the immediately previous CT scan, but in some cases, the oncologist considered earlier scans in their evaluation. The determination of the presence or absence of tumor progression was often based not on a single mass being measured, but rather based on an overall impression by the oncologist.

[0726] As a metric for determining tumor progression, the oncologist based this on the Response Evaluation Criteria in Solid Tumours (RECIST) criteria first published 2000, and updated in 2008. See Eisenhauer et al., “New response evaluation criteria in solid tumors: Revised RECIST guideline (version 1.1),” European Journal of Cancer, 45 (2009) 228-247, the entire contents of which is herein incorporated by reference. As noted in Eisenhauer et al., progressive disease is considered to be present where there is at least a 20% increase in the sum of diameters of target lesions, taking as reference the smallest sum on study (this includes the baseline sum if that is the smallest on study). In addition to the relative increase of 20%, the sum must also demonstrate an absolute increase of at least 5 mm. The appearance of one or more new lesions is also considered tumor progression.

[0727] As shown in Figure 28A, PIPEN1 was confirmed to be responsive to Pembrolizumab in the short term, based by a low normalized ctDNA fraction of methylation after about 7

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[0729] weeks, until around 35 weeks. However, at 40 weeks, PIPEN1 indicated methylation score progression, and radiological tumor progression was observed at about 63 weeks (445 days). PIPEN1 was still alive at least until a “follow up” was performed 1915 days after treatment, which was the last date for which data was available.

[0730] On the other hand, as shown in Figure 28B, PIPEN2 had tumor progression, despite Pembrolizumab treatment. PIPEN2 had an increased normalized ctDNA fraction of methylation at weeks 2 and 7, for example. At week 7 (52 days), PIPEN 2 was confirmed by CT scan to have tumor progression. PIPEN2 died at 325 days.

[0731] Similar data is presented in Figures 28C-28K with respect to PIPENs 4, 5, 6, 8, 10, 11, 14, 15, and 16, respectively. It is noted that some of the 19 PIPEN patents are not shown for various reasons. PIPEN3 and PIPEN12 died before treatment began and / or dropped out of the study. PIPEN18 and PIPEN19 were excluded because too few samples were obtained (3 samples and 2 samples, respectively). PIPEN 9 was excluded because their week 0 data was lost. PIPEN7 was excluded because methylation was not detectable. It is suspected that this was due to very low levels of methylation. PIPEN13 and PIPEN17 were excluded because their week 0 data was suspected to be invalid

[0732] Figures 29 and 30 demonstrate a comparison between “good responders” (Fig. 29) to Pembrolizumab and “poor responders” (Fig. 30) to Pembrolizumab, as determined by CT scan and oncologist’s report. These figures compile the data of figures among Figs. 28A-28K. As shown in Figure 29, PIPEN2, PIPEN6, PIPEN10, PIPEN11, and PIPEN14 all exhibited increased or relatively unchanged normalized ctDNA fractions of methylation. All of these patients had tumor progression at weeks 7, 12, and 13, as denoted by a star, with the exception of PIPEN 14, who died before tumor progression could be confirmed.

[0733] Additionally, days until death are indicated with PIPEN2, PIPEN6, PIPEN10, PIPEN11, and

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[0735] PIPEN14 dying at days 325, 325, 225, 275, and 34, respectively, after initiation of Pembrolizumab treatment.

[0736] On the other hand, as shown in Figure 30, PIPEN1, PIPEN5, PIPEN15 and PIPEN16 exhibited markedly decreased normalized ctDNA fractions of methylation. None of these patients had tumor progression during the first 16 weeks of observation, as denoted by the lack of a star. All of PIPEN1, PIPEN5, PIPEN15, and PIPEN16 survived for at least one year.

[0737] 5,4, Prediction of death or tumor progression based on slope

[0738] Next, statistical analyses were performed to determine a prediction of death or tumor progression that could be made based on the observed data. In particular, statistical analyses were performed using IBM SPSS Statistics Software (version 29.0.1.0). All tests were two-sided, and significance was based on a p-value of <0.05. A multi -variate analysis was performed using Pearson’s p to assess the correlation between a slope of a line between a ctDNA sum fraction of methylation at 0 weeks and a ctDNA sum fraction of methylation approximately 3 months later. This analysis evaluated how these changes correlate with the patient’s response to treatment, overall outcome, time to progression, and time to death. A significant correlation was found between days to death and slope (Pearson correlation r = 0.911; p = 0.011). Similarly, a strong correlation was found between the slope and progression free survival (Pearson correlation r = -0.946; p = 0.004). Both suggest there is a significant correlation between increasing or decreasing levels ctDNA fraction of methylation levels and outcomes.

[0739] To further characterize this relationship, Kaplan-Meier curves were generated. In Figures 31 and 32, patients were analyzed based on a plot across multiple time points of the sum fractions of methylation within the first 7-11 weeks (approx. 3 months) following the

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[0741] initiation of Pembrolizumab. In other words, the slope of a chart such as that in Figure 27W, comprising the full 24 target region signature, was determined for each patient. Patients were divided by stable or increasing slope (above -0.02) or decreasing slope (below -0.02). This data was plotted against days to death (Figure 31) and days to tumor progression (Figure 32). Vertical bars indicated as “censored” means patients were lost to further follow-up either due to death by another cause or the inability to obtain further information.

[0742] Figure 31 shows that patients with slopes less than -0.02 have higher survival rates with a mean survival of 933 (95% CI 722 to 1144) days compared to those with slopes greater than -0.02 who have a mean survival of 271 days (95% CI 124 to 419). This indicates that patients with decreasing sum fractions of methylation levels are responding to Pembrolizumab treatment as reflected in their superior long-term outcome which is 3.4 times longer than non-responders who have stable or increasing levels of sum fractions of methylation as determined by this assay.

[0743] This relationship between levels of sum fractions of methylation and outcomes is even more apparent for progression free survival. Figure 32 demonstrates patients with slopes below -0.02, indicative of decreasing levels, had median progression free survival of 626 days (95% CI 372 to 880) compared to patients with slopes above -0.02 who had median progression free survival of 61 days (95% CI 38 to 84). This 10-fold difference clearly indicates that decreasing sum fractions of methylation levels are consistent with a good response to therapy, while stable of increasing sum fractions of methylation levels are associated with rapid progression. Overall, these results indicate that changes in methylated ctDNA within the first three months or less are indicative of outcomes in response to Pembrolizumab treatment in NSCLC patients.

[0744] Next, Figure 33 is a swimmer plot was created to summarize the compiled response and outcome data of all patients.

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[0746] Thus, when predicting whether the patient is likely to have tumor progression or death, a slope of -0.02 is a threshold, above which the patient is likely to have a negative outcome (tumor progression or death) and equal to or below which the patient is likely to have a positive outcome (lack of tumor progression or death).

[0747] Next, Figure 33 is a swimmer plot was created to summarize the compiled response and outcome data of all patients.

[0748] 5,5 Area Under the Curve (AUC) analysis

[0749] Figure 34 is an Area Under the Curve (AUC) analysis using TCGA data as an indication of the test performance. This is not a direct measure of the test in patients, but rather is a direct indication of what would be expected in differentiating a tumor sample from a normal sample, as would happen in patients.

[0750] To assess the ability of methylation of the regions encompassed by the above-noted target regions to correctly identify tumor samples from normal tissue, the following bioinformatics analysis was done.

[0751] TCGA Illumina 450k methylation data was obtained for 172 Non-Small Cell Lung Cancer (NSCLC) tumor samples (eliminating low purity and un-characterized samples) and for 502 normal tissue samples from bladder, colon, kidney, liver, pancreas, prostate, breast and lung. The Beta values (0 to 1) were obtained for CG sites closest to or within the 24 regions defining the 24 target regions noted above. Target regions were considered methylated if they were greater or equal to a Beta value of 0.4, while they were considered negative if they were less than a Beta value of 0.4.

[0752] For each patient or control sample, the number of positive target regions was then calculated and used to carry out an Area Under the Curve (AUC) using the tumor samples as patients and the normal tissue as controls. The observed AUC of 0.9856 is extremely high,

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[0754] demonstrating the ability of this test to accurately discriminate between tumor and normal samples. Furthermore, the sensitivity of 97.67% for a specificity of 97.21% also reinforces the high performance of this test.

[0755] 5.6 Verification of methylation of 24 target regions in PIPEN patients as compared to healthy subjects

[0756] The ability of a test to differentiate between a patient with lung cancer and a subject who does not have lung cancer is an important characteristic for using the test for diagnosis. Results were compared between 19 patients from the PIPEN study and 19 healthy controls who did not have known signs of disease. See Figure 43. The 19 PIPEN patient plasma samples were taken before the patients started treatment and used 1.8 mL of plasma for analysis. The controls also used 1.8 mL of plasma.

[0757] In Figure 43, the methylation levels for individual target regions are shown, where darker shading indicates a higher number of reads that were fully methylated compared to all of the reads. It can clearly be seen that the overall level of methylation in the 19 PIPEN patient samples is much greater than the healthy controls, with both higher levels of methylation for each target region as well as more target regions having methylation.

[0758] The “Methylation Index” was calculated for each sample, and was the sum of the individual fraction of methylation for each target region. The values for the 19 PIPEN patients and the 19 healthy controls were then used to calculate the classification power of the test using an Area Under the Curve (AUC) analysis. The AUC analysis is an aggregate measure of performance taking into consideration both sensitivity and specificity. The AUC of the test was 0.9723. Although the sample size is relatively small, this is extremely high performance for any test of this nature. This data corresponds to a sensitivity of 95% for a specificity of 95%, with a likelihood ratio of 18.

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[0760] This demonstrates the ability of the test to diagnose lung cancer patients with a high level of accuracy without exposure to radiation. This test achieves a comparable AUC as low dose CT scans (LDCT), but avoids radiation exposure. Though the 19 PIPEN patients are metastatic lung cancer patients who may have higher circulating tumour DNA levels compared to patients who have not previously been diagnosed (primary cancer), this data indicate that the test may also be used for detection of minimal residual disease, relapse detection, and tumour progression, given the low background in most subjects without cancer. The low background of this test in subjects with no known lung cancer also indicates that the test may be used as a screening and diagnosis tool in addition to its use for monitoring therapy response.

[0761] The particulars shown herein are by way of example and for purposes of illustrative discussion of the various embodiments only and are presented in the cause of providing what is believed to be the most useful and readily understood description of the principles and conceptual aspects of the methods and compositions described herein. In this regard, no attempt is made to show more detail than is necessary for a fundamental understanding, the description making apparent to those skilled in the art how the several forms may be embodied in practice.

[0762] The above-described invention may be embodied in different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope to those skilled in the art.

[0763] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description herein is for describing particular

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[0765] embodiments only and is not intended to be limiting. As used in the description and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. All publications, patent applications, patents, and other references mentioned herein are expressly incorporated by reference in their entirety.

[0766] Unless indicated to the contrary, the numerical parameters set forth in the following specification and attached claims are approximations that may vary depending upon the desired properties sought to be obtained and thus may be modified by the term “about”. At the very least, and not as an attempt to limit the application of the doctrine of equivalents to the scope of the claims, each numerical parameter should be construed in light of the number of significant digits and ordinary rounding approaches.

[0767] Notwithstanding that the numerical ranges and parameters setting forth the broad scope are approximations, the numerical values set forth in the specific examples are reported as precisely as possible. Any numerical value, however, inherently contains certain errors necessarily resulting from the standard deviation found in their respective testing measurements. Every numerical range given throughout this specification will include every narrower numerical range that falls within such broader numerical range, as if such narrower numerical ranges were all expressly written herein. Applicant also contemplates ranges derived from data points and express ranges disclosed herein.

[0768] CITATIONS

[0769] Ansari, J., Yun, J. W., Kompelli, A. R., Moufarrej, Y. E., Alexander, J. S., Herrera, G. A., & Shackelford, R. E. (2016). The liquid biopsy in lung cancer. Genes & Cancer, 7(11-12), 355-367. doi. org / http s: / / doi. org / 10.18632 / gene sandcancer.127

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[0771] Clinic, C. (2022). Lung Cancer. my.clevelandclinic.org / health / diseases / 4375-lung-cancer Eisenhauer et al., “New response evaluation criteria in solid tumors: Revised RECIST guideline (version 1.1),” European Journal of Cancer, 45 (2009) 228-247

[0772] Elazezy, M., & Joosse, S. A. (2018). Techniques of using circulating tumor DNA as a liquid biopsy component in cancer management. Computational and Structural Biotechnology Journal, 16, 370-378. doi.org / https: / / doi.org / 10.1016 / j.csbj.2018.10.002

[0773] Hie, M., & Hofman, P. (2016). Pros: Can tissue biopsy be replaced by liquid biopsy?.

[0774] Translational Lung Cancer Research, 5(4), 420-423. doi.org / https: / / doi.org / 10.21037 / tlcr.2016.08.06

[0775] Ma, Y, W., Q., Dong, Q., Zhan, L., & Zhang, J. (2019). How to differentiate pseudoprogression from true progression in cancer patients treated with immunotherapy. American Journal of Cancer Research, 9(8), 1546-1553.

[0776] Nooreldeen, R., & Bach, H. (2021). Current and Future Development in Lung Cancer Diagnosis. National Journal of Molecular Sciences, 22(16), 8661. doi.org / https: / / doi.org / 10.3390 / ijms22168661

[0777] Public Health Agency of Canada. (2019). Lung Cancer. Government of Canada. Retrieved April 2 from www.canada.ca / en / public-health / services / chronic-diseases / cancer / lung-cancer.html

[0778] Santarpia, M., Liguori, A., D'Aveni, A., Karachaliou, N., Gonzalez-Cao, M., Daffina, M. G., Lazzari, C., Altavilla, G., & Resell, R. (2018). Liquid biopsy for lung cancer early detection. Journal of Thoracic Disease, 10, S882-S897. doi.org / https: / / doi.org / 10.21037 / jtd.2018.03.81 Sozzi, G., Conte, D., Leon, M., Ciricione, R., Roz, L., Ratcliffe, C., Roz, E., Cirenei, N., Bellomi, M., Pelosi, G., Pierotti, M. A., & Pastorino, U. (2003). Quantification of free circulating DNA as a diagnostic marker in lung cancer. Journal of Clinical Oncology: Official Journal of the American Society of Clinical Oncology, 27(21), 3902-3908. doi.org / https: / / doi.org / 10.1200 / JC0.2003.02.006

[0779] {P7473606844378. DOCX} 64

Claims

Attorney Docket No. P74736CLAIMS1. A method of determining methylation status in a lung cancer patient, the method comprising:amplifying a plurality of target regions of DNA extracted from a cell-free sample obtained from the lung cancer patient;generating sequencing reads from each of the plurality of amplified target regions; anddetermining a methylation status of two or more adjacent CpG residues within each of the plurality of target regions for each of the sequencing reads;wherein, with reference to Human Genome version 19, the plurality of target regions is a plurality of target regions found within the group consisting ofADCY5 chr3:123167226-123167572,CD200 chr3: 112051941-112052455,CHST8 chrl9: 34112683-34113010,CPLX2 chr5: 175298564-175299035,GATA3 chrlO: 8095484-8095687,GEM chr8: 95246514-95246814,H0XA7 chr7: 27195602-27196153,KCNS2 chr8: 99439441-99439685,KLF14 chr7: 130418549-130419057,LINC014 chrlO: 101279944-101280256,LOC101 chr5: 77268452-77268888,MDFI chr6: 41606439-41606770,MIR129-2 chrll: 43602795-43602929,PAX5 chr9: 37037526-37038053,PAX6 chr 11: 31837379-31838724,PITX2 chr4: 111544016-111544299,PRKC8 chrl6: 23847325-23847675,SLC30A10 chrl: 220101728-220101962,TSHZ3 chrl9: 31841391-31841663, andTWIST1 chr7: 19156621-19157193,wherein, with reference to Human Genome version 19, at least one of the plurality of target regions is found within the group consisting ofADCY5 chr3:123167226-123167572CD200 chr3: 112051941-112052455CHST8 chrl9: 34112683-34113010{P7473606844378. DOCX} 65Attorney Docket No. P74736CPLX2 chr5: 175298564-175299035GATA3 chrlO: 8095484-8095687,GEM chr8: 95246514-95246814,KCNS2 chr8: 99439441-99439685,KLF14 chr7: 130418549-130419057,LINC014 chrlO: 101279944-101280256,LOC101 chr5: 77268452-77268888,MDFI chr6: 41606439-41606770,PAX5 chr9: 37037526-37038053,PITX2 chr4: 111544016-111544299,SLC30A10 chrl: 220101728-220101962,TSHZ3 chrl9: 31841391-31841663, andTWIST1 chr7: 19156621-19157193.

2. The method according to claim 1,wherein the plurality of target regions is a plurality of target regions selected from the group consisting ofthe ADCY5 target region amplified by SEQ ID NOS: 1 and 2,the CD200 target region amplified by SEQ ID NOS: 3 and 4,the CHST8 target region amplified by SEQ ID NOS: 5 and 6,the CPLX2 target region amplified by SEQ ID NOS: 7 and 8,the CPLX2 target region amplified by SEQ ID NOS: 9 and 10,the GATA3 target region amplified by SEQ ID NOS: 11 and 12,the GEM target region amplified by SEQ ID NOS: 13 and 14,the GEM target region amplified by SEQ ID NOS: 15 and 16,the HOXA7 target region amplified by SEQ ID NOS: 17 and 18,the HOXA7 target region amplified by SEQ ID NOS: 19 and 20,the KCNS2 target region amplified by SEQ ID NOS: 21 and 22,the KLF14 target region amplified by SEQ ID NOS: 23 and 24,the LINC014 target region amplified by SEQ ID NOS: 25 and 26,the LOC101 target region amplified by SEQ ID NOS: 27 and 28,the MDFI target region amplified by SEQ ID NOS: 29 and 30,the MDFI target region amplified by SEQ ID NOS: 31 and 32,the MIR129-2 target region amplified by SEQ ID NOS: 33 and 34,the PAX5 target region amplified by SEQ ID NOS: 35 and 36,the PAX6 target region amplified by SEQ ID NOS: 37 and 38,the PITX2 target region amplified by SEQ ID NOS: 39 and 40,the PRKC8 target region amplified by SEQ ID NOS: 41 and 42,the SLC30A10 target region amplified by SEQ ID NOS: 43 and 44,the TSHZ3 target region amplified by SEQ ID NOS: 45 and 46, andthe TWIST1 target region amplified by SEQ ID NOS: 47 and 48.{P7473606844378. DOCX} 66Attorney Docket No. P747363. The method according to claim 1 or 2, wherein prior to amplifying the plurality of target regions of DNA extracted from the cell-free sample obtained from the lung cancer patient, the DNA is subjected to bisulfite conversion.

4. The method according to any one of claims 1-3, wherein prior to amplifying the plurality of target regions of DNA extracted from the cell-free sample obtained from the lung cancer patient, the DNA is subjected to enzymatic modification.

5. The method according to any one of claims 1-4, wherein, with reference to Human Genome version 19, the plurality of target regions is at least 10 regions found within the group consisting ofADCY5 chr3:123167226-123167572,CD200 chr3: 112051941-112052455,CHST8 chrl9: 34112683-34113010,CPLX2 chr5: 175298564-175299035,GATA3 chrlO: 8095484-8095687,GEM chr8: 95246514-95246814,H0XA7 chr7: 27195602-27196153,KCNS2 chr8: 99439441-99439685,KLF14 chr7: 130418549-130419057,LINC014 chrlO: 101279944-101280256,LOC101 chr5: 77268452-77268888,MDFI chr6: 41606439-41606770,MIR129-2 chrll: 43602795-43602929,PAX5 chr9: 37037526-37038053,PAX6 chr 11: 31837379-31838724,PITX2 chr4: 111544016-111544299,PRKC8 chrl6: 23847325-23847675,SLC30A10 chrl: 220101728-220101962,TSHZ3 chrl9: 31841391-31841663, andTWIST1 chr7: 19156621-19157193.

6. A method of monitoring a disease state of a lung cancer patient, the method comprising:{P7473606844378. DOCX} 67Attorney Docket No. P74736amplifying a plurality of target regions of DNA extracted from a cell-free sample obtained from the lung cancer patient, the patient having previously been administered a treatment for lung cancer;generating sequencing reads from each of the plurality of amplified target regions; determining a methylation status of two or more adjacent CpG residues within each of the plurality of target regions for each of the sequencing reads;assigning a methylation score to each of the plurality of target regions based on the methylation status of the two or more adjacent CpG residues; andcalculating a total methylation score which is the sum of the methylation scores of each of the plurality of target regions,wherein, with reference to Human Genome version 19, the plurality of target regions is a plurality of regions found within the group consisting ofADCY5 chr3:123167226-123167572,CD200 chr3: 112051941-112052455,CHST8 chrl9: 34112683-34113010,CPLX2 chr5: 175298564-175299035,GATA3 chrlO: 8095484-8095687,GEM chr8: 95246514-95246814,H0XA7 chr7: 27195602-27196153,KCNS2 chr8: 99439441-99439685,KLF14 chr7: 130418549-130419057,LINC014 chrlO: 101279944-101280256,LOC101 chr5: 77268452-77268888,MDFI chr6: 41606439-41606770,MIR129-2 chrll: 43602795-43602929,PAX5 chr9: 37037526-37038053,PAX6 chr 11: 31837379-31838724,PITX2 chr4: 111544016-111544299,PRKC8 chrl6: 23847325-23847675,SLC30A10 chrl: 220101728-220101962,TSHZ3 chrl9: 31841391-31841663, andTWIST1 chr7: 19156621-19157193,wherein, with reference to Human Genome version 19, at least one of the plurality of target regions is found within the group consisting of{P7473606844378. DOCX} 68Attorney Docket No. P74736ADCY5 chr3:123167226-123167572,CD200 chr3: 112051941-112052455,CHST8 chrl9: 34112683-34113010,CPLX2 chr5: 175298564-175299035,GATA3 chrlO: 8095484-8095687,GEM chr8: 95246514-95246814,KCNS2 chr8: 99439441-99439685,KLF14 chr7: 130418549-130419057,LINC014 chrlO: 101279944-101280256,LOC101 chr5: 77268452-77268888,MDFI chr6: 41606439-41606770,PAX5 chr9: 37037526-37038053,PITX2 chr4: 111544016-111544299,SLC30A10 chrl: 220101728-220101962,TSHZ3 chrl9: 31841391-31841663, andTWIST1 chr7: 19156621-19157193.

7. The method according to claim 6, wherein prior to amplifying the plurality of target regions of DNA extracted from the cell-free sample obtained from the lung cancer patient, the DNA is subjected to bisulfite conversion.

8. The method according to claim 6 or 7, wherein prior to amplifying the plurality of target regions of DNA extracted from the cell-free sample obtained from the lung cancer patient, the DNA is subjected to enzymatic modification.

9. The method of any one of claims 6-8, wherein the methylation score for each target region is generated by:determining a number of sequencing reads of the target region where two or more adjacent CpG residues within the target region are methylated;calculating a percentage of sequencing reads of the target region where two or more adjacent CpG residues within the target region are methylated relative to the total number of sequencing reads of the region; andassigning the methylation score based on the calculation.{P7473606844378. DOCX} 69Attorney Docket No. P7473610. The method of any one of claims 6-9, wherein the total methylation score is plotted over time, and a slope of the methylation score over time is determined, a slope of greater than -0.2 being indicative of a need to administer an increased dose of the treatment or to administer an alternative treatment.

11. The method of any one of claims 6-10,wherein when greater than or equal to 10% of the sequencing reads of the target region contain methylation of two more adjacent CpG residues in the target region, the methylation score of the target region is 1, andwherein when less than 10% of the sequencing reads of the target region contain methylation of two more adjacent CpG residues in the target region, the methylation score of the target region is 0.

12. The method of any one of claims 6-11, wherein the total methylation score is plotted over time, and a slope of the methylation score over time is determined, a slope of greater than -0.2 being indicative of a need to administer an increased dose of the treatment or to administer an alternative treatment.

13. The method of any one of claims 6-12, wherein a percentage of sequencing reads where two more adjacent CpG residues within the target region are methylated relative to the total number of sequencing reads of the target region is calculated, the percentage being the methylation score of the target region.{P7473606844378. DOCX} 70Attorney Docket No. P7473614. The method of any of claims 6-13, wherein the total methylation score is plotted over time, and a slope of the methylation score over time is determined, a slope of greater than -0.2 being indicative of a need to administer an increased dose of the treatment or to administer an alternative treatment.

15. A method of treatment, the method comprising:administering a lung cancer medicament to a patient in need thereof, amplifying a plurality of target regions of DNA extracted from a cell-free sample obtained from the lung cancer patient;generating sequencing reads from each of the plurality of amplified target regions; determining a methylation status of two more adjacent CpG residues within each of the plurality of target regions for each of the sequencing reads;assigning a methylation score to each of the plurality of target regions based on the methylation status of the two more adjacent CpG residues; andcalculating a total methylation score which is the sum of the methylation scores of each of the plurality of target regions,wherein, with reference to Human Genome version 19, the plurality of target regions is a plurality of target regions found within the group consisting ofADCY5 chr3:123167226-123167572,CD200 chr3: 112051941-112052455,CHST8 chrl9: 34112683-34113010,CPLX2 chr5: 175298564-175299035,GATA3 chrlO: 8095484-8095687,GEM chr8: 95246514-95246814,H0XA7 chr7: 27195602-27196153,KCNS2 chr8: 99439441-99439685,KLF14 chr7: 130418549-130419057,LINC014 chrlO: 101279944-101280256,LOC101 chr5: 77268452-77268888,MDFI chr6: 41606439-41606770,MIR129-2 chrll: 43602795-43602929,PAX5 chr9: 37037526-37038053,{P7473606844378. DOCX} 71Attorney Docket No. P74736PAX6 chr 11: 31837379-31838724,PITX2 chr4: 111544016-111544299,PRKC8 chrl6: 23847325-23847675,SLC30A10 chrl: 220101728-220101962,TSHZ3 chrl9: 31841391-31841663, andTWIST1 chr7: 19156621-19157193,wherein, with reference to Human Genome version 19, at least one of the plurality of target regions is found within the group consisting ofADCY5 chr3:123167226-123167572,CD200 chr3: 112051941-112052455,CHST8 chrl9: 34112683-34113010,CPLX2 chr5: 175298564-175299035,GATA3 chrlO: 8095484-8095687,GEM chr8: 95246514-95246814,KCNS2 chr8: 99439441-99439685,KLF14 chr7: 130418549-130419057,LINC014 chrlO: 101279944-101280256,LOC101 chr5: 77268452-77268888,MDFI chr6: 41606439-41606770,PAX5 chr9: 37037526-37038053,PITX2 chr4: 111544016-111544299,SLC30A10 chrl: 220101728-220101962,TSHZ3 chrl9: 31841391-31841663, andTWIST1 chr7: 19156621-19157193.

16. The method according to claim 15, wherein prior to amplifying the plurality of target regions of DNA extracted from the cell-free sample obtained from the lung cancer patient, the DNA is subjected to bisulfite conversion.

17. The method according to claim 15 or 16, wherein prior to amplifying the plurality of target regions of DNA extracted from the cell-free sample obtained from the lung cancer patient, the DNA is subjected to enzymatic modification.

18. The method of any one of claims 15-17, wherein the methylation score for each target region is generated by:{P7473606844378. DOCX} 72Attorney Docket No. P74736determining a number of sequencing reads of the target region where two or more adjacent CpG residues within the target region are methylated;calculating a percentage of sequencing reads of the target region where two or more adjacent CpG residues within the target region are methylated relative to the total number of sequencing reads of the region; andassigning the methylation score based on the calculation.

19. The method of any one of claims 15-18, wherein the total methylation score is plotted over time, and a slope of the methylation score over time is determined, a slope of greater than -0.2 being indicative of a need to administer an increased dose of the treatment or to administer an alternative treatment.

20. The method of any one of claims 15-19,wherein when greater than or equal to 10% of the sequencing reads of the target region contain methylation of two or more adjacent CpG residues in the target region, the methylation score of the target region is 1, andwherein when less than 10% of the sequencing reads of the target region contain methylation of two or more adjacent CpG residues in the target region, the methylation score of the target region is 0.

21. The method of any one of claims 15-20, wherein the total methylation score is plotted over time, and a slope of the methylation score over time is determined, a slope of greater than -0.2 being is indicative of a need to administer an increased dose of the treatment or to administer an alternative treatment.{P7473606844378. DOCX} 73Attorney Docket No. P7473622. The method of any one of claims 15-21, wherein a percentage of sequencing reads where two or more adjacent CpG residues within the target region are methylated relative to the total number of sequencing reads of the target region is calculated, the percentage being the methylation score of the target region.

23. The method of any one of claims 15-22, wherein the total methylation score is plotted over time, and a slope of the methylation score over time is determined, a slope of greater than -0.2 being is indicative of a need to administer an increased dose of the treatment or to administer an alternative treatment.

24. A method of monitoring progression or stability of disease in a lung cancer patient, the method comprising:(I) a first methylation analysis, comprising:(a) amplifying a plurality of target regions of tumor DNA extracted from a first cell-free sample obtained from the lung cancer patient;(b) generating sequencing reads from each of the plurality of amplified target regions;(c) determining whether each CpG residue within each of the plurality of target regions for each of the sequencing reads is methylated;(d) assigning a methylation score to each of the plurality of target regions based on the methylation status of two or more adjacent CpG residues;(e) calculating a first total methylation score which is the sum of the methylation scores of each of the plurality of target regions,(II) a second methylation analysis, comprising:{P7473606844378. DOCX} 74Attorney Docket No. P74736(f) amplifying the plurality of target regions of tumor DNA extracted from a second cell-free sample obtained from the lung cancer patient, the second cell-free sample being obtained chronologically after the first cell-free sample;(g) generating sequencing reads from each of the plurality of amplified target regions;(h) determining whether each CpG residue within each of the plurality of target regions for each of the sequencing reads is methylated;(i) assigning a methylation score to each of the plurality of target regions based on the methylation status of the two or more adjacent CpG residues;(j) calculating a second total methylation score which is the sum of the methylation scores of each of the plurality of target regions,wherein the second total methylation score in analysis (II) higher than the first total methylation score in analysis (I) is indicative of disease progression,wherein the second total methylation score in analysis (II) substantially equal to the first total methylation score in analysis (I) is indicative of stable disease,wherein the second total methylation score in analysis (II) lower than the first total methylation score in analysis (I) is indicative of improving disease,wherein, with reference to Human Genome version 19, the plurality of target regions is a plurality of target regions found within the group consisting ofADCY5 chr3:123167226-123167572,CD200 chr3: 112051941-112052455,CHST8 chrl9: 34112683-34113010,CPLX2 chr5: 175298564-175299035,GATA3 chrlO: 8095484-8095687,GEM chr8: 95246514-95246814,H0XA7 chr7: 27195602-27196153,KCNS2 chr8: 99439441-99439685,KLF14 chr7: 130418549-130419057,LINC014 chrlO: 101279944-101280256,LOC101 chr5: 77268452-77268888,MDFI chr6: 41606439-41606770,{P7473606844378. DOCX} 75Attorney Docket No. P74736MIR129-2 chrll: 43602795-43602929,PAX5 chr9: 37037526-37038053,PAX6 chr 11: 31837379-31838724,PITX2 chr4: 111544016-111544299,PRKC8 chrl6: 23847325-23847675,SLC30A10 chrl: 220101728-220101962,TSHZ3 chrl9: 31841391-31841663, andTWIST1 chr7: 19156621-19157193,wherein, with reference to Human Genome version 19, at least one of the plurality of target regions is found within the group consisting ofADCY5 chr3:123167226-123167572,CD200 chr3: 112051941-112052455,CHST8 chrl9: 34112683-34113010,CPLX2 chr5: 175298564-175299035,GATA3 chrlO: 8095484-8095687,GEM chr8: 95246514-95246814,KCNS2 chr8: 99439441-99439685,KLF14 chr7: 130418549-130419057,LINC014 chrlO: 101279944-101280256,LOC101 chr5: 77268452-77268888,MDFI chr6: 41606439-41606770,PAX5 chr9: 37037526-37038053,PITX2 chr4: 111544016-111544299,SLC30A10 chrl: 220101728-220101962,TSHZ3 chrl9: 31841391-31841663, andTWIST1 chr7: 19156621-19157193.

25. The method according to claim 24, wherein prior to amplifying the plurality of target regions of DNA extracted from the cell-free sample obtained from the lung cancer patient, the DNA is subjected to bisulfite conversion.

26. The method according to claim 24 or 25, wherein prior to amplifying the plurality of target regions of DNA extracted from the cell-free sample obtained from the lung cancer patient, the DNA is subjected to enzymatic modification.{P7473606844378. DOCX} 76Attorney Docket No. P7473627. The method of any one of claims 24-26, wherein the methylation score for each target region is generated by:determining a number of sequencing reads of the target region where two or more adjacent CpG residues within the target region are methylated;calculating a percentage of sequencing reads of the target region where two or more adjacent CpG residues within the target region are methylated relative to the total number of sequencing reads of the region; andassigning the methylation score based on the calculation.

28. The method of any one of claims 24-27, wherein the total methylation score is plotted over time, and a slope of the methylation score overtime is determined, a slope of greater than -0.2 being indicative of a need to administer an increased dose of the treatment or to administer an alternative treatment.

29. The method of any one of claims 24-28,wherein when greater than or equal to 10% of the sequencing reads of the target region contain methylation of two or more adjacent CpG residues in the target region, the methylation score of the target region is 1, andwherein when less than 10% of the sequencing reads of the target region contain methylation of two or more adjacent CpG residues in the target region, the methylation score of the target region is 0.

30. The method of any one of claims 24-29, wherein the total methylation score is plotted over time, and a slope of the methylation score over time is determined, a slope of greater{P7473606844378. DOCX} 77Attorney Docket No. P74736than -0.2 being indicative of a need to administer an increased dose of the treatment or to administer an alternative treatment.

31. The method of any one of claims 24-30, wherein a percentage of sequencing reads where two or more adjacent CpG residues within the target region are methylated relative to the total number of sequencing reads of the target region is calculated, the percentage being the methylation score of the target region.

32. The method of any one of claims 24-31, wherein the total methylation score is plotted over time, and a slope of the methylation score overtime is determined, a slope of greater than -0.2 being indicative of a need to administer an increased dose of the treatment or to administer an alternative treatment.

33. A kit comprising:primers for amplifying a plurality of DNA target regions,wherein, with reference to Human Genome version 19, the plurality of target regions is a plurality of target regions found within the group consisting ofADCY5 chr3:123167226-123167572,CD200 chr3: 112051941-112052455,CHST8 chrl9: 34112683-34113010,CPLX2 chr5: 175298564-175299035,GATA3 chrlO: 8095484-8095687,GEM chr8: 95246514-95246814,H0XA7 chr7: 27195602-27196153,KCNS2 chr8: 99439441-99439685,KLF14 chr7: 130418549-130419057,LINC014 chrlO: 101279944-101280256,LOC101 chr5: 77268452-77268888,MDFI chr6: 41606439-41606770,MIR129-2 chrll: 43602795-43602929,PAX5 chr9: 37037526-37038053,PAX6 chr 11: 31837379-31838724,PITX2 chr4: 111544016-111544299,{P7473606844378. DOCX} 78Attorney Docket No. P74736PRKC8 chrl6: 23847325-23847675,SLC30A10 chrl: 220101728-220101962,TSHZ3 chrl9: 31841391-31841663, andTWIST1 chr7: 19156621-19157193,wherein, with reference to Human Genome version 19, at least one of the plurality of target regions is found within the group consisting ofADCY5 chr3:123167226-123167572,CD200 chr3: 112051941-112052455,CHST8 chrl9: 34112683-34113010,CPLX2 chr5: 175298564-175299035,GATA3 chrlO: 8095484-8095687,GEM chr8: 95246514-95246814,KCNS2 chr8: 99439441-99439685,KLF14 chr7: 130418549-130419057,LINC014 chrlO: 101279944-101280256,LOC101 chr5: 77268452-77268888,MDFI chr6: 41606439-41606770,PAX5 chr9: 37037526-37038053,PITX2 chr4: 111544016-111544299,SLC30A10 chrl: 220101728-220101962,TSHZ3 chrl9: 31841391-31841663, andTWIST1 chr7: 19156621-19157193.

34. The kit according to claim 33, wherein the primers are configured to amplify DNA which was subjected to bisulfite conversion.

35. The kit according to claim 33 or 34, wherein the primers are configured to amplify DNA which was subjected to enzymatic modification.

36. A method of determining methylation status in a lung cancer patient, the method comprising:amplifying a plurality of target regions of DNA extracted from a cell-free sample obtained from the lung cancer patient;generating sequencing reads from each of the plurality of amplified target regions;{P7473606844378. DOCX} 79Attorney Docket No. P74736determining a methylation status of two or more adjacent CpG residues within each of the plurality of target regions for each of the sequencing reads;wherein the plurality of target regions each satisfy all of the following:(i) the target region comprises at least one methylation site having a methylation value at or below -0.3 in normal tissue;(ii) a difference between the average methylation value in lung cancer and in normal tissue is greater than 0.3, and(iii) the target region is methylated in at least 50% of the tumors in a population of samples for lung cancer and are not methylated in tissues and blood of subjects without lung cancer tumors.

37. A method of diagnosing lung cancer, the method comprising:amplifying a plurality of target regions of DNA extracted from a cell-free sample obtained from a subject;generating sequencing reads from each of the plurality of amplified target regions; determining a methylation status of two or more adjacent CpG residues within each of the plurality of target regions for each of the sequencing reads; anddetermining that the subject has lung cancer when a least two of the plurality of target regions have a fraction of methylation of greater than that of a population of individuals who do not have lung cancer,wherein, with reference to Human Genome version 19, the plurality of target regions is a plurality of target regions found within the group consisting ofADCY5 chr3:123167226-123167572,CD200 chr3: 112051941-112052455,CHST8 chrl9: 34112683-34113010,CPLX2 chr5: 175298564-175299035,GATA3 chrlO: 8095484-8095687,GEM chr8: 95246514-95246814,{P7473606844378. DOCX} 80Attorney Docket No. P74736H0XA7 chr7: 27195602-27196153,KCNS2 chr8: 99439441-99439685,KLF14 chr7: 130418549-130419057,LINC014 chrlO: 101279944-101280256,LOC101 chr5: 77268452-77268888,MDFI chr6: 41606439-41606770,MIR129-2 chrll: 43602795-43602929,PAX5 chr9: 37037526-37038053,PAX6 chr 11: 31837379-31838724,PITX2 chr4: 111544016-111544299,PRKC8 chrl6: 23847325-23847675,SLC30A10 chrl: 220101728-220101962,TSHZ3 chrl9: 31841391-31841663, andTWIST1 chr7: 19156621-19157193,wherein, with reference to Human Genome version 19, at least one of the plurality of target regions is found within the group consisting ofADCY5 chr3:123167226-123167572,CD200 chr3: 112051941-112052455,CHST8 chrl9: 34112683-34113010,CPLX2 chr5: 175298564-175299035,GATA3 chrlO: 8095484-8095687,GEM chr8: 95246514-95246814,KCNS2 chr8: 99439441-99439685,KLF14 chr7: 130418549-130419057,LINC014 chrlO: 101279944-101280256,LOC101 chr5: 77268452-77268888,MDFI chr6: 41606439-41606770,PAX5 chr9: 37037526-37038053,PITX2 chr4: 111544016-111544299,SLC30A10 chrl: 220101728-220101962,TSHZ3 chrl9: 31841391-31841663, andTWIST1 chr7: 19156621-19157193.

38. A method of determining methylation status of two or more adjacent CpG residues in a subject having or at risk of having lung cancer, the method comprising:generating sequencing reads of a plurality of target regions of DNA extracted from a cell-free sample obtained from the subject;determining a methylation status of two or more adjacent CpG residues within each of the plurality of target regions for each of the sequencing reads;{P7473606844378. DOCX} 81Attorney Docket No. P74736wherein, with reference to Human Genome version 19, the plurality of target regions is a plurality of target regions found within the group consisting ofADCY5 chr3:123167226-123167572,CD200 chr3: 112051941-112052455,CHST8 chrl9: 34112683-34113010,CPLX2 chr5: 175298564-175299035,GATA3 chrlO: 8095484-8095687,GEM chr8: 95246514-95246814,H0XA7 chr7: 27195602-27196153,KCNS2 chr8: 99439441-99439685,KLF14 chr7: 130418549-130419057,LINC014 chrlO: 101279944-101280256,LOC101 chr5: 77268452-77268888,MDFI chr6: 41606439-41606770,MIR129-2 chrll: 43602795-43602929,PAX5 chr9: 37037526-37038053,PAX6 chr 11: 31837379-31838724,PITX2 chr4: 111544016-111544299,PRKC8 chrl6: 23847325-23847675,SLC30A10 chrl: 220101728-220101962,TSHZ3 chrl9: 31841391-31841663, andTWIST1 chr7: 19156621-19157193,wherein, with reference to Human Genome version 19, at least one of the plurality of target regions is found within the group consisting ofADCY5 chr3:123167226-123167572CD200 chr3: 112051941-112052455CHST8 chrl9: 34112683-34113010CPLX2 chr5: 175298564-175299035GATA3 chrlO: 8095484-8095687,GEM chr8: 95246514-95246814,KCNS2 chr8: 99439441-99439685,KLF14 chr7: 130418549-130419057,LINC014 chrlO: 101279944-101280256,LOC101 chr5: 77268452-77268888,MDFI chr6: 41606439-41606770,PAX5 chr9: 37037526-37038053,PITX2 chr4: 111544016-111544299,SLC30A10 chrl: 220101728-220101962,TSHZ3 chrl9: 31841391-31841663, andTWIST1 chr7: 19156621-19157193.

39. The method according to any one of claims 1-32 and 36-38,wherein each of the target regions is 75 to 150 bp in length, and{P7473606844378. DOCX} 82Attorney Docket No. P74736wherein each of the target regions comprises 2 to 12 CpG methylation sites.

40. The kit according to any one of claims 33-35,wherein each of the target regions is 75 to 150 bp in length, and wherein each of the target regions comprises 2 to 12 CpG methylation sites.{P7473606844378. DOCX} 83

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