Identification of instance-specific somatic genome alterations with functional impact

Patent metadata

Field	Value
Publication number	US-11990209-B2
Application number	US-201716349192-A
Country	US
Kind code	B2
Filing date	Nov 13, 2017
Priority date	Nov 11, 2016
Publication date	May 21, 2024
Grant date	May 21, 2024

Abstract

The present application provides methods for the identification of somatic genome alterations with functional impact in the genome of a tumor. In several embodiments, the methods comprise generating a bipartite causal Bayesian network with maximal posterior probability including causal edges pointing from genes including somatic mutations and somatic copy number alterations in the genome of the tumor to genes having differential expression in the tumor. The methods can be used, for example, to identify driver somatic genome alterations in the genome of a tumor.

First claim

It is claimed:

1. A method for detecting a somatic genome alteration (SGA) with tumor-specific functional impact in a genome of a specific tumor t of a subject, comprising:
receiving a training dataset that comprises gene expression data indicating respective levels of expression of a plurality of genes in a collection of tissue samples including tumors;
receiving a set of SGAs in the genome of t;
training a bipartite causal Bayesian network (CBN) with maximal posterior probability, including:
identifying a set of differentially expressed genes (DEGs) in the genome of t by designating each gene in a set of genes in the genome of t as a DEG if the respective level of expression of the gene in t falls outside a significance boundary determined based on respective levels of expression of the gene in the collection of tissue samples represented in the training dataset;
for each DEG in the set of DEGs:
(i) identifying, from among multiple SGAs in the set of SGAs that are possible causes of the DEG, one SGA that is the most probable cause of the DEG, and
(ii) providing the DEG with just a single causal edge in the CBN with maximal posterior probability, the single causal edge connecting the DEG in the CBN to the SGA that has been identified as the most probable cause of the DEG, wherein the DEG is not connected in the CBN to any other SGAs except for the SGA that has been identified as the most probable cause of the DEG; and

identifying a particular SGA that is connected by respective causal edges to at least a threshold number of DEGs in the CBN with maximal posterior probability as the SGA with tumor-specific functional impact in the genome of t.

2. The method of claim 1 , wherein training the CBN with maximal posterior probability comprises:
generating a plurality of test CBNs for the set of SGAs and the set of DEGs, determining a posterior probability for each test CBN in the plurality of test CBNs, and identifying the test CBN with the maximal posterior probability.

3. The method of claim 2 , wherein determining the posterior probability for a test CBN in the plurality of test CBNs comprises calculating P(D|M)×P(M), wherein:
P(D|M) is a marginal likelihood of the test CBN; and
P(M) is a prior probability of the test CBN.

4. The method of claim 3 , wherein P(M) comprises a product of the frequencies at which the SGAs of the test CBN are present in a reference set of tumor genomes, D.

5. The method of claim 3 , wherein P(D|M) comprises a product of marginal likelihoods of causal edges of the test CBN for tumor t.

6. The method of claim 3 , wherein P(D|M) comprises a product of posterior probabilities of causal edges of the test CBN, and calculating said product comprises:
calculating a posterior probability of each causal edge A h →E i of the CBN as:

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Assignees

• Univ Of Pittsburgh—Of The Commonwealth System Of Higher Education
• Univ Of Pittsburgh—Of The Commonwealth System Of Higher Educat

Inventors

• Lu Xinghua
• Cooper Gregory
• Cai Chunhui
• Visweswaran Shyam

Classifications

• G16B40/20 Primary

  Supervised data analysis · CPC title

• G16B20/10

  Ploidy or copy number detection · CPC title

• G16B20/20

  Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection · CPC title

• G16B25/00

  ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression · CPC title

• G16B40/00

  ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding · CPC title