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: 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.

Abstract: 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 molecular breast cancer (BC) type of a subject and identifying the subject's prognosis and/or one or more therapeutic agents for treating the subject based upon the molecular BC type determination.

Abstract: 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.

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: Aspects of the disclosure relate to methods, systems, and computer-readable storage media that are useful for characterizing subjects having certain cancers, for example cancers typified by solid tumors. The disclosure is based, in part, on methods for determining a tumor microenvironment (TME) type of a cancer subject and determining the subject's prognosis and/or likelihood of responding to one or more therapies based upon the TME 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 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 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.

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 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.

Abstract: Methods described herein relate to constructing therapeutic fusion-specific vaccine libraries, selecting a therapeutic fusion-specific vaccine for a cancer patient, and/or constructing a de novo therapeutic fusion-specific vaccine for patients having a gene fusion that is absent from a fusion-specific vaccine library.

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: Techniques for determining therapy scores for at least two of an anti-PD1 therapy, an anti-CTLA4 therapy, an IL-2 therapy, an IFN alpha therapy, an anti-cancer vaccine therapy, an anti-angiogenic therapy, and an anti-CD20 therapy. The techniques include determining, using sequencing data for the subject and information indicating distribution of biomarker values across one or more reference populations, a first set of normalized biomarker scores for a first set of biomarkers associated with a first therapy; and a second set of normalized biomarker scores for a second set of biomarkers associated with a second therapy; providing the first set of normalized biomarker scores as input to a statistical model to obtain a first therapy score for the first therapy; and providing the second set of normalized biomarker scores as input to the statistical model to obtain a second therapy score for the second therapy.

Abstract: Techniques for determining therapy scores for at least two of an anti-PD1 therapy, an anti-CTLA4 therapy, an IL-2 therapy, an IFN alpha therapy, an anti-cancer vaccine therapy, an anti-angiogenic therapy, and an anti-CD20 therapy. The techniques include determining, using sequencing data for the subject and information indicating distribution of biomarker values across one or more reference populations, a first set of normalized biomarker scores for a first set of biomarkers associated with a first therapy; and a second set of normalized biomarker scores for a second set of biomarkers associated with a second therapy; providing the first set of normalized biomarker scores as input to a statistical model to obtain a first therapy score for the first therapy; and providing the second set of normalized biomarker scores as input to the statistical model to obtain a second therapy score for the second therapy.

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 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.

Abstract: Techniques for generating therapy biomarker scores and visualizing same. The techniques include determining, using a patient's sequence data and distributions of biomarker values across one or more reference populations, a first set of normalized scores for a first set of biomarkers associated with a first therapy, and a second set of normalized scores for a second set of biomarkers associated with a second therapy, generating a graphical user interface (GUI) including a first portion associated with the first therapy and having at least one visual characteristic determined based on a normalized score of the respective biomarker in the first set of normalized scores; and a second portion associated with a second therapy and having at least one visual characteristic determined based on a normalized score of the respective biomarker in the second set of normalized scores; and displaying the generated GUI.

Abstract: 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.

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: 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.