JP2022546908A - Biomarkers and methods for personalized treatment of small cell lung cancer using KDM1A inhibitors - Google Patents

Abstract

The present application provides biomarkers and methods for predicting responsiveness of patients with small cell lung cancer (SCLC) to treatment with KDM1A inhibitors, and treating subgroups of SCLC patients identified using the above methods. Disclose how.

Description

The present invention relates to biomarkers and methods for individualized treatment of small cell lung cancer (SCLC) using KDM1A inhibitors. The present invention provides methods of identifying patients with SCLC who may benefit from treatment with a KDM1A inhibitor, and methods of treating such patients with a KDM1A inhibitor.

Lysine-specific demethylase 1 (LSD1, also known as KDM1A) is a histone modifying enzyme involved in the demethylation of dimethyl histone 3 lysine 4 (H3K4me2) (Shi et al., Cell 2004). In several human cancers, KDM1A overexpression is associated with disease progression and poor prognosis, and its inhibition has been shown to reduce cancer cell proliferation, migration and invasion. KDM1A has therefore been recognized as a target of interest in drug development to treat cancer, and several KDM1A inhibitors are currently undergoing clinical trials in oncology.

In particular, KDM1A inhibitors have been reported to be effective in treating small cell lung cancer (SCLC). KDM1A inhibition has been shown to reduce proliferation of SCLC cell lines in vitro and delay tumor growth in vivo in SCLC xenograft-bearing mice (Mohammad et al., 2015, Cancer Cell 28:57- 69). However, published data indicate that although some SCLCs are sensitive to KDM1A inhibition, sensitivity to KDM1A inhibition is not a widespread feature of SCLC cells. This provides a method of individualizing SCLC treatment with a KDM1A inhibitor, particularly a method of patient selection that can identify patients with SCLC who would benefit or most benefit from receiving treatment with a KDM1A inhibitor. There is an increasing need for development.

Accordingly, the technical problem underlying the present invention is to provide means and methods for identifying and treating patients with SCLC who are most amenable to treatment with KDM1A inhibitors.

The present invention provides means and methods for individualized treatment of small cell lung cancer (SCLC) using KDM1A inhibitors.
In one aspect, the invention provides a method of identifying patients with SCLC who are likely to respond to treatment comprising a KDM1A inhibitor, wherein a sample from the patient is administered prior to initiating treatment comprising a KDM1A inhibitor. A method is provided comprising measuring the levels of ASCL1 and SOX2 in a subject. In some embodiments, a patient is identified as more likely to respond to treatment comprising a KDM1A inhibitor if each level of ASCL1 and SOX2 in the sample exceeds a threshold value.

For example, a patient has SCLC and the levels of ASCL1 and SOX2 in a sample from the patient were measured prior to initiation of treatment comprising a KDM1A inhibitor. Identified as more likely to respond to treatment, including a KDM1A inhibitor, compared to not exceeded. Comparable patients (whose respective levels of ASCL1 and SOX2 in the sample do not exceed the threshold), by contrast, are less likely to respond to treatment containing a KDM1A inhibitor. This exemplary description is provided herein involving, encompassing or including identifying patients with SCLC who are likely to respond/respond to treatments including KDM1A inhibitors, etc. It applies to all embodiments and uses described herein.

In some embodiments, the method further comprises using the levels of ASCL1 and SOX2 in the sample to generate a score for the sample, wherein if the score for the sample exceeds a threshold, the patient is , as more likely to respond to treatment, including a KDM1A inhibitor.

In another aspect, the invention provides a method of identifying a patient with SCLC who may benefit from treatment comprising a KDM1A inhibitor, wherein prior to initiating treatment comprising a KDM1A inhibitor, in a sample from the patient A method is provided comprising measuring levels of ASCL1 and SOX2. In some embodiments, the patient is identified as likely to benefit from treatment comprising a KDM1A inhibitor if each level of ASCL1 and SOX2 in the sample exceeds a threshold value. In some embodiments, the method further comprises using the levels of ASCL1 and SOX2 in the sample to generate a score for the sample, wherein if the score for the sample exceeds a threshold, the patient is , identified as those that may benefit from treatment involving a KDM1A inhibitor.

In a further aspect, the invention provides a method of predicting the responsiveness of a patient with SCLC to treatment comprising a KDM1A inhibitor, wherein, prior to initiating treatment comprising a KDM1A inhibitor, ASCL1 and A method is provided comprising measuring the level of SOX2. In some embodiments, the patient is identified as likely to be responsive to treatment comprising a KDM1A inhibitor if each level of ASCL1 and SOX2 in the sample exceeds a threshold value. In some embodiments, the method further comprises using the levels of ASCL1 and SOX2 in the sample to generate a score for the sample, wherein if the score for the sample exceeds a threshold, the patient is , as likely to be responsive to treatment, including KDM1A inhibitors.

In a further aspect, the invention provides a method of assessing the likelihood of a patient with SCLC to respond to treatment comprising a KDM1A inhibitor, wherein prior to initiating treatment comprising a KDM1A inhibitor, in a sample from the patient methods comprising measuring levels of ASCL1 and SOX2 in a subject. In some embodiments, a patient is identified as likely to respond to treatment comprising a KDM1A inhibitor if each level of ASCL1 and SOX2 in the sample exceeds a threshold value. In some embodiments, the method further comprises using the levels of ASCL1 and SOX2 in the sample to generate a score for the sample, wherein if the score for the sample exceeds a threshold, the patient is , as likely to respond to treatment, including KDM1A inhibitors.

In a further aspect, the invention provides a method of assessing the likelihood of SCLC in a patient responsive to treatment comprising a KDM1A inhibitor, wherein a sample from a patient with SCLC is administered prior to initiation of treatment comprising a KDM1A inhibitor. A method is provided comprising measuring the levels of ASCL1 and SOX2 in a subject. In some embodiments, SCLC is identified as likely to respond to treatment comprising a KDM1A inhibitor when each level of ASCL1 and SOX2 in the sample exceeds a threshold value. In some embodiments, the method further comprises using the levels of ASCL1 and SOX2 in the sample to generate a score for the sample, wherein SCLC is defined as , as likely to respond to treatment, including KDM1A inhibitors.

In a further aspect, the invention provides a method of selecting treatment for a patient with SCLC comprising measuring levels of ASCL1 and SOX2 in a sample from the patient prior to initiating treatment. . In some embodiments, the method comprises providing a recommendation that selected treatment for the patient includes a KDM1A inhibitor if each level of ASCL1 and SOX2 in the sample exceeds a threshold value. . In some embodiments, the method uses the levels of ASCL1 and SOX2 in the sample to generate a score for the sample, and if the score for the sample exceeds a threshold, the patient is selected. providing a recommendation that the treatment includes a KDM1A inhibitor. In other words, the treatment of choice for a patient with SCLC is, for example, KDM1A if each level of ASCL1 and SOX2 in the sample exceeds a threshold, or if the score in the sample exceeds a threshold. Treatments that include inhibitors. Other treatment options other than treatment with a KDM1A inhibitor, e.g., treatment with a drug/therapeutic agent other than a KDM1A inhibitor, if ASCL1 and SOX2 levels do not exceed threshold or score does not exceed threshold may be contemplated for the patient.

In a further aspect, the invention provides a method of treating a patient with SCLC, wherein prior to initiating treatment comprising a KDM1A inhibitor, the patient is treated with a KDM1A inhibitor using a method according to any of the preceding aspects. Methods are provided comprising administering to a patient a therapeutically effective amount of a treatment comprising a KDM1A inhibitor when identified as likely to respond to treatment comprising the agent.

In a further aspect, the invention provides a method of treating a patient with SCLC, wherein, prior to initiating treatment, levels of ASCL1 and SOX2 are measured in a sample from the patient, and levels of each of ASCL1 and SOX2 in the sample are determined. Identifying a patient as likely to respond to treatment comprising a KDM1A inhibitor if the level exceeds a threshold; Features include administering a therapeutically effective amount of treatment.

In a further aspect, the invention provides a method of treating a patient with SCLC, comprising measuring levels of ASCL1 and SOX2 in a sample from the patient prior to initiating treatment, and using these levels to A score is generated for the sample, and the patient is identified as likely to respond to treatment comprising a KDM1A inhibitor if the score for the sample exceeds a threshold, and the patient is identified as likely to respond. A method comprising administering to a patient a therapeutically effective amount of a treatment comprising a KDM1A inhibitor, if administered.

In a further aspect, the invention provides a KDM1A inhibitor for use in treating a patient with SCLC, wherein prior to initiating treatment comprising the KDM1A inhibitor, the patient undergoes a method according to any of the preceding aspects. is used to provide KDM1A inhibitors that have been identified as likely to respond to treatment comprising a KDM1A inhibitor.

In a further aspect, the invention provides the use of ASCL1 and SOX2 in methods of identifying patients with SCLC who are likely to respond to treatment comprising a KDM1A inhibitor.

In a further aspect, the invention provides the use of ASCL1 and SOX2 in methods of assessing the likelihood of response of a patient with SCLC to treatment comprising a KDM1A inhibitor.

In a further aspect, the invention provides the use of one or more agents for measuring levels of ASCL1 and SOX2 in methods of identifying patients with SCLC who are likely to respond to treatment comprising a KDM1A inhibitor. offer.

In a further aspect, the invention provides the use of one or more agents for measuring ASCL1 and SOX2 levels in a method of assessing the likelihood of response of a patient with SCLC to treatment comprising a KDM1A inhibitor. do.

In a further aspect, the invention provides the use of ASCL1 and SOX2 to manufacture a diagnostic for identifying patients with SCLC who are likely to respond to treatment comprising a KDM1A inhibitor.

In a further aspect, the invention provides the use of ASCL1 and SOX2 for the manufacture of a diagnostic agent for assessing the likely response of patients with SCLC to treatment comprising a KDM1A inhibitor.

In another aspect, the invention provides a kit for identifying patients with SCLC who are likely to respond to treatment comprising a KDM1A inhibitor, comprising: Kits are provided that include one or more agents and optionally instructions for use.

In another aspect, the invention provides a kit for assessing the likely response of a patient with SCLC to treatment comprising a KDM1A inhibitor, the kit for measuring levels of ASCL1 and SOX2 in a sample. Kits are provided that include one or more agents and optionally instructions for use.

As described in more detail in Example 2, RNA-seq (Log2 ( FIG. 10 shows dot plots representing the expression of ASCL1 (Y-axis) and SOX2 (X-axis) as measured by RPKM) values). ASCL1 and SOX2 qRT-PCR (absolute FIG. 2 shows dot plots representing gene expression as measured by Cp values). Plotted values are means of independent experiments. Values in brackets have Cp values greater than 40 for SOX2 expression. Microarray Affymetrix analysis of ASCL1 and SOX2 in an extended panel of SCLC cell lines sensitivity (grey dots), partial sensitivity (empty squares) or resistance (black triangles) to KDM1A inhibition, as described in Example 4. FIG. 2 shows dot plots representing gene expression measured by (RMA values). FIG. 4 shows ROC curves based on gene expression of ASCL1 (FIG. 4A) and SOX2 (FIG. 4B) for distinguishing between KDM1Ai-sensitive and resistant SCLC cells, as described in Example 4. The sensitivity and specificity of each confidence interval for a given threshold are shown in the table below each graph. FIG. 4 shows ROC curves based on gene expression of ASCL1 (FIG. 4A) and SOX2 (FIG. 4B) for distinguishing between KDM1Ai-sensitive and resistant SCLC cells, as described in Example 4. The sensitivity and specificity of each confidence interval for a given threshold are shown in the table below each graph. Western blot (WB) (Fig. 5A) and quantification (Fig. 5B) of ASCL1 and SOX2 protein levels in NCI-H146, NCI-H510A, NCI-H446 and NCI-H526 cell lines as described in Example 5.1. ). Western blot (WB) (Fig. 5A) and quantification (Fig. 5B) of ASCL1 and SOX2 protein levels in NCI-H146, NCI-H510A, NCI-H446 and NCI-H526 cell lines as described in Example 5.1. ). FIG. 6 shows the correlation of protein and mRNA (CCLE Affymetrix) levels for SOX2 (FIG. 6A) and ASCL1 (FIG. 6B), as described in Example 5.1. FIG. 6 shows the correlation of protein and mRNA (CCLE Affymetrix) levels for SOX2 (FIG. 6A) and ASCL1 (FIG. 6B), as described in Example 5.1. Fluorescent immunohistochemical staining of SOX2 (FIG. 7A), ASCL1 (FIG. 7B) and a negative control (no primary antibody, FIG. 7C) according to Example 5.2 showing low levels of both biomarkers in NCI-H526 cells. Levels/undetectable levels, low/undetectable levels of ASCL1 in NCI-H446 cells, and highest levels of both biomarkers in NCI-H146 cells are shown, consistent with their mRNA levels. DAPI: DAPI (4',6-diamidino-2-phenylindole) is a fluorescent dye that binds strongly to AT-rich regions in DNA and stains the nucleus. No signal is detected in the secondary antibody only negative control (AF546: Alexa Fluor 546). Fluorescent immunohistochemical staining of SOX2 (FIG. 7A), ASCL1 (FIG. 7B) and a negative control (no primary antibody, FIG. 7C) according to Example 5.2 showing low levels of both biomarkers in NCI-H526 cells. Levels/undetectable levels, low/undetectable levels of ASCL1 in NCI-H446 cells, and highest levels of both biomarkers in NCI-H146 cells are shown, consistent with their mRNA levels. DAPI: DAPI (4',6-diamidino-2-phenylindole) is a fluorescent dye that binds strongly to AT-rich regions in DNA and stains the nucleus. No signal is detected in the secondary antibody only negative control (AF546: Alexa Fluor 546). Fluorescent immunohistochemical staining of SOX2 (FIG. 7A), ASCL1 (FIG. 7B) and a negative control (no primary antibody, FIG. 7C) according to Example 5.2 showing low levels of both biomarkers in NCI-H526 cells. Levels/undetectable levels, low/undetectable levels of ASCL1 in NCI-H446 cells, and highest levels of both biomarkers in NCI-H146 cells are shown, consistent with their mRNA levels. DAPI: DAPI (4',6-diamidino-2-phenylindole) is a fluorescent dye that binds strongly to AT-rich regions in DNA and stains the nucleus. No signal is detected in the secondary antibody only negative control (AF546: Alexa Fluor 546). FIG. 8 shows the correlation of SOX2 (FIG. 8A) and ASCL1 (FIG. 8B) protein and mRNA (CCLE Affymetrix) levels, as described in more detail in Example 5.2. FIG. 8 shows the correlation of SOX2 (FIG. 8A) and ASCL1 (FIG. 8B) protein and mRNA (CCLE Affymetrix) levels, as described in more detail in Example 5.2. Representative ASCL1, SOX2 and their corresponding DAPI fluorescence immunohistochemistry images from SCLC PDX TMA are shown for each staining classification level 0, 1, 2 and 3, as described in Example 5.3. be Two-sided Spearman correlation tests between RNASeq (Log ₂ FPKM) and IF (visual score) datasets for SOX2 (FIGS. 10A and 10B) and ASCL1 (FIGS. 10C and 10D) with 95% confidence intervals are shown in Example 5. 3 is shown for two independent experiments (coefficients are identified at the bottom of each graph), as described in FIG. Two-sided Spearman correlation tests between RNASeq (Log ₂ FPKM) and IF (visual score) datasets for SOX2 (FIGS. 10A and 10B) and ASCL1 (FIGS. 10C and 10D) with 95% confidence intervals are shown in Example 5. 3 is shown for two independent experiments (coefficients are identified at the bottom of each graph), as described in FIG. Two-sided Spearman correlation tests between RNASeq (Log ₂ FPKM) and IF (visual score) datasets for SOX2 (FIGS. 10A and 10B) and ASCL1 (FIGS. 10C and 10D) with 95% confidence intervals are shown in Example 5. 3 is shown for two independent experiments (coefficients are identified at the bottom of each graph), as described in FIG. Two-sided Spearman correlation tests between RNASeq (Log ₂ FPKM) and IF (visual score) datasets for SOX2 (FIGS. 10A and 10B) and ASCL1 (FIGS. 10C and 10D) with 95% confidence intervals are shown in Example 5. 3 is shown for two independent experiments (coefficients are identified at the bottom of each graph), as described in FIG. WB and Ponceau staining for ASCL1 and SOX2 in exosome fractions according to Example 6. FIG. Consistent with mRNA expression, ASCL1 was not detected in NCI-H446- and NCI-H526-derived exosomes, and SOX2 was absent in NCI-H526-derived exosomes. WB (left) and Ponceau staining (right) for ASCL1, SOX2 and CD151 (lung cancer-specific exosome markers) in both exosomes and corresponding parental cells according to Example 6. FIG. ASCL1, SOX2 and CD151 signals were significantly reduced or eliminated in the exosome fraction after treatment of NCI-H510A cells with 5 μM GW4869 (an exosome production inhibitor) for 48 h, whereas the expression of these proteins in cells was Unaffected, detection of ASCL1, SOX2 and CD151 is exosome-specific.

Definitions Unless defined otherwise, 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. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, suitable methods and materials are described below.

All publications, patent applications, patents, and other references mentioned herein are hereby incorporated by reference in their entirety.
The nomenclature used in this application is based on the IUPAC systematic nomenclature unless otherwise indicated.

Any open valence appearing on a carbon, oxygen, sulfur or nitrogen atom in a structure herein indicates the presence of hydrogen unless otherwise indicated.
The stereochemical definitions and conventions used herein are generally according to S.M. P. Parker, ed., McGraw-Hill Dictionary of Chemical Terms (1984) McGraw-Hill Book Company, New York; and Wilen, S.; , "Stereochemistry of Organic Compounds", John Wiley & Sons, Inc.; , New York, 1994. In describing optically active compounds, the prefixes D and L, or R and S are used to indicate the absolute configuration of the molecule about its chiral center(s). Substituents attached to the chiral center under consideration are ranked according to the sequence rules of Cahn, Ingold and Prelog (Cahn et al., Angew. Chem. Inter. eds. 1966, 5:385; errata 511). The prefixes D and L or (+) and (-) are used to indicate the sign of rotation of plane-polarized light by the compound, with (-) or L indicating that the compound is levorotatory. Compounds prefixed with (+) or D are dextrorotatory.

The terms "optionally" or "optionally" mean that the event or situation subsequently described may, but need not, occur and the description includes instances where the event or situation does and does not occur. indicates that

The term "pharmaceutically acceptable salt" denotes a salt that is not biologically or otherwise undesirable. Pharmaceutically acceptable salts include both acid and base addition salts. Pharmaceutically acceptable salts are well known in the art.

The term "pharmaceutically acceptable acid addition salts" includes inorganic acids such as hydrochloric acid, hydrobromic acid, sulfuric acid, nitric acid, carbonic acid, phosphoric acid, as well as organic acids such as formic acid, acetic acid, propionic acid, glycol. Acid, gluconic acid, lactic acid, pyruvic acid, oxalic acid, malic acid, maleic acid, malonic acid, succinic acid, fumaric acid, tartaric acid, citric acid, aspartic acid, ascorbic acid, glutamic acid, anthranilic acid, benzoic acid, cinnamic acid aliphatic, cycloaliphatic, aromatic, araliphatic, heterocyclic, carboxylic, and sulfonic acids of , mandelic, embonic, phenylacetic, methanesulfonic, ethanesulfonic, p-toluenesulfonic, and salicylic acids Refers to those pharmaceutically acceptable salts formed with organic acids selected from the class of acids.

The term "pharmaceutically acceptable base addition salt" refers to pharmaceutically acceptable salts formed with organic or inorganic bases. Examples of acceptable inorganic bases include sodium, potassium, ammonium, calcium, magnesium, iron, zinc, copper, manganese, and aluminum salts. Salts derived from pharmaceutically acceptable organic non-toxic bases include salts of primary, secondary and tertiary amines, substituted amines such as naturally occurring substituted amines, cyclic amines and basic bases. Ion exchange resins such as isopropylamine, trimethylamine, diethylamine, triethylamine, tripropylamine, ethanolamine, 2-diethylaminoethanol, trimethamine, dicyclohexylamine, lysine, arginine, histidine, caffeine, procaffeine, hydrabamine, choline, betaine , ethylenediamine, glucosamine, methylglucosamine, theobromine, purines, piperizine, piperidine, N-ethylpiperidine, and polyamine resins.

The terms "KDM1A inhibitor" or "KDM1Ai" are used interchangeably and as used herein refer to any compound capable of inhibiting KDM1A activity. Methods for determining KDM1A inhibitory activity are well known in the art. In preferred embodiments, the KDM1A inhibitor is a small molecule. Examples of KDM1A inhibitors are described in detail elsewhere herein.

As used herein, "small molecule" refers to organic compounds having a molecular weight of 900 Daltons or less, preferably 500 Daltons or less. Molecular weight is the mass of a molecule and is calculated as the sum of the atomic weight of each constituent element multiplied by the number of atoms of that element in the molecular formula.

"Treatment comprising a KDM1A inhibitor" means a therapy or treatment regimen that incorporates a KDM1A inhibitor, either as the sole active pharmaceutical ingredient (API) or along with other anticancer agents. whether in combination with one or more additional APIs. The treatment, including the KDM1A inhibitor, is typically in the form of a pharmaceutical composition. If a treatment comprising a KDM1A inhibitor comprises one or more APIs in addition to the KDM1A inhibitor, they may be administered in the form of a single pharmaceutical composition incorporating all APIs, or otherwise can be administered by the same route or by different routes (e.g., one can be administered orally and the other can be administered parenterally), and can be administered simultaneously or sequentially, each API (i.e., KDM1A inhibitor agent and one or more additional APIs) in the form of individual pharmaceutical compositions.

The terms "pharmaceutical composition" and "pharmaceutical formulation" are used interchangeably and are administered to a mammal, e.g., a human in need thereof, along with one or more pharmaceutically acceptable excipients. Compositions (eg, mixtures or solutions) containing effective amounts of active pharmaceutical ingredients (eg, KDM1A inhibitors) are presented.

The term "pharmaceutically acceptable" refers to the preparation of pharmaceutical compositions that are generally safe, non-toxic, not biologically or otherwise undesirable, and acceptable for veterinary and human pharmaceutical use. means an attribute of a material that is useful for

The terms "pharmaceutically acceptable excipient", "pharmaceutically acceptable carrier" and "therapeutically inert excipient" can be used interchangeably and are devoid of therapeutic activity, Administration of disintegrants, binders, fillers, solvents, buffers, osmotic agents, stabilizers, antioxidants, surfactants, carriers, diluents or lubricants used in the formulation of pharmaceutical products Any pharmaceutically acceptable ingredient in the pharmaceutical composition that is non-toxic to the intended subject.

The term "therapeutically effective amount" (or "effective amount") means that, when administered to a patient, (i) treat or prevent a particular disease, (ii) alleviate or ameliorate one or more symptoms of the disease. or eliminate, or (iii) prevent or delay the onset of one or more symptoms of the disease. A therapeutically effective amount will depend on the compound, the condition being treated, the severity of the disease being treated, the age and relative health of the patient, the route and form of administration, the judgment of the attending physician or veterinarian, and other factors. change by

"Treating" or the term "treatment" of a disease (eg, SCLS) includes reversing, alleviating, or inhibiting the progression of the disease or one or more symptoms thereof.
"Patient" or "subject" can be used interchangeably and mean a mammal in need of treatment. Mammals include, but are not limited to, primates (e.g., human and non-human primates such as monkeys), livestock animals (e.g., cows, sheep, cats, dogs and horses) and laboratory animals (mice, rats, guinea pigs, etc.). ) is included. In preferred embodiments, the patient is human. Intended to be included as a patient is any subject who participates in a clinical research trial. The patient may have been previously treated with other drugs and/or any KDM1A inhibitor, for example. In one aspect, the patient has not been previously treated with any KDM1A inhibitor. The patient may be treated with other drugs, particularly at the time the sample is obtained, but should not be treated with any KDM1A inhibitor at the time the sample is obtained for use in the methods of the invention (i.e., the patient may should not be treated simultaneously with a KDM1A inhibitor when obtaining . Alternatively, if the biomarker level can be modulated by the (residual) KDM1A inhibitor within that time period (e.g., the biomarker level to the level prior to previous treatment with the KDM1A inhibitor (or administration of the KDM1A inhibitor). If not returned), the patient must not be treated with any KDM1A inhibitor within the period prior to obtaining the sample. For example, the patient should not have been treated with any KDM1A inhibitor within two weeks, more preferably within one month, prior to obtaining the sample. The latter is to avoid that biomarker levels can still be regulated by (residual) KDM1A inhibitors.

The term "biomarker" or "marker" as used herein refers to a protein or polynucleotide, the expression or presence of which can be detected in or on mammalian tissues or cells by standard methods. (or methods disclosed herein) and correlates with the sensitivity of mammalian cells or tissues to treatment comprising a KDM1A inhibitor. Biomarkers according to the invention are ASCL1 and SOX2.

The term "measuring" the level of a biomarker, as used herein, refers to biomarkers in a sample using a suitable method of detection as described elsewhere herein. It refers to experimentally determining the amount of a marker.

The term "threshold" as used herein refers to two categories/subsets of the population, such as SCLC patients likely to respond to KDM1A inhibitor treatment versus SCLC patients less likely to respond to KDM1A inhibitor treatment. Refers to a given value, a straight line or a more complex n-dimensional function that defines the frontier between. In the methods according to the invention, different thresholds are applied to individual biomarker levels (i.e. each biomarker, ASCL1 and SOX2, has respective thresholds) or classification as described elsewhere herein. An algorithm can be applied to the score derived from the biomarker levels. As one skilled in the art will appreciate, thresholds are established to optimally discriminate between different categories of samples. Thresholds can be established according to methods known in the art. Typically, the threshold can be determined experimentally or theoretically using a training set of samples with known susceptibility or resistance to treatment with a KDM1A inhibitor. Training samples can be, for example, SCLC cell lines, patient-derived xenografts (PDX), or human clinical samples with known sensitivity or resistance to treatment with a KDM1A inhibitor. Thresholds can also be arbitrarily selected based on existing experimental and/or clinical and/or regulatory requirements, as recognized by those skilled in the art. Preferentially, thresholds are established to obtain optimal sensitivity and specificity according to the function of the test and the benefit/risk balance (false positive and false negative clinical results). Typically, optimal sensitivity and specificity (and thus thresholds) can be determined using receiver operating characteristic (ROC) curves based on experimental data, as shown in the accompanying examples. In some implementations, the threshold is a threshold value. In some embodiments, the biomarker thresholds are ASCL1 and SOX2 measured in one or more samples of SCLC cells from patients that are sensitive or resistant to treatment comprising a KDM1A inhibitor. derived from the level. In some embodiments, the threshold is derived from ASCL1 and SOX2 levels measured in one or more samples of (human) patients who responded or did not respond to treatment comprising a KDM1A inhibitor. In some embodiments, the threshold is from ASCL1 and SOX2 levels measured in one or more samples obtained from xenograft models from patients who responded or did not respond to treatment comprising a KDM1A inhibitor. Induced. In some embodiments, the threshold is obtained from the mRNA level of the biomarker. In some embodiments, the threshold is obtained from the protein level of the biomarker.

The term "score" as used herein refers to the output calculated from the biomarker levels measured in a sample by a classification algorithm. The score is compared to a threshold value and used to determine if the patient from which the sample was derived is likely or unlikely to respond to treatment containing a KDM1A inhibitor. For example, if the score exceeds a threshold, the patient is identified as likely to respond to treatment with a KDM1A inhibitor/as one who may benefit from treatment with a KDM1A inhibitor. be.

A "classification algorithm", as used herein, is used to calculate a score for a sample and assess which category the sample belongs to ("classify"), i.e. is a mathematical function used when Classification algorithms are well known in the art. Examples of classification algorithms include Linear Classifier, Fisher Linear Discriminant, Linear Boolean Classification, Logistic Regression, Naive Bayes Classifier, Perceptron, Support Vector Machine, Least Squares Support Vector Machine, Quadratic Classifier, Kernel Estimation, k-Nearest , decision trees, random forests, neural networks, and learning vector quantization. Software packages incorporating one or more classification algorithms are readily available for online use or download, eg, DTREG, XLSTat, http://www. support-vector-machines. org/SVM_soft. html etc. In some embodiments, the classification algorithm is a Boolean function (truth function).

Samples can be classified using the Boolean integration function A AND B, where A and B are whether the level of each biomarker (ASCL1 and SOX2) in the sample exceeds the respective threshold for that biomarker. Evaluate. A Boolean integration function is typically determined if all criteria are met (e.g., if the level of each biomarker (ASCL1 and SOX2) in the sample is above/exceeds the biomarker's respective threshold(s) , 1 (true), or if one (or both) of the criteria are not met (e.g., only one level of each biomarker (ASCL1 and SOX2) in the sample exceeds the biomarker's respective threshold Above/above(s) or not above/above the level of any of the biomarkers) yields a score represented by 0 (false). The threshold applied to the score generated by the Boolean algorithm to classify samples is 0, i.e., samples above this threshold (i.e., score > 0) respond to treatment with a KDM1A inhibitor. categorized as likely to benefit from treatment including a /KDM1A inhibitor.

In some embodiments, the classification algorithm is a support vector machine (SVM). SVM is used to map the training dataset in space and construct an N-dimensional hyperplane that optimally separates the sample data into two categories (e.g. susceptibility and resistance to KDM1Ai) and thus acts as a threshold function. Classification is performed by In addition to performing linear classification, SVMs can use kernel tricks to perform non-linear classification, mapping sample data to a high-dimensional feature space. Then, using the trained SVM's scoring function, new data are mapped into that same space and predicted to belong to a category based on which side of the hyperplane they fall on. The performance of a classification algorithm (thresholds included) is measured by comparing the classification predicted by the algorithm with experimental values and calculating true positives, false positives, true negatives, and false negatives, as well as sensitivity, specificity, etc. can be further evaluated and optionally subjected to multiple training rounds using training samples of known responsiveness/tolerance to KDM1Ai to adjust model parameters and/or optimize performance. can.

The term "exceeds" as used herein means: a test sample's biomarker level or score (with unknown susceptibility to KDM1Ai), e.g. samples from SCLC patients who have been diagnosed with KDM1Ai by comparing the level or score (as the case may be) of the test sample to the respective threshold to classify the sample into the category of samples known to be susceptible to KDM1Ai. exceeds or crosses the threshold when In some embodiments, the threshold is a threshold value, and if the biomarker level or score exceeds its respective threshold, the biomarker level or score is the threshold (threshold value ). The comparison of the level of the biomarker or score in the sample to the respective threshold value of the biomarker or score can be done by thought, manually, or automatically by a computer program.

The term "sample" as used herein with respect to a patient sample used in the method according to the invention is a tumor sample (e.g. a biopsy such as a biopsy sample from either a primary or metastatic SCLC lesion). sample), body fluid or patient-derived cell line, PDX sample (“PDX” is “patient-derived xenograft”, ie, human tumor grown in mice), or exosome(s). Preferably, the sample is enriched/enriched for tumor cells. Patient-derived samples used to perform methods according to the invention should be obtained prior to initiation of treatment with a KDM1A inhibitor (i.e., during periods of no current treatment with a KDM1A inhibitor and/or For example, if a biomarker level can be modulated by a (residual) KDM1A inhibitor within that period, it should be obtained during a period that is not the period following previous treatment with a KDM1A inhibitor (administration of a KDM1A inhibitor). For example, a patient-derived sample to be used in accordance with a method according to the invention should not be obtained within two weeks, preferably one month, after previous treatment with a KDM1A inhibitor (or administration of a KDM1A inhibitor). ). Biopsy samples can be obtained by well known techniques and can be fresh or post-collection fractionation and storage techniques (e.g., frozen, fixed and/or embedded, e.g., formalin-fixed, paraffin-embedded, fresh snap frozen, fixed and frozen OCT embedding, etc.). A sample of bodily fluid can be obtained by well-known techniques and includes a sample of blood, sputum, bronchoalveolar lavage fluid, or any other biological secretion or derivative thereof that may contain SCLC cells. Separated cells can be obtained from body fluids or tissues or organs by separation techniques such as centrifugation or cell sorting. Of course, the cell sample may be subjected to various well-known post-collection preparation and storage techniques (e.g., nucleic acid and/or protein extraction, fixation, storage, freezing, ultrafiltration, concentration) prior to assessing the level of markers in the sample. , evaporation, centrifugation, etc.). Preferably, the sample in which biomarker levels are measured is enriched/enriched for the presence of SCLC cells or SCLC cell-derived vesicles (eg, exosomes, etc.). For example, SCLC cells can be isolated from sputum using methods described in the literature (Chest. 1992 August; 102(2):372-4). SCLC circulating tumor cells (CTCs) are described in the literature (Peeters et al., Br J Cancer 2 Apr 2013; 108(6): 1358-67; Hodgkinson et al., Nat Med. Aug 2014; 20 ( 8): 897-903; Carter et al., Nat Med. 2017 Jan;23(1):114-119). 2017; 7(3):789-804; Sandfeld-Paulsen et al., J Thorac Oncol. October 2016; 11(10):1701-10). can be purified from blood by various methods described in . Biomarker levels can also be analyzed in spatially defined regions from SCLC cell-enriched samples, e.g., determined by an anatomic pathologist using standard methods used in the field. can be done.

The terms "responsive to", "responsive to", "responsive to", "susceptible to", "susceptible to" and like expressions are used in the context of treatments involving KDM1A inhibitors to SCLC ( or sample, SCLC cell line, etc.) shows a positive response to a KDM1A inhibitor, ie, a treatment containing a KDM1A inhibitor. In a more simplified form, the terms "responsive to treatment comprising a KDM1A inhibitor," etc. can be expressed as "responsive to a KDM1A inhibitor," "responsive to KDM1A inhibition," and the like. For example, a "positive response to treatment with a KDM1A inhibitor" or "treatment with a KDM1A inhibitor is beneficial" indicates that the progression of the disease SCLC or one or more symptoms thereof is reversed, alleviated or inhibited. can or include The term "likely to respond" as used herein can mean "more responsive" or simply "responsive."

The phrases "identifying a patient" or "selecting a patient" can be used interchangeably and as used herein are those who respond to (or benefit from) treatment comprising a KDM1A inhibitor. information generated in relation to biomarker levels in patient samples to identify or select patients who are likely to respond (or are unlikely to benefit) or to use the data. The information or data used or generated may be in any form and may be written, oral or electronic. The methods and uses provided herein include communicating results, information or data to the patient and/or to any person(s) involved in or responsible for the treatment of the patient. Treatment may include a KDM1A inhibitor. In some embodiments, using generated information or data includes communicating, presenting, reporting, storing, transmitting, forwarding, supplying, transmitting, distributing, or combinations thereof. In some embodiments, communicating, presenting, reporting, storing, transmitting, forwarding, providing, transmitting, distributing, or combinations thereof is performed by computing devices, analyzer units, or combinations thereof. In some further embodiments, communicating, presenting, reporting, storing, transmitting, forwarding, supplying, transmitting, distributing, or combinations thereof is performed by a laboratory or medical professional. In some embodiments, the information or data includes comparison of biomarker levels and thresholds. In some embodiments, the information or data includes an indication that the patient is likely or unlikely to respond to (or benefit from) treatment comprising a KDM1A inhibitor.

The phrase "predicting patient responsiveness" as used herein refers to biomarker levels in a patient sample to assess the likelihood that the patient will respond to treatment comprising a KDM1A inhibitor. Refers to the use of information or data generated in relation to it. Information or data used or generated may be in any form, written, oral or electronic. In some embodiments, using generated information or data includes communicating, presenting, reporting, storing, transmitting, forwarding, supplying, transmitting, distributing, or combinations thereof. In some embodiments, communicating, presenting, reporting, storing, transmitting, forwarding, providing, transmitting, distributing, or combinations thereof is performed by computing devices, analyzer units, or combinations thereof. In some further embodiments, communicating, presenting, reporting, storing, transmitting, forwarding, supplying, transmitting, distributing, or combinations thereof is performed by a laboratory or medical professional. In some embodiments, the information or data includes comparison of biomarker levels and thresholds. In some embodiments, the information or data comprises an indication that the patient is likely or unlikely to respond to treatment comprising a KDM1A inhibitor.

The phrase "select treatment", as used herein, was generated in relation to biomarker levels in a patient sample to identify or select a treatment (therapy) for the patient. Refers to using information or data. In some embodiments, treatment may include a KDM1A inhibitor. Information or data used or generated may be in any form, written, oral or electronic. In some embodiments, using generated information or data includes communicating, presenting, reporting, storing, transmitting, forwarding, supplying, transmitting, distributing, or combinations thereof. In some embodiments, communicating, presenting, reporting, storing, transmitting, forwarding, supplying, transmitting, distributing, or combinations thereof is performed by computing devices, analyzer units, or combinations thereof. In some further embodiments, communicating, presenting, reporting, storing, transmitting, forwarding, supplying, transmitting, distributing, or combinations thereof is performed by a laboratory or medical professional. In some embodiments, the information or data includes comparison of biomarker levels and thresholds. In some embodiments, the information or data comprises an indication that treatment comprising a KDM1A inhibitor is suitable for the patient (ie, the patient is likely to respond to said treatment).

The phrase "recommend treatment," as used herein, refers to a KDM1A inhibitor for patients identified or selected as likely or unlikely to respond to treatment, including a KDM1A inhibitor. Refers to using information or data generated in relation to biomarker levels to suggest or select treatments, including inhibitors. Information or data used or generated may be in any form, written, oral or electronic. In some embodiments, using generated information or data includes communicating, presenting, reporting, storing, transmitting, forwarding, supplying, transmitting, distributing, or combinations thereof. In some embodiments, communicating, presenting, reporting, storing, transmitting, forwarding, providing, transmitting, distributing, or combinations thereof is performed by computing devices, analyzer units, or combinations thereof. In some further embodiments, communicating, presenting, reporting, storing, transmitting, forwarding, supplying, transmitting, distributing, or combinations thereof is performed by a laboratory or medical professional. In some embodiments, the information or data includes comparison of biomarker levels and thresholds. In some embodiments, the information or data comprises an indication that treatment with a KDM1A inhibitor is suitable for the patient.

As used herein, a "kit" is any article of manufacture (e.g., package or container), facilitated for manufacture, distributed, or sold as a unit for practicing the method of the present invention.

As used herein, an "agent" for measuring levels of ASCL1 or SOX2 includes, but is not limited to, any reagent typically used in the art for measuring biomarker levels. is an antibody that specifically recognizes an ASCL1 or SOX2 protein, or a probe and/or primer that hybridizes with an ASCL1 or SOX2 polynucleotide for specifically detecting a biomarker according to the present invention, and measures biomarker levels Any other reagents described in more detail elsewhere herein in connection with methods for .

The present invention provides means and methods for identifying patients with SCLC who are likely to respond to treatment with a KDM1A inhibitor and are therefore most suitable for treatment comprising a KDM1A inhibitor, and using a KDM1A inhibitor. A therapeutic method for treating a patient is provided. The present invention is based, at least in part, on the discovery that levels of ASCL1 and SOX2 can be used as biomarkers (e.g., predictive biomarkers) in methods of predicting the likelihood of responding to treatment with a KDM1A inhibitor. . As described herein and in the accompanying examples, we have found that high levels of both ASCL1 and SOX2 in SCLC cell lines correlate with SCLC responsiveness (sensitivity) to KDM1A inhibitor therapy. have been found to do. As shown in Examples 2, 3 and 4 and Figures 1-3, SCLC cell lines expressing high levels of both ASCL1 and SOX2 are normally responsive (sensitive) to KDM1A inhibitors. However, SCLC cell lines that exhibit low levels of either or both ASCL1 and SOX2 are usually resistant to KDM1A inhibitory treatment. Therefore, ASCL1 and SOX2 levels can be used to stratify SCLC patients against treatment with KDM1A inhibitors, with patients more likely to respond to treatment with KDM1A inhibitors and those more likely to respond to KDM1A inhibition identify patients who are unlikely to be sexually active. The methods of the invention using ASLC1 and SOX2 have high sensitivity and specificity, which is remarkable in view of the reduced number of biomarkers used, as shown in more detail in the accompanying examples. , can predict the responsiveness of SCLC to KDM1A inhibition. Using either SCLC cell lines or patient-derived samples, such as SCLC PDX samples, the method according to the present invention demonstrates a good correlation between their mRNA and protein expression levels, as shown in Example 5. is shown, biomarkers can be measured either as mRNA or protein levels. This is because the most readily available samples from cancer patients are typically fixed tumor biopsies, and standard biopsy sample fixation procedures are known to fragment and reduce RNA. The suitability for protein level analysis makes the method according to the invention particularly advantageous for use in clinical settings, especially in hospitals.

As noted above, the methods of the invention involve measuring levels of ASCL1 and SOX2. These markers themselves are known in the art and are described below.

Public database entries for ASCL1 and SOX2:
The DNA and protein sequences of human ASCL1 and SOX2 have been previously reported. See the GenBank numbers (NCBI-GenBank Flat File Release 225.0, April 15, 2018) and UniProtKB/Swiss-Prot numbers (Knowledgebase Release 2018_04, April 25, 2018) listed below. each of which is hereby incorporated by reference in its entirety for all purposes. Such sequences can be used to design procedures for measuring ASCL1 and SOX2 levels by methods known to those skilled in the art.

Exemplary nucleotide and amino acid sequences for human ASCL1 and SOX2 are shown in SEQ ID NOs: 1-4 herein. The table below assigns the markers and their respective sequences.

In one aspect, the invention provides a method of identifying patients with SCLC who are likely to respond to treatment comprising a KDM1A inhibitor, wherein a sample from the patient is administered prior to initiating treatment comprising a KDM1A inhibitor. A method is provided comprising measuring the levels of ASCL1 and SOX2 in a subject. In some embodiments, a patient is identified as likely to respond to treatment comprising a KDM1A inhibitor if each level of ASCL1 and SOX2 in the sample exceeds a threshold value. In some embodiments, the method further comprises using the levels of ASCL1 and SOX2 in the sample to generate a score for the sample, wherein if the score for the sample exceeds a threshold, the patient are identified as likely to respond to treatment, including KDM1A inhibitors. Accordingly, in some embodiments, the invention provides a method of identifying patients with SCLC who are likely to respond to treatment comprising a KDM1A inhibitor, wherein prior to initiating treatment comprising a KDM1A inhibitor, , measuring levels of ASCL1 and SOX2 in a sample from the patient, wherein the likelihood that the patient will respond to treatment comprising a KDM1A inhibitor if each level of ASCL1 and SOX2 in the sample exceeds a threshold value. is identified as high. In some embodiments, the invention provides a method of identifying SCLC patients who are likely to respond to treatment comprising a KDM1A inhibitor, wherein prior to initiating treatment comprising a KDM1A inhibitor, measuring the levels of ASCL1 and SOX2 in the sample; using the levels of ASCL1 and SOX2 in the sample to generate a score for the sample; , is identified as likely to respond to treatment comprising a KDM1A inhibitor.

In another aspect, the invention provides a method of identifying a patient with SCLC who may benefit from treatment comprising a KDM1A inhibitor, comprising: methods comprising measuring levels of ASCL1 and SOX2 in a subject. In some embodiments, the patient is identified as likely to benefit from treatment comprising a KDM1A inhibitor if each level of ASCL1 and SOX2 in the sample exceeds a threshold value. In some embodiments, the method further comprises using the levels of ASCL1 and SOX2 in the sample to generate a score for the sample, wherein if the score for the sample exceeds a threshold, the patient are identified as those that may benefit from treatment involving a KDM1A inhibitor. Accordingly, in some embodiments, the invention provides a method of identifying a patient with SCLC who may benefit from treatment comprising a KDM1A inhibitor, wherein prior to initiating treatment comprising a KDM1A inhibitor, the patient measuring the levels of ASCL1 and SOX2 in a sample from which the patient is determined to benefit from treatment comprising a KDM1A inhibitor if each level of ASCL1 and SOX2 in the sample exceeds a threshold value; A method is characterized. In some embodiments, the invention provides a method of identifying a patient with SCLC who may benefit from treatment comprising a KDM1A inhibitor, wherein prior to initiating treatment comprising a KDM1A inhibitor, measuring the levels of ASCL1 and SOX2 in the sample; using the levels of ASCL1 and SOX2 in the sample to generate a score for the sample; , which identify methods for which treatment involving a KDM1A inhibitor may be beneficial.

In a further aspect, the invention provides a method of predicting the responsiveness of a patient with SCLC to treatment comprising a KDM1A inhibitor, wherein ASCL1 in a sample derived from the patient prior to initiation of treatment comprising a KDM1A inhibitor. and SOX2 levels. In some embodiments, the patient is identified as likely to be responsive to treatment comprising a KDM1A inhibitor if each level of ASCL1 and SOX2 in the sample exceeds a threshold value. In some embodiments, the method further comprises using the levels of ASCL1 and SOX2 in the sample to generate a score for the sample, wherein if the score for the sample exceeds a threshold, the patient are identified as likely to be responsive to treatment, including KDM1A inhibitors. Accordingly, in some embodiments, the invention provides a method of predicting the responsiveness of a patient with SCLC to treatment comprising a KDM1A inhibitor, comprising: wherein the patient is likely responsive to treatment comprising a KDM1A inhibitor if each level of ASCL1 and SOX2 in the sample exceeds a threshold value. Provide a method that is identified as high. In some embodiments, the invention provides a method of predicting responsiveness of a patient with SCLC to treatment comprising a KDM1A inhibitor, wherein a sample from the patient is administered prior to initiation of treatment comprising a KDM1A inhibitor. measuring the levels of ASCL1 and SOX2 in the sample and using the levels of ASCL1 and SOX2 in the sample to generate a score for the sample; Methods are provided that are identified as likely to be responsive to treatment comprising a KDM1A inhibitor.

In a further aspect, the invention provides a method of assessing the likelihood of a patient with SCLC to respond to treatment comprising a KDM1A inhibitor, wherein, prior to initiating treatment comprising a KDM1A inhibitor, in a sample from the patient methods comprising measuring levels of ASCL1 and SOX2 in a subject. In some embodiments, a patient is identified as likely to respond to treatment comprising a KDM1A inhibitor if each level of ASCL1 and SOX2 in the sample exceeds a threshold value. In some embodiments, the method further comprises using the levels of ASCL1 and SOX2 in the sample to generate a score for the sample, wherein if the score for the sample exceeds a threshold, the patient is , as likely to respond to treatment, including KDM1A inhibitors. Accordingly, in some embodiments, the invention provides a method of assessing the likelihood of a patient with SCLC to respond to treatment comprising a KDM1A inhibitor, wherein prior to initiating treatment comprising a KDM1A inhibitor, comprising measuring levels of ASCL1 and SOX2 in a sample from the patient, wherein the patient is likely to respond to treatment comprising a KDM1A inhibitor if each level of ASCL1 and SOX2 in the sample exceeds a threshold value. Provide a method that is identified as high. In some embodiments, the invention provides a method of assessing a patient's likelihood of having SCLC to respond to treatment comprising a KDM1A inhibitor, wherein prior to initiating treatment comprising a KDM1A inhibitor, measuring levels of ASCL1 and SOX2 in a sample of the patient and using the levels of ASCL1 and SOX2 in the sample to generate a score for the sample; provide methods of identifying as likely to respond to treatment comprising a KDM1A inhibitor.

In a further aspect, the invention provides a method of assessing the likelihood of SCLC in a patient responsive to treatment comprising a KDM1A inhibitor, wherein a sample from a patient with SCLC is administered prior to initiation of treatment comprising a KDM1A inhibitor. A method is provided comprising measuring the levels of ASCL1 and SOX2 in a subject. In some embodiments, SCLC is identified as likely to respond to treatment comprising a KDM1A inhibitor when each level of ASCL1 and SOX2 in the sample exceeds a threshold value. In some embodiments, the method further comprises using the levels of ASCL1 and SOX2 in the sample to generate a score for the sample, wherein if the score for the sample exceeds a threshold, SCLC are identified as likely to respond to treatment, including KDM1A inhibitors. Accordingly, in some embodiments, the invention provides a method of assessing the likelihood of SCLC response to treatment comprising a KDM1A inhibitor, wherein prior to initiation of treatment comprising a KDM1A inhibitor, a patient with SCLC comprising measuring levels of ASCL1 and SOX2 in a sample from which SCLC is likely to respond to treatment with a KDM1A inhibitor if each level of ASCL1 and SOX2 in the sample exceeds a threshold A method is provided that is identified as a In some embodiments, the invention provides a method of assessing the likelihood that SCLC will respond to treatment comprising a KDM1A inhibitor, wherein prior to initiating treatment comprising a KDM1A inhibitor, measuring the levels of ASCL1 and SOX2 in the sample; using the levels of ASCL1 and SOX2 in the sample to generate a score for the sample; , is identified as likely to respond to treatment comprising a KDM1A inhibitor.

In a further aspect, the invention provides a method of selecting treatment for a patient with SCLC comprising measuring levels of ASCL1 and SOX2 in a sample from the patient prior to initiating treatment. . In some embodiments, the method comprises providing a recommendation that selected treatment for the patient includes a KDM1A inhibitor if each level of ASCL1 and SOX2 in the sample exceeds a threshold value. . In some embodiments, the method uses the levels of ASCL1 and SOX2 in the sample to generate a score for the sample, and if the score for the sample exceeds a threshold, the patient is selected. providing a recommendation that the treatment includes a KDM1A inhibitor. Accordingly, in some embodiments, the invention further provides a method of selecting treatment for a patient with SCLC, comprising measuring levels of ASCL1 and SOX2 in a sample from the patient prior to initiating treatment. , providing a recommendation that the selected treatment for the patient includes a KDM1A inhibitor if each level of ASCL1 and SOX2 in the sample exceeds a threshold. In some embodiments, the invention further provides a method of selecting treatment for a patient with SCLC, comprising, prior to initiating treatment, measuring levels of ASCL1 and SOX2 in a sample from the patient; levels of ASCL1 and SOX2 are used to generate a score for the sample and provide a recommendation that selected treatment for the patient includes a KDM1A inhibitor if the score for the sample exceeds a threshold providing a method comprising:

All of the above methods according to the invention involve measuring the levels of the biomarkers of the invention (ASCL1 and SOX2) in a sample and assessing said biomarker levels or a derived score (based on said levels) versus a threshold. include. Typically, each biomarker (i.e., ASCL1 and SOX2) has its threshold (which can be established as described elsewhere herein) and the (measured) Levels are each assessed against its respective threshold, and if each level of ASCL1 and SOX2 in the sample exceeds its threshold, the patient will receive a KDM1A inhibitor (or, if applicable, the patient will receive a KDM1A identified as likely to respond to treatment, including inhibitors, etc., identified as those that may benefit from treatment. Alternatively, the (measured) levels of ASCL1 and SOX2 in the sample can be used to generate a score for the sample using a classification algorithm; can be established as described elsewhere herein), the patient is identified as likely to respond to treatment comprising a KDM1A inhibitor if the score for the sample exceeds a threshold (Alternatively, if applicable, patients are identified as those who may benefit from treatment, including KDM1A inhibitors and the like).

In some embodiments of any of the preceding aspects, the method further comprises obtaining or providing a sample from the patient. The obtaining/providing step is prior to measuring the level of the biomarker (and prior to administration of any treatment comprising a KDM1A inhibitor to the patient from whom the sample is obtained/provided).

In some embodiments of any of the preceding aspects, the method comprises administering to the patient a treatment comprising a KDM1A inhibitor if the patient is identified as likely to respond to treatment comprising a KDM1A inhibitor. It further includes recommending, prescribing or administering a therapeutically effective amount.

In some embodiments of any of the preceding aspects, the method comprises removing the patient from treatment with a KDM1A inhibitor if the patient is identified as unlikely to respond to treatment comprising a KDM1A inhibitor. can optionally further include recommending

In the methods according to the invention, the levels of ASCL1 and SOX2, either as mRNA levels or protein levels, are known in the art for measuring mRNA or protein levels, including the methods described herein. It can be determined using any method.

In the method according to the invention, mRNA from the sample can be used directly to determine biomarker levels. In the method according to the invention the levels can be determined by hybridization. In the method according to the invention, RNA can be transformed into cDNA (complementary DNA) copies using methods known in the art. Detection methods include, but are not limited to, quantitative reverse transcription polymerase chain reaction (qRT-PCR), gene expression analysis, RNA sequencing, nanopore sequencing, microarray analysis, gene expression chip analysis, (in situ) hybridization techniques. , RNAscope, and chromatography, and any other technique known in the art, such as Ralph Rapley, "The Nucleic Acid Protocols Handbook", published 2000, ISBN: 978-0-89603-459-4 may include those described in Methods of detecting RNA include, but are not limited to, PCR, real-time PCR, digital PCR, hybridization, microarray analysis, and any other technique known in the art, such as Leland et al., Handbook of Molecular and Cellular Methods in Biology and Medicine”, published 2011, ISBN 9781420069389.

In a method according to the invention, the method can comprise detecting the protein expression level of the biomarker. Any suitable method for protein detection, quantization and comparison, eg, John M. et al. Walker, The Protein Protocols Handbook, published 2009, ISBN 978-1-59745-198-7, may be used. Biomarker protein expression levels can be determined using, but not limited to, enzyme-linked immunosorbent assays (ELISA), "sandwich" immunoassays, immunoradiometric assays, in situ immunoassays, alpha LISA immunoassays, protein proximity assays, proximity ligation techniques (e.g., protein qPCR), western blot assays, immunoprecipitation assays, immunofluorescence assays, flow cytometry, immunohistochemistry (IHC), immunoelectrophoresis, protein immunostaining, confocal microscopy. or the antibody or antibody fragment is displaced in a specific way by a chemical probe, aptamer, receptor, interacting protein, or any other biomolecule that recognizes the biomarker protein by similar methods; or by Forster/fluorescence resonance energy transfer (FRET), differential scanning fluorescence spectroscopy (DSF), microfluidics, spectroscopy, mass spectroscopy, enzymatic assays, surface plasmon resonance, or combinations thereof. can. Immunoassays can be homogeneous or heterogeneous. In homogeneous assays, an immunological response usually involves a specific antibody, labeled analyte, and sample of interest. The signal resulting from the label is modified, either directly or indirectly, upon binding of the antibody to the labeled analyte. Both the immunological reaction and the detection of its extent can be performed in a homogeneous solution. Immunochemical labels that may be used include free radicals, radioisotopes, fluorochromes, enzymes, bacteriophages, or coenzymes. In a heterogeneous assay approach, the reagents are usually sample, antibody, and a means of producing a detectable signal. Antibodies can be immobilized on a support such as a bead, plate or slide and contacted with a specimen suspected of containing the antigen in a liquid phase. The support is then separated from the liquid phase and either the support phase or the liquid phase is interrogated for detectable signal using means for producing such signal. A signal is related to the presence of the analyte in the sample. Means of producing a detectable signal include the use of radioactive, fluorescent, or enzymatic labels.

Antibodies against biomarkers of interest can be used in the methods according to the invention. A kit for detection can be used in the method according to the invention. Such antibodies and kits are available from EMD Millipore, R&D Systems for biochemical assays, Thermo Scientific Pierce Antibody, Novus Biologicals, Aviva Systems Biology, Abnova Corporation, AbD Serotec or other commercial sources. Alternatively, antibodies can also be synthesized by any known method. The term "antibody" as used herein is intended to include monoclonal antibodies, polyclonal antibodies, single chain antibodies and chimeric antibodies. Antibodies are attached to a suitable solid support (e.g., beads such as protein A or protein G agarose, microspheres formed from materials such as latex or polystyrene, plates, slides or wells) according to known techniques such as passive conjugation. can be conjugated. Antibodies described herein can also be radioactively labeled (e.g. ³⁵ S), enzymatically labeled (e.g. horseradish peroxidase, alkaline phosphatase), fluorescently labeled (e.g. fluorescein, Alexa, green fluorescent protein, rhodamine). , phthalocyanine containing beads capable of releasing singlet oxygen after irradiation at 680 nM and subsequently eliciting luminescence after absorption of light by acceptor beads containing europium or ceribium, and oligonucleotide labels. can be conjugated to a detectable label or group capable of A label can directly or indirectly produce a signal. The signal generated can include, for example, fluorescence, radioactivity, or luminescence, according to known techniques.

Antibodies can be replaced by alternative protein capture agents that have high affinity and selectivity for the protein biomarker being analyzed, eg, aptamers, affimers, or chemoprobes.

In some embodiments, when measuring biomarker protein levels, biomarker levels can be assessed within the nuclei of tumor cells in immunofluorescence analysis of a portion of SCLC tumor cells, e.g., a biopsy. .

Biomarker levels can be expressed in any form of mRNA expression or protein expression measurement used in the field, raw data or processed data, i.e. background subtraction, normalization or other correction, or It can be raw data that is subjected to other mathematical manipulations or transformations typically used in the field. For example, when measured by microarray hybridization, the biomarker level is the hybridization signal intensity value of the sample, Log2(hybridization intensity value of sample), or Log2(hybridization signal intensity value of sample/hybridization of reference sample signal intensity value). Examples of suitable reference samples are tumor samples from patients with high expression levels of ASCL1 and SOX2 obtained from xenografts or PDX models, or SCLC cell pellets. Hybridization signal values can be obtained using single-color or two-color hybridizations. For example, when measured by qRT-PCR, biomarker levels are determined by cross-point PCR-cycle (Cp) values, which represent the number of cycles required for amplification-associated fluorescence to reach a threshold level for specific detection. ΔC _p =C _p -C p, can be expressed by the 2- ^{Cp value or the 2-ΔCp value} by _{the reference gene} . For example, when measured by RNA sequencing, biomarker levels may be represented by RPM (Reads Per Million), RPKM (Reads Per Kilobase Million), FPKM (Fragments Per Kilobase Million), or TPM (Transcripts Per Million) values. can be done. For example, when measured by Western blot, biomarker levels are normalized as integrated densities (AU) of the corresponding bands after image analysis, as raw integrated densities or by protein content and/or reference It can be expressed as a ratio to the sample. For example, when measured by immunostaining, biomarker levels are expressed as integrated density (A.U) of the nuclear signal after image analysis, raw integrated density (A.U)/area unit ( ^{pixel2 or μm 2 )} . , or as integrated density/nucleus, or as a ratio to a reference sample. For example, when measured by ELISA, biomarker levels are measured by R. L. It can be expressed as U (relative light units) or absorbance units, raw or background corrected, or normalized by total protein content and/or as a ratio to a reference sample.

In some embodiments of any of the methods according to the invention, the biomarker level (ie ASCL1 level and SOX2 level) is mRNA expression level. Preferably, mRNA expression levels are measured by qRT-PCR.

In some embodiments of any of the methods according to the invention, the biomarker level is protein expression level. Preferably, protein expression levels are measured by fluorescence immunohistochemistry.

In some embodiments, biomarker expression levels in immunofluorescent staining are visually classified as high, medium, low or undetectable with values of 3, 2, 1 and 0, respectively, based on staining intensity level. . Samples with intermediate and high levels (values 2 and 3) are considered "positive" (i.e., exceed the respective biomarker threshold), whereas undetectable or low levels (values 0 and 1) of samples are considered "negative" (ie, do not exceed the respective biomarker threshold). If the sample is deemed "positive" for both biomarkers (i.e., if the levels of both ASCL1 and SOX2 in the sample are classified as level 2 or 3, respectively), the patient: Patients/patients identified as likely to respond to treatment comprising a KDM1A inhibitor are identified as those who may benefit from treatment comprising a KDM1A inhibitor.

Alternatively, in some embodiments, biomarker expression levels in immunofluorescence staining images can be quantified. DNA dyes, such as DAPI staining, can be used to localize the nucleus using fluorometry. SOX2 and ASCL1 expression in the nucleus can be analyzed using immunofluorescence quantification. Individual images from biomarkers and DAPI staining can be analyzed using image software such as ImageJ. The signal can be obtained by background subtraction and normalized to a reference (calibrator) sample. A suitable calibrator sample is one with high uniform nuclear expression of both biomarkers, eg, a xenograft sample from NCI-H1417. Normalized quantitation values can be expressed as % or ratio relative to the calibrator sample. A threshold value for each biomarker can be established as a fraction of the signal of the calibrator samples and the negative control sample(s) used (e.g., a normal lung biopsy, or is selected to be higher than the (mean) signal of the xenograft sample, which is undetectable. Preferably, the threshold is at least the mean signal of the negative control samples plus 1 SD, 2 SD or 3 SD, where SD means standard deviation.

In some embodiments of methods and uses according to the present invention, terms such as "when each level of ASCL1 and SOX2 in a sample exceeds a threshold value" is defined as "when each level of ASCL1 and SOX2 in a sample exceeds a can mean "if increased by In this context, a patient is identified as likely to respond to treatment comprising a KDM1A inhibitor if the respective levels of ASCL1 and SOX2 in the sample are increased compared to controls. Treatment with an inhibitor is identified as one that may be beneficial. The terms "control" or "reference" are used interchangeably herein. A non-limiting example of a "control" (e.g., "control value") or "reference" (e.g., "reference value") is the The levels of ASCL1 and SOX2, respectively, in the sample or sample pool. A healthy individual/subject may, for example, be an individual/subject not suffering from SCLC as defined herein, particularly one not suffering from SCLC at the time the sample is obtained from the individual/subject. Alternatively, a healthy individual/subject can be, for example, an individual/subject not suffering from a disease or disorder associated with increased levels of ASCL1 and SOX2. Preferably, the healthy individual/subject is human. Another non-limiting example of a "control" (e.g., "control value") or "reference" (e.g., "reference value") is ASCL1 and SOX2, respectively, in a sample or sample pool from a "non-responder." For example, one or more patients with SCLC known not to respond to KDM1A inhibitors. Another example of a "non-responder" control is (a) cell line(s)/cell(s)/tissue(s), in vitro, ex vivo or (patient-derived) xenograft studies No response to inhibitors. Another non-limiting example of a "control" is an "internal standard", e.g., a purified or synthetically produced protein and/or peptide, or a mixture thereof, or a corresponding nucleic acid, each protein/ The amount of peptide (or corresponding nucleic acid) is determined by using the "non-responder" control described above. In particular, this "internal standard" can contain the protein(s) (or corresponding nucleic acids) ASCL1 and SOX2, as described and defined herein. A non-limiting example of a "control" (e.g., "control value") or "reference" (e.g., "reference value") is e.g. herein if the sample was obtained before being predisposed (or at risk of) to suffering from The levels of ASCL1 and SOX2, respectively, in a sample from the patient to be identified.

In some embodiments of any of the preceding aspects, the sample is an SCLC biopsy, preferably an SCLC biopsy enriched with SCLC cells.
Preferably, in any of the methods of the invention, the patient is a human patient. It is preferred herein that said (diagnostic) method is an in vitro method. "In vitro" as used herein means that the methods of the invention described above, such as methods of identifying patients with SCLC, who are likely to respond to treatment, including KDM1A inhibitors, are Not in vivo, i.e., not directly on the patient, but obtained from the patient and separated/isolated from the patient (i.e., in vivo location) outside of a living human (or other mammal) (removed from the sample).

In a further aspect, the invention provides the use of ASCL1 and SOX2 in methods of identifying patients with SCLC who are likely to respond to treatment comprising a KDM1A inhibitor.

In a further aspect, the invention provides the use of ASCL1 and SOX2 in methods of assessing the likelihood of response of a patient with SCLC to treatment comprising a KDM1A inhibitor.

In a further aspect, the invention provides the use of one or more agents for measuring levels of ASCL1 and SOX2 in methods of identifying patients with SCLC who are likely to respond to treatment comprising a KDM1A inhibitor. offer.

In a further aspect, the invention provides the use of one or more agents for measuring ASCL1 and SOX2 levels in a method of assessing the likelihood of response of a patient with SCLC to treatment comprising a KDM1A inhibitor. .

In a further aspect, the invention provides the use of ASCL1 and SOX2 for the manufacture of a diagnostic for identifying patients with SCLC who are likely to respond to treatment comprising a KDM1A inhibitor.

In a further aspect, the invention provides the use of ASCL1 and SOX2 for the manufacture of a diagnostic to assess the likelihood of response of patients with SCLC to treatment comprising a KDM1A inhibitor.

In another aspect, the invention provides a kit for identifying patients with SCLC who are likely to respond to treatment comprising a KDM1A inhibitor, comprising: Kits are provided that include one or more agents and optionally instructions for use.

In another aspect, the invention provides a kit for assessing the likelihood of response of a patient with SCLC to treatment comprising a KDM1A inhibitor, the kit for measuring levels of ASCL1 and SOX2 in a sample. Kits containing the above agents and optionally instructions for use are provided.

Identifying subgroups of patients with SCLC who have a high chance of response, i.e. who are likely to respond to or benefit from treatment comprising KDM1Ai, by using the above methods. is possible. The invention also relates to therapeutic methods for treating SCLC patients so identified.

Thus, in a further aspect, the invention provides a method of treating a patient with SCLC, wherein, prior to initiating treatment comprising a KDM1A inhibitor, the patient, using a method according to any of the preceding aspects, comprises: When identified as likely to respond to treatment comprising a KDM1A inhibitor, methods are provided comprising administering to a patient a therapeutically effective amount of a treatment comprising a KDM1A inhibitor.

In a further aspect, the invention provides a method of treating a patient with SCLC, wherein, prior to initiating treatment, levels of ASCL1 and SOX2 are measured in a sample from the patient, and levels of each of ASCL1 and SOX2 in the sample are determined. Identifying a patient as likely to respond to treatment comprising a KDM1A inhibitor if the level exceeds a threshold; Features include administering a therapeutically effective amount of treatment.

In a further aspect, the invention provides a method of treating a patient with SCLC, comprising measuring levels of ASCL1 and SOX2 in a sample from the patient prior to initiating treatment, and using these levels to A score is generated for the sample, and the patient is identified as likely to respond to treatment comprising a KDM1A inhibitor if the score for the sample exceeds a threshold, and the patient is identified as likely to respond. A method comprising administering to a patient a therapeutically effective amount of a treatment comprising a KDM1A inhibitor, if administered.

In a further aspect, the invention provides a KDM1A inhibitor for use in treating a patient with SCLC, wherein prior to initiating treatment comprising the KDM1A inhibitor, the patient undergoes a method according to any of the preceding aspects. is used to provide KDM1A inhibitors that have been identified as likely to respond to treatment comprising a KDM1A inhibitor.

In some embodiments of any of the preceding aspects, the method further comprises obtaining or providing a sample from the patient.
Preferably, in any of the methods of treatment and uses according to the invention, the patient is a human patient.

The disclosures elsewhere regarding methods of measuring biomarker levels, sample types, etc. are similarly applicable to the above treatment methods and related therapeutic applications.
KDM1A inhibitors that can be used in accordance with the present invention include any KDM1A inhibitor currently known in the art or that may be reported in the future. Preferably, the KDM1A inhibitor is a small molecule. Both irreversible and reversible KDM1A inhibitors have been reported. Irreversible KDM1A inhibitors exert their inhibitory activity by covalently binding to a FAD cofactor within the KDM1A active site, typically a 2-cyclyl- Based on the cyclopropylamino moiety. Reversible inhibitors of KDM1A have also been disclosed. Preferably, KDM1A inhibitors should be active in cells. Cellular activity of KDM1A inhibitors can be assessed, for example, in SCLC cell viability assays (such as those described in Example 1 herein, or those described in Mohammad et al., 2015, supra) or acute myeloid leukemia. Cell line differentiation assays (eg, as described in Lynch et al., Anal Biochem. 2013 Nov 1:442(1):104-6.doi:10.1016/j.ab.2013.07.032). can be determined using well-established in vitro cellular assays for KDM1A inhibitors, such as the assay in

Examples of KDM1A inhibitors that can be used in accordance with the present invention include, but are not limited to: WO 2011/131697, WO 2012/013727, WO 2012/013728, WO 2012/045883, WO 2013/057320, WO 2013/057322, WO2010/143582, WO2011/022489, WO2011/131576, WO2012/034116, WO2012/135113, WO2013/022047, WO2013/022047 WO 2013/025805, WO 2014/058071, WO 2014/084298, WO 2014/086790, WO 2014/164867, WO 2014/205213, WO 2015 /021128, WO2015/031564, WO2007/021839, WO2008/127734, WO2014/164867, WO2015/089192, WO2015/123408 WO 2015/123424, WO 2015/123437, WO 2015/123465, WO 2015/156417, WO 2015/181380, WO 2016/123387, WO2016/130952, WO2016/172496, WO2016/177656, WO2017/027678, WO2012/071469, WO2013/033688, WO2013/033688 WO 2014/085613, WO 2015/120281, WO 2015/134973, WO 2015/168466, WO 2015/200843, WO 2016/003917, WO 2016 /004105, WO2016/007722, WO2016/007727, WO2016/007731, WO2016/007736, WO2016/034946, WO2 016/037005, WO2016/161282, WO2016172496, WO2017/004519, WO2017/027678, WO2017/079476, WO2017/079670 , WO 2017/090756, WO 2017/109061, WO 2017/116558, WO 2017/114497, WO 2017/149463, WO 2017/157322, WO Publication No. 2017/195216, WO 2017/198780, WO 2017/215464, WO 2018/081342, WO 2018/081343, US Patent Application Publication No. 2010-0324147, United States Patent Application Publication No. 2015-0065434, US Patent Application Publication No. 2017-0283397, China Patent Application Publication No. 106045862, China Patent Application Publication No. 104119280, China Patent Application Publication No. 103961340, China Patent Application Publication No. 103893163 , CN 103319466, CN 103054869, CN 105985265, CN 106432248, CN 106478639, CN 106831489, China Patent Application No. 106928235, CN 107033148, CN 107174584, CN 107176927, CN 107474011, CN 107501169, and CN Patent including those disclosed in Application Publication No. 107936022, as well as any optically active stereoisomer of, or any pharmaceutically acceptable salt thereof, of

is mentioned.
A particularly preferred KDM1A inhibitor is (trans)-N1-((1R,2S)-2-phenylcyclopropyl)cyclohexane-1,4-diamine [CAS Registry Number 1431304-21-0] or a pharmaceutically acceptable more preferably (trans)-N1-((1R,2S)-2-phenylcyclopropyl)cyclohexane-1,4-diaminebis-hydrochloride [CAS Registry Number 1431303-72-8]. The compound (trans)-N1-((1R,2S)-2-phenylcyclopropyl)cyclohexane-1,4-diamine is (1r,4S)-N1-((1R,2S)-2-phenylcyclopropyl) It can also be named as cyclohexane-1,4-diamine, known as ORY-1001 or Iadademstat, and has the chemical structure shown below.

ORY-1001 is disclosed, for example, in WO2013/057322, see Example 5 therein. Pharmaceutical formulations containing ORY-1001 for administration to a patient can be prepared according to methods known to those skilled in the art, for example, the methods described in WO2013/057322.

A KDM1A inhibitor can be administered as a single API, i.e., monotherapy, or in combination with one or more additional APIs such as other anticancer agents used in the treatment of SCLC. be able to.

A KDM1A inhibitor (or a treatment comprising a KDM1A inhibitor) can thus be administered for direct therapeutic use, although typically one or more pharmaceutical agents are used with the compound as the active pharmaceutical ingredient. It is administered in the form of a pharmaceutical composition containing together with a pharmaceutically acceptable excipient or carrier. Any reference herein to KDM1A inhibitors refers to such compounds, i.e., in non-salt form (e.g., as the free base) or any pharmaceutically acceptable salt or solvate form thereof. and to pharmaceutical compositions comprising said compound (or a pharmaceutically acceptable salt or solvate thereof) and one or more pharmaceutically acceptable excipients or carriers. including.

KDM1A inhibitors can be administered by any means that achieve their intended purpose. Examples include administration by oral or parenteral (eg, intravenous or subcutaneous) routes.

For oral delivery, the compounds may be combined with binders (eg gelatin, cellulose, gum tragacanth), excipients (eg starch, lactose), lubricants (eg magnesium stearate, silicon dioxide), disintegrants (eg , alginates, primogel, and corn starch), and sweeteners or flavoring agents (e.g., glucose, sucrose, saccharin, methyl salicylate, and peppermint). . Formulations can be delivered orally, for example, in the form of enclosed gelatin capsules or compressed tablets. Capsules and tablets can be prepared by any conventional technique. Capsules and tablets can also be coated with various coating agents known in the art to modify the flavor, taste, color, and shape of capsules and tablets. Additionally, liquid carriers such as fatty oils can also be included in the capsule.

Suitable oral formulations may be in the form of suspensions, syrups, gums, wafwers, elixirs and the like. Conventional agents can also be included to modify the flavor, taste, color and shape of the particular form, if desired. Additionally, for convenient administration via an enteral feeding tube in patients who are unable to swallow, the active compounds can be dissolved in an acceptable fat-soluble vegetable oil vehicle such as olive oil, corn oil and safflower oil.

The compounds can also be administered parenterally in the form of solutions or suspensions, or in lyophilized form which can be converted to the form of solutions or suspensions prior to use. Such formulations may employ diluents or pharmaceutically acceptable carriers such as sterile water and saline buffer. Other conventional solvents, pH buffers, stabilizers, antimicrobial agents, surfactants, and antioxidants can all be included. For example, useful ingredients include sodium chloride, acetate, citrate or phosphate buffers, glycerin, dextrose, fixed oils, methylparaben, polyethylene glycol, propylene glycol, sodium bisulfate, benzyl alcohol, ascorbic acid, and the like. mentioned. Parenteral formulations can be stored in any conventional container, such as vials and ampoules.

Pharmaceutical compositions, such as oral and parenteral compositions, can be formulated in dosage unit form for ease of administration and uniformity of dosage. As used herein, "unit dosage form" refers to physically discrete units suitable as unitary dosages for administration to a subject, each unit comprising one or more suitable pharmaceutical agents. It contains a predetermined amount of active ingredient calculated to produce the desired therapeutic effect in association with a therapeutic carrier.

For therapeutic use, the pharmaceutical compositions should be administered in a manner appropriate for the disease to be treated, as determined by those skilled in the medical arts. The appropriate dosage and appropriate duration and frequency of administration will be determined by factors such as, among other things, the patient's condition, the severity of the disease, the particular KDM1A inhibitor administered, the method of administration, and the judgment of the attending physician. In general, an appropriate dose and dosing regimen will provide therapeutic benefit, e.g., more frequent complete or partial remissions, or longer disease-free and/or overall survival, or reduced severity of symptoms, or as indicated by the clinician. A sufficient amount of the pharmaceutical composition is provided to provide an improved clinical outcome, such as, or any other clearly discernible improvement. Effective doses can generally be estimated or extrapolated using experimental models such as dose-response curves derived from in vitro or animal model test systems. One of ordinary skill in the art can determine the appropriate dosage and treatment regimen based on the above factors.

A pharmaceutical composition of the invention can be included in a container, pack, or dispenser together with instructions for administration.

The following examples are provided to illustrate the invention. They should not be considered as limiting the scope of the invention, but merely as being representative thereof.

Example 1: Classification of Cell Lines Sensitive and Resistant to KDM1A Inhibitors For biomarker identification, seven SCLC cell lines were challenged with the KDM1A inhibitor ORY-1001 (described elsewhere herein). were classified for their response to KDM1A inhibitor treatment based on the results of viability assays performed after either 4 or 10 days of treatment. For 4-day viability assays, SCLC cell lines were supplemented with ORY-1001 (maximum concentration: 50 μM; 18 serial 1:2 dilutions were tested) at the optimal dose recommended by each cell line provider. 384-well plates were seeded in a final volume of 40 μL of transformation medium. After 4 days of incubation at 37° C. in a 5% CO ₂ -controlled atmosphere, cell viability was assessed using the CellTiterGlo® assay according to the manufacturer's protocol (Promega). EC50 values were calculated using Microsoft Excel software and normalized to untreated cells (100% proliferation) and no CellTiterGlo® reagent (100% proliferation inhibition). Viability quantification after 10 days exposure to ORY-1001 was performed in a 96-well plate format (maximum concentration 1 μM, 100 nM for NCI-H187 cell line). Cells were initially cultured in 100 μL medium (RPMI-1640 10% FBS 2 mM glutamine, except for the NCI-H1876 cell line cultured in HITES medium, 4.5 mM glutamine, 0.005 mg/mL insulin, 0.01 mg /mL transferrin, 30 nM sodium selenate, 10 nM hydrocortisone, 10 nM beta-estradiol (prepared in DMEM:F12 5% FBS) and incubated at 37° C. in a 5% CO ₂ -controlled atmosphere. On day 6, an additional 100 μL of ORY-1001 containing medium was added. After an additional 4 days, residual viability was measured using the Alamar Blue® assay (Thermo Fisher Scientific). After background subtraction, normalization was performed to vehicle-treated cells. EC50 values were calculated after fitting a non-linear model using GraphPad Prism software.

Cell lines were classified as sensitive to KDM1A inhibition if the growth inhibition was 25% or greater and the EC50 was less than 10 nM after treatment with ORY-1001. SHP77 was classified as partially sensitive because it is not sensitive under the conditions tested here, but has been reported to be sensitive to ORY-1001 in other assays. The results obtained are shown in Table 1 below. Reported in the table are the concentration that achieved 50% growth reduction (EC50) and the maximum percentage of growth inhibition achieved at the highest dose tested (% maximum response).

Example 2. Identification of ASCL1 and SOX2 as biomarkers of responsiveness to KDM1A inhibitors. RNASeq analysis was performed on the four SCLC cell lines identified as being KDM1Ai resistant to the two. Details regarding their responsiveness and classification to KDM1Ai are provided in Example 1 above.

Cell Pellet for Gene Expression Analysis Cells were grown in flasks for 6 consecutive days. On the final day of the assay, cells were harvested into Falcon tubes, counted, and then centrifuged at 1200 rpm for 4 minutes. The supernatant was discarded, the cells were suspended in 1 mL of PBS, transferred to the sample in an Eppendorf, and centrifuged at 3000 rpm for 5 minutes in an Eppendorf centrifuge at 4°C. Finally the supernatant was discarded and the pellet was frozen at -80°C.

RNA Sequencing Total RNA was isolated from specimens using the QIAGEN miRNeasy mini kit. RNA isolation was performed using the QIAgen RNeasy mini kit. The RNeasy Mini Kit combines the selective binding properties of silica gel-based membranes with the speed of microspin technology. Briefly, tissues were homogenized and lysed in RLT buffer (containing beta-mercaptoethanol) using Lysing Matrix D tubes (MP Biomedicals LLC) or vortexing. Ethanol was added to the homogenate to provide suitable binding conditions for all total RNA molecules longer than 200 nucleotides (nt). Samples were applied to RNeasy mini-columns, allowing total RNA to bind to the membrane and efficiently washing away contaminants. High quality RNA was then eluted with nuclease-free water. The resulting RNA was quantified and assessed for integrity using an Agilent Bioanalyzer.

Library generation was performed using the Illumina TruSeq Stranded mRNA library prep. Library cluster generation and sequencing were performed on the Illumina HiSeq. Total RNA samples were converted to cDNA libraries using the TruSeq Stranded mRNA sample preparation kit (Illumina). Starting with 100 ng of total RNA, polyadenylated RNA (mainly mRNA) was selected and purified using oligo-dT coupled magnetic beads. The mRNA was chemically fragmented, converted to single-stranded cDNA using reverse transcriptase and random hexamer primers, and actinomycin D was added to suppress DNA-dependent synthesis of the second strand. Double-stranded cDNA was made by removing the RNA template and synthesizing the second strand in the presence of dUTP instead of dTTP. A single A base was added to the 3' end to facilitate ligation of sequencing adapters containing a single T base overhang. The adaptor-ligated cDNA was amplified by the polymerase chain reaction to enrich the sequence-ready library. During this amplification, the polymerase stops when it encounters a U base, making the second strand a poor template. Thus, the amplified material used the first strand as template, thereby preserving strand information. The final cDNA library was analyzed for size distribution and quantified by qPCR (KAPA Library Quant Kit) using an Agilent Bioanalyzer (DNA 1000 kit) and then normalized to 2 nM in preparation for sequencing. A standard cluster generation kit v5 bound the cDNA library to the flow cell surface. The cBot isothermally amplified the attached cDNA constructs to generate clonal clusters of approximately 1000 copies each. DNA sequences were determined directly using sequencing-by-synthesis technology via the TruSeq SBS Kit.

The following quality control indicators were applied: To proceed with library preparation, samples had 100 ng of input RNA and a RIN value of 7.0 or greater. At least 30 million 50 bp paired end reads were generated for each sample. At least 28.5 million reads were delivered after subtracting various off-target sequences such as ribosomal RNA, phiX, homopolymer repeats, and globin RNA.

The Illumina Hiseq software measures the total number of clusters (DNA fragments) loaded in each lane, the percentage of sequencing quality filters that pass (which identifies errors due to overloading and sequencing chemistry), the phred quality of each base in each sequence read. Scores, overall average phred scores for each sequencing cycle, and overall error percentages (based on alignment to the reference genome) are reported. For each RNA-seq sample, calculate the percentage of reads containing mitochondrial and ribosomal RNA. The FASTQC package is used to provide additional QC metrics (base distribution, sequence overlap, overrepresented sequences, and enriched kmers) and graphical summaries. Raw reads were aligned against the human genome (hg19) using GSNAP and the recommended option for RNASeq data. In addition to genomic sequences, GSNAP is provided with a database of human splice junctions and transcripts based on Ensembl v73. The resulting SAM file is then converted to a filtered BAM file using Samtools. Gene expression values were calculated as RPKM values and as read counts according to Mortazavi et al. (Nat Methods (2008) 5(7):621-8). Normalized read counts were obtained using the R package DESeq2. Data are reported as the mean of Log2 (RPKM) of three independent experiments. RPKM stands for reads per kilobase per million.

Results Gene expression of ASCL1 and SOX2 in SCLC cell lines measured by RNASeq is shown in Table 2 below. The table shows the mean (Av.) and standard deviation (SD) of Log2(RPKM). The color codes shown are based on gene expression levels, the darker the color, the higher the biomarker expression. For comparison, GAPDH expression is reported in parallel to show stable expression across samples of this reference gene.

These results are graphically represented in FIG. 1 and measured by RNA-seq as described above for SCLC cell lines that are sensitive, partially sensitive or resistant to KDM1A inhibition. Dot plots representing the expression of ASCL1 (Y-axis) and SOX2 (X-axis).

Based on the RNASeq data generated for these cell lines, all ORY-1001 sensitive and partially sensitive cell lines were identified to express high levels of ASCL1 and moderate to high levels of SOX2; In SCLC cell lines resistant to ORY-1001 treatment, either ASCL1 or SOX2 were detected at very low levels (Log2(RPKM)≦0), as shown in Table 2 and FIG. Thus, ASCL1 and SOX2 identify SCLC cells, or subjects with SCLC, that are sensitive (i.e. responsive) or likely to be sensitive (responsive) to treatment with a KDM1A inhibitor such as ORY-1001. can be used as a biomarker for

Example 3. Validation of ASCL1 and SOX2 using qRT-PCR Next, the biomarkers ASCL1 and SOX2 responsive to KDM1A inhibitors identified in Example 2 were detected in the SCLC cell lines described in Examples 1 and 2. Validated by Taqman qRT-PCT analysis on the same panel, one was identified as sensitive to KDM1Ai treatment (DMS53) and one as partially sensitive (NCIH526); Includes additional SCLC cell lines.

Gene Expression Analysis by qRT-PCR Total RNA was extracted using the RNeasy mini kit and cDNA was obtained using the High Capacity RNA-cDNA Master Mix (ThermoFisher Scientific #4390779) according to standard procedures. qRT-PCR was performed using a LightCycler 480 Probes Master (PNT-L-034; Roche #04887301001) using pre-designed and pre-optimized TaqMan gene expression assays from ThermoFisher Scientific. qRT-PCR was performed in triplicate using a Lightcycler 480 Instrument II (Roche; PNT-L-035). Analysis of Cp values by qRT-PCR was performed in triplicate using the following Taqman primer/probe sets:
- ASCL1: Hs04187546_g1 (Life Technologies; amplicon length 81 bp, targeting exon 1-2 boundary, see RefSeq NM_004316.3, SEQ ID NO: 1)
- SOX2: Hs01053049_s1 (Life Technologies; amplicon length 91 bp, targeting exon 1-1 boundary, see RefSeq NM_003106.3, SEQ ID NO:3)
- GAPDH: Hs02758991_g1 (Life Technologies; amplicon length 93 bp, targeting exon 6-7 boundaries, RefSeq NM_001256799.2)
Results ASCL1 and SOX2 gene expression in this panel of SCLC cell lines measured by qRT-PCR are shown in Table 3. The table reports Cp values. Exp. R: experimental replicates; Av: average; n.p. d. :Not detected. Each experimental replicate value is the average of three technical replicates. The same amount of RNA per sample was analyzed by qRT-PCR. As a comparison, the mean Cp expression of the GAPDH reference gene was reported and they ranged from 23-26 Cp among all samples (see Table 3).

Table 4 shows the mean Cp values of all experimental replicates for ASCL1 and SOX2 in SCLC cell lines measured by qRT-PCR. The color codes shown are based on gene expression levels, the darker the color, the higher the biomarker expression.

As shown in Tables 3 and 4, the expression of ASCL1 or SOX2 in KDM1Ai-resistant cells was either not detected at all (Cp values >40) or very low with absolute Cp values >35. Indicated. On the other hand, all KDM1Ai-sensitive SCLC cell lines expressed both ASCL1 and SOX2, thus RNASeq data described in Example 2 were used to confirm the findings. Of the partially sensitive cell lines, one was confirmed to express high levels of both ASCL1 and SOX2, while the other expressed very low levels of ASCL1 and SOX2.

These results are also shown graphically in FIG. 2, where qRT- Dot plots representing ASCL1 (Y-axis) and SOX2 (X-axis) gene expression (absolute Cp values) measured by PCR. Plotted values are the average of independent experiments, as shown in Table 3. One of the cell lines showed a Cp value of over 40 for SOX2 expression, which is indicated in FIG. 2 by the bracketed dots.

Thus, the results described herein demonstrate that SCLCs that are sensitive to KDM1A inhibitor treatment show overall high expression of both ASCL1 and SOX2, whereas resistant SCLCs show either or both ASCL1 and SOX2. is further confirmed to have low expression of Therefore, ASCL1 and SOX2 levels can be used as predictive biomarkers to identify SCLC cells or subjects with SCLC who have an increased likelihood of responding to KDM1A inhibitor therapy.

Example 4. Evaluation of predictive biomarkers of response to KDM1A inhibitors in an expanded panel of SCLC cell lines To construct a larger dataset for further biomarker validation of ASCL1 and SOX2 as biomarkers of responsiveness to KDM1A inhibition, analyzed. SCLC viability assay data obtained with ORY-1001 are described in Mohammad et al., Cancer Cell 2015, 28:57-69 (specifically Figure S2A-B, incorporated herein by reference). ), and combined with publicly available data on the response of SCLC cell lines to two additional KDM1A inhibitors, GSK2879552 and GSK-LSD1, and then tested a larger set of SCLC cell lines. were used to classify as sensitive or resistant to treatment with KDM1A inhibitors. A magnified panel of this SCLC cell line is shown in Table 5 below. The chemical structures of GSK2879552 and GSK-LSD1 are provided in the literature.

Certain cell lines were classified as partially sensitive, as the sensitivity of these cell lines to KDM1A inhibition was assay dependent and different results were obtained under other assay conditions. The NCIH526 cell line was classified as partially sensitive because it is sensitive under the conditions tested here, but has been reported to be resistant to ORY-1001 in other assays. Although some response to ORY-1001 was observed in vitro, the maximum growth inhibition was very low (10%) and NCIH2081 was classified as partially sensitive.

Expression of ASCL1 and SOX2 was then compared to cell line transcriptome data compiled by the Broad Institute (Cancer Cell Line Encyclopedia; https://portals.broadinstitute.org/ccle; CCLE_Expression_Entrez_2012-09-29.gct.txt). A set was used to evaluate in these KDM1Ai sensitive and resistant cells. Gene expression of ASCL1, SOX2 and GAPDH in the SCLC cell lines as in the CCLE database (Affymetrix microarray data; RMA values) is shown in Table 6 below. Two-tailed Student's t-test p-values were calculated using Microsoft Office Excel.

ASCL1 and SOX2 were differentially expressed among cell lines sensitive and resistant to treatment with KDM1Ai (Table 6). Specifically, mean expression of ASCL1 was 12.25 and 7.19 normalized probe intensity units for sensitive and resistant cell lines, respectively (p-value=3.0E-05). In the same cell lines, mean normalized probe intensity values were 8.96 (sensitive group) and 5.75 (resistant group) for SOX2 (p-value=2.0E-03). Expression of the GAPDH reference gene was similar in all samples (14.60 average normalized probe intensity units for sensitive cell lines, 14.56 average normalized probe intensity units for resistant cell lines, p-value = 6.1E- 01).

Consistent with the results described in Examples 2 and 3, 7 out of 8 SCLC cell lines sensitive to KDM1Ai highly expressed both ASCL1 and SOX2, whereas almost all Resistant SCLC cell lines expressed low levels of either or both ASCL1 and SOX2. This is further highlighted in Figure 3, where microarray Affymetrix analyzes (RMA Dot plots representing gene expression measured by (values) are shown. As shown in Figure 3, in cell lines sensitive to KDM1A inhibitors, a clear enrichment was observed among cell lines with high levels of both ASCL1 and SOX2 (and vice versa), indicating that KDM1A inhibitors There is a clear enrichment in cell lines that are resistant to , among cell lines with low expression of one or both of ASCL1 and SOX2.

Therefore, the results described herein in Example 4 suggest that these two biomarkers should be combined to identify/select subjects likely to be responsive to treatment with a KDM1A inhibitor such as ORY-1001. It further supports the combined use of

Gene expression values for sensitive and resistant cell lines were selected using receiver operating characteristic curves (ROC curves) with GraphPad Prism 5.01 software ("partially sensitive" cell lines excluded from analysis). were analyzed for each biomarker. A ROC curve is generated by plotting the true positive rate (TPR) against the false positive rate (FPR) at various threshold settings. FIG. 4 shows ROC curves for expression of ASCL1 (FIG. 4A) and SOX2 (FIG. 4B) to distinguish SCLC cells sensitive and resistant to KDM1A inhibitors. Based on the ROC curve for each biomarker, the respective threshold level is reported for the best trade-off between selectivity and specificity (maximum likelihood ratio). Using only the ASCL1 biomarker with a threshold level ≧8.935 for the expression dataset of Table 6, KDM1A sensitive and resistant cell lines can be distinguished with 68.42% sensitivity and 100% specificity. . Using only SOX2 biomarkers with a threshold level of ≧8.030 in a given expression dataset, KDM1A sensitive and resistant cell lines can be distinguished with a sensitivity of 84.21% and a specificity of 87.50%.

A different algorithm was then used to determine the response to KDM1A inhibition based on the combination of ASCL1 and SOX2 on a panel of SCLC cell lines listed in Table 6 (“partially sensitive” cell lines excluded from analysis). Predictions about responses were made.

First, based on the simultaneous compliance of biomarkers above the individual thresholds for ASCL1 and SOX2 shown above, a Boolean joint model classification algorithm was constructed (FIGS. 4A and B); If the conditions indicated in the first row were met, a score of '1' was obtained, otherwise a score of '0' was obtained. Cell lines were then classified as likely to respond to KDM1Ai if the score exceeded the threshold, i.e. >0 (equal to 1 in this case), and scored to 0. If they were equal, they were classified as unlikely to respond to KDM1Ai (see Table 7 below). Finally, we evaluated the performance of the Boolean joint classification algorithm. Sensitivity (also called true positive rate; TPR), specificity (also called true negative rate; TNR), positive predictive value (PPV, also called precision), negative predictive value (NPV) are TPR and TNR, and PPV and NPV was calculated with the geometric mean of Sensitivity was calculated as the ratio between true positives and total number of positives. Similarly, specificity was obtained as the ratio between the number of true negatives and the total number of negatives. Positive predictive value (PPV; also called precision) and negative predictive value (NPV) are calculated using the following formulas:
PPV=TP/(TP+FP)
NPV=TN/(TN+FN)
TP is the number of true positives, TN is the number of true negatives, FP is the number of false positives and FN is the number of false negatives.

The ASCL1/SOX2 signature shows high sensitivity (88%), specificity (100%), precision (100%) and negative predictive value (95%).
Alternatively, support vector machine (SVM) modeling of the DTREG predictive modeling software was used to develop algorithms that can be used to classify samples using biomarkers. In particular, to evaluate the combination of ASCL1 and SOX2 as predictive biomarkers of responsiveness to KDM1A inhibitors, different kernel functions, polynomials and radial basis functions (RBFs) reflected in the upper row of Table 8 and their respective parameters ( C: cost parameter; gamma: kernel coefficient) were used. Using the scores and thresholds (functions) determined by these algorithms, cell lines were classified as likely or unlikely to respond to KDM1Ai, as indicated, and samples were The performance of the SVM model for classification is shown at the bottom of Table 8, which shows the specificity, sensitivity and confusion matrices for the SVM model generated to predict susceptibility and resistance using a combination of ASCL1 and SOX2 expression. reflects.

Using this algorithm, the SVM algorithm employing the ASCL1/SOX2 combination has high sensitivity (88%) and high specificity (≧95%) using both polynomial and RBF fitting.

Alternatively, an algorithm that classifies samples that are likely or unlikely to respond to KDM1Ai in function of the selected biomarker combinations on the dataset of Table 6 using linear regression in the DTREG predictive modeling software. was developed (Table 9). The algorithm classifies cell lines as sensitive or resistant to KDM1Ai by calculating a score for each sample and comparing the score for each sample to a threshold. The performance of the linear regression model generated based on the combined ASCL1/SOX2 biomarkers has a sensitivity of 87.5% and a specificity of 94.74% in predicting susceptibility to KDM1Ai (Table 9).

Table 9 below shows the parameters, specificity, sensitivity and confusion matrix of a linear regression model generated to predict susceptibility and resistance using combinations of ASCL1 and SOX2 expression. C: Cost parameter, Gamma: kernel coefficient of each function.

Overall, the use of different classification algorithms (Boolean integration model, SVM model and linear model) combining ASCL1 and SOX2 expression levels indicates that this two-marker signature has optimal performance in predicting SCLC response to KDM1A inhibitors. Confirm that

Example 5. Correlation of ASCL1 and SOX2 mRNA and protein expression levels by Western blot (WB) and fluorescence immunohistochemistry (IF) , by fluorescence immunohistochemistry of SCLC cell lines with either moderate or low/undetectable expression, and the same sectioned SCLC cell pellets and SCLC patient-derived xenografts (PDX) with known mRNA levels. Analyzed by WB.

5.1 SOX2 and ASCL1 analysis in SCLC cell lines by WB Human small cell lung cancer (SCLC) cell lines NCI-H146, NCI-H510A, NCI-H446, NCI-H526 supplemented with 2 mM glutamine and 10% FBS (Sigma) were grown at 37° C. and 5% CO ₂ in a humidified atmosphere in RPMI medium (Sigma). A pellet of 5 million exponentially growing cells was generated and used for total protein extraction in RIPA buffer (Sigma) supplemented with protease inhibitors (Sigma), followed by WB in 12% PAGE (Life Technologies). ASCL1 (Abcam, ab213151) and SOX2 (Abcam, ab97959) levels were determined. Proteins were transferred using the iBlot System (Life Technologies) and after secondary antibody incubation and washing, blots were developed using ECL Prime (Amersham) and photographed on a G:BOX Chemi XRQ (Syngene). Ponceau S staining of transfer blots was used as a loading control. WB signal quantification was performed using Image J. The integrated density of each WB band was normalized by the corresponding total protein integrated density from Ponceau staining and generated relative to the NCI-H146 signal.

Results ASCL1 and SOX2 protein levels were increased by WB in SCLC cell lines with either high, moderate or low/undetectable expression of these biomarkers as determined by qRT-PCR (Example 3). Analyzed and confirmed by publicly available Affymetrix mRNA expression data from the Cancer Cell Line Encyclopedia (CCLE) (see Example 4). The resulting WBs are shown in Figure 5A and the corresponding quantification of ASCL1 and SOX2 protein levels in NCI-H146, NCI-H510A, NCI-H446 and NCI-H526 cell lines is shown in Figure 5B. ASCL1 was not detected in NCI-H446 and neither ASCL1 nor SOX2 were detected in NCI-H526 cells, but their expression was highest in NCI-H146 cells, indicating the mRNA expression level and specificity of the antibodies used. WB-induced protein expression levels of ASCL1 and SOX2 correlated with their respective mRNA levels. Correlations between protein and mRNA (CCLE Affymetrix) levels of SOX2 and ASCL1 were plotted in FIGS. 6A and 6B, respectively; good correlations were observed with R values of 0.8957 for SOX2 and 0.9910 for ASCL1. rice field.

5.2 SOX2 and ASCL1 Analysis by Fluorescence Immunohistochemistry on SCLC Cell Pellets for 1 hour at room temperature, washed in 1×PBS (Sigma), pelleted and included in 1.3% agarose (Sigma), then dehydrated and included in paraffin for microtome sectioning. . 5 μm sections were placed on Superfrost slides, deparaffinized in HistoChoice remover (Sigma), 2×5 min, reduced ethanol series (2×100% for 5 min, 90% for 1 min, 70% for 1 min). Hydrate through 30% for 1 min, 2x running water). Sections were then subjected to heat-induced antigen retrieval for 20 min in boiling pH 6 citrate buffer 1× (Sigma). After 20 minutes at room temperature, the slides were washed in PBS-Triton X100 0.1% (0.1% PBS-Tx) and washed in 5% goat serum in 0.1% PBS-Tx for 1 hour. Block at room temperature. After blocking, excess liquid was removed with paper tissue by capillary tube and sections were treated with diluted primary antibody (1:500 dilution for SOX2, Abcam ab 97959; for ASCL1) in 1% goat serum in 0.1% PBS-Tx. was incubated at 1:100 dilution, Abcam ab 213151) and with the corresponding negative control (1% goat serum only in 0.1% PBS-Tx) overnight at 4°C. After three washes of 5 minutes each in 0.1% PBS-Tx, the slides were washed with goat anti-rabbit Alexa Fluor 546 secondary antibody (1:1500 dilution, Life Technologies A11010) for 1 hour at room temperature, protected from light. and incubated. After five washes in 0.1% PBS-Tx for 5 minutes each, excess liquid was removed by capillary with paper tissue and samples were mounted in Fluoroshield mounting medium supplemented with DAPI (Sigma). DAPI is 4',6-diamidino-2-phenylindole, a fluorescent dye that binds strongly to AT-rich regions in DNA and is used to stain the nucleus. Signals from DAPI-stained nuclei were used to identify and analyze the co-localization of nuclear-specific SOX2 and ASCL1 signals (see below). Images were taken on a Zeiss Axio fluorescence microscope with an attached AxioCam camera (Zeiss).

Quantification of IF Intensity IF images were processed and quantified with imageJ software. For quantification of SOX2 and ASCL1 nucleus-specific signals, a mask enclosing the nucleus-covered area was created from the DAPI-stained image and then transferred to the corresponding IF image, whereby the integrated density was defined by nuclei only. determined by the selected area.

Results In order to validate the antibodies for use in fluorescence immunohistochemistry and to further confirm the correlation between ASCL1 and SOX2 proteins and mRNA levels, immunohistochemistry was performed in formalin-fixed, paraffin-embedded, sectioned SCLC cell pellets. To examine the levels of these biomarkers, the same antibodies used for WB were tested in fluorescence immunohistochemistry. Fluorescence immunohistochemistry of SOX2, ASCL1 and negative controls is shown in Figure 7 (Figure 7A - SOX2, Figure 7B - ASCL1, Figure 7C - negative control with secondary antibody only (AF546)). Consistent with previously presented data, SOX2 levels were high in NCI-H146, moderate in NCI-H510A, and low in NCI-H446 cell lines, although nuclear-specific expression was detectable in NCI-H526 cells. it wasn't. On the other hand, ASCL1 levels were high in NCI-H146, moderate in NCI-H510A, and not observed/detected in NCI-H446 and NCI-H526 cells. No expression was observed in the secondary antibody only (AF546: Alexa Fluor 546) negative control. Based on the staining intensity, the following staining intensity levels were established and will be adopted in the examples below: high level is "level 3", medium level is "level 2", low level is "level 1", Absence of signal is defined as "level 0".

Table 10 below shows the nuclear biomarker signals in the immunofluorescence images shown in FIG. Show quantification. Signals were background corrected and expressed relative to the NCI-H146 signal (corresponding to 100%).

Quantification of nuclear signals reveals highly statistically significant correlations between ASCL1 and SOX2 protein levels detected by fluorescence immunohistochemistry and corresponding mRNA levels for the cell lines analyzed. (See FIG. 8A-SOX2 and FIG. 8B-ASCL1). The corresponding R values are 0.8710 and 0.9594 for SOX2 and ASCL1, respectively.

As shown in Examples 5.1 and 5.2 above, a good correlation was obtained between ASCL1 and SOX2 mRNA and protein levels, suggesting that ASCL1 and SOX2 It was confirmed that it can be used as a predictive biomarker of responsiveness to KDM1A inhibition, measuring any of

5.3 SOX2 and ASCL1 analysis by fluorescence immunohistochemistry on patient-derived SCLC xenograft (PDX) tissue microarrays (TMA) To further confirm the correlation between SOX2 and ASCL1 mRNA and protein levels in human samples , SOX2 and ASCL1 IF were performed on SCLC xenograft tissue microarrays from patients with available SOX2 and ASCL1 RNASeq data.

Tissue microarray sections containing 44 patient-derived SCLC xenografts were obtained from Molecular Response (now Crown Biosciences) and https://oncoexpress. crown bio. com/OncoExpress/index. were purchased from their corresponding available RNASeq data downloaded from aspx.

TMA was deparaffinized twice with HistoChoice detergent (Sigma) for 5 min and subjected to a decreasing ethanol series (2 x 100% for 5 min, 90% for 1 min, 70% for 1 min, 30% for 1 min, 2 x running water). Sections were then subjected to heat-induced antigen retrieval for 20 min in boiling pH 6 citrate buffer 1× (Sigma). After 20 minutes at room temperature, the slides were washed in PBS-Triton X100 0.1% (0.1% PBS-Tx) and washed in 5% goat serum in 0.1% PBS-Tx for 1 hour. Block at room temperature. After blocking, excess liquid was removed with paper tissue by capillary tube and sections were treated with diluted primary antibody (1:500 dilution for SOX2, Abcam ab 97959; for ASCL1) in 1% goat serum in 0.1% PBS-Tx. was incubated at 1:100 dilution, Abcam ab 213151) and with the corresponding negative control (1% goat serum only in 0.1% PBS-Tx) overnight at 4°C. After three washes of 5 minutes each in 0.1% PBS-Tx, the slides were washed with goat anti-rabbit Alexa Fluor 546 secondary antibody (1:1500 dilution, Life Technologies A11010) for 1 hour at room temperature, protected from light. and incubated. After five washes in 0.1% PBS-Tx for 5 minutes each, excess liquid was removed by capillary with paper tissue and samples were mounted in Fluoroshield mounting medium supplemented with DAPI (Sigma). Images were taken on a Zeiss Axio fluorescence microscope with an attached AxioCam camera (Zeiss).

Classification of PDX IF Intensity IF staining was analyzed using a Zeiss Axio fluorescence microscope and tumor areas were defined by nuclear morphology. Nuclear-specific signal intensity within the tumor region was visually classified into four levels, as described in Example 5.2:
Level Intensity 3 High 2 Medium 1 Low 0 None Based on the intensity obtained in fluorescence immunohistochemistry performed on SCLC cell pellets and their corresponding known mRNA expression (Example 5.2) Samples with expression levels above 1 individual biomarker threshold for both (i.e. medium to high expression levels 2 and 3), or alternatively formulated above threshold (>0) (i.e. individual biomarkers Samples with a combined ASCL1/SOX2 Boolean score that met both conditions for the marker simultaneously) were classified as coming from patients likely to respond to KDM1Ai. In FIG. 9, samples whose scores exceed the threshold are indicated as "positive" and samples that do not exceed the threshold are indicated as "negative."

Statistics Two-tailed Spearman correlation tests with 95% confidence intervals between RNASeq (Log ₂ FPKM) and IF (visual score) datasets for SOX2 and ASCL1 were performed using GraphPad Prism software.

Results SOX2 and ASCL1 IF were performed on patient-derived SCLC xenograft tissue microarrays using available SOX2 and ASCL1 RNASeq data. All samples were visually analyzed by fluorescence microscopy, tumor areas defined by nuclear morphology and given intensity levels according to the nuclear signals observed within the tumor areas, as described above. Representative ASCL1 and SOX2 staining from SCLC PDX TMA are shown in FIG. 9 for each staining intensity classification level.

In two independent stainings of two consecutive SCLC PDX TMA sections (N=35 and N=43, respectively), IF visual scores and RNASeq data showed a highly statistically significant (P<0.0001) correlation. with Spearman r values of 0.7535 and 0.7659 for SOX2 (FIGS. 10A and 10B) and 0.8803 and 0.8989 for ASCL1 (FIGS. 10C and 10D).

The above results demonstrate that SOX2 and ASCL1 protein and mRNA levels are also correlated in patient-derived samples, thus ASCL1 and SOX2 are responsive to KDM1A inhibition, measuring either their mRNA or protein levels. Confirm that it can be used in human samples as a predictive biomarker.

Example 6: ASCL1 and SOX2 can be detected in exosomes Exosomes are microvesicles present in body fluids that in their contents mirror the proteasome, genome and transcriptome of the parental cell. Exosomes therefore constitute an excellent minimally invasive tool for quantitative biomarker detection. We therefore tested whether detection of predictive biomarkers of responsiveness to KDM1A inhibitors, ASCL1 and SOX2 would be appropriate in methods employing exosome-containing samples.

Exosomes were isolated by precipitation from SCLC cell lines and SOX2 and ASCL1 protein levels were determined by WB.
ASCL1 and SOX2 detection in exosomes 20 million NCI-H146, NCI-H510A, NCI-H446 and NCI-H526 SCLC cells were supplemented with 2 mM glutamine (Sigma) and 10% exosome-free FBS (System Biosciences). Inoculated in 20 ml RPMI medium (Sigma) and incubated in T75 flasks at 37° C. and 5% CO ₂ in a humid atmosphere. After 48 hours, 15 ml of well-resuspended cells were centrifuged at 2,000 xg for 30 minutes at room temperature, the supernatant was transferred to a clean tube, and the cell pellet was kept at -20°C. Using 10 ml of clean medium, exosomes were precipitated with Total Exosome Isolation Reagent (from Cell Culture Medium) (Life Technologies) according to the manufacturer's instructions exactly. The exosome pellet was then resuspended in either 80 μl 1×SDS loading buffer for WB or 40 μl RIPA buffer supplemented with protease inhibitors (Sigma) for protein extraction. After quantification, 2 volumes of RIPA extract were mixed with 1 volume of 3×SDS loading buffer, heated at 95° C. and kept at −20° C. until used in WB.

The exosome isolation method involved growing NCI-H510A cells under the conditions described above in the presence of 5 μM exosome release inhibitor GW4869 (SelleckChem) or vehicle, and total exosome isolation reagent (from cell culture medium) (Life Technologies) isolated exosomes were used to verify accuracy according to the manufacturer's instructions.

Cell pellets kept at −20° C. were used for protein extraction in RIPA buffer (Sigma) supplemented with protease inhibitors. After quantification of protein using protein assay dye reagents (Bio-Rad), 7 μg of total protein preheated at 95° C. in 1×SDS loading buffer was assayed with ASCL1 (Abcam, ab213151), SOX2 (Abcam, ab97959). and lung cancer exosome-specific CD151 marker (Abcam, ab3315) antibodies were used for WB on 12% PAGE (Life Technologies) with 15 μl of exosomal protein extract. Blots were developed with ECL Prime (Amersham) and photographed with a G:BOX Chemi XRQ (Syngene). Ponceau S staining of transferred blots was used as a loading control.

Results To investigate the potential for ASCL1 and SOX2 detection in exosomes, microvesicle fractions from NCI-H146, NCI-H510A, NCI-H446 and NCI-H526 SCLC cells were isolated by precipitation and ASCL1 and SOX2 protein levels were measured. It was analyzed by WB using the method described in Example 5.1. The results obtained are shown in FIG. 11 and demonstrate that both ASCL1 and SOX2 can be detected in exosomes and their expression is consistent with that in parental cells (see FIG. 5). ASCL1 was not detected in NCI-H446- or NCI-H526-derived exosomes (see Figure 11), but was highly detected in NCI-H146 and NCI-H510A cells. Instead, SOX2 was not detected in NCI-H526-derived exosomes (FIG. 11), consistent with SOX2 expression reported in FIG.

In addition, ASCL1, SOX2 and lung cancer-specific exosome marker CD151 signals were significantly reduced or eliminated in the exosome fraction of NCI-H510A cells treated with 5 μM GW4869 (an exosome release inhibitor) compared to vehicle. , whereas the expression of these proteins in parental cells was unchanged (FIG. 12). Thus, these results indicate that the exosome-derived ASCL1 and SOX2 signals are specific for this microvesicle fraction obtained by precipitation and validate the exosome isolation method employed.

Overall, the results of this Example 6 demonstrate that measurements of protein levels of the predictive biomarkers of the present invention, ASCL1 and SOX2, are performed to determine responsiveness to KDM1A inhibitors using exosomes as starting material/sample. make sure you can.

The present invention refers to the following nucleotide and amino acid sequences:
The sequences provided herein are available in the NCBI database, www. ncbi. nlm. nih. gov/sites/entrez? db=gene; these sequences also relate to annotated and modified sequences. The invention also provides techniques and methods in which homologous sequences and variants of the short sequences provided herein are used. Preferably such "variants" are genetic variants, eg splice variants.

Exemplary amino acid and nucleotide sequences for human ASCL1 and SOX2 are shown herein below in SEQ ID NOS: 1-4. Exemplary nucleotide and amino acid sequences for human GAPDH (glyceraldehyde-3-phosphate dehydrogenase) used as a regulatory gene in some of the examples are shown in SEQ ID NOS:5 and 6.

SEQ ID NO: 1: Homo sapiens Achaete-Scute family bHLH transcription factor 1 (ASCL1), nucleotide sequence encoding mRNA NCBI Reference Sequence: NM_004316.3. The coding region extends from nucleotide 572 to nucleotide 1282 (highlighted in bold). It is understood that the mRNA corresponds to (i.e., is identical to) the sequence below, except that "t" (thymidine) residues are replaced with "uracil" (u) residues. .
origin
1 agcactctct cacttctggc cagggaacgt ggaaggcgca ccgacaggga tccggccagg
61 gagggcgagt gaaaagaagga aatcagaaag gaagggagtt aacaaaataa taaaaacagc
121 ctgagccacg gctggagaga ccgagacccg gcgcaagaga gcgcagcctt agtaggagag
181 gaacgcgaga cgcggcagag cgcgttcagc actgactttt gctgctgctt ctgctttttt
241 ttttcttaga aacaagaagg cgccagcggc agcctcacac gcgagcgcca cgcgaggctc
301 ccgaagccaa cccgcgaagg gaggagggga gggaggagga ggcggcgtgc agggaggaga
361 aaaagcattt tcactttttt tgctcccact ctaagaagtc tcccggggat tttgtatata
421 ttttttaact tccgtcagg ctcccgcttc atatttcctt ttctttccct ctctgttcct
481 gcacccaagt tctctctgtg tccccctcgc gggccccgca cctcgcgtcc cggatcgctc
541 tgattccgcg actccttggc cgccgctgcg catggaaagc tctgccaaga tggagagcgg
601 cggcgccggc cagcagcccc agccgcagcc ccagcagccc ttcctgccgc ccgcagcctg
661 tttctttgcc acggccgcag ccgcggcggc cgcagccgcc gcagcggcag cgcagagcgc
721 gcagcagcag cagcagcagc agcagcagca gcagcaggcg ccgcagctga gaccggcggc
781 cgacggccag ccctcagggg gcggtcacaa gtcagcgccc aagcaagtca agcgacagcg
841 ctcgtcttcg cccgaactga tgcgctgcaa acgccggctc aacttcagcg gctttggcta
901 cagcctgccg cagcagcagc cggccgccgt ggcgcgccgc aacgagcgcg agcgcaaccg
961 cgtcaagttg gtcaacctgg gctttgccac ccttcgggag cacgtcccca acggcgcggc
1021 caacaagaag atgagtaagg tggagacact gcgctcggcg gtcgagtaca tccgcgcgct
1081 gcagcagctg ctggacgagc atgacgcggt gagcgccgcc ttccaggcag gcgtcctgtc
1141 gccccaccatc tcccccaact actccaacga cttgaactcc atggccggct cgccggtctc
1201 atcctactcg tcggacgagg gctcttacga cccgctcagc cccgaggagc aggagcttct
1261 cgacttcacc aactggttct gaggggctcg gcctggtcag gccctggtgc gaatggactt
1321 tggaagcagg gtgatcgcac aacctgcatc tttagtgctt tcttgtcagt ggcgttggga
1381 gggggagaaa aggaaaagaa aaaaaaaaga agaagaagaa gaaaagagaa gaagaaaaaa
1441 acgaaaacag tcaaccaacc ccatcgccaa ctaagcgagg catgcctgag agacatggct
1501 ttcagaaaaac gggaagcgct cagaacagta tctttgcact ccaatcattc acggagatat
1561 gaagagcaac tgggacctga gtcaatgcgc aaaatgcagc ttgtgtgcaa aagcagtggg
1621 ctcctggcag aagggagcag cacacgcgtt atagtaactc ccatcacctc taacacgcac
1681 agctgaaagt tcttgctcgg gtcccttcac ctcctcgccc tttcttaaag tgcagttctt
1741 agccctctag aaaacgagttg gtgtctttcg tctcagtagc ccccacccca ataagctgta
1801 gacattggtt tacagtgaaa ctatgctatt ctcagccctt tgaaactctg cttctcctcc
1861 agggcccgat tcccaaaccc catggcttcc ctcacactgt cttttctacc attttcatta
1921 tagaatgctt ccaatctttt gtgaattttt tattataaaa aatctatttg tatctatcct
1981 aaccagttcg gggatatatt aagatatttt tgtacataag agagaaagag agagaaaaat
2041 ttatagaagt tttgtacaaa tggtttaaaa tgtgtatatc ttgatacttt aacatgtaat
2101 gctattacct ctgcatattt tagatgtgta gttcacctta caactgcaat tttccctatg
2161 tggttttgta aagaactctc ctcataggtg agatcaagag gccaccagtt gtacttcagc
2221 accaatgtgt cttactttat agaaatgttg ttaatgtatt aatgatgtta ttaaatactg
2281 ttcaagaaga acaaagttta tgcagctact gtccaaactc aaagtggcag ccagttggtt
2341 ttgataggtt gccttttgga gatttctatt actgcctttt tttttcttac tgttttatta
2401 caaacttaca aaaatatgta taaccctgtt ttatacaaaac tagtttcgta ataaaacttt
2461 ttcctttttt taaaatgaaa ataaaaaaa//
SEQ ID NO: 2: Amino acid sequence of Homo sapiens Achaete-Scute family bHLH transcription factor 1 (ASCL1) protein UniProtKB/Swiss-Prot: ASCL1_HUMAN, P50553
MESSAKMESGGAGQQPQPQPQQPFLPPAACFFATAAAAAAAAAAAAAQSAQQQQQQQQQQ
QQAPQLRPAADGQPSGGGHKSAPKQVKRQRSSSPELMRCKRRLNFSGFGYSLPQQQPAAV
ARRNERERNRVKLVNLGFATLREHVPNGAANKKMSKVETLRSAVEYIRALQQLLDEHDAV
SAAFQAGVLSPTISPNYSNDLNSMAGSPVSSYSSDEGSYDPLSPEEQELLDFTNWF
SEQ ID NO: 3: Homo sapiens SRY box 2 (SOX2), nucleotide sequence encoding mRNA NCBI Reference Sequence: NM_0003106.3. The coding region extends from nucleotide 438 to nucleotide 1391 (highlighted in bold). It is understood that the mRNA corresponds to (i.e., is identical to) the sequence below, except that "t" (thymidine) residues are replaced with "uracil" (u) residues. .
origin
1 ggatggttgt ctattaactt gttcaaaaaa gtatcaggag ttgtcaaggc agagaagaga
61 gtgtttgcaa aagggggaaa gtagtttgct gcctctttaa gactaggact gagagaaaga
121 agaggagaga gaaagaaagg gagagaagtt tgagccccag gcttaagcct ttccaaaaaa
181 taataataac aatcatcggc ggcggcagga tcggccagag gaggagggaa gcgctttttt
241 tgatcctgat tccagtttgc ctctctcttt ttttccccca aattattctt cgcctgattt
301 tcctcgcgga gccctgcgct cccgacaccc ccgcccgcct cccctcctcc tctccccccg
361 cccgcgggcc ccccaaagtc ccggccgggc cgagggtcgg cggccgccgg cgggccgggc
421 ccgcgcacag cgcccgcatg tacaacatga tggagacgga gctgaagccg ccgggcccgc
481 agcaaacttc ggggggcggc ggcggcaact ccaccgcggc ggcggccggc ggcaaccaga
541 aaaacagccc ggaccgcgtc aagcggccca tgaatgcctt catggtgtgg tcccgcgggc
601 agcggcgcaa gatggcccag gagaacccca agatgcacaa ctcggagatc agcaagcgcc
661 tgggcgccga gtggaaactt ttgtcggaga cggagaagcg gccgttcatc gacgaggcta
721 agcggctgcg agcgctgcac atgaaggagc acccggatta taaataccgg ccccggcgga
781 aaaccaagac gctcatgaag aaggataagt acacgctgcc cggcgggctg ctggcccccg
841 gcggcaatag catggcgagc ggggtcgggg tgggcgccgg cctgggcgcg ggcgtgaacc
901 agcgcatgga cagttacgcg cacatgaacg gctggagcaa cggcagctac agcatgatgc
961 aggaccagct gggctacccg cagcacccgg gcctcaatgc gcacggcgca gcgcagatgc
1021 agcccatgca ccgctacgac gtgagcgccc tgcagtacaa ctccatgacc agctcgcaga
1081 cctacatgaa cggctcgccc acctacagca tgtcctactc gcagcagggc acccctggca
1141 tggctcttgg ctccatgggt tcggtggtca agtccgaggc cagctccagc ccccctgtgg
1201 tacctcttc ctcccactcc aggcgccct gccaggccgg ggacctccgg gacatgatca
1261 gcatgtatct ccccggcgcc gaggtgccgg aacccgccgc ccccagcaga cttcacatgt
1321 cccagcacta ccagagcggc ccggtgcccg gcacggccat taacggcaca ctgcccctct
1381 cacacatgtg aggccggac agcgaactgg aggggggaga aattttcaaa gaaaaacgag
1441 ggaaatggga ggggtgcaaa agaggagagt aagaaacagc atggagaaaa cccggtacgc
1501 tcaaaaagaa aaaggaaaaa aaaaaatccc atcaccccaca gcaaatgaca gctgcaaaag
1561 agaacaccaa tcccatccac actcacgcaa aaaccgcgat gccgacaaga aaacttttat
1621 gagagagatc ctggacttct ttttggggga ctatttttgt acagagaaaa cctggggagg
1681 gtggggaggg cgggggaatg gaccttgtat agatctggag gaaagaaagc tacgaaaaac
1741 ttttttaaaag ttctagtggt acggtaggag ctttgcagga agtttgcaaa agtctttacc
1801 aataatattt agagctagtc tccaagcgac gaaaaaaatg ttttaatatt tgcaagcaac
1861 ttttgtacag tatttatcga gataacatg gcaatcaaaa tgtccattgt ttataagctg
1921 agaatttgcc aatatttttc aaggaggc ttcttgctga attttgattc tgcagctgaa
1981 atttaggaca gttgcaaacg tgaaaagaag aaaattattc aaatttggac attttaattg
2041 tttaaaaatt gtacaaaagg aaaaaattag aataagtact ggcgaaccat ctctgtggtc
2101 ttgtttaaaa aggcaaag ttttagactg tactaaattt tataacttac tgttaaaagc
2161 aaaaatggcc atgcaggttg acaccgttgg taatttataa tagcttttgt tcgatcccaa
2221 ctttccattt tgttcagata aaaaaaacca tgaaattact gtgtttgaaa tattttctta
2281 tggtttgtaa tatttctgta aatttattgt gatattttaa ggttttcccc cctttatttt
2341 ccgtagttgt attttaaaag attcggctct gtattatttg aatcagtctg ccgagaatcc
2401 atgtatatat ttgaactaat atcatcctta taacaggtac attttcaact taagttttta
2461 ctccattatg cacagtttga gataataaa tttttgaaat atggacactg aaaaaaaaaa//
SEQ ID NO: 4: Homo sapiens SRY-box 2 (SOX2) protein amino acid sequence UniProtKB/Swiss-Prot: SOX2_HUMAN, P48431
MYNMMETELKPPGPQQTSGGGGGNSTAAAAGGNQKNSPDRVKRPMNAFMVWSRGQRRKMA
QENPKMHNSEISKRLGAEWKLLSETEKRPFIDEAKRLRALHMKEHPDYKYRPRRKTKTLM
KKDKYTLPGGLLAPGGNSMASGVGVGAGLGAGVNQRMDSYAHMNGWSNGSYSMMQDQLGY
PQHPGLNAHGAAQMQPMHRYDVSALQYNSMTSSQTYMNGSPTYSMSYSQQGTPGMALGSM
GSVVKSEASSSPPVVTSSSHSRAPCQAGDLRDMISMYLPGAEVPEPAAPSRLHMSQHYQS
GPVPGTAINGTLPLSHM
SEQ ID NO: 5: Homo sapiens glyceraldehyde-3-phosphate dehydrogenase (GAPDH), mRNA
NCBI reference sequence: NM_002046.6. The coding region extends from nucleotide 77 to nucleotide 1084 (highlighted in bold). It is understood that the mRNA corresponds to (i.e., is identical to) the sequence below, except that "t" (thymidine) residues are replaced with "uracil" (u) residues. .
origin
1 gctctctgct cctcctgttc gacagtcagc cgcatcttct tttgcgtcgc cagccgagcc
61 acatcgctca gacaccatgg ggaaggtgaa ggtcggagtc aacggatttg gtcgtattgg
121 gcgcctggtc accagggctg cttttaactc tggtaaagtg gatattgttg ccatcaatga
181 ccccttcatt gacctcaact acatggttta catgttccaa tatgattcca cccatggcaa
241 attccatggc accgtcaagg ctgagaacgg gaagcttgtc atcaatggaa atcccatcac
301 catcttccag gagcgagatc cctccaaaat caagtggggc gatgctggcg ctgagtacgt
361 cgtggagtcc actggcgtct tcaccaccat ggagaaggct ggggctcatt tgcagggggg
421 agccaaaagg gtcatcatct ctgccccctc tgctgatgcc cccatgttcg tcatgggtgt
481 gaaccatgag aagtatgaca acagcctcaa gatcatcagc aatgcctcct gcaccaccaa
541 ctgcttagca cccctggcca aggtcatcca tgacaacttt ggtatcgtgg aaggactcat
601 gaccacagtc catgccatca ctgccaccca gaagactgtg gatggcccct ccgggaaact
661 gtggcgtgat ggccgcgggg ctctccagaa catcatccct gcctctactg gcgctgccaa
721 ggctgtgggc aaggtcatcc ctgagctgaa cgggaagctc actggcatgg ccttccgtgt
781 ccccactgcc aacgtgtcag tggtggacct gacctgccgt ctagaaaaac ctgccaaata
841 tgatgacatc aagaaggtgg tgaagcaggc gtcggagggc cccctcaagg gcatcctggg
901 ctacactgag caccaggtgg tctcctctga cttcaacagc gacacccact cctccacctt
961 tgacgctggg gctggcattg ccctcaacga ccactttgtc aagctcattt cctggtatga
1021 caacgaattt ggctacagca acagggtggt ggacctcatg gcccacatgg cctccaagga
1081 gtaagacccc tggaccacca gccccagcaa gagcacaaga ggaagagaga gaccctcact
1141 gctggggagt ccctgccaca ctcagtcccc caccacactg aatctcccct cctcacagtt
1201 gccatgtaga ccccttgaag agggagggg cctagggagc cgcaccttgt catgtaccat
1261 caataaagta ccctgtgctc aaccagttaa aaaaaaaaaa aaaaaaaaa//
SEQ ID NO: 6: Homo sapiens glyceraldehyde-3-phosphate dehydrogenase (GAPDH), amino acid sequence of protein UniProtKB/Swiss-Prot: G3P_HUMAN, P04406
MGKVKVGVNGFGRIGRLVTRAAFNSGKVDIVAINDPFIDLNYMVYMFQYDSTHGKFHGTV
KAENGKLVINGNPITIFQERDPSKIKWGDAGAEYVVESTGVFTTMEKAGAHLQGGAKRVI
ISAPSADAPMFVMGVNHEKYDNSLKIISNASCTTNCLAPLAKVIHDNFGIVEGLMTTVHA
ITATQKTVDGPSGKLWRDGRGALQNIIPASTGAAKAVGKVIPELNGKLTGMAFRVPTANV
SVVDLTCRLEKPAKYDDIKKVVKQASEGPLKGILGYTEHQVVSSDFNSDTHSSTFDAGAG
IALNDHFVKLISWYDNEFGYSNRVVDLMAHMASKE

Claims (19)

A method of identifying patients with SCLC who are likely to respond to treatment comprising a KDM1A inhibitor, comprising measuring levels of ASCL1 and SOX2 in a sample from the patient prior to initiating treatment comprising a KDM1A inhibitor a method comprising the step of 2. The method of claim 1, further comprising identifying the patient as likely to respond to treatment comprising a KDM1A inhibitor if each level of ASCL1 and SOX2 in the sample exceeds a threshold value. The level of ASCL1 and SOX2 in the sample is used to generate a score for the sample, and the patient is likely to respond to treatment comprising a KDM1A inhibitor if the score for the sample exceeds a threshold value. 2. The method of claim 1, wherein the subject is identified as having high potential. A method of identifying a patient with SCLC who may benefit from treatment comprising a KDM1A inhibitor, comprising measuring levels of ASCL1 and SOX2 in a sample from the patient prior to initiating treatment comprising a KDM1A inhibitor. method including. 5. The method of claim 4, further comprising identifying the patient as one who may benefit from treatment comprising a KDM1A inhibitor if each level of ASCL1 and SOX2 in the sample exceeds a threshold value. further comprising using the levels of ASCL1 and SOX2 in the sample to generate a score for the sample, wherein if the score for the sample exceeds a threshold, the patient will benefit from treatment with a KDM1A inhibitor; 5. The method of claim 4 identified as obtaining. A method of selecting treatment for a patient with SCLC comprising measuring levels of ASCL1 and SOX2 in a sample from the patient prior to initiating treatment. The method of any one of claims 1-7, wherein ASCL1 level and SOX2 level are mRNA expression levels. 9. The method of claim 8, wherein the mRNA expression level is measured by qRT-PCR. The method of any one of claims 1-7, wherein ASCL1 and SOX2 levels are protein expression levels. 11. The method of claim 10, wherein protein expression levels are measured by fluorescence immunohistochemistry. The method of any one of claims 1-11, wherein the sample is a biopsy. If the patient is identified as likely to respond to treatment comprising a KDM1A inhibitor, recommending, prescribing or administering to the patient a therapeutically effective amount of a treatment comprising a KDM1A inhibitor. A method according to any one of claims 1 to 12, comprising A KDM1A inhibitor for use in treating a patient with SCLC, wherein the patient has used the method of any one of claims 1-12 prior to initiating treatment comprising the KDM1A inhibitor. A KDM1A inhibitor that has been identified as likely to respond to treatment comprising a KDM1A inhibitor as described above. A method of treating a patient with SCLC, wherein the patient is administered a KDM1A inhibitor using the method of any one of claims 1-12 prior to initiating treatment comprising a KDM1A inhibitor. administering to the patient a therapeutically effective amount of a treatment comprising a KDM1A inhibitor when identified as likely to respond to treatment comprising. Use of ASCL1 and SOX2 in methods of identifying patients with SCLC who are likely to respond to treatment comprising a KDM1A inhibitor. Claims 1-13, wherein the KDM1A inhibitor is (trans)-N1-(((1R,2S)-2-phenylcyclopropyl)cyclohexane-1,4-diamine or a pharmaceutically acceptable salt thereof. a KDM1A inhibitor for use according to claim 14; a method of treating according to claim 15; or a use according to claim 16. A method according to claims 1 to 13 or 17, a KDM1A inhibitor for use according to claim 14 or 17, a method of treating according to claim 15 or 17, or claim, wherein the patient is a human patient. Use according to 16 or 17. A kit for assessing the likely response of a patient with SCLC to treatment comprising a KDM1A inhibitor, the kit comprising one or more agents for measuring levels of ASCL1 and SOX2 in a sample, and optionally use Kit including instructions.

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