Methods for partner agnostic gene fusion detection

Patent metadata

Field	Value
Publication number	US-2025084470-A1
Application number	US-202418896256-A
Country	US
Kind code	A1
Filing date	Sep 25, 2024
Priority date	Mar 22, 2019
Publication date	Mar 13, 2025
Grant date	—

Abstract

A method for detecting a gene fusion includes amplifying a nucleic acid sample in the presence of primer pool to produce a plurality of amplicons. The primer pool includes primers targeting a plurality of exon-exon junctions of a driver gene. The amplicons correspond to the exon-exon junctions. The amplicons are sequenced and aligned to a reference sequence. The number of reads corresponding to each amplicon is normalized to give a normalized read count. A baseline correction is applied to the normalized read counts for the amplicons to form corrected read counts. A binary segmentation score is calculated for each corrected read count. A predicted breakpoint for the gene fusion is determined based on the amplicon index corresponding to the maximum absolute binary segmentation score. Gene fusion events may be detected in a partner agnostic manner, i.e. without prior knowledge of the specific fusion partner genes or specific breakpoint information.

First claim

What is claimed is:

1 . A system for detecting a gene fusion, comprising:
a machine-readable memory; and
a processor in communication with the memory, wherein the processor is configured to execute machine-readable instructions, which, when executed by the processor, cause the system to perform a method, comprising:
receiving, at the processor, a plurality of nucleic acid sequence reads for a plurality of amplicons produced by amplification of a nucleic acid sample a presence of a primer pool, the primer pool including primers targeting a plurality of exon-exon junctions of a driver gene, wherein the amplicons correspond to the exon-exon junctions;
aligning the reads to a reference sequence, the reference sequence including nucleic acid sequences of the amplicons corresponding to the targeted exon-exon junctions of the driver gene;
determining a number of reads for each amplicon corresponding to each targeted exon-exon junction;
dividing the number of reads for each amplicon by a maximum number of reads among the amplicons of the driver gene to give a normalized read count for each amplicon;
applying a baseline correction to the normalized read counts for the amplicons to form corrected read counts, wherein the baseline correction uses baseline values based on read counts for amplicons of a plurality of normal samples;
determining an imbalance between the corrected read counts for the amplicons corresponding to a 5′ end of the driver gene and the corrected read counts for the amplicons corresponding to a 3′end of the driver gene; and
detecting the gene fusion in the driver gene based on the imbalance.

2 . The system of claim 1 , wherein the determining an imbalance further comprises:
calculating a partial sum, S i , of the corrected read counts X from a first amplicon to an i th amplicon, where S i =X 1 + . . . +X i ; and calculating a sum, S n , of the corrected read counts from the first amplicon to an n th amplicon, where S n =X 1 + . . . +X n , where n is a total number of corrected read counts.

3 . The system of claim 2 , wherein the determining an imbalance further comprises determining a binary segmentation score, Z i , for the i th amplicon by:

Z
i

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i

-

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-
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n
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1
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+

1

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4 . The system of claim 3 , wherein the determining an imbalance further comprises determining a predicted breakpoint for the gene fusion based on an amplicon index corresponding to a maximum absolute binary segmentation score.

5 . The system of claim 1 , wherein the determining an imbalance further comprises determining an imbalance score based on a ratio of an observed imbalance value and an expected imbalance value.

6 . The system of claim 5 , wherein the expected imbalance value is based on a first array of the baseline values and the observed imbalance value is based on a second array of the normalized read counts, wherein a number of array elements in each array is N.

Assignees

• Life Technologies Corp

Inventors

• Gottimukkala Rajesh
• Marcovitz Amir
• Schageman Jeoffrey
• Bagai Varun
• Gu Jian
• Veitch James
• Bramlett Kelli
• Myrand Scott
• Hyland Fiona
• Sadis Seth
• Williams Paul

Classifications

• G06F17/18

  for evaluating statistical data {, e.g. average values, frequency distributions, probability functions, regression analysis (forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04)} · CPC title

• G16B25/10

  Gene or protein expression profiling; Expression-ratio estimation or normalisation · CPC title

• G16B30/00

  ICT specially adapted for sequence analysis involving nucleotides or amino acids · CPC title

• C12Q1/6851 Primary

  Quantitative amplification · CPC title

• G16B20/00 Primary

  ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations · CPC title