CN117795098A - chromosomal interaction markers - Google Patents

Abstract

A method for analyzing chromosomal interactions associated with cancer immunotherapy.

Description

Chromosome interaction markers

Technical Field

The present invention relates to immunotherapy.

Background

Cancer is a major disease burden worldwide. Every year, tens of millions of people worldwide are diagnosed with cancer, and more than half of patients eventually die from cancer. Cancer is the second most common cause of death in many countries, next to cardiovascular disease. With significant improvements in cardiovascular disease treatment and prevention, cancer has become or is about to become the first killer in many parts of the world. Since elderly people are most susceptible to cancer and population aging in many countries is still continuing, cancer will still be a major health problem worldwide.

Although the primary purpose of the immune system is to combat infections caused by external factors such as pathogens, it also has an important function of attacking and eliminating cancer cells. Immunotherapy of cancer generally works by somehow assisting the immune system against cancer cells.

Disclosure of Invention

The inventors have identified chromosomal conformational features that define the state of the immune system associated with cancer therapy. This illustrates the role of this mode in the regulation of the immune system and allows the immune system of a "readout" patient to respond to immunotherapy. It also allows identification of certain types of responder populations for which immunotherapy is unsuitable and in fact very detrimental. This analysis at the genomic 3D structural level defined by chromosomal interactions provides an extremely early readout of the patient's response to immunotherapy, allowing decisions to be made regarding the most appropriate therapy at the early stages of the disease. Detection of relevant chromosomal interactions according to the invention has been found to be robust, suitable for different immunotherapies and cancers.

The identified markers are consistent with deregulation (deregulation) in T cells, NK (natural killer) cells, macrophages, B cells and Dendritic Cells (DCs), showing the role played by specific settings at the cellular level of the adaptive immune system and the innate immune system as part of cancer-host interactions in individual patients, defining disease progression (super-progressors) and responsiveness to immunotherapy.

Accordingly, the present invention provides a method of determining how an individual responds to cancer immunotherapy comprising detecting the presence or absence in said individual:

-all of the chromosomes shown in table 8 interact to determine whether the individual will respond to immunotherapy; and/or

All chromosomes shown in table 2 interact to determine if the individual is a super-progressor (super-progressor) whose immunotherapy would accelerate the progression of the disease.

A method of determining how an individual responds to cancer immunotherapy may comprise detecting the presence or absence of all of the chromosomal interactions shown in table 1 in the individual, thereby determining whether the individual will respond to immunotherapy.

Drawings

Figure 1 shows a preferred method for typing chromosomal interactions, which is based mainly on the EpiSwitch method.

FIG. 2 shows a predicted prognostic baseline patient profile and shows response data to PD-L1 (Avelumab) in two-line (2L) non-small cell lung cancer (NSCLC). For the top graph in each of fig. 2a, 2b and 2c, the vertical axis represents the probability of survival from 0.00 to 1.00, the horizontal axis represents the time from 0 to 1200, and the p values of the dashed lines in fig. 2a, 2b and 2c are < 0.0001, 0.76 and < 0.0001, respectively. The bottom table of each graph shows the corresponding number of at-risk for each line of the graph for the time from 0 to 1200.

Figure 3 relates to the validation of the EpiSwitch anti-PD-L1 response marker in independent cohorts of 21 patients treated with different cancer types and checkpoint inhibitors.

Figure 4 shows a high degree of agreement between baseline EpiSwitch determination (call), PD-L1 expression and observed clinical response.

Fig. 5 shows training set data based on 11 marker models for 80 NSCLC patients, who were a mix of 1L, 2L Avelumab (54) and 2L pamglizumab (Pembrolizumab) (36).

Fig. 6 shows test set data based on 11 marker models for 38 NSCLC patients, who were a mix of 1L, 2L Avelumab (27) and 2L palbociclizumab (11).

Fig. 7 and 8 show the second test set of data for malaysia observational studies, which observe the mixing of checkpoint inhibitors and tumors.

Fig. 9 shows the vertical determination in blind asian samples.

Fig. 10 shows actual EpiSwitch decisions for patients sampled at multiple time points.

Fig. 11 and 12 show sample selection and patient details of work associated with the super-progressors.

Figure 13 shows EpiSwitch chromosome conformational marker selection and associated genetic positions.

Fig. 14 and 15 show the pathway analysis of genetic locations.

Fig. 16 shows a training set of super-progressors.

Fig. 17 shows a test set of super-progressors.

Fig. 18 shows the logical PCA (principal component analysis) of the super-progressor training set. The squares represent H and the circles represent S.

Fig. 19 shows the training set of super-progressors and the logical PCA of the predicted test samples. The squares represent H and the black circles represent S.

Fig. 20 shows the logical PCA of the training set with PFS as a marker for the super-progressors. The squares represent H and the circles represent S.

Fig. 21 shows the logical PCA of the super-progressors' training set labeled OS. The squares represent H and the circles represent S.

Figure 22 shows the confusion matrix and statistics. The training model was 78 patients. 30 patients were NR (non-responders). The 39 patients were R (responders). The 9 patients were SD (disease stabilization).

Figure 23 shows the confusion matrix and statistics. The test set was 24 patients. 8 patients were NR. The 12 patients were R. The 4 patients were SD.

Figure 24 shows the confusion matrix and statistics. The test set was 128 patients.

Figure 25 shows the global variable importance of different markers. From top to bottom, the results are shown as: (i) obd189_q65-q67_p65, (ii) obd189_q53_q55_p53, (iii) obd189_q81_q83_p81, (iv) obd148_q893_q895_p893, (v) obd189_q49_q51_p49, (vi) obd189_q29_q31_p31, (vii) obd189_q57_q59_p57, (viii) obd189_q05_q07_p05. The horizontal axis represents top model features. The vertical axis shows values from 0 to 20.

FIG. 26 shows the genetic location of markers.

FIG. 27 shows the pathways associated with the genes of FIG. 26.

Description of the form

Table 1 shows the universal marker sets and how each marker correlates with responsiveness (R) or non-responsiveness (NR) to immunotherapy.

Table 2 shows the set of markers used to detect the super-progressors and how each marker is related to super-progression (HS) or stabilization (S).

Table 3 shows immune checkpoint molecules that can be targeted and/or modulated by immunotherapy.

Tables 4 to 6 provide examples of cancer immunotherapy for which the responder status can be determined by the present invention, and which is also a therapy that can be administered to an individual based on the result of the responder status determination according to the present invention. These tables also show the preferred cancers.

Table 7 shows the markers associated with the screening performed in example 2 to develop the marker sets shown in table 8.

Table 8 shows another universal set of markers and how each marker correlates with responsiveness (R) or non-responsiveness (NR) to immunotherapy.

Patient data for example 2 are given in table 9. Patients shown with asterisks were studied in the second screen described in example 2.

Detailed Description

The terminology used herein

The method of the present invention may be referred to herein as the "process" of the present invention.

The typed chromosomal interactions may be referred to herein as "markers," CCS, "" chromosomal conformational features, "" epigenetic interactions, "or" epigenetic switch markers.

The term "typing" will be interpreted in terms of context, but generally refers to detecting the presence or absence of a particular chromosomal interaction. Typing will typically be by physically determining whether the chromosomal interaction is present.

The term "responder" is used to refer to a response to immunotherapy and includes aspects related to both responsiveness to immunotherapy (universal marker set) and detection of super-progressors. The term "responder group" includes all four different groups discussed herein:

-responders to immunotherapy

-non-responders to immunotherapy

Super-progressors on administration of immunotherapy

Disease stabilization when immunotherapy is administered (non-super-progressors).

The chromosomal interactions of the typing in the methods of the invention are defined in tables 2 and 8. The chromosomal interactions of the typing in the methods of the invention are further defined in table 1. They are defined by means of probe sequences which detect the ligation products generated by the EpiSwitch method (see fig. 1). They are also defined by the position numbers of interactions within the probe name, and they are also defined by primer sequences that allow detection of the ligation sequences.

Epigenetic interactions associated with the present invention

The chromosomal interactions typed in the present invention are typically interactions between the distal regions of the chromosome that are dynamic and that change, form or break depending on the state of the chromosomal region. This state will reflect how the immune system interacts with the administered immunotherapy, which responder group the individual falls into.

For example, chromosomal interactions may reflect whether it is transcribed or repressed. Chromosomal interactions specific to the "panel" of responders as defined herein have been found to be stable, providing a reliable method of measuring differences between groups (e.g., reflecting different responses to immunotherapy).

For example, in comparison to other epigenetic markers (e.g., methylation or changes in histone binding), the group-specific chromosomal interactions of the responders typically occur prior to or at an early stage of the disease process. Thus, the method of the invention is able to provide valuable information about the way the immune system responds in an early stage. This enables early intervention (e.g. treatment) and thus also more effective, and also enables early selection of the type of treatment appropriate for the patient and the treatment that should not be used. Chromosome interactions also reflect the current state of an individual and can therefore be used to assess changes in disease states. Furthermore, there was little change in the relevant chromosomal interactions between individuals within the same group.

The chromosomal interactions detected in the present invention may be affected by: alterations in potential DNA sequences, environmental factors, DNA methylation, non-coding antisense RNA transcripts, non-mutagenic carcinogens, histone modifications, chromatin remodeling, and specific local DNA interactions. However, it must be remembered that the chromosomal interactions defined herein are themselves a regulatory manner, and do not have a one-to-one correspondence with any genetic marker (DNA sequence change) or any other epigenetic marker.

Changes that result in chromosomal interactions can be affected by changes in potential nucleic acid sequences that do not themselves directly affect gene products or gene expression patterns. Such changes may be, for example, SNPs, intra-and/or extra-gene, gene fusions of inter-gene DNA, micrornas and non-coding RNAs and/or gene deletions. For example, about 20% of SNPs are known to be located in non-coding regions, so the described methods can provide useful information also in non-coding situations. Typically, the chromosomal regions that are clustered together to form interactions are less than 5kb, 3kb, 1kb, 500 base pairs, or 200 base pairs apart on the same chromosome.

The method of the invention

The method of the invention comprises a typing system for detecting chromosomal interactions associated with a responder status. Any combination can be usedSuitable typing methods, for example, methods of detecting the proximity of chromosomes in an interaction and/or methods of detecting markers reflecting the state of chromosome interactions. The typing method may use the EpiSwitch mentioned herein ^TM The system proceeds, for example, by a method comprising the steps of (e.g., for DNA and/or sample from a subject):

(i) Crosslinking the chromosomal regions that have been brought together in the chromosomal interaction;

(ii) Optionally isolating cross-linked DNA from the chromosomal locus;

(iii) Cutting the crosslinked DNA; and

(iv) Ligating nucleic acids present in the crosslinking entity to obtain a ligated nucleic acid having sequences from two regions forming chromosomal interactions.

Detection of the linked nucleic acid allows for the determination of the presence or absence of a specific chromosomal interaction. Thus, the linked nucleic acids serve as markers for the existence of chromosomal interactions. Preferably, the linked nucleic acids are detected by PCR or probe-based methods, including qPCR methods.

In the method, the chromosome may be crosslinked by any suitable method, for example by a crosslinking agent, typically a compound. In a preferred aspect, the interaction is cross-linked using formaldehyde, but may be cross-linked with any aldehyde or D-biotin-e-aminocaproic acid-N-hydroxysuccinimide ester or digoxin-3-O-methylcarbonyl-e-aminocaproic acid-N-hydroxysuccinimide ester. Paraformaldehyde can crosslink DNA strands that are 4 angstroms apart. Preferably, the chromosomes interact on the same chromosome. Typically, the chromosome interactions are 2 to 10 angstroms apart.

Preferably, the crosslinking is performed in vitro. Preferably, cleavage is performed by restriction with an enzyme (e.g., taqI). Ligation may form a DNA loop.

When PCR (polymerase chain reaction) is used to detect or identify a linked nucleic acid, the size of the PCR product produced can be indicative of the specific chromosomal interactions present and thus can be used to identify the status of the locus. In a preferred aspect, primers as set forth in any of the tables herein are used, for example, primer pairs as set forth in table 2 or table 8 (corresponding to the chromosome interactions being detected) are used. Primers shown in Table 1 can be used. Homologs of the primer or primer pair may also be used, which homologs may have at least 70% identity to the original sequence.

When probes are used to detect or identify the linked nucleic acids, this is typically accomplished by Watson-Crick based base pairing between the probe and the linked nucleic acid. Probe sequences shown in any of the tables herein, such as those shown in table 2 or 8 (corresponding to the chromosome interactions being detected), may be used. The probe sequences shown in Table 1 may be used. Homologs of these probe sequences, which may have at least 70% identity to the original sequence, may also be used.

Typing according to the method of the invention may be performed at various time points, for example to monitor the progression of a disease. This may be at one or more defined points in time, for example at least 1, 2, 5, 8 or 10 different points in time. The duration between at least 1, 2, 5 or 8 time points may be at least 5 days, 10 days, 20 days, 50 days, 80 days or 100 days. There are typically 3 time points at least 50 days apart.

Individuals to be tested and/or treated

Preferably, the individual tested in the method of the invention is a eukaryote, an animal, a bird, or a mammal. Most preferably, the individual is a human. The individual may be male or female. In the case of human individuals, their age is typically 65 years or older.

The invention includes detecting and treating specific groups in a population whose responder status (e.g., their response to immunotherapy) is generally different. The inventors have found that chromosomal interactions between these groups are differential, and identifying these differences will allow doctors to categorize their patients as part of a particular group of the population. Accordingly, the present invention provides a method for a physician to provide personalized medicine to an individual based on the individual's epigenetic chromosome interactions. Such tests may be used to select how the patient is to be subsequently treated, for example, the type of drug to be administered. The methods of the invention can be practiced to select a treatment for an individual, such as whether to administer any of the specific treatments mentioned herein to the individual.

The individual tested in the method of the invention may have been selected in some way, e.g. based on risk factors, symptoms or physical characteristics. The individual may have been selected based on having symptoms of cancer and/or being at an early stage of cancer.

The individual may be susceptible to any of the cancers mentioned herein and/or may need any of the therapies mentioned herein. The individual may be receiving any of the treatments mentioned herein. In particular, the individual may have cancer or be suspected of having cancer, such as any of the specific cancers mentioned herein.

Type of cancer

Cancers associated with the present invention may include any of the cancers mentioned herein, and are preferably melanoma, lung cancer, non-small cell lung cancer (NSCLC), diffuse large B-cell lymphoma, liver cancer, hepatocellular carcinoma, prostate cancer, breast cancer, leukemia, acute myelogenous leukemia, pancreatic cancer, thyroid cancer, nasal cancer, brain cancer, bladder cancer, cervical cancer, non-hodgkin lymphoma, ovarian cancer, colorectal cancer or renal cancer. The cancer may be a cancer that can be treated by immunotherapy (e.g., any of the specific immunotherapies mentioned herein).

Type of immunotherapy

The present invention relates to determining whether an individual responds to immunotherapy and/or whether they are super-progressors where immunotherapy would cause disease acceleration.

Preferably, the determined response is to a therapy comprising molecules or cells associated with the immune system (e.g. a composition comprising antibodies or immune cells (e.g. T cells or dendritic cells)) or any of the therapeutic substances mentioned herein. It may be a response to a substance that modulates or stimulates the immune system (e.g. vaccine therapy). Immunotherapy may modulate, block or stimulate an immune checkpoint and thus may target or modulate PD-L1, PD-L2 or CTLA4 or any other immune checkpoint molecule disclosed herein, thus the immunotherapy is preferably an immune checkpoint therapy. Preferably, the response is to antibody therapy or to any particular therapy disclosed herein. The therapy may be a combination therapy, such as any of the specific combination therapies disclosed herein.

In one embodiment, the response is to a PD-1 inhibitor or a PD-L1 inhibitor, including a PD-1 specific antibody or a PD-L1 specific antibody. PD-1 is "programmed cell death protein" and PD-L1 is "programmed death ligand 1".

The term "antibody" includes all fragments and derivatives of antibodies that retain the ability to bind to an antigen target, e.g., all fragments and derivatives of single chain scFV or all fragments and derivatives of Fab.

The therapy may be a monotherapy or a combination therapy, for example a monotherapy or a combination therapy using an immune checkpoint modulator (preferably an inhibitor) of PD-1 and/or its ligand PD-L1. The therapy may include administration of at least one immune checkpoint modulator, such as an immune checkpoint modulator disclosed herein, e.g., in any of the tables, figures, or examples. The therapy may be a combination of anti-PD-1 or anti-PD-L1 in combination with another drug (Ipilimumab)/Yervoy) targeting a checkpoint such as CTLA4 or a small molecule. The PD-1 inhibitor may be palbociclizumab (Keytruda) or nal Wu Liyou mab (nivolumab) (Opdivo). The modulator or therapeutic agent of PD-L1 may be atilizumab (Atezolizumab) (tecantrioq), avelumab (Bavencio), dulcitalopram You Shan antibody (Durvalumab) (Imfinzi), CA-170, ipilimumab, tremelimumab (Tremelimumab), na Wu Liyou mab, palbociclizumab, pidizumab (Pidilizumab), BMS935559, GVAXMLL 3280A, MEDI4736, MSB0010718C, MDX-1105/BMS-936559, AMP-224, MEDI0680.

The therapy may include administration of agents that target and/or modulate the interferon gamma or JAK-START pathway.

The therapeutic agent may be any such agent disclosed in any of the tables herein, or the therapeutic agent may be targeted to any "target" disclosed herein, including any protein disclosed herein. It should be understood that any agents disclosed in combination are to be considered as also disclosed for separate administration.

Super progressors

The progression of the cancer may be identified by the skilled person in a straightforward manner, and preferably the progression of the cancer is an increase in the progression of the cancer disease and/or an adverse response to the immunotherapy following administration of the immunotherapy in an individual suffering from the cancer. Any suitable disease parameter may be used for measurement, for example a 2-fold increase in tumor size. Tumor burden typically increases by more than 50% within 60 days of immunotherapy administration. In one aspect, a super-progressor may be defined as having no progression less than 60 days after administration of immunotherapy and/or having a total survival of less than 150 days after administration of immunotherapy.

Treatment selection

Based on the test results of the methods of the invention, a determination can be made as to which treatments will be administered or not administered to the individual.

If a person is found to be responsive to immunotherapy, they may be given any of the immunotherapies mentioned herein. In one aspect, if an individual is found to be non-responsive, they may be administered a combination therapy, such as any of the combination therapies listed herein. Combination therapies typically include antibodies and small molecules.

Data in the tables provided herein

Tables 1, 2 and 8 show specific markers that may be used to detect the status of a responder. Their presence or absence may be used for such detection (i.e. they are "transmissibility" markers). Tables 1 and 8 show markers to detect responsiveness to immunotherapy and the table shows which are related to responsiveness and which are related to non-responsiveness. Table 2 shows the markers for detecting the super-progressors and the table shows which are related to the super-progressors and which are related to the disease stabilization.

The markers are defined using probe sequences that detect ligation products as defined herein. The start-end positions of the first two pools show the probe positions and the start-end positions of the last two pools show the relevant 4kb region.

The probe data table provides the following information:

RP-Rank sum (Rsum) of Rank Product (Rank Product) statistics assessed per chromosomal interaction.

FC-interaction frequency (positive or negative).

Pfp-estimated percentage of false positive predictions (Pfp), taking into account both positive and negative chromosome interactions.

Pval-estimated p value for each positive CCS and negative CCS.

P value (FDR) -p value after error discovery rate adjustment.

Type-in which state the ring is.

Simple permutation-based (permutation) estimates are used to determine the likelihood of a given RP value or better being observed in random experiments. This comprises the steps of:

1. p permutations of k ordered lists of length n are generated.

2. The rank product of n CCS in p permutations is calculated.

3. Counting the number of times the rank product of CCS in the permutation is less than or equal to the observed rank product (c). C is set to this value.

4. The average expected value of the rank product is calculated by Erp (g) =c/p.

5. The percentage of false positives is calculated as follows: pfp (g) =erp (g)/rank (g), where rank (g) is the rank of CCS g in the list of all n CCS ordered incrementally by RP.

Rank product statistics the chromosomal interactions are ordered according to intensity in each microarray and the product of these ranks in the plurality of microarrays is calculated. This technique allows identification of consistently detected chromosomal interactions among the most differential chromosomal interactions in many replicated microarrays. When the p value is 0, this indicates that the rank product of CCS in the samples varies little, which is a good example of the signal-to-noise ratio and effect size of CCS. When the p value is 0 and pfp is 0, this means that the permuted rank product is not different from the actually observed rank product. These methods are described in Breitling R and Herzyk P (2005) Rank-based methods as anon-parametric alternative of the t-test for the analysis of biological microarray data.J Bioinf Comp Biol 3,1171-1189.

FC represents the occurrence of the marker in each comparison, 2 represents twice the average test, 1.5 represents 1.5 times the average test, and so on, so FC represents the weight of the marker to phenotype/group. The FC value can be used to indicate how many markers are needed for efficient testing.

The probe was designed to be 30bp from Taq1 site. In the case of PCR, PCR primers are usually designed to detect the ligation products, but they are located differently from the Taq1 site. Probe position:

30 bases upstream of TaqI site on the initial 1-fragment 1

Stop 1-fragment 1 TaqI restriction site

TaqI restriction site on Start 2-fragment 2

30 bases downstream of TaqI site on termination 2-fragment 2

4kb sequence position:

4000 bases upstream of TaqI site on the initial 1-fragment 1

Stop 1-fragment 1 TaqI restriction site

TaqI restriction site on Start 2-fragment 2

4000 bases downstream of TaqI site on terminator 2-fragment 2

Detection type

When using probes for detection, sequences from two regions of the probe (i.e., from two sites of chromosomal interaction) can typically be detected. In a preferred aspect, the probes used in the method comprise or consist of the same or complementary sequences as the probes shown in any of the tables. In some aspects, probes used include sequences homologous to any of the probe sequences shown in the tables.

Method for identifying markers and marker sets (panels)

The invention described herein relates to chromosome conformation spectra and 3D structures as modulation means per se, closely related to phenotype. The discovery of biomarkers is based on annotation by pattern identification and screening of representative cohorts of clinical samples representing phenotypic differences. We have annotated and screened important parts of the genome spanning the coding and non-coding parts of known genes and their non-coding 5 'and 3' large sways for identifying statistically consistent conditionally transmissible chromosome conformations, e.g., non-coding sites anchored in (introns) or out of the open reading frame.

In selecting the best marker, we are driven by statistics of the marker lead and the p-value. Regardless of the expression profile of the gene used in the reference, the chromosome conformation selected and verified within the feature is itself a transmissible classification entity (disseminating stratifying entities). Related regulatory patterns, such as SNPs at the anchor site, changes in gene transcription profile, changes in H3K27ac levels, etc., remain to be studied further.

We are studying the problems of clinical phenotype differences and their classification on the basis of underlying biology and epigenetic phenotype control, including for example from the framework of regulatory networks. Thus, to assist in classification, changes in the network may be captured, preferably by features of several biomarkers, such as by narrowing the markers by following a machine learning algorithm, including evaluating the optimal number of markers, thereby classifying the test queue with minimal noise. This can end with 3 to 20 markers.

Markers can be selected for a stack by cross-validating statistical properties (rather than, for example, by functional correlation of adjacent genes for reference names).

One marker stack (with the name of the adjacent gene) is the product of cluster selection from screening for a significant portion of the genome, which is analyzed in an unbiased manner for statistical transmission capacity of over 14,000 to 60,000 annotated EpiSwitch sites. For classification problems, they should not be considered as custom capture of the chromosomal conformation of genes of known functional value. The total number of sites for chromosomal interactions is 120 tens of thousands, so the number of potential combinations is 120 tens of thousands to the power of 120 tens of thousands. Nevertheless, the approach we followed allows identification of relevant chromosomal interactions.

The specific markers provided herein have been statistically (significantly) associated with a pathology or subgroup by selection. This is illustrated by the data in the correlation table. Each marker may be considered to represent a biological epigenetic event as part of a network disorder (network deregulation) manifested under the relevant condition. In practice, this means that these markers are prevalent in each group of patients compared to the control group. On average, for example, a single marker may typically be present in 80% of the relevant responders and 10% of the control groups, so the test results by the method of the present invention are directly interpreted and are essentially equivalent to "binary information readout".

Simple summation of all markers does not directly represent some deregulated network correlations. This is where standard multivariate biomarker analysis glanet (R software package) was introduced. The GLMNET software package helps identify the interdependence between some markers, reflecting their co-action in achieving a deregulation that leads to a disease phenotype. Modeling and then testing the marker with the highest GLMNET score can identify not only the smallest number of markers that accurately identify the patient cohort, but also the smallest number of markers that provide the least false positive results in the control group of patients due to low incidence of background statistical noise in the control group. Typically, a set (combination) of selected markers (e.g., 3 to 11) provides the best balance between detection sensitivity and specificity, appearing in the context of multivariate analysis of each attribute from all statistically significant markers selected for a pathology.

The table herein shows the reference name, chromosome number, and initiation and termination of two juxtaposed chromosome segments of an array probe (60-mer) overlapping the junction between long-range interaction sites for array analysis.

In a preferred aspect, all 11 markers of table 1 are typed. In another preferred aspect, all 11 markers of table 2 are typed. In another preferred aspect, all 8 markers of table 8 are typed.

Sample and sample processing

The method of the present invention is typically performed on a sample. The sample may be obtained at a defined point in time, e.g., any point in time defined herein. The sample typically comprises DNA from the individual. The sample typically comprises cells. In one aspect, the sample is obtained by minimally invasive means, and may be, for example, a blood sample. The DNA may be extracted and cleaved with standard restriction enzymes. This can predetermine which chromosome conformations are preserved and will be determined by EpiSwitch ^TM And (5) detecting a platform. Because of the synchronicity of chromosomal interactions, including horizontal transfer, between tissue and blood, blood samples may be used to detect chromosomal interactions in tissue (e.g., tissue associated with disease).

Preferred aspects of sample preparation and detection of chromosome interactions

Methods of preparing a sample and detecting chromosome conformation are described herein. Optimized (non-conventional) versions of these methods may be used, such as described in this section.

Typically, the sample comprises at least 2X 10 ⁵ Individual cells. The samples may contain up to 5 x 10 ⁵ Individual cells. In one aspect, the sample comprises 2×10 ⁵ To 5.5X10 ⁵ Individual cells.

Described herein are cross-links to epigenetic chromosome interactions present at chromosomal loci. This may be done before cell lysis occurs. Cell lysis may be performed for 3 minutes to 7 minutes, for example 4 minutes to 6 minutes or about 5 minutes. In some aspects, cell lysis is performed for at least 5 minutes and less than 10 minutes.

Digestion of DNA with restriction enzymes is described herein. Typically, DNA restriction is performed at about 55 ℃ to about 70 ℃, e.g., at about 65 ℃, for about 10 minutes to 30 minutes, e.g., about 20 minutes.

Preferably, a high frequency shearing restriction endonuclease (frequent cutter restriction enzyme) is used which produces fragments of ligated DNA having an average fragment size of up to 4000 base pairs. Optionally, the restriction enzyme produces a fragment of the ligated DNA having an average fragment size of about 200 to 300 base pairs (e.g., about 256 base pairs). In one aspect, the fragment size is typically 200 base pairs to 4,000 base pairs, such as 400 to 2,000 or 500 to 1,000 base pairs.

In one aspect of the EpiSwitch method, no DNA precipitation step is performed between the DNA restriction digestion step and the DNA ligation step.

DNA ligation is described herein. Typically DNA ligation is performed for 5 minutes to 30 minutes, for example about 10 minutes.

The proteins in the sample may be enzymatically digested (e.g., using protease, optionally proteinase K). The protein may be enzymatically digested for about 30 minutes to 1 hour, for example about 45 minutes. In one aspect, after protein digestion (e.g., proteinase K digestion), there is no crosslinking reversing DNA extraction or phenolic DNA extraction step.

In one aspect, PCR detection is capable of detecting a single copy of the linked nucleic acid, preferably by binary information readout of the presence/absence of the linked nucleic acid.

FIG. 1 shows a preferred method of detecting chromosomal interactions.

The method and use of the invention

The method of the invention can be described in different ways. It can be described as a method of preparing one or more linked nucleic acids comprising: (i) In vitro cross-linking of the chromosomal regions that have been brought together in the chromosomal interactions; (ii) Cutting or restriction digestion of the cross-linked DNA; and (iii) ligating the cross-linked cleaved DNA ends to form one or more ligating nucleic acids, wherein optionally detection of the ligating nucleic acids may be used to determine the chromosomal status of a locus, and wherein preferably the chromosomal interactions may be 1, 3, 5, 8 or all chromosomal interactions of table 1 or table 2. In this method, the chromosomal interactions may be 1, 3, 5 or 8 chromosomal interactions of table 8.

Homologs of

This document relates to homologs of polynucleotide/nucleic acid (e.g., DNA) sequences. Such homologues generally have at least 70% homology, preferably at least 80%, at least 85%, at least 90%, at least 95%, at least 97%, at least 98% or at least 99% homology, for example over a region of at least 10, 15, 20, 30, 100 or more consecutive nucleotides, or over a nucleic acid portion spanning a region from a chromosome involved in chromosomal interaction. Homology may be calculated based on nucleotide identity (sometimes referred to as "hard homology").

Thus, in a particular aspect, homologs of a polynucleotide/nucleic acid (e.g., DNA) sequence are referred to herein as percent sequence identity. Typically such homologues have at least 70% sequence identity, preferably at least 80%, at least 85%, at least 90%, at least 95%, at least 97%, at least 98% or at least 99% sequence identity, for example over a region of at least 10, 15, 20, 30, 100 or more consecutive nucleotides, or over a nucleic acid portion spanning a region from a chromosome involved in chromosome interaction. The homologue may have at least 70% sequence identity, preferably at least 80%, at least 85%, at least 90%, at least 95%, at least 97%, at least 98% or at least 99% sequence identity throughout the probe, primer or primer pair.

For example, the UWGCG software package provides a BESTFIT program that can be used to calculate homology and/or sequence identity (e.g., for use at its default settings) (Devereux et al (1984) Nucleic Acids Research, p 387-395). The PILEUP and BLAST algorithms may be used to calculate% homology and/or sequence identity and/or alignment (e.g., identify equivalent sequences or corresponding sequences (typically under their default settings)), e.g., as Altschul s.f. (1993) J MoI Evol 36:290-300; altschul, S, F et al (1990) J MoI Biol 215:403-10.

Software for performing BLAST analysis is publicly available through the national center for biotechnology information. The algorithm includes first identifying pairs of high scoring sequences (high scoring sequence pair, HSPs) by identifying short fields of the same length in the query sequence that either match or satisfy some positive threshold score T when aligned with a field of length W (word) in the database sequence. T is referred to as the neighbor field score threshold (Altschul et al, supra). These initial neighbor matching fields (word hit) act as seeds (seed) for initiating searches to find HSPs containing them. The match field extends in both directions along each sequence until the cumulative alignment score can be increased. The extension of the matching field in each direction is terminated if: the cumulative alignment score drops by an amount X from its maximum implementation value; the cumulative score becomes zero or lower due to the accumulation of one or more negative score residue alignments; or to the end of either sequence. The BLAST algorithm parameters W5T and X determine the sensitivity and speed of the alignment. The BLAST program defaults to a field length (W) of 11, BLOSUM62 scoring matrix (see Henikoff and Henikoff (1992) Proc. Natl. Acad. Sci. USA 89:10915-10919) vs (B) of 50, expected (E) of 10, M= 5,N =4, and a comparison of the two chains.

The BLAST algorithm performs a statistical analysis of the similarity between two sequences; see, e.g., karlin and Altschul (1993) Proc.Natl. Acad.Sci.USA 90:5873-5787. One measure of similarity provided by the BLAST algorithm is the minimum sum probability (P (N)), which provides an indication of the probability of an accidental match between two polynucleotide sequences. For example, one sequence is considered similar to another sequence if the smallest sum probability of a first sequence compared to a second sequence is less than about 1, preferably less than about 0.1, and more preferably less than about 0.01, and most preferably less than about 0.001.

Homologous sequences typically have a difference of 1, 2, 3, 4 or more bases, for example a difference of less than 10, 15 or 20 bases (which may be nucleotide substitutions, deletions or insertions). These changes can be measured in any of the regions mentioned above for the calculation of percent homology and/or percent sequence identity.

The homology of a "primer pair" can be calculated, for example, by treating the two sequences as a single sequence (as if the two sequences were joined together) for the purpose of subsequent comparison with another primer pair, which is also treated as a single sequence.

Detection threshold

The markers disclosed herein have been found to be "transmissible markers" capable of determining responder status, and tables 1 and 2 show in which responder group (responder to immunotherapy/non-responder to immunotherapy, or hyper-progression/disease stabilization) each marker is present.

In practice, this means that these markers are prevalent in the relevant responder group (e.g. as indicated by FC values) compared to the control group. On average, for example, a single marker may typically be present in 80% of the relevant responder group and 10% of the control group. When an individual is tested, the result will be a combination of "present" and "absent" of chromosomal interactions for each of the markers shown in tables 1 and 2, allowing the determination of the individual's responder group. In general, the presence/absence of at least 8 of the 11 markers can be used to assign individuals to the responder group, as compared to the "ideal" results shown in the table.

Therapeutic agents and treatments

This section relates to both immunotherapy defining a group of responders to an individual and to therapies that can be administered to an individual based on the results of the test methods of the present invention.

The present invention provides therapeutic agents for preventing or treating any of the conditions mentioned herein. This may include administering to an individual in need thereof a therapeutically effective amount of an agent. The invention provides the use of said agent in the manufacture of a medicament for the prevention or treatment of a condition, for example in an individual tested by the method of the invention.

The formulation of the agent will depend on the nature of the agent. The agent will be provided in the form of a pharmaceutical composition comprising the agent and a pharmaceutically acceptable carrier or diluent. Suitable carriers and diluents include isotonic saline solutions, for example phosphate buffered saline. Typical oral dosage compositions include tablets, capsules, liquid solutions and liquid suspensions. The agent may be formulated for parenteral, intravenous, intramuscular, subcutaneous, transdermal or oral administration.

The dosage of the agent may be determined according to various parameters, in particular according to the substance used, the age, weight and condition of the individual to be treated, the route of administration, and the desired regimen. The physician can determine the route of administration and dosage required for any particular agent. However, suitable dosages may be from 0.1mg/kg body weight to 100mg/kg body weight, for example from 1mg/kg body weight to 40mg/kg body weight, for example from 1 to 3 times per day.

The present invention provides an immunotherapeutic agent, preferably selected from any one of tables 4 to 6, for use in a method of treating an individual identified as responsive to immunotherapy, optionally the method comprising:

identifying whether the individual is responsive to immunotherapy by the method of the invention, and

-administering the agent to any individual identified as responsive to immunotherapy.

The present invention provides the use of (i) a therapeutic agent in combination with immunotherapy or (ii) non-immunotherapy, in a method of treating an individual identified as non-responsive to immunotherapy, optionally comprising:

identifying whether the individual is responsive to immunotherapy by the method of the invention, and

-administering (i) and/or (ii) to any individual identified as non-responsive, wherein optionally the combination therapy of (i) is any combination therapy shown in table 4 or any combination therapy comprising at least one agent selected from table 4 and table 5.

Screening of therapeutic agents

The present invention provides a screening method for identifying cancer therapeutic agents comprising determining whether a candidate agent is capable of causing a change in all of the chromosomal interactions shown in table 1 and/or table 2. The screening method may include determining whether the candidate agent is capable of causing a change in all of the chromosomal interactions shown in table 8.

Nucleic acids of the invention

The invention provides certain nucleic acids, including probes and primers. Preferably, the nucleic acid is DNA. It is to be understood that where specific sequences are provided, the invention may use complementary sequences as desired for specific aspects.

Primers or probes shown in Table 1 or Table 2 can be used in the present invention. In one aspect, probes or primers are used that include any of the following: the sequences shown in table 1 or table 2; or a fragment and/or homologue of any of the sequences shown in table 1 or table 2. Primers or probes shown in Table 8 can be used in the present invention. In one aspect, probes or primers are used that include any of the following: the sequences shown in table 8; or a fragment and/or homologue of any of the sequences shown in Table 8.

Labeling nucleic acids and hybridization patterns

The nucleic acids mentioned herein may be labeled, preferably with a separate label, such as a fluorophore (fluorescent molecule) or a radiolabel, that aids in the detection of successful hybridization. Some labels may be detected under UV light.

Forms of the substances mentioned herein

Any of the substances mentioned herein, e.g., nucleic acids or therapeutic agents, may be in purified or isolated form. They may exist in forms other than those found in nature, for example they may exist in combination with other substances not found in nature. Nucleic acids (including portions of sequences defined herein) may have sequences that differ from sequences found in nature, e.g., have at least 1, 2, 3, 4, or more nucleotide changes in the sequence, as described in the section for homology. The nucleic acid may have a heterologous sequence at the 5 'end or the 3' end. Nucleic acids may be chemically different from those found in nature, e.g., they may be modified in some way, but preferably still be able to undergo Watson-Crick base pairing. Where appropriate, the nucleic acid will be provided in double-stranded or single-stranded form. The present invention provides all of the specific nucleic acid sequences referred to herein, in single-stranded or double-stranded form, and thus includes the complementary strand of any of the sequences disclosed.

The invention provides kits for practicing any of the methods of the invention, including detecting a chromosomal interaction associated with prognosis. Such a kit may comprise a specific binding agent capable of detecting the relevant chromosomal interactions, e.g. an agent capable of detecting the linked nucleic acids generated by the method of the invention. Preferred agents in the kit include probes capable of hybridizing to the ligation nucleic acid or primer pairs capable of amplifying the ligation nucleic acid in a PCR reaction, such as those described herein. Preferred agents include any of the specific primers and probes disclosed herein and/or homologues of such primers and probes.

The present invention provides a device capable of detecting interactions of related chromosomes. The device preferably comprises any particular binding agent, probe or primer pair capable of detecting chromosomal interactions, such as any such binding agent, probe or primer pair described herein.

Detection method

In one aspect, a ligation sequence associated with chromosomal interactions is quantitatively detected using a probe that is detectable upon activation during a PCR reaction, wherein the ligation sequence comprises sequences from two chromosomal regions that are clustered together in epigenetic chromosomal interactions, wherein the method comprises: the ligation sequence is contacted with a probe during the PCR reaction and the degree of activation of the probe is detected, and wherein the probe binds to the ligation site. This method typically uses dual-labeled fluorescent hydrolysis probes to detect specific interactions in a manner consistent with MIQE.

Probes are typically labeled with a detectable label having an inactive and active state, so that the probe is only detected when activated. The extent of activation will be related to the extent of template (ligation product) present in the PCR reaction. The detection may be performed during all or part of the PCR process, for example during at least 50% or 80% of the PCR cycles.

The probe may include a fluorophore covalently linked to one end of the oligonucleotide and a quencher linked to the other end of the oligonucleotide such that fluorescence of the fluorophore is quenched by the quencher. In one aspect, the fluorophore is attached to the 5 'end of the oligonucleotide and the quencher is covalently attached to the 3' end of the oligonucleotide. Fluorophores that may be used in the methods of the invention include FAM, TET, JOE, yakima yellow, HEX, anthocyanin 3 (Cyanine 3), ATTO 550, TAMRA, ROX, texas Red (Texas Red), anthocyanin 3.5, LC610, LC 640, ATTO 647N, anthocyanin 5, anthocyanin 5.5, and ATTO 680. Quenchers that may be used with suitable fluorophores include TAM, BHQ1, DAB, eclip, BHQ, and BBQ650, optionally wherein the fluorophores are selected from HEX, texas red, and FAM. Preferred combinations of fluorophores and quenchers include FAM and BHQ1 and texas red and BHQ2.

Use of probes in qPCR detection

The hydrolysis probes of the present invention are typically temperature gradient optimized and have a concentration-matched negative control. Preferably, a single step PCR reaction is optimized. More preferably, a standard curve is calculated. One advantage of using specific probes that bind across the junction of the ligation sequences is that specificity for the ligation sequences can be achieved without the use of nested PCR methods. The methods described herein can accurately and precisely quantify low copy number targets. The target binding sequence may be purified, e.g., gel purified, prior to temperature gradient optimization. The target junction sequence may be sequenced. Preferably, the PCR reaction is performed using about 10ng, or 5ng to 15ng, or 10ng to 20ng, or 10ng to 50ng, or 10ng to 200ng of template DNA. The forward and reverse primers are designed such that one primer binds to the sequence of one of the chromosomal regions represented in the ligation DNA sequence and the other primer binds to the other chromosomal region represented in the ligation DNA sequence, e.g., by sequence complementation.

Selection of ligation DNA targets

The invention includes selecting primers and probes as defined herein for use in a PCR method, including selecting primers based on their ability to bind and amplify a ligation sequence, and selecting probe sequences based on the characteristics of the target sequence to which the probes will bind, particularly the curvature of the target sequence.

Probes are typically designed/selected to bind to a ligation sequence that is a juxtaposed restriction fragment spanning a restriction site. In one aspect of the invention, the predicted curvature of the possible linked sequences associated with a particular chromosomal interaction is calculated, for example, using the specific algorithms cited herein. The curvature may be expressed as degrees/helical angle, for example 10.5 °/helical angle. The junction sequence is selected for targeting at a curvature propensity peak fraction of at least 5 °/helical turn, typically at least 10 °, 15 ° or 20 °/helical turn, for example 5 ° to 20 °/helical turn. Preferably, the curvature propensity score for each helical turn is calculated for at least 20, 50, 100, 200 or 400 bases, e.g. 20 to 400 bases, upstream and/or downstream of the ligation site. Thus, in one aspect, the target sequence in the ligation product has any of these levels of curvature. The target sequence may also be selected based on the lowest thermodynamic structural free energy.

Detailed description of the invention

In particular aspects, certain chromosomal interactions are not typed, such as any particular interactions not mentioned herein. In certain aspects, only the markers of table 1 or table 2 are typed and no other markers are typed. In certain aspects, only the markers of table 2 or table 8 are typed and no other markers are typed. In certain aspects, only the markers of table 1 and table 2 are typed and no other markers are typed. In certain aspects, only the markers of table 2 and table 8 are typed and no other markers are typed.

Description of the invention

The invention includes aspects described in the following numbered paragraphs:

1. a method of determining how an individual responds to cancer immunotherapy comprising detecting the presence or absence in said individual:

-all of the chromosomes shown in table 1 interact to determine whether the individual will respond to immunotherapy; and/or

All chromosomes shown in table 2 interact to determine if the individual is a super-progressor whose immunotherapy would accelerate the progression of the disease.

2. The method according to paragraph 1, wherein the presence or absence of said chromosomal interaction is determined:

-in a sample from said individual, and/or

-in DNA from said individual, and/or

By detecting the presence or absence of a DNA loop at the chromosomal interaction site, and/or

Detecting the presence or absence of distal regions of chromosomes that have clustered together in a chromosome conformation, and/or

By detecting the presence of a connecting nucleic acid generated during said typing and the sequence of said connecting nucleic acid comprising two regions, each region corresponding to a region of a chromosome which has been brought together in said chromosomal interaction, and/or

-a method by detecting the proximity of chromosomal regions that are clustered together in said chromosomal interactions.

3. The method according to paragraph 1 or paragraph 2, wherein the detecting the presence or absence of chromosomal interactions is performed by a method comprising the steps of:

(i) In vitro cross-linking of the epigenetic chromosomal interactions present;

(ii) Optionally isolating the crosslinked DNA;

(iii) Cutting the crosslinked DNA;

(iv) Ligating the cross-linked cleaved DNA ends to form ligated DNA; and

(v) Identifying the presence or absence of DNA sequences corresponding to each chromosomal interaction in the ligated DNA;

thereby determining whether each chromosomal interaction is present.

4. The method according to paragraph 2 or 3, wherein the ligated DNA is detected by PCR or by using a probe.

5. The method according to paragraph 4, wherein:

(i) Detection is performed by using probes, wherein the probes preferably have at least 70% identity to any of the probes shown in table 1 or table 2; or (b)

(ii) Detection is performed by using PCR, wherein the PCR preferably uses a primer pair having at least 70% identity to any of the primer pairs shown in table 1 or table 2.

6. A method according to any one of the preceding paragraphs, wherein:

(i) Administering the method prior to the subject receiving immunotherapy, and/or administering the method to select a cancer therapy that the subject should receive, and/or

(ii) Performing the method on an individual having cancer or suspected of having cancer, and/or

(iii) The method is performed on individuals pre-selected based on physical characteristics, risk factors, or the presence of cancer symptoms.

7. The method according to any one of the preceding paragraphs, wherein the individual:

-at an early stage of cancer; and/or

Undergoing or about to undergo cancer therapy, such as cancer immunotherapy.

8. The method according to any one of the preceding paragraphs, wherein the cancer is:

(i) Cancer treated with immune checkpoint inhibitor PD-1/PD-L1; and/or

(ii) Melanoma, lung cancer, hepatocellular carcinoma (liver cancer), bladder cancer, prostate cancer, nasal cancer, parotid cancer (salivary gland cancer), acinar soft tissue sarcoma (soft tissue cancer); and/or

(iii) Breast cancer, cervical cancer, colon cancer, head and neck cancer, hodgkin's lymphoma, renal cancer, gastric cancer, rectal cancer or solid tumors.

9. The method according to any one of the preceding paragraphs, wherein the immunotherapy:

(i) Including antibodies or immune cells, preferably T cells or dendritic cells; and/or

(ii) Including vaccines, preferably against cancer; and/or

(iii) Modulating, blocking or stimulating an immune checkpoint, and preferably targeting or modulating PD-L1, PD-L2 or CTLA4 or any other immune checkpoint molecule disclosed in table 3; and/or

(iv) Comprising the therapies shown in any one of tables 4 to 6; and/or

(v) Increasing the killing of cancer cells by the immune system, preferably wherein such killing is achieved by T cells.

10. The method according to any one of the preceding paragraphs, wherein the immunotherapy is:

(i) A PD-1 inhibitor or a PD-L1 inhibitor, preferably a PD-1 specific antibody or a PD-L1 specific antibody; and/or

(ii) The PD-2 inhibitor or PD-L2 inhibitor is preferably a PD-2 specific antibody or a PD-L2 specific antibody.

11. The method according to any one of the preceding paragraphs, wherein said typing of chromosomal interactions comprises specific detection of said ligation products by quantitative PCR (qPCR) using primers capable of amplifying said ligation products and a probe that binds to a ligation site during a PCR reaction, wherein said probe comprises a sequence complementary to a sequence from each chromosomal region that is clustered together in chromosomal interactions, wherein preferably said probe comprises:

-an oligonucleotide that specifically binds to said ligation product, and/or

-a fluorophore covalently linked to the 5' -end of said oligonucleotide, and/or

-a quencher covalently linked to the 3' -end of the oligonucleotide, and

Optionally, the composition may be used in combination with,

-the fluorophore is selected from HEX, texas red and FAM; and/or

-the probe comprises a nucleic acid sequence of 10 to 40 nucleotide bases in length, preferably a nucleic acid sequence of 20 to 30 nucleotide bases in length.

12. Use of cancer immunotherapy for a method of treatment of cancer in an individual, wherein the method of treatment comprises:

-identifying whether the individual is responsive to immunotherapy by a method according to any of the preceding paragraphs, and

-administering an immunotherapy to an individual who has been identified as responsive to said immunotherapy.

13. Use of a combination therapy for cancer in a method of treatment of cancer in a subject, wherein the method of treatment comprises:

-identifying whether the individual is responsive to immunotherapy by a method according to any of the preceding paragraphs, and

-administering the combination therapy to an individual who has been identified as not responding to an immunotherapy, wherein the combination therapy comprises a therapeutic agent as disclosed in any one of tables 4 to 6 or a combination therapy as disclosed in any one of tables 4 to 6.

14. Use of a non-immunotherapeutic anti-cancer therapy for use in a method of treatment of cancer in an individual, wherein the method of treatment comprises:

-identifying by the method of any of the preceding paragraphs whether the individual is a super-progressor to immunotherapy, and

-administering the anti-cancer therapy to an individual who has been identified as a super-progressor of immunotherapy.

Publication and priority application

The contents of all publications mentioned herein are incorporated by reference into the present specification and may be used to further define the features relevant to the present invention. The contents of all priority applications are incorporated by reference into this specification and may be used to define features relevant to the invention.

Techniques for identifying specific related chromosomal interactions

EpiSwitch ^TM Platform technology detects epigenetic regulatory features that regulate changes between normal and abnormal conditions at loci. EpiSwitch ^TM The platform identifies and monitors the basic epigenetic level of gene regulation associated with regulatory higher-order structures of the human chromosome (also known as chromosomal conformational features). Chromosomal characteristics are unique initial steps in a cascade of gene dysregulation. They are advanced biomarkers with a range of unique advantages over biomarker platforms that utilize late epigenetic and gene expression biomarkers (e.g., DNA methylation and RNA profiling).

EpiSwitch ^TM Array analysis

Custom EpiSwitch ^TM The array screening platform had a unique chromosomal conformation of 4 densities (15K, 45K, 100K and 250K), and each chimeric fragment was repeated 4 times on the array to an effective density of 60K, 180K, 400K and 100 ten thousand, respectively.

Custom designed EpiSwitch ^TM Array

15K EpiSwtch ^TM The array can screen the entire genome, including by EpiSwitch ^TM About 300 loci queried by biomarker search technology. EpiSwitch ^TM The array is built on a Agilent SurePrint G3 custom CGH microarray platform; this technique provides 4 densities (60K, 180K, 400K and 100 tens of thousands) of probes. The density of each array was reduced to 15K, 45K, 100K and 250K because of each EpiSwtch ^TM Probes are present in quadruplicates so reproducibility can be statistically assessed. Potential EpiSwitch for query per locus ^TM The average number of markers was 50, so the number of loci that could be investigated was 300, 900, 2000 and 5000.

EpiSwitch ^TM Custom array flow (pipeline)

EpiSwitch ^TM The array is a two-color system in which a collection of samples is tested at EpiSwitch ^TM The library was labeled with Cy5 after generation, while the samples of the other set to be compared/analyzed (control) were labeled with Cy 3. The array was scanned using an Agilent suresecan scanner and the resulting features extracted using Agilent Feature Extraction software. Then use the EpiSwitch in R ^TM The array processing script processes the data. The arrays were processed using standard bicolor software packages in Bioconductor in Limma. Normalization of the array was done using the normalisedWithinArrays function in Limma, relative to the Agilent positive control and EpiSwitch on chip ^TM Positive control was completed. The screening data were filtered according to Agilent Flag determination, agilent control probes were removed, and the technology repeat probes were averaged for analysis using Limma. The probe was modeled based on the differences between the 2 comparison scenarios and then corrected using the error discovery rate (False Discovery Rate). Coefficient of Variation (CV)<=30% and is<= -1.1 or =>1.1 and by p<Probes with a value of =0.1 FDR p were used for further screening. To further reduce the probe set, a multifactor analysis was performed using the FactorMineR software package in R (Multiple Factor Analysis).

* And (3) injection: LIMMA is a linear model and empirical Bayesian method for assessing differential expression in microarray experiments. Limma is an R software package for analyzing gene expression data from a microarray or RNA-Seq.

The pool of probes was initially selected for final selection based on the adjusted p-value, FC and CV <30% (arbitrary cut-off) parameters. The further analysis and final list is plotted from the first two parameters only (adjusted p-value (adj. P); FC).

Statistical flow

Using EpiSwitch in R ^TM Analytical Package pair of EpiSwitch ^TM Screening the array for processing to select high value EpiSwitch ^TM Markers for transfer to EpiSwitch ^TM On a PCR platform.

Step 1

The probe is selected according to the corrected p-value (false discovery rate, FDR), which is the result of the modified linear regression model. The probe with p-value < = 0.1 is selected, then the probe is further narrowed down according to its Epigenetic Ratio (ER), which probe ER must < = -1.1 or = >1.1 to be selected for further analysis. The last filter is the Coefficient of Variation (CV), which must be lower than < = 0.3.

Step 2

The first 40 markers in the statistical list were selected according to their selection as ER markers for PCR transfer. The first 20 markers with the highest negative ER amounts and the first 20 markers with the highest positive ER amounts make up the list.

Step 3

The markers from step 1, i.e. probes with statistical significance, form the basis for enrichment analysis using Hypergeometric Enrichment (HE). This analysis enabled the reduction of the markers in the significant probe list and the markers from step 2, both of which form a shift to EpiSwitch ^TM List of probes on PCR platform.

The statistical probes are processed by HE to determine which genetic loci are enriched with statistically significant probes, indicating which genetic loci are the center of epigenetic differences.

Most significant enrichment loci based on corrected p-valuesSelected for use in generating the probe list. Genetic positions with p-values below 0.3 or 0.2 are selected. Statistical probes mapped to these genetic positions, together with the markers from step 2, form a set for EpiSwitch ^TM High value markers for PCR transfer.

Array design and processing

Array design

Loci were processed using SII software (currently v 3.2) to:

extracting the sequences of the genome at these specific loci (gene sequences with 50kb upstream and 20kb downstream)

-defining a probability of sequence participation CC within the region

-using specific RE cleavage sequences

-determining which restriction fragments are likely to interact in a certain direction

Ranking the possibilities of interaction of different CCs together

-determining the array size, thereby determining the number (x) of available probe positions

Extracting x/4 interactions.

For each interaction, a sequence of 30bp from the restriction site of part 1 and a sequence of 30bp from the restriction site of part 2 are defined. Check if those areas repeat, exclude if repeat, and record the next interaction in the list. Two 30bp were ligated to define the probe.

Creating a list of x/4 probes plus defined control probes and copying 4 times to create a list to be built on the array

Uploading the probe list to the Agilent Sure design website for custom CGH array

-using a set of probes to design an Agilent custom CGH array.

Array processing

-using EpiSwitch ^TM Standard Operating Procedure (SOP) process samples for template production.

Cleaning by ethanol precipitation through an array processing laboratory.

-processing samples according to the Agilent SureTag complete DNA labelling kit-CGH based on Agilent oligonucleotide arrays for genomic DNA analysis enzyme labelling of blood, cells or tissues.

Scanning using an Agilent C scanner, by using Agilent feature extraction software.

EpiSwitch ^TM Biomarker signatures exhibit high robustness, sensitivity and specificity in classification of complex disease phenotypes. The technology utilizes the latest breakthrough of epigenetic science to monitor and evaluate chromosome conformational features as a kind of epigenetic biomarkers with rich information. Current research methods used in academic settings require biochemical treatment of cellular material for 3 to 7 days to detect CCS. These procedures have limited sensitivity and reproducibility; furthermore, these programs also do not have an EpiSwtch ^TM Analytical Package provides the advantage of targeted understanding during the design phase.

EpiSwitch ^TM Array in silico marker identification

The CCS site of the whole genome is defined by EpiSwitch ^TM The array evaluates the clinical samples from the test cohort directly to identify all relevant class leader biomarkers. EpiSwitch ^TM Array platforms are used for marker identification due to their high throughput and their ability to rapidly screen a large number of loci. The array used was an Agilent custom CGH array that allowed querying for markers identified by in silico software.

EpiSwitch ^TM PCR

By EpiSwitch ^TM PCR or DNA sequencer (i.e., roche 454, nanopore Minion, etc.) pair is determined by EpiSwitch ^TM The potential markers identified by the array were validated. The top-ranked PCR markers that are statistically significant and show the best reproducibility were selected for further reduction to final EpiSwitch ^TM Feature set and validated in a separate sample queue. EpiSwitch ^TM PCR can be performed by trained technicians according to established standardized protocols. All protocols and reagent preparation were performed under ISO 13485 and 9001 certification to ensure that the workerCapability to act as a mass and transfer scheme. EpiSwitch ^TM PCR and EpiSwtch ^TM The array biomarker platform is compatible with both whole blood analysis and cell line analysis. These tests are sensitive enough to detect very low copy number abnormalities using a small amount of blood.

Use of classifier

Methods of the invention may include analysis of chromosomal interactions identified in an individual, for example using a classifier, which may improve performance such as sensitivity or specificity. The classifier is typically one that has been "trained" on samples from a population, and such training may help the classifier detect any of the groups of respondents mentioned herein.

The invention is illustrated by the following examples:

examples

Example 1 development of Universal marker sets and marker sets for detecting super-progressors

In working on patient populations undergoing cancer immunotherapy, two different sets of markers were developed: one is a universal set of markers that allows for detection of responsiveness to therapy in a range of cancers and specific therapies; the second is to detect a set of markers for the super-progressors that should not be treated with a particular type of immunotherapy.

We have now defined a specific, unique and optimized set (universal set) of 11 biomarkers from the hundreds of biomarkers identified in the initial array screen and later tested in a particular patient cohort. The unique feature of each of these 11 markers is that in all tumor indications tested, each of them is statistically significant in all PD-1/PD-L1 cases (as part of the core found), defining a common core of response/non-response to PD-1/PD-L1 treatment. The classifier using these 11 markers works very robustly as one unique performance entity in all patient queues tested.

As background of the current work, figure 1 shows the performance of baseline prediction of response/non-response to Avelumab (PD-L1) in NSCLC based on a large set of test markers.

In contrast, for the universal 11 marker sets, a list of all treatments using multiple therapeutic actives (asset) PD-1/PD-L1 and multiple tumor indications we studied is shown in table a below: melanoma, NSCLC, lung cancer, HCC, bladder cancer, prostate cancer, NPC, parotid cancer, and follicular soft tissue sarcoma, such as palbociclizumab, divali You Shan antibody, avelumab, and atti Li Zhushan antibody. The universal 11 marker classifier works well in all these cohorts and identifies a universal profile (universal profile) that provides a robust baseline classification for respondents/non-respondents regardless of the exact PD-1 treatment or PD-L1 treatment tested and the exact cancer type. We captured a very specific favorable/non-favorable epigenetic system network with 11 markers, which consisted of features defining immune checkpoint therapy outcomes.

Turning to the second marker set, a very serious problem in cancer immunotherapy is the continued presence of a subset of patients who should not be treated with PD-1/PD-L1 therapy. They are called super progressors (or super progressors), where a progressor means progression to a disease. These patients respond very differently to treatment-their tumor growth rate rises dramatically and they die substantially quickly within weeks.

A super-progressor may be defined as a patient who produces an adverse response to immune checkpoint immune tumor therapy, exhibiting a significant reduction in progression-free survival (as a measure of survival in response to drug therapy) (PFS < 60 days) or overall survival (OS < 150 days).

Average trials showed that 8% to 15% of these patients exhibited a superior progressive profile. Currently, there is no way to identify and exclude these patients to prevent serious side effects of immunotherapy. In most studies, super-progressors are categorized into a larger group of non-responders, also known as progressors/disease progression (progressive disease, PD). Most non-responders are patients who do not benefit from immunotherapy. Today, the use of checkpoint inhibitors justifies by the overall benefit of the percentage of patients (10% to 70%) who respond to immunotherapy.

Here we utilized patients from the immunotherapy cohort, focusing on those patients whose PFS/OS is in the super-progressor range and those who exceeded the time-to-live limit (labeled S as "standard" in slides and tables). The super-progressor markers identified patient profiles predicted to exhibit short PFS/OS after treatment. In the group of 32 patients tested prior to treatment (H and S equal), the 11 super-progressor markers correctly predicted 15 out of 17H (sensitivity 0.88), 14/15 (specificity 0.93), 15/16 (PPV 0.94), 14/16 (0.875) when compared to PFS and OS after treatment.

These markers were specifically selected for identifying and excluding patients prior to treatment based on the predicted severe reduction in survival resulting from immunotherapy. This can be considered as a subset of a larger non-responder cohort that can be identified and predicted by a common 11 marker set.

While the current work has been done in patients with melanoma, lung cancer, hepatocellular carcinoma (liver cancer), bladder cancer, prostate cancer, nasal cancer, parotid gland cancer (salivary gland cancer), acinar soft tissue sarcoma (soft tissue cancer), it is also applicable to other cancers treated with immune checkpoint inhibitors PD-1/PD-L1, such as breast cancer, cervical cancer, colon cancer, head and neck cancer, hodgkin lymphoma, renal cancer, gastric cancer, rectal cancer, and any solid tumor.

Detection mechanism

The collection of markers that have been identified capture deregulated networks at the cellular 3D genome level, reflecting networks of deregulated cell types that co-act to maintain and promote the pathological or physiological phenotype of cancer. Up to now, statistically significant chromosomal conformations have been observed as evidence of disorders associated with cell subtype CD loci, and we can state that the observed common features comprise and represent disorders of T cells, NK (natural killer) cells, macrophages, B cells and Dendritic Cells (DCs). This underscores the role played by specific settings at the cellular level of the adaptive immune system and the innate immune system as part of cancer-host interactions in individual patients, which define disease progression (super-progressors) and responsiveness to the immune checkpoint inhibitor PD-1/PD-L1.

Method

Initial studies of chromosome interactions were performed in the following populations (work depicted in fig. 1):

list a-initial work

-16 anti-PD-1 (palbock-lizumab) melanoma queues

-16 anti-PD-L1 NSCLC queues

-99 anti-PD-L1 NSCLC queues

-49 anti-PD-1 NSCLC queues

-50 anti-PD-1 and combination therapy NSCLC cohorts

-48 anti-PD-1 (palbock-mab) melanoma cohorts, including super-progressors

-550 anti-PD-L1 urinary tract cancers

And (3) longitudinal observation:

anti-PD-L1 (Duvalli You Shan anti, abilizumab) and anti-PD 1 (palbociclizumab)

Lung cancer, HCC, bladder cancer, prostate cancer, NPC, parotid cancer, acinar soft tissue sarcoma.

The following patient development marker sets were used:

training: 80 patients, all NSCLC

1L, 2L Avelumab (54) and 2L palbociclizumab (36) were mixed

And (3) testing: 38 patients, all NSCLC

1L, 2L Avelumab (27) and 2L palbociclizumab (11) were mixed

And (3) testing: 20 mixed samples

Mixing of 2L patients with different checkpoint inhibitors and different solid tumors

April bead mab (7), dulcis You Shan antigen (3) and palbociclib bead mab (10)

Lung cancer (13), NPC (2), HCC (2), bladder cancer (1), acinar sarcoma (1) and parotid cancer (1)

A total of 138 samples.

To test universal markers, we collected a blind queue to test for non-responsive features. The cohort was collected in malaysia and consisted of 21 patients who provided baseline blood samples prior to immunotherapy. All patients had prior history of treatment. 3 checkpoints were used: atenolizumab (anti-PD-L1), dulcis You Shan anti (anti-PD-L1) and palbociclizumab (anti-PD-1). There are 7 disease indications: lung cancer, HCC, bladder cancer, prostate cancer, NPC, parotid cancer, and acinar soft tissue sarcoma. There were multiple acquisitions for 11 patients: 2-4 times. Up to 4 acquisitions were performed for 3 patients. The ethnicity of the patient is han or the patient is indonesia.

Figure 4 shows a high degree of agreement between baseline EpiSwitch decisions, PD-L1 expression and observed clinical responses.

Fig. 5 shows training set data based on 11 marker models for 80 NSCLC patients, who were a mix of 1L, 2L Avelumab (54) and 2L palbociclizumab (36).

Figure 6 shows test set data based on 11 marker models for 38 NSCLC patients, who were a mix of 1L, 2L Avelumab (27) and 2L palbociclizumab (11).

Fig. 7 and 8 show the second test set of data from malaysia observational studies, which observe CPI and tumor mixing.

Fig. 9 shows a determination of a patient with multiple acquisitions. The decisions at these time points are mostly consistent and coordinated:

patient 12 using the dutch Li Youshan antibody showed a responder profile at 4 acquisitions and a score of R/NR into the gray zone at the second acquisition. Over time, the overall probability spectrum of the respondent may actually become stronger.

Patient 1 using atilizumab showed an interesting initial no response but became an advanced responder.

Patient 17, who used palbociclib, was an NPC patient and showed a response spectrum in both samples.

This emphasizes the EpiSwitch ^TM The ability of markers to find non-responders and responders to capture common characteristics of the host response spectrum of multi-checkpoint inhibitors in different tumor cases.

Fig. 10 shows the EpiSwitch determination of patients sampled at multiple time points, obd.261.263 and obd.301.303 representing universal response markers, while obd.029.031, obd.045.047, obd.645.647, obd.753.755 and obd.8.69.871 represent universal non-response markers.

For work related to the super-progressors, figure 11 shows sample selection where the super-progressors were selected based on having PFS < = 60 days (2 months) and OS < = 150 days (4.5 months), with survival group (S) selected because most of them had PFS over 1 year.

Fig. 13 shows 11 epigwitch CCS selected from a total of 60 markers selected based on binary difference, OS and PFS rank log analysis (rank log analysis). Related genetic positions are also shown. Fig. 10 and 11 show the pathway analysis of genetic locations.

Fig. 16 shows training set data for the super-progressors: XGBoost 11 marker models. Figure 17 shows test set data for the super-progressors, where 6 samples were excluded from the marker selection. Fig. 18 shows a logical Principal Component Analysis (PCA) of the training set. Fig. 19 shows the training set and the logical PCA of the predicted test samples. This is the second classification method and is consistent with XGBoost. Fig. 20 shows the logical PCA of the training set with PFS as a marker. Fig. 21 shows the logical PCA of the training set with OS as a marker.

Method for analyzing patient

A specific method is employed to analyze a patient population. The relevant patient cohorts have received either prior therapy (one round of cisplatin-based chemotherapy) or only checkpoint inhibitor therapy. We also observed only patients with defined responses, so either complete, partial or no response. We removed disease-stable patients from the analysis.

For EpiSwtch, a variety of statistical techniques are used, including but not limited to established univariate (Fisher exact test) and multivariate (arranged GLMNET, random forest with Shapley sum of interpretation (SHAP)) programs ^TM And (5) analyzing the data output of the nested PCR platform.

To develop diagnostic and prognostic EpiSwitch ^TM A classifier using the following statistical analysis: (i) XGBoost: a gradient enhanced decision tree algorithm. Generating a set of weak decision tree models and combining them to generate a strong classification model (layer-by-layer tree growth (level wise tree growth)); (ii) logical Principal Component Analysis (PCA): optimizing to use principal component analysis of the binary data; (iii) GLMNET: a generalized linear model fitted by punishment maximum likelihood technique; (iv) LightGBM: gradient enhancement framework using a tree-based learning algorithm (vertical She Shusheng long (vertical leaf tree growth)).

SHAP analysis of the universal marker sets is shown below.

The markers are ranked according to their SHAP score, with the best marker being obd117_029_0.31.SHAP (SHapley add and interpret (SHapley Additive exPlanations)) values are a unified method of interpreting any machine learning model output. There are three important benefits.

The first benefit is global interpretability—how much each predictor's contribution (whether positive or negative) to the target variable can be shown by the collective SHAP value. This is similar to the variable importance map, but it can show a positive or negative correlation of each variable with the target.

The second benefit is local interpretability-each observation has its own set of SHAP values. This greatly increases its transparency. We can explain why a case will get its predicted value and the contribution of the predictor. Conventional variable importance algorithms only display the results of the entire population, not the results of each individual case. Local interpretability enables us to determine and compare the impact of factors.

Third, SHAP values may be calculated for any tree-based model (our model is XGboost, an enhanced tree-based model), while other methods use a linear regression model or a logistic regression model as proxy models.

This represents the analytical procedure for marker selection. The high performance of these two marker sets is shown in the figures and tables. In particular for the universal marker set, all 7 types of cancers shown in fig. 3 were represented in 21 patients observed longitudinally, with 100% performance in terms of specificity and positive predictive value in the entire cohort.

Example 2 further work leading to the development of marker sets as shown in Table 8

Immune checkpoint inhibitors are a class of drugs that target a limited set of proteins in a specific regulatory network present in immune cells (e.g., T cells) and some cancer cells in a patient. The checkpoint protein targets and their controlled network help prevent the immune response from being too strong and provide additional protection against autoimmune disease, but in the case of cancer, may prevent T cells from killing cancer cells. The use of immune checkpoint inhibitors helps to re-activate the immune response in cancer patients with effective outcome and increased patient survival.

Immune checkpoint inhibitors act by targeting 1) PD-1 (nano Wu Liyou mab, palbociclizumab, cemiplimab, camrelizumab, tislelizumab, sasanlimab) or 2) a PD-1 ligand called PD-L1 (Avelumab, actigraphy Li Zhushan antibody, and divaline You Shan antibody) to reset and activate the immune response.

Current work presents several problems, including: given the role played by the patient's immune system in a successful response to immune checkpoint inhibitors and the limited number of targets for this therapy (in particular the PD-1 receptor and its ligand PD-L1), it is possible to detect and verify in qPCR format the baseline patient's epigwitch biomarker, which would have previously generally been predicted to be responsive/non-responsive to treatment, regardless of the type of checkpoint inhibitor used and the range of neoplastic conditions.

qPCR formats that meet MIQE are standard for clinical PCR-based testing. This format is very different from nested PCR or array format because it places limitations on primer and probe sequence design and continuous detection range, traditionally measured by the number of Cq cycles.

The following steps were employed as part of the discovery and validation of these biomarkers (patient data shown in table 9):

stage 1: from the markers previously identified by the array, the first 24 markers that met the initial theoretical limitations and requirements of qPCR primer and probe sequence design (i.e., the unique sequences for detection, correct annealing temperature for primer and probe annealing, overlapping 3C junctions) were identified for detection of chromosome conformation. During the experimental stage, the 20 markers were designed with quality control and satisfactory temperature gradient optimization.

Stage 2: the qPCR format of lead markers was used to identify whether there were minimal numbers of biomarkers that would have strong classification capacity as a feature for predicting in a broad cohort the responsiveness and non-responsiveness of patients treated with immune checkpoint inhibitors (palbocuzumab, avelumab, atelizumab, dulcis You Shan antibodies) against each of the three targets (PD-1, PD-L1) from a broad selection of the following tumor conditions: melanoma, non-small cell lung cancer, urethra cancer, HCC, bladder cancer, prostate cancer, NPC, parotid cancer, acinar soft tissue sarcoma, nasopharyngeal carcinoma, vulvar cancer, colon cancer, breast cancer, bone cancer, brain cancer, sagittal sinus cancer, lymphoma, laryngeal cancer, cervical cancer, oral cancer.

Stage 3: in screen 1, all 20 markers were first assessed on pooled DNA templates from 3 patient clinical outcome categories (responders, non-responders and disease stabilization). Sample cohorts represent patients with different types of cancer (see accompanying notes) that receive multiple immune checkpoint inhibitors as monotherapy: avelumab, pamil mab, acti Li Zhushan, and dulcitol You Shan.

Stage 4: in screen 2, the first 13 markers enclosed from screen 1 were evaluated on individual samples of the same patient used in screen 1: a total of 24 patients/samples (see table 9, patients with asterisks shown).

The selection of the best markers for stage 2 and stage 3 was performed using a linear model. A linear model was fitted to the Cq value for each marker and PR v PD, PR v SD and PD v SD were compared. The coefficients of the fitting model describe the differences between CCS in each comparison. The adjusted log-statistics probability (log-odds) of the difference CCS is then calculated using a linear model by empirical bayesian adjustment (empirical Bayes moderation) to standard error to approximate global values (0 log or 1 linear). The markers were then ranked according to the adjusted p-value and CCS abundance differences between the markers in the groups. Markers between PR and PD are given greater weight.

Stage 5: in screen 3, the first 8 markers (enclosed from screen 2) were validated on IO samples consisting of patients with the following clinical outcome: 55% NR (non-responders), 24% SD (disease stable, SD should receive IO therapy as with the potential responder group according to regulatory rules and clinical practices), 20% R (responders, also referred to as partial responders PR), and 1% CR (complete responders). The classification effect of the model based on 8 markers is shown in fig. 22 to 24. Predicted Value (PPV) =100 xTP/(tp+fp). Negative Predictive Value (NPV) =100 xTN/(fn+tn).

An immunotherapy checkpoint classifier was constructed using Catboost. Catboost is a member of the gradient enhanced decision tree (GBDT) machine learning integration technique (see Hancock and Khoshgoftaar; J.big Data (2020) 7:94).

Form type of test

Both the nested and qPCR formats severely limit which array-based lead markers can be successfully converted to one format or the other. This is especially the case in qPCR format, where we have to determine if we can use two primers and fluorescent probes at the 3C junction (this is similar to the array probes), which 1) have unique sequences for specific detection across the entire genome; 2) Having very similar annealing temperatures; 3) It was shown how the amplification was effective in a single PCR procedure rather than in two consecutive reactions (one for the first and the other for the second) as required for nested PCR. qPCR is more stringent and selective.

Conclusion(s)

Based on qPCR evaluation of 8 conditional chromatin conformations as blood-based regulatory biomarkers, individuals can be evaluated for the likelihood of responding to Immune Checkpoint Inhibitor (ICI) monotherapy. In interactions between the patient's tumor microenvironment and the patient's immune system, the Pd-1 pathway, including the receptor programmed death 1 (Pd-1) and its ligand Pd-L1, mediates local immunosuppression in the tumor microenvironment. Current work is directly related to ICI as an antagonist targeting PD-1 (palbociclizumab) or its ligand PD-L1 (atilizumab, avelumab and divali You Shan antibodies). Prior to application of ICI monotherapy, patients were classified into groups likely to be responders to ICI monotherapy or groups not responders to ICI monotherapy based on a classification of 8 marker classifiers. This classification applies to all ICI monotherapy against PD-L1 and its ligand PD-L1 in the context of all tumor indications used in ICI monotherapy treatment.

Figures 25-27 depict features of the identified chromosomal interaction markers. Figure 25 shows the importance of markers in terms of their efficacy in the model. FIG. 26 shows genetic locations. FIG. 27 shows the pathways associated with these genes. These are all pathways involved in checkpointing. This suggests that the model is predictive and notably that the markers in the model have biological relevance. In fact, one of the markers is located between PD-L1 and PD-L2 (q057_q059).

	Probe with a probe tip	RP/Rsum	FC
				1	PDCD1LG2_9_5495992_5498009_5563479_5572986_RR	6499	-1.13
2	MYC_8_127691489_127694045_127738939_127740424_FR	6220	1.15
				3	IKBKB_8_42264241_42271203_42331044_42332799_FR	1585	1.3
4	ORF712_9_120888366_120893320_120913546_120919710_RR	8471	1.11
				5	ITK_5_157178319_157181048_157266725_157271762_RR	4605	1.15
6	IL17D_13_20664875_20671757_20688261_20691044_FF	6848	-1.1
				7	IKBKB_8_42264241_42271203_42290979_42292124_FR	2465	1.25
8	IGF1R_15_98731539_98737034_98785670_98790114_FF	5450	1.15
				9	CASP6_4_109703339_109705583_109735036_109741090_RF	1727	1.29
10	TRAF2_9_136904007_136906211_136939587_136941363_RF	5090	1.16
				11	ORF313_13_20664875_20671757_20695143_20698635_FF	4910	-1.16

TABLE 1A 1

	PFP	P value	FDR
				1	0.3857	0.01934	0.188603463
2	0.3231	0.01577	0.169755579
				3	0.001699	0.00000779	0.00118933
4	0.6186	0.06146	0.308076103
				5	0.1332	0.003607	0.076416861
6	0.4249	0.02454	0.210154857
				7	0.01154	0.0001149	0.00763801
8	0.2259	0.008388	0.12237213
				9	0.002472	0.0000133	0.001705674
10	0.1832	0.005986	0.101918497
				11	0.2061	0.004999	0.091747389

TABLE 1A 2

TABLE 1A 3

TABLE 1 a4

TABLE 1A 5

TABLE 1A 6

TABLE 1 a7

					Probe with a probe tip	RP/Rsum	FC
1	ORF479_8_81007411_81018107_81095100_81099880_FR	5655	1.14
				2	ORF482_5_168579937_168582137_168614429_168620163_RR	8291	-1.11
3	PDCD1LG2_9_5495992_5498009_5563479_5572986_RR	6499	-1.13
				4	IL17D_13_20664875_20671757_20688261_20691044_FF	6848	-1.1
5	CASP6_4_109703339_109705583_109735036_109741090_RF	1727	1.29
				6	ORF102_17_34316073_34325822_34367538_34373948_RF	1958	-1.26
7	TNFSF8_9_114957908_114962933_114975258_114977746_RF	8214	-1.11
				8	ORF712_9_120888366_120893320_120913546_120919710_RR	8471	1.11
9	ORF698_18_62296384_62304812_62385139_62386748_FF	7473	1.12
				10	ORF197_8_26561792_26565691_26638318_26644530_FR	3611	-1.2
11	IKBKB_8_42264241_42271203_42331044_42332799_FR	1585	1.3

TABLE 2A 1

					PFP	P value	FDR
1	0.252	0.01003	0.13564257
				2	0.5777	0.05624	0.298153549
3	0.3857	0.01934	0.188603463
				4	0.4249	0.02454	0.210154857
5	0.002472	0.0000133	0.001705674
				6	0.009056	0.0000289	0.002854389
7	0.5681	0.0541	0.293528769
				8	0.6186	0.06146	0.308076103
9	0.4918	0.03615	0.248717729
				10	0.09085	0.0009981	0.034849498
11	0.001699	0.00000779	0.00118933

TABLE 2 a2

TABLE 2A 3

TABLE 2 a4

TABLE 2A 5

TABLE 2A 6

	Probe with a probe tip	Marker(s)	Parting type
				1	ORF479_8_81007411_81018107_81095100_81099880_FR	OBD148_105.107	S
2	ORF482_5_168579937_168582137_168614429_168620163_RR	OBD148_669.671	H
				3	PDCD1LG2_9_5495992_5498009_5563479_5572986_RR	OBD117_029.031	H
4	IL17D_13_20664875_20671757_20688261_20691044_FF	OBD148_645.647	H
				5	CASP6_4_109703339_109705583_109735036_109741090_RF	0BD148_821.823	S
6	ORF102_17_34316073_34325822_34367538_34373948_RF	OBD148_893.895	S
				7	TNFSF8_9_114957908_114962933_114975258_114977746_RF	OBD148_917.919	S
8	ORF712_9_120888366_120893320_120913546_120919710_RR	OBD148_301.303	S
				9	ORF698_18_62296384_62304812_62385139_62386748_FF	OBD148_505.507	S
10	ORF197_8_26561792_26565691_26638318_26644530_FR	OBD148_661.663	S
				11	IKBKB_8_42264241_42271203_42331044_42332799_FR	OBD148_261.263	S

TABLE 2 a7

Stimulatory checkpoint molecules	Inhibitory checkpoint molecules
		CD27	A2AR
CD28	B7-H3
		CD40	B7-H4
CD122	CTLA-4
		CD137	IDO
OX40	KIR
		GITR	LAG3
ICOS	PD-1
			TIM-3
	VISTA

TABLE 3 Table 3

TABLE 4 combinations in cancer immunotherapy (biological agents, immunocytokines (L19-IL 2 and L19-TNF), cytotoxins (paclitaxel))

TABLE 5 other single molecules, immunocytokines and biologicals for cancer therapy

Medicament	Target(s)
		Alantuzumab (monoclonal antibody)	CD52
Ofautumumab (second generation human IgG1 antibody)	CD20
		Pegylated Liposomal Doxorubicin (PLD) plus motolimod (VTX 2337)
Sipuleucel-T (approved cancer vaccine)
		Rituximab (Rituximab) (monoclonal antibody)	CD20
Interferon gamma
		Combined ablation and immunotherapy
polysaccharide-K
		Adoptive cell therapy
anti-CD 47 antibodies	CD47
		Purine reverse Hoogsteen oligonucleotides (PPRHs)
anti-GD 2 antibodies	GD2
		BGB-A317 (monoclonal antibody)	PD-1 inhibitors
Affimer biotherapeutic agent	PD-L1 inhibitors
		Polysaccharide
New antigens

TABLE 6

	Probe with a probe tip	RP/Rsum	FC
				1	PDCD1LG2_9_5495992_5498009_5563479_5572986_RR	6499	-1.13
2	MYC_8_127691489_127694045_127738939_127740424_FR	6220	1.15
				3	IKBKB_8_42264241_42271203_42331044_42332799_FR	1585	1.3
4	ORF712_9_120888366_120893320_120913546_120919710_RR	8471	1.11
				5	ITK_5_157178319_157181048_157266725_157271762_RR	4605	1.15
6	IL17D_13_20664875_20671757_20688261_20691044_FF	6848	-1.1
				7	IKBKB_8_42264241_42271203_42290979_42292124_FR	2465	1.25
8	IGF1R_15_98731539_98737034_98785670_98790114_FF	5450	1.15
				9	CASP6_4_109703339_109705583_109735036_109741090_RF	1727	1.29
10	TRAF2_9_136904007_136906211_136939587_136941363_RF	5090	1.16
				11	ORF313_13_20664875_20671757_20695143_20698635_FF	4910	-1.16
12	ORF479_8_81007411_81018107_81095100_81099880_FR	5655	1.14
				13	ORF482_5_168579937_168582137_168614429_168620163_RR	8291	-1.11
14	ORF102_17_34316073_34325822_34367538_34373948_RF	1958	-1.26
				15	TNFSF8_9_114957908_114962933_114975258_114977746_RF	8214	-1.11
16	ORF698_18_62296384_62304812_62385139_62386748_FF	7473	1.12
				17	ORF197_8_26561792_26565691_26638318_26644530_FR	3611	-1.2
18	ORF243_1_161633494_161637462_161657362_161661864_RF	1345	1.16
				19	ORF313_13_20664875_20671757_20737979_20744490_FR	2891	1.2
20	ORF369_13_46087370_46090583_46186579_46193039_RF	2719	1.11
				21	ORF480_11_77430379_77437843_77514783_77519103_RF	3101	-1.1
22	ORF698_18_62330039_62332469_62356961_62362521_FR	6988	-1.28
				23	ORF703_1_6461604_6466207_6514024_6515315_FR	1109	-1.25
24	ORF705_9_114855753_114859111_114920994_114929419_FR	898	-1.16

TABLE 7a

	PFP	P value	FDR
				1	0.3857	0.01934	0.188603463
2	0.3231	0.01577	0.169755579
				3	0.001699	0.00000779	0.00118933
4	0.6186	0.06146	0.308076103
				5	0.1332	0.003607	0.076416861
6	0.4249	0.02454	0.210154857
				7	0.01154	0.0001149	0.00763801
8	0.2259	0.008388	0.12237213
				9	0.002472	0.0000133	0.001705674
10	0.1832	0.005986	0.101918497
				11	0.2061	0.004999	0.091747389
12	0.252	0.01003	0.13564257
				13	0.5777	0.05624	0.298153549
14	0.009056	0.0000289	0.002854389
				15	0.5681	0.0541	0.293528769
16	0.4918	0.03615	0.248717729
				17	0.09085	0.0009981	0.034849498
18	0.001212	6.11E-06	0.000216321
				19	0.0315	0.000344332	0.002847073
20	0.031	0.000300988	0.002596993
				21	0.04015	0.000446091	0.003407902
22	0.4301	0.026579755	0.067536623
				23	0.000988	5.35E-07	6.03E-05
24	0.000547	2.59E-07	4.28E-05

TABLE 7b

TABLE 7c

TABLE 7d

TABLE 7e

	Probe with a probe tip	Primer numbering
			1	PDCD1LG2_9_5495992_5498009_5563479_5572986_RR	OBD189-q057
2	MYC_8_127691489_127694045_127738939_127740424_FR	OBD189-q013
			3	IKBKB_8_42264241_42271203_42331044_42332799_FR	OBD148-q261
4	ORF712_9_120888366_120893320_120913546_120919710_RR	OBD189-q017
			5	ITK_5_157178319_157181048_157266725_157271762_RR	OBD189-q065
6	IL17D_13_20664875_20671757_20688261_20691044_FF	OBD189-q077
			7	IKBKB_8_42264241_42271203_42290979_42292124_FR	OBD189-q025
8	IGF1R_15_98731539_98737034_98785670_98790114_FF	OBD189-q001
			9	CASP6_4_109703339_109705583_109735036_109741090_RF	OBD189-q005
10	TRAF2_9_136904007_136906211_136939587_136941363_RF	OBD189-q009
			11	ORF313_13_20664875_20671757_20695143_20698635_FF	OBD189-q081
12	ORF479_8_81007411_81018107_81095100_81099880_FR	OBD189-q061
			13	ORF482_5_168579937_168582137_168614429_168620163_RR	OBD189-q021
14	ORF102_17_34316073_34325822_34367538_34373948_RF	OBD148-q893
			15	TNFSF8_9_114957908_114962933_114975258_114977746_RF	OBD148-q917
16	ORF698_18_62296384_62304812_62385139_62386748_FF	OBD189-q045
			17	ORF197_8_26561792_26565691_26638318_26644530_FR	OBD189-q069
18	ORF243_1_161633494_161637462_161657362_161661864_RF	OBD189-q041
			19	ORF313_13_20664875_20671757_20737979_20744490_FR	OBD189-q073
20	ORF369_13_46087370_46090583_46186579_46193039_RF	OBD189-q053
			21	ORF480_11_77430379_77437843_77514783_77519103_RF	OBD189-q033
22	ORF698_18_62330039_62332469_62356961_62362521_FR	OBD189-q037
			23	ORF703_1_6461604_6466207_6514024_6515315_FR	OBD189-q029
24	ORF705_9_114855753_114859111_114920994_114929419_FR	OBD189-q049

TABLE 7f

TABLE 7g

TABLE 7h

TABLE 7i

TABLE 7j

TABLE 7k

	Probe with a probe tip	Marker(s)
			1	PDCD1LG2_9_5495992_5498009_5563479_5572986_RR	0BD189-q057.q059.p057
2	ITK_5_157178319_157181048_157266725_157271762_RR	OBD189-q065.q067.p065
			3	CASP6_4_109703339_109705583_109735036_109741090_RF	OBD189-q005.q007.p005
4	ORF313_13_20664875_20671757_20695143_20698635_FF	OBD189-q081.q083.p081
			5	ORF102_17_34316073_34325822_34367538_34373948_RF	OBD148-q0893.q0895.p0893
6	ORF369_13_46087370_46090583_46186579_46193039_RF	OBD189-q053.q055.p053
			7	ORF703_1_6461604_6466207_6514024_6515315_FR	OBD189-q029.q031.p031
8	ORF705_9_114855753_114859111_114920994_114929419_FR	OBD189-q049.q051.p049

TABLE 8a

TABLE 8b

TABLE 8c

85

TABLE 9.1

TABLE 9.2

TABLE 9.3

TABLE 9.4

TABLE 9.5

Claims (15)

1. A method of determining how an individual responds to cancer immunotherapy comprising detecting the presence or absence in said individual:

-all of the chromosomes shown in table 8 interact to determine whether the individual will respond to immunotherapy; and/or

All chromosomes shown in table 2 interact to determine if the individual is a super-progressor whose immunotherapy would accelerate the progression of the disease.

2. The method of claim 1, further comprising detecting in the individual the presence or absence of all of the chromosomal interactions shown in table 1, thereby determining whether the individual will respond to immunotherapy.

3. The method of claim 1 or 2, wherein the presence or absence of the chromosomal interaction is determined:

-in a sample from said individual, and/or

-in DNA from said individual, and/or

-by detecting the presence or absence of DNA at said chromosomal interaction site

Rings, and/or

Detecting the presence or absence of distal regions of chromosomes that have clustered together in a chromosome conformation, and/or

By detecting the presence of a connecting nucleic acid generated during said typing and the sequence of said connecting nucleic acid comprising two regions, each region corresponding to a region of a chromosome which has been brought together in said chromosomal interaction, and/or

-a method by detecting the proximity of chromosomal regions that are clustered together in said chromosomal interactions.

4. The method of any one of the preceding claims, wherein the detecting the presence or absence of chromosomal interactions is performed by a method comprising the steps of:

(i) In vitro cross-linking of the epigenetic chromosomal interactions present;

(ii) Optionally isolating the crosslinked DNA;

(iii) Cutting the crosslinked DNA;

(iv) Ligating the cross-linked cleaved DNA ends to form ligated DNA; and

(v) Identifying the presence or absence of DNA sequences corresponding to each chromosomal interaction in the ligated DNA;

thereby determining whether each chromosomal interaction is present.

5. The method of claim 3 or 4, wherein the ligated DNA is detected by PCR or by using a probe.

6. The method according to claim 5, wherein:

(i) Detection is performed by using probes, wherein the probes preferably have at least 70% identity to any of the probes shown in table 1, table 2 or table 8; or (b)

(ii) Detection is performed by using PCR, wherein the PCR preferably uses a primer pair having at least 70% identity to any of the primer pairs shown in table 1, table 2 or table 8.

7. The method of any of the preceding claims, wherein:

(i) Administering the method prior to the subject receiving immunotherapy, and/or administering the method to select a cancer therapy that the subject should receive, and/or

(ii) Performing the method on an individual having cancer or suspected of having cancer, and/or

(iii) The method is performed on individuals pre-selected based on physical characteristics, risk factors, or the presence of cancer symptoms.

8. The method of any one of the above claims, wherein the individual:

-at an early stage of cancer; and/or

Undergoing or about to undergo cancer therapy, such as cancer immunotherapy.

9. The method of any one of the above claims, wherein the cancer is:

(i) Cancer treated with immune checkpoint inhibitor PD-1/PD-L1; and/or

(ii) Melanoma, lung cancer, hepatocellular carcinoma (liver cancer), bladder cancer, prostate cancer, nasal cancer, parotid cancer (salivary gland cancer), acinar soft tissue sarcoma (soft tissue cancer); and/or

(iii) Breast cancer, cervical cancer, colon cancer, head and neck cancer, hodgkin's lymphoma, renal cancer, gastric cancer, rectal cancer or solid tumors.

10. The method of any one of the above claims, wherein the immunotherapy:

(i) Including antibodies or immune cells, preferably T cells or dendritic cells; and/or

(ii) Including vaccines, preferably against cancer; and/or

(iii) Modulating, blocking or stimulating an immune checkpoint, and preferably targeting or modulating PD-L1, PD-L2 or CTLA4 or any other immune checkpoint molecule disclosed in table 3; and/or

(iv) Comprising the therapies shown in any one of tables 4 to 6; and/or

(v) Increasing the killing of cancer cells by the immune system, preferably wherein such killing is achieved by T cells.

11. The method of any one of the above claims, wherein the immunotherapy is:

(i) A PD-1 inhibitor or a PD-L1 inhibitor, preferably a PD-1 specific antibody or a PD-L1 specific antibody; and/or

(ii) The PD-2 inhibitor or PD-L2 inhibitor is preferably a PD-2 specific antibody or a PD-L2 specific antibody.

12. The method according to any one of the preceding claims, wherein said typing of chromosomal interactions comprises specific detection of said ligation products by quantitative PCR (qPCR) using primers capable of amplifying said ligation products and a probe binding to a ligation site during a PCR reaction, wherein said probe comprises a sequence complementary to a sequence from each chromosomal region that is clustered together in a chromosomal interaction, wherein preferably said probe comprises:

-an oligonucleotide that specifically binds to said ligation product, and/or

-a fluorophore covalently linked to the 5' -end of said oligonucleotide, and/or

-a quencher covalently linked to the 3' -end of the oligonucleotide, and

optionally, the composition may be used in combination with,

-the fluorophore is selected from HEX, texas red and FAM; and/or

-the probe comprises a nucleic acid sequence of 10 to 40 nucleotide bases in length, preferably a nucleic acid sequence of 20 to 30 nucleotide bases in length.

13. Use of cancer immunotherapy for a method of treatment of cancer in an individual, wherein the method of treatment comprises:

-identifying whether the individual is responsive to immunotherapy by the method of any one of the preceding claims, and

-administering an immunotherapy to an individual who has been identified as responsive to said immunotherapy.

14. Use of a combination therapy for cancer in a method of treatment of cancer in a subject, wherein the method of treatment comprises:

-identifying whether the individual is responsive to immunotherapy by the method of any one of the preceding claims, and

-administering the combination therapy to an individual who has been identified as not responding to an immunotherapy, wherein the combination therapy comprises a therapeutic agent as disclosed in any one of tables 4 to 6 or a combination therapy as disclosed in any one of tables 4 to 6.

15. Use of a non-immunotherapeutic anti-cancer therapy for use in a method of treatment of cancer in an individual, wherein the method of treatment comprises:

-identifying whether the individual is a super-progressor to immunotherapy by the method of any one of the preceding claims, and

-administering the anti-cancer therapy to an individual who has been identified as a super-progressor of immunotherapy.