US 2026/0018291 A1 · Domain Adaptation Engine(s) For Cell-Free DNA Fragmentomics

Abstract

Systems and methods for predicting tissue-of-origin for diseased tissues using cell-free DNA (cfDNA) are provided herein. In an aspect, a domain adaptation engine receives a cfDNA sample from a subject and generates cfDNA fragmentation data from the cfDNA sample. The domain adaptation engine deconvolutes the cfDNA fragmentation data using Assay for Transposase-Accessible Chromatin using sequencing (ATAC-Seq) data from multiple tissue and cell types to generate deconvoluted cfDNA fragmentation data. In an example, the domain adaptation engine deconvolutes the cfDNA fragmentation data using a machine learning (ML) system trained to translate between the ATAC-Seq data and cfDNA data. Subsequently, the domain adaptation engine detects a diseased tissue signature within the deconvoluted cfDNA fragmentation data and generates a tissue or cell-type-of-origin prediction for the diseased tissue signature based on the deconvoluted cfDNA fragmentation data.

Claims26

Dependent claims

A computing apparatus comprising:

a computer-readable storage media comprising processor-executable instructions stored thereon;
and
a processor coupled to the computer-readable storage media and configured to execute the processor-executable instructions that, when executed by the processor, direct the computing apparatus to at least:
receive a cell-free DNA (cfDNA) sample from a subject;
process the cfDNA sample to generate cfDNA fragmentation data;
input the cfDNA fragmentation data into a pre-trained machine learning (ML) system, wherein the pre-trained ML system:
(a) has been trained on cell-type-specific Assay for Transposase-Accessible Chromatin using sequencing (ATAC-Seq) data from multiple tissue types;
and
(b) is configured to translate between ATAC-Seq data and cfDNA fragmentation data;
analyze the cfDNA fragmentation data using the pre-trained ML system to identify tissue-specific chromatin accessibility patterns;
predict a tissue-of-origin for the cfDNA sample based on the identified tissue-specific chromatin accessibility patterns;
and
generate a prediction of the tissue-of-origin for the cfDNA sample.

10 more elements

A computer-implemented method for predicting tissue-of-origin from cell-free DNA (cfDNA) samples, wherein the method comprises:

receiving, by a domain adaptation engine, a cell-free DNA (cfDNA) sample from a subject;
generating, by the domain adaptation engine, cfDNA fragmentation data from the cfDNA sample;
deconvoluting, by the domain adaptation engine, the cfDNA fragmentation data using Assay for Transposase-Accessible Chromatin using sequencing (ATAC-Seq) data from multiple tissue types to generate deconvoluted cfDNA fragmentation data;
detecting, by the domain adaptation engine, a diseased tissue signature within the deconvoluted cfDNA fragmentation data;
and
generating, by the domain adaptation engine, a tissue-of-origin prediction for the diseased tissue signature based on the deconvoluted cfDNA fragmentation data.

3 more elements

A non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause a computing system to:

receive cell-free DNA (cfDNA) fragmentation data corresponding to a cfDNA sample from a subject;
process the cfDNA fragmentation data using a pre-trained machine learning (ML) system to identify tissue-specific chromatin accessibility patterns, wherein the pre-trained ML system:
(a) has been trained on single-cell Assay for Transposase-Accessible Chromatin using sequencing (ATAC-Seq) data from multiple tissue types, and
(b) is configured to translate between ATAC-Seq data and cfDNA fragmentation data;
detect, based on the tissue-specific chromatin accessibility patterns, a diseased tissue signature within the cfDNA fragmentation data;
and
generate a tissue-of-origin prediction for the diseased tissue signature based on the identified tissue-specific chromatin accessibility patterns.

4 more elements

A method for training a machine learning model to detect tissue-of-origin from cell-free DNA (cfDNA) samples, comprising:

obtaining single-cell Assay for Transposase-Accessible Chromatin using sequencing (ATAC-Seq) data from multiple tissue types;
generating simulated cfDNA fragmentation data by combining blood cell data with the ATAC-Seq data at varying proportions;
training a machine learning (ML) system using the ATAC-Seq data and the simulated cfDNA fragmentation data, wherein the machine learning model is configured to:
(a) learn tissue-specific chromatin accessibility patterns from the ATAC-Seq data,
(b) translate between ATAC-Seq data and cfDNA fragmentation data, and
(c) predict tissue-of-origin from cfDNA fragmentation data;
validating the ML system as trained using a set of real cfDNA samples with known tissue-of-origin;
and
storing the ML system as validated for subsequent use in detecting tissue-of-origin from cfDNA samples.

6 more elements

Dependencies are inferred from the claim text.

Domain Adaptation Engine(s) For Cell-Free DNA Fragmentomics

Life of this patent

Domain Adaptation Engine(s) For Cell-Free DNA Fragmentomics

Life of this patent

Abstract

Claims26