Integrated machine-learning framework to estimate homologous recombination deficiency

Patent metadata

Field	Value
Publication number	US-10975445-B2
Application number	US-202016789363-A
Country	US
Kind code	B2
Filing date	Feb 12, 2020
Priority date	Feb 12, 2019
Publication date	Apr 13, 2021
Grant date	Apr 13, 2021

Abstract

Methods, systems, and software are provided for determining a homologous recombination pathway status of a cancer in a test subject, e.g., to improve cancer treatment predictions and outcomes. In some embodiments, classifiers using one or more of (i) a heterozygosity status for DNA damage repair genes in a cancerous tissue, (ii) a measure of the loss of heterozygosity across the genome of the cancerous tissue, (iii) a measure of variant alleles detected in a second plurality of DNA damage repair genes in the genome of the cancerous tissue, (iv) a measure of variant alleles detected in the second plurality of DNA damage repair genes in the genome of a non-cancerous tissue, and (v) tumor sample purity are provided.

First claim

What is claimed is:

1. A method of determining a homologous recombination pathway status of a cancer in a test subject, the method comprising:
at a computer system having one or more processors, and memory storing one or more programs for execution by the one or more processors:
(A) obtaining a first plurality of sequence reads, in electronic form, of a first DNA sample from the test subject, the first DNA sample comprising DNA molecules from a cancerous tissue of the subject;
(B) obtaining a second plurality of sequence reads, in electronic, of a second DNA sample from the test subject, the second DNA sample consisting of DNA molecules from a non-cancerous tissue of the subject;
(C) aligning each respective sequence read in the first plurality of sequence reads and each respective sequence read in the second plurality of sequence reads to a reference human genome, thereby generating a corresponding first plurality of aligned sequence reads and a corresponding second plurality of aligned sequence reads;
(D) generating, based on the first plurality of aligned sequence reads and the second plurality of aligned sequence reads, a genomic data construct for the subject, the genomic data construct comprising a plurality of features of the genomes of the cancerous and non-cancerous tissues of the subject, the plurality of features including (i) a heterozygosity status for a first plurality of DNA damage repair genes in the genome of the cancerous tissue of the subject, (ii) a measure of the loss of heterozygosity across the genome of the cancerous tissue of the subject, (iii) a measure of variant alleles detected in a second plurality of DNA damage repair genes in the genome of the cancerous tissue of the subject, and (iv) a measure of variant alleles detected in the second plurality of DNA damage repair genes in the genome of the non-cancerous tissue of the subject; and
(E) inputting the genomic data construct into a classifier trained to distinguish between cancers with homologous recombination pathway deficiencies and cancers without homologous recombination pathway deficiencies, thereby determining the homologous recombination pathway status of the test subject.

2. The method of claim 1 , wherein the first DNA sample is from a solid tumor biopsy of the cancerous tissue of the subject.

3. The method of claim 1 , wherein the second DNA sample is from a buffy coat preparation of a blood sample from the subject.

4. The method of claim 1 , wherein the first plurality of sequence reads was generated by targeted sequencing using a plurality of nucleic acid probes to enrich nucleic acids from the cancerous tissue of the subject for a panel of genomic regions.

5. The method of claim 1 , wherein the first plurality of sequence reads was generated by whole genome sequencing of nucleic acids from the cancerous tissue of the subject.

6. The method of claim 1 , wherein the second plurality of sequence reads was generated by targeted sequencing using a plurality of nucleic acid probes to enrich nucleic acids from the non-cancerous tissue of the subject for a panel of genomic regions.

7. The method of claim 1 , wherein the second plurality of sequence reads was generated by whole genome sequencing of nucleic acids from the non-cancerous tissue of the subject.

8. The method of claim 1 , wherein the measure of the loss of heterozygosity across the genome of the cancerous tissue of the subject is determined by:
determining a loss of genomic heterozygosity in the first plurality of sequence reads, and
normalizing the determined loss of heterozygosity by an estimate of the tumor purity for the first plurality of sequence reads, wherein the estimate of the tumor purity is based on the first plurality of sequence reads and the second plurality of sequence reads.

9. The method of claim 1 , wherein the heterozygosity status for the first plurality of DNA damage repair genes comprises a count of the number of unique frameshift mutations detected in the first plurality of DNA damage repair genes.

10. The method of claim 1 , wherein the heterozygosity status for the first plurality of DNA damage repair genes comprises a count of the number of unique truncating mutations detected in the first plurality of DNA damage repair genes.

11. The method of claim 1 , wherein the first plurality of DNA damage repair genes comprises BRCA1 and BRCA2.

12. The method of claim 1 , wherein the measure of variant alleles detected in the second plurality of DNA damage repair genes in the genome of the cancerous tissue of the subject comprises a count of the number of unique mutations associated with loss of homologous recombination detected in the first plurality of sequence reads.

13. The method of claim 1 , wherein the measure of variant alleles detected in the second plurality of DNA damage repair genes in the genome of the non-cancerous tissue of the subject comprises a count of the number of unique mutations associated with loss of homologous recombination detected in the second plurality of sequence reads.

14. The method of claim 1 , wherein the second plurality of DNA damage repair genes comprises BRCA1 and BRCA2.

15. The method of claim 14 , wherein the unique mutations associated with loss of homologous recombination in BRCA1 and BRCA2 include at least 50 of the mutations listed in Table 1.

16. The method of claim 14 , wherein the unique mutations associated with loss of homologous recombination in BRCA1 and BRCA2 comprises the mutations listed in Table 1.

17. The method of claim 1 , wherein the method further comprises:
when it is determined that the cancer in the test subject is homologous recombination deficient, treating the cancer by administering a poly ADP ribose polymerase (PARP) inhibitor to the test subject; and
when it is determined the cancer in the test subject is not homologous recombination deficient, treating the cancer with a therapy that does not include administration of a PARP inhibitor to the test subject.

18. The method of claim 17 , wherein the PARP inhibitor is selected from the group consisting of olaparib, veliparib, rucaparib, niraparib, and talazoparib.

19. The method of claim 1 , wherein the cancer is breast cancer.

20. The method of claim 1 , wherein the cancer is ovarian cancer.

21. The method of claim 1 , wherein the cancer is colorectal cancer.

22. The method of claim 1 , wherein the classifier is a neural network algorithm, a support vector machine algorithm, a Naive Bayes algorithm, a nearest neighbor algorithm, a boosted trees algorithm, a random forest algorithm, a convolutional neural network algorithm, a decision tree algorithm, a regression algorithm, or a clustering algorithm.

23. The method of claim 1 , wherein the classifier is a random forest algorithm.

24. The method of claim 1 , wherein the first plurality of sequence reads was generated by exome sequencing of cDNA molecules generated from the cancerous tissue of the subject.

25. The method of claim 1 , wherein the second plurality of sequence reads was generated by exome sequencing of cDNA molecules generated from the non-cancerous tissue of the subject.

26. The method of claim 1 , wherein the first plurality of sequence reads comprises at least 300 respective unique sequence reads for each of at least 10 different loci in the human genome and the second plurality of sequence reads comprises at least 300 respective unique sequence reads for e

Assignees

• Tempus Labs Inc

Inventors

• Venkat Aarti
• Parsons Jerod
• Bell Joshua S K
• Igartua Catherine
• Zhang Yilin
• Salahudeen Ameen
• Sánchez Freire Verónica
• Tell Robert

Classifications

• G06N5/01

  Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound · CPC title

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• G06N3/045

  Combinations of networks · CPC title

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title

• G06N3/09

  Supervised learning · CPC title