WO2024051097A1 - Neoantigen identification method and device for tumor-specific circular rnas, apparatus and medium - Google Patents

Abstract

The present invention belongs to the technical field of bioinformatics. Disclosed in the present invention is a neoantigen identification method for tumor-specific circular RNAs. The method comprises: for candidate circular RNAs that have been detected, constructing pseudo-reference sequences for realignment, respectively aligning sequencing data of tumor tissue samples and paracancerous tissue samples to the pseudo-reference sequences, extracting determined first candidate reads and second candidate reads after alignment, determining candidate circular RNAs supported by the first candidate reads as first circular RNAs in the tumor tissues and candidate circular RNAs supported by the second candidate reads as second circular RNAs in the paracancerous tissues, fusing the first circular RNAs and the second circular RNAs, filtering the circular RNAs, which are present in both the tumor tissues and the paracancerous tissues, to obtain tumor-specific circular RNAs, further predicting a translation ability score, and scoring and ranking neoantigens derived from the circular RNAs to obtain top-ranked neoantigens. In the present invention, sources of tumor neoantigens can be increased, the identification accuracy of circular RNAs can also be improved, and the neoantigens thereof are more immunogenic.

Description

New antigen identification method and device, equipment and medium for tumor-specific circular RNA

The present invention requires the priority of the Chinese patent application submitted to the China Patent Office on September 6, 2022, with the application number 2022110862377, and the application name is "Neoantigen identification method and device, equipment and medium for tumor-specific circular RNA", which The entire contents are incorporated herein by reference.

Technical field

The invention belongs to the technical field of bioinformatics, and specifically relates to a neoantigen identification method and device, electronic equipment and storage medium for tumor-specific circular RNA.

Background technique

Different from traditional understanding, many long non-coding RNAs that stably exist in the body exist in covalently closed circular structures, most of which are produced by back splicing and are called circular RNAs (circRNAs). In recent years, with the development and application of new technologies for transcriptome sequencing and the continuous optimization of corresponding computational biology analysis processes, scientists have discovered hundreds of thousands of exon back-spliced circRNAs in eukaryotes .

Studies have found that proteins encoded by circular RNAs play an important role in regulating the growth of cancer cells. Because circRNA has a covalently closed stable structure, its degradation rate is lower than that of mRNA. The neoantigen it encodes persists in tumor cells for a long time and is more likely to be recognized by T cells.

However, since most of the sequences of circular RNAs are completely identical to the normal genome sequence, they only differ near the back-splicing junction, and linear RNA transcribed from the same gene will interfere with the detection of circular RNAs. Therefore, currently, circular RNAs are False positive detection results for circRNA remain high, resulting in low accuracy in the identification of circRNA neoantigens.

Contents of the invention

The object of the present invention is to provide a new antigen identification method and device for tumor-specific circular RNA. Settings, equipment, and storage media can improve the accuracy of identification of circRNA, thereby making circRNA-derived neoantigens more immunogenic.

A first aspect of the present invention discloses a method for identifying neoantigens of tumor-specific circular RNA, which includes: obtaining first sequencing data of tumor tissue samples and second sequencing data of adjacent cancer tissue samples;

Perform circRNA detection on the first sequencing data and the second sequencing data respectively to obtain multiple candidate circRNAs;

For each of the candidate circRNAs, construct a pseudo-reference sequence of the first specified length upstream and downstream of the reverse BSJ site according to its sequence order;

Determine the reads in the first sequencing data that have the BSJ site and the second specified length sequences upstream and downstream of the BSJ site that match the pseudo-reference sequence as first candidate reads; and, The reads in the second sequencing data that have the BSJ site and the second specified length sequences upstream and downstream of the BSJ site that match the pseudo-reference sequence are determined as second candidate reads; wherein, the second The specified length is less than or equal to the first specified length;

determining the candidate circRNA supported by the first candidate reads as the first circRNA detected from the tumor tissue sample; and determining the candidate circRNA supported by the second candidate reads as a second circular RNA detected from the adjacent cancer tissue sample;

Filter out the first circRNA that is the same as the second circRNA from the plurality of first circRNAs to obtain a plurality of tumor-specific circRNAs;

predicting a translation ability score for each of the tumor-specific circular RNAs;

Obtain multiple neoantigen candidate peptide segments derived from multiple tumor-specific circular RNAs;

According to the translation ability score of the tumor-specific circular RNA, perform immunogenicity scoring and ranking on multiple neoantigen candidate peptide segments;

The specified number of neoantigen candidate peptides ranked first are determined as the neoantigen target peptides.

A second aspect of the present invention discloses a neoantigen identification device for tumor-specific circular RNA, which includes: a data acquisition unit, used to acquire first sequencing data of tumor tissue samples and second sequencing data of adjacent cancer tissue samples;

A detection unit, configured to detect circRNAs on the first sequencing data and the second sequencing data respectively, to obtain a plurality of candidate circRNAs;

A pseudo-reference unit, used to construct a pseudo-reference sequence of the first specified length upstream and downstream of the reverse BSJ site according to its sequence order for each of the candidate circular RNAs;

Alignment unit, used to determine the reads in the first sequencing data that have the BSJ site and the second specified length sequences upstream and downstream of the BSJ site that match the pseudo-reference sequence as first candidates. reads; and, determine the reads in the second sequencing data that have the BSJ site and the sequences of the second specified length upstream and downstream of the BSJ site that match the pseudo-reference sequence as second candidate reads; Wherein, the second specified length is less than or equal to the first specified length;

A first determination unit, configured to determine the candidate circular RNA supported by the first candidate reads as the first circular RNA detected from the tumor tissue sample; and, determine the candidate circular RNA supported by the second candidate reads. The candidate circRNA is determined to be the second circRNA detected from the adjacent cancer tissue sample;

a filtering unit, configured to filter out the first circular RNA that is the same as the second circular RNA from the plurality of first circular RNAs to obtain a plurality of tumor-specific circular RNAs;

a translation prediction unit for predicting the translation ability score of each of the tumor-specific circular RNAs;

a peptide acquisition unit, used to acquire a plurality of neoantigen candidate peptides derived from a plurality of tumor-specific circular RNAs;

A scoring unit, configured to score and rank multiple neoantigen candidate peptide segments for immunogenicity according to the translation ability score of the tumor-specific circular RNA;

The second determination unit is used to determine a specified number of neoantigen candidate peptides that are ranked first as neoantigen target peptides.

A third aspect of the present invention discloses an electronic device, including a memory storing executable program code. and a processor coupled to the memory; the processor calls the executable program code stored in the memory for executing the neoantigen identification method of tumor-specific circular RNA disclosed in the first aspect.

A fourth aspect of the present invention discloses a computer-readable storage medium that stores a computer program, wherein the computer program causes the computer to execute the neoantigen identification method of tumor-specific circular RNA disclosed in the first aspect.

The beneficial effect of the present invention is that the provided neoantigen identification method, device, equipment, and storage medium for tumor-specific circular RNAs construct pseudo-reference sequences for re-alignment of the detected candidate circular RNAs, respectively. The sequencing data of tumor tissue samples and adjacent cancer tissue samples are compared with pseudo-reference sequences, the first candidate reads and the second candidate reads on the comparison are extracted respectively, and the candidate circRNA supported by the first candidate reads is determined as The first circRNA detected from the tumor tissue sample, and the candidate circRNA supported by the second candidate reads was determined to be the second circRNA detected from the paracancerous tissue sample. The fusion of the two can simultaneously Normal circRNAs present in tumor tissue samples and paracancerous tissue samples are filtered out to obtain tumor-specific circRNAs, and then the translation ability scores of tumor-specific circRNAs are further predicted, and neoantigen candidate peptides derived from them are then The segments are scored and sorted for immunogenicity, and the top ones are finally determined as neoantigen target peptide segments, which can increase the source of tumor neoantigens, broaden the scope of neoantigen screening, and improve the accuracy of identification of circRNAs. , thereby making circRNA-derived neoantigens more immunogenic.

Description of the drawings

The drawings here show specific examples of the technical solutions described in the present invention, and constitute a part of the specification together with the specific implementation modes, and are used to explain the technical solutions, principles and effects of the present invention.

Unless otherwise specified or otherwise defined, the same reference signs in different drawings represent the same or similar technical features, and the same or similar technical features may also be represented by different reference signs.

Figure 1 is a flow chart of a method for neoantigen identification of tumor-specific circular RNA;

Figure 2 is a schematic structural diagram of the reverse 100bp pseudo-reference sequence of the circular RNA;

Figure 3 is a schematic structural diagram of two different circular RNA sequences formed by the same BSJ site;

Figure 4 is a schematic structural diagram of a tumor-specific circular RNA neoantigen identification device;

FIG. 5 is a schematic structural diagram of an electronic device disclosed in an embodiment of the present invention.

Explanation of reference symbols:

401. Data acquisition unit; 402. Detection unit; 403. Pseudo reference unit; 404. Comparison unit; 405. First determination unit; 406. Filtering unit; 407. Translation prediction unit; 408. Peptide acquisition unit; 409 , scoring unit; 410, second determination unit; 501, memory; 502, processor.

Detailed ways

In order to facilitate understanding of the present invention, specific embodiments of the present invention will be described in more detail below with reference to the accompanying drawings.

Unless otherwise specified or defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. In the case of combining the technical solutions of the present invention with realistic scenarios, all technical and scientific terms used herein may also have meanings corresponding to the purpose of realizing the technical solutions of the present invention. The "first, second..." used in this article is only used to distinguish names and does not represent a specific number or order. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.

Unless otherwise specified or otherwise defined, "said" and "the" used in this article refer to the technical features or technical content mentioned or described before the corresponding position, and the technical features or technical content are different from the mentioned technical features or technical content. The technical content can be the same or similar.

There is no doubt that technical content or technical features that are contrary to the purpose of the present invention or are obviously contradictory shall be excluded.

As shown in Figure 1, the embodiment of the present invention discloses a method for identifying neoantigens of tumor-specific circular RNA. method, including the following steps S1 ~ S10:

S1. Obtain the first sequencing data of the tumor tissue sample, the second sequencing data of the adjacent cancer tissue sample, and the third sequencing data of the tumor tissue sample based on polyribosome analysis.

Preferably, the sample library construction is processed using the A tailing+RNase R (Li+buffer) method. Studies have shown that this method can digest linear RNA to the greatest extent, which is not only conducive to the accurate detection of circular RNA (circRNA), but is also very important for determining the full-length sequence of circRNA.

In the embodiment of the present invention, the samples include tumor tissue samples and adjacent cancer tissue samples. In this step, the first sequencing data, the second sequencing data, and the third sequencing data are all second-generation high-throughput circular RNA sequencing data, and all It can be high-quality sequencing data obtained after quality control and filtering of the original sequencing data that was removed from the machine.

Specifically, in step S1, the same process is performed on the original sequencing (circleSeq) data of the off-machine tumor tissue samples, the adjacent cancer tissue samples, and the original sequencing (polysome profiling RNASeq) data of the tumor tissue samples based on polysome profiling. Quality control filtering is performed to obtain the first sequencing data, the second sequencing data and the third sequencing data. Among them, the quality control filtering process may include the following steps S101 to S102:

S101. Obtain the raw sequencing data off the computer, perform adapter removal, and filter out low-quality reads to obtain filtered reads.

In this step, the adapter removal operation is performed on the original sequencing data to filter out adapter (adapter) sequences; at the same time, low-quality reads are filtered out. Specifically, a window with a length of 10 and a step size of 1 is used to right on the original data that is downloaded. Sliding, the average sequencing quality of the 10 bases in the window is calculated every time it slides. If the average sequencing quality is <15, the window sequence and the sequence to its right are judged to be low-quality areas, and the window sequence and the sequence to its right are located Delete the entire reads, and then further judge the length of the reads that are retained for the first time after deletion. The sequence length of the reads is required to be >20 bp. That is to say, if the sequence length of the reads that are retained for the first time after deletion is <20 bp, it is considered that the sequence length is <20 bp. The reads are low-quality reads, and the entire reads are deleted. After deletion, the filtered reads are ultimately retained.

S102. Align the filtered reads to the human ribosomal RNA sequence, and compare the unaligned reads to as high-quality sequencing data.

Among them, the filtered reads are aligned to the human ribosomal RNA sequence, the aligned ribosomal reads are filtered out, and the unaligned reads are retained as high-quality sequencing data for subsequent analysis, which can avoid ribosomal RNA Contamination impact analysis results.

S2. Perform circRNA detection on the first sequencing data and the second sequencing data respectively, and obtain multiple candidate circRNAs.

In step S2, the same processing is performed on the first sequencing data of the tumor tissue sample and the second sequencing data of the adjacent cancer tissue sample to perform circular RNA detection. In order to avoid redundant description, the following uses tumor tissue circleSeq sequencing data (i.e., the first sequencing data) as an example. tumor.circ.R1.fq and tumor.circ.R2.fq used below represent the sequencing data file names.

Preferably, the standard algorithm processes of detection algorithms CIRCexplorer2 and CIRI2 are respectively used to detect circRNA in tumor tissues. Among them, the CIRCexplorer2 standard algorithm process includes three steps: Align, Parse, and Annotate, which are summarized as follows:

In the Align step, the comparison software STAR is used to compare the first sequencing data. The example command is:

STAR\

--chimSegmentMin 10\

--runThreadN 10\

--genomeDir hg38_star_index\

--readFilesIn tumor.circ.R1.fq tumor.circ.R2.fq

Among them, --chimSegmentMin specifies that the number of bases aligned at one end of the chimeric alignment is at least 10bp;

--runThreadN specifies the number of running threads; --genomeDir specifies the reference index file path used; --readFilesIn specifies the input sequencing data.

In the Parse step, use the CIRCexplorer2 parse command to parse the output in the Align step. junction information, the example command is:

CIRCexplorer2 parse\

-t STAR\

-b back_spliced_junction.bed\

Chimeric.out.junction

Among them, -t indicates the comparison tool used in the Align step; -b indicates the output file name; Chimeric.out.junction indicates the input file name.

In the Annotate step, the back splicing site is annotated based on the provided gene annotation file. The example command is:

CIRCexplorer2 annotate\

-r GTF\

-g hg38.fa\

-b back_spliced_junction.bed\

-o tumor_circRNA.txt

Among them, -r specifies the gene annotation file; -g specifies the human reference genome file; -b specifies the junction information output by the Parse step; -o specifies the output file name.

The standard process example command of another detection algorithm CIRI2 is as follows:

perlCIRI2.pl\

--in tumor.circ.sam\

--ref_file hg38.fa\

--anno GTF\

--out outfile

Among them, --in specifies the sam format file, which is generated by comparing circRNA sequencing data with the bwa-mem tool; --ref_file specifies the human reference genome file; --anno specifies the gene annotation file; --out specifies the output result file.

Finally, all candidate circRNAs detected by the two detection algorithms CIRCexplorer2 and CIRI2 on the first sequencing data and the second sequencing data were summarized.

S3. For each candidate circRNA, construct the reverse pseudo-reference sequence of the first specified length upstream and downstream of the BSJ site according to its sequence order.

The BSJ site refers to the backsplicing junction (BSJ). The first specified length can be set to 100 bp. Therefore, step S3 is specifically for each candidate circRNA, construct the reverse splicing junction according to the sequence order of the circRNA. The pseudo-reference sequence and its index information of 100 bp upstream and downstream of the BSJ site are shown in Figure 2. The reverse pseudo-reference sequence constructed for the candidate circRNA is shown in Figure 2.

S4. Determine the reads that have the BSJ site in the first sequencing data and match the second specified length sequence upstream and downstream of the BSJ site with the pseudo-reference sequence as the first candidate reads; and, identify the reads that have the BSJ site in the second sequencing data. The reads that match the second specified length sequence upstream and downstream of the BSJ site and the pseudo-reference sequence are determined as second candidate reads.

Wherein, the second specified length is less than or equal to the first specified length. For example, the second specified length may be set to 3bp, 5bp, or 10bp, etc., or in some other possible embodiments, the second specified length may also be set equal to the first specified length. Specify the length, but should not be greater than the second specified length, which is 100bp.

In this embodiment, the second specified length is set to 3 bp as an example. In step S4, all reads supporting the circular RNA BSJ site in the first sequencing data and the second sequencing data are specifically extracted, and according to the index information Re-align to the pseudo-reference sequence constructed in the previous step S3. It is required that the 3 bp sequences upstream and downstream of the BSJ site in the reads completely match the pseudo-reference sequence. That is to say, when the first sequencing data and the second sequencing data There are reads with a BSJ site and the 3 bp sequences upstream and downstream of the BSJ site that completely match the pseudo-reference sequence, which can be determined as candidate circular RNA reads (i.e., the first candidate reads and the second candidate reads).

In order to further eliminate false positive reads, the screened candidate circRNA reads can be compared with the normal human genome and transcriptome, and the candidate circRNA reads on the normal alignment can be filtered out to obtain the real candidate circRNA reads.

S5. Determine the candidate circRNA supported by the first candidate reads as the first circRNA detected from the tumor tissue sample; and determine the candidate circRNA supported by the second candidate reads as detected from the adjacent cancer tissue. Second circular RNA detected in the sample.

In step S5, among multiple candidate circRNAs, the candidate circRNAs supported by reads with 3 bp sequences upstream and downstream of the BSJ site that completely match the pseudo-reference sequence are selected from tumor tissue samples and adjacent cancer tissue samples. The final detected circular RNA (i.e. the first circular RNA and the second circular RNA).

S6. Filter out the first circRNA that is identical to the second circRNA from the plurality of first circRNAs to obtain multiple tumor-specific circRNAs.

As the name suggests, tumor-specific circular RNA refers to circular RNA that only exists in tumor cells. Therefore, the circRNAs also detected in adjacent tissue samples can be filtered out. These circRNAs belong to the circRNAs that normally exist in cells. Therefore, these circRNAs need to be filtered out, that is, multiple first circRNAs need to be filtered out. The first circular RNA in RNA that is identical to the second circular RNA.

However, due to individual differences and limitations of sequencing data, circRNAs in adjacent cancer tissue samples cannot represent all normal circRNAs. Therefore, more circRNAs from normal cells can also be collected from published circRNA databases to combine The first circRNA detected in the tumor tissue sample is further compared with them to identify tumor tissue-specific circRNAs. Among them, the circRNA database preferably uses one or more combinations of circBase, CIRCpedia v2 and CircAtlas.

Therefore, step S6 may specifically include: filtering out the first circRNAs that are identical to the second circRNAs from the plurality of first circRNAs, and filtering out the circRNAs in the specified circRNA database from the plurality of first circRNAs. The first circRNA that is the same as the normal cell circRNA is obtained, and the retained first circRNA is obtained as the final tumor-specific circRNA.

S7. Predict the translation ability score of each tumor-specific circular RNA.

After the tumor-specific circRNA is detected, the translation ability of the tumor-specific circRNA can be further evaluated, that is, the translation potential of the tumor-specific circRNA can be determined. The specific implementation includes the following steps S701 to S704:

S701. Determine the reads in the third sequencing data that have the BSJ site and that the second specified length sequences upstream and downstream of the BSJ site match the pseudo-reference sequence of the tumor-specific circular RNA as reads spanning the BSJ site.

Since the third sequencing data is already high-quality sequencing data after quality control filtering and processing according to the above steps S101 to S102, the third sequencing data can be directly compared with the pseudo-reference sequence of tumor-specific circular RNA. For the results, if there are reads with a BSJ site in the third sequencing data and the second specified length (3bp) sequence upstream and downstream of the BSJ site matches the pseudo-reference sequence of tumor-specific circular RNA, it is determined to span the BSJ site. The third candidate reads.

S702. Determine whether the position of each tumor-specific circular RNA across the BSJ site is aligned with the third candidate read. If yes, execute step S703; otherwise, execute step S704.

S703. Set the translation ability score of the tumor-specific circular RNA of the third candidate read across the position of the BSJ site to the maximum score.

Polyribosome analysis can extract the RNA that is being translated with ribosomes in the cell, and combined with high-throughput RNA sequencing can accurately identify the RNA sequence that is being translated. That is to say, if the position of any tumor-specific circular RNA across the BSJ site is aligned with the third candidate read, it means that the tumor-specific circular RNA is being combined with ribosomes for translation, and its translation potential can be considered to be the greatest. Then its translation ability score can be set to the maximum score, such as 1. Assume that the value range of the translation ability score is [0, 1], then the minimum score is 0 and the maximum score is 1.

S704. Construct the first full-length sequence of the tumor-specific circular RNA that does not align with the third candidate read across the BSJ site, determine the multiple IRES fragments in the first full-length sequence, and calculate the value of each IRES fragment. Raw score; the maximum raw score among multiple IRES fragments is normalized to obtain the translation ability score of the tumor-specific circRNA.

A large number of studies have confirmed that circular RNA is not similar to mRNA due to its closed loop characteristics. The 5'-end capped structure uses an internal ribosome entry site (IRES) element with a special secondary structure to recruit ribosomes to initiate translation. Therefore, its translation ability can be predicted by analyzing the endogenous IRES elements in the circRNA sequence. In this example, for tumor-specific circular RNAs that do not have third candidate reads aligned across the BSJ site, the translation ability is predicted by analyzing the endogenous IRES element in the circular RNA sequence.

Then the specific implementation of step 704 is: construct the full-length sequence of all tumor-specific circular RNAs that do not have third candidate reads across the BSJ site as the first full-length sequence, and combine multiple designated hexamers The nucleic acid sequence is mapped to the first full-length sequence, multiple target hexamer nucleic acid sequences that overlap with the specified hexamer nucleic acid sequence in the first full-length sequence are determined, and adjacent target hexamer nucleic acid sequences are merged, The merged sequence is regarded as an IRES fragment, in which the target hexamer nucleic acid sequences that overlap on the first full-length sequence need to be merged, but are not mutually exclusive with each other after merging in the entire first full-length sequence. Overlapping ones are regarded as different IRES fragments, so that multiple IRES fragments in the first full-length sequence can be obtained. For example, assume that the specified hexamer nucleic acid sequence includes the following four types:

AATAAA, AAAAGA, ACAAAA, CAAAAA;

The first full-length sequence is: AATAAAAGATTGGAGGACAAAAACCGG. The bolded part is the merged two IRES fragments.

The raw score of each IRES fragment is equal to the sum of the Z scores of all target hexamer nucleic acid sequences in the IRES fragment divided by the combined sequence length of the IRES fragment. Finally, among multiple IRES fragments in tumor-specific circular RNA The maximum raw score is normalized so that it is distributed in a specified value range (such as [0, 1]), thereby obtaining the translation ability score of the tumor-specific circRNA.

Among them, the specified hexamer nucleic acid sequence can be a pre-collected hexamer nucleic acid sequence with Z score>7. These IRES-like functional hexamer nucleic acid sequence short elements are significantly enriched in circular RNA to drive circular RNA. translate.

S8. Obtain multiple neoantigen candidate peptides derived from multiple tumor-specific circular RNAs.

Step S8 specifically includes the following steps S801 to S804:

S801. Construct second full-length sequences of multiple tumor-specific circular RNAs.

Circular RNA is formed by abnormal back-splicing of precursor RNA molecules, and circular RNA in the cytoplasm is mainly formed from the exons of conventional transcripts. For circRNAs spanning multiple exons, completely different circRNA sequences may be formed due to alternative splicing. As shown in Figure 3, the same BSJ site can form two different circRNA sequences. Therefore, it is necessary to construct the full-length sequence of tumor-specific circular RNA.

During the construction process, samtools was first used to extract all reads within the interval covered by the tumor-specific circular RNA. Since the experimental processing of sample library construction has filtered out the sequences of all linear transcripts, these reads can be used to determine the internal structure of the circular RNA. structure. Then the number of reads for all possible exon junctions in tumor-specific circular RNAs was counted, and the number of reads ≥ 3 was considered a valid junction. Finally, the full-length sequences of all tumor-specific circular RNAs were constructed based on the exon junction information within the region as the second full-length sequence.

S802. Predict the second full-length sequence according to three reading frames and obtain multiple open reading frame sequences.

Non-"ATG" start codons are relatively common in the IRES-mediated translation process, so all "NTG" are considered as their start codons when predicting the open reading frame (Open Reading Frame, ORF) sequence, where N represents Any base among A, C, T, and G.

The second full-length sequence constructed based on all tumor-specific circular RNAs is predicted according to three reading frames, with "NTG" as the start codon extending backward and stopping when it encounters the stop codon. For a base sequence starting from the start codon and not interrupted by the stop codon, it is determined as an ORF sequence with the potential to encode proteins in the DNA sequence. Each predicted ORF sequence is at least 60 bp in length and must span the domain BSJ site. If there is a longer ORF sequence that completely covers the shorter ORF sequence, the short ORF sequence will no longer be considered.

S803. Translate the open reading frame sequence of the third specified length and the cross-domain BSJ site into an amino acid sequence according to the codon table.

After multiple ORF sequences are predicted, the ORF sequences that meet the criteria (i.e., are at least 60 bp in length and must cross domain BSJ sites) are translated into amino acid sequences according to the codon table and are regarded as full-length protein sequences derived from tumor-specific circular RNAs. .

S804. Divide the amino acid sequence into multiple peptide segments, and filter out the peptide segments included in the human normal proteome to obtain multiple neoantigen candidate peptide segments.

Because the sequence of a circular RNA molecule is highly consistent with the sequence of the transcript from which it is derived, the protein sequence is also very similar to a large extent. Therefore, after being divided into multiple peptide segments, these peptide segments need to be further filtered to filter out peptide segments that exist in the normal human proteome to obtain tumor-specific cyclic RNA neoantigen candidate peptide segments.

S9. Score and rank the immunogenicity of multiple neoantigen candidate peptides according to the translation ability score of the tumor-specific circular RNA.

Among them, the translation ability score is specifically used as a scoring indicator of the neoantigen immunogenicity scoring model, and then the neoantigen immunogenicity scoring model is used to score each neoantigen candidate peptide segment separately and then rank.

In addition, preferably, after performing the above step S6 to filter out the first circRNA that is the same as the second circRNA from the plurality of first circRNAs to obtain multiple tumor-specific circRNAs, each of them can also be counted. The number of first candidate reads on the tumor-specific circRNA alignment, and then the abundance of each tumor-specific circRNA was calculated based on the number of first candidate reads, expressed in reads per million (RPM). Abundance can also be used as a scoring indicator in the neoantigen immunogenicity scoring model. Therefore, step S9 specifically includes: scoring and ranking the immunogenicity of multiple neoantigen candidate peptides according to the translation ability score and abundance of the tumor-specific circRNA.

The immunogenicity process of neoantigens is relatively complex, including proteasome cleavage of protein sequences to obtain peptide fragments, processing of peptide fragments in the endoplasmic reticulum and presentation to the cell surface by MHC molecules, pMHC (peptide-MHC) complexes and T cells Binding of receptors (TCR) initiates immune responses and so on.

The embodiments of the present invention further take into account that the peptide segments recognized and presented by human histocompatibility antigen molecules (human leukocyte antigen, HLA) are usually shorter, among which class I HLA mainly recognizes peptide segments in the length range of 8-12aa, and class II HLA The sequence length of the identified peptides is slightly longer, mainly 15aa.

Therefore, in step S804, the full-length protein sequence obtained in step S804 can be slidingly divided into peptide segments corresponding to the length range according to the above-mentioned sequence length mainly recognized by HLA, thereby dividing the sequence into Neoantigen candidate peptides whose sequence length is within the first length range are determined to be class I neoantigen candidate peptides; and neoantigen candidate peptides whose sequence length is within the second length range are determined to be class II neoantigen candidate peptides. ; Wherein, the second length range is greater than the first length range.

Among them, peptides whose sequence length is within the first length range (such as 8-12aa) are regarded as class I neoantigen candidate peptides and bind to HLA class I molecules; peptides whose sequence length is within the second length range including 15aa Peptides within the range (such as 14-16aa) are considered class II neoantigen candidate peptides. The preferred class II neoantigen candidate peptide is 15aa in length and binds to HLA class II molecules.

Then the binding affinity between the class I neoantigen candidate peptide and the patient's HLA class I molecule corresponding to the tumor tissue sample can be predicted respectively, as well as the binding affinity between the class II neoantigen candidate peptide and the patient's HLA class II molecule corresponding to the tumor tissue sample. Affinity, the binding affinity can also be used as a scoring index in the neoantigen immunogenicity scoring model. Then step S9 is specifically: based on the translation ability score and abundance of the tumor-specific circRNA, and the binding affinity of each neoantigen candidate peptide to the corresponding HLA molecule, perform immunogenicity on multiple neoantigen candidate peptides respectively. Score and sort.

In addition, the physical and chemical properties of the neoantigen candidate peptide itself can also be considered, such as the possibility of the peptide being cleaved by the proteasome, the hydrophobicity score of the T cell-binding residues in the peptide, etc.

Ultimately, the preferred neoantigen immunogenicity scoring model takes the above process into consideration. The scoring indicators used for neoantigen candidate peptides (peptides for short) include at least: the translation ability score of the tumor-specific circRNA from which the peptides are derived. , the abundance of tumor-specific circRNA from which the peptide is derived, the binding affinity of the peptide to the corresponding HLA molecule, the possibility that the peptide is cleaved by the proteasome, and the hydrophobicity score of the T cell-binding residues in the peptide. multiple combinations.

Preferably, the neoantigen immunogenicity scoring model can be set as a linear model, and the neoantigen immunogenicity score is obtained by assigning different weights to each scoring index after normalization and summing. Specifically, for each neoantigen candidate peptide, the translation ability score of the tumor-specific circRNA from which the peptide is derived, the abundance of the tumor-specific circRNA from which the peptide is derived, and the binding of the peptide to the corresponding HLA molecule Multiple combinations of affinity, the possibility that the peptide is cleaved by the proteasome, and the hydrophobicity score of the T cell-binding residues in the peptide are calculated with the corresponding weight coefficients to obtain each neoantigen candidate peptide. The neoantigen immunogenicity scores of the segments are then sorted from high to low according to the neoantigen immunogenicity scores.

S10. Determine the specified number of neoantigen candidate peptides that are ranked first as the neoantigen target peptides.

Finally, the top-ranked neoantigen candidate peptides can be determined as circRNA-derived neoantigen target peptides, and their immunogenicity can be further verified through experiments and used in clinical immunotherapy for tumor patients.

It can be seen that by implementing the embodiments of the present invention, by constructing a circRNA pseudo-reference sequence for re-alignment from the detected candidate circRNAs, the first sequencing data of the tumor tissue samples and the second sequencing data of the adjacent cancer tissue samples are respectively Compare with the pseudo-reference sequence, extract the first candidate reads and second candidate reads of the pseudo-reference sequence, and determine the first circRNA detected in the tumor tissue sample and the first circRNA detected in the adjacent tissue sample. The second circRNA, the two are fused to filter out the normal circRNA that exists in both tumor tissue samples and adjacent tissue samples to obtain tumor-specific circRNAs, and then verify that each tumor-specific circRNA is in the corresponding pseudo-cRNA. The number of aligned first candidate reads in the reference sequence is used to calculate the abundance of tumor-specific circular RNAs from which each neoantigen candidate peptide segment is derived based on the number of first candidate reads; and for tumor-specific circular RNAs that span BSJ sites For circRNAs, the translation ability score is set to the maximum score; for tumor-specific circRNAs that do not have a cross-BSJ site, the translation ability score is predicted based on the endogenous IRES element, and the tumor-specific circRNA from which the fusion peptide is derived is Based on scoring indicators such as the abundance of circRNA, the translation ability score, and the binding affinity of the peptide to the corresponding HLA molecule, the immunogenicity of the neoantigen candidate peptides derived from it is scored and ranked, and the top ranked ones are finally determined. Neoantigen target peptides can increase the source of tumor neoantigens and broaden the scope of neoantigen screening. At the same time, it can further improve the identification accuracy of circRNA, thereby further making circRNA-derived neoantigens more immunogenic. sex.

Compared with neoantigens derived mainly from somatic point mutations (SNV) and insertion and deletion variations (INDEL), embodiments of the present invention provide a computer-implemented method to explore proteins that translate circular RNA based on second-generation sequencing data. As a potential source of neoantigens for tumor-specific immunotherapy, it expands the screening scope of neoantigens, which is especially beneficial for tumor types with low mutation load; and comprehensively considers the translation potential of tumor-specific circRNAs, and by integrating the two most advanced The results of the circular RNA detection algorithm (CIRCexplorer2, CIRI2) were used to construct a circular RNA pseudo-reference sequence for re-alignment, and verify the number of aligned reads of each candidate circular RNA in the corresponding pseudo-sequence reference, achieving a more accurate Accurate circRNA identification can be used to more accurately identify circRNA-based immunotherapy neoantigens.

As shown in Figure 4, the embodiment of the present invention discloses a neoantigen identification device for tumor-specific circular RNA. The device can be embedded in a computer. The device includes a data acquisition unit 401, a detection unit 402, a pseudo reference unit 403, a comparison unit 404, a first determination unit 405, a filtering unit 406, a translation prediction unit 407, and a peptide acquisition unit. Unit 408, scoring unit 409 and second determining unit 410, wherein,

The data acquisition unit 401 is used to acquire the first sequencing data of tumor tissue samples and the second sequencing data of adjacent cancer tissue samples;

The detection unit 402 is used to detect circRNAs on the first sequencing data and the second sequencing data respectively to obtain multiple candidate circRNAs;

Pseudo-reference unit 403 is used to construct pseudo-reference sequences of the first specified length upstream and downstream of the reverse BSJ site according to its sequence order for each candidate circRNA;

The comparison unit 404 is used to determine the reads in the first sequencing data that have the BSJ site and the second specified length sequences upstream and downstream of the BSJ site that match the pseudo-reference sequence as first candidate reads; and, determine the second candidate reads The reads that have a BSJ site in the sequencing data and the sequences of the second specified length upstream and downstream of the BSJ site match the pseudo-reference sequence are determined as second candidate reads; where the second specified length is less than or equal to the first specified length;

The first determination unit 405 is used to determine the candidate circRNA supported by the first candidate reads as the first circRNA detected from the tumor tissue sample; and, determine the candidate circRNA supported by the second candidate reads. Identified as a second circular RNA detected in paracancerous tissue samples;

The filtering unit 406 is used to filter out the first circular RNA that is the same as the second circular RNA from the plurality of first circular RNAs to obtain a plurality of tumor-specific circular RNAs;

The translation prediction unit 407 is used to predict the translation ability score of each tumor-specific circular RNA;

The peptide acquisition unit 408 is used to acquire multiple neoantigen candidate peptides derived from multiple tumor-specific circular RNAs;

The scoring unit 409 is used to score and rank the immunogenicity of multiple neoantigen candidate peptide segments according to the translation ability score of the tumor-specific circular RNA;

The second determination unit 410 is used to determine a specified number of neoantigen candidate peptide segments ranked first as Neoantigen target peptides.

As shown in Figure 5, an embodiment of the present invention discloses an electronic device, including a memory 501 storing executable program code and a processor 502 coupled with the memory 501;

The processor 502 calls the executable program code stored in the memory 501 to execute the tumor-specific circular RNA neoantigen identification method described in the above embodiments.

Embodiments of the present invention also disclose a computer-readable storage medium that stores a computer program, wherein the computer program causes the computer to execute the neoantigen identification method of tumor-specific circular RNA described in the above embodiments.

The purpose of the above embodiments is to exemplarily reproduce and deduce the technical solutions of the present invention, and thereby completely describe the technical solutions, purposes and effects of the present invention. The purpose is to enable the public to have a more thorough understanding of the disclosed content of the present invention. , comprehensive, and does not limit the scope of the present invention.

The above embodiments are not an exhaustive list of the present invention. In addition, there may be many other unlisted implementations. Any substitutions and improvements made without violating the concept of the present invention shall fall within the protection scope of the present invention.

Claims (10)

1. The method for identifying new antigens of tumor-specific circular RNA is characterized by including:

   Obtain the first sequencing data of the tumor tissue sample and the second sequencing data of the adjacent cancer tissue sample;

   Perform circRNA detection on the first sequencing data and the second sequencing data respectively to obtain multiple candidate circRNAs;

   For each of the candidate circRNAs, a pseudo-reference sequence of the first specified length upstream and downstream of the reverse BSJ site is constructed according to its sequence order;

   Determine the reads in the first sequencing data that have the BSJ site and the second specified length sequences upstream and downstream of the BSJ site that match the pseudo-reference sequence as first candidate reads; and, The reads in the second sequencing data that have the BSJ site and the second specified length sequences upstream and downstream of the BSJ site that match the pseudo-reference sequence are determined as second candidate reads; wherein, the second The specified length is less than or equal to the first specified length;

   determining the candidate circRNA supported by the first candidate reads as the first circRNA detected from the tumor tissue sample; and determining the candidate circRNA supported by the second candidate reads as a second circular RNA detected from the adjacent cancer tissue sample;

   Filter out the first circRNA that is the same as the second circRNA from the plurality of first circRNAs to obtain a plurality of tumor-specific circRNAs;

   predicting a translation ability score for each of the tumor-specific circular RNAs;

   Obtain multiple neoantigen candidate peptide segments derived from multiple tumor-specific circular RNAs;

   According to the translation ability score of the tumor-specific circular RNA, perform immunogenicity scoring and ranking on multiple neoantigen candidate peptide segments;

   The specified number of neoantigen candidate peptides ranked first are determined as the neoantigen target peptides.
2. The neoantigen identification method of tumor-specific circRNA as claimed in claim 1, characterized in that predicting the translation ability score of each tumor-specific circRNA includes:

   Obtain third sequencing data based on polyribosome analysis of the tumor tissue sample;

   The reads in the third sequencing data that have the BSJ site and the sequences of the second specified length upstream and downstream of the BSJ site that match the pseudo-reference sequence of the tumor-specific circular RNA are determined as the third candidate reads across the BSJ site. ;

   Determine in turn whether the position of each tumor-specific circular RNA across the BSJ site is aligned with the third candidate read;

   If aligned, the translation ability score of the tumor-specific circular RNA of the third candidate read across the BSJ site location is set to the maximum score;

   If there is no alignment, construct the first full-length sequence of the tumor-specific circular RNA that does not have the third candidate read aligned across the BSJ site, and determine the multiple IRES fragments in the first full-length sequence, The raw score of each of the IRES fragments is calculated; the maximum raw score among multiple IRES fragments is normalized to obtain the translation ability score of the tumor-specific circRNA.
3. The neoantigen identification method of tumor-specific circular RNA according to claim 2, characterized in that determining multiple IRES fragments in the first full-length sequence includes:

   Map multiple specified hexamer nucleic acid sequences to the first full-length sequence, determine multiple target hexamer nucleic acid sequences that overlap with the specified hexamer nucleic acid sequence in the first full-length sequence, and compare the positions. The adjacent target hexamer nucleic acid sequences are merged to obtain multiple IRES fragments.
4. The neoantigen identification method of tumor-specific circular RNA as claimed in claim 3, characterized in that calculating the original score of each IRES fragment includes:

   The raw score for each IRES fragment is obtained by dividing the sum of the Z scores of all target hexamer nucleic acid sequences in each of the IRES fragments by the sequence length of the IRES fragment.
5. The neoantigen identification method of tumor-specific circular RNA according to any one of claims 2 to 4, characterized in that, a plurality of neoantigen candidate peptide segments derived from the tumor-specific circular RNA are obtained, including:

   Construct multiple second full-length sequences of the tumor-specific circular RNA, predict the second full-length sequences according to three reading frames, and obtain multiple open reading frame sequences;

   Translate the open reading frame sequence of the third specified length and the cross-domain BSJ site into an amino acid sequence according to the codon table;

   The amino acid sequence is divided into multiple peptide segments, and peptide segments included in the human normal proteome are filtered out to obtain multiple neoantigen candidate peptide segments.
6. The method for identifying neoantigens of tumor-specific circular RNAs according to any one of claims 2 to 4, characterized in that, from a plurality of the first circular RNAs, those identical to the second circular RNAs are filtered out. After the first circRNA obtains a plurality of tumor-specific circRNAs, the method further includes:

   Count the number of first candidate reads on each of the tumor-specific circRNA alignments;

   Calculate the abundance of each tumor-specific circular RNA based on the number of first candidate reads;

   And, according to the translation ability score of the tumor-specific circular RNA, the immunogenicity scores and rankings of multiple neoantigen candidate peptide segments are respectively performed, including:

   According to the translation ability score and abundance of the tumor-specific circular RNA, multiple neoantigen candidate peptide segments are individually scored and ranked for their immunogenicity.
7. The neoantigen identification method of tumor-specific circRNA according to claim 6, wherein after obtaining a plurality of neoantigen candidate peptide segments derived from the tumor-specific circRNA, the method further includes:

   Determining the neoantigen candidate peptides whose sequence length is within the first length range as Class I neoantigen candidate peptides; and determining the neoantigen candidate peptides whose sequence length is within the second length range as Class II neoantigen candidate peptides segment; wherein the second length range is greater than the first length range;

   Predict the binding affinity between the class I neoantigen candidate peptide segment and the HLA class I molecule of the patient corresponding to the tumor tissue sample; and, predict the binding affinity between the class II neoantigen candidate peptide segment and the patient corresponding to the tumor tissue sample. Binding affinity between HLA class II molecules;

   And, according to the translation ability score and abundance of the tumor-specific circRNA, immunogenicity scores and rankings are performed on multiple neoantigen candidate peptide segments, including:

   According to the translation ability score and abundance of the tumor-specific circular RNA, and the binding affinity of each neoantigen candidate peptide segment to the corresponding HLA molecule, immunogenicity is performed on multiple neoantigen candidate peptide segments respectively. Score and sort.
8. The tumor-specific circular RNA neoantigen identification device is characterized by including:

   a data acquisition unit, used to acquire first sequencing data of tumor tissue samples and second sequencing data of adjacent cancer tissue samples;

   A detection unit, configured to detect circRNAs on the first sequencing data and the second sequencing data respectively, to obtain a plurality of candidate circRNAs;

   A pseudo-reference unit, used to construct a pseudo-reference sequence of the first specified length upstream and downstream of the reverse BSJ site according to its sequence order for each of the candidate circular RNAs;

   Alignment unit, used to determine the reads in the first sequencing data that have the BSJ site and the second specified length sequences upstream and downstream of the BSJ site that match the pseudo-reference sequence as first candidates. reads; and, determine the reads in the second sequencing data that have the BSJ site and the sequences of the second specified length upstream and downstream of the BSJ site that match the pseudo-reference sequence as second candidate reads; Wherein, the second specified length is less than or equal to the first specified length;

   A first determination unit, configured to determine the candidate circular RNA supported by the first candidate reads as the first circular RNA detected from the tumor tissue sample; and, determine the candidate circular RNA supported by the second candidate reads. The candidate circRNA is determined to be the second circRNA detected from the adjacent cancer tissue sample;

   a filtering unit, configured to filter out the first circular RNA that is the same as the second circular RNA from the plurality of first circular RNAs to obtain a plurality of tumor-specific circular RNAs;

   a translation prediction unit for predicting the translation ability score of each of the tumor-specific circular RNAs;

   a peptide acquisition unit, used to acquire a plurality of neoantigen candidate peptides derived from a plurality of tumor-specific circular RNAs;

   A scoring unit is used to score a plurality of all tumor-specific circular RNAs based on their translation ability scores. The above-mentioned neoantigen candidate peptides were scored and ranked according to their immunogenicity;

   The second determination unit is used to determine a specified number of neoantigen candidate peptides that are ranked first as neoantigen target peptides.
9. Electronic device, characterized in that it includes a memory storing executable program code and a processor coupled to the memory; the processor calls the executable program code stored in the memory to execute claim 1 The neoantigen identification method of tumor-specific circular RNA according to any one of to 7.
10. Computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program, wherein the computer program causes the computer to perform the neoantigen identification of tumor-specific circular RNA according to any one of claims 1 to 7 method.