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.
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 - Produits d’acides nucléiques utilisés dans l’analyse d’acides nucléiques, p.ex. amorces ou sondes pour les maladies provoquées par des altérations du matériel génétique pour le 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 - Produits d’acides nucléiques utilisés dans l’analyse d’acides nucléiques, p.ex. amorces ou sondes pour les maladies provoquées par des altérations du matériel génétique pour le cancer
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 - Produits d’acides nucléiques utilisés dans l’analyse d’acides nucléiques, p.ex. amorces ou sondes pour les maladies provoquées par des altérations du matériel génétique pour le cancer
5.
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.
G06V 10/26 - Segmentation de formes dans le champ d’image; Découpage ou fusion d’éléments d’image visant à établir la région de motif, p.ex. techniques de regroupement; Détection d’occlusion
G06V 10/74 - Appariement de motifs d’image ou de vidéo; Mesures de proximité dans les espaces de caractéristiques
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
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.
G16B 40/00 - TIC spécialement adaptées aux biostatistiques; TIC spécialement adaptées à l’apprentissage automatique ou à l’exploration de données liées à la bio-informatique, p.ex. extraction de connaissances ou détection de motifs
G16B 20/00 - TIC spécialement adaptées à la génomique ou protéomique fonctionnelle, p. ex. corrélations génotype-phénotype
G16B 25/10 - Profilage de l’expression de gènes ou de protéines; Estimation ou normalisation de ratio d’expression
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.
C12Q 1/6809 - Méthodes de détermination ou d’identification des acides nucléiques faisant intervenir la détection différentielle
C12Q 1/6806 - Préparation d’acides nucléiques pour analyse, p.ex. pour test de réaction en chaîne par polymérase [PCR]
G16B 20/00 - TIC spécialement adaptées à la génomique ou protéomique fonctionnelle, p. ex. corrélations génotype-phénotype
G16B 25/00 - TIC spécialement adaptées à l’hybridation; TIC spécialement adaptées à l’expression de gènes ou de protéines
G16B 25/10 - Profilage de l’expression de gènes ou de protéines; Estimation ou normalisation de ratio d’expression
G16B 30/00 - TIC spécialement adaptées à l’analyse de séquences impliquant des nucléotides ou des aminoacides
C12Q 1/68 - Procédés de mesure ou de test faisant intervenir des enzymes, des acides nucléiques ou des micro-organismes; Compositions à cet effet; Procédés pour préparer ces compositions faisant intervenir des acides nucléiques
9.
SYSTEMS AND METHODS FOR IDENTIFYING CANCER TREATMENTS FROM NORMALIZED BIOMARKER SCORES
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.
G16B 20/00 - TIC spécialement adaptées à la génomique ou protéomique fonctionnelle, p. ex. corrélations génotype-phénotype
G16H 20/00 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p.ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients
G16H 50/00 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies
G16B 5/00 - TIC spécialement adaptées à la modélisation ou aux simulations dans la biologie des systèmes, p. ex. réseaux de régulation génétique, réseaux d’interaction entre protéines ou réseaux métaboliques
G16B 30/00 - TIC spécialement adaptées à l’analyse de séquences impliquant des nucléotides ou des aminoacides
G16B 40/00 - TIC spécialement adaptées aux biostatistiques; TIC spécialement adaptées à l’apprentissage automatique ou à l’exploration de données liées à la bio-informatique, p.ex. extraction de connaissances ou détection de motifs
10.
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.
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.
G16B 20/00 - TIC spécialement adaptées à la génomique ou protéomique fonctionnelle, p. ex. corrélations génotype-phénotype
C12Q 1/6809 - Méthodes de détermination ou d’identification des acides nucléiques faisant intervenir la détection différentielle
G16B 5/00 - TIC spécialement adaptées à la modélisation ou aux simulations dans la biologie des systèmes, p. ex. réseaux de régulation génétique, réseaux d’interaction entre protéines ou réseaux métaboliques
G16B 25/10 - Profilage de l’expression de gènes ou de protéines; Estimation ou normalisation de ratio d’expression
brevets
