CN110305965B - A method for predicting the sensitivity of non-small cell lung cancer (NSCLC) patients to immunotherapy - Google Patents

Abstract

The present invention discloses methods of predicting the sensitivity of a cancer (non-small cell lung cancer) patient to immunotherapy, for example immunotherapy with immune checkpoint inhibitors, using two genes, KMT2C and TP53 as biomarkers; also disclosed is the use of an agent that specifically detects the biomarker for the manufacture of a kit for predicting the sensitivity of a cancer patient (non-small cell lung cancer) to immunotherapy, for example immunotherapy using an immune checkpoint inhibitor. In the invention, TP53 and KMT2C gene mutation states are considered in a combined manner, so that a population sensitive to ICI in NSCLC patients can be accurately predicted, blind medication is avoided, and the economic performance of ICI treatment is improved.

Description

A method for predicting the sensitivity of non-small cell lung cancer (NSCLC) patients to immunotherapy

Technical Field

The invention relates to the field of biomedicine. In particular, the present invention relates to a method, kit and system for predicting the sensitivity of non-small cell lung cancer (NSCLC) patients to immunotherapy, for example immunotherapy using immune checkpoint inhibitors. More specifically, the present invention relates to the use of the KMT2C gene and TP53 gene as biomarkers to predict the sensitivity of cancer patients to immunotherapy, for example immunotherapy with immune checkpoint inhibitors.

Background

Clinically, lung cancer is divided into two major categories: small Cell Lung Cancer (SCLC) and non-small cell lung cancer (NSCLC). This distinction is important because the treatment regimens for these two types of lung cancer are distinct. The identification of the two types can be determined by the diagnosis of a pathologist, by obtaining the pathology through a bronchoscope, a needle biopsy or after surgery. Non-small cell lung cancer is a generic term for a large group of diseases including squamous cell carcinoma, adenocarcinoma, large cell carcinoma, and the like, as opposed to small cell lung cancer. The name is now used less and less in clinical practice, and more directly by squamous carcinoma, adenocarcinoma and the like. The squamous carcinoma is the most common, accounting for about 50%, and has slow growth speed, long disease course and relatively high sensitivity to radiotherapy and chemotherapy. Adenocarcinoma is relatively common in women, generally growing slowly, but sometimes early onset of hematogenous metastasis. Non-small cell lung cancer can be cured radically by operation in early stage, and recurrence is less. The locally advanced non-small cell lung cancer is mostly treated by adopting a multidisciplinary means, and doctors in thoracic surgery, radiotherapy department and internal medicine need to discuss and then make a treatment scheme suitable for patients. Some patients may also get a cure. For most metastatic lung cancers, systemic treatment is the main treatment. Some patients have better treatment effect, and can survive for a long time, for example, for ALK positive lung cancer, about 50% of patients survive for more than 4 years after treatment. Small cell lung cancer accounts for about 20%, belongs to neuroendocrine cancer, is often accompanied by endocrine abnormality or carcinoid syndrome, is a tumor with high malignancy degree, is mostly located in the central part of lung, grows rapidly, and has early metastasis. Most patients are very sensitive to initial chemotherapy or radiation therapy, but resistance occurs quickly in most patients and, once resistant, subsequent treatment is difficult. The small cell lung cancer is divided into a limited stage and a wide stage, and the limited stage small cell lung cancer emphasizes the early combined radiotherapy after the start of chemotherapy; in the extensive period, partial patients can be subjected to chest radiotherapy after chemotherapy is finished.

Tumor immunotherapy has been actively developed, and among them, immune checkpoint inhibitors (anti-PD-1/PD-L1 inhibitors) are more "star" drugs in the field of tumor therapy in recent years, and have entered first-line treatment of non-small cell lung cancer. However, it is also seen that although the immune checkpoint inhibitor has good effect, the overall ORR is still only about 20%, so how to accurately screen the population with benefit becomes a problem that needs to be solved by the clinician urgently.

PD-L1, TMB (tumor mutational burden) and MSI (microsatellite instability) are three immunotherapeutic biomarkers (biomarker) that have been approved by FDA or recommended by NCCN guidelines, but each of these three biomarkers has advantages and disadvantages. PD-L1 is most widely used as an immunotherapy biomarker, and PD-L1 IHC detection is also approved by the FDA as a concomitant diagnosis of Pembrolizumab first-line drug administration. However, the results of multiple clinical trials show that the prediction ability of the expression of PD-L1 on the curative effect of immunotherapy is inconsistent, part of PD-L1 negative patients still can benefit from the immunotherapy, and the sustained remission time is not inferior to that of PD-L1 positive patients; TMB is also the immunotherapeutic biomarker recommended by the non-small cell lung cancer NCCN guidelines, but TMB thresholds are difficult to establish consensus given the differences in TMB algorithms by different companies or laboratories; MSI has been used as a key biomarker for tumors to allow FDA to agree to administer medication based on MSI status, rather than histopathological type, but the tumor MSI-H ratio is too low, and clinical popularization has certain limitations. The most important point is that the overlapping rate of PD-L1 positive, TMB high expression and MSI-H is only 0.6% in the existing research (including 11348 cases of solid tumors), which suggests that many potential immunotherapy benefit groups are missed by any biomar alone. Further exploration of immunotherapeutic biorarkers is required.

With the development of the second generation sequencing in the precise treatment of tumors, somatic mutations of specific genes are found to possibly influence the immune function of the tumors or the response to immunotherapy, namely, the specific somatic mutations are suggested to be potential predictors of immunotherapy. EGFR mutations and ALK rearrangements are potential predictors of poor prognosis for ICI immunotherapy. A retrospective analysis found that only 3.6% of these patients responded to ICI immunotherapy, while the response rate for EGFR wild-type and ALK-negative or unknown patients was 23.3%. To include the use ofMeta-analysis of 5 trials in 3025 patients with advanced NSCLC treated with PD- (L)1 inhibitor found no improvement in overall survival compared to docetaxel in EGFR-mutated patients. EGFR-mutated or ALK-rearranged lung cancer shows lower PD-L1 and CD8⁺T cell infiltration, which may be responsible for poor ICI immunotherapy response. In addition to the alterations in EGFR and ALK, synergistic mutations in TP53 and KRAS, and synergistic mutations in the DNA Damage Response (DDR) pathway of homologous recombination repair and mismatch repair (HRR-MMR) or HRR and base excision repair (HRR-BER), are considered to be positive predictors of better efficacy of ICI immunotherapy, whereas synergistic mutations in KRAS and STK11 are associated with poor prognosis of ICI immunotherapy. However, these gene mutations still do not cover all potential immunotherapeutic benefit groups as biorarers, and there is still a need in the art for methods and tools for more efficient and accurate identification of non-small cell lung cancer patients for treatment with immune checkpoint inhibitors.

PD-L1 and TMB have been considered as the 2 most common predictive biomarkers for the selection of NSCLC patients susceptible to ICI treatment. However, the PD-L1 test lacks a uniform standard due to the different anti-PD- (L)1 drugs having their own corresponding PD- (L)1 detection antibodies and platforms; in addition, the expression of PD-L1 has the characteristic of dynamic change, so that the relationship between the expression of PD-L1 and the effect of immunotherapy is still controversial; on the other hand, although a large number of random control studies and large-sample real-world studies have confirmed the correlation between TMB and the immune efficacy, TMB still can only reflect the tumor mutation number, but cannot prompt the state of the tumor microenvironment, and TMB detection has high requirements on a technical platform, a long working period and high cost, which restricts clinical application thereof. Therefore, a method for predicting the sensitivity to immunotherapy, which is highly effective, is urgently required.

Disclosure of Invention

In order to solve the above problems, the present invention provides a method and a kit, and a device and a system for predicting the sensitivity of a cancer patient to immunotherapy, for example, immunotherapy using an immune checkpoint inhibitor, using the KMT2C gene and TP53 gene as biomarkers.

The KMT2C gene sequence disclosed by the invention is shown in GenBank accession number NG _033948.1, and the TP53 gene sequence disclosed by the invention is shown in GenBank accession number NG _ 017013.2.

In a first aspect, the present invention provides the use of an agent that specifically detects TP53 gene mutation and/or expression levels, and/or specifically detects KMT2C gene mutation and/or expression levels, in the manufacture of a kit or device for predicting the sensitivity of a cancer patient to immunotherapy.

In some embodiments, the immunotherapy is an immunotherapy using an immune checkpoint inhibitor.

In some embodiments, the cancer is non-small cell lung cancer (NSCLC).

In some embodiments, the reagents are, for example, genomic sequencing reagents, gene-specific primers or probes, antibodies specific for gene expression products, and the like.

In a second aspect, the invention provides a method for predicting the sensitivity of a cancer patient to immunotherapy, for example immunotherapy using an immune checkpoint inhibitor, the method comprising

Step a) assessing TP53 gene mutation and/or expression in tumor tissue of the patient; and

step b) assessing mutation and/or expression of the KMT2C gene in tumour tissue of the patient; and

step c) predicting the sensitivity of the cancer patient to immunotherapy, e.g. immunotherapy with an immune checkpoint inhibitor, based on the results of the evaluation of a), b).

In some embodiments, the cancer is non-small cell lung cancer (NSCLC).

As used herein, the term "immune checkpoint" refers to some inhibitory signaling pathway present in the immune system. Under normal conditions, the immune checkpoint can maintain immune tolerance by adjusting the strength of autoimmune reaction, however, when the organism is invaded by tumor, the activation of the immune checkpoint can inhibit autoimmunity, which is beneficial to the growth and escape of tumor cells. By using the immune checkpoint inhibitor, the normal anti-tumor immune response of the body can be restored, so that the tumor can be controlled and eliminated. A variety of immune checkpoint inhibitors are known in the art for use in tumor therapy. For example, the immune checkpoint inhibitors of the present invention include, but are not limited to, PD1 inhibitors or PD-L1 inhibitors, such as domestic terieprimab, netralizumab, carpriluzumab, as well as pembrolizumab, nivolumab, alemtuzumab, Avelumab, and Durvalumab.

In some embodiments of the various aspects of the invention, the cancer is non-small cell lung cancer (NSCLC).

In some embodiments of various aspects of the invention, the TP53 gene mutation and the KMT2C gene mutation are assessed by comparing sequencing data, e.g., genomic sequencing data and/or exome sequencing data, of tumor tissue to control tissue.

In some embodiments of the various aspects of the invention, the control tissue is a normal tissue (non-tumor tissue), e.g., a blood tissue, from the subject.

In some embodiments of the various aspects of the invention, the sequencing is high throughput sequencing, also known as next generation sequencing ("NGS"). Second generation sequencing produces thousands to millions of sequences simultaneously in a parallel sequencing process. NGS is distinguished from "Sanger sequencing" (one generation sequencing), which is based on electrophoretic separation of chain termination products in a single sequencing reaction. Sequencing platforms that can be used with the NGS of the present invention are commercially available and include, but are not limited to, Roche/454 FLX, Illumina/Solexa genome Analyzer, and Applied Biosystems SOLID system, among others.

Exome sequencing is a genome analysis method of high-throughput sequencing after capturing and enriching genomic exome region DNA by using a sequence capture technology. Because it has high sensitivity to common and rare variations, only 2% of the genome need be sequenced to discover most disease-related variations in exon regions. The sequencing can be whole genome sequencing, and can also cover sequencing of partial genes or regions in a genome.

In some embodiments, assessing TP53 gene mutation comprises determining whether a mutation, such as a frameshift mutation, is present in its coding region. In some embodiments, the mutations are located within nucleotides 285-864 and 954-1074 of the TP53 gene. In some preferred embodiments, assessing TP53 gene mutations comprises determining whether there is a mutation in its coding region that truncates the TP53 protein.

In some embodiments, TP53 gene expression is assessed by transcriptome sequencing, Polymerase Chain Reaction (PCR), or immunoassay. Various methods are known in the art for detecting the expression of a particular gene, and these may be applied to the present invention.

For example, transcriptome sequencing is to rapidly and comprehensively obtain almost all transcripts and gene sequences of a specific cell or tissue of a certain species in a certain state through a second-generation sequencing platform, and can be used for researching gene expression amount, gene function, structure, alternative splicing, new transcript prediction and the like. In addition, by designing appropriate primers, the transcriptional expression level of TP53 gene can be determined by PCR such as reverse transcription PCR. In addition, the protein expression level of TP53 gene can be measured by immunoassay such as Immunohistochemistry (IHC), ELISA, etc. using TP53 protein-specific antibody.

In some embodiments, TP53 gene expression, e.g., protein expression level of TP53 gene, is assessed after determining the presence of a mutation in the coding region of TP53 gene that truncates the TP53 protein.

In some embodiments, assessing mutations in the KMT2C gene includes assessing the presence or absence of mutations in its coding region.

In some embodiments, the mutation in the KMT2C gene is a mutation that results in a high Tumor Mutational Burden (TMB). Tumor Mutation Burden (TMB) is defined as the total number of somatic gene coding errors, base substitutions, gene insertion or deletion errors detected per million bases. TMB is generally expressed as the total number of mutations or the number of mutations per 1Mb (1 megabase) (muts/Mb).

In some embodiments, the mutation is located within nucleotide 3999-7788, nucleotide 9375-13071 of the KMT2C gene. In some preferred embodiments, assessing mutations in the KMT2C gene includes determining whether there is a mutation in its coding region that truncates the KMT2C protein.

In some embodiments, the patient is predicted to be sensitive to the immunotherapy if i) there is a mutation in the TP53 gene and/or a deletion in TP53 gene expression, and ii) there is a mutation in the KMT2C gene and/or a deletion in KMT2C gene expression.

In some embodiments, the patient is predicted to be insensitive to the immunotherapy if i) there is no mutation in the TP53 gene and/or the TP53 gene expression is intact, and ii) there is a mutation in the KMT2C gene and/or the KMT2C gene expression is absent.

In some embodiments, the patient is predicted to be insensitive to the immunotherapy if i) there is no mutation in the TP53 gene and/or TP53 gene expression is intact, and ii) there is no mutation in the KMT2C gene and/or KMT2C gene expression is intact.

In some embodiments, the method comprises the step of obtaining the tumor tissue from the cancer patient.

In some embodiments, the method further comprises the step of performing a quality check on the obtained tumor tissue.

In some embodiments, the quality detection is performed by amplifying the ACTIN gene from nucleic acids extracted from the tumor tissue. In some embodiments, the ACTIN gene is amplified using the following three sets of primer pairs:

i) 5'-CACACTGTGCCCATCTATGAGG-3' (SEQ ID NO: 1) and 5'-CACGCTCGGTGAGGATCTTC-3' (SEQ ID NO: 2),

ii) 5'-CACACTGTGCCCATCTATGAGG-3' (SEQ ID NO: 1) and 5'-TCGAAGTCCAGGGCAACATAGC-3' (SEQ ID NO: 3), and

iii) 5'-CACACTGTGCCCATCTATGAGG-3' (SEQ ID NO: 1) and 5'-AAGGCTGGAAGAGCGCCTCGGG-3' (SEQ ID NO: 4) which amplify fragments of 100bp, 200bp and 300bp, respectively. And when the three groups of primers are amplified to the target fragments, judging that the quality of the tumor tissues is qualified.

The quality of the tumor tissue is detected through the steps, and the tissue with qualified quality is subjected to subsequent sequencing and evaluation steps, so that the missing detection of variation information caused by the quality difference of tissue samples can be avoided, and the accuracy of the sensitivity prediction of the immune checkpoint inhibitor is improved.

In some embodiments, when evaluating genetic mutations by high throughput sequencing data, only mutations that meet the following criteria are evaluated:

(1) for point mutations:

the sequencing coverage depth of the position of the point mutation is more than 500 times; a quality value for each read comprising the point mutation of >40, and a base quality value corresponding to the point mutation on each read comprising the point mutation of > 21; the number of the reads containing the point mutation is more than or equal to 5; a ratio of reads in forward to reads in reverse of all reads comprising the point mutation < 1/6; and the frequency of the variant allele of the tumor tissue/the frequency of the variant allele of the control tissue is more than or equal to 20;

(2) for indels (indels):

if the consecutive identical bases in the indel are <5, the sequencing coverage depth of the position of the indel is >600 times; the quality value of each read containing the indels is > 40; (ii) a base quality value corresponding to the indel mutation on each read comprising the indel of > 21; the number of reads containing the insertion deletion is more than or equal to 5; the ratio of forward read length to reverse read length in all reads containing the indel is < 1/6; the frequency of the variant allele of the tumor tissue/the frequency of the variant allele of the control tissue is more than or equal to 20;

if the continuous identical basic groups in the insertion deletion are more than or equal to 5 and less than 7, the sequencing coverage depth of the position of the insertion deletion is more than 60 times; the quality value of each read containing the indels is > 40; (ii) a base quality value corresponding to the indel mutation on each read comprising the indel of > 21; the number of reads containing the insertion deletion is more than or equal to 5; the ratio of forward read length to reverse read length in all reads containing the indel is < 1/6; (ii) the variant allele frequency of the tumor tissue/variant allele frequency of a control tissue > 20; and the frequency of the variant allele of the tumor tissue is more than or equal to 10 percent;

if the continuous same basic groups in the insertion deletion are more than or equal to 7, the sequencing coverage depth of the position of the insertion deletion is more than 60 times; the quality value of each read containing the indels is > 40; (ii) a base quality value corresponding to the indel mutation on each read comprising the indel of > 21; the number of reads containing the insertion deletion is more than or equal to 5; the ratio of forward read length to reverse read length in all reads containing the indel is < 1/6; (ii) the variant allele frequency of the tumor tissue/variant allele frequency of a control tissue > 20; and the frequency of the variant allele of the tumor tissue is more than or equal to 20 percent.

Those skilled in the art will appreciate that all or part of the functions of the above-described method steps may be implemented by hardware, or may be implemented by a computer program. When all or part of the functions of the above method steps are implemented by means of a computer program, the program may be stored in a computer-readable storage medium, and the storage medium may include: a read only memory, a random access memory, a magnetic disk, an optical disk, a hard disk, etc., and the program is executed by a computer to realize the above functions. For example, the program may be stored in a memory of the device, and when the program in the memory is executed by the processor, all or part of the functions described above may be implemented. In addition, when all or part of the functions in the above embodiments are implemented by a computer program, the program may be stored in a storage medium such as a server, another computer, a magnetic disk, an optical disk, a flash disk, or a removable hard disk, and may be downloaded or copied to a memory of a local device, or may be version-updated in a system of the local device, and when the program in the memory is executed by a processor, all or part of the functions in the above embodiments may be implemented.

In a third aspect, the present invention provides an apparatus for predicting the sensitivity of a cancer patient to immunotherapy, for example immunotherapy using an immune checkpoint inhibitor, said cancer being non-small cell lung cancer (NSCLC), said apparatus comprising the following three modules:

an evaluation module I) which evaluates the TP53 gene mutation and/or expression in the tumor tissue of the patient;

an evaluation module II) that evaluates mutation and/or expression of the KMT2C gene in tumor tissue of the patient; and

prediction module III) which predicts the sensitivity of the cancer patient to an immunotherapy, for example an immunotherapy using an immune checkpoint inhibitor, based on the results of the evaluation modules I), II).

In some embodiments, evaluation module I), II) evaluates the TP53 gene mutation and the KMT2C gene mutation by comparing sequencing data, e.g., genomic sequencing data and/or exome sequencing data, of tumor tissue to control tissue.

In some embodiments, assessment module II) determines whether there is a mutation, e.g., a frameshift mutation, in the coding region of the TP53 gene. In some embodiments, the mutations are located within nucleotides 285-864 and 954-1074 of the TP53 gene. In some preferred embodiments, evaluation module II) determines whether the coding region of the TP53 gene has a mutation that truncates the TP53 protein.

In some embodiments, the assessment module II) determines whether there is a mutation in the coding region of the KMT2C gene. In some embodiments, the mutation in the KMT2C gene is a mutation that results in a high Tumor Mutational Burden (TMB). In some embodiments, the mutation is located within nucleotide 3999-7788, nucleotide 9375-13071 of the KMT2C gene.

In some embodiments, the evaluation module I), II) evaluates only mutations that meet the following criteria:

(1) for point mutations:

the sequencing coverage depth of the position of the point mutation is more than 500 times; a quality value for each read comprising the point mutation of >40, and a base quality value corresponding to the point mutation on each read comprising the point mutation of > 21; the number of the reads containing the point mutation is more than or equal to 5; a ratio of reads in forward to reads in reverse of all reads comprising the point mutation < 1/6; and the frequency of the variant allele of the tumor tissue/the frequency of the variant allele of the control tissue is more than or equal to 20;

(2) for indels (indels):

if the consecutive identical bases in the indel are <5, the sequencing coverage depth of the position of the indel is >600 times; the quality value of each read containing the indels is > 40; (ii) a base quality value corresponding to the indel mutation on each read comprising the indel of > 21; the number of reads containing the insertion deletion is more than or equal to 5; the ratio of forward read length to reverse read length in all reads containing the indel is < 1/6; the frequency of the variant allele of the tumor tissue/the frequency of the variant allele of the control tissue is more than or equal to 20;

if the continuous identical basic groups in the insertion deletion are more than or equal to 5 and less than 7, the sequencing coverage depth of the position of the insertion deletion is more than 60 times; the quality value of each read containing the indels is > 40; (ii) a base quality value corresponding to the indel mutation on each read comprising the indel of > 21; the number of reads containing the insertion deletion is more than or equal to 5; the ratio of forward read length to reverse read length in all reads containing the indel is < 1/6; (ii) the variant allele frequency of the tumor tissue/variant allele frequency of a control tissue > 20; and the frequency of the variant allele of the tumor tissue is more than or equal to 10 percent;

if the continuous same basic groups in the insertion deletion are more than or equal to 7, the sequencing coverage depth of the position of the insertion deletion is more than 60 times; the quality value of each read containing the indels is > 40; (ii) a base quality value corresponding to the indel mutation on each read comprising the indel of > 21; the number of reads containing the insertion deletion is more than or equal to 5; the ratio of forward read length to reverse read length in all reads containing the indel is < 1/6; (ii) the variant allele frequency of the tumor tissue/variant allele frequency of a control tissue > 20; and the frequency of the variant allele of the tumor tissue is more than or equal to 20 percent.

In some embodiments, the patient is predicted to be sensitive to the immunotherapy if i) there is a mutation in the TP53 gene and/or a deletion in TP53 gene expression, and ii) there is a mutation in the KMT2C gene and/or a deletion in KMT2C gene expression.

In some embodiments, the patient is predicted to be insensitive to the immunotherapy if i) there is no mutation in the TP53 gene and/or the TP53 gene expression is intact, and ii) there is a mutation in the KMT2C gene and/or the KMT2C gene expression is absent.

In some embodiments, the patient is predicted to be insensitive to the immunotherapy if i) there is no mutation in the TP53 gene and/or TP53 gene expression is intact, and ii) there is no mutation in the KMT2C gene and/or KMT2C gene expression is intact.

In a fourth aspect, the present invention also provides an apparatus for predicting the sensitivity of a cancer patient to immunotherapy, for example immunotherapy using an immune checkpoint inhibitor, the cancer being non-small cell lung cancer (NSCLC), the apparatus comprising: a memory for storing a program; a processor for implementing all or part of the steps of the method of the second aspect of the invention by executing the program stored in the memory.

In a fifth aspect, the present invention also provides a computer readable storage medium comprising a program executable by a processor to perform all or part of the steps of the method of the second aspect of the present invention.

Compared with the prior art, the invention has the following technical effects:

1. currently, Immune Checkpoint Inhibitor (ICI) treatment of NSCLC patients uses only PD-L1 and TMB as biomarkers for suitable treatment. However, the objective response rate is still only around 20% in NSCLC patients selected by PD-L1 or TMB, while some NSCLC patients with PD-L1 negative expression have also been reported to respond to ICI. In the invention, TP53 and KMT2C gene mutation and/or expression states are considered in a combined manner, so that a population sensitive to ICI in NSCLC patients can be accurately predicted, blind medication is avoided, and the economic performance of ICI treatment is improved.

2. The TP53 and KMT2C gene co-mutation is screened out to be used as a biomarker for predicting a population sensitive to ICI in NSCLC patients, and compared with the co-mutation of other gene combinations, the prediction result is more accurate; and the TP53 and KMT2C gene co-mutation adopted in the invention can be used as an independent prediction risk factor in practical application, thereby improving the detection efficiency.

The mutation states of the TP53 and KMT2C genes reveal that the patient is sensitive to immunotherapy, such as immunotherapy using immune checkpoint inhibitors, which is beneficial to simplify the detection content, reduce the detection cost of the patient, and shorten the detection report time, and the detection of the gene mutation states is more reliable than the PD-L1 immunohistochemical method which requires manual interpretation of immunohistochemical tablets and TMB which requires manual determination of a threshold value.

Drawings

FIG. 1 comparison of KMT2C mutation with wild type patient Tumor Mutational Burden (TMB);

FIG. 2 comparison of KMT2C mutation with wild type patient tumor PD-L1 expression;

FIG. 3 comparison of Tumor Mutation Burden (TMB) for different mutation states of KMT2C and TP 53;

FIG. 4 comparison of PD-L1 expression in different mutational states of KMT2C and TP 53;

FIG. 5 comparison of the efficacy of the KMT2C mutation with that of wild type patients receiving immunotherapy with immune checkpoint inhibitors;

FIG. 6 comparison of the efficacy of patients with different mutation states of KMT2C and TP53 receiving immunotherapy with immune checkpoint inhibitors;

figure 7 KMT2C in combination with TP53 co-mutation, Cox multifactorial regression analysis independent risk factors associated with the efficacy of immunotherapy using immune checkpoint inhibitors;

figure 8 comparison of the efficacy of patients with different mutational states of KRAS and TP53 receiving immunotherapy with immune checkpoint inhibitors;

figure 9 KRAS in combination with TP53 co-mutation, Cox multifactorial regression analysis independent risk factors associated with the efficacy of immunotherapy using immune checkpoint inhibitors;

figure 10 comparison of the efficacy of patients with PTPRD and TP53 in different mutation states receiving immunotherapy with immune checkpoint inhibitors;

figure 11 PTPRD in combination with TP53 co-mutation, Cox multifactorial regression analysis independent risk factors associated with the efficacy of immunotherapy using immune checkpoint inhibitors;

fig. 12 comparison of the efficacy of different mutant states of HGF and TP53 in receiving immunotherapy with an immune checkpoint inhibitor;

fig. 13 HGF in combination with TP53 co-mutations, Cox multifactorial regression analysis independent risk factors associated with the efficacy of immunotherapy using immune checkpoint inhibitors.

Detailed Description

The present invention will be described in detail below with reference to the accompanying drawings and embodiments.

While specific embodiments of the present invention or prior art will be described briefly in order to more clearly illustrate it, it should be apparent that the following description of the embodiments is illustrative of some embodiments of the present invention and that others can be devised by those skilled in the art without departing from the inventive concept.

Example 1

Genomic analysis

FFPE tumor samples from chinese non-small cell lung cancer (NSCLC) patients and paired peripheral whole blood control samples were studied. All patients provided written informed consent. Next generation sequencing for targeted capture (NGS) at OrigiMed involves a combination comprising 450 cancer-associated genes. DNA was extracted from all unstained FFPE sections and whole blood containing not less than 20% of tumor content by DNA FFPE Tissue Kit and DNA Mini Kit (QIAamp), respectively, and then quantified by dsDNA HS determination Kit (Qubit). The approximately 250bp sonicated DNA was fragmented using KAPA Hyper Prep Kit (KAPA Biosystems) to construct a library, which was then PCR amplified and quantified. Hybrid capture was performed using custom combinations, this group and the human genome covering 2.6Mb, targeting 450 cancer-associated genes and some frequently rearranged introns. The captured library was mixed, denatured and diluted to 1.5-1.8pM, followed by paired-end sequencing on illumina nextseq 500 according to the manufacturer's protocol.

Wherein the samples were subjected to quality detection using the following three primer pairs for amplifying the ACTIN gene:

i) 5'-CACACTGTGCCCATCTATGAGG-3' (SEQ ID NO: 1) and 5'-CACGCTCGGTGAGGATCTTC-3' (SEQ ID NO: 2),

ii) 5'-CACACTGTGCCCATCTATGAGG-3' (SEQ ID NO: 1) and 5'-TCGAAGTCCAGGGCAACATAGC-3' (SEQ ID NO: 3), and

iii) 5'-CACACTGTGCCCATCTATGAGG-3' (SEQ ID NO: 1) and 5'-AAGGCTGGAAGAGCGCCTCGGG-3' (SEQ ID NO: 4) which amplify fragments of 100bp, 200bp and 300bp, respectively. And when the three groups of primers are amplified to the target fragment, judging that the tissue sample is qualified.

Genome alteration analysis

Genomic alterations, including single base Substitutions (SNVs), short and long insertion deletions, Copy Number Variations (CNVs), and gene rearrangements and fusions were evaluated. Alignment of the original reads to the human genome reference sequence (hg19) was performed using a Burrows-Wheeler Aligner, followed by PCR deduplication using Picard's MarkDuplicates algorithm. Variants with read depths less than 30x, strand bias greater than 10% or VAF < 0.5% were removed. Common Single Nucleotide Polymorphisms (SNPs) defined as from the dbSNP database (version 147) or with frequencies exceeding 1.5% of exome sequencing project 6500 (ESP6500) or exceeding 1.5% of the 1000 genome project were also excluded.

Whether the identified mutation is true is judged by the following criteria:

(1) for point mutations:

the sequencing coverage depth of the position of the point mutation is more than 500 times; a quality value for each read comprising the point mutation of >40, and a base quality value corresponding to the point mutation on each read comprising the point mutation of > 21; the number of the reads containing the point mutation is more than or equal to 5; a ratio of reads in forward to reads in reverse of all reads comprising the point mutation < 1/6; and the frequency of the variant allele of the tumor tissue/the frequency of the variant allele of the control tissue is more than or equal to 20;

(2) for indels (indels):

if the consecutive identical bases in the indel are <5, the sequencing coverage depth of the position of the indel is >600 times; the quality value of each read containing the indels is > 40; (ii) a base quality value corresponding to the indel mutation on each read comprising the indel of > 21; the number of reads containing the insertion deletion is more than or equal to 5; the ratio of forward read length to reverse read length in all reads containing the indel is < 1/6; the frequency of the variant allele of the tumor tissue/the frequency of the variant allele of the control tissue is more than or equal to 20;

if the continuous identical basic groups in the insertion deletion are more than or equal to 5 and less than 7, the sequencing coverage depth of the position of the insertion deletion is more than 60 times; the quality value of each read containing the indels is > 40; (ii) a base quality value corresponding to the indel mutation on each read comprising the indel of > 21; the number of reads containing the insertion deletion is more than or equal to 5; the ratio of forward read length to reverse read length in all reads containing the indel is < 1/6; (ii) the variant allele frequency of the tumor tissue/variant allele frequency of a control tissue > 20; and the frequency of the variant allele of the tumor tissue is more than or equal to 10 percent;

if the continuous same basic groups in the insertion deletion are more than or equal to 7, the sequencing coverage depth of the position of the insertion deletion is more than 60 times; the quality value of each read containing the indels is > 40; (ii) a base quality value corresponding to the indel mutation on each read comprising the indel of > 21; the number of reads containing the insertion deletion is more than or equal to 5; the ratio of forward read length to reverse read length in all reads containing the indel is < 1/6; (ii) the variant allele frequency of the tumor tissue/variant allele frequency of a control tissue > 20; and the frequency of the variant allele of the tumor tissue is more than or equal to 20 percent.

TMB calculation

In addition to routine detection of genomic changes, TMB is also determined by NGS-based algorithms. TMB was estimated by counting somatic mutations including SNVs and indels per megabase of coding region sequence examined. The driver mutations and known germline changes in dbSNP were excluded.

Example 2

A total of 637 non-small cell lung cancer (NSCLC) patients were enrolled in the study. The characteristics of the patients are shown in table 1. Most patients are male (355/637, 55.7%), with a median age at diagnosis of 60 years (IQR, 53-67 years). The most common histological types are non-squamous cell carcinomas (N =553, 86.8%), including adenocarcinomas (N =537) and other non-squamous cell carcinomas (N = 16). The preparation comprises 211 cases in phase I (33.1%), 64 cases in phase II (10%), 138 cases in phase III (21.7%) and 224 cases in phase IV (35.2%). PD-L1 and TMB tests were performed on 637 patients. The expression of PD-L1 and the distribution of TMB according to demographic characteristics are shown in Table 1. The weak positive (PD-L1 TPS score =1-49%) and strong positive PD-L1 expression (PD-L1 TPS score ≧ 50%) were 16.5% and 10%, respectively. We performed a one-way analysis of the relationship between PD-L1 expression (evaluated as categorical variables with cutoff values of 1% and 50%) and clinical features of non-small cell lung cancer (table 1). There was no significant difference in mean age between TPS <1%, 1-49% and > 50% (P > 0.05). PD-L1 was expressed higher in men (P =0.002) and squamous cell carcinoma (P < 0.001). The median TMB of the whole population was 4.6 muts/Mb (IQR, 2.3-10). 29.4 percent of tumors with TMB more than or equal to 10 muts/Mb. We found that age was positively correlated with TMB (p < <0.001). In addition, TMB values were higher in the male patient group (p < > 0.001) and in the squamous cell carcinoma patient group (p < 0.001).

Example 3

The first three mutant genes in the 637 NSCLC patient cohort were TP53 (55.9%), EGFR (51.3%) and KRAS (12.6%). Of the 637 NSCLC patients, 32 patients carried the mutation of the KMT2C gene, accounting for 5%. 24 of 32 KMT2C mutant patients carried the TP53 gene mutation at the same time, accounting for 75%. The TMB of KMT2C mutant patients was significantly higher than that of KMT2C wild-type patients (median TMB: 13.1 vs. 4.6, p <0.001) (FIG. 1). The positive expression rate of PD-L1 (PD-L1 TPS is more than or equal to 1%) of KMT2C mutant patients is higher than that of KMT2C wild-type patients (34.4% vs. 26.1%, p is more than 0.05) (FIG. 2).

Of the 637 NSCLC cohorts of patients, 42.9% (273/637) were TP53-WT/KMT2C-WT patients, 1.3% (8/637) were TP53-WT/KMT2C-MUT patients, 52.1% (332/637) were TP53-MUT/KMT2C-WT patients, and 3.7% (24/637) were TP53-MUT/KMT2C-MUT patients. TP53-WT/KMT2C-MUT patients have significantly higher tumor TMB than TP53-WT/KMT2C-WT patients (median TMB: 10.8 vs. 3.1, p = 0.007); the tumor TMB of the TP53-MUT/KMT2C-MUT patient is obviously higher than that of the TP53-MUT/KMT2C-WT patient (the median TMB: 15.2 vs. 6.6, p = 0.002); TP53-MUT/KMT2C-MUT patients tumor TMB was significantly higher than TP53-WT/KMT2C-WT patients (median TMB: 15.2 vs. 3.1, p <0.001) (FIG. 3). The PD-L1 positive expression rate (PD-L1 TPS is more than or equal to 1%) of TP53-WT/KMT2C-MUT patient tumor is higher than that of TP53-WT/KMT2C-WT patient (25% vs. 16.1%, p is more than 0.05). The PD-L1 positive expression rate (PD-L1 TPS is more than or equal to 1%) of TP53-MUT/KMT2C-MUT patient tumor is higher than that of TP53-MUT/KMT2C-WT patient (37.5% vs. 34.4%, p is more than 0.05) (FIG. 4).

Therefore, clinical experimental data show that the TP53 and KMT2C gene mutation and/or expression state can accurately predict a population sensitive to ICI in NSCLC patients, and improve the economic performance of ICI treatment.

Example 4

To further validate the predictive value of KMT2C mutations for Immune Checkpoint Inhibitor (ICIs) treatment, we performed external validation by downloading public database cohort information. We downloaded Rizvi et al, at the cBioPortal website (http:// www.cbioportal.org /), the data in the queue, which included 240 non-small cell lung cancer patients receiving anti-PD- (L)1 monotherapy or anti-PD- (L)1+ anti-CTLA-4 combination therapy, and specific patient baseline data were available for reference. Of the 240 patients, 25 patients with KMT2C mutation (10.4%) had longer median PFS after immunotherapy than KMT2C wild-type patients (median PFS: 5.47 vs. 3.17 months, p = 0.058) (fig. 5). Further grouped according to KMT2C and TP53 mutation status, 92 patients were TP53-WT/KMT2C-WT, 123 patients were TP53-MUT/KMT2C-WT, 7 patients were TP53-WT/KMT2C-MUT and 18 patients were TP53-MUT/KMT 2C-MUT. Median PFSs for these four groups were 2.47 months (95% CI 2.03-3.5), 2.57 months (95% CI 1.63-NA), 4.2 months (95% CI 3.23-5.37), 7.33 months (95% CI 2.50-NA) (fig. 6), Cox multifactorial analysis results further demonstrated that KMT2C/TP53 co-mutation was an independent predictive risk factor for immunotherapy prognosis (TP 53-MUT/KMT2C-MUT vs TP53-WT/KMT2C-WT, HR: 0.51, 95% CI: 0.27-0.96, p = 0.036) (fig. 7).

Therefore, the KMT2C/TP53 gene mutation can be used as an immunotherapy biomarker, and compared with the PD-L1 immunohistochemical method which needs manual interpretation of immunohistochemical tablets and TMB which needs manual determination of a threshold value, the detection of the gene mutation state is more reliable.

Example 5

In view of the fact that both KRAS and TP53 genes can be used to predict response of NSCLC patients to PD-1 inhibitor treatment and show positive correlation with GMS, the present invention was similarly studied for KRAS/TP53 co-mutation in order to further illustrate that the KMT2C/TP53 co-mutation of the present invention is an independent risk factor for prognosis of immunotherapy. We downloaded Rizvi et al, the queue data uploaded at the cBioPortal website (http:// www.cbioportal.org /), which included 240 non-small cell lung cancer patients receiving anti-PD- (L)1 monotherapy or anti-PD- (L)1+ anti-CTLA-4 combination therapy regimens. Of the 240 patients, 45 patients were further grouped according to KRAS and TP53 mutation status, 109 patients were KRAS-WT/TP53-WT, 109 patients were KRAS-WT/TP53-MUT, 54 patients were KRAS-MUT/TP53-WT, and 32 patients were KRAS-MUT/TP 53-MUT. Median PFS for these four groups were 2.6 months (95% CI 1.93-5.27), 3.6 months (95% CI 3.07-5.37), 2.33 months (95% CI 1.93-4.37), 5.77 months (95% CI 4.27-NA) (fig. 8), Cox multifactorial analysis results further demonstrated that KRAS/TP53 co-mutation was not an independent predictive risk factor for immunotherapy prognosis (KRAS-MUT/TP 53-MUT vs KRAS-WT/TP53-WT, HR: 0.58, 95% CI: 0.33-1.04, p = 0.067) since p of Cox regression >0.05 (fig. 9).

Example 6

In view of the fact that PTPRD and TP53 genes can be used to predict the response of NSCLC patients to PD-1 inhibitor treatment and show positive correlation with GMS, the present invention was similarly studied for PTPRD/TP53 co-mutation in order to further illustrate that the KMT2C/TP53 co-mutation of the present invention is an independent risk prediction factor for immunotherapy prognosis. We downloaded Rizvi et al, the queue data uploaded at the cBioPortal website (http:// www.cbioportal.org /), which included 240 non-small cell lung cancer patients receiving anti-PD- (L)1 monotherapy or anti-PD- (L)1+ anti-CTLA-4 combination therapy regimens. Of the 240 patients, 91 were named PTPRD-WT/TP53-WT, 119 were named PTPRD-WT/TP 53-MUT, 8 were named PTPRD-MUT/TP53-WT and 22 were further grouped according to PTPRD and TP53 mutation status. Median PFSs for these four groups were 2.47 months (95% CI 2.03-3.5), 3.8 months (95% CI 3.1-5.33), 2.79 months (95% CI 2.1-NA), 6.33 months (95% CI 4.17-NA) (fig. 10), Cox multifactorial analysis results further demonstrated that PTPRD/TP53 co-mutation was an independent predictive risk factor for immunotherapy prognosis (PTPRD-MUT/TP 53-MUT vs PTPRD-WT/TP53-WT, HR: 0.52, 95% CI: 0.30-0.92, p = 0.026) (fig. 11).

Example 7

In view of the fact that both HGF and TP53 genes can be used to predict response of NSCLC patients to inhibitor therapy of PD-1 and show positive correlation with GMS, in order to further illustrate that the KMT2C/TP53 co-mutation of the present invention is an independent predictive risk factor for prognosis of immunotherapy, the present invention has been similarly studied against HGF/TP53 co-mutation. We downloaded Rizvi et al, the queue data uploaded at the cBioPortal website (http:// www.cbioportal.org /), which included 240 non-small cell lung cancer patients receiving anti-PD- (L)1 monotherapy or anti-PD- (L)1+ anti-CTLA-4 combination therapy regimens. Among 240 patients, 96 patients were HGF-WT/TP53-WT patients, 127 patients were HGF-WT/TP53-MUT patients, 3 patients were HGF-MUT/TP 53-WT patients and 14 patients were HGF-MUT/TP53-MUT patients, further grouped according to the mutation status of HGF and TP 53. Median PFS for these four groups were 2.57 months (95% CI 2.1-3.5), 4.17 months (95% CI 3.3-5.43), 1.6 months (95% CI 1.57-NA), 5.1 months (95% CI 3.17-NA) (fig. 12), Cox multifactorial analysis results further demonstrated that HGF/TP53 co-mutation was not an independent predictive risk factor for immunotherapy prognosis (HGF-MUT/TP 53-MUT/vsHGF-WT/TP 53-WT, HR: 0.57, 95% CI: 0.29-1.12, p = 0.104) (fig. 13) since p of Cox regression > 0.05.

Examples of effects

Through the survival analysis study of KMT2C/TP53 co-mutation, KRAS/TP53 mutation, PTPRD/TP53 co-mutation and HGF/TP53 co-mutation in examples 4-7, respectively, the results showed that the median PFS in TP53-MUT/KMT2C-MUT patients was 7.33 months (95% CI 2.5-NA), the median PFS in TP53-MUT/KRAS-MUT patients was 5.77 months (95% CI 4.27-NA), the median PFS in TP53-MUT/PTPRD-MUT patients was 6.33 months (95% CI 4.17-NA), the median PFS in TP53-MUT/HGF-MUT patients was 5.1 months (95% CI 3.17-NA), and the best predicted effect of immunotherapy effect of TP 53/TP 2C combination was observed from the median survival aspect.

Meanwhile, the Cox proportional-hazard model (Cox model) is a semi-parametric regression model proposed in british statistician d.r.cox (1972). The Cox model can simultaneously process the influence of a plurality of factors on the survival outcome, so that the Cox model is the most classical multi-factor analysis method for the survival data in clinical research. The risk ratio (HR) is the most important concept in Cox models, and in cancer research: the hazard ratio >1 is considered to be a bad cognitive factor, the hazard ratio <1 is considered to be a good cognitive factor, and the smaller HR represents the better prognosis for the group (the smaller HR represents the lower risk of the occurrence of the event for the group, e.g., HR =0.51 represents a 49% reduction in the risk of the occurrence of the event for the experimental group relative to the control group), TP53-MUT/KMT2C-MUT group HR: 0.51, 95% CI: 0.27-0.96, p =0.036, TP53-MUT/KRAS-MUT group HR: 0.58, 95% CI: 0.33-1.04, p =0.067, TP53-MUT/PTPRD-MUT group 0.52, 95% CI: 0.30-0.92, p =0.026, TP 53-MUT/MUT group 0.57-MUT group, HGF = 0.29-0.104, HGF =0.104, and 12% CI = 0.51. The TP53-MUT/KMT2C-MUT combination is optimal from the HR point of view.

The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes performed by the present specification and drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.

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Claims (7)

1. Use of an agent that specifically detects TP53 gene mutations and specifically detects KMT2C gene mutations in the preparation of a kit or device for predicting the sensitivity of a non-small cell lung cancer (NSCLC) patient to immunotherapy with an immune checkpoint inhibitor.

2. Use according to claim 1, characterized in that said prediction comprises the steps of,

step a) assessing a mutation of the TP53 gene in tumor tissue of the patient; and

step b) assessing KMT2C gene mutation in tumour tissue of the patient; and

step c) predicting the sensitivity of the cancer patient to immunotherapy with an immune checkpoint inhibitor based on the results of the evaluation of a), b).

3. The use according to claim 2, wherein said assessing is carried out by comparing the sequencing data of the tumor tissue with the control tissue to assess the TP53 gene mutation and the KMT2C gene mutation.

4. A kit for predicting the sensitivity of a non-small cell lung cancer (NSCLC) patient to immunotherapy with an immune checkpoint inhibitor, consisting of reagents that specifically detect TP53 gene mutations and specifically detect KMT2C gene mutations.

5. An apparatus for predicting the sensitivity of a non-small cell lung cancer (NSCLC) patient to immunotherapy with an immune checkpoint inhibitor, comprising the following three modules:

an evaluation module I) which evaluates the TP53 gene mutation in the tumor tissue of the patient;

an evaluation module II) that evaluates the mutation of the KMT2C gene in the tumor tissue of the patient; and

prediction module III) which predicts the sensitivity of the cancer patient to immunotherapy with an immune checkpoint inhibitor based on the results of the evaluation modules I), II).

6. An apparatus for predicting the sensitivity of a non-small cell lung cancer (NSCLC) patient to immunotherapy with an immune checkpoint inhibitor, the apparatus comprising: a memory for storing a program; a processor for implementing, by executing the above-described memory-stored program, prediction of the sensitivity of a cancer patient to immunotherapy with an immune checkpoint inhibitor, the prediction comprising the steps of,

step a) assessing a mutation of the TP53 gene in tumor tissue of the patient; and

step b) assessing KMT2C gene mutation in tumour tissue of the patient; and

step c) predicting the sensitivity of the cancer patient to immunotherapy with an immune checkpoint inhibitor based on the results of the evaluation of a), b).

7. A computer readable storage medium comprising a program executable by a processor to perform all the steps of predicting the sensitivity of a non-small cell lung cancer (NSCLC) patient to immunotherapy with an immune checkpoint inhibitor, said all the steps comprising,

step a) assessing a mutation of the TP53 gene in tumor tissue of the patient; and

step b) assessing KMT2C gene mutation in tumour tissue of the patient; and

step c) predicting the sensitivity of the cancer patient to immunotherapy with an immune checkpoint inhibitor based on the results of the evaluation of a), b).