Techniques for determining whether a sample obtained from a subject includes cells having homologous recombination deficiency (HRD). The techniques include: obtaining data about segments of the subject's genome; identifying a first subset of the segments, the first subset including segments associated with at least one chromosome arm of the genome and having a common copy number; identifying a second subset of the segments, each of the segments of the second subset having (i) a respective copy number different from the common copy number and (ii) a respective length that satisfies a predetermined length criterion; determining a proportion of a number of segments in the second subset to a number of chromosome arms of the at least one chromosome arm; and determining, based on the determined proportion, whether the biological sample includes cells having HRD.
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
Aspects of the disclosure relate to methods, systems, computer-readable storage media, and graphical user interfaces (GUIs) that are useful for characterizing subjects having certain cancers, for example solid tumor cancers or blood cancers. The disclosure is based, in part, on methods for determining a cytokine signature of a subject and the subject's prognosis and/or likelihood of responding to a therapy based upon the cytokine signature determination.
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
G06F 16/28 - Databases characterised by their database models, e.g. relational or object models
G06F 17/18 - Complex mathematical operations for evaluating statistical data
G16B 25/10 - Gene or protein expression profiling; Expression-ratio estimation or normalisation
G16B 30/00 - ICT specially adapted for sequence analysis involving nucleotides or amino acids
G16B 40/00 - ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
G16B 50/30 - Data warehousing; Computing architectures
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
Aspects of the disclosure relate to methods, systems, computer-readable storage media, and graphical user interfaces (GUIs) that are useful for characterizing subjects having certain cancers, for example bladder cancers or urothelial cancers. The disclosure is based, in part, on methods for determining the urothelial cancer (UC) tumor microenvironment (TME) type of a urothelial cancer subject and the subject's prognosis and/or likelihood of responding to a therapy based upon the UC TME type determination.
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
4.
MACHINE LEARNING TECHNIQUES FOR TERTIARY LYMPHOID STRUCTURE (TLS) DETECTION
Techniques for identifying a tertiary lymphoid structure (TLS) in an image of tissue. The techniques include obtaining a set of overlapping sub-images of the image; processing the set of overlapping sub-images using a neural network model to obtain a set of pixel-level sub-image masks, each of the set of pixel-level sub-image masks indicating, for each of multiple pixels in a respective sub-image, a probability that the pixel is part of a TLS; determining a pixel-level mask for at least a portion of the image covered by at least some of the sub-images, the determining comprising determining the pixel-level mask using at least some of the set of pixel-level sub-image masks; identifying boundaries of a TLS in at least the portion of the image using the pixel-level mask; and identifying one or more features of the TLS using the identified boundaries and at least the portion of the image.
Techniques for determining a respective cell type for each of at least some of a plurality of cells. The techniques includes: obtaining cytometry data for a biological sample from a subject, the biological sample comprising a plurality of cells including a first cell, the cytometry data including first cytometry data for the first cell; and determining a respective type for each of at least some of the plurality of cells using a hierarchy of machine learning models corresponding to a hierarchy of cell types, the determining comprising determining a first type for the first cell by processing the first cytometry data using a first subset of the hierarchy of machine learning models.
Aspects of the disclosure relate to methods, systems, and computer-readable storage media, which are useful for characterizing subjects having certain cancers, for example breast cancer. The disclosure is based, in part, on methods for determining the breast cancer molecular type and/or tumor microenvironment (TME) type of a breast cancer subject, and identifying the subject's prognosis and/or one or more therapeutic agents for treating the subject based upon the TME type determination.
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
7.
TECHNIQUES FOR SINGLE SAMPLE EXPRESSION PROJECTION TO AN EXPRESSION COHORT SEQUENCED WITH ANOTHER PROTOCOL
Aspects of the disclosure relate to methods for improving compatibility of nucleic acid sequencing data obtained using different techniques. The disclosure is based, in part, on methods for mapping expression levels for genes expressed in a biological sample and obtained from a subject using a first protocol to expression levels as would have been determined through a second protocol if the second protocol were used to process the biological sample instead of the first protocol.
Techniques for using machine learning to estimate tumor expression levels of genes in tumor cells. The techniques include obtaining expression data for a set of genes comprising a first plurality of genes associated with the tumor cells and a second plurality of genes associated with tumor microenvironment cells; determining the tumor expression levels of the first plurality of genes in the tumor cells using a plurality of machine learning models, the determining comprising: generating a first set of features for the first gene; providing the first set of features as input to the first machine learning model to obtain an output comprising a tumor microenvironment expression level estimate of the first gene in the tumor microenvironment cells; and determining a first tumor expression level for the first gene in the tumor cells using the output of the first machine learning model and a total expression level for the first gene.
Techniques for identifying, based at least in part on a gastric cancer (GC) tumor microenvironment (TME) type for a subject having, suspected of having, or at risk of having gastric cancer, whether the subject is likely to respond to an immunotherapy. The techniques include: obtaining RNA expression data for the subject; generating a GC TME signature for the subject using the RNA expression data, the GC TME signature comprising gene group scores for respective gene groups in a plurality of gene groups, the generating comprising: determining the gene group scores using the RNA expression data; identifying, using the GC TME signature and from among a plurality of GC TME types, a GC TME type for the subject; and identifying, using the GC TME type of the subject, whether or not the subject is likely to respond to the immunotherapy.
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
10.
PREDICTING RESPONSE TO TREATMENTS IN PATIENTS WITH CLEAR CELL RENAL CELL CARCINOMA
Aspects of the disclosure relate to methods, systems, computer-readable storage media, and graphical user interfaces (GUIs) that are useful for characterizing subjects having certain cancers, for example renal cell carcinomas such as clear cell renal carcinoma (ccRCC). The disclosure is based, in part, on methods for determining the renal cancer (RC) tumor microenvironment (TME) type (RC TME type) of a renal cancer subject and the subject's prognosis and/or likelihood of responding to certain therapies (e.g., immunotherapy or tyrosine kinase inhibitors) based upon the renal cancer type determination.
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
Techniques for identifying a gastric cancer (GC) tumor microenvironment (TME) type for a subject having, suspected of having, or at risk of having gastric cancer. The techniques include: obtaining RNA expression data for the subject; generating a GC TME signature for the subject using the RNA expression data, the GC TME signature comprising gene group scores for respective gene groups in the at least some of the plurality of gene groups, the generating comprising: determining the gene group scores using the RNA expression data; and identifying, using the GC TME signature and from among a plurality of GC TME types, a GC TME type for the subject.
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
12.
MACHINE LEARNING TECHNIQUES FOR IDENTIFYING MALIGNANT B-AND T-CELL POPULATIONS
Techniques for identifying malignant cell populations. The techniques include: obtaining sequencing data previously obtained from a biological sample from a subject; processing the sequencing data to identify: a plurality of cell population estimates for a cell of a first type, the plurality of cell population estimates including a first cell population estimate and a second cell population estimate associated respectively with largest and second largest cell population estimates from among the identified plurality of cell population estimates; and features associated with the plurality of cell population estimates, the features including: a first feature indicative of a size of the first cell population estimate; and a second feature indicative of a ratio between sizes of the first cell population estimate and the second cell population estimate; and determining, using the features and a trained machine learning model, whether the first cell population estimate includes malignant cells of the first type.
Aspects of the disclosure relate to methods, systems, computer-readable storage media, and graphical user interfaces (GUIs) that are useful for characterizing subjects having certain cancers, for example lymphomas. The disclosure is based, in part, on methods for determining the tumor microenvironment (TME) type of a lymphoma (e.g., follicular lymphoma) subject and identifying the subject's prognosis based upon the TME type determination.
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
14.
HIERARCHICAL MACHINE LEARNING TECHNIQUES FOR IDENTIFYING MOLECULAR CATEGORIES FROM EXPRESSION DATA
Described herein in some embodiments is a method comprising: obtaining expression data previously obtained by processing a biological sample obtained from a subject; processing the expression data using a hierarchy of machine learning classifiers corresponding to a hierarchy of molecular categories to obtain machine learning classifier outputs including a first output and a second output, the hierarchy of molecular categories including a parent molecular category and first and second molecular categories that are children of the parent molecular category in the hierarchy of molecular categories, the hierarchy of machine learning classifiers comprising first and second machine learning classifiers corresponding to the first and second molecular categories; and identifying, using at least some of the machine learning classifier outputs including the first output and the second output, at least one candidate molecular category for the biological sample.
G16B 25/10 - Gene or protein expression profiling; Expression-ratio estimation or normalisation
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
15.
TUMOR MICROENVIRONMENT-BASED METHODS FOR ASSESSING CAR-T AND OTHER IMMUNOTHERAPIES
Aspects of the disclosure relate to methods for determining whether or a subject is likely to respond to certain adoptive cell therapies (e.g., chimeric antigen receptor (CAR) T-cell therapy, etc.). In some embodiments, the methods comprise the steps of identifying a subject as having a tumor microenvironment (TME) type based upon a molecular- functional (ME) expression signature of the subject, and determining whether or not the subject is likely to respond to a chimeric antigen receptor (CAR) T-cell therapy based upon the TME type. In some embodiments, the methods comprise determining the lymphoma microenvironment (LME) type of a lymphoma (e.g., Diffuse Large B cell lymphoma (DLBCL)) subject and identifying the subject's prognosis based upon the LME type determination.
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
16.
SYSTEMS AND METHODS FOR DECONVOLUTION OF EXPRESSION DATA
Techniques for determining one or more cell composition percentages from expression data. The techniques include obtaining expression data for a biological sample, the biological sample previously obtained from a subject, the expression data including first expression data associated with a first set of genes associated with a first cell type; determining a first cell composition percentage for the first cell type using the expression data and one or more non-linear regression models including a first non-linear regression model, wherein the first cell composition percentage indicates an estimated percentage of cells of the first cell type in the biological sample, wherein determining the first cell composition percentage for the first cell type comprises: processing the first expression data with the first non-linear regression model to determine the first cell composition percentage for the first cell type; and outputting the first cell composition percentage.
Techniques for processing multiplexed immunofluorescence (MxIF) images. The techniques include: obtaining at least one MxIF image of a same tissue sample; obtaining information indicative of locations of cells in the at least one MxIF image; identifying multiple groups of cells in the at least one MxIF image at least in part by: determining feature values for at least some of the cells using the at least one MxIF image and the information indicative of locations of the at least some cells in the at least one MxIF image; and grouping the at least some of the cells into the multiple groups using the determined feature values; and determining at least one characteristic of the tissue sample using the multiple cell groups.
Techniques for determining one or more characteristics of a biological sample using rankings of gene expression levels in expression data obtained using one or more sequencing platforms is described. The techniques may include obtaining expression data for a biological sample of a subject. The techniques further include ranking genes in a set of genes based on their expression levels in the expression data to obtain a gene ranking and determining using the gene ranking and a statistical model, one or more characteristics of the biological sample.
Described herein are various methods of collecting and processing of tumor and/or healthy tissue samples to extract nucleic acid and perform nucleic acid sequencing. Also described herein are various methods of processing nucleic acid sequencing data to remove bias from the nucleic acid sequencing data. Also described herein are various methods of evaluating the quality of nucleic acid sequence information. The identity and/or integrity of nucleic acid sequence data is evaluated prior to using the sequence information for subsequent analysis (for example for diagnostic, prognostic, or clinical purposes). The methods enable a subject, doctor, or user to characterize or classify various types of cancer precisely, and thereby determine a therapy or combination of therapies that may be effective to treat a cancer in a subject based on the precise characterization.
Techniques for determining predicted response of a subject to multiple therapies using the subject's sequencing data. The techniques include accessing biomarker information indicating a distribution of values for each biomarker in at least a reference subset of a plurality of biomarkers across a respective group of people, each of the plurality of biomarkers being associated with at least one therapy in a plurality of therapies; determining, using the sequencing data and the biomarker information, a normalized score for each biomarker in at least a subject subset of the plurality of biomarkers to obtain a set of normalized biomarker scores for the subject; and determining, using the set of normalized biomarker scores for the subject, therapy scores for the plurality of therapies, each of the therapy scores indicative of predicted response of the subject to administration of a respective therapy in the plurality of therapies.
G06F 19/18 - for functional genomics or proteomics, e.g. genotype-phenotype associations, linkage disequilibrium, population genetics, binding site identification, mutagenesis, genotyping or genome annotation, protein-protein interactions or protein-nucleic acid interactions
G06F 19/24 - for machine learning, data mining or biostatistics, e.g. pattern finding, knowledge discovery, rule extraction, correlation, clustering or classification
G06F 19/26 - for data visualisation, e.g. graphics generation, display of maps or networks or other visual representations
21.
SYSTEMS AND METHODS FOR GENERATING, VISUALIZING AND CLASSIFYING MOLECULAR FUNCTIONAL PROFILES
Various methods, systems, computer-readable storage media, and graphical user interfaces (GUIs) are presented and described that enable a subject, doctor, or user to characterize or classify various types of cancer precisely. Additionally, described herein are methods, systems, computer-readable storage media, and GUIs that enable more effective specification of treatment and improved outcomes for patients with identified types of cancer.
G06F 19/10 - Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology (in silico methods of screening virtual chemical libraries C40B 30/02;in silico or mathematical methods of creating virtual chemical libraries C40B 50/02)
22.
SYSTEMS AND METHODS FOR IDENTIFYING RESPONDERS AND NON-RESPONDERS TO IMMUNE CHECKPOINT BLOCKADE THERAPY
Techniques for determining whether a subject is likely to respond to an immune checkpoint blockade therapy. The techniques include obtaining expression data for the subject, using the expression data to determine subject expression levels for at least three genes selected from the set of predictor genes consisting of BRAF, ACVR1B, MPRIP, PRKAG1, STX2, AGPAT3, FYN, CMIP, ROB04, RAB40C, HAUS8, SNAP23, SNX6, ACVR1B, MPRIP, COPS3, NLRX1, ELAC2, MON1B, ARF3, ARPIN, SPRYD3, FLU, TIRAP, GSEl, POLR3K, PIGO, MFHAS l, NPIPAl, DPH6, ERLIN2, CES2, LHFP, NAIFl, ALCAM, SYNE1, SPINT1, SMTN, SLCA46A1, SAP25, WISP2, TSTD1, NLRX1, NPIPAl, HIST1H2AC, FUT8, FABP4, ERBB2, TUBA1A, XAGE1E, SERPINF1, RAI14, SIRPA, MTIX, NEK3, TGFB3, USP13, HLA-DRB4, IGF2, and MICALl; and determining, using the determined expression levels and a statistical model trained using expression data indicating expression levels for a plurality of genes for a plurality of subjects, whether the subject is likely to respond to the immune checkpoint blockade therapy.
G06F 19/10 - Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology (in silico methods of screening virtual chemical libraries C40B 30/02;in silico or mathematical methods of creating virtual chemical libraries C40B 50/02)
patents
