METHODS FOR IDENTIFYING CHROMOSOMAL SPATIAL INSTABILITY SUCH AS HOMOLOGOUS REPAIR DEFICIENCY IN LOW COVERAGE NEXT- GENERATION SEQUENCING DATA

A genomic data analyzer may be configured to detect and characterize, with a machine learning model such as a trained convolutional neural network, the presence of a genomic instability in a tumor sample. The genomic data analyzer may use whole genome sequencing reads as input data even at low seque...

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Bibliographic Details
Main Authors Andre, Gregoire, Pozzorini, Christian, Coletta, Tommaso, Xu, Zhenyu
Format Patent
LanguageEnglish
Published 17.03.2022
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS
PHYSICS
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Summary:A genomic data analyzer may be configured to detect and characterize, with a machine learning model such as a trained convolutional neural network, the presence of a genomic instability in a tumor sample. The genomic data analyzer may use whole genome sequencing reads as input data even at low sequencing coverage in a high throughput sequencing workflow as may be routinely employed in a diversity of clinical oncology setups. The genomic data analyzer may arrange the aligned read data coverage from chromosome arms or full chromosomes to form a coverage data signal array possibly as an image. The trained machine learning model may process the coverage data signal array to determine whether a chromosomal spatial instability (CSI) such as for instance a genomic instability caused by a homologous repair or recombination deficiency (HRD) is present in the tumor sample. The latter indication may guide the choice of a preferred anticancer treatment for the tumor.
Bibliography:Application Number: US202117534368