KR20230080281A - Novel biomarker for predicting of prognosis of metastatic renal cell carcinoma and uses thereof - Google Patents

Abstract

The present invention relates to a novel biomarker for predicting the prognosis of metastatic renal cancer and its use, and provides a composition and kit for predicting the prognosis of metastatic renal cancer by presenting a novel biomarker combination capable of predicting the prognosis of metastatic renal cancer, thereby providing a metastatic renal cancer patient The survival prognosis of the patient can be quickly and accurately determined and appropriate treatment can be provided.

Description

Novel biomarker for predicting of prognosis of metastatic renal cell carcinoma and uses thereof}

The present invention relates to a novel biomarker for predicting the prognosis of metastatic kidney cancer and its use, and more specifically, to a method for providing information for predicting the prognosis of metastatic kidney cancer, and a composition and kit for predicting the prognosis of metastatic kidney cancer for the same will be.

Kidney cancer refers to renal cell carcinoma (RCC) that occurs in cells constituting the lining of tubules of the kidney, which mainly plays a role in producing urine, and corresponds to the most frequently occurring malignant tumor in the kidney. Renal cell carcinoma is largely divided into clear cell type and non-clear cell type, and clear cell renal cell carcinoma (ccRCC) is a representative type that accounts for more than 75% of all renal cell carcinomas.

Clear cell renal cell carcinoma is rich in blood vessels, metastasizes to other organs, and responds well to targeted therapy and immunotherapy. Chromosomal abnormalities or inactivation are found in more than 70 to 90% of cases. In addition, in recent studies, subsequent genomic alterations, including genes related to chromosome 3, such as PBRM1 , SETD2 , and BAP1 , or KDM5C located on the X chromosome, are associated with the progression and aggressiveness of clear cell renal cell carcinoma. It has been reported that in cellular renal cell carcinoma, T cell infiltration is increased and the expression of immune checkpoints such as PD-L1 and CTLA-4 is high (Braun DA, et al. Nature Medicine. 2020. 26(6): 909-18;Cancer Genome Atlas Research N. Nature.2013.499(7456):43-9;Miao D, et al.Science.2018.359(6377):801-6;Thompson RH, et al.Clinical Cancer Research. 2007. 13(6):1757-61). Considering these characteristics of high angiogenesis, immune cell infiltration, and PD-L1 expression, positive clinical effects can be seen when drugs targeting VEGF related to angiogenesis or inhibitors related to PD-L1 are used for the treatment of clear cell renal cell carcinoma. can

Loss-of-function mutations in PBRM1, a PBAF-complex gene commonly mutated in clear cell renal cell carcinoma, are related to the clinical utility of immune checkpoint inhibitors and predict response to anti-PD-1 therapy. (Sung WW, et al. Scientific Reports. 2021. 11(1):1479; Braun DA, et al. Nature Medicine. 2020. 26(6):909-18). However, since loss of PBRM1 function is observed only in some patients, it is difficult to predict the treatment response of immune checkpoint inhibitors in all patients with clear renal cell carcinoma through PBRM1 mutation. Therefore, in order to more accurately predict patient response and establish new treatment strategies, comprehensive understanding and research on the molecular level of renal cancer are urgently required.

Korean Patent Publication No. 10-2021-0113137 Korean Patent Publication No. 10-2021-0090147

An object of the present invention is to provide a method for providing information for predicting the prognosis of metastatic renal cancer.

In addition, an object of the present invention is to provide a composition and kit for predicting the prognosis of metastatic renal cancer.

The present inventors classified subtypes according to characteristics related to mutations and loss of function of the PBRM1 gene in patients with metastatic clear cell renal cell carcinoma who were administered immune checkpoint inhibitors, and selected subtypes with high PBRM1 mutation frequency, and differentially expressed genes related thereto. After selecting the up-regulated genes GATM and USH1C as candidate biomarkers, the association between each gene expression and patient survival was verified, and it was confirmed that progression-free survival and overall survival were high when GATM expression was high while having a PBRM1 mutation. Proposing GATM together with PBRM1 as a biomarker for predicting the prognosis of metastatic renal cancer, providing a method of providing information for predicting the prognosis of metastatic renal cancer using these biomarkers, and providing a composition and kit for predicting the prognosis of metastatic renal cancer completed the present invention.

One aspect of the present invention comprises the steps of a) taking a biological sample from a subject; b) measuring whether PBRM1 is mutated and the GATM expression level in the biological sample; and c) providing information for predicting the prognosis of metastatic renal cancer, comprising comparing the measured PBRM1 mutation and GATM expression level with a control group.

Steps a) to c) will be described in detail below.

Step a) is a process of collecting a biological sample from an individual in need of an examination for survival prognosis of metastatic renal cancer.

According to one embodiment of the present invention, the subject in step a) may be a metastatic renal cancer patient before or after treatment.

Here, treatment refers to a therapy commonly used to inhibit the growth, proliferation, or metastasis of renal cancer, and includes both targeted therapy and immunotherapeutic agents alone or in combination.

The renal cancer is divided into cancer arising from the parenchyma and the renal pelvis, but since more than 90% are tumors (renal cell cancer) originating from the parenchyma, renal cancer generally refers to renal cell cancer. Therefore, in the present invention, renal cancer and renal cell carcinoma may be used interchangeably. Renal cell carcinoma is divided into clear cell renal cell carcinoma, which accounts for more than 75% of the total, and non-clear cell renal cell carcinoma, such as papillary (type I/II), hemochromic, urinary collecting duct, and aqueous humor, and is not clearly distinguished. If not, it can be classified as unclassified renal cell carcinoma.

According to one embodiment of the present invention, the renal cancer may be clear cell renal cell carcinoma.

As used herein, “prognosis” refers to determining recurrence, metastasis, drug responsiveness, resistance, etc. of an individual before/after treatment for an individual who has not yet been diagnosed or has been diagnosed. In the present invention, metastatic renal cancer patients, more specifically, to predict whether a future survival prognosis will be good by checking the PBRM1 mutation and GATM expression level of patients who have been treated or scheduled to be treated with immunotherapeutic agents and/or targeted therapies. In the present invention, survival prognosis can be predicted using overall survival (OS) and progression free survival (PFS).

As used in the present invention, "biomarker" is a substance that is generally detectable in biological samples, and organic biomolecules such as polypeptides, proteins, nucleic acids, genes, lipids, glycolipids, glycoproteins, sugars, etc. All inclusive. In the present invention, the mutated PBRM1 gene and GATM gene or protein can be used as biomarkers.

According to one embodiment of the present invention, the biological sample in step a) may be at least one selected from the group consisting of blood, plasma, serum, lymph, saliva, urine, and tissue.

According to one embodiment of the present invention, cancer tissue is preferably used to accurately measure gene changes and molecular expression levels of cancer cells.

Step b) is a process of measuring the expression level of the GATM gene or protein together with the presence or absence of PBRM1 mutation as a biomarker in the biological sample.

The PBRM1 gene encodes a subunit constituting an ATP-dependent chromatin-remodeling complex. Mutations in the PBRM1 gene are only repeatedly detected in 30 to 40% of clear cell renal cell carcinomas, and there are also clear cell renal cell carcinomas without PBRM1 mutations, so there is a limitation in using only PBRM1 mutations as renal cancer biomarkers.

On the other hand, the GATM gene encodes a mitochondrial enzyme belonging to the amidinotransferase family, and is known to be involved in creatine biosynthesis, which plays an important role in cancer progression and immunotherapy, but is associated with PBRM1 mutation or renal cancer has not been studied on

In the present invention, the prognosis of metastatic renal cancer patients can be more accurately predicted by measuring the GATM expression level together with the presence or absence of PBRM1 mutation.

According to one embodiment of the present invention, the PBRM1 mutation in step b) is one or more selected from the group consisting of single nucleotide sequence mutation, substitution, deletion or insertion of a nucleotide sequence of 1 to 50 nucleotides, and copy number mutation. it could be

As used herein, “mutation” means an alteration of bases, nucleotides, polynucleotides or nucleic acids in a genome. The mutation may include substitution, insertion, or deletion of a base, nucleotide, polynucleotide, or nucleic acid. Here, substitution means a change in which a base, nucleotide, polynucleotide or nucleic acid is replaced with another base, nucleotide, polynucleotide or nucleic acid. An insertion is a change in which another base, nucleotide, polynucleotide or nucleic acid is added. Deletion refers to an alteration in which a base, nucleotide, polynucleotide or nucleic acid is removed.

The single nucleotide variant refers to a change or mutation of a sequence showing a difference of one base or nucleotide in the genome, and one specific base is changed to another base at the same location in the genome of several people, resulting in a different trait It can be used interchangeably with single nucleotide polymorphism, which means expressed as

The 1 to 50 nucleotide sequence deletion or insertion refers to a sequence change or mutation showing a difference of 1 to 50 or more consecutive or discontinuous bases, nucleotides, polynucleotides or nucleic acids on the genome.

Such nucleotide sequence variations can affect even one amino acid composed of three nucleotide sequences, and base differences can contribute to individual differences in susceptibility to specific diseases, disease expression patterns, and therapeutic response.

The copy number mutation refers to a mutation section of 1 kb or more in length showing a difference in the number of repeated sequences when compared to a reference sequence, and susceptibility to a specific disease and expression of a disease due to the difference in copy number It can contribute to the expression of individual differences in aspects, treatment responsiveness, etc.

In order to determine whether these genes are mutated, sequencing or amplification reactions can be used.

According to one embodiment of the present invention, the PBRM1 mutation in step b) may be measured using next-generation sequencing (NGS).

The NGS has a multiplexing ability to simultaneously perform hundreds of thousands of reactions, and sequencing is possible even with a small amount of sample. NGS has slightly different specific application techniques depending on the commercialized technology, but in general, clonal amplification, massively parallel sequencing, and a new sequencing method with a different mechanism of action from the Sanger method are used. Commercialized technologies include the 454 GS improved FLX model sequencer released by Roche in 2007, Genome Analyzer HiSeq released by Illumina in 2006, and SOLiD released by Applied Biosystems in 2007. In common, these three platforms abandoned complex library construction and cloning processes and adopted clonal amplification technology, chose massively parallel sequencing technology that can process a large amount at once, and adopted cyclic sequencing. The nucleotide sequence was determined by sequencing by synthesis, thereby eliminating the cumbersome electrophoresis process. It also uses an algorithm that arranges the short reads read using the shotgun method into a computer to find overlapping parts and complete the whole.

More specifically, the next-generation sequencing may include, but is not limited to, whole genome sequencing, whole exome sequencing, targeted sequencing, and the like, depending on the subject to be analyzed.

In addition, according to one embodiment of the present invention, the PBRM1 mutation in step b) is polymerase chain reaction (PCR), reverse transcription polymerase chain reaction (RT-PCR), competitive RT-PCR, real-time RT-PCR, nucleic acid degradation It may be measured by one or more methods selected from the group consisting of nuclease protection assay, in situ hybridization, DNA microarray, and Northern blot.

In one embodiment of the present invention, target sequencing was performed using a panel of 380 cancer-related genes to measure or confirm PBRM1 mutations in tumor tissues of 60 patients with metastatic clear cell renal cell carcinoma, and as a result, the human reference genome (human reference genome) genome) (UCSC Genome Browser on Human Feb. 2009 (GRCh37/hg19) Assembly), it was confirmed that 1 to 4 bases were substituted, deleted, or inserted in 18 tissues, resulting in PBRM1 mutations (see Table 2).

GATM expression can also be measured at the level of the GATM gene or the GATM protein encoded therefrom.

According to one embodiment of the present invention, the GATM expression level in step b) is polymerase chain reaction (PCR), reverse transcription polymerase chain reaction (RT-PCR), competitive RT-PCR, real-time RT-PCR, nuclease It may be measured at the nucleic acid level by one or more methods selected from the group consisting of nuclease protection assay, in situ hybridization, DNA microarray, and Northern blot.

According to one embodiment of the present invention, the GATM expression level in step b) is one selected from the group consisting of ELISA, Western blot, radioimmunoassay, immunodiffusion, immunoprecipitation, immunohistochemistry, immunofluorescence, and protein microarray. Anything above can be measured at the protein level.

Step c) is a process of determining the survival prognosis of metastatic renal cancer patients by comparing the subject's PBRM1 mutation and GATM expression levels with those of the control group.

According to one embodiment of the present invention, the control group in step c) may be a tumor tissue without PBRM1 mutation derived from a patient with primary renal cancer or metastatic renal cancer.

According to one embodiment of the present invention, in step c), when the subject has a PBRM1 mutation and the GATM expression level is higher than that of the control group, the metastatic renal cancer prognosis may be determined as good.

More specifically, if the expression level of GATM in the subject is greater than the average value of GATM expressed in tumor tissue without PBRM1 mutation in primary renal cancer or metastatic renal cancer patients, the expression level can be said to be high. Predictable.

According to one embodiment of the present invention, data related to patients with metastatic clear cell renal cell carcinoma were classified into four groups according to PBRM1 mutation and GATM expression, and survival rates were compared. Both the progression-free survival rate and overall survival rate of the patient group were the highest, and it was confirmed that there was statistical significance with other patient groups.

Another aspect of the invention is a PBRM1 mutation; And it provides a composition for predicting the prognosis of metastatic renal cancer comprising an agent for measuring the level of GATM expression.

Here, the description of the contents common to the foregoing is omitted in order to avoid excessive complexity.

According to one embodiment of the present invention, the PBRM1 mutation may be one or more selected from the group consisting of single nucleotide sequence mutation, substitution, deletion or insertion of a nucleotide sequence of 1 to 50 nucleotides, and copy number mutation.

The agent may be one capable of specifically binding to each gene or protein to measure the mutation and expression level of the gene or protein.

According to one embodiment of the present invention, the agent comprises at least one selected from the group consisting of primers, probes, antibodies, aptamers, oligopeptides, and PNAs that specifically bind to genes or proteins of PBRM1 and GATM, respectively. it could be

More specifically, the agent is preferably a primer or probe specifically binding to the PBRM1 and GATM genes.

As used herein, “specifically binding” means that the binding ability to a target substance is superior to other substances to the extent that the presence or absence of the target substance can be detected by binding.

As used herein, "primer" refers to a primer capable of acting as a starting point for template-directed DNA synthesis under suitable conditions (i.e., four different nucleoside triphosphates and polymerases) at a suitable temperature and buffer. It refers to a single stranded oligonucleotide. The suitable length of a primer varies depending on various factors, such as temperature and use of the primer, but typically consists of 15 to 30 nucleotides.

As used herein, “probe” refers to a linear oligomer of natural or modified monomers or linkages, comprising deoxyribonucleotides and ribonucleotides, and specifically hybridizing to a target nucleotide sequence It can exist naturally, or it can be artificially synthesized.

In addition, one aspect of the present invention provides a kit for predicting the prognosis of metastatic renal cancer comprising the composition.

As used in the present invention, the "kit for predicting metastatic renal cancer prognosis" refers to a substance that can predict whether or not a future survival prognosis will be good through a biological sample collected from a test subject or a metastatic renal cancer patient, through which the test subject's survival Prognosis can be diagnosed quickly, accurately and simply. In the present invention, an agent for measuring the GATM expression level together with the mutation of the PBRM1 gene is included.

The kit may include, without limitation, a diagnostic kit based on conventional genetic quantitative analysis.

According to one embodiment of the present invention, the kit may be at least one selected from the group consisting of a PCR kit, an RT-PCR kit, and a DNA chip kit.

For example, when the kit is applied to a PCR amplification process, the kit of the present invention may optionally include reagents necessary for PCR amplification, such as a buffer, DNA polymerase, DNA polymerase cofactor, and dNTPs, When the kit is applied to an immunoassay, the kit of the present invention may optionally include a substrate of a secondary antibody and a label. In addition, the kit according to the present invention may be manufactured in a plurality of separate packaging or compartments including the reagent components described above, and the kit of the present invention may be a diagnostic kit including essential elements necessary for performing a DNA chip. there is. A DNA chip kit may include a substrate to which a cDNA corresponding to a gene or a fragment thereof is attached as a probe, and reagents, reagents, enzymes, and the like for producing a fluorescently labeled probe. In addition, the substrate may include a cDNA corresponding to a quantitative control gene or a fragment thereof.

The present invention provides a composition and kit for predicting the prognosis of metastatic kidney cancer by presenting a novel biomarker combination capable of predicting the prognosis of metastatic kidney cancer, thereby quickly and accurately determining the survival prognosis of metastatic kidney cancer patients and providing appropriate treatment. .

1 is a heatmap showing the mutation frequencies for 17 genes in 60 patients with metastatic ccRCC who received immune checkpoint inhibitors.
Figure 2 shows the p values and odds ratios for 17 genes by performing Fisher's exact test between (A) clinical usefulness (CB) and nonclinical usefulness (NCB) groups of 60 patients with metastatic ccRCC who received immune checkpoint inhibitors ( It is a plot showing odds ratio (OR), and (B) is a Kaplan-Meier plot showing overall survival (OS) and progression free survival (PFS) according to the presence or absence of PBRM1 mutation.
Figure 3 shows the characteristics of molecular subtypes of metastatic ccRCC patients who received immune checkpoint inhibitors using merged data combining the internal data set and the checkmate 025 data set. Enrichment scores of ssGSEA for the four gene sets associated with loss of function are shown (top), and mutation profiling shows 6 commonly mutated genes in both the internal and checkmate 025 data sets. Dogs are shown (middle), and radial plots show the gene ontology for upregulated genes in each subtype (bottom).
Figure 4 (A) is the result of Kaplan-Meier plot analysis of overall survival (OS) and progression-free survival (PFS) for each subtype, and (B) is the average Z score of ssGSEA of the Hallmark gene for each subtype. It is a heat map.
5 (A) shows the expression level of TDO2 in each subtype, (B) shows the ratio of immune cell types by CIBERSORTx, and (C) shows the active immune type of each subtype and the PBRM1 mutant/wild type group. (D) shows the ratio of immune types including (active-immune type, AIT), exhausted-immune type (EIT) and non-immune type (NIT), and (D) shows the Results of Kaplan-Meier plot analysis of progression-free survival (PFS) and overall survival (OS) for
Figure 6 (A) is a heat map of ssGSEA enrichment scores using up- and down-regulated differentially expressed genes (DEGs) obtained from 786-O and A-704 cell line data, and (B) is each subtype and PBRM1 mutation / subgeneration A plot comparing the ssGSEA enrichment scores of the older siblings.
7 (A) is a Venn diagram overlapping five DEGs that are up- and down-regulated and a list of common genes, and (B) is the expression level of AMACR , SLC6A13 , GATM , and USH1 C depending on whether or not PBRM1 is mutated in the merged data. is a plot showing
8(A) shows the expression levels of GATM and USH1 C in non-tumor (NT) (n = 77) and primary tumor (PT) (n = 530) in the TCGA-KIRC data set. (B) is a plot showing the expression levels of GATM and USH1 C in non-tumor (NT) (n = 8) and primary tumor (PT) (n = 9) in the GSE105288 data set , (C) is the result of Kaplan-Meier plot analysis of overall survival and disease free survival (DFS) based on average GATM and USH1 C expression levels in the TCGA-KIRC data set (n = 530), (D) are the results of Kaplan-Meier plot analysis of overall survival and progression-free survival based on mean GATM and USH1 C expression levels in the merged data (n=177).
9 (A) is a graph showing the expression levels of GATM and USH1C in PBRM1-defective 786-O cells or Caki-1 cells, and (B) is a graph showing actinomycin D in 786-O cells transfected with siScr or siPBRM1. It is a graph measuring the GATM expression level after treatment for 0, 4, 8 or 16 hours.
10 shows the results of (A) cell migration assay and (B) colony formation assay in 786-O cells treated with siRNA (siPBRM1 and/or siGATM).
Figure 11 is an immunohistochemical analysis of GATM expression using TMA (tissue microarray) of 51 metastatic ccRCC patients administered with immune checkpoint inhibitors. (A) shows GATM high expression (A; x4, B; x20) and GATM Cell photographs of the GATM positive group including low expression (C; x4, D; x20) and the GATM negative group including no GATM expression (E; x4, F; x20), (B) is Kaplan-Meier plot analysis results of progression-free survival (PFS) and overall survival (OS) based on GATM protein expression status. (C) is a graph showing the ratio of GATM-positive group (+, red) and GATM-negative group (-, gray) according to PBRM1 mutation (top) and subtype (bottom).
12 shows (A) combined data (n = 177) and (B) TCGA-KIRC data set (n = 430) using PBRM1 mutations and GATM expression levels for progression-free survival (PFS) and disease-free survival ( DFS) and Kaplan-Meier plot analysis of overall survival (OS).

Hereinafter, the present invention will be described in more detail. However, these descriptions are merely presented as examples to aid understanding of the present invention, and the scope of the present invention is not limited by these exemplary descriptions.

1. Materials and Methods

1-1. internal data set

1-1-1. patient

Ipilimumab (anti-CTLA-4) along with Nivolumab (anti-Programmed cell death protein 1, anti-PD-1) as the first-line therapy from 2017 to 2020 at Samsung Seoul Hospital We recruited 60 patients with metastatic clear cell renal cell carcinoma (ccRCC) who received either combination therapy (n=8) or nivolumab as second-line or higher therapy (n=52). Abdominal-pelvic and chest CT scans were taken every 3 to 4 months after treatment to evaluate the treatment response of immune checkpoint blockade (ICB). response, PR), stable disease (SD) or progressive disease (PD).

1-1-2. Target sequencing pre-processing

Targeted sequencing was performed on 380 cancer-related genes (CancerSCAN v3.1) by collecting tumor tissues from 60 patients with metastatic ccRCC. The tumor tissue was used after being fixed in formalin and embedded in paraffin. The average coverage of most of the samples was up to 900 times, with hotspot coverage much higher than average. Paired-end reads were aligned to the human reference genome (hg19) using BWA (v.0.7.5), file processing, local alignment and removal of duplicate reads. For this, SAMTOOLS (v0.1.18), GATK (v3.1-1) and Picard (v1.93) were used, respectively. Base quality scores were recalibrated using GATK BaseRecalibrator based on known single-nucleotide polymorphisms (SNPs) and insertions/deletions (Indels) of dbSNP138.

1-1-3. RNA sequencing pretreatment

For RNA-sequencing, total RNA was extracted with an RNA extraction kit (RNeasy Mini Kit, QIAGEN) from 61 tissues of metastatic ccRCC patients and 5 adjacent non-tumor tissues, and 2100 Bioanalyzer (Agilent) was used. Through this, RNA integrity was verified. Libraries for sequencing were generated using the QuantSeq 3' Library Prep Kit (Lexogen Inc.) according to the manufacturer's instructions, and sequencing was performed using the HiSeq 2000 system (Illumina). Reads were mapped to the hg19 human reference genome using STAR set to default parameters. The number of reads mapped to each gene was calculated using RSEM, and data processing and analysis were performed using the R/Bioconductor library. For preprocessing of the transcriptome data, genes with a value of 0 were excluded from all samples, and the average expression values of adjacent non-tumor tissues per gene were subtracted to normalize the expression values of each sample and gene. In addition, 4 metastatic ccRCC samples with outliers for the overall mean and standard deviation and 2 metastatic ccRCC samples with no information on PBRM1 mutations were excluded (internal data set, n=55).

1-2. external data set

To merge transcriptome data for subtype classification, the Checkmate 025 data set (n = 122) published by DA Braun et al. was used (Nature Medicine, 2020. 26: 909-918).

Data sets from TCGA-KIRC and GEO websites (accession numbers: GSE102806 and GSE105288) were used to verify the study results.

1-3. GSEA and ssGSEA

For gene set enrichment analysis (GSEA), Hallmark gene sets from the Molecular Signatures Database (MSIGDB 7.0) were used (Liberzon A, et al. Cell systems. 2015. 1(6):417-25). Single sample gene set enrichment analysis (Single sample GSEA, ssGSEA) was calculated using the "GSVA" package (Hanzelmann S, et al. BMC Bioinformatics. 2013. 14:7).

1-4. Immune cell types and immune types

The proportion of immune cell types was calculated by CIBERSORTx (Newman AM, et al. Nature biotechnology. 2019. 37(7):773-82). The ratio of 10 immune cell types was calculated by aggregation. For example, the ratio of macrophages was calculated by counting macrophages M0, macrophages M1, and macrophages M2.

Immune types, including active-immune and exhausted-immune, were performed by the Nearest Template Prediction (NTP) algorithm based on the expression of active stroma and normal stroma characteristics (Sia D, et al Gastroenterology 2017. 153(3):812-26).

1-5. Characteristics of differentially expressed genes

A set of differentially expressed genes (DEGs) was calculated in the three conditions to find up- or down-regulated genes using GSE102806. DEG1 was calculated by comparing shPBRM1 and shControl in one replicate using 786-O cells, DEG2 was calculated by comparing shPBRM1 and shControl in two replicates using 786-O cells, and DEG3 was calculated by comparing shPBRM1 and shControl in two replicates using 786-O cells, and DEG3 was calculated using A-704 cells. was calculated by comparing PBRM1 mutant and PBRM1 wild type in .

DEG4 and DEG5 were calculated from the merged data (n=177) combining the internal data set and the checkmate 025 data set, DEG4 is a differentially expressed gene for subtype 2, and DEG5 was calculated by comparing PBRM1 mutant and PBRM1 wild type .

DEG1-5 performed permuted t test, cut off was p < 0.05 and log2 fold differences > 0.5.

1-6. gene knockdown

786-O cells or Caki-1 cells (confluency = 60 to 80%) were treated with siScramble (siScr), siPBRM1 (Thermo Fisher, AM16708) or siGATM (ThermoFisher, AM16708) for gene deletion or loss of function. For transcriptional inhibition, 0.5 μM actinomycin D (11805017, Thermo Fisher) was treated at the indicated times.

1-7. Real-time qPCR

786-O cells or Caki cells were transfected with siScr or siPBRM1 and used for qRT-PCR analysis after 48 hours according to the manufacturer's instructions. 0.5 μg of total RNA from each cell was used as a template to perform reverse transcription with ReverTra Ace ^TM qPCR RT Master Mix (TOYOBO). Real-time PCR analysis was performed with the QuantStudio system using SYBR Premix Ex Taq (TaKaRa). The primers used here are shown in Table 1 below.

Primer name Primer sequence (5'-3') sequence number GATM F: CAC TAC ATC GGA TCT CGG CTT One R: CTA AGG GGT CCC ATT CGT TGT 2 USH1C F: TTC CGG CAT AAG GTG GAT TTT C 3 R: GTA CAT TCG CAG CAC ATC ATA GA 4

1-8. cell migration assay

786-O cells were transfected with siRNA (control, PBRM1 or GATM) and used. The transfected 786-O cells were cultured for 24 hours on a glass-bottomed dish pre-coated with fibronectin (100 g/ml), and when the cells reached 90% growth, they were scraped into monolayers using a pipette tip. After that, they were cultured in a medium containing 300 μM of hydrogen peroxide for 12 hours in a humidified CO ₂ incubator at 37°C.

1-9. Colony formation assay

The cells transfected with siRNA were seeded in a 6-well plate the next day at 1,000 to 2,000 cells/well. After 24 hours, the culture medium was replaced with 300 μM hydrogen peroxide or low glucose medium (11966025, Thermo Fisher). After 7 days, cells were fixed and stained with crystal violet. Each experiment was repeated in 3 wells.

1-10. Immunohistochemical analysis

A tissue microarray (TMA) method was applied to perform immunohistochemical analysis using 51 available tissues among 60 patients with metastatic ccRCC. Briefly, representative tumor tissues (2 mm in diameter) were individually extracted from paraffin-embedded tumors (donor blocks) and placed in new recipient paraffin blocks (TMA blocks) using a trephine apparatus. One core tissue was extracted from each tissue. 5 mm thick sections were cut from each TMA block, deparaffinized, and dehydrated for immunohistochemical staining. GATM (1:100 dilution) (ab32936, Abcam PLC) was stained using Ventana XT Benchmark (Ventana Medical Systems). The membranous and cytoplasmic staining of GATM was evaluated, and the immunoreactivity to GATM was diffuse (immunoreactivity was shown in more than 50% of tumor cells) and cases with strong expression of GATM in the middle. High expression (2+), diffuse or focal (less than 50% of tumor cells show immunoreactivity), weak GATM expression low expression (1+), and no GATM expression ( 0) was scored. Specimens with weak or high GATM expression were classified as GATM-positive, and specimens without GATM expression were classified as GATM-negative. Immunohistochemistry data were reviewed by an experienced pathologist blinded to other clinical data.

1-11. statistical analysis

The Kaplan-Meier test was used to estimate disease free survival (DFS), progression free survival (PFS) and overall survival (OS). Categorical variables between the two groups were compared using Fisher's exact test. A one way-Anova test was performed for three groups and a Student's t test was performed for two groups. Univariate and multivariate analyzes of overall survival were performed with Cox regression analysis. All statistical analyzes were performed using R software.

2. Results

2-1. Characteristics of genomic mutations in metastatic ccRCC patients treated with immune checkpoint inhibitors

As a result of analyzing recurrently altered genes in metastatic ccRCC patients (n=60) who received immune checkpoint inhibitors, 17 recurrently altered genes were found, and VHL (n=34, 56.7%) , PBRM1 (n=18, 30.0%), SETD2 (n=16, 26.7%) and BAP1 (n=12, 20.0%) were identified as common modified genes (Fig. 1). Gene mutation types are shown in Table 2 below.

mutation type explanation Frame_shift_Del Mutations caused by deletion of bases in the coding region (ORF modification) Frame_shift_Ins Mutations caused by the addition of bases to the coding region (ORF variants) In_Frame_Del Mutations caused by deletion of bases in the coding region (ORF maintenance) In_Frame_Ins Mutations caused by the addition of bases to the coding region (ORF maintenance) Nonsynonymous_SNVs Mutations that encode the same amino acid even if the base is changed Synonymous_SNVs Mutations in which amino acids also change due to base changes Splice_site Mutations appearing at splice sites Stopgain_SNV A mutation in which a stop codon is created at a point ahead of the existing stop codon due to mutation * ORF (open reading frame): DNA sequence translated into amino acid sequence

And in the gene-specific alteration comparing the clinical benefit (CB) group and the non-clinical benefit (NCB) group, only the PBRM1 mutation among 17 repeat variant genes was significant in the CB group. (Fisher's exact test, p=0.03, CB odds ratio (OR)=3.67, 95% confidence interval (CI) 0.98 to 14.69) (Fig. 2A), PBRM1 mutation patients PBRM1 mutation information for 18 people is shown in Table 3 below.

chromosome start end existing change One chr3 52584502 52584502 A G 2 chr3 52702608 52702608 AT - 3 chr3 52661315 52661315 A - 4 chr3 52668738 52668738 CTAA - 5 chr3 52610608 52610608 - T 6 chr3 52643700 52643700 A - 7 chr3 52621464 52621464 G A 8 chr3 52651513 52651513 T A 9 chr3 52643627 52643627 G - 10 chr3 52651315 52651315 A T 11 chr3 52595859 52595859 G C 12 chr3 52597502 52597502 T C 13 chr3 52696229 52696229 C A 14 chr3 52637589 52637589 T - 15 chr3 52651282 52651282 G T 16 chr3 52668632 52668632 T - 17 chr3 52713610 52713610 TGG TT 18 chr3 52595815 52595815 T -

In addition, PBRM1 mutant patients had significantly higher overall survival (OS) and progression-free survival (PFS) compared to PBRM1 wild-type patients (log-rank test, p=0.018 and p=0.142, respectively) (FIG. 2B). These results suggest that the PBRM1 mutation is advantageous for survival.

2-2. Molecular subtype characteristics of patients with metastatic ccRCC treated with immune checkpoint inhibitors.

To broaden our understanding of the molecular phenotype of patients with metastatic ccRCC receiving immune checkpoint inhibitors, we merged (n=177) the internal data set (n=55) with the CheckMate 025 data set (n=122) and identified PBRM1 -mutations and PBRM1 - Subtypes were classified using four features (up- or down-regulated genes, angiogenesis-related genes, and immunoregulation-related genes) related to loss of function (LOF). First, patients were classified into three subtypes through unsupervised clustering analysis. Subtype 1 (n=64, 36%) displays a mixed pattern of immunomodulatory with relatively low angiogenesis expression and is characterized by moderate expression of up- and down-regulated genes associated with PBRM1 -mutation; There were 12 patients with PBRM1 mutations. Subtype 2 (n=75, 42%) exhibited relatively high angiogenic expression and low immunomodulation and was characterized by a consistent expression pattern of up- and down-regulated genes associated with PBRM1 -mutation and showed a high mutation rate for PBRM1 . Subtype 3 (n=38, 22%) was characterized by inverse expression of up- and down-regulated genes associated with PBRM1 -mutation, moderate expression of angiogenesis and high immunoregulatory enhancement, in which there were 5 patients with PBRM1 mutations. (Fig. 3, top).

In addition, as a result of analyzing the incidence of 6 commonly mutated genes by overlapping repetitively mutated genes in both the internal data and the checkmate 025 data, subtype 2 showed a higher incidence of VHL , PBRM1 and SETD2 compared to subtypes 1 and 3. A higher mutation rate was observed (Fig. 3, center).

Pairwise comparisons were performed for each subtype to evaluate the key biological features driving these subtypes. In subtype 1, 397 upregulated genes were identified, especially cell-cell signaling (ES=2.46, p=4.39X10 ^-4 ) and cell development (ES=2.34, p=4.39X10 -4 ). 2.15X10 ^-5 ) related genes were enriched. In subtype 2 with a high PBRM1 mutation rate, 1,569 upregulated genes were identified, metabolic process (ES=25.93, p=5.80X10 ^-42 ) and xenobiotic metabolism (ES=12.56, p=12.56). 7.04X10 ^-14 ) had many related genes. In subtype 3, 459 upregulated genes were identified, and cell cycle (ES=12.55, p=3.62X10 ^-23 ) and immune response (ES=6.97, p=1.19X10 ^-10 ) and The activity of related genes was shown (Fig. 3, bottom).

2-3. Subtype 2 associated with high metabolic processes and low immune type

As a result of comparing subtype 2 with subtypes 1 and 3 to evaluate the prognostic relevance of each subtype, the OS of subtype 2 (p=0.042) showed good prognostic results, whereas the PFS (p=0.381) showed good prognostic results. There was no difference with other subtypes (Fig. 4A). In addition, as a result of evaluating the transcriptomic pathway program in ssGSEA using the Hallmark gene set (n=50), 18 gene sets were activated in each subtype (one-way ANOVA, p<0.0005, Fig. 4B). In both subtypes 1 and 3, immune-related pathways (INFLAMMATORY_RESPONSE, COMPLEMENT and IL6_JAK_STAT3) were activated. Subtype 3 differentiated from subtypes 1 and 2 due to high activation of cell cycle progression pathways (G2M_CHECKPOINT, E2F_TARGETS and MITOTIC_SPINDLE). In subtype 2, metabolism-related pathways (OXIDATIVE_PHOSPHORYLATION, FATTY_ACID_METABOLISM and ADIPOGENESIS) were activated, and were also enriched in HYPOXIA and REACTIVE OXYGEN SPEICIES PATHWAY. These results suggest that metabolic pathways can provide a favorable microenvironment for the survival of metastatic ccRCC patients treated with immune checkpoint inhibitors.

Meanwhile, previous studies have reported that kynurenine, a tryptophan metabolite, is associated with immunosuppressive roles and responses to nivolumab treatment, and the kynurenine pathway enzymes IDO and TDO, which metabolize tryptophan to kynurenine, have been reported to be effective in ccRCC patients. It has been confirmed to be activated in the kynurenine pathway (Li H, et al. Nature Communications. 2019. 10(1):4346; Pilotte L, et al. PNAS. 2012. 109(7):2497-502; Uyttenhove C, et al.Nature Medicine.2003.9(10):1269-74). Accordingly, as a result of evaluating kinurenin pathway enzymes including IDO1, IDO2, and TDO2 in each subtype, there was no difference in the expression of IDO1 and IDO2 according to the subtype, but the expression of TDO2 was significantly reduced in subtype 2 compared to subtypes 1 and 3. (p=0.0006) (Fig. 5A). These results suggest that tryptophan metabolic programming in metastatic ccRCC patients may serve as a potential immunoregulatory mechanism associated with non-resistance to immune checkpoint inhibitors.

In addition, to further evaluate the immune-related properties of each subtype, the proportions of various immune cell types were examined using the CIBERSORTx deconvolution algorithm, and subtype 2 did not show significant differences in cell types compared to subtypes 1 and 3. (Fig. 5B). Expanding the analysis of immune cell types, we analyzed using the NTP algorithm for immune types, including active immunity and exhaustion immunity, which have been reported to play distinct roles in cancer progression and to be associated with patients' clinical outcomes. As a result, it was found that subtypes 1 and 2 had the same ratio of active immune type, and the exhaustion type was higher as the clinical prognosis was worse. Subtype 3 had a predominance of the exhaustion immune type, whereas subtype 2 had the lowest proportion of the exhaustion immune type (Fig. 5C, left). In addition, as a result of evaluating the immune type according to the presence or absence of PBRM1 mutation, patients with PBRM1 mutations showed a highly active immune type, whereas patients with wild-type PBRM1 showed a high exhaustion immune type (Fig. 5C, right). And as a result of evaluating OS and PFS according to active immunity, exhaustion immunity and non-immunity type, the active immunity type showed better PFS (p=0.052) and OS (p=0.469) (Fig. 5D). These results suggest that the immune type of each subtype may affect the patient's microenvironmental immunotherapy, and in particular, exhaustion immunity may lead to an aggressive phenotype with a poor prognosis.

2-4. Proposal of candidate biomarkers for metastatic ccRCC patients treated with immune checkpoint inhibitors

To find key regulatory genes associated with subtype 2 with high PBRM1 mutation rates, GSE102806 was used to analyze 786-O and A-704 cell line data associated with PBRM1 -loss of function. First, six gene sets for DEG were generated using the cell line data comparing the treatment group sample and the control sample, and ssGSEA was performed to confirm the expression pattern for each subtype of the merged data (n = 177) (Fig. 6A) . In particular, subtype 2 showed higher expression of up-regulated DEGs and lower expression of down-regulated DEGs compared to subtypes 1 and 3. In addition, the expression level of up-regulated DEGs was higher in PBRM1 mutants than in PBRM1 wild-type, but there was no difference in down-regulated DEGs (Fig. 6B).

In addition, by overlapping the 5 up- and down-regulated DEGs, 4 up-regulated genes and 0 down-regulated genes were identified (FIG. 7A). And four up-regulated genes AMACR , SLC6A3 , GATM and USH1C were found to be increased in the PBRM1 mutant (Fig. 7B). Among them, GATM (p=1.01X10 ^-5 ) and USH1C (p=1.94X10 ^-4 ) with significant differences in PBRM1 mutation were further analyzed. In particular, the GATM gene showed non-tumor specific expression compared to tumor tissue (PT) in TCGA-KIRC (n = 607, p = 3.07X10 ^-7 ) and GSE105288 (n = 43, p = 0.045), whereas the USH1C gene TCGA-KIRC (p=6.0X10 ⁻⁹ ) and GSE105288 (p=0.503) showed tumor-specific expression compared to non-tumor tissue (NT) ( FIGS. 8A and B).

We also evaluated the clinical relevance of the genes GATM and USH1C (Figures 8C and D). GATM expression was consistently found to be associated with clinical outcome of OS in TCGA-KIRC (p=3.64X10 ^-7 ) and merged data (p=0.030). USH1C expression was found to be associated with the clinical outcome of OS (p=0.00015) in TCGA-KIRC, whereas it was not associated with the clinical outcome of OS (p=0.203) in the merged data. Therefore, GATM was considered as a candidate biomarker predicting the prognosis of metastatic ccRCC patients treated with immune checkpoint inhibitors, and then the association with GATM was analyzed.

2-5. under stress conditions PBRM1 - Fruiting and GATM Decreased cell proliferation by expression

In order to induce GATM expression changes by PBRM1 loss in ccRCC cell lines, PBRM1 was knocked down in Caki-1 and 786-O cells, and GATM or USH1C expression was increased (FIG. 9A). And as a result of testing whether GATM expression is regulated by PBRM1 at the transcriptional level using actinomycin D (transcription inhibitor), the PBRM1 knockdown cells (siPBRM1) showed an increased GATM expression level compared to control cells (siScr). persisted (FIG. 9B). These results suggest that GATM expression was increased at the transcriptional level by PBRM1 knockdown.

In previous studies, PBRM1 plays a role in protecting cell proliferation in an environment where cell proliferation can be reduced due to stress, so it can protect cancer cells in an environment such as exhaustion of active oxygen and energy caused by excessive proliferation. PD-1 inhibitor responses have been reported in ccRCC patients with PBRM1 mutations (Miao D, et al. Science. 2018. 359(6377):801-6; Piva F, et al. Expert Review of Molecular Diagnostics. 2015 15(9):1201-10). Accordingly, to confirm the protective role of PBRM1 in the stress response, we investigated whether GATM induced by PBRM1 deletion is involved in the anticancer state under stress conditions. 786-O cells transfected with siScr, siPBRM1, or siGATM were scratched and incubated in high-concentration hydrogen peroxide for 12 hours. When PBRM1 was knocked down in 786-O cells exposed to hydrogen peroxide (siPBRM1), there was little migration. However, when GATM was knocked down together with PBRM1 (siPBRM1 + siGATM), cell migration and motility increased to a level similar to that of the control group (siScr) (FIG. 10A). This indicates that GATM induction by PBRM1 loss results in loss of cell migration ability.

Next, the effect of GATM induced by PBRM1 knockdown on the proliferation of 786-O cells under stress conditions such as hydrogen peroxide or low-glucose medium was investigated. PBRM1- depleted cells cultured in a medium containing high-concentration hydrogen peroxide or low glucose lost colony-forming ability, but colony-forming ability was restored by GATM silencing (FIG. 10B). These results suggest that the ability to inhibit cell proliferation by PBRM1 mutation was accompanied by an increase in GATM expression under stress conditions.

2-6. Increased survival of metastatic ccRCC patients by GATM expression

To verify the prognostic role of GATM in metastatic renal cancer, tissues from 51 metastatic ccRCC patients were evaluated for GATM protein expression using immunohistochemical staining, and stratified into GATM-positive and GATM-negative groups according to GATM expression status (Fig. 11A). We then investigated the association between GATM protein levels and survival outcomes. In particular, the GATM-positive group showed significantly better PFS (P=0.0156) (Fig. 11B, top) and OS result (P=0.0013) (Fig. 11B, bottom) compared to the GATM-negative group after immunotherapy. In addition, as a result of analyzing the GATM protein level based on the PBRM1 mutation status and molecular subtype, the proportion of GATM-positive samples was higher in patients with PBRM1 mutation than in patients without PBRM1 mutation (Fig. 11C, top). In addition, subtype 2 had the highest proportion of GATM-positive patients, whereas subtype 3, which had an aggressive phenotype and low survival rate, had the lowest proportion of GATM-positive specimens compared to subtypes 1 and 2 (Fig. 11C, bottom). These results suggest that GATM protein levels are associated with PBRM1 mutation status and low aggressive phenotype, and have potential clinical utility as a predictive marker for metastatic ccRCC in combination with immunotherapy.

2-7. PBRM1 mutation and GATM Increased overall survival by expression

To investigate the association of GATM expression and PBRM1 mutations with clinical prognosis, merged data (n=177) were analyzed according to PBRM1 mutation and GATM expression: PBRM1_MUT+HIGH_GATM (n=42), PBRM1_MUT+LOW_GATM (n=19), It was divided into PBRM1_WT+HIGH_GATM (n=55) and PBRM1_WT+LOW_GATM (n=61). As a result, as expected, patients with PBRM1 mutation and high expression of GATM showed the best PFS (p=0.069) and OS (p=0.0012) (FIG. 12A).

In addition, the predictive significance of GATM expression and PBRM1 mutation was revealed through univariate and multivariate analysis of the overall survival rate of the merged data, and the results are shown in Table 4 below (hazard ratio (HR) = 2.067, 95 % CI, 1.147-3.726; P=0.016).

variable Univariate analysis multivariate analysis HR (95% CI) P value HR (95% CI) P value age <median reference - reference - ≥median 0.855 (0.58-1.26) 0.428 0.895 (0.599-1.339) 0.589 gender female reference - reference - male 2.047 (1.235-3.394) 0.005 ^** 1.576 (0.939-2.645) 0.085 IMDC Good reference - reference - Medium & Poor 2.134 (1.336-3.409) 0.0015 ^** 2.012 (1.252-3.233) 0.004 ^** PBRM1&GATM MUT&HIGH reference - reference - MUT&LOW, WT&HIGH and WT&LOW 2.532 (1.414-4.532) 0.0017 ^** 2.067 (1.147-3.726) 0.016 ^** IMDC; International Metastatic RCC Database Consortium

Further analysis of the TCGA-KIRC data set revealed that PBRM1 mutation and GATM expression were associated with significant OS (P=9.40X10 ^-7 ) (Fig. 12B, right) but not DFS (P=0.332) (Fig. 12B, left). appeared to be Although the TCGA-KIRC data set consisted primarily of patients not receiving immunotherapy, survival outcomes showed that patients with PBRM1 mutations and high GATM expression were consistently associated with superior OS.

Taken together, the PBRM1 mutations and GATM expression status identified through these retrospective analyzes can be suggested as key factors predicting the prognosis of patients with metastatic renal cancer.

So far, the present invention has been looked at with respect to its preferred embodiments. Those of ordinary skill in the art to which the present invention pertains will understand that the present invention can be implemented in a modified form without departing from the essential characteristics of the present invention. Therefore, the disclosed embodiments should be considered from an illustrative rather than a limiting point of view. The scope of the present invention is shown in the claims rather than the foregoing description, and all differences within the equivalent scope will be construed as being included in the present invention.

<110> SAMSUNG LIFE PUBLIC WELFARE FOUNDATION Oncocross Co., Ltd. <120> Novel biomarker for predicting of prognosis of metastatic renal cell carcinoma and uses its <130> BPN211008P1 <150> KR 10-2021-0167429 <151> 2021-11-29 <160> 4 <170> KoPatentIn 3.0 <210> 1 <211> 21 <212> DNA <213> artificial sequence <220> <223> GATM F <400> 1 cactacatcg gatctcggct t 21 <210> 2 <211> 21 <212> DNA <213> artificial sequence <220> <223> GATM R <400> 2 ctaaggggtc ccattcgttg t 21 <210> 3 <211> 22 <212> DNA <213> artificial sequence <220> <223> USH1C F <400> 3 ttccggcata aggtggattt tc 22 <210> 4 <211> 23 <212> DNA <213> artificial sequence <220> <223> USH1C R <400> 4 gtacattcgc agcacatcat aga 23

Claims (10)

a) taking a biological sample from the subject;
b) measuring whether PBRM1 is mutated and the GATM expression level in the biological sample; and
c) comparing the measured PBRM1 mutation and GATM expression level with a control group
Method for providing information for predicting the prognosis of metastatic renal cancer comprising a. The method of claim 1,
Wherein the biological sample of step a) is at least one selected from the group consisting of blood, plasma, serum, lymph, saliva, urine and tissue. The method of claim 1,
Wherein the PBRM1 mutation in step b) is at least one selected from the group consisting of single nucleotide sequence mutation, substitution, deletion or insertion of a nucleotide sequence of 1 to 50 nucleotides, and copy number mutation. The method of claim 1,
The PBRM1 mutation in step b) is performed by next-generation sequencing, polymerase chain reaction (PCR), reverse transcription polymerase chain reaction (RT-PCR), competitive RT-PCR, real-time RT-PCR, nucleic acid degradation A method that is measured by at least one selected from the group consisting of nuclease protection assay, in situ hybridization, DNA microarray, and Northern blot. The method of claim 1,
The GATM expression level in step b) was determined by polymerase chain reaction (PCR), reverse transcription polymerase chain reaction (RT-PCR), competitive RT-PCR, real-time RT-PCR, nuclease protection assay, in measured at the nucleic acid level by one or more methods selected from the group consisting of in situ hybridization, DNA microarray and Northern blot, or ELISA, Western blot, radioimmunoassay, immunodiffusion, immunoprecipitation, immunohistochemistry, immunofluorescence and protein A method that is measured at the protein level by one or more methods selected from the group consisting of microarrays. The method of claim 1,
Step c) is to determine that the prognosis of metastatic renal cancer is good when the subject has a PBRM1 mutation and the GATM expression level is higher than that of the control group. PBRM1 mutation; and
A composition for predicting the prognosis of metastatic renal cancer comprising an agent for measuring the level of GATM expression. The method of claim 7,
Wherein the PBRM1 mutation is at least one selected from the group consisting of a single nucleotide sequence mutation, a substitution, deletion or insertion of a nucleotide sequence of 1 to 50 nucleotides, and a copy number mutation. The method of claim 7,
The agent is a composition comprising at least one selected from the group consisting of primers, probes, antibodies, aptamers, oligopeptides and PNAs that bind specifically to genes or proteins of PBRM1 and GATM, respectively. A kit for predicting the prognosis of metastatic renal cancer comprising the composition of any one of claims 7 to 9.

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