CN116129998A

CN116129998A - Esophageal squamous cell carcinoma data processing method and system

Info

Publication number: CN116129998A
Application number: CN202310062863.0A
Authority: CN
Inventors: 刘芝华; 陈洪岩
Original assignee: Cancer Hospital and Institute of CAMS and PUMC
Current assignee: Cancer Hospital and Institute of CAMS and PUMC
Priority date: 2023-01-19
Filing date: 2023-01-19
Publication date: 2023-05-16
Anticipated expiration: 2043-01-19
Also published as: CN116129998B

Abstract

The invention discloses a processing method, a processing system, processing equipment and a computer readable storage medium of esophageal squamous cell carcinoma data, wherein the processing method comprises the following steps: acquiring sequencing data of a sample to be tested; inputting the sequencing data of the sample to be tested into the constructed classification model to obtain classification results of DDR-silent subtype and non-DDR-silent subtype; based on the classification results of the DDR-silent subtype, a treatment regimen of whether anti-PD-1 antibody+anti-GITR promoter/anti-BTLA inhibitor is administered is given. According to the method, sequencing data of the sample to be tested are utilized to divide the sample to be tested into two classification results of the DDR-silent subtype and the non-DDR-silent subtype, then a combined treatment strategy of PD-1 blocking combined GITR triggering or BTLA blocking is given out according to the classification result of the DDR-silent subtype, and the effectiveness of the combined immunotherapy strategy is verified.

Description

Esophageal squamous cell carcinoma data processing method and system

Technical Field

The invention relates to the field of data analysis, in particular to a processing method and a processing system of esophageal squamous cell carcinoma data.

Background

Esophageal squamous cell carcinoma (Esophageal squamous cell carcinoma, ESCC) is a malignant tumor that threatens human health. Five year survival in ESCC patients is less than 20% in developed countries and less than 5% in many developing countries. Notably, some primary esophageal cancer patients often relapse rapidly after esophageal resection, and the prognosis of these patients remains poor. To date, no accurate molecular biomarkers can predict the development of these primary ESCC patients, resulting in inadequate clinical management. Thus, there is an urgent need to identify new prognostic biomarkers for primary ESCC.

A variety of synergistic repair mechanisms can rapidly and properly repair DNA damage in normal cells; DNA double strand breaks are repaired primarily by Homologous Recombination (HR) and non-homologous end joining (NHEJ), DNA single strand breaks are repaired primarily by mismatch repair (MMR) and nucleotide excision repair pathways (NER). DNA Damage Repair (DDR) defects can lead to accumulation of DNA damage and genomic instability, production of neoantigens, and up-regulation of expression of immune checkpoints, ultimately altering immune balance in the Tumor Microenvironment (TME). Interestingly, DDR deficiency becomes an important determinant of anti-tumor immune response by affecting antigenicity, adjuvanticity and responsiveness, which may contribute to the response of immunotherapy. Recent studies have revealed the potential of some DDR-based biomarkers in predicting immune therapeutic responses; however, the value of DDR-related features for prognostic evaluation and personalized immunotherapy has not yet been fully elucidated. Thus, revealing the correlation between the change in tumor DDR pathway and prognosis, and the regimen of personalized immunotherapy based on DDR-specific features is of paramount importance.

ESCC treatment generally involves a variety of modalities including surgery, radiation therapy and chemotherapy. Recently, immune checkpoint inhibitors (e.g., anti-PD-1) have produced significant survival benefits for advanced and metastatic ESCC. For primary esophageal cancer, chemo-radiotherapy or adjuvant chemotherapy after esophageal resection is the primary treatment modality; however, many patients are inherently resistant to the conventional treatments described above and have limited clinical efficacy. To date, no immunotherapy has been approved for the treatment of primary ESCC. Thus, there is an urgent need to fully understand the immune microenvironment and develop optimal immunotherapeutic approaches for primary ESCC patients.

Disclosure of Invention

The present invention aims to solve at least one of the technical problems existing in the prior art. Therefore, the invention provides a processing method of esophageal squamous cell carcinoma data, which utilizes sequencing data of a sample to be detected to divide the sample to be detected into two classification results of DDR-silent subtype and non-DDR-silent subtype, then gives a combined treatment strategy of PD-1 blocking combined GITR triggering or BTLA blocking according to the classification result of DDR-silent subtype, and verifies the effectiveness of the combined immunotherapy strategy. According to the method, two independent prognosis prediction genes are obtained through analysis of sequencing data, classification of the primary ESCC samples to be tested is achieved, and prognosis of primary ESCC patients is effectively improved.

The first aspect of the application discloses a method for processing esophageal squamous cell carcinoma data, which comprises the following steps:

acquiring sequencing data of a sample to be tested;

inputting the sequencing data of the sample to be tested into the constructed classification model to obtain classification results of DDR-silent subtype and non-DDR-silent subtype;

based on the classification results of the DDR-silent subtype, a treatment regimen of whether anti-PD-1 antibody+anti-GITR promoter/anti-BTLA inhibitor is administered is given.

A second aspect of the present application discloses a method for processing esophageal squamous cell carcinoma data, comprising:

acquiring sequencing data of a sample to be tested;

based on gene expression data of sequencing data of the sample to be tested, classification results of DDR-silent subtype and non-DDR-silent subtype are obtained, and the gene expression data comprises the expression quantity of one or more genes: HFM1, BRCA1;

giving a treatment regimen of whether to administer anti-PD-1 antibody + anti-GITR promoter/anti-BTLA inhibitor based on the classification result of the DDR-silent;

alternatively, the classification result of the DDR-silent subtype corresponds to high HFM1 gene expression.

The sequencing data of the sample to be tested is RNA-seq data of a primary ESCC patient;

optionally, the anti-PD-1 antibody comprises: inVivoMabanti-mouse PD-1; the anti-PD-1 antibody is preferably clone RMP1-14;

optionally, the anti-GITR promoter comprises: inVivoMAbanti-mouse GITR; the anti-GITR promoter is preferably clone DTA-1; GITR acts as a co-stimulatory receptor, becoming a potential target for enhancing immunotherapy, and plays a key role in T cell activation, with its activity enhancing other anti-cancer therapies through synergy;

optionally, the anti-BTLA inhibitor comprises: inVivoMAbanti-mouse BTLA; the anti-BTLA inhibitor is preferably clone 6A6; BTLA acts as an inhibitory receptor and the ligand is herpes virus invasion mediator (HVEM).

Both BTLA and PD-1 are highly expressed, and/or GITR is low and PD-1 is highly expressed in a T cell subset of the classification result of the DDR-silent subtype.

The construction method of the classification model comprises the following steps:

acquiring sequencing data of a training set sample and a life cycle condition corresponding to the sample;

extracting a path related to the survival rate and the gene expression condition thereof from the sequencing data of the training set sample;

performing cluster analysis on the training set samples based on the lifetime condition to obtain two groups of classification of DDR-silent subtype and non-DDR-silent subtype, and characterizing the passage of each group of classification and the gene expression condition thereof to obtain the classification model;

optionally, the survival-related pathway includes one or more of the following: MMR pathway, NER pathway, FA pathway, and NHEJ pathway.

The construction method further comprises the following steps: based on the gene expression condition of the passage related to the survival rate, obtaining (8) DDR gene sets and corresponding gene expression conditions related to survival results by utilizing a univariate regression analysis method; the DDR gene set related to the survival result and the corresponding gene expression situation are processed by utilizing a multivariate analysis method, so that the gene expression situations of a prognosis prediction gene and a prognosis prediction gene are obtained;

and carrying out cluster analysis on the training set sample based on the survival condition to obtain two groups of classification of DDR-silent subtype and non-DDR-silent subtype, and representing the prognosis prediction gene of each group of classification and the gene expression condition thereof to obtain a classification model.

The cluster analysis method comprises the following steps: a consistency clustering algorithm;

optionally, the sequencing data of the training set sample includes: RNA-seq data of primary ESCC tumor tissue samples and metastatic ESCC tumor tissue samples.

A third aspect of the present application discloses a system for processing esophageal squamous cell carcinoma data, comprising:

the acquisition unit is used for acquiring sequencing data of the sample to be tested;

the classification unit is used for inputting the sequencing data of the sample to be tested into the constructed classification model to obtain classification results of the DDR-silent subtype and the non-DDR-silent subtype;

and an output unit for giving a treatment scheme of whether the anti-PD-1 antibody+the anti-GITR antibody/the anti-BTLA antibody is given or not based on the classification result of the DDR-silent subtype.

In a fourth aspect the present application discloses a device for processing esophageal squamous cell carcinoma data, said device comprising: a memory and a processor;

the memory is used for storing program instructions; the processor is used for calling program instructions which, when executed, are used for executing the processing method of esophageal squamous cell carcinoma data.

A fifth aspect of the present application discloses a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the above-described method of processing esophageal squamous cell carcinoma data.

The application has the following beneficial effects:

1. the application creatively discloses a processing method of esophageal squamous cell carcinoma data, which utilizes the specificity of DDR-silent subtype to give a combined treatment strategy of PD-1 blocking combined GITR triggering or BTLA blocking to a sample to be tested of classified DDR-silent subtype, and provides potential clinical significance for the treatment and management strategy of primary ESCC with DDR-silent subtype.

2. The method creatively classifies primary ESCC patients into DDR-silent subtype and non-DDR-silent subtype, and carries out clinical prognosis evaluation on the patients based on analysis of sequencing data or prognosis prediction genes of the patients. Meanwhile, the application also discloses a model construction method for parting the primary ESCC according to the DDR pathway gene set and the gene expression condition thereof, in the model construction process, two independent prognosis biomarkers BRCA1 and HFM1 are determined, the classification model can be used for effectively predicting the subsequent survival rate of a primary ESCC patient with frequent rapid recurrence and poor prognosis, the immune treatment scheme is effectively guided, a new clue and visual angle are provided for tumor heterogeneity based on the identification of a novel DDR molecular subtype, and the potential clinical significance of the treatment and management strategy of the primary ESCC patient of the DDR-silent subtype is revealed.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a schematic flow chart of a method for processing esophageal squamous cell carcinoma data provided by a first aspect of an embodiment of the invention;

FIG. 2 is a schematic view of an apparatus for processing and analyzing esophageal squamous cell carcinoma data provided in a fourth aspect of the invention;

FIG. 3 is a schematic flow chart of a processing analysis system for esophageal squamous cell carcinoma data provided by a third aspect of an embodiment of the invention;

FIG. 4 is an ESCC tumor cluster analysis chart based on DDR gene map provided by the embodiment of the invention;

FIG. 5 is a graph showing the results of the BRCA1 and HFM1 mediated DNA damage in ESCC provided in the examples of the present invention;

FIG. 6 is a schematic of ESCC tumor growth inhibition more effective in combination PD-1 and BTLA blocking/GITR trigger induction provided by an embodiment of the invention.

Detailed Description

In order to enable those skilled in the art to better understand the present invention, the following description will make clear and complete descriptions of the technical solutions according to the embodiments of the present invention with reference to the accompanying drawings.

In some of the flows described in the specification and claims of the present invention and in the foregoing figures, a plurality of operations occurring in a particular order are included, but it should be understood that the operations may be performed out of order or performed in parallel, with the order of operations such as 101, 102, etc., being merely used to distinguish between the various operations, the order of the operations themselves not representing any order of execution. In addition, the flows may include more or fewer operations, and the operations may be performed sequentially or in parallel. It should be noted that, the descriptions of "first" and "second" herein are used to distinguish different messages, devices, modules, etc., and do not represent a sequence, and are not limited to the "first" and the "second" being different types.

The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments according to the invention without any creative effort, are within the protection scope of the invention.

Fig. 1 is a schematic flow chart of a processing method of esophageal squamous cell carcinoma data provided by a first aspect of an embodiment of the invention, specifically, the method comprises the following steps:

101: acquiring sequencing data of a sample to be tested;

in one embodiment, the sequencing data of the test sample is RNA-seq data of a primary ESCC patient. Primary is relative to secondary and metastatic. That is, a disease first occurs in a tissue or organ for which the disease is primary. As an example: primary hepatocellular carcinoma, i.e., hepatocellular carcinoma occurs first, while secondary liver cancer is cancer in other areas, which is transferred to the liver along with blood flow or lymphatic path, and the primary area is in other tissues or organs, not the liver. The primary ESCC patient in this example refers to a surgically resected patient who received a primary tumor, followed by radiation therapy, with or without chemotherapy.

In one embodiment, the RNA-seq, i.e., transcriptome sequencing, technique is a sequencing analysis using high throughput sequencing techniques that reflects the expression levels of mRNA, smallRNA, noncodingRNA, etc., or some of them. In the last decade, RNA-Seq technology has evolved rapidly and has become an indispensable tool for analyzing differential gene expression/variable cleavage of mRNA at the transcriptome level. With the development of the next generation sequencing technology, the application range of the RNA-Seq technology becomes wider: firstly, in the field of RNA biology, RNA-Seq can be applied to single cell gene expression/protein expression/RNA structure analysis; secondly, the concept of spatial transcriptomes is also growing. Long read long/direct RNA-Seq technology and better data analysis and calculation tools have the advantage of helping biologists to gain insight into RNA biology with RNA-Seq-e.g. when and where transcription starts; and how to influence RNA functions by in vivo folding and intermolecular actions.

A transcriptome is a collection of all transcripts produced by a particular species or cell type. Transcriptome research can research gene functions and gene structures from an overall level, reveals specific biological processes and molecular mechanisms in disease occurrence processes, and has been widely applied to the fields of basic research, clinical diagnosis, drug development and the like.

In one embodiment, the sample to be tested is a primary ESCC patient clinically used to receive a prognostic evaluation.

102: inputting the sequencing data of the sample to be tested into the constructed classification model to obtain classification results of DDR-silent subtype and non-DDR-silent subtype;

in one embodiment, both BTLA and PD-1 are highly expressed, and/or GITR is low and PD-1 is highly expressed, in a T cell subset of the classification results for the DDR-silent subtype. Wherein PD1, BTLA and GITR are transcriptome analysis parts, and anti-PD-1, anti-BTLA and anti-GITR are inhibitors of the tumorigenesis experiment.

In one embodiment, the method for constructing the classification model includes:

extracting a path related to the survival rate and the gene expression condition thereof from the sequencing data of the training set sample; the training set samples included RNA-seq data for tumor tissue of 82 primary ESCCs and 73 ESCCs with lymph node metastasis; the patients received surgical excision of the primary tumor and lymph node dissection followed by radiotherapy, with or without chemotherapy. The data of the 155 patients are from ESCC queues of the Shanxi province tumor Hospital (SCH), the RNA-seq data of the SCH queues are stored in Gene Expression Omnibus (GEO), the accession number is GSE53625, and clinical and pathological data of 97 patients are determined through retrospective examination of SCH electronic medical records, and the follow-up period is finished 2019/06 8 months. RNA-seq data analysis of the HiSeq Illumina platform was from UCSC Xena atlas

(https:// xenabrowser. Net/datapages /) collection, the RNA-seq data covers TPM levels and log2 (x+1) normalization;

and carrying out cluster analysis on the training set samples based on the lifetime condition to obtain two groups of classification of DDR-silent subtype and non-DDR-silent subtype, and characterizing the passage of each group of classification and the gene expression condition thereof to obtain the classification model.

Optionally, the survival-related pathway includes one or more of the following: MMR pathway, NER pathway, FA pathway, and NHEJ pathway; the gene expression conditions of the survival rate-related pathway comprise gene expression profiles of one or more of the following genes: POLD1, POLD2, POLD3, POLD4, MSH2, MSH3, MSH6, MLH1, MLH3, PMS1, PMS2, MSH4, MSH5, EXO1, HMGB1, LIG1, PCNA, RFC2, RFC4, RFC3, RFC5, RFC1, RPA2, RPA3, RPA4, POLD1, POLD2, POLD3, POLD4, PCNA, RFC1, RFC2, RFC3, RFC4, RFC1, RFC3 RFC5, POLE2, POLE3, POLE4, POLK, CUL4A, DDB, DDB2, RBX1, CUL4A, DDB1, DDB2, RBX1, CETN2, RAD23B, XPC, POLR2A, POLR2A, POLR2A, POLR2A, POLR2A, POLR2A, POLR2A, POLR2A, POLR2A, POLR2A, POLR 3, CUL5, ERCC1, ERCC4, ERCC5, LIG1, TCEB2, TCEB3, A, POLR, RPA2, TCEB1, RPA2, RED 2A, POLR2A, POLR2A, POLR2A, POLR2A, POLR2A, POLR2A, POLR2A, POLR2A, POLR 3, TCEB1, RPA2 RPA3, RPA4, CDK7, ERCC2, ERCC3, GTF2H1, GTF2H2, GTF2H3, GTF2H4, GTF2H5, MNAT1, ERCC6, ERCC8, LIG3, RAD23A, POLR2, XRCC1, GADD45A, POLR 45A, POLR2, TOP3A, POLR 3A, POLR 1, BRCA2, BRIP1, PALB2, FAAP100, FAAP24, A, POLR 1, HES1, STRA13, UBE 2A, POLR2, A, POLR, DNA2, FAN1, HELQ, KAT5, RAD 51A, POLR2, USP1, WDR48, A, POLR 1, MRE11A, POLR 3, RAD50, RNF168, RNF8, TP53BP1, DCLRE 1A, POLR 4, NHEJ1, kdc, XRCC5, brcc 6, LIG4, mbe 2A, POLR, prcc 37, XRCC 37, A, POLR, XRCC5, XRCC 37.

Optionally, the survival-related pathway is obtained by: extracting a DDR channel gene set and a gene expression condition thereof from sequencing data of the training set sample; performing univariate Cox regression analysis on the DDR pathway gene set to obtain a pathway related to survival rate and a gene expression condition of the pathway related to survival rate; the DDR pathway gene set comprises: BER pathway (base excision repair, n=43), MMR pathway (mismatch repair, n=27), NER pathway (nucleotide excision repair, n=70), FA pathway (fanconi anemia, n=36), HR pathway (homologous recombination, n=55) and NHEJ pathway (non-homologous end joining, n=37);

in one embodiment, to characterize the DDR subtype, the DDR subtype is first subjected to Differential Expression (DE) analysis using R-package limma (V3.50.3) to determine subtype-specific genes. Differentially Expressed Genes (DEG) were defined as log fc) < = -1 or > = 1 and adjusted for P value <0.05. Then, a pathway enrichment analysis was performed on the DEG from the MSigDB (genome database: https:// www.jianshu.com/p/99369b2f7a7 d) for a set of carefully selected marker pathways to identify enriched pathways in the DDR subtype, as implemented by the R-packet cluster analysis program clusterifier (version 4.2.2).

In one embodiment, the building method further comprises: based on the gene expression condition of the passage related to the survival rate, obtaining (8) DDR gene sets and corresponding gene expression conditions related to survival results by utilizing a univariate regression analysis method; the DDR gene set related to the survival result and the corresponding gene expression situation are processed by utilizing a multivariate analysis method, so that the gene expression situations of a prognosis prediction gene and a prognosis prediction gene are obtained; sex, grade, smoking history and drinking history are controlled in the multivariate analysis;

and carrying out cluster analysis on the training set sample based on the survival condition to obtain two groups of classification of DDR-silent subtype and non-DDR-silent subtype, and representing the prognosis prediction gene of each group of classification and the gene expression condition thereof to obtain a classification model. The DDR-silent subtype corresponds to the gene expression condition of a low survival rate pathway. There is no specific threshold for the survival rate, and it is concluded by statistical comparative analysis between the DDR-active subtype and the DDR-silent subtype.

In one embodiment, the method of cluster analysis is: a consistency clustering algorithm; consistency clustering is also called consensus clustering, and is a method for aggregating the results of various clustering algorithms, and is also called clustering integration or aggregation of clusters. It is meant that a number of different (input) clusters have been obtained for a particular dataset and that it is desirable to find a single (consistent) cluster, in some sense more appropriate than existing clusters. Thus, consistent clustering is a problem of coordinating clustering information about the same dataset from different sources or different runs of the same algorithm. This clustering procedure was performed using the R-packet consissuclusteriplus, iterated 1000 times and resampled 90%. The core algorithm is a k-means algorithm based on Euclidean distance, and a single algorithm cannot be realized.

Optionally, the univariate regression analysis is univariate Cox regression analysis;

optionally, the sequencing data of the training set sample includes: RNA-seq data of primary ESCC tumor tissue samples and metastatic ESCC tumor tissue samples. By analyzing the RNA-seq data of primary and metastatic ESCC tumor tissue samples, DDR pathway analysis determined that the DDR active subtype and DDR silent subtype have independent prognostic value in primary ESCC, but not in metastatic ESCC.

In one example, the non-DDR-side subtype was the DDR-side subtype, and the correlation between DDR subtype and ESCC survival with and without LNM (lymph node metastasis) was studied using a hierarchical analysis method, which showed that the primary ESCC tumor of DDR-side subtype had the worst survival rate (log-rankp=0.032) compared to the metastatic ESCC tumor, but no significant difference was observed in survival rate of metastatic ESCC tumor between DDR subtypes (log-rankp=0.34). DDR pathway analysis established that the DDR active subtype and DDR silent subtype have independent prognostic value in primary ESCC, but not in metastatic ESCC.

In one embodiment, to further verify the association between DDR subtype and survival outcomes, DDR subtypes of 74 tumors in the TCGA-ESCC cohort and 117 tumors in the Chen cohort were also summarized. Consistent with the findings in this cohort, DDR subtype assisted survival prediction was only used for primary ESCC tumors, allowing identification of a subset of patients with good or poor outcome (TCGA-ESCC cohort, hr=0.075, 95% ci 0.008-0.674, log-rankp=0.004; for Chen cohort, hr=0.430, 95% ci 0.186-0.995, log-rankp=0.042), and failure to stratify survival of ESCC tumors with LNM. Multivariate Cox regression analysis showed that the DDR subtype was a powerful predictor of survival outcome and it was independent of clinical variables and underscores the value of the DDR subtype and its robustness in predicting primary ESCC patient survival outcome. Stratified analysis is to separate the population into different layers (sub-groups) according to a certain characteristic, such as gender, age, etc., and analyze the association of exposure and disease in each layer separately. The objective of hierarchical analysis is to control confounding factors, adjust the interference of these factors-estimate the magnitude of the confounding factors' impact on the relationship between exposure and outcome. Hierarchical analysis is a scenario to cope with mean value failure. Wherein, the TCGA-ESCC queue comprises RNA-seq data of 74 patients, collected from UCSC Xenaatlas (https:// xenabowser. Net/datapages /); the Chen cohort was 117 cases of ESCC patient microarray data of the academy of medical science and the college of beijing synergetics, and clinical data was obtained from Gene Expression Omnibus (GEO, www.ncbi.nlm.nih.gov/GEO/query/acc.cgiac=gse 53624).

Optionally, the prognostic prediction gene includes: BRCA1 gene and HFM1 gene; the DDR-active subtype corresponds to the high BRCA1 gene expression quantity, and the DDR-silent subtype corresponds to the high HFM1 gene expression quantity; there is no specific threshold for the amount of gene expression, and it is concluded by statistical comparative analysis between the DDR-active subtype and the DDR-silent subtype.

In one embodiment, 3 independent queues are used for prognosis evaluation of DDR genes in primary and metastatic ESCC tumor tissues respectively by using meta analysis, and BRCA1 and HFM1 are predictors of survival results of primary ESCC patients, but do not contribute to prognosis of metastatic ESCC; BRCA1 was identified as a favorable prognostic factor, with high expression associated with improved survival, combined HR of 0.22, while HFM1 is a risk factor, with increased expression associated with poor survival results, with a different aggregate HR of 4.41.

In one embodiment, the cells sense and repair DNA damage, maintain genomic integrity and prevent tumorigenesis in the presence of BRCA 1. BRCA1 deficiency can disrupt normal DDR and lead to accumulation of DNA damage. However, the role of HFM1 (an ATP dependent DNA helicase homolog) in DDR has not been studied. To determine the role of BRCA1 and HFM1 in ESCC cell DDR, cell models of cisplatin (DDP) and X-IR induced in vitro DNA damage were constructed, expression of BRCA1 or HFM1 was silenced using transient siRNA transfection, and cells were treated with cisplatin (DDP) or X-IR. The knockout efficiency of BRCA1 and HFM1 was examined by Western blotting. To directly assess DDR, γh2ax (a mature DNA DSB marker) was visualized by immunofluorescence. Spontaneous and DDP or IR induced γh2ax lesions were counted and analyzed. After DDP or X-IR treatment, γH2AX accumulates. Furthermore, immunofluorescence analysis showed a significant increase in endogenous γh2ax accumulation in KYSE410 and KYSE450 cells following BRCA1 knockout under IR and DDP treatment. In contrast, HFM1 knockdown significantly reduced the number of γH2AX lesions in KYSE30 and KYSE450 cells treated with X-IR or DDP. These results indicate that the loss of BRCA1 results in DDR defects, which support the role of BRCA1 as an advantageous prognostic factor, whereas the loss of HFM1 promotes DDR, supporting the role of HFM1 as a prognostic risk factor.

103: giving a treatment regimen of whether to administer anti-PD-1 antibody + anti-GITR promoter/anti-BTLA inhibitor based on the classification result of the DDR-silent subtype;

in one embodiment, the anti-PD-1 antibody comprises: inVivoMabanti-mouse PD-1; the anti-PD-1 antibody is preferably clone RMP1-14; PD-1 is programmed death receptor 1, an important immunosuppressive molecule, which is an immunoglobulin superfamily; wherein each antibody has a clone number, followed by a name and followed by a clone number;

optionally, the anti-GITR promoter comprises: inVivoMAbanti-mouse GITR; the anti-GITR promoter is preferably clone DTA-1; wherein each antibody has a clone number, followed by a name and followed by a clone number; GITR acts as a co-stimulatory receptor, becoming a potential target for enhancing immunotherapy, and plays a key role in T cell activation, with its activity enhancing other anti-cancer therapies through synergy; GITR promoters are attractive targets in immunotherapy; GITR promotes activation and proliferation of effector T cells and reduces the level of regulatory T cells. GITR (TNFRSF 18/CD 357/AITR) is a type 1 transmembrane protein belonging to the TNFRSF superfamily, and other members also include OX40, CD27, CD40 and 4-1BB. Human GITR is expressed at high levels on cd4+cd25+foxp3+ Tregs and at low levels on naive and memory T cells. In activation of cd8+ and cd4+ effector T cells, GITR expression on Tregs and effector T cells increases rapidly, reaching maximum levels on activated Tregs. GITR is also expressed on natural killer cells (NK), and is also expressed at low levels on B cells, macrophages and dendritic cells, and can be upregulated by activation, particularly on NK cells. In recent years, GITR has been widely studied as a pharmacological target. The agonist mab activates GITR to enhance immune and inflammatory responses, thereby enhancing anti-tumor responses. In contrast, GITR inhibitors inhibit T cell activation and immune responses. Thus, GITR agonist mab was further developed as an anti-tumor drug. GITR, like other co-stimulatory molecules, plays a key role in T cell activation, and its activity may enhance other anti-cancer therapies through synergy. anti-PD-1 and GITR agonist mab combination therapy could achieve long-term survival in ovarian and breast cancer mouse models, stimulate IFN-gamma producing conventional T cells, suppress immunosuppressive Tregs and myeloid-derived suppressor cells. The combination therapy successfully restored the activity of cd8+ T cells and induced proliferation of precursor effector memory T cell phenotypes in a CD 226-dependent manner.

Optionally, the anti-BTLA inhibitor comprises: inVivoMAbanti-mouse BTLA; the anti-BTLA inhibitor is preferably clone 6A6; wherein each antibody has a clone number, followed by a name and followed by a clone number; BTLA acts as an inhibitory receptor and the ligand is herpes virus invasion mediator (HVEM). The BTLA inhibitor is an inhibitory receptor of the immunoglobulin superfamily; BTLA belongs to the CD28 family and has structural similarity to PD-1 and CTLA-4. It has an extracellular immunoglobulin domain, an Immunoreceptor Tyrosine Inhibitory Motif (ITIM), and an immunoreceptor tyrosine-based switching motif (ITSM). BTLA signaling involves phosphorylation of ITIMs and SH2 domain-containing phosphatase 1 (SHP-1)/SHP-2 binding, thereby inhibiting T cell proliferation and cytokine production. BTLA expression in malignant tissue is higher than normal tissue, with higher BTLA expression being positively correlated with higher HVEM expression. Furthermore, BTLA expression affects prognosis, total 5-year survival (OS) for low BTLA expression groups was 48.3%, decreasing to 17.9% when BTLA was highly expressed. Higher BTLA expression is also associated with lymph node metastasis. BTLA is associated with other co-inhibitory receptor expression. In advanced melanoma, foucade et al demonstrated that 42% of NY-ESO-1 specific CD8+ T lymphocytes co-express BTLA and PD-1, and that these cells have a partially dysfunctional phenotype. TIM-3 and PD-1 are upregulated when NY-ESO-1 specific cd8+ T lymphocytes are stimulated with cognate antigen for prolonged periods of time. BTLA expression follows different patterns, suggesting that BTLA upregulation depends on different conditions, not functional depletion driven by high antigen loading. Furthermore, blocking BTLA by anti-BTLA antibodies can enhance production of IFN- γ, tnfα and IL-2 by NY-ESO-1 specific cd8+ T cells. Interestingly, when anti-BTLA was combined with anti-PD-1, a synergistic effect was observed in the functional assay.

In one embodiment, to characterize the gene expression status of PD-1, BTLA, and GITR on the T-package compartment involved in ESCC in a single cell state, scRNA-seq data of tumor tissue of 31 primary ESCC patients were obtained, the scRNA-seq data including 32918T cells; 7T cell subsets were identified based on typical gene markers (T helper 17,cytotoxic T cells,NK T cells,exhausted CD8T cells,memory CD8T cells,

t cells, and regulatory CD 4T cells), the regulatory CD 4T cells are labeled with transcripts including CD4, IL32, FOXP3 and IL2 RA. exhausted CD 8T cells have typical markers of failure, including TOX, CTLA-4, TIGIT and CXCL13, and cytotoxic T cell subsets characterized by high expression of GNLY, GZMA, GZMB and NKG 7.

acquiring sequencing data of a sample to be tested;

Fig. 2 is a processing analysis device for esophageal squamous cell carcinoma data provided by an embodiment of the invention, the device comprising: a memory and a processor; the memory is used for storing program instructions; the processor is used for calling program instructions which, when executed, are used for executing the processing method of esophageal squamous cell carcinoma data.

Fig. 3 is a processing analysis system for esophageal squamous cell carcinoma data provided by an embodiment of the invention, comprising:

an acquiring unit 301, configured to acquire sequencing data of a sample to be tested;

the classification unit 302 is configured to input sequencing data of the sample to be tested into the constructed classification model to obtain classification results of the DDR-silent subtype and the non-DDR-silent subtype;

an output unit 303 that gives a treatment regimen of whether to administer the anti-PD-1 antibody+the anti-GITR antibody/the anti-BTLA antibody based on the classification result of the DDR-silent subtype.

The processing and analyzing system for esophageal squamous cell carcinoma data provided by the embodiment of the invention comprises:

the acquisition unit is used for acquiring gene expression data of the sample to be detected; the gene expression data of the sample to be tested comprises the gene expression data of one or more of the following genes: BRCA1 gene, HFM1 gene;

the classification unit is used for obtaining classification results of the DDR-silent subtype and the non-DDR-silent subtype based on gene expression data of sequencing data of the sample to be detected, wherein the gene expression data comprises the expression quantity of one or more genes: HFM1, BRCA1;

and an output unit for giving a treatment scheme of whether the anti-PD-1 antibody+the anti-GITR promoter/the anti-BTLA inhibitor is given or not based on the classification result of the DDR-silent.

A computer readable storage medium having stored thereon a computer program which when executed by a processor implements a method of processing esophageal squamous cell carcinoma data as described above.

FIG. 4 is a chart of ESCC tumor cluster analysis based on DDR gene map provided by the embodiment of the invention, wherein,

(A) Heat map of fold change in DDR gene expression between DDR subtypes. Red bars represent DDR-active subtypes and green bars represent DDR-silent subtypes. DDR subtypes are classified by consensus clustering methods. (B-D) Kaplan-Meier curves compare the OS (log rank test) of DDR-active subtype, DDR-silent subtype and transition subtype groups. HR and 95% ci were calculated by double sided Wald test using univariate Cox regression. (E) Kaplan-Meier curves compare the OS of DDR-active subtype and DDR-silent subtype in primary esophageal squamous cell carcinoma (log rank test). HR and 95% ci were calculated by double sided Wald test using univariate Cox regression.

FIG. 5 is a graph of the results of the BRCA1 and HFM1 mediated DNA damage reaction in ESCC provided in the examples of the present invention, wherein (A, B) KYSE410 and KYSE450 cells are transfected with BRCA1 siRNA, treated with 2. Mu.g/ml DDP, and analyzed by Western blotting for γH2AX. (C, D) KYSE410 and KYSE450 cells were transfected with BRCA1 siRNA, exposed to IR (4 Gy), harvested at the indicated times and analyzed by Western blot for γH2AX. (E, F) representative pictures and quantification of gamma H2AX lesions in control and BRCA1 knockdown KYSE410 and KYSE450 cells were treated with 2. Mu.g/ml DDP for the indicated times. Data represent three independent experiments. Each dot represents one cell, and 50 cells per group were counted for this experiment with Image J. Error bars represent ± SD of the experiment. The P-value was determined by unpaired double sided t-test. (G, H) representative pictures and quantification of γH2AX lesions in control and BRCA1 knockdown KYSE410 and KYSE450 cells, treatment with IR (4 Gy) for the indicated times. Data represent three independent experiments. Each dot represents one cell, and 50 cells per group were counted for this experiment with Image J. Error bars represent ± SD of the experiment. The P-value was determined by unpaired double sided t-test. (I, J) KYSE30 and KYSE450 cells transfected with HFM1 siRNA, treated with 2. Mu.g/ml DDP and analyzed by Western blotting

γh2ax. (K, L) KYSE30 and KYSE450 cells were transfected with HFM1 siRNA, exposed to IR (4 Gy), harvested at the indicated times and analyzed by Western blot for γH2AX. Representative pictures and quantification of γh2ax lesions in (M, N) control and HFM1 knockdown KYSE30 and KYSE450 cells were treated with 2 μg/ml DDP for the indicated times. Data represent three independent experiments. Each dot represents one cell and Image J counted 50 cells for each group of the experiment. Error bars represent ± SD of the experiment. The P-value was determined by unpaired double sided t-test. (O, P) control and HFM1 knockdown KYSE30 and KYSE450 cells with IR (4 Gy) treatment for a specified time of representative pictures and quantification of gamma H2AX lesions. Data represent three independent experiments. Each dot represents one cell and Image J counted 50 cells for each group of the experiment. Error bars represent ± SD of the experiment. The P-value was determined by unpaired double sided t-test.

FIG. 6 is a schematic of a treatment plan for (A) alpha-GITR, alpha-PD-1, or combination therapy, providing a more effective ESCC tumor growth inhibition profile for combined PD-1 and BTLA blocking/GITR trigger induction in accordance with an embodiment of the present invention. (B, C) tumor images and statistics of tumor weight from isogenic mEC model receiving indicated treatment. (D) Schematic of treatment plan for α -BTLA, α -PD-1 or combination therapy. (E, F) tumor images and statistics of tumor weight from isogenic mEC model receiving indicated treatment. Data in C and E represent mean ± SD and are analyzed by unpaired double sided t-test.

The results of the verification of the present verification embodiment show that assigning an inherent weight to an indication may moderately improve the performance of the present method relative to the default settings.

It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.

In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.

The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.

Those of ordinary skill in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be implemented by a program to instruct related hardware, the program may be stored in a computer readable storage medium, and the storage medium may include: read Only Memory (ROM), random access Memory (RAM, random Access Memory), magnetic or optical disk, and the like.

Those of ordinary skill in the art will appreciate that all or a portion of the steps in implementing the methods of the above embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, where the storage medium may be a read only memory, a magnetic disk or optical disk, etc.

While the foregoing describes a computer device provided by the present invention in detail, those skilled in the art will appreciate that the foregoing description is not meant to limit the invention thereto, as long as the scope of the invention is defined by the claims appended hereto.

Claims

1. A method of processing esophageal squamous cell carcinoma data, comprising:

acquiring sequencing data of a sample to be tested;

2. A method of processing esophageal squamous cell carcinoma data, comprising:

acquiring sequencing data of a sample to be tested;

3. The method for processing esophageal squamous cell carcinoma data according to claim 1 or 2, wherein the sequencing data of the sample to be tested is RNA-seq data of a primary ESCC patient;

optionally, the anti-BTLA inhibitor comprises: inVivoMAbanti-mouse BTLA; the anti-BTLA inhibitor is preferably clone 6A6; BTLA acts as an inhibitory receptor and ligands are mediators of herpes virus invasion.

4. The method of processing esophageal squamous cell carcinoma data according to claim 1 or 2, wherein both BTLA and PD-1 are highly expressed, and/or GITR is low-expressed and PD-1 is highly expressed in a T cell subset of the classification result of the DDR-silent subtype.

5. The method for processing esophageal squamous cell carcinoma data according to claim 1, wherein the method for constructing the classification model comprises:

extracting a path related to the survival rate and the gene expression condition thereof from the sequencing data of the training set sample; performing cluster analysis on the training set samples based on the lifetime condition to obtain two groups of classification of DDR-silent subtype and non-DDR-silent subtype, and characterizing the passage of each group of classification and the gene expression condition thereof to obtain the classification model;

6. The method for processing esophageal squamous cell carcinoma data of claim 5, wherein the constructing method further comprises: based on the gene expression condition of the passage related to the survival rate, obtaining a DDR gene set related to the survival result and a corresponding gene expression condition by utilizing a univariate regression analysis method; the DDR gene set related to the survival result and the corresponding gene expression situation are processed by utilizing a multivariate analysis method, so that the gene expression situations of a prognosis prediction gene and a prognosis prediction gene are obtained;

7. The method for processing esophageal squamous cell carcinoma data according to claim 5, wherein the method for cluster analysis is as follows: a consistency clustering algorithm;

8. A system for processing esophageal squamous cell carcinoma data, comprising:

9. A device for processing esophageal squamous cell carcinoma data, the device comprising: a memory and a processor;

the memory is used for storing program instructions; the processor is adapted to invoke program instructions, which when executed, are adapted to carry out the method of processing esophageal squamous cell carcinoma data of any of claims 1-7.

10. A computer-readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, implements a method for processing esophageal squamous cell carcinoma data as set forth in any of the preceding claims 1-7.