Method for estimating tumor fraction

Abstract

A computer-based method for estimating the tumor fraction in a patient sample may be provided, comprising the steps of obtaining a catalog of tumor-specific mutations and whole-genome sequencing data from the patient sample. Furthermore, the method may include the steps of aligning reads to a reference genome, determining an index of signals in the patient sample read alignment file that support the presence of mutations in the catalog of tumor-specific mutations, and determining an index of noise in the patient sample read alignment file that is associated with mutations similar to those in the catalog of tumor-specific mutations. Given the indexes of signals and noise at all tumor-specific locations, the method may include estimating the tumor DNA fraction (eTF) in the patient sample through iterations k, and generating a list of final eTFs and somatic mutations in the patient sample.

Claims

1. A computer method for estimating the tumor fraction in a patient sample, The steps include obtaining a catalog of tumor-specific mutations constructed based on baseline (t=0) analysis of at least one patient sample, The steps include obtaining whole genome sequencing data from the aforementioned patient sample, The steps include aligning the reads of the whole genome sequencing data from the patient sample to a reference genome to obtain a patient sample read alignment file, The steps include determining an indicator of a signal that supports the presence of a mutation in the catalog of tumor-specific mutations in the patient sample read alignment file, The steps include determining an index of noise associated with mutations similar to the mutations in the tumor-specific mutation catalog in the patient sample read alignment file, Given indicators of the signal and noise at all tumor-specific locations, the step of estimating the fraction of tumor DNA (hereinafter referred to as eTF) in the patient sample through one or more iterations k, A computer-based method comprising the steps of generating a final eTF in the last iteration of the one or more iterations and a list of somatic mutations in the patient sample.

2. The step of estimating the eTF is: The steps include assigning a probability that each mutation in the catalog of tumor-specific mutations is a somatic mutation, The steps include: calculating a signal supporting the presence of a mutation in the catalog of tumor-specific mutations in the patient sample using a first weighted sum; A computer-based method according to claim 1, comprising the step of calculating noise supporting the presence of mutations in the catalog of tumor-specific mutations in the patient sample using a second weighted sum.

3. At k=0, the probability that each mutation in the catalog of tumor-specific mutations is a somatic mutation is 1 (Psomatic). k The computer-based method according to claim 2, wherein [x]=1), where x is a tumor mutation in the catalog of tumor-specific mutations, and k is the current number of iterations.

4. At k=0, the probability that each mutation in the catalog of tumor-specific mutations is a somatic mutation (Psomatic k [x]) a computer-based method according to claim 2, which is based on information about mutations obtained from a common mutation database.

5. The probability that each tumor mutation in the aforementioned catalog of tumor-specific mutations is a somatic mutation (Psomatic k The step of calculating [x] is, For k > 0, the steps include calculating the likelihood (Lgermline[x]) that each mutation in the catalog of tumor-specific mutations is a germline mutation, Here, Lgermline[x] is Binomial(Ntot_signal[x],1;Nalt_signal[x])+Binomial(Ntot_signal[x],0.5;Nalt_signal[x]), For k > 0, for each mutation in the catalog of tumor-specific mutations, the likelihood that the mutation is a somatic mutation (Lsomatic k The step of calculating [x], Here, Lsomatic k [x]=Binomial(Ntot_signal[x],TF k ;Nalt_signal[x]) and The step includes calculating the probability that mutation x is a somatic mutation when k > 0, Here, Psomatic k [x] = Lsomatic k [x] / (Lsomatic k [x] + Lgermline[x]) as described in any one of claims 2 to 4, a method executed by a computer.

6. The step of determining the fraction of reads in the read alignment file that support the presence of representative mutations is: Steps to obtain a list of representative genome segments, The steps include examining multiple positions in each of the multiple representative genomic sections in the list of representative genomic sections that have the same reference nucleotide base (ref nucleotide base) as the mutation in the catalog of tumor-specific mutations, A computer-operated method according to any one of claims 1 to 5, further comprising the step of excluding from a plurality of positions in each of the list of representative genomic sections having the same reference nucleotide base as the mutation in the catalog of tumor-specific mutations a mutation with an allele frequency of more than 5% and positions containing the mutation in the catalog of tumor-specific mutations.

7. The computer-executed method according to claim 6, wherein in the list of representative genomic segments, each of the multiple representative genomic segments is adjacent on the genome and is defined as a 100 bp window centered on a mutation in the catalog of tumor-specific mutations.

8. The computer-based method according to claim 6, wherein in the list of representative genomic segments, each of the multiple representative genomic segments shares one or more similar characteristics with the genomic segment in which the mutation in the catalog of tumor-specific mutations is located.

9. The computer-based method according to claim 8, wherein the similar characteristics include similar sequence composition and chromatin state.

10. The signal index supporting the presence of mutations in the catalog of tumor-specific mutations in the patient sample is defined as Signal = Nalt_signal / Ntot_signal using a first weighted sum, where Nalt_signal = sum(Nalt_signal[x] * Psomatic k [x]) over all variants, x is Ntot_signal = sum(Ntot_signal[x] * Psomatic k [x]) over all variants, x, a method to be performed by a computer according to any one of claims 2 to 5.

11. For each mutation in the catalog of tumor-specific mutations, the number of reads in the patient sample that support the alt sequence (Nalt_signal[x]) is calculated. The computer-operated method according to claim 10, further comprising the step of calculating the number of reads (Ntot_signal[x]) in the patient sample that span the mutation for each mutation in the catalog of tumor-specific mutations.

12. The noise index supporting the presence of mutations in the catalog of tumor-specific mutations in the patient sample is defined as Noise = Nalt_noise / Ntot_noise using a second weighted sum, Here, Nalt_noise = sum(Nalt_noise [x] * Psomatic k [x]) over all variants, x is Ntot_noise = sum(Ntot_noise [x] * Psomatic k [x]) over all variants, x, a method to be performed by a computer according to any one of claims 2 to 5.

13. The steps include determining the fraction of reads in the patient sample read alignment file that support the representative mutation, The steps include: calculating the number of reads (Nalt_noise[x]) in the patient sample that support the alt sequence in the representative mutation; A computer-operated method according to claim 12, further comprising the step of calculating the number of reads (Ntot_noise[x]) in the patient sample that cross the mutation in a representative mutation.

14. A computer-based method according to any one of claims 1 to 13, wherein the coverage of whole-genome sequencing data from the patient sample is less than 100 times.

15. A computer-based method according to any one of claims 1 to 13, wherein the coverage of whole-genome sequencing data from the patient sample is less than 10 times.

16. A computer-based method according to any one of claims 1 to 13, wherein the coverage of whole-genome sequencing data from the patient sample is less than 5 times.

17. The computer-based method according to any one of claims 1 to 16, wherein the catalog of tumor-specific mutations is obtained from whole-genome sequencing data from tumor-normal pairs.

18. The steps include determining the mutations present in the tumor sequencing data and germline sequencing data at the baseline using a mutation calling method, The steps include classifying the mutations present in the tumor sequencing data and germline sequencing data at the baseline as either highly probable germline mutations or highly probable somatic mutations, The computer-operated method according to claim 17, further comprising the step of constructing a catalog of tumor-specific mutations by comparing the germline mutations with the somatic mutations that are likely to be found.

19. The step of classifying the mutations present in the tumor sequencing data and germline sequencing data at baseline as either highly probable germline mutations or highly probable somatic mutations is: The computer-based method according to claim 18, comprising the step of defining the mutations in the germline sequencing data at the baseline as highly probable germline mutations.

20. The step of classifying the mutations present in the tumor sequencing data and germline sequencing data at baseline as either highly probable germline mutations or highly probable somatic mutations is: The computer-operated method according to claim 19, comprising the step of defining a mutation in the tumor sequencing data at a baseline as a likely germline mutation if the mutation is present in the mutation in the germline sequencing data at a baseline.

21. The step of classifying the mutations present in the tumor sequencing data and germline sequencing data at baseline as either highly probable germline mutations or highly probable somatic mutations is: The computer-based method according to claim 20, comprising the step of defining mutations not included in the germline sequencing data at the baseline as likely somatic mutations.

22. The computer-based method according to any one of claims 1 to 21, wherein the patient sample comprises cell-free DNA (cfDNA).

23. A computer-based method according to any one of claims 1 to 22, wherein, given indicators of the signal and noise at all tumor-specific locations, the step of estimating the fraction of tumor DNA (eTF) in the patient sample through one or more iterations k further comprises the step of utilizing an expectation maximization algorithm.

24. Psomatic k A computer-based method according to any one of claims 1 to 23, wherein if [x] is greater than a predetermined value p, the mutation is defined as a somatic mutation.

25. The computer-executed method according to any one of claims 1 to 24, wherein the tumor DNA is circulating tumor DNA (ctDNA).

26. The computer-based method according to any one of claims 1 to 25, wherein the whole genome sequencing data from the patient sample is obtained at a later time (t=1).

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