WO2016049929A1 - Method for constructing sequencing library and application thereof - Google Patents

Abstract

The present invention provides a method for constructing a sequencing library, a sequencing method, a method for determining a nucleic acid sequence, an apparatus for constructing a sequencing library, a sequencing device and a system for determining a nucleic acid sequence.

Description

Method for constructing sequencing library and its application Technical field

The invention relates to the field of biomedicine. In particular, the invention relates to methods of constructing sequencing libraries, sequencing methods, methods of determining nucleic acid sequences, devices for constructing sequencing libraries, sequencing devices, and systems for determining nucleic acid sequences.

Background technique

High-throughput sequencing is gaining increasing attention, but the detection of low-frequency mutations for high-throughput sequencing is still to be improved.

Summary of the invention

The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, in accordance with embodiments of the present invention, the present invention proposes methods for constructing sequencing libraries and means for detecting low frequency mutations.

In a first aspect of the invention, the invention proposes a method of constructing a sequencing library. According to an embodiment of the invention, the method comprises: (a) separately joining a linker at both ends of the double-stranded DNA fragment to obtain a ligation product, wherein the linker comprises a first strand and a second strand, the first strand Matching the second strand portion and the first strand comprises a first tag sequence such that the linker defines a double stranded region and two single stranded tails, the sequence of one of the two single stranded tails comprising the first a label; (b) cleavage of the ligated product into a single-stranded DNA fragment; (c) performing a strand extension reaction on the single-stranded DNA fragment using a first primer to obtain a strand extension product, wherein the first primer comprises a second tag sequence, and the first primer is adapted to form a double stranded structure with the first strand of the linker, except that there is a mismatch between the first tag sequence and the second tag sequence; (d) pair The strand extension product is amplified to obtain an amplification product using primers suitable for simultaneously amplifying the first tag sequence and the second tag sequence.

Thus, with the method of constructing a sequencing library according to an embodiment of the present invention, a sequencing library can be efficiently constructed, and at the same time, the constructed sequencing library is directed to the same double-stranded DNA fragment (also referred to herein as a "source sequence"). Each of the chains has an amplification product having a first tag sequence and a second tag sequence, respectively, whereby in the analysis of subsequent sequencing results, mutual calibration can be performed according to the sequencing results of the two tags, and the analysis is improved. The reliability of the results.

According to an embodiment of the present invention, the double-stranded DNA fragment is obtained by subjecting a nucleic acid sample to end repair to obtain a repaired nucleic acid sample; and adding a base A at the 5' end of the nucleic acid sample, In order to obtain a nucleic acid sample having viscous terminal bases A at both ends, respectively, the nucleic acid samples having viscous terminal bases A at both ends constitute the double-stranded DNA fragment. Thus, a linker can be conveniently added to both ends of the double-stranded DNA fragment in a subsequent operation. Thereby, the efficiency of constructing a sequencing library is improved.

According to an embodiment of the invention, the nucleic acid sample is at least a portion of human genomic DNA or a free nucleic acid. According to an embodiment of the invention, the human free nucleic acid is extracted from the peripheral blood of the patient. According to an embodiment of the invention, the The patient has cancer, which is at least one selected from the group consisting of bladder cancer, prostate cancer, lung cancer, colorectal cancer, stomach cancer, breast cancer, kidney cancer, pancreatic cancer, ovarian cancer, endometrial cancer, thyroid cancer. , cervical cancer, esophageal cancer and liver cancer. Therefore, the method of the embodiment of the present invention can effectively analyze gene mutations of human disease patients, and can be effectively used for early diagnosis, individualized medication, and postoperative monitoring of common tumors.

According to an embodiment of the invention, at least a portion of the human genomic DNA is obtained by random disruption of human genomic DNA. Thus, a linker can be conveniently added to both ends of the double-stranded DNA fragment in a subsequent operation. Thereby, the efficiency of constructing a sequencing library is improved.

According to an embodiment of the invention, the linker has a 3' base T sticky end. Thus, a linker can be conveniently added to both ends of the double-stranded DNA fragment in a subsequent operation. Thereby, the efficiency of constructing a sequencing library is improved.

According to an embodiment of the present invention, the single-stranded DNA fragment is obtained by subjecting the ligation product to denaturation treatment. Thereby, a single-stranded DNA fragment can be obtained quickly and efficiently. According to some embodiments of the invention, the denaturation treatment may be a heat denaturation treatment or an alkali denaturation treatment.

According to an embodiment of the present invention, the single-stranded DNA fragment is screened using a probe prior to performing the strand extension, wherein the probe specifically recognizes a predetermined region. Thereby, the sequencing library can be efficiently constructed for the region of interest, and the efficiency of constructing the sequencing library and subsequent sequencing is improved.

According to an embodiment of the invention, the predetermined area comprises one of the following:

(1) at least one of the genes shown in Tables 1 to 5;

(2) the CDS area of (1);

(3) A region of at least 10 bp upstream and downstream of (2).

Thereby, it is possible to efficiently construct a sequencing library and subsequent nucleic acid sequence analysis of cancer-related genes.

According to an embodiment of the invention, the probe is provided in the form of a chip. Thereby, the efficiency of probe screening can be improved.

According to an embodiment of the invention, the strand extension reaction is carried out in the presence of a UDG enzyme/FPG enzyme. Thereby, the damaged DNA can be effectively repaired during the chain extension process, the generation of false positives is reduced, and the quality of the constructed sequencing library is improved.

According to an embodiment of the invention, the first tag sequence and the second tag sequence are each independently 4 to 10 nt in length. According to an embodiment of the invention, the first tag sequence and the second tag sequence are both 8 nt in length. According to an embodiment of the invention, there is a mismatch of at least 2 nt between the first tag sequence and the second tag sequence. The inventors have surprisingly found that with such an arrangement, the efficiency of correction using the first tag sequence and the second tag sequence in subsequent analysis can be effectively improved.

According to an embodiment of the invention, the first strand of the linker has the sequence set forth in SEQ ID NO: 1, the second strand of the linker has the sequence set forth in SEQ ID NO: 2, the first tag having SEQ ID NO: shown in any of 3-6 a sequence, the second tag having the sequence set forth in at least one of SEQ ID NOs: 7-10, the first primer having the sequence set forth in SEQ ID NO: 11, the The primers of the first tag sequence and the second tag sequence have the sequences set forth in SEQ ID NO: 12 and SEQ ID NO: 13.

Wherein "XXXXXXXX" in the sequence of the first strand of the linker represents the first tag sequence, and "XXXXXXXX" in the sequence in the first primer represents the second tag sequence.

In accordance with embodiments of the present invention, the labels include, but are not limited to, the four pairs described above, and multiple pairs of labels can be designed as needed for simultaneous detection of multiple samples.

In a second aspect of the invention, the invention proposes a sequencing method comprising: constructing a sequencing library according to the method described above; sequencing the sequencing library.

The sequencing was performed on Hiseq2000 or Hiseq 2500 according to an embodiment of the invention.

Thereby, the efficiency of sequencing can be effectively improved. In addition, the features and advantages described above with respect to the method of constructing a sequencing library are equally applicable to the sequencing method and will not be described herein.

In a third aspect of the invention, the invention provides a method of determining a nucleic acid sequence, comprising:

For nucleic acid samples, sequencing is performed according to the methods previously described in the claims to obtain sequencing results consisting of multiple sequencing data;

Based on the sequencing result, at least one subset of sequencing data is constructed, wherein all sequencing data in each subset of sequencing data corresponds to the same source sequence on the nucleic acid sample;

For each subset of sequencing data, determining that the sequencing data corresponding to the first tag sequence is positive strand sequencing data, and the sequencing data corresponding to the second tag sequence is negative strand sequencing data;

Correcting the sequencing data for each of the sequencing data subsets based on the positive strand sequencing data and the negative strand sequencing data, respectively, to determine corrected sequencing data;

A sequence of the nucleic acid sample is determined based on the corrected sequencing data.

Thereby, the calibration can be effectively performed based on the positive strand sequencing data and the negative strand sequencing data, thereby improving the reliability of the analysis result.

According to an embodiment of the invention, the sequencing is a double-end sequencing, the sequencing result consisting of pairs of pairs of sequencing data.

According to an embodiment of the invention, constructing at least one subset of sequencing data based on the sequencing results is performed by the following steps:

Determining a paired sequencing data index for each pair of the plurality of pairs of sequenced data, the paired sequencing data index consisting of an initial N bases of each of the paired sequencing data, wherein N is An integer between 10 and 20;

Constructing at least one preliminary sequencing data subset based on the paired sequencing data index, wherein each of the sequencing data subsets has the same paired sequencing data index;

The at least one preliminary sequencing data subset is subdivided based on a Hamming distance between the sequencing data in the preliminary sequencing data subset to obtain a plurality of the sequencing data subsets.

According to an embodiment of the invention, N is 12.

According to an embodiment of the invention, in each of the plurality of sequencing data subsets, the Hamming distance of any two pairs of paired sequencing data does not exceed 20.

According to an embodiment of the invention, in each of the plurality of sequencing data subsets, the positive strand sequencing data and the negative strand sequencing data are at least two, respectively.

According to an embodiment of the invention, determining the corrected sequencing data based on the positive strand sequencing data and the negative strand sequencing data is based on the following principles:

Each base in the corrected sequencing data is simultaneously supported by at least 50% positive strand sequencing data and at least 50% negative strand sequencing data.

According to an embodiment of the invention, each base in the corrected sequencing data simultaneously obtains at least 80% positive stranding Order data and support for at least 80% negative strand sequencing data.

According to an embodiment of the present invention, the method further includes:

The corrected sequencing data is aligned to a reference sequence and the sequencing data with a quality of less than 30 is deleted.

According to an embodiment of the invention, SNV analysis or Indel analysis is performed based on the sequence of the nucleic acid sample.

In a fourth aspect of the invention, the invention proposes an apparatus for constructing a sequencing library. According to an embodiment of the invention, the apparatus comprises:

a linking unit for respectively connecting a linker at both ends of the double-stranded DNA fragment to obtain a ligation product, wherein the linker includes a first strand and a second strand, the first strand and the second strand portion are matched and The first strand comprises a first tag sequence such that the linker defines a double-stranded region and two single-stranded tails, the sequence of one of the two single-stranded tails comprising a first label;

a cleavage unit for cleaving the ligation product into a single-stranded DNA fragment;

a strand extension unit for performing a strand extension reaction on the single-stranded DNA fragment with a first primer to obtain a strand extension product, wherein the first primer includes a second tag sequence, and the first primer is adapted to The first strand of the linker forms a double-stranded structure, except that there is a mismatch between the first tag sequence and the second tag sequence;

An amplification unit for amplifying the strand extension product to obtain an amplification product, the amplification product constituting the sequencing library, wherein the amplification is adapted to simultaneously amplify the first label a sequence and a primer for the second tag sequence.

According to an embodiment of the present invention, the above apparatus can effectively implement the method for constructing a sequencing library described above, and can efficiently construct a sequencing library, and at the same time, the constructed sequencing library targets the same double-stranded DNA fragment (in this paper) Each of the strands, also referred to as "source sequences", obtains an amplification product having a first tag sequence and a second tag sequence, respectively, whereby, in the analysis of subsequent sequencing results, sequencing of the two tags can be performed. The results are mutually corrected to improve the reliability of the analysis results.

According to an embodiment of the present invention, the method further includes:

An end repair unit for end-repairing a nucleic acid sample to obtain a repaired nucleic acid sample;

a terminal modification unit for adding a base A at the 5' end of the nucleic acid sample to obtain a nucleic acid sample having a sticky terminal base A at each end, wherein the two ends respectively have a nucleic acid sample having a sticky terminal base A The double-stranded DNA fragment.

According to an embodiment of the present invention, there is further included a screening unit for screening the single-stranded DNA fragment using a probe before the chain extension is performed, wherein the probe specifically recognizes a predetermined region.

According to an embodiment of the invention, the predetermined area comprises one of the following:

(1) at least one of the genes shown in Tables 1 to 5;

(2) the CDS area of (1);

(3) A region of at least 10 bp upstream and downstream of (2).

According to an embodiment of the invention, the probe is provided in the form of a chip.

According to an embodiment of the invention, the strand extension reaction is carried out in the presence of a UDG enzyme/FPG enzyme. Thereby, the damaged DNA can be effectively repaired during the chain extension process, the generation of false positives is reduced, and the quality of the constructed sequencing library is improved.

According to an embodiment of the invention, the first tag sequence and the second tag sequence are each independently 4 to 10 nt in length.

According to an embodiment of the invention, the first tag sequence and the second tag sequence are both 8 nt in length.

According to an embodiment of the invention, there is a mismatch of at least 2 nt between the first tag sequence and the second tag sequence.

According to an embodiment of the invention, the first strand of the linker has the sequence set forth in SEQ ID NO: 1, the second strand of the linker has the sequence set forth in SEQ ID NO: 2, the first tag having SEQ ID NO: the sequence of any one of 3-6, wherein the second tag has the sequence set forth in at least one of SEQ ID NOs: 7-10, the first primer having the sequence set forth in SEQ ID NO:11 The primers suitable for simultaneously amplifying the first tag sequence and the second tag sequence have the sequences set forth in SEQ ID NO: 12 and SEQ ID NO: 13.

In accordance with embodiments of the present invention, the labels include, but are not limited to, the four pairs described above, and multiple pairs of labels may be involved as needed for simultaneous detection of multiple samples.

Those skilled in the art will appreciate that the features and advantages previously described for the method of constructing a sequencing library are equally applicable to the apparatus for constructing a sequencing library and will not be described herein.

In a fifth aspect of the invention, the invention proposes a sequencing device. According to an embodiment of the invention, the sequencing device comprises: a device for constructing a sequencing library according to the foregoing; a sequencing device for sequencing the sequencing library.

Thereby, the efficiency of sequencing can be effectively improved. In addition, the features and advantages described above with respect to the methods and apparatus for constructing a sequencing library are equally applicable to the sequencing apparatus and will not be described herein.

According to an embodiment of the invention, the sequencing device is Hiseq2000 or Hiseq 2500.

In a sixth aspect of the invention, the invention proposes a system for determining a nucleic acid sequence. According to an embodiment of the invention, the system comprises:

The sequencing device described above for sequencing a nucleic acid sample to obtain a sequencing result composed of a plurality of sequencing data;

a sequencing data subset construction device for constructing at least one subset of sequencing data based on the sequencing result, wherein all sequencing data in each subset of sequencing data corresponds to the same source sequence on the nucleic acid sample;

a sequencing data classification device, configured to determine, for each subset of the sequencing data, sequencing data corresponding to the first label sequence as positive strand sequencing data, and sequencing data corresponding to the second label sequence as negative strand sequencing data ;

a sequencing data correction device for correcting the sequencing data for each of the sequencing data subsets based on the positive strand sequencing data and the negative strand sequencing data, respectively, to determine corrected sequencing data;

A sequence determining device for determining a sequence of the nucleic acid sample based on the corrected sequencing data.

Thus, the method of determining a nucleic acid sequence as described above can be efficiently carried out using a system for determining a nucleic acid sequence according to an embodiment of the present invention. Therefore, the calibration can be effectively performed based on the positive strand sequencing data and the negative strand sequencing data, thereby improving the reliability of the analysis result.

According to an embodiment of the invention, the sequencing is a double-end sequencing, the sequencing result consisting of pairs of pairs of sequencing data.

According to an embodiment of the invention, the sequencing data subset construction device comprises:

a sequencing data index determining device for determining a paired sequencing data index for each pair of the plurality of pairs of paired sequencing data, the paired sequencing data indexing from the first N of each of the paired sequencing data Base composition, wherein N is an integer between 10 and 20;

a preliminary screening device for constructing at least one preliminary sequencing data subset based on the paired sequencing data index, wherein each of the sequencing data subsets has the same paired sequencing data index;

And a secondary screening device for subdividing the at least one preliminary sequencing data subset based on a Hamming distance between the sequencing data in the preliminary sequencing data subset to obtain a plurality of the sequencing data subsets.

According to an embodiment of the invention, N is 12.

According to an embodiment of the invention, in each of the plurality of sequencing data subsets, the Hamming distance of any two pairs of paired sequencing data does not exceed 20.

According to an embodiment of the invention, in each of the plurality of sequencing data subsets, the positive strand sequencing data and the negative strand sequencing data are at least two, respectively.

According to an embodiment of the invention, determining the corrected sequencing data based on the positive strand sequencing data and the negative strand sequencing data is based on the following principles:

Each base in the corrected sequencing data is simultaneously supported by at least 50% positive strand sequencing data and at least 50% negative strand sequencing data.

According to an embodiment of the invention, each base in the corrected sequencing data is simultaneously supported by at least 80% positive strand sequencing data and at least 80% negative strand sequencing data.

According to an embodiment of the present invention, the method further includes:

The corrected sequencing data is aligned to a reference sequence and the sequencing data with a quality of less than 30 is deleted.

According to an embodiment of the invention, there is further included a sequence analysis device for performing SNV analysis or Indel analysis based on the sequence of the nucleic acid sample.

It will be understood by those skilled in the art that the advantages and features described above with respect to methods for determining nucleic acid sequences are equally applicable to the system for determining nucleic acid sequences, and are not described herein.

The additional aspects and advantages of the invention will be set forth in part in the description which follows.

DRAWINGS

The above and/or additional aspects and advantages of the present invention will become apparent and readily understood from

1 shows a flow chart of a method of constructing a sequencing library in accordance with an embodiment of the present invention;

2 shows an analysis result of the same index reads cluster according to an embodiment of the present invention;

Figure 3 shows the results of analysis of a catastrophe spectrum according to an embodiment of the present invention;

4 shows an analysis result of the same index reads cluster according to an embodiment of the present invention;

Figure 5 shows the results of analysis of a mutated spectrum in accordance with one embodiment of the present invention;

6 shows an analysis result of the same index reads cluster according to an embodiment of the present invention;

Figure 7 shows the results of analysis of a mutation spectrum according to an embodiment of the present invention;

Figure 8 shows an analysis result of the same index reads cluster in accordance with one embodiment of the present invention;

Figure 9 shows the results of analysis of a mutation spectrum according to an embodiment of the present invention;

Figure 10 shows the results of an analysis of the same indexed reads cluster in accordance with one embodiment of the present invention;

Figure 11 shows the results of analysis of the catastrophe spectrum according to one embodiment of the present invention.

detailed description

The invention is illustrated by the following examples, which are intended to be illustrative only and not to be construed as limiting the invention.

General method

Unless otherwise stated, in the following examples, the following general methods are carried out:

First, design the probe

According to the human genome HG19, the exon sequence of the relevant gene was retrieved. Considering the size and cost of the capture region, the final chip only involved the CDS region of the above gene and extended the CDS region by 20 bp. The chip is covered with a rich capture probe with a 98% coverage area, which enriches the target DNA fragment from the complex genome and captures the genomic region with high specificity and high coverage on the same chip.

Second, build sequencing libraries and sequencing

Referring to Figure 1, the steps for constructing the library and sequencing are as follows:

1. 5 ml of peripheral blood was taken from the patient, plasma and leukocytes were separated by centrifugation, DNA was extracted from plasma samples and white blood cell samples, respectively, and DNA extracted by leukocytes was used as a control for detection of somatic mutations.

2. The free circulating DNA extracted from plasma averaged 170BP, then proceeded to the 3-step enzymatic reaction directly according to the conventional database construction method: end-repair, plus “A” and a special-handled sequencing linker (8BP on the linker) The label, named index1, not only has the ability to distinguish between different samples, it will also be used for subsequent positive-chain markings).

3. The obtained ligation product was subjected to chip hybridization capture, and the eluted single-stranded template product was amplified by one round of one cycle of primers labeled with index 2, so that the anti-strand was labeled. At the same time, UDG/FPG enzyme was added during the PCR to incubate to eliminate the DNA damage in the template strand and reduce the occurrence of false positives.

4. The product obtained by double-indexing of the positive and negative chains is purified, and then subjected to a second round of PCR enrichment to complete the preparation of the library.

5. The sequencing method adopts Hiseq2000 or Hiseq2500. According to the difference in the amount of sequencing and the number of samples, the appropriate sequencing platform can be flexibly selected.

The specific steps include:

1.cfDNA extraction

About 2-3 ml of plasma isolated from 5 ml of peripheral blood was taken, and plasma cfDNA was extracted according to the QIAamp Circulating Nucleic Acid Kit extraction reagent specification. Qubit (Invitrogen, the Quant-iT TM dsDNA HS Assay Kit) quantification of the extracted DNA, a total of about 5 ~ 50ng.

2. Preparation of the sample library:

The cfDNA extracted from the plasma was then subjected to a three-step enzymatic reaction according to the KAPA LTP Library Preparation Kit.

1) End repair

Thereafter, 120 μL of Agencourt AMPure XP reagent was added to carry out magnetic bead purification, and finally 42 μL of ddH ₂ O was dissolved, and magnetic beads were used for the next reaction.

2) Add A

Thereafter, 90 μL of a PEG/NaCl SPRI solution was added, thoroughly mixed, magnetic bead purification, and finally dissolved (35-linker) μL ddH ₂ O, and magnetic beads were used for the next reaction.

3) Connector connection

Then, 50 μL of PEG/NaCl SPRI solution was added twice, and magnetic beads were purified twice, and finally 25 μL of ddH ₂ O was dissolved.

3 chip hybrid capture

In the present invention, an early screening related chip for cancer designed by the inventors is used, and hybridization capture is performed with reference to a specification provided by the chip manufacturer. Finally eluted back to dissolve 21 μL of ddH ₂ O band hybrid eluting magnetic beads.

4. Double index positive and negative chain tagging and enrichment:

A total of 2 rounds of PCR were performed, PCR1 was subjected to reverse strand labeling and template DNA damage repair, and PCR2 was subjected to amplification and enrichment to complete library preparation.

1) PCR1

PCR1 program:

The hybrid elution magnetic beads were first removed, and then 40 μL of Agencourt AMPure XP reagent was added for magnetic bead purification, and finally 20 ul of ddH ₂ O was dissolved, and magnetic beads were used for the next reaction.

2) PCR2

PCR2 program

The magnetic beads of the previous step were removed first, then 50 μL of Agencourt AMPure XP reagent was re-added, magnetic beads were purified, and finally 25 μL of ddH ₂ O was dissolved, and QC and the machine were performed.

Third, the analysis of sequencing results

1, the paired reads (paired sequencing data) of the first 12 bp base of reads1 and the first 12 bp base of reads2 (ie, the breakpoint sequence) are connected into a short sequence of 24 bp, and the 24 bp is used as an index of paired reads, and according to Its index marks the positive and negative chains.

2. Externally sort the index to achieve the purpose of bringing together copies of the same DNA template.

3. Center clustering the collected reads with the same index, and clustering each large cluster with the same index into several small clusters according to the Hamming distance between the sequences, any two pairs in each small cluster Paired reads have a Hamming distance of no more than 10 in order to distinguish between reads that have the same index but come from different DNA templates.

4. The copy clusters of the same DNA template obtained in step 3 are screened. If the number of reads of the positive and negative strands is more than 2 pairs, subsequent analysis is performed.

5, correcting the clusters satisfying the conditions of 4, and generating a pair of new readings without errors. For each sequence of bases of the DNA template, if the certain base type has a coincidence rate of 80 in the positive chain of reads. %, and the agreement rate in the anti-chain reads is also 80%, then the base of the new reads is the base type, otherwise it is denoted as N, thus obtaining a new read representing the original DNA template sequence.

6. The new reads are re-aligned to the genome using the bwa mem algorithm, and the reads with a quality less than 30 are screened out.

7. SNV analysis:

1) According to the statistics obtained in 6 to obtain the base type distribution of each site in the capture region, the base type which is inconsistent with the mainstream base type (base ratio greater than 15%) is a mutated base. type. Statistical target area coverage size, average sequencing depth, positive and negative chain intermix rate, low frequency mutation rate, etc.

2) Using SDS, human genome database (NCBI36.3), dbSNP (v130) information to annotate SNPs, identify genes, coordinates, mRNA sites, amino acid changes, and SNP functions at the site of mutation (missense mutation/nonsense) Mutation/variable cleavage site), SIFT predicts SNP affects protein function prediction, etc.

3) Based on the comparison of patient sample and control sample information, Call Somatic Mutation. SNPs appearing in the dbSNP, HAPMAP, 1000 human genome, and other exon sequencing projects were also removed from the candidate SNVs as the final disease-related candidate SNV.

8.INDEL analysis:

1) According to the statistics of the indel-containing reads obtained in 6 to obtain all indels and select indel with 2 or more reads as a reliable mutation indel,

2) Indicating Indel using CCDS, Human Genome Database (NCBI36.3), and dbSNP (v130) information to determine the gene, coordinates, mRNA site, sequence of the coding region, and the effect on the amino acid of the mutation site, InDel Function (amino acid insertion / amino acid deletion / frameshift mutation);

3) Based on the comparison of patient sample and control sample information, Call Somatic Mutation. Indel was also removed from the dbSNP and other exon sequencing projects in the candidate Indel as the final disease-related candidate Indel.

Example 1: Early screening of gynecological reproductive tract tumors

First, the chip design

Based on the TCGA, ICGC, COSMIC and other related databases, the inventors designed the gene chip WCNpan for early screening of gynecological reproductive tract tumors. The WCNpan chip includes: Driver Gene (driver gene) related to gynecological genital tract tumors (cervical cancer, endometrial cancer, ovarian cancer), high-frequency mutated genes, and important genes in 12 signaling pathways of cancer, totaling 42 genes. , 300KB.

The specific design process of the chip: According to the human genome HG19, the exon sequences of the above 42 genes are retrieved. Considering the size and cost of the capture region, the final chip only covers the CDS region of the above gene, and extends the CDS region before and after. 20bp, the chip totals 300kb. The chip is covered with a rich capture probe with a 98% coverage area, which enriches the target DNA fragment from a complex genome and captures approximately 300KB of genomic region with high specificity and high coverage on the same chip. .

See Table 1 for details of the gene list.

Table 1

AFF3 BRCA2 FBXW7 MED12 PDE4DIP STK11 AKAP9 CDK12 FGFR2 MLL2 PIK3CA TP53 AKT1 CDKN2A FGFR3 MLL3 PIK3R1 APC CREBBP FOXL2 MSH6 PPP2R1A ARID1A CSMD3 GNAS NF1 PTEN BCOR CTNNB1 HRAS NFE2L2 RB1 BRAF EGFR KIT NRAS RNF213 BRCA1 FAT3 KRAS NSD1 RNF43

Second, sequencing analysis

One patient with cervical dysplasia was analyzed according to the steps of the above methods. The statistical results of the sequencing data are shown in the following table:

Note: The positive and negative chain interoperability rate: based on the ratio of the total clusters on the clusters/3 reads on the positive and negative chains of 3 reads, to evaluate the positive and negative chain interoperability in the available data; effective data utilization: based on The ratio of the number of reads error correction of at least 2+/2-cluster to the total number of sequencing reads is satisfied; the average sequencing depth: the average coverage of bases in the target region after error correction based on effective data.

The analysis result of the same index reads cluster is shown in Fig. 2, in which the abscissa indicates the number of duplication (dup) of the table cluster, and the ordinate indicates the total number of reads of the cluster satisfying a certain number of dup. It can be seen from the results of Fig. 2 that most of the dup clusters are around 8, and the larger part of the clusters can satisfy the condition of 2 plus + 2 inverses. The effective utilization rate of the final data is 5.14%, and the average sequencing depth is 1153.6X.

The results of the catastrophe spectrum analysis are shown in Fig. 3, in which the complementary mutation type is substantially the same as the theoretical mutation frequency for the double-stranded molecule (DNA). The abscissa represents the type of base mutation; the ordinate represents the number of mutations. The results in Fig. 3 show that the mutated base type distribution is balanced, and the mutation frequency (Mutations per nucleotide) is: 1.7 × 10 -6 . .

The details of the mutation detection list (based on the exon region and non-synonymous mutation statistics) are shown in the table below.

gene cHGVS pHGVS Mutation type Mutation frequency PIK3CA c.2119G>A p.Glu707Lys Missense mutation 5.60% TP53 c.817C>T p.Arg273Cys Missense mutation 2.40%

CDKN2A c.217A>C p.Ser73Arg Missense mutation 1.80% NRAS c.182A>G p.Gln61Arg Missense mutation 1.40%

Analysis of results: According to TCGA, COSMIC, ClinVar, HMGD and other related databases and literatures, Driver mutations such as PIK3CAp.Glu707Lys, TP53p.Arg273Cys were detected in the plasma of patients, indicating that patients have a higher cancer risk rate, and patients are recommended to relevant. Medical institutions conduct more comprehensive testing and take relevant interventions.

Example 2 Twelve common tumor individualized medication

First, the chip design

1) Design of tumor individualized gene chip:

Based on TCGA, ICGC, COSMIC and other related literature references, an iterative algorithm was used to design a tumor individualized gene chip CANPer-YY for 12 common cancers. The CANPer-YY chip includes: oncogenes, tumor suppressor genes, 12 common cancer high-frequency genes, important genes in 12 signal pathways of cancer, target drugs and chemotherapeutic drugs, etc., a total of 524 genes, 750KB.

The main design process of the chip is divided into 4 steps:

1. Count the number of samples of each exon region of the 12 cancer-related driver genes in the cosmic database, the sample of the variation, the number of samples with the most hot variation, and the PI value (to assess the frequency of patient responses at each The level on each exon, PI = cumulative number of patients carrying mutations per exon / exon length), and ranked in descending order of PI values. Then iterative algorithm is adopted: the sample of the first exon region variation is used as the sample database, and the number of different samples of all other intervals and sample databases is counted, and the sample interval with the largest number of different samples is listed as the second screening chip. Interval, at this time, the mutated samples of the two selected intervals are used as the sample database, and the third interval is screened in the same way until the sample database includes all the samples to count the exon region set, and for the unfiltered All intervals of the genes in any interval are added to the chip interval.

2. Based on the TCGA, ICGC and other databases, to remove the driver gene interval and include the interval of the hotspot variation of 5 samples or more (SNV>=5) as the candidate interval, repeat the iterative calculation of the previous step.

3. Based on TCGA, ICGC and other databases, in the interval where the screening has been removed, PI>=30, SNV>=3 and :PI>=20, SNV>=3 are selected as the candidate interval, and the screening makes the single sample database sample. The interval with the largest number reduction is taken as the first chip interval, and the above process is repeated for iterative calculation.

4. Add the fusion gene and the region of the chemotherapy detection site gene.

See Table 2 for details of the gene list.

Table 2

ABL1 C1R DIS3 FGF19 HSPA4 MIR142 PAX5 RB1 SRSF2 ABL2 C1S DNMT1 FGF23 IDH1 MITF PBRM1 REL SSTR2 ACVR1B CARD11 DNMT3A FGF3 IDH2 MLH1 PCBP1 RET STAG2

ACVR2A CASP8 DOT1L FGF4 IFNAR1 MLH3 PCM1 RHEB STAT4 AJUBA CBFB DUSP6 FGF6 IFNAR2 MLL PDGFRA RICTOR STAT5B AKT1 CBL EDNRA FGF7 IGF1 MLL2 PDGFRB RNASEL STK11 AKT2 CBLB EGFR FGFR1 IGF1R MLL3 PDK1 RNF43 SUFU AKT3 CBR1 EGR3 FGFR2 IGF2 MLL4 PHF6 ROBO1 SUZ12 ALK CCND1 EIF4A2 FGFR3 IKBKB MPL PIGF ROBO2 SYK ALOX12B CCND2 ELAC2 FGFR4 IKBKE MRE11A PIK3C2A ROS1 TAF1 ANGPT1 CCND3 ELF3 FH IKZF1 MS4A1 PIK3C2B RPA1 TBL1XR1 ANGPT2 CCNE1 EML4 FLCN IL7R MSH2 PIK3C2G RPL22 TBX3 APC CD79A EP300 FLT1 INHBA MSH3 PIK3C3 RPL5 TEK APCDD1 CD79B EPCAM FLT3 IRF4 MSH4 PIK3CA RPS14 TERT AR CDC25C EPHA2 FLT4 IRS2 MSH5 PIK3CB RPS6KB1 TET2 ARAF CDC42 EPHA3 FNTA ITGB2 MSH6 PIK3CG RPTOR TFG ARFRP1 CDC73 EPHA5 FOXA1 JAK1 MSR1 PIK3R1 RUNX1 TGFBR2 ARHGAP35 CDH1 EPHB1 FOXA2 JAK2 MTOR PIK3R2 RUNX1T1 TIPARP ARID1A CDK12 EPHB2 FOXL2 JAK3 MUC1 PLK1 RXRA TLR4 ARID1B CDK2 EPHB6 FPGS JUN MUTYH PML RXRB TMEM127 ARID2 CDK4 EPPK1 FUBP1 KAT6A MYC PMS1 RXRG TNFAIP3 ARID5B CDK6 ERBB2 FYN KDM5A MYCL1 PMS2 SDHAF2 TNFRSF14 ASXL1 CDK8 ERBB3 GAB2 KDM5C MYCN PNRC1 SDHB TNFRSF8 ATM CDKN1A ERBB4 GATA1 KDM6A MYD88 POLQ SDHC TNFSF11 ATR CDKN1B ERCC2 GATA2 KDR NAV3 PPP2R1A SDHD TNFSF13B ATRX CDKN2A ERCC3 GATA3 KEAP1 NBN PRDM1 SEMA3A TOP1 AURKA CDKN2B ERG GID4 KIF1B NCOA1 PRKAA1 SEMA3E TOP2A AURKB CDKN2C ESR1 GNA11 KIF5B NCOA2 PRKAR1A SETBP1 TOP2B AXIN1 CDX2 ETV1 GNA13 KIT NCOR1 PRKCA SETD2 TP53 AXIN2 CEBPA ETV6 GNAQ KLF4 NEK11 PRKCB SF1 TRAF7 AXL CFLAR EWSR1 GNAS KLHL6 NF1 PRKCG SF3B1 TSC1 B2M CHD1 EXT1 GNRHR KRAS NF2 PRKDC SH2B3 TSC2 B4GALT3 CHD2 EXT2 GPR124 LCK NFE2L2 PRSS8 SIN3A TSHR BACH1 CHD4 EZH2 GRIN2A LIMK1 NFE2L3 PSMB1 SLAMF7 TSHZ2 BAK1 CHEK1 FAM123B GRM3 LRRK2 NFKBIA PSMB2 SLC4A1 TSHZ3 BAP1 CHEK2 FAM46C GSK3B LYN NKX2-1 PSMB5 SLIT2 TUBA1A BARD1 CHUK FANCA H3F3A MALAT1 NKX3-1 PTCH1 SMAD2 TUBB BCL2 CIC FANCC H3F3C MAP2K1 NOTCH1 PTCH2 SMAD3 TUBD1 BCL2A1 CRBN FANCD2 HCK MAP2K2 NOTCH2 PTEN SMAD4 TUBE1 BCL2L1 CREBBP FANCE HDAC1 MAP2K4 NOTCH3 PTP4A3 SMARCA1 TUBG1 BCL2L11 CRIPAK FANCF HDAC2 MAP3K1 NOTCH4 PTPN11 SMARCA4 TYR BCL2L2 CRKL FANCG HDAC3 MAP3K13 NPM1 PTPRD SMARCB1 U2AF1 BCL6 CRLF2 FANCI HDAC4 MAPK1 NR3C1 RAC1 SMARCD1 USP9X BCOR CROT FANCL HDAC6 MAPK3 NRAS RAC2 SMC1A VEGFA BCORL1 CSF1R FANCM HDAC8 MAPK8 NSD1 RAD21 SMC3 VEGFB BCR CTCF FAT3 HGF MAPK8IP1 NTRK1 RAD50 SMO VEZF1 BLM CTLA4 FBXW7 HIF1A MAX NTRK2 RAD51 SOCS1 VHL

BMPR1A CTNNA1 FCGR1A HIST1H1C MC1R NTRK3 RAD51B SOX10 WHSC1L1 BRAF CTNNB1 FCGR2A HIST1H2BD MCL1 NUP93 RAD51C SOX17 WISP3 BRCA1 CUL4A FCGR2B HIST1H3B MDM2 PAK3 RAD51D SOX2 WWP1 BRCA2 CUL4B FCGR2C HNF1A MDM4 PAK7 RAD52 SOX9 XIAP BRIP1 CYLD FCGR3A HRAS MECOM PALB2 RAD54L SPEN XPA BTG1 CYP17A1 FCGR3B HRH2 MED12 PARP1 RAF1 SPOP XPC BTK DAXX FGF10 HSD17B3 MEF2B PARP2 RARA SPRY4 XPO1 C11orf30 DDR1 FGF12 HSD3B2 MEN1 PARP3 RARB SRC XRCC3 C1QA DDR2 FGF14 HSP90AA1 MET PARP4 RARG SRD5A2 YES1 ZNF217 ZNF703 ZRSR2 WT1 XRCC1 GSTP1 ERCC1 MTHFR SOD2 CBR3 ATIC MTRR DPYD UMPS TPMT UGT1A1 MDR1 CDA CYP19A1 CYP2D6

2) Gene prediction drug efficacy database construction:

The killing effect of chemotherapeutic drugs on tumor cells is significantly correlated with the expression and/or polymorphism of a specific (a group of) genes. The detection of related genes predicts the efficacy of chemotherapeutic drugs and selects appropriate drugs for individualized chemotherapy. It has become a reasonable choice to improve efficacy and reduce ineffective treatment. Based on the above characteristics of chemotherapeutic drugs, the PharmGKB database is used to integrate all the current chemotherapeutic drugs and the genes related to curative effect and predictive evaluation of therapeutic effects, and to form a database for interpretation of individualized drugs for chemotherapy. The chemotherapy data was integrated into the individualized information flow of the tumor to complete the automated interpretation of the chemotherapy drug.

Targeted drugs have the characteristics of significant drug efficacy and few side effects in tumor therapy, but they are dependent on targets (including protein, DNA, etc.). Target analysis must be performed on patients before they can determine whether patients can take drugs. Integrate current FDA-approved targeted drugs, as well as drugs in clinical III and IV. According to the NCCN clinical guidelines, the clinical drug gene research collates the relationship between the drug target gene and the target drug, and forms a database of individualized target drug interpretation.

Individualized interpretation of the mutated data after bioinformatics analysis, reference to the constructed tumor database and related literature, analysis of the variability detected by the patient, determine the cause of the variability, the expected efficacy and side effects of various chemotherapeutic drugs The most suitable benefit-targeted drugs and drug-resistant targeted drugs allow clinicians to be more targeted to the treatment of patients with cancer, avoiding the valuable time and the side effects of ineffective medications. pain.

Second, sequencing analysis

According to the present invention, a patient with advanced gastric cancer (one of the 12 common tumors) is subjected to the individualized drug guidance test according to the steps of the above method, and the results are as follows:

The statistical results of the sequencing data are shown in the following table:

Note: The positive and negative chain interoperability rate: based on the ratio of the clusters above 3 positive and negative chains/3 total reads, to evaluate the positive and negative chain interoperability in the available data; effective data utilization: based on The ratio of the number of reads error correction of at least 2+/2-cluster to the total number of sequencing reads is satisfied; the average sequencing depth: the average coverage of bases in the target region after error correction based on effective data.

Cluster analysis:

The analysis result of the same index reads cluster is shown in Fig. 4, in which the abscissa represents the number of duplication (dup) of the cluster, and the ordinate represents the total number of reads of the cluster satisfying a certain number of dup. The results in Figure 4 show that most of the dup clusters are around 5, and most of the clusters can satisfy the conditions of 2 plus + 2 inverses. The final data effective utilization rate is 3.5%, and the average sequencing depth is: 667X.

Mutation spectrum analysis:

The results of the mutational profiling are shown in Fig. 5, in which the complementary mutation type is substantially the same for the double-stranded molecule (DNA), the abscissa represents the type of base mutation, and the ordinate represents the number of mutations. The results in Figure 5 show that the distribution of the mutated base type is basically balanced, and the mutation frequency (Mutations per nucleotide) is: 4.2 × 10 ^-6 .

The details of the mutation detection list (based on the exon region and non-synonymous mutation statistics) are shown in the following table:

gene Base mutation Amino acid mutation Mutation type Mutation frequency TP53 c.241C>T p.R81X Stop codon to obtain mutation 10.83% PIK3CA c.2816A>G p.D939G Missense mutation 6.34% KRAS c.35G>A p.G12D Missense mutation 4.36% ZNF678 c.1628G>C p.R543P Missense mutation 3.40% ALMS1 c.3971T>G p.V1324G Missense mutation 3.20% MLH1 c.1427A>T p.E476V Missense mutation 2.80% ZNF721 c.2061C>G p.H687Q Missense mutation 2.76% MUC17 c.392G>C p.S131T Missense mutation 2.73% GNAQ c.286A>T p.T96S Missense mutation 2.46% CASC1 c.97C>A p.R33S Missense mutation 2.20% ZNF20 c.1016G>A p.R339K Missense mutation 2.00% CYP4F2 c.1448C>G p.A483G Missense mutation 2.00%

The chemotherapy sites are shown in the following table:

Drug prediction:

According to the target drug chemotherapy interpretation database, combined with the above test results, the following conclusions are only for the clinician to develop a treatment plan:

Example 3: Early screening of colorectal cancer

First, the chip design

1) Design of colorectal cancer early screening chip:

Based on TCGA, ICGC, COSMIC and other related literature references, an iterative algorithm was used to design a color chip Colorectalpan for early colorectal cancer screening. The Colorectalpan chip includes: Driver Gene, a high-frequency mutated gene, and an important gene in 12 signaling pathways of cancer, a total of 60 genes, 123KB.

The main design process of the chip is divided into 4 steps:

1. Count the number of samples of each exon region of the colorectal cancer driver gene in the cosmic database, the variation sample, the number of samples with the most hot variation, and the PI value (to assess the frequency of patient responses on each exon) Level, PI = cumulative number of patients carrying mutations per exon / exon length), and ranked in descending order of PI values. Then iterative algorithm is adopted: the sample of the first exon region variation is used as the sample database, and the number of different samples of all other intervals and sample databases is counted, and the sample interval with the largest number of different samples is listed as the second screening chip. Interval, at this time, the mutated samples of the two selected intervals are used as the sample database, and the third interval is screened in the same way until the sample database includes all the samples to count the exon region set, and for the unfiltered All intervals of the genes in any interval are added to the chip interval.

2. Based on the TCGA, ICGC and other databases, to remove the driver gene interval and include the interval of the hotspot variation of 5 samples or more (SNV>=5) as the candidate interval, repeat the iterative calculation of the previous step.

3. Based on TCGA, ICGC and other databases, in the interval where the screening has been removed, PI>=30, SNV>=3 and :PI>=20, SNV>=3 are selected as the candidate interval, and the screening makes the single sample database sample. The interval with the largest number reduction is taken as the first chip interval, and the above process is repeated for iterative calculation.

4. Add a fusion gene and other intervals.

See Table 3 for details of the gene list.

table 3

KRAS SRC TLR3 EP300 TMPRSS13 EPHA5 BRAF PTEN MC4R CYLD PHF2 EPHA3 APC AXIN1 MLH1 FBN2 OPRD1 PTPRD TP53 FLG AKT1 NF1 LILRB5 NTRK3 PIK3CA LIG1 CASD1 ASXL1 COL18A1 NTRK1 CTNNB1 MAP2K1 PTCH1 SMAD4 LARP4B ALK

NRAS PIK3R1 ADAMTS18 IRF5 DMKN ROS1 EGFR ERBB2 MSH2 DOCK3 ROBO2 RET FBXW7 STK11 BAP1 MYOM1 KCNN3 PDGFRA ARID1A IL7R CTNNA1 NEFH INHBA FGFR1

Second, sequencing analysis

According to the present invention, a colorectal cancer early screening test is performed on a patient with intestinal polyps according to the steps of the above method, and the results are as follows:

The statistical results of the sequencing data are shown in the following table:

Note: The positive and negative chain interoperability rate: based on the ratio of the clusters above 3 positive and negative chains/3 total reads, to evaluate the positive and negative chain interoperability in the available data; effective data utilization: based on The ratio of the number of reads error correction of at least 2+/2-cluster to the total number of sequencing reads is satisfied; the average sequencing depth: the average coverage of bases in the target region after error correction based on effective data.

Cluster analysis:

The analysis of the same index reads cluster is shown in Fig. 6, where the abscissa represents the number of duplications (dup) of the cluster, and the ordinate represents the total number of reads of the cluster satisfying a certain number of dups. The results in Figure 6 show that most of the dup clusters are around 6, and most of the clusters can satisfy the conditions of 2 plus + 2 inverses. The final data effective utilization rate is 5.12%, and the average sequencing depth is: 1033X.

Mutation spectrum analysis:

The results of the mutational profiling are shown in Figure 7, in which the complementary mutation type is substantially the same for the double-stranded molecule (DNA), the abscissa represents the type of base mutation, and the ordinate represents the number of mutations. The results of Fig. 7 show that the distribution of the mutated base type is basically balanced, and the mutation frequency (Mutations per nucleotide) is: 2.2 × 10 ^-6 .

Mutation detection list details (based on exon area and non-synonymous mutation statistics):

gene Base mutation Amino acid mutation Mutation type Mutation frequency SMAD4 c.2119G>A p.Y301F Missense mutation 2.8% ARID1A c.817C>T p.A1872T Missense mutation 2.34% APC c.217A>C p.A426T Missense mutation 1.80%

Analysis of results: According to TCGA, COSMIC, ClinVar, HMGD and other related databases and literature, in the disease In the plasma, SMAD4 p.Y301F was detected, and the APC p.A426T-driven mutation predicted that the patient had a high cancer risk rate. It is recommended that patients go to relevant medical institutions for more comprehensive testing and relevant interventions.

Example 4: Early screening of lung cancer

First, the chip design

1) Design of lung cancer early screening chip:

Based on TCGA, ICGC, COSMIC and other related literature references, an iterative algorithm was used to design a gene chip lungpan for early lung cancer screening. The Lungpan chip includes: lung cancer-related Driver Gene, high-frequency mutated gene, and important genes in 12 signaling pathways of cancer, totaling 145 genes, 250KB.

The main design process of the chip is divided into 4 steps:

1. Count the number of samples of each exon region of the lung cancer driver gene in the cosmic database, the variation sample, the number of samples with the most hot variation, and the PI value (to assess the level of patient response frequency on each exon, PI = cumulative number of patients/exon lengths carrying mutations per exon) and ranked in descending order of PI values. Then iterative algorithm is adopted: the sample of the first exon region variation is used as the sample database, and the number of different samples of all other intervals and sample databases is counted, and the sample interval with the largest number of different samples is listed as the second screening chip. Interval, at this time, the mutated samples of the two selected intervals are used as the sample database, and the third interval is screened in the same way until the sample database includes all the samples to count the exon region set, and for the unfiltered All intervals of the genes in any interval are added to the chip interval.

2. Based on the TCGA, ICGC and other databases, to remove the driver gene interval and include the interval of the hotspot variation of 5 samples or more (SNV>=5) as the candidate interval, repeat the iterative calculation of the previous step.

3. Based on TCGA, ICGC and other databases, in the interval where the screening has been removed, PI>=30, SNV>=3 and :PI>=20, SNV>=3 are selected as the candidate interval, and the screening makes the single sample database sample. The interval with the largest number reduction is taken as the first chip interval, and the above process is repeated for iterative calculation.

4. Add a fusion gene and other intervals.

See Table 4 for details of the gene list.

Table 4

KRAS ALK ROS1 ADAM23 KIAA0907 KRTAP5-5 MAP1B EGFR RB1 FGFR3 DNMT3B GAB1 TSHZ3 ZNF814 TP53 PDGFRA FGFR4 SDHAP2 OR10Z1 XIRP2 ZFHX4 BRAF KDR JAK3 DHX9 CNTNAP3B NYAP2 ZNF804A PIK3CA FBXW7 APC CSNK2A1 IL32 NUDT11 OR5D18 ERBB2 HRAS FRG1B CNTN5 NAV3 SNAPC4 ZNF479 CDKN2A JAK2 CHEK2 ATXN3 TNRC6A ZNF598 OR51V1 NRAS ERBB4 KLK1 CLIP1 FAM135B KIAA2022 OR4N2

STK11 KIT NBPF10 OR4M2 VGLL3 DDX11L2 OR4C15 NFE2L2 SMAD4 PARG OR10G8 KRTAP4-11 MUC6 OR14C36 CTNNB1 FGFR2 FBN2 PAPPA2 ANAPC1 ATXN1 CROCC MET DDR2 HSD17B7P2 OR8H2 FAM47C MUC16 OR2T2 PTEN ATM WASH2P PBX2 AKAP6 BEST3 PCDH11X AKT1 RET POTEC POLDIP2 ZNF804B DSPP REG3A KEAP1 NOTCH1 EEF1B2 SLC6A10P ZEB1 MB21D2 REG1B DDX11 EPB41L4A TBX6 PRB2 OR2T34 NTRK3 LRRIQ3 DNAH8 OR2M2 WDR62 CNTNAP2 LPA NTRK1 EPHA5 OR2B11 OR4C16 DCAF4L2 CDH10 MMP27 NF1 OR5L2 OR4K2 KCNB2 EPHA3 CDH12 VAV3 INHBA OR2T33 FAM47A STAG3L2 PTPRD RALGAPB THSD4 FGFR1 GNA15 RYR2 KRTAP4-8 NOTCH2 FOLH1 OR4N4

Second, sequencing analysis

According to the present invention, a lung nodule patient is subjected to early screening of lung cancer according to the steps of the above method, and the results are as follows:

The statistical results of the sequencing data are shown in the following table:

Note: The positive and negative chain interoperability rate: based on the ratio of the clusters above 3 positive and negative chains/3 total reads, to evaluate the positive and negative chain interoperability in the available data; effective data utilization: based on The ratio of the number of reads error correction of at least 2+/2-cluster to the total number of sequencing reads is satisfied; the average sequencing depth: the average coverage of bases in the target region after error correction based on effective data.

Cluster analysis:

The analysis result of the same index reads cluster is shown in Fig. 8. The abscissa represents the number of duplication (dup) of the cluster, and the ordinate represents the total number of reads of the cluster satisfying a certain number of dup. The results of Fig. 8 show that most of the dup clusters are around 10, and the larger part of the cluster can satisfy the condition of 2 plus + 2 inverse. The effective utilization rate of the final data is 4.12%, and the average sequencing depth is 898X.

Mutation spectrum analysis:

The results of the mutational profiling are shown in Fig. 9, in which the complementary mutation type is substantially the same for the double-stranded molecule (DNA), the abscissa represents the type of base mutation, and the ordinate represents the number of mutations. The results in Figure 9 show that the mutated base type distribution is basically balanced, and its mutation frequency (Mutations per nucleotide) is: 2.6 × 10 ^-6 .

Mutation detection list details (based on exon area and non-synonymous mutation statistics):

gene Base mutation Amino acid mutation Mutation type Mutation frequency ZNF804A c.126G>C p.K42N Missense mutation 2.6%

CDH10 c.2240C>T p.S747F Missense mutation 1.3%

Analysis of results: According to TCGA, COSMIC, ClinVar, HMGD and other related databases and literature, no relevant driving mutations were detected in the patient's plasma, indicating that patients have a lower risk of cancer.

Example 5: Postoperative monitoring of twelve common cancers

1) Design of 12 common tumor early screening and postoperative monitoring related gene chips:

Based on TCGA, ICGC, COSMIC and other related literature references, an iterative algorithm was used to design a gene chip CANPer-JK for 12 common cancer postoperative monitoring. The CANPer-JK chip includes: 12 common cancer-related Driver Genes, high-frequency mutated genes, and important genes in 12 cancer signaling pathways, totaling 547 genes, 800 KB.

The main design process of the chip is divided into 4 steps:

1. Count the number of samples per mutation in each exon region of the 12 cancer-associated driver genes in the cosmic database, the number of samples, the number of samples with the most hot variation, and the PI value (to assess the frequency of patient responses in each exon) The upper level, PI = cumulative number of patients carrying mutations per exon / exon length), and ranked in descending order of PI values. Then iterative algorithm is adopted: the sample of the first exon region variation is used as the sample database, and the number of different samples of all other intervals and sample databases is counted, and the sample interval with the largest number of different samples is listed as the second screening chip. Interval, at this time, the mutated samples of the two selected intervals are used as the sample database, and the third interval is screened in the same way until the sample database includes all the samples to count the exon region set, and for the unfiltered All intervals of the genes in any interval are added to the chip interval.

2. Based on the TCGA, ICGC and other databases, to remove the driver gene interval and include the interval of the hotspot variation of 5 samples or more (SNV>=5) as the candidate interval, repeat the iterative calculation of the previous step.

3. Based on TCGA, ICGC and other databases, in the interval where the screening has been removed, PI>=30, SNV>=3 and :PI>=20, SNV>=3 are selected as the candidate interval, and the screening makes the single sample database sample. The interval with the largest number reduction is taken as the first chip interval, and the above process is repeated for iterative calculation.

4. Add a fusion gene and other intervals.

See Table 5 for details of the gene list.

table 5

ABCB1 BRAF CHD2 ERBB4 FOXA2 IKBKE MECOM NTRK1 PTCH2 SF3A1 TIPARP ABL1 BRCA1 CHD4 ERCC2 FOXL2 IKZF1 MED12 NTRK2 PTEN SF3B1 TLR4 ABL2 BRCA2 CHEK1 ERCC3 FPGS IL13RA2 MEF2B NTRK3 PTP4A3 SH2B3 TMEM127 ACVR1B BRIP1 CHEK2 ERG FUBP1 IL2RA MEN1 NUP93 PTPN11 SIK1 TNFAIP3 ACVR2A BTG1 CHUK ESR1 FYN IL2RB MET PAK3 PTPRD SIN3A TNFRSF14 AJUBA BTK CIC ETV1 GAB2 IL2RG MIR142 PAK7 RAC1 SLAMF7 TNFRSF8 AKT1 C11orf30 CRBN ETV6 GATA1 IL7R MITF PALB2 RAC2 SLC4A1 TNFSF11

AKT2 C1QA CREBBP EWSR1 GATA2 INHBA MLH1 PARP1 RAD21 SLIT2 TNFSF13B AKT3 C1QB CRIPAK EXT1 GATA3 IRF4 MLH3 PARP2 RAD50 SMAD2 TOP1 ALK C1QC CRKL EXT2 GID4 IRS2 MLL PARP3 RAD51 SMAD3 TOP2A ALOX12B C1R CRLF2 EZH2 GNA11 ITGB2 MLL2 PARP4 RAD51B SMAD4 TOP2B ANGPT1 C1S CROT FAM123B GNA13 JAK1 MLL3 PAX5 RAD51C SMARCA1 TP53 ANGPT2 CAMK2G CSF1R FAM46C GNAQ JAK2 MLL4 PBRM1 RAD51D SMARCA4 TRAF7 APC CARD11 CTCF FANCA GNAS JAK3 MPL PCBP1 RAD52 SMARCB1 TRRAP APCDD1 CASP8 CTLA4 FANCC GNRHR JUN MRE11A PCM1 RAD54L SMARCD1 TSC1 AR CBFB CTNNA1 FANCD2 GPR124 KAT6A MS4A1 PDGFRA RAF1 SMC1A TSC2 ARAF CBL CTNNB1 FANCE GRIN2A KCNH2 MSH2 PDGFRB RARA SMC3 TSHR ARFRP1 CBLB CUL4A FANCF GRM3 KDM5A MSH3 PDK1 RARB SMO TSHZ2 ARHGAP35 CBR1 CUL4B FANCG GSK3B KDM5C MSH4 PHF6 RARG SOCS1 TSHZ3 ARID1A CCND1 CYLD FANCI H3F3A KDM6A MSH5 PIGF RB1 SOX10 TUBA1A ARID1B CCND2 CYP17A1 FANCL H3F3C KDR MSH6 PIK3C2A REL SOX17 TUBB ARID2 CCND3 DAXX FANCM HCK KEAP1 MSR1 PIK3C2B RET SOX2 TUBD1 ARID5B CCNE1 DDR1 FAT3 HDAC1 KIF1B MTOR PIK3C2G RFC1 SOX9 TUBE1 ASXL1 CD22 DDR2 FBXW7 HDAC2 KIF5B MUC1 PIK3C3 RHEB SPEN TUBG1 ATM CD33 DIS3 FCGR1A HDAC3 KIT MUTYH PIK3CA RICTOR SPOP TXNRD1 ATR CD3D DNMT1 FCGR2A HDAC4 KLF4 MYC PIK3CB RNASEL SPRY4 TYR ATRX CD3E DNMT3A FCGR2B HDAC6 KLHL6 MYCL1 PIK3CG RNF43 SRC U2AF1 AURKA CD3G DOCK2 FCGR2C HDAC8 KRAS MYCN PIK3R1 ROBO1 SRD5A2 U2AF2 AURKB CD52 DOT1L FCGR3A HGF LCK MYD88 PIK3R2 ROBO2 SRSF1 USP9X AXIN1 CD79A DUSP6 FCGR3B HIF1A LHCGR NAV3 PLK1 ROS1 SRSF2 VEGFA AXIN2 CD79B EDNRA FGF10 HIST1H1C LIFR NBN PML RPA1 SRSF7 VEGFB AXL CD80 EGFR FGF12 HIST1H2BD LIMK1 NCOA1 PMS1 RPL22 SSTR2 VEZF1 B2M CDC25C EGR3 FGF14 HIST1H3B LMO1 NCOA2 PMS2 RPL5 SSTR3 VHL B4GALT3 CDC42 EIF4A2 FGF19 HLA-A LRRK2 NCOR1 PNRC1 RPS14 SSTR5 WHSC1L1 BACH1 CDC73 ELAC2 FGF23 HNF1A LYN NEK11 POLQ RPS6KB1 STAG2 WISP3 BAK1 CDH1 ELF3 FGF3 HRAS MALAT1 NF1 PPP2R1A RPTOR STAT4 WWP1 BAP1 CDK12 ELMO1 FGF4 HRH2 MAP2K1 NF2 PRDM1 RUNX1 STAT5B XBP1 BARD1 CDK2 EML4 FGF6 HSD17B3 MAP2K2 NFE2L2 PRKAA1 RUNX1T1 STK11 XIAP BCL2 CDK4 EP300 FGF7 HSD3B2 MAP2K4 NFE2L3 PRKAR1A RXRA SUFU XPA BCL2A1 CDK6 EPCAM FGFR1 HSH2D MAP3K1 NFKBIA PRKCA RXRB SUZ12 XPC BCL2L1 CDK8 EPHA2 FGFR2 HSP90AA1 MAP3K13 NKX2-1 PRKCB RXRG SYK XPO1 BCL2L11 CDKN1A EPHA3 FGFR3 HSPA4 MAPK1 NKX3-1 PRKCG SDHAF2 TACR1 XRCC3 BCL2L2 CDKN1B EPHA5 FGFR4 IDH1 MAPK3 NOTCH1 PRKDC SDHB TAF1 YES1 BCL6 CDKN2A EPHB1 FH IDH2 MAPK8 NOTCH2 PRPF40B SDHC TBL1XR1 ZNF217 BCOR CDKN2B EPHB2 FLCN IFNAR1 MAPK8IP1 NOTCH3 PRSS8 SDHD TBX3 ZNF703 BCORL1 CDKN2C EPHB6 FLT1 IFNAR2 MAX NOTCH4 PRX SEMA3A TEK ZRSR2 BCR CDX2 EPOR FLT3 IGF1 MC1R NPM1 PSMB1 SEMA3E TERT WT1 BLM CEBPA EPPK1 FLT4 IGF1R MCL1 NR3C1 PSMB2 SETBP1 TET2 BMPR1A CFLAR ERBB2 FNTA IGF2 MDM2 NRAS PSMB5 SETD2 TFG BRAF CHD1 ERBB3 FOXA1 IKBKB MDM4 NSD1 PTCH1 SF1 TGFBR2

Second, sequencing analysis

According to the present invention, a postoperative breast cancer patient (one of 12 common tumors) is subjected to postoperative monitoring and detection of breast cancer according to the steps of the above method, and the results are as follows:

The statistical results of the sequencing data are shown in the following table:

Note: The positive and negative chain interoperability rate: based on the ratio of the clusters above 3 positive and negative chains/3 total reads, to evaluate the positive and negative chain interoperability in the available data; effective data utilization: based on The ratio of the number of reads error correction of at least 2+/2-cluster to the total number of sequencing reads is satisfied; the average sequencing depth: the average coverage of bases in the target region after error correction based on effective data.

Cluster analysis:

The analysis result of the same index reads cluster is shown in Fig. 10, in which the abscissa represents the number of duplication (dup) of the cluster, and the ordinate represents the total number of reads of the cluster satisfying a certain number of dup. The results in Figure 10 show that most of the dup clusters are around 6, and most of the clusters can satisfy the conditions of 2 plus + 2 inverses. The effective utilization rate of the final data is 4.74%, and the average sequencing depth is: 1028.6X.

Mutation spectrum analysis:

The results of the catastrophe spectrum analysis are shown in Fig. 11, in which the complementary mutation type is substantially the same for the double-stranded molecule (DNA), the abscissa represents the type of base mutation, and the ordinate represents the number of mutations. The results of Fig. 11 show that the distribution of the mutated base type is basically balanced, and the mutation frequency (Mutations per nucleotide) is: 3.1 × 10 ^-6 .

Mutation detection list details (based on exon area and non-synonymous mutation statistics):

Analysis of results: In the postoperative plasma, not only the mutations in the cancer were detected, such as: ROS1 p.A2106T, AR p.G457del; HLA-A p.R138G, but also high frequency PML p.R284P, IRF4 p. E11* and other variations. It indicates that the patient has poor postoperative condition, and it is recommended that the patient go to the relevant medical institution for more comprehensive testing and relevant intervention measures.

In the description of the present specification, the description with reference to the terms "one embodiment", "some embodiments", "illustrative embodiment", "example", "specific example", or "some examples", etc. Particular features, structures, materials or features described in the examples or examples are included in at least one embodiment or example of the invention. In the present specification, the schematic representation of the above terms does not necessarily mean the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in a suitable manner in any one or more embodiments or examples. In addition, it should be noted that those skilled in the art can understand that the order of the steps included in the solution proposed by the present invention can be adjusted by those skilled in the art, and it is also included in the scope of the present invention.

While the embodiments of the present invention have been shown and described, the embodiments of the invention may The scope of the invention is defined by the claims and their equivalents.

Claims (50)

A method of constructing a sequencing library, comprising: (a) attaching a linker at each end of the double-stranded DNA fragment to obtain a ligation product, wherein the linker includes a first strand and a second strand, the first strand and the second strand portion are matched and the first The chain comprises a first tag sequence such that the linker defines a double-stranded region and two single-stranded tails, the sequence of one of the two single-chain tails comprising a first tag; (b) cleaving the ligation product into a single-stranded DNA fragment; (c) performing a strand extension reaction on the single-stranded DNA fragment using a first primer to obtain a strand extension product, wherein the first primer includes a second tag sequence, and the first primer is adapted to be coupled to the linker The first strand forms a double-stranded structure, except that there is a mismatch between the first tag sequence and the second tag sequence; (d) amplifying the strand extension product to obtain an amplification product, the amplification product constituting the sequencing library, wherein the amplification is adapted to simultaneously amplify the first tag sequence and A primer for the second tag sequence. The method according to claim 1, wherein said double-stranded DNA fragment is obtained by the following steps: Performing end-repair of the nucleic acid sample to obtain a repaired nucleic acid sample; A base A is added to the 5' end of the nucleic acid sample to obtain a nucleic acid sample having a sticky terminal base A at each end, and a nucleic acid sample having a sticky terminal base A at each end constitutes the double-stranded DNA fragment . The method of claim 2 wherein said nucleic acid sample is at least a portion of human genomic DNA or a free nucleic acid. The method of claim 3 wherein said human free nucleic acid is extracted from peripheral blood of a patient. The method according to claim 4, wherein said patient has cancer, said cancer being at least one selected from the group consisting of: Bladder cancer, prostate cancer, lung cancer, colorectal cancer, stomach cancer, breast cancer, kidney cancer, pancreatic cancer, ovarian cancer, endometrial cancer, thyroid cancer, cervical cancer, esophageal cancer, and liver cancer. The method of claim 5 wherein at least a portion of said human genomic DNA is obtained by random disruption of human genomic DNA. The method of claim 2 wherein said linker has a 3' base T sticky end. The method according to claim 1, wherein said single-stranded DNA fragment is obtained by subjecting said ligation product to denaturation treatment. The method of claim 1 wherein the probe pair is used prior to performing the chain extension The single-stranded DNA fragment is screened, wherein the probe specifically recognizes a predetermined region. The method of claim 9 wherein said predetermined area comprises one of: (1) at least one of the genes shown in Tables 1 to 5; (2) the CDS area of (1); (3) A region of at least 10 bp upstream and downstream of (2). The method of claim 10 wherein said probe is provided in the form of a chip. The method according to claim 1, wherein the chain extension reaction is carried out in the presence of a UDG enzyme/FPG enzyme. The method according to claim 1, wherein the first tag sequence and the second tag sequence are each independently 4 to 10 nt in length, preferably 8 nt. The method of claim 1 wherein the first tag sequence and the second tag sequence are each 8 nt in length. The method of claim 1 wherein there is at least a 2 nt mismatch between the first tag sequence and the second tag sequence. The method according to claim 1, wherein the first strand of the linker has the sequence of SEQ ID NO: 1, and the second strand of the linker has the sequence of SEQ ID NO: 2, The first tag has the sequence set forth in at least one of SEQ ID NOs: 3-6, and the second tag has the sequence set forth in at least one of SEQ ID NOs: 7-10, the first primer having SEQ ID NO: The sequence shown in Figure 11, wherein the second primer has the sequence shown in SEQ ID NO: 12, and the third primer has the sequence shown in SEQ ID NO: 13. A sequencing method, comprising: Constructing a sequencing library according to the method of any one of claims 1 to 16; The sequencing library was sequenced. The method of claim 17 wherein said sequencing is performed on Hiseq2000 or Hiseq 2500. A method of determining a nucleic acid sequence, comprising: For nucleic acid samples, sequencing is performed according to the method of claim 17 or 18 to obtain sequencing results consisting of multiple sequencing data; Based on the sequencing result, at least one subset of sequencing data is constructed, wherein all sequencing data in each subset of sequencing data corresponds to the same source sequence on the nucleic acid sample; For each subset of sequencing data, determining that the sequencing data corresponding to the first tag sequence is positive strand sequencing data, and the sequencing data corresponding to the second tag sequence is negative strand sequencing data; Sequencing each subset of sequencing data based on the positive strand sequencing data and the negative strand sequencing data, respectively Data is corrected to determine corrected sequencing data; A sequence of the nucleic acid sample is determined based on the corrected sequencing data. The method of claim 19, wherein the sequencing is double-end sequencing, the sequencing result consisting of pairs of pairs of sequencing data. The method of claim 20, wherein constructing the at least one subset of sequencing data based on the sequencing result is performed by the following steps: Determining a paired sequencing data index for each pair of the plurality of pairs of sequenced data, the paired sequencing data index consisting of an initial N bases of each of the paired sequencing data, wherein N is An integer between 10 and 20; Constructing at least one preliminary sequencing data subset based on the paired sequencing data index, wherein each of the sequencing data subsets has the same paired sequencing data index; The at least one preliminary sequencing data subset is subdivided based on a Hamming distance between the sequencing data in the preliminary sequencing data subset to obtain a plurality of the sequencing data subsets. The method of claim 21 wherein N is 12. The method of claim 21 wherein in each of said plurality of sequencing data subsets, the Hamming distance of any two pairs of paired sequencing data does not exceed 20. The method of claim 21 wherein in each of said plurality of sequencing data subsets, the positive strand sequencing data and the negative strand sequencing data are at least two, respectively. The method of claim 20, wherein determining the corrected sequencing data based on the positive strand sequencing data and the negative strand sequencing data is based on the following principles: Each base in the corrected sequencing data is simultaneously supported by at least 50% positive strand sequencing data and at least 50% negative strand sequencing data. The method of claim 20 wherein each base in the corrected sequencing data is simultaneously supported by at least 80% positive strand sequencing data and at least 80% negative strand sequencing data. The method of claim 20, further comprising: The corrected sequencing data is aligned to a reference sequence and the sequencing data with a quality of less than 30 is deleted. The method according to claim 20, wherein the SNV analysis or the Indel analysis is performed based on the sequence of the nucleic acid sample. An apparatus for constructing a sequencing library, comprising: a linking unit for respectively connecting a linker at both ends of the double-stranded DNA fragment to obtain a ligation product, wherein the linker includes a first strand and a second strand, the first strand and the second strand portion are matched and The first strand comprises a first tag sequence such that the linker defines a double-stranded region and two single-stranded tails, the sequence of one of the two single-stranded tails comprising a first label; a cleavage unit for cleaving the ligation product into a single-stranded DNA fragment; a strand extension unit for performing a strand extension reaction on the single-stranded DNA fragment with a first primer to obtain a strand extension product, wherein the first primer includes a second tag sequence, and the first primer is adapted to The first strand of the linker forms a double-stranded structure, except that there is a mismatch between the first tag sequence and the second tag sequence; An amplification unit for amplifying the strand extension product to obtain an amplification product, the amplification product constituting the sequencing library, wherein the amplification employs a second primer and a third primer, A second primer identifies a second strand of the adaptor, the third primer being configured to simultaneously amplify the first tag sequence and the second tag sequence. The device according to claim 29, further comprising: An end repair unit for end-repairing a nucleic acid sample to obtain a repaired nucleic acid sample; a terminal modification unit for adding a base A at the 5' end of the nucleic acid sample to obtain a nucleic acid sample having a sticky terminal base A at each end, wherein the two ends respectively have a nucleic acid sample having a sticky terminal base A The double-stranded DNA fragment. The device according to claim 29, further comprising a screening unit for screening said single-stranded DNA fragment using a probe prior to performing said strand extension, wherein said probe-specific recognition Scheduled area. The apparatus according to claim 31, wherein said predetermined area comprises one of: (1) at least one of the genes shown in Tables 1 to 5; (2) the CDS area of (1); (3) A region of at least 10 bp upstream and downstream of (2). The device of claim 31 wherein said probe is provided in the form of a chip. The device according to claim 29, wherein said strand extension reaction is carried out in the presence of a UDG enzyme/FPG enzyme. The apparatus according to claim 29, wherein said first tag sequence and said second tag sequence are each independently 4 to 10 nt in length. The apparatus according to claim 29, wherein said first tag sequence and said second tag sequence are each 8 nt in length. The apparatus of claim 29 wherein there is at least a 2 nt mismatch between said first tag sequence and said second tag sequence. The device according to claim 29, wherein the first strand of the linker has the sequence of SEQ ID NO: 1, and the second strand of the linker has the sequence of SEQ ID NO: 2, The first tag has the sequence set forth in at least one of SEQ ID NOs: 3-6, and the second tag has the sequence set forth in at least one of SEQ ID NOs: 7-10, the first primer having SEQ ID NO: a sequence shown by 11, the second primer having SEQ ID NO: The sequence shown in 12, the third primer having the sequence shown in SEQ ID NO: 13. A sequencing device, comprising: An apparatus for constructing a sequencing library according to any one of claims 29 to 38; A sequencing device for sequencing the sequencing library. The sequencing device according to claim 39, wherein the sequencing device is Hiseq2000 or Hiseq 2500. A system for determining a nucleic acid sequence, comprising: The sequencing device according to claim 39 or 40, for sequencing a nucleic acid sample to obtain a sequencing result composed of a plurality of sequencing data; a sequencing data subset construction device for constructing at least one subset of sequencing data based on the sequencing result, wherein all sequencing data in each subset of sequencing data corresponds to the same source sequence on the nucleic acid sample; a sequencing data classification device, configured to determine, for each subset of the sequencing data, sequencing data corresponding to the first label sequence as positive strand sequencing data, and sequencing data corresponding to the second label sequence as negative strand sequencing data ; a sequencing data correction device for correcting the sequencing data for each of the sequencing data subsets based on the positive strand sequencing data and the negative strand sequencing data, respectively, to determine corrected sequencing data; A sequence determining device for determining a sequence of the nucleic acid sample based on the corrected sequencing data. The system of claim 41 wherein said sequencing is double-end sequencing, said sequencing results consisting of pairs of pairs of sequencing data. The system of claim 41 wherein said sequencing data subset building device comprises: a sequencing data index determining device for determining a paired sequencing data index for each pair of the plurality of pairs of paired sequencing data, the paired sequencing data indexing from the first N of each of the paired sequencing data Base composition, wherein N is an integer between 10 and 20; a preliminary screening device for constructing at least one preliminary sequencing data subset based on the paired sequencing data index, wherein each of the sequencing data subsets has the same paired sequencing data index; And a secondary screening device for subdividing the at least one preliminary sequencing data subset based on a Hamming distance between the sequencing data in the preliminary sequencing data subset to obtain a plurality of the sequencing data subsets. The system of claim 43 wherein N is 12. The system of claim 43 wherein in each of said plurality of sequencing data subsets, the Hamming distance of any two pairs of paired sequencing data does not exceed 20. The system of claim 43 wherein in each of said plurality of sequencing data subsets, the positive strand sequencing data and the negative strand sequencing data are at least two, respectively. The system of claim 41, based on said positive strand sequencing data and said negative stranding The sequencing data determines that the corrected sequencing data is based on the following principles: Each base in the corrected sequencing data is simultaneously supported by at least 50% positive strand sequencing data and at least 50% negative strand sequencing data. The system of claim 47, wherein each base in the corrected sequencing data is simultaneously supported by at least 80% positive strand sequencing data and at least 80% negative strand sequencing data. The system of claim 41, further comprising: The corrected sequencing data is aligned to a reference sequence and the sequencing data with a quality of less than 30 is deleted. The system of claim 41, further comprising sequence analysis means for performing SNV analysis or Indel analysis based on the sequence of said nucleic acid samples.

Images