CN117551767A - Application of cfDNA fragment characteristic combination in prediction of cancer - Google Patents

Application of cfDNA fragment characteristic combination in prediction of cancer Download PDF

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CN117551767A
CN117551767A CN202311603742.9A CN202311603742A CN117551767A CN 117551767 A CN117551767 A CN 117551767A CN 202311603742 A CN202311603742 A CN 202311603742A CN 117551767 A CN117551767 A CN 117551767A
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汪强虎
吴玲祥
吴维
张若寒
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Abstract

The invention discloses an application of cfDNA fragment characteristics in predicting cancers, and belongs to the technical field of cancer genomics. The cfDNA fragment characteristic combination comprises a first cfDNA fragment characteristic sub-combination and a second cfDNA fragment characteristic sub-combination, wherein the first cfDNA fragment characteristic sub-combination comprises cfDNA fragment characteristics falling between 130bp and 175bp, and the second cfDNA fragment characteristic sub-combination comprises cfDNA fragment characteristics falling between 330bp and 360bp. The cfDNA fragment characteristic combination is utilized to predict the cancer, so that the requirement and the dependence of a cfDNA fragment analysis-based cancer prediction method on an upstream experimental end are reduced, the interpretability and the utilization rate of other histology sequencing data are remarkably widened, the experimental cost of diagnosing tumors based on cfDNA is greatly reduced, and the accuracy of cfDNA-based cancer prediction is improved.

Description

Application of cfDNA fragment characteristic combination in prediction of cancer
Related patent
The application is a divisional application of Chinese patent application with the application number of 2022112033941 and the application date of 2022, 09 and 29, and the invention name of cfDNA fragment characteristic combination, system and application for predicting cancer.
Technical Field
The invention belongs to the technical field of cancer genomics, and particularly relates to application of cfDNA fragment characteristic combination in prediction of cancers.
Background
Free DNA (cfDNA, circulating free DNA or Cell free DNA) in blood can change in concentration with tissue damage, cancer, inflammatory response and the like, and has important potential value in early diagnosis, prognosis, monitoring and the like of diseases. In recent years, cfDNA has been widely used in research fields such as early screening of cancer. Studies have shown that tumor tissue sources can be classified using specific cfDNA fragment characteristics, and that cfDNA fragment lengths can also reveal tissue origin or tumor origin.
However, most liquid biopsy methods are currently focused on detecting genetic mutations or chromosomal abnormalities in blood, and existing methods of fragment histology rely on Whole Genome Sequencing (WGS) methods, which cannot fully exploit other histologic sequencing data information.
Disclosure of Invention
In order to solve at least one of the above technical problems, the present invention develops a system for analyzing fragment histology based on multiple histology data to identify cfDNA fragments to distribute tumor markers, thereby identifying whether the sample is a tumor sample. Specifically, the technical scheme adopted by the invention is as follows:
the first aspect of the invention provides a cfDNA fragment characteristic combination comprising a first cfDNA fragment characteristic sub-combination comprising cfDNA fragment characteristics falling between 60bp and 200bp and having an increased fragment number ratio in a population cancer sample, and/or a second cfDNA fragment characteristic sub-combination comprising cfDNA fragment characteristics falling between 300 and 400bp and having a reduced fragment number ratio in a population cancer sample, said increase or decrease being relative to a representative value of the fragment number ratio of the corresponding fragment characteristics of a normal population sample.
In the present invention, the definition of the relevant terms is as follows:
fragment characteristics: the cfDNA fragments are divided into different fragment intervals according to different lengths, and all cfDNA fragments in each fragment interval are one fragment characteristic. For example, the fragment features are: 61-65bp, including cfDNA fragments with fragment lengths of 61bp, 62bp, 63bp, 64bp and 65 bp. For example, the fragment features are: 74-75bp, including cfDNA fragments with fragment lengths of 74bp and 75 bp.
Ratio of number of fragments: refers to the ratio of cfDNA fragments in a fragment signature to total fragments.
In the present invention, the cfDNA fragment length and number data refers to data obtained using a sequencing method selected from any one of the group consisting of WGS sequencing, WES sequencing, meDIP and MBD-Seq. In fact, one skilled in the art may use any method, either sequenced or not, provided that the length and number of cfDNA fragments can be obtained.
In the present invention, the cfDNA fragments contained in each fragment characteristic in the first cfDNA fragment characteristic sub-combination are relatively short, and the inventors of the present invention unexpectedly found that fragment characteristics falling between 60bp and 200bp can be used to identify cancer, and have higher accuracy. More surprisingly, the inventors found that cfDNA fragments falling between 130bp and 175bp are characterized with higher cancer recognition accuracy.
Further, if a subject having a cancer is selected to have a risk of at least one of 163-164bp, 163-165bp, 161-164bp, 165-166bp, 159-165bp, 157-164bp, 155-156bp, 163-168bp, 160-168bp, 157-158bp, 154-156bp, 161-170bp, 156-160bp, 161-162bp, 157-159bp, 165-168bp, 157-162bp, 157-160bp, 151-160bp, 152-158bp, 160-162bp, 153-156bp, 151-159bp, 166-168bp, 148-150bp, 149-156bp, 159-160bp, 151-156bp, 167-168bp, 147-148bp, 146-150bp, 165-172bp, 166-170bp, 151-155bp, 153-154bp, 149-152bp, 145-150bp, 145-151bp, 166-172bp, 160bp, 151-152bp, 151-172 bp, 151-152bp, 151-150 bp, and 169 bp, or 169, if any of the characteristics fall between 130-175 bp and 175 bp.
The inventors have further found that the selection of the above fragment characteristics is not as good as possible, and that the selection of 163-164bp, 163-165bp, 161-164bp and 165-166bp as markers has a very good cancer recognition effect.
In the present invention, the cfDNA fragments contained in each fragment characteristic in the second cfDNA fragment characteristic sub-combination are relatively long, and the inventors of the present invention unexpectedly found that fragment characteristics falling between 300bp and 400bp can be used to identify tumors, and have higher accuracy. More surprisingly, the inventors found that cfDNA fragments falling between 330bp and 360bp feature higher tumor recognition accuracy.
Further, if among the cfDNA fragment characteristics falling between 330bp and 360bp, 339-340bp, 341-342bp, 343-344bp, 337-339bp, 340-342bp, 337-340bp, 341-344bp, 336-340bp, 341-345bp, 337-342bp, 337-338bp, 343-345bp, 341-347bp, 340-348bp, 334-340bp, 341-348bp, 345-346bp, 343-348bp, 341-350bp, 345-348bp, 346-348bp, 333-340bp, 347-348bp, 346-350bp, 335-336bp, 331-340bp, 334-336bp, 349-350bp, 349-351bp, 349-339 bp, 349-352bp, 333-336bp, 348-354bp, 351-352bp, 349-356bp, 352-354bp, 351-355bp, 333-334bp, 331-336bp, 349-357bp, 355-357bp, 355-355 bp, 355-357bp, 360bp, and 357bp are selected, whether a subject has cancer or is at risk of having cancer can be accurately predicted.
Likewise, the inventors have further found that the selection of the above fragment characteristics is not as much as possible, and has a very good cancer recognition effect when 339-340bp, 341-342bp, 343-344bp, 337-339bp and 340-342bp are selected as markers.
A second aspect of the invention provides a system for predicting whether a subject has or is at risk of having cancer, comprising the following modules:
the data input module is used for inputting the cfDNA fragment length and quantity data of the subject;
the distribution spectrum analysis module is connected with the data input module and is used for obtaining the fragment quantity proportion of each cfDNA fragment characteristic in the cfDNA fragment characteristic combination;
and the cancer prediction module is connected with the distribution spectrum analysis module and is used for judging whether the subject has cancer or is at risk of having cancer according to the fragment quantity proportion of the cfDNA fragment characteristics.
In some embodiments of the invention, the subject is determined to have or be at risk of having cancer if the proportion of the number of fragments of at least one cfDNA fragment feature in the first subset of cfDNA fragment features is increased and/or the proportion of the number of fragments of at least one cfDNA fragment feature in the second subset of cfDNA fragment features is decreased.
In other embodiments of the present invention, the cfDNA fragment signature combination includes a first cfDNA fragment signature sub-combination and a second cfDNA fragment signature sub-combination, and the cancer prediction module obtains the judgment value using the following formula:
wherein,
the Score is used as a judgment value and,
m is the number of cfDNA fragment features in the first cfDNA fragment feature sub-combination, and n is the number of cfDNA fragment features in the second cfDNA fragment feature sub-combination;
ti is the proportion of the number of fragments of the ith cfDNA fragment characteristic in the first cfDNA fragment characteristic sub-combination;
N j the ratio of the number of fragments characteristic of the jth cfDNA fragment in the first cfDNA fragment characteristic sub-combination,
if Score is greater than a preset threshold, the subject is judged to have cancer or to be at risk of having cancer.
In some embodiments of the invention, the predictive threshold is determined from a population cancer sample Score value and/or a population normal sample Score value.
Optionally, the predictive threshold is determined from a representative value of Score values of a population of cancer samples.
Optionally, the predictive threshold is determined from a representative value of a Score value of a population normal sample.
Optionally, the predictive threshold is determined from a representative value of an increased value of the Score value of the cancer sample of the population relative to the Score value of the normal sample of the population. The cancer sample and the normal sample are paired samples, so that the added value has clinical significance.
In some embodiments of the invention, the population of cancer samples refers to more than 10 cancer samples, such as 10, 20, 50, 100, 200, 500 or more.
In some embodiments of the invention, the representative value refers to one of an average, a mode, a median, a 1/4 fraction, and a 3/4 fraction.
In the present invention, the cancers include, but are not limited to, solid tumors and blood cancers such as fibrosarcoma, myosarcoma, liposarcoma, chondrosarcoma, osteogenic sarcoma, chordoma, angiosarcoma, endothelial sarcoma, lymphangiosarcoma, lymphangioendothelioma, synovioma, mesothelioma, ewing's tumor, leiomyosarcoma, rhabdomyosarcoma, colon cancer, pancreatic cancer, prostate cancer, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary adenocarcinoma, cystic adenocarcinoma, medullary carcinoma, bronchogenic carcinoma, hepatoma, cholangiocarcinoma, choriocarcinoma, seminoma, embryonal carcinoma, nephroblastoma, cervical cancer, testicular tumor, lung cancer, small cell lung cancer, epithelial carcinoma, glioma, astrocytoma, medulloblastoma, craniopharyoma, ependymoma, pineal tumor, angioblastoma, auditory glioma, oligodendroglioma, meningioma, melanoma, neuroblastoma, glioblastoma; leukemias such as acute lymphoblastic leukemia and acute myeloblastic leukemia (myeloblastic, promyelocytic, myelomonocytic, monocytic and erythrocytic leukemia); chronic leukemia (chronic myelogenous (granulocytic) leukemia and chronic lymphocytic leukemia); and polycythemia vera, lymphomas (hodgkin's and non-hodgkin's), multiple myeloma, waldenstrom's macroglobulinemia and heavy chain disease.
In a third aspect, the present invention provides the use of a detection reagent of a cfDNA fragment feature combination according to the first aspect of the present invention for the preparation of a kit for predicting whether a subject has cancer or is at risk of having cancer.
In some embodiments of the invention, the detection reagent comprises a capture reagent and/or a sequencing reagent.
In some embodiments of the invention, the kit further comprises cfDNA extraction reagents.
The beneficial effects of the invention are that
Compared with the prior art, the invention has the following effective effects:
the cfDNA fragment feature combination and system of the present invention can be used for cancer prediction, not only using data of any one of the sequencing methods selected from the group consisting of WGS sequencing, WES sequencing, meDIP and MBD-Seq, but also using data obtained by any sequencing or non-sequencing method, as long as the length and number of cfDNA fragments can be obtained.
The cfDNA fragment characteristic combination and the cfDNA fragment characteristic system are utilized to predict cancers, comprehensive characteristic analysis of cfDNA fragments can be utilized, and the prediction performance on cancers is better.
The cfDNA fragment characteristic combination and the cfDNA fragment characteristic system are utilized for carrying out cancer prediction, so that the requirement and the dependence of a cfDNA fragment analysis-based cancer prediction method on an upstream experimental end are reduced, the interpretability and the utilization rate of other histology sequencing data are remarkably widened, the experimental cost of cfDNA-based tumor diagnosis is greatly reduced, and the accuracy of cfDNA-based cancer prediction is improved.
Drawings
Fig. 1 shows the results of tumor identification in training and validation sets using 10 cfDNA fragment features.
Fig. 2 shows the results of tumor identification in training and validation sets using 20 cfDNA fragment features.
Fig. 3 shows the results of tumor identification in training and validation sets using 30 cfDNA fragment features.
Fig. 4 shows the results of tumor identification in training and validation sets using 40 cfDNA fragment features.
Fig. 5 shows the results of tumor identification in training and validation sets using 50 cfDNA fragment features.
Fig. 6 shows the results of tumor identification in training and validation sets using 60 cfDNA fragment features.
Fig. 7 shows the results of tumor identification in an external test set using 10 cfDNA fragment features.
Detailed Description
Unless otherwise indicated, implied from the context, or common denominator in the art, all parts and percentages in the present application are based on weight and the test and characterization methods used are synchronized with the filing date of the present application. Where applicable, the disclosure of any patent, patent application, or publication referred to in this application is incorporated by reference in its entirety, and the equivalent patents to those cited are incorporated by reference, particularly as they relate to the definitions of terms of art and the like. If the definition of a particular term disclosed in the prior art does not conform to any definition provided in this application, the definition of that term provided in this application controls.
Numerical ranges in this application are approximations, so that it may include the numerical values outside of the range unless otherwise indicated. The numerical range includes all values from the lower value to the upper value that increase by 1 unit, provided that there is a spacing of at least 2 units between any lower value and any higher value. These are merely specific examples of what is intended to be provided, and all possible combinations of numerical values between the lowest value and the highest value enumerated are to be considered to be expressly stated in this application.
In order to make the technical problems, technical schemes and beneficial effects solved by the invention more clear, the invention is further described in detail below with reference to the embodiments.
Examples
The following examples are presented herein to demonstrate preferred embodiments of the present invention. It will be appreciated by those skilled in the art that the techniques disclosed in the examples which follow represent techniques discovered by the inventor to function in the practice of the invention, and thus can be considered to constitute preferred modes for its practice. Those of skill in the art should, in light of the present disclosure, appreciate that many changes can be made in the specific embodiments which are disclosed and still obtain a like or similar result without departing from the spirit or scope of the invention.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs, the disclosure of which is incorporated herein by reference as is commonly understood by reference.
Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific embodiments of the invention described herein. Such equivalents are intended to be encompassed by the claims.
The molecular biology experiments described in the following examples, which are not specifically described, were performed according to the specific methods listed in the "guidelines for molecular cloning experiments" (fourth edition) (j. Sambrook, m.r. Green, 2017) or according to the kit and product specifications. Other experimental methods, unless otherwise specified, are all conventional. The instruments used in the following examples are laboratory conventional instruments unless otherwise specified; the test materials used in the examples described below, unless otherwise specified, were purchased from conventional biochemical reagent stores.
Example 1 identification of cfDNA fragment distribution tumor markers
Cfdna sequencing
To obtain cfDNA fragment distribution tumor markers, the inventors obtained blood samples of 417 tumor patients (183 colorectal cancer, 40 liver cancer, 92 gastric cancer, 68 pancreatic cancer, 9 esophageal cancer and 25 glioblastomas) and 813 normal persons. cfDNA was extracted and sequenced using methylation DNA enrichment Sequencing technique (MBD-Seq, methylated DNA Binding Domain-Sequencing).
2. Data preprocessing
a) Data cleaning: the adaptor sequence introduced during library construction was removed using fastp-0.20.0 software and low quality base fragments (more than 40% of the bases had a mass value below Q15 and more than 5N for the whole fragment, with a sliding window based cut end average mass < 4 bases of Q20).
b) Data comparison: the base sequence of fastq file was aligned to human reference genome hg19 (GRCH 37) using bowtie2-2.3.4.2 software to generate bam file, and the bam file was ranked according to genome coordinates, the ranked bam was deduplicated using picard MarkDuplicates-2.18.25-snappshot, and paired reads were screened for reads aligned to the reference genome and MAPQ > 20.
c) cfDNA screening: to delete cfDNA fragments of MBD proteins that are non-specifically captured, fragments of the bam file that do not contain CG base pairs are filtered out. cfDNA with fragment length (60, 400) was further retained for subsequent analysis.
Cfdna fragment distribution profile
The final processed bam file was analyzed using R package Rsamtools to calculate the fragment length of each cfDNA. Then, dividing the cfDNA fragment length into different fragment intervals according to the lengths of 2bp, 3bp, 4bp and 5bp and … … bp respectively (if the step length is 2bp, the divided fragment intervals are 61-62bp, 63-64bp … … and 398-400bp, if the step length is 3bp, the divided fragment intervals are 61-63bp, 64-66bp … … and 396-399bp, if the step length is 10bp, the divided fragment intervals are 61-70bp, 71-80bp … … 391-400 bp), defining all cfDNA fragments included in each fragment interval as fragment characteristics, and calculating the proportion of the cfDNA fragments in each fragment characteristic to the total fragment number so as to generate the fragment distribution spectrum of the cfDNA.
4. Tumor markers for recognizing cfDNA fragments
In both tumor and healthy samples, wilcox rank sum test was performed on each cfDNA fragment feature and BH correction was used to obtain corrected p-values, and the area under ROC curve (AUC) values for each fragment feature to distinguish tumor from healthy samples were further calculated. Fragment characteristics with corrected p-value <0.05 and AUC >0.6 were identified as differentially distributed in tumor and healthy samples.
Dividing tumor samples in a training set into two parts randomly and averagely, randomly generating two parts of samples with the same number as the tumor samples in a healthy sample, respectively mixing the two parts of tumor samples and the healthy sample, sequentially sequencing the two parts of samples according to each fragment characteristic, and calculating the dominance ratio OR value of distinguishing the tumor from the healthy sample by the fragment characteristics in the two parts of samples. The above process is repeated 100 times, then the average OR value is calculated 100 times for each segment feature, and segment features with average OR values >1.5 are retained.
Thus, 100 fragment features were obtained, wherein the proportion of the number of fragments of 50 fragment features to the total number of fragments was increased in the tumor sample, and the proportion of the number of fragments of 50 fragment features was decreased in the tumor sample, as shown in table 1:
TABLE 1 100 fragment characteristics
As can be seen from Table 1, the size is concentrated in 131-172bp in the fragment features increased in the tumor samples, and in 331-360bp in the fragment features decreased in the tumor samples.
Example 2 determination of tumor efficacy by different segment characteristics
And calculating the increasing or decreasing proportion of each feature in the tumor sample relative to the normal control sample in the training set by using the 50 fragment features increased in the tumor sample and the 50 fragment features decreased in the tumor sample, judging whether the sample belongs to the tumor or normal according to the standard, and verifying in the test set. Their efficacy in distinguishing tumor samples from normal samples, respectively, are shown in tables 2 and 3 below:
TABLE 2 determination of 50 increased fragment characteristics in tumor samples
TABLE 3 determination of 50 increased fragment characteristics in tumor samples
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It follows that the 100 fragment features described above can be used as markers for identifying tumors. Judging whether the sample belongs to a tumor sample or not by judging the proportion of the sample in the sample.
Example 3 identification of tumors by different segment feature combinations
1.10 markers
The increased fragment features (T5) in the first 5 tumor samples and the decreased fragment features (N5) in the first 5 tumor samples were combined, respectively, in order of AUC values for the individual features.
Wherein,
t5 comprises: 163-164bp, 163-165bp, 161-164bp and 165-166bp
N5 comprises: 339-340bp, 341-342bp, 343-344bp, 337-339bp and 340-342bp
For each sample, a score 10=sum (T5)/sum (N5) is calculated, and then the proportion of the score increase in the tumor sample relative to the normal control sample is calculated in the training set, based on which criteria the sample is judged to be tumor or normal, and validated in the test set.
2.20 markers
The increased fragment features (T10) in the first 10 tumor samples and the decreased fragment features (N10) in the first 10 tumor samples were combined, respectively, in order of AUC values for the individual features.
Wherein,
t10 comprises: 163-164bp, 163-165bp, 161-164bp, 165-166bp, 159-165bp, 157-164bp, 155-156bp, 163-168bp, 160-168bp
N10 includes: 339-340bp, 341-342bp, 343-344bp, 337-339bp, 340-342bp, 337-340bp, 341-344bp, 336-340bp, 341-345bp, 337-342bp
For each sample, a score of score20 = sum (T10)/sum (N10) is calculated, and then the proportion of the score increase in the tumor sample relative to the normal control sample is calculated in the training set, based on which criteria the sample is judged to be tumor or normal, and validated in the test set.
3.30 markers
The increased fragment features (T15) in the first 15 tumor samples and the decreased fragment features (N15) in the first 15 tumor samples were combined, respectively, in order of AUC values of the individual features.
Wherein,
t15 comprises: 163-164bp, 163-165bp, 161-164bp, 165-166bp, 159-165bp, 157-164bp, 155-156bp, 163-168bp, 160-168bp, 157-158bp, 154-156bp, 161-170bp, 156-160bp, 161-162bp
N15 includes: 339-340bp, 341-342bp, 343-344bp, 337-339bp, 340-342bp, 337-340bp, 341-344bp, 336-340bp, 341-345bp, 337-342bp, 337-338bp, 343-345bp, 341-347bp, 340-348bp, 334-340bp
For each sample, a score of 30=sum (T15)/sum (N15) was calculated, and then the proportion of the score increase in tumor samples relative to normal control samples was calculated in the training set, based on which criteria the samples were judged to be tumor or normal, and verified in the test set.
4.40 markers
The increased fragment features (T20) in the first 20 tumor samples and the decreased fragment features (N20) in the first 20 tumor samples were combined, respectively, in order of AUC values for the individual features.
Wherein,
t20 comprises: 163-164bp, 163-165bp, 161-164bp, 165-166bp, 159-165bp, 157-164bp, 155-156bp, 163-168bp, 160-168bp, 157-158bp, 154-156bp, 161-170bp, 156-160bp, 161-162bp, 157-159bp, 165-168bp, 157-162bp, 157-160bp, 151-160bp
N20 comprises: 339-340bp, 341-342bp, 343-344bp, 337-339bp, 340-342bp, 337-340bp, 341-344bp, 336-340bp, 341-345bp, 337-342bp, 337-338bp, 343-345bp, 341-347bp, 340-348bp, 334-340bp, 341-348bp, 345-346bp, 343-348bp, 341-350bp, 345-348bp
For each sample, a score 40=sum (T20)/sum (N20) is calculated, and then the proportion of the score increase in the tumor sample relative to the normal control sample is calculated in the training set, based on which criteria the sample is judged to be tumor or normal, and validated in the test set.
5.50 markers
The increased fragment features (T25) in the first 25 tumor samples and the decreased fragment features (N25) in the first 25 tumor samples were combined, respectively, in order of AUC values for the individual features.
Wherein,
t25 comprises: 163-164bp, 163-165bp, 161-164bp, 165-166bp, 159-165bp, 157-164bp, 155-156bp, 163-168bp, 160-168bp, 157-158bp, 154-156bp, 161-170bp, 156-160bp, 161-162bp, 157-159bp, 165-168bp, 157-162bp, 157-160bp, 151-160bp, 152-158bp, 160-162bp, 153-156bp, 151-159bp, 166-168bp
N25 includes: 339-340bp, 341-342bp, 343-344bp, 337-339bp, 340-342bp, 337-340bp, 341-344bp, 336-340bp, 341-345bp, 337-342bp, 337-338bp, 343-345bp, 341-347bp, 340-348bp, 334-340bp, 341-348bp, 345-346bp, 343-348bp, 341-350bp, 345-348bp, 346-348bp, 333-340bp, 347-348bp, 346-350bp, 335-336bp
For each sample, a score 50=sum (T25)/sum (N25) was calculated, and then the proportion of the score increase in the tumor sample relative to the normal control sample was calculated in the training set, based on which criteria the sample was judged to be tumor or normal, and verified in the test set.
6.60 markers
The increased fragment features (T30) in the first 30 tumor samples and the decreased fragment features (N30) in the first 30 tumor samples were combined, respectively, in order of AUC values of the individual features.
Wherein,
t30 includes: 163-164bp, 163-165bp, 161-164bp, 165-166bp, 159-165bp, 157-164bp, 155-156bp, 163-168bp, 160-168bp, 157-158bp, 154-156bp, 161-170bp, 156-160bp, 161-162bp, 157-159bp, 165-168bp, 157-162bp, 157-160bp, 151-160bp, 152-158bp, 160-162bp, 153-156bp, 151-159bp, 166-168bp, 148-150bp, 149-156bp, 159-160bp, 151-156bp
N30 includes: 339-340bp, 341-342bp, 343-344bp, 337-339bp, 340-342bp, 337-340bp, 341-344bp, 336-340bp, 341-345bp, 337-342bp, 337-338bp, 343-345bp, 341-347bp, 340-348bp, 334-340bp, 341-348bp, 345-346bp, 343-348bp, 341-350bp, 345-348bp, 346-348bp, 333-340bp, 347-348bp, 346-350bp, 335-336bp, 331-340bp, 334-336bp, 349-350bp, 349-351bp, 331-331 bp
For each sample, a score of 60=sum (T30)/sum (N30) was calculated, and then the proportion of the score increase in tumor samples relative to normal control samples was calculated in the training set, based on which criteria the samples were judged to be tumor or normal, and verified in the test set.
7. Judgment result of different marker combinations
The results of the determination in the training set and the test set according to the scores of the above 6 marker combinations are shown in fig. 1 to 6 and table 4:
TABLE 4 tumor recognition results for different marker combinations
From the above table, it can be seen that the tumor sample can be well identified by using 10 fragment features, further increasing fragment features does not make the identification effect better, and the reverse side has a certain degree of reduction, which indicates that the tumor sample has a better tumor identification effect by using 10 fragment features, and it is possible to predict whether the subject has tumor or has a risk of having tumor by calculating the score.
Example 4 verification of marker combinations of 10 fragment signature in external test set
To further verify the performance of the above 10 fragment features as markers for predicting tumors, the inventors performed further verification using an external test set (external data), the results of which are shown in fig. 7.
As can be seen from fig. 7, the score obtained by using the 10 fragment characteristics can significantly distinguish between the tumor sample and the normal sample, and in particular, the score in the tumor sample is significantly higher than that in the normal sample, and the ROC curve AUC reaches 0.827.
All documents mentioned in this application are incorporated by reference as if each were individually incorporated by reference. Further, it will be appreciated that various changes and modifications may be made by those skilled in the art after reading the above teachings, and such equivalents are intended to fall within the scope of the claims appended hereto.

Claims (10)

  1. Use of a detection reagent of a cfDNA fragment signature combination in the preparation of a kit for predicting whether a subject has cancer, characterized in that the cfDNA fragment signature combination comprises a first cfDNA fragment signature sub-combination comprising cfDNA fragment signatures falling between 130bp and 175bp and a second cfDNA fragment signature sub-combination comprising cfDNA fragment signatures falling between 330bp and 360bp.
  2. 2. The use of claim 1, wherein the cfDNA fragment falling between 130-175 bp is characterized by at least one member selected from the group consisting of 163-164bp, 163-165bp, 161-164bp, 165-166bp, 159-165bp, 157-164bp, 155-156bp, 163-168bp, 160-168bp, 157-158bp, 154-156bp, 161-170bp, 156-160bp, 161-162bp, 157-159bp, 165-168bp, 157-162bp, 157-160bp, 151-160bp, 152-158bp, 160-162bp, 153-156bp, 151-159bp, 166-168bp, 148-150bp, 149-156bp, 159-160bp, 151-156bp, 167-168bp, 147-148bp, 146-150bp, 165-172bp, 166-170bp, 151-155, 153-154bp, 149-152bp, 145-150bp, 166-172bp, 151-172 bp, and 169-150 bp.
  3. 3. The use of claim 2, wherein the first cfDNA fragment signature sub-combination comprises cfDNA fragment signatures falling between 160bp and 170bp, the cfDNA fragment signatures falling between 160bp and 170bp being selected from at least one of the group comprising 163-164bp, 163-165bp, 161-164bp and 165-166 bp.
  4. 4. The use according to claim 1, wherein the cfDNA fragment falling between 330bp and 360bp is characterized by at least one group selected from the group consisting of 339-340bp, 341-342bp, 343-344bp, 337-339bp, 340-342bp, 337-340bp, 341-344bp, 336-340bp, 341-345bp, 337-342bp, 337-338bp, 343-345bp, 341-347bp, 340-348bp, 334-340bp, 341-348bp, 345-346bp, 343-348bp, 341-350bp, 345-348bp, 346-348bp, 333-340bp, 347-348bp, 346-350bp, 335-336bp, 331-340bp, 334-336bp, 349-350bp, 349-351bp, 331-339bp, 349-352bp, 333-336bp, 348bp, 351-354 bp, 351-355bp, 333-334bp, 354bp, 355-348 bp, 355bp, 331-348 bp, 355bp, 357-356 bp, 355bp, 357-357 bp, 360bp, 357bp, 360-357 bp, and 357 bp.
  5. 5. The use of claim 4, wherein the second subset of cfDNA fragment features comprises cfDNA fragment features falling between 335bp and 345bp, the cfDNA fragment features falling between 335bp and 345bp being selected from at least one of the group consisting of 339-340bp, 341-342bp, 343-344bp, 337-339bp, and 340-342 bp.
  6. 6. The use according to any one of claims 1 to 5, wherein the detection reagent is a reagent that detects the combination of cfDNA fragment features based on a non-sequencing based method.
  7. 7. Use according to any one of claims 1 to 5, wherein a ratio of the number of fragments of each cfDNA fragment signature in the combination of cfDNA fragment signatures is obtained; judging whether the subject has cancer or is at risk of having cancer according to the fragment quantity proportion of the cfDNA fragment characteristics.
  8. 8. The use according to claim 7, wherein the subject is judged to have or to be at risk of cancer if the proportion of the number of fragments of at least one cfDNA fragment feature in the first cfDNA fragment feature sub-combination is increased and/or the proportion of the number of fragments of at least one cfDNA fragment feature in the second cfDNA fragment feature sub-combination is decreased.
  9. 9. The use of claim 7, wherein the cfDNA fragment signature combination comprises a first cfDNA fragment signature sub-combination and a second cfDNA fragment signature sub-combination, and wherein the cancer prediction module obtains the determination using the following formula:
    wherein,
    the Score is used as a judgment value and,
    m is the number of cfDNA fragment features in the first cfDNA fragment feature sub-combination, and n is the number of cfDNA fragment features in the second cfDNA fragment feature sub-combination;
    ti is the proportion of the number of fragments of the ith cfDNA fragment characteristic in the first cfDNA fragment characteristic sub-combination;
    N j the ratio of the number of fragments characteristic of the jth cfDNA fragment in the first cfDNA fragment characteristic sub-combination,
    if Score is greater than a preset threshold, the subject is judged to have cancer or to be at risk of having cancer.
  10. 10. A system for predicting whether a subject has or is at risk of having cancer, comprising the following modules:
    the data input module is used for inputting the cfDNA fragment length and quantity data of the subject;
    the distribution spectrum analysis module is connected with the data input module and is used for obtaining the fragment quantity proportion of each cfDNA fragment characteristic in the cfDNA fragment characteristic combination of claim 1;
    and the cancer prediction module is connected with the distribution spectrum analysis module and is used for judging whether the subject has cancer or is at risk of having cancer according to the fragment quantity proportion of the cfDNA fragment characteristics.
CN202311603742.9A 2022-09-29 2022-09-29 Application of cfDNA fragment characteristic combination in prediction of cancer Pending CN117551767A (en)

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