Methods and systems for determining biological sample integrity
Inventors
CHOI, Yoonha • Babiarz, Joshua • Kennedy, Giulia C. • Huang, Jing
Assignees
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Publication Number
US-11217329-B1
Publication Date
2022-01-04
Expiration Date
Abstract
The methods disclosed herein can be used to determine sample integrity, such as sample identity, by using kinship coefficients. Kinship coefficients between two samples can be determined by measuring genetic relatedness in order to determine whether the samples are related or not related.
Core Innovation
The invention provides a method for biological sample processing that identifies a biological sample by using an expression profile derived from transcripts corresponding to genes having one or more genetic aberrations. The method obtains a biological sample comprising a plurality of transcripts, amplifies the plurality of transcripts to generate amplification products, and performs a sequencing assay to generate an expression profile corresponding to the at least one gene having the genetic aberrations. The resulting expression profile is then processed by a computer classifier to determine identity information.
A central aspect is that the computer classifier performs kinship analysis of the expression profile against one or more other expression profiles. The kinship analysis compares the expression profile of the biological sample to expression profiles of one or more other biological samples of the same subject or one or more other subjects, so that the classifier identifies the biological sample as belonging to a subject. The kinship analysis is based on comparing genetic aberrations reflected in the expression profile.
The document further frames the approach as supporting biological sample integrity and identity, including addressing sample mix-ups, by estimating genetic relatedness between samples using kinship coefficients. The kinship coefficients are computed from genetic aberrations in the biological sample by comparing to genetic aberrations in other biological samples, and the kinship framework distinguishes replicates versus non-replicates and detects experimental errors and contamination.
Claims Coverage
The independent claim defines the core workflow: obtain a sample with transcripts from genes with genetic aberrations; amplify; sequence to generate an expression profile; and use a computer classifier to perform kinship analysis for subject identification. The claim set adds multiple inventive features that refine sequencing, kinship computation, assay format, and downstream classification.
Transcript-based amplification and sequencing for aberration genes
Obtaining a biological sample comprising a plurality of transcripts corresponding to at least one gene having one or more genetic aberrations, nucleic acid amplifying the plurality of transcripts to generate amplification products, and subjecting the amplification products to a sequencing assay to generate an expression profile of the plurality of transcripts corresponding to the at least one gene having the one or more genetic aberrations.
Computer classifier kinship analysis against other samples
Using a computer classifier to process the expression profile to identify the biological sample as belonging to a subject, wherein the computer classifier performs a kinship analysis of the expression profile against one or more other expression profiles of one or more other biological samples of the subject or one or more other subjects.
Kinship coefficients from genetic-aberration subsets
Determining kinship coefficients based on at least a subset of the one or more genetic aberrations in the biological sample by comparing them to genetic aberrations in one or more different biological samples.
Sequencing coverage constraints for aberrations
Sequencing the amplification products with sequencing coverage of 20× or more for the one or more genetic aberrations, and sequencing coverage in a range from about 30× to about 700× for the one or more genetic aberrations.
Sequencing assay modalities and amplification formats
Sequencing assay comprising microarray and serial analysis of gene expression (SAGE), and nucleic acid amplification comprising reverse transcription PCR or quantitative PCR.
Disease classification after subject identification
Upon identifying the biological sample as belonging to the subject, classifying the biological sample as malignant, benign or normal for a disease.
Overall, the claim set covers a transcript-to-expression-profile workflow tied to genes with genetic aberrations and uses computer classifier kinship analysis against other expression profiles for subject identification, with additional refinements to kinship computation, sequencing coverage constraints, assay modality/amplification formats, and optional malignant, benign, or normal disease classification outcomes.
Stated Advantages
Supports determining biological sample identity by using kinship analysis derived from expression profiles.
Supports detecting sample mix-ups via kinship-based relatedness comparisons.
Distinguishes replicates from non-replicates and detects experimental errors and contamination through the kinship analysis framework.
Enables downstream classification of a biological sample as malignant, benign or normal for a disease after subject identification.
Documented Applications
Analysis and evaluation on thyroid and lung transbronchial biopsy (TBB) RNA-seq and DNA-seq datasets.
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