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Methods and systems for determining proportions of distinct cell subsets

A technology of cell subgroups and subgroups, applied in biochemical equipment and methods, biostatistics, molecular entity identification, etc., can solve problems such as difficult to explain results, not very effective, and no statistical significance test

Active Publication Date: 2017-12-01
THE BOARD OF TRUSTEES OF THE LELAND STANFORD JUNIOR UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

While such methods are accurate for mixtures with well-defined composition (e.g. blood), they are less effective for mixtures with unknown content and noise (e.g. solid tumors) and for distinguishing closely related cell types (e.g. naive B cells from memory B cells) are less effective
Furthermore, the absence of a statistical significance test in previous methods made the results difficult to interpret

Method used

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  • Methods and systems for determining proportions of distinct cell subsets
  • Methods and systems for determining proportions of distinct cell subsets
  • Methods and systems for determining proportions of distinct cell subsets

Examples

Experimental program
Comparison scheme
Effect test

example 1

[0208] Example 1: Enhanced Enumeration of Cell Subpopulations by Expression Profiling of Composite Tissues

[0209] CIBERSORT uses an input matrix of reference gene expression markers to estimate the relative proportion of each cell type of interest. However, for each gene, no cell type specific expression pattern (method) is required. To deconvolve mixtures, a machine learning method is robust to noise using a novel application of linear support vector regression (SVR) 9 . Unlike many other methods, SVR performs feature selection, where genes from a marker matrix are adaptively selected to deconvolute a given mixture. An empirically defined global P-value for deconvolution is then determined ( Figure 1a ,method).

[0210] As a preliminary application, the feasibility of leukocyte deconvolution from bulk tumors, and thus the designed and validated leukocyte marker matrix, was determined. Defined LM22, this marker matrix consists of 547 genes that accurately distinguish...

example 1

[0224] Use the following method for Example 1.

[0225] patient sample

[0226] All patient samples in this study were reviewed and approved by the Stanford Institutional Review Board in accordance with the Declaration of Helsinki. for Figure 5b , with informed consent for research use, at Lucile Packard Children's Hospital, Stanford University, tonsils were collected as part of routine tonsillectomy surgery and then mechanically disintegrated prior to cryopreservation in cell suspension. for Figure 6c "Patient 1" shown in "Patient 1", in subjects without measurable circulatory disease, infused rituximab (375 mg m- 2) Peripheral blood mononuclear cells (PBMC) were isolated from the samples before and immediately after monotherapy. for Figure 6c In patients 2 and 3, PBMCs were isolated from samples collected immediately after 4 and 6 cycles of RCHOP immunochemotherapy for the treatment of DLBCL, respectively. for Figure 6c In patient 4, PBMCs were isolated from a sub...

example 2

[0301] Example 2: Inferred leukocyte frequency and prognostic association in 25 human cancers using CIBERSORT

[0302] Materials and methods

[0303] The following materials and methods were used for Examples 2 and 3.

[0304] Prediction of Clinical Outcomes for Genomic Profiles (PRECOG) Composite and Quality Control. To identify cancer gene expression datasets with corresponding patient outcome data, the NCBI Gene Expression Omnibus (GEO), EBI ArrayExpress, NCI caArray, and Stanford Microarray Databases were queried for the terms survival, prognosis, prognostic, or outcome. A Perl script was implemented to download the processed raw data along with associated annotations. For data in NCBI, array platforms were determined from SOFT format files and corresponding annotation files were retrieved from GEO. From these, ProbeIDs, Genbank accession numbers, HUGO gene symbols and gene descriptions were extracted based on the internal headers of the SOFT annotation files. If thi...

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Abstract

Methods of deconvolving a feature profile of a physical system are provided herein. The present method may include: optimizing a regression between a) a feature profile of a first plurality of distinct components and b) a reference matrix of feature signatures for a second plurality of distinct components, wherein the feature profile is modeled as a linear combination of the reference matrix, and wherein the optimizing includes solving a set of regression coefficients of the regression, wherein the solution minimizes 1) a linear loss function and 2) an L2-norm penalty function; and estimating the fractional representation of one or more distinct components among the second plurality of distinct components present in the sample based on the set of regression coefficients. Systems and computer readable media for performing the subject methods are also provided.

Description

[0001] Cross References to Related Applications [0002] Pursuant to 35 U.S.C. §119(e), this application claims the benefit of U.S. Provisional Patent Application No. 62 / 106,601, filed January 22, 2015, which is incorporated herein by reference in its entirety. [0003] government power [0004] This invention was made with Government support under Grant No. 5T32CA09302-35 (A.M.N.) awarded by the National Institutes of Health (NIH) and Grant No. W81XWH-12-1-0498 (A.M.N.) awarded by the Department of Defense. The government has certain rights in this invention. Background technique [0005] Variations in cellular composition underlie distinct physiological states in metazoans and their composite tissues. For example, in malignancies, levels of infiltrating immune cells correlate with tumor growth, cancer progression, and patient outcome. Common methods for studying cellular heterogeneity, such as immunohistochemistry and flow cytometry, rely on a limited variety of phenotypi...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F15/18G16B25/10
CPCG01N33/5005C12Q1/6809G16C20/20G16B40/10C12Q1/6886C12Q2600/158G16B25/10C12Q2537/165C12Q2600/106G16B25/00C12Q1/6881
Inventor A·M·纽曼A·A·阿里扎德
Owner THE BOARD OF TRUSTEES OF THE LELAND STANFORD JUNIOR UNIV
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