Abstract: Various methods, systems, computer readable 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 media, and GUIs that enable more effective specification of treatment and improved outcomes for patients with identified types of cancer. Some embodiments of the methods, systems, computer readable media, and GUIs described herein comprise obtaining RNA expression data and/or whole exome sequencing (WES) data for a biological sample from a plurality of subjects, determining a respective plurality of molecular-functional (MF) profiles for the plurality of subjects, and storing the plurality of MF profiles in association with information identifying the particular cancer type.

Abstract: 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.

Abstract: 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.

Abstract: 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 (MF) 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 subjects prognosis based upon the LME type determination.

Abstract: 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.

Abstract: 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 (MF) 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.

Abstract: Various methods, systems, computer readable 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 media, and GUIs that enable more effective specification of treatment and improved outcomes for patients with identified types of cancer. Some embodiments of the methods, systems, computer readable media, and GUIs described herein comprise obtaining RNA expression data and/or whole exome sequencing (WES) data for a biological sample; determining a molecular-functional (MF) profile for a subject using the data; determining visual characteristics GUI elements using the data; generating a GUI personalized to the subject using the determined visual characteristics; and presenting the generated personalized GUI to a user.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Various methods, systems, computer readable 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 media, and GUIs that enable more effective specification of treatment and improved outcomes for patients with identified types of cancer. Some embodiments of the methods, systems, computer readable media, and GUIs described herein comprise obtaining RNA expression data and/or whole exome sequencing (WES) data for a biological sample; determining a molecular-functional (MF) profile for the subject at least in part by determining first and second visual characteristics for first and second GUI elements using the data; generating a personalized GUI personalized to the subject using the first and second visual characteristics; and presenting the generated personalized GUI to a user.

Abstract: 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.

Abstract: Various methods, systems, computer readable 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 media, and GUIs that enable more effective specification of treatment and improved outcomes for patients with identified types of cancer. Some embodiments of the methods, systems, computer readable media, and GUIs described herein comprise obtaining RNA expression data and/or whole exome sequencing (WES) data for a biological sample from a subject; determining a molecular-functional (MF) profile for the subject; identifying an MF profile cluster with which to associate the MF profile for the subject; and clustering the plurality of MF profiles to obtain the MF profile clusters.

Abstract: Various methods, systems, computer readable 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 media, and GUIs that enable more effective specification of treatment and improved outcomes for patients with identified types of cancer. Some embodiments of the methods, systems, computer readable media, and GUIs described herein comprise obtaining RNA expression data and/or whole exome sequencing (WES) data for a biological sample from a subject; determining a molecular-functional (MF) profile for the subject; identifying an MF profile cluster with which to associate the MF profile for the subject; and clustering the plurality of MF profiles to obtain the MF profile clusters.

Abstract: 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.

Abstract: 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.

Abstract: Various methods, systems, computer readable 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 media, and GUIs that enable more effective specification of treatment and improved outcomes for patients with identified types of cancer. Some embodiments of the methods, systems, computer readable media, and GUIs described herein comprise obtaining RNA expression data and/or whole exome sequencing (WES) data for a biological sample from a subject; determining a molecular-functional (MF) profile for the subject; identifying an MF profile cluster with which to associate the MF profile for the subject; and clustering the plurality of MF profiles to obtain the MF profile clusters.

Abstract: Various methods, systems, computer readable 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 media, and GUIs that enable more effective specification of treatment and improved outcomes for patients with identified types of cancer. Some embodiments of the methods, systems, computer readable media, and GUIs described herein comprise obtaining RNA expression data and/or whole exome sequencing (WES) data for a biological sample; determining a molecular-functional (MF) profile using the data; determining sets of visual characteristics for GUI elements using the data; generating a personalized GUI using the determined visual characteristics; and presenting the generated personalized GUI to a user.

Abstract: Various methods, systems, computer readable 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 media, and GUIs that enable more effective specification of treatment and improved outcomes for patients with identified types of cancer. Some embodiments of the methods, systems, computer readable media, and GUIs described herein comprise obtaining RNA expression data and/or whole exome sequencing (WES) data for a biological sample; determining a molecular-functional (MF) profile for a subject using the data; determining visual characteristics GUI elements using the data; generating a GUI personalized to the subject using the determined visual characteristics; and presenting the generated personalized GUI to a user.