Systems and methods for generating, visualizing and classifying molecular functional profiles

Inventors

Bagaev, Alexander • Frenkel, Feliks • Kotlov, Nikita • Ataullakhanov, Ravshan • Isaeva, Olga

Assignees

BostonGene Corp

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.

Core Innovation

The invention provides a computer-based approach for cancer analytics that obtains RNA expression data from biological samples from a plurality of subjects having a cancer of a particular type. For each subject, the system determines a respective plurality of molecular-functional (MF) profiles by determining a respective gene group expression level for each group in a set of gene groups associated with cancer malignancy and cancer microenvironment.

The cancer microenvironment gene groups include a cancer associated fibroblasts group, an angiogenesis group, a MHCI group, a MHCII group, a coactivation molecules group, an effector cells group, an NK cells group, a T cells group, a B cells group, a M1 signatures group, a Th1 signature group, an antitumor cytokines group, a checkpoint inhibition group, a Treg group, a MDSC group, a granulocytes group, a Th2 signature group, and a protumor cytokines group. Determining the MF profiles comprises determining a gene group expression level for each of these groups, and determining the gene group expression level for the M1 signatures group comprises using a gene expression level obtained from the RNA expression data for at least three genes in the M1 signatures group.

The invention further clusters the respective plurality of MF profiles to obtain MF profile clusters associated with inflamed and/or non-inflamed biological samples and vascularized and/or non-vascularized biological samples, and optionally also with fibroblast-enriched and/or non-fibroblast-enriched biological samples. The MF profiles are stored in association with information identifying the particular cancer type.

The invention also generates a personalized graphical user interface (GUI) for a subject of the plurality of subjects. It determines at least one visual characteristic of a first GUI element using a first gene group expression level for at least one gene group associated with cancer malignancy, and determines at least one visual characteristic of a second GUI element using a second gene group expression level for at least one gene group associated with cancer microenvironment, and presents the generated personalized GUI to a user.

Claims Coverage

The provided content includes three independent claims. Across these independent claims, the inventive coverage centers on determining MF profiles from RNA expression data using cancer malignancy and cancer microenvironment gene groups, including an M1 signatures group defined using at least three genes, clustering into four inflamed/non-inflamed and vascularized/non-vascularized categories with fibroblast-enriched/non-fibroblast-enriched variants, storing profiles by cancer type, and generating and presenting a personalized GUI with GUI elements whose visual characteristics are based on cancer malignancy versus cancer microenvironment gene group expression levels.

Across the independent claims, the invention covers computing MF profiles from RNA expression data via gene group expression levels for cancer malignancy and cancer microenvironment, computing the M1 signatures group using at least three genes, clustering MF profiles into four categories defined by inflamed/non-inflamed and vascularized/non-vascularized with fibroblast-enriched/non-fibroblast-enriched variants, storing MF profiles by cancer type, and generating a personalized GUI with distinct malignancy and microenvironment portions using gene-group expression levels to determine visual characteristics.

Stated Advantages

Documented Applications