FIELD

Aspects of the technology described herein relate to generating, visualizing and classifying molecular-functional (MF) profiles of cancer patients.

Some aspects of the technology described herein relate to generating a graphical user interface (GUI) for visualizing a molecular-functional profile of a cancer patient.

Some aspects of the technology described herein relate to identifying the type of MF profile of a patient, and predicting prognoses, identifying therapies, and/or otherwise aiding in the personalized care of the patient using the identified type.

BACKGROUND

Correctly characterizing the type or types of cancer a patient or subject has and, potentially, selecting one or more effective therapies for the patient can be crucial for the survival and overall wellbeing of that patient. Advances in characterizing cancers, predicting prognoses, identifying effective therapies, and otherwise aiding in personalized care of patients with cancer are needed.

SUMMARY

Provided herein, inter alia, are systems and methods for generating a molecular-functional (MF) profile for a subject and identifying an existing MF profile cluster that is associated with the generated MF profile. Such information, in some embodiments, is output to a user in a graphical user interface (GUI).

Systems and methods for identifying a molecular-functional (MF) profile cluster with which to associate a MF profile for a subject comprises, in some embodiments, obtaining RNA expression data and/or whole exome sequencing (WES) data for the subject; determining a MF profile for the subject, in part, by determining a gene group expression level for each gene group in a set of gene groups using the RNA expression data and/or WES data, the set of gene groups comprising gene groups associated with cancer malignancy and different gene groups associated with cancer microenvironment; and identifying a MF profile cluster with which to associate the MF profile for the subject from among multiple MF profile clusters that were generated by determining a plurality of MF profiles for a respective plurality of subjects using RNA expression data obtained from biological samples for the plurality of subjects, each of the plurality of MF profiles containing a gene group expression level for each gene group in the set of gene groups, and clustering the plurality of MF profiles to obtain the MF profile clusters. Provided herein, inter alia, are systems and methods for generating MF profile clusters. Such information, in some embodiments, is stored in one or more databases.

Systems and methods for generating MF profile clusters comprises, in some embodiments, obtaining RNA expression data and/or whole exome sequencing (WES) data for a plurality of subjects having a cancer of a particular type; determining a respective plurality of MF profiles for the plurality of subjects, in part, by determining, for each subject, a respective gene group expression level for each gene group in a set of gene groups using the RNA expression data and/or WES data, the set of gene groups comprising gene groups associated with cancer malignancy and different gene groups associated with cancer microenvironment; clustering the plurality of MF profiles to obtain MF profile clusters comprising a first MF profile cluster, a second MF profile cluster, a third MF profile cluster, and a fourth MF profile cluster; and storing the plurality of MF profiles in association with information identifying the particular cancer type.

Provided herein, inter alia, are systems and methods for generating a molecular-functional (MF) profile for a subject using at least four (e.g., at least five) gene group expression levels and identifying an existing MF profile cluster that is associated with the generated MF profile. Such information, in some embodiments, is output to a user in a graphical user interface (GUI).

Systems and methods for identifying a molecular-functional (MF) profile cluster with which to associate a MF profile for a subject comprises, in some embodiments, obtaining RNA expression data and/or whole exome sequencing (WES) data for the subject; determining a MF profile for the subject, in part, by determining a gene group expression level for each gene group in a set of gene groups using the RNA expression data and/or WES data, the set of gene groups comprising gene groups associated with cancer malignancy that consists of a tumor properties group and gene groups associated with cancer microenvironment that consists of a tumor-promoting immune microenvironment group, a an anti-tumor immune microenvironment group, an angiogenesis group, and a fibroblasts group; and identifying a MF profile cluster with which to associate the MF profile for the subject from among multiple MF profile clusters that were generated by determining a plurality of MF profiles for a respective plurality of subjects using RNA expression data obtained from biological samples for the plurality of subjects, each of the plurality of MF profiles containing a gene group expression level for each gene group in the set of gene groups, and clustering the plurality of MF profiles to obtain the MF profile clusters.

Provided herein, inter alia, are systems and methods for generating molecular-functional (MF) profile clusters, generating MF profiles for a subject, and associating the patient's MF profile with the MF profile cluster. Such information, in some embodiments, is output to a user in a graphical user interface (GUI).

Systems and methods for generating molecular-functional (MF) profile clusters, generating MF profiles for a subject, and associating the patient's MF profile with the MF profile cluster comprises, in some embodiments, obtaining RNA expression data and/or whole exome sequencing (WES) data for a plurality of subjects; determining a respective plurality of MF profiles for the plurality of subjects, in part, by determining, for each subject, a respective gene group expression level for each gene group in a set of gene groups using the RNA expression data and/or WES data, the set of gene groups comprising gene groups associated with cancer malignancy and different gene groups associated with cancer microenvironment; clustering the plurality of MF profiles to obtain MF profile clusters comprising a first MF profile cluster, a second MF profile cluster, a third MF profile cluster, and a fourth MF profile cluster; obtaining second RNA expression data from a subject, determining a MF profile for the subject, in part, by determining a gene group expression level for each gene group in the set of gene groups using the second RNA expression data; and identifying a MF profile cluster with which to associate the MF profile for the subject from among multiple MF profile clusters.

Provided herein, inter alia, are systems and methods for generating a MF profile and generating a MF portrait for visualizing the MF profile in a graphical user interface (GUI).

Systems and methods for generating a MF profile and generating a MF portrait for visualizing the MF profile in a graphical user interface (GUI) comprises, in some embodiments, obtaining RNA expression data and/or whole exome sequencing (WES) data for a subject; determining a MF profile for the subject, in part, by determining a gene group expression level for each gene group in a set of gene groups using the RNA expression data and/or WES data, the set of gene groups comprising gene groups associated with cancer malignancy and different gene groups associated with cancer microenvironment; determining a first visual characteristic for a first GUI element using the first gene group expression level; determining a second visual characteristic for a second GUI element using the second gene group expression level; generating a personalized GUI personalized to the subject; and presenting the generated personalized GUI to a user.

Provided herein, inter alia, are systems and methods for generating a MF profile by determining expression levels for e.g., four or five gene groups and generating a MF portrait for visualizing the MF profile in a graphical user interface (GUI).

Systems and methods for generating a MF profile by determining expression levels for e.g., four or five gene groups and generating a MF portrait for visualizing the MF profile in a graphical user interface (GUI) comprises, in some embodiments, obtaining RNA expression data and/or whole exome sequencing (WES) data for a subject; determining a MF profile for the subject, in part, by determining a gene group expression level for each gene group in a set of gene groups using the RNA expression data and/or WES data, the set of gene groups comprising gene groups associated with cancer malignancy that consists of X and gene groups associated with cancer microenvironment that consist of a tumor-promoting immune microenvironment group, an anti-tumor immune microenvironment group, an angiogenesis group, and a fibroblasts group; determining a first visual characteristic for a first GUI element using the first gene group expression level; determining a second visual characteristic for a second GUI element using the second gene group expression level; generating a personalized GUI personalized to the subject; and presenting the generated personalized GUI to a user.

In one aspect, provided herein is a system, comprising: at least one computer hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform: 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 at least in part by determining, using the RNA expression data, a gene group expression level for each gene group in a set of gene groups, the set of gene groups comprising gene groups associated with cancer malignancy and different gene groups associated with cancer microenvironment; and identifying, from among multiple MF profile clusters, an MF profile cluster with which to associate the MF profile for the subject, the MF profile clusters comprising: a first MF profile cluster associated with inflamed and vascularized biological samples and/or inflamed and fibroblast-enriched biological samples, a second MF profile cluster associated with inflamed and non-vascularized biological samples and/or inflamed and non-fibroblast-enriched biological samples, a third MF profile cluster associated with non-inflamed and vascularized biological samples and/or non-inflamed and fibroblast-enriched biological samples, and a fourth MF profile cluster associated with non-inflamed and non-vascularized biological samples and/or non-inflamed and non-fibroblast-enriched biological samples, wherein the MF profile clusters were generated by: determining a plurality of MF profiles for a respective plurality of subjects using RNA expression data obtained from biological samples from the plurality of subjects, each of the plurality of MF profiles containing a gene group expression level for each gene group in the set of gene groups; and clustering the plurality of MF profiles to obtain the MF profile clusters.

In one aspect, provided herein is a method, comprising: using at least one computer hardware processor to perform: 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 at least in part by determining, using the RNA expression data, a gene group expression level for each gene group in a set of gene groups, the set of gene groups comprising gene groups associated with cancer malignancy and different gene groups associated with cancer microenvironment; and identifying, from among multiple MF profile clusters, an MF profile cluster with which to associate the MF profile for the subject, the MF profile clusters comprising: a first MF profile cluster associated with inflamed and vascularized biological samples and/or inflamed and fibroblast-enriched biological samples, a second MF profile cluster associated with inflamed and non-vascularized biological samples and/or inflamed and non-fibroblast-enriched biological samples, a third MF profile cluster associated with non-inflamed and vascularized biological samples and/or non-inflamed and fibroblast-enriched biological samples, and a fourth MF profile cluster associated with non-inflamed and non-vascularized biological samples and/or non-inflamed and non-fibroblast-enriched biological samples, wherein the MF profile clusters were generated by: determining a plurality of MF profiles for a respective plurality of subjects using RNA expression data obtained from biological samples from the plurality of subjects, each of the plurality of MF profiles containing a gene group expression level for each gene group in the set of gene groups; and clustering the plurality of MF profiles to obtain the MF profile clusters.

In one aspect, provided herein is at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by at least one computer hardware processor, cause at least one computer hardware processor to perform: 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 at least in part by determining, using the RNA expression data, a gene group expression level for each gene group in a set of gene groups, the set of gene groups comprising gene groups associated with cancer malignancy and different gene groups associated with cancer microenvironment; and identifying, from among multiple MF profile clusters, an MF profile cluster with which to associate the MF profile for the subject, the MF profile clusters comprising: a first MF profile cluster associated with inflamed and vascularized biological samples and/or inflamed and fibroblast-enriched biological samples, a second MF profile cluster associated with inflamed and non-vascularized biological samples and/or inflamed and non-fibroblast-enriched biological samples, a third MF profile cluster associated with non-inflamed and vascularized biological samples and/or non-inflamed and fibroblast-enriched biological samples, and a fourth MF profile cluster associated with non-inflamed and non-vascularized biological samples and/or non-inflamed and non-fibroblast-enriched biological samples, wherein the MF profile clusters were generated by: determining a plurality of MF profiles for a respective plurality of subjects using RNA expression data obtained from biological samples from the plurality of subjects, each of the plurality of MF profiles containing a gene group expression level for each gene group in the set of gene groups; and clustering the plurality of MF profiles to obtain the MF profile clusters.

In one aspect, provided herein is a system, comprising: at least one computer hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform: obtaining RNA expression data and/or whole exome sequencing (WES) data from biological samples from a plurality of subjects, at least some of the subjects having a cancer of a particular type; determining a respective plurality of molecular-functional (MF) profiles for the plurality of subjects at least in part by, for each of the plurality of subjects, determining, using the RNA expression data, a respective gene group expression level for each group in a set of gene groups, the set of gene groups comprising gene groups associated with cancer malignancy and different gene groups associated with cancer microenvironment; clustering the plurality of MF profiles to obtain MF profile clusters comprising: a first MF profile cluster associated with inflamed and vascularized biological samples and/or inflamed and fibroblast-enriched biological samples, a second MF profile cluster associated with inflamed and non-vascularized biological samples and/or inflamed and non-fibroblast-enriched biological samples, a third MF profile cluster associated with non-inflamed and vascularized biological samples and/or non-inflamed and fibroblast-enriched biological samples, and a fourth MF profile cluster associated with non-inflamed and non-vascularized biological samples and/or non-inflamed and non-fibroblast-enriched biological sample; and storing the plurality of MF profiles in association with information identifying the particular cancer type.

In one aspect, provided herein is a method, comprising: using at least one computer hardware processor to perform: obtaining RNA expression data and/or whole exome sequencing (WES) data from biological samples from a plurality of subjects, at least some of the subjects having a cancer of a particular type; determining a respective plurality of molecular-functional (MF) profiles for the plurality of subjects at least in part by, for each of the plurality of subjects, determining, using the RNA expression data, a respective gene group expression level for each group in a set of gene groups, the set of gene groups comprising gene groups associated with cancer malignancy and different gene groups associated with cancer microenvironment; clustering the plurality of MF profiles to obtain MF profile clusters comprising: a first MF profile cluster associated with inflamed and vascularized biological samples and/or inflamed and fibroblast-enriched biological samples, a second MF profile cluster associated with inflamed and non-vascularized biological samples and/or inflamed and non-fibroblast-enriched biological samples, a third MF profile cluster associated with non-inflamed and vascularized biological samples and/or non-inflamed and fibroblast-enriched biological samples, and a fourth MF profile cluster associated with non-inflamed and non-vascularized biological samples and/or non-inflamed and non-fibroblast-enriched biological sample; and storing the plurality of MF profiles in association with information identifying the particular cancer type.

In one aspect, provided herein is at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by at least one computer hardware processor, cause at least one computer hardware processor to perform: obtaining RNA expression data and/or whole exome sequencing (WES) data from biological samples from a plurality of subjects, at least some of the subjects having a cancer of a particular type; determining a respective plurality of molecular-functional (MF) profiles for the plurality of subjects at least in part by, for each of the plurality of subjects, determining, using the RNA expression data, a respective gene group expression level for each group in a set of gene groups, the set of gene groups comprising gene groups associated with cancer malignancy and different gene groups associated with cancer microenvironment; clustering the plurality of MF profiles to obtain MF profile clusters comprising: a first MF profile cluster associated with inflamed and vascularized biological samples and/or inflamed and fibroblast-enriched biological samples, a second MF profile cluster associated with inflamed and non-vascularized biological samples and/or inflamed and non-fibroblast-enriched biological samples, a third MF profile cluster associated with non-inflamed and vascularized biological samples and/or non-inflamed and fibroblast-enriched biological samples, and a fourth MF profile cluster associated with non-inflamed and non-vascularized biological samples and/or non-inflamed and non-fibroblast-enriched biological sample; and storing the plurality of MF profiles in association with information identifying the particular cancer type.

In one aspect, provided herein is a system, comprising: at least one computer hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform: 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 at least in part by determining, using the RNA expression data, a gene group expression level for each gene group in a set of gene groups, the set of gene groups comprising a first gene group associated with cancer malignancy and a second gene group associated with cancer microenvironment, wherein the first and second gene groups are different, the determining comprising: determining a first gene group expression level for the first gene group, and determining a second gene group expression level for the second gene group; determining a first visual characteristic for a first graphical user interface (GUI) element using the first gene group expression level; determining a second visual characteristic for a second GUI element using the second gene group expression level; generating a personalized GUI personalized to the subject, the GUI comprising: a first GUI portion associated with cancer malignancy and containing the first GUI element having the first visual characteristic, and a second GUI portion associated with cancer microenvironment and containing the second GUI element having the second visual characteristic; and presenting the generated personalized GUI to a user.

In one aspect, provided herein is a method, comprising: using at least one computer hardware processor to perform: 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 at least in part by determining, using the RNA expression data, a gene group expression level for each gene group in a set of gene groups, the set of gene groups comprising a first gene group associated with cancer malignancy and a second gene group associated with cancer microenvironment, wherein the first and second gene groups are different, the determining comprising: determining a first gene group expression level for the first gene group, and determining a second gene group expression level for the second gene group; determining a first visual characteristic for a first graphical user interface (GUI) element using the first gene group expression level; determining a second visual characteristic for a second GUI element using the second gene group expression level; generating a personalized GUI personalized to the subject, the GUI comprising: a first GUI portion associated with cancer malignancy and containing the first GUI element having the first visual characteristic, and a second GUI portion associated with cancer microenvironment and containing the second GUI element having the second visual characteristic; and presenting the generated personalized GUI to a user.

In one aspect, provided herein is at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by at least one computer hardware processor, cause at least one computer hardware processor to perform: 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 at least in part by determining, using the RNA expression data, a gene group expression level for each gene group in a set of gene groups, the set of gene groups comprising a first gene group associated with cancer malignancy and a second gene group associated with cancer microenvironment, wherein the first and second gene groups are different, the determining comprising: determining a first gene group expression level for the first gene group, and determining a second gene group expression level for the second gene group; determining a first visual characteristic for a first graphical user interface (GUI) element using the first gene group expression level; determining a second visual characteristic for a second GUI element using the second gene group expression level; generating a personalized GUI personalized to the subject, the GUI comprising: a first GUI portion associated with cancer malignancy and containing the first GUI element having the first visual characteristic, and a second GUI portion associated with cancer microenvironment and containing the second GUI element having the second visual characteristic; and presenting the generated personalized GUI to a user.

In one aspect, provided herein is a system, comprising: at least one computer hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform: obtaining RNA expression data and/or whole exome sequencing (WES) data for a biological sample from a subject having a particular type of cancer; determining a molecular-functional (MF) profile for the subject at least in part by: determining, using the RNA expression data and reference RNA expression data, a gene group expression level for each gene group in a first set of gene groups associated with cancer malignancy and consisting of the tumor properties group; and determining, using the RNA expression data and the reference RNA expression data, a gene group expression level for each gene group in a second set of gene groups associated with cancer microenvironment and consisting of the tumor-promoting immune microenvironment group, the anti-tumor immune microenvironment group, the angiogenesis group, and the fibroblasts group; and accessing information specifying multiple MF profile clusters for the particular cancer type; identifying, from among the multiple MF profile clusters, an MF profile cluster with which to associate the MF profile for the subject, the MF profile clusters comprising: a first MF profile cluster associated with inflamed and vascularized biological samples and/or inflamed and fibroblast-enriched biological samples, a second MF profile cluster associated with inflamed and non-vascularized biological samples and/or inflamed and non-fibroblast-enriched biological samples, a third MF profile cluster associated with non-inflamed and vascularized biological samples and/or non-inflamed and fibroblast-enriched biological samples, and a fourth MF profile cluster associated with non-inflamed and non-vascularized biological samples and/or non-inflamed and non-fibroblast-enriched biological sample, wherein the MF profile clusters were generated by: determining a plurality of MF profiles for a respective plurality of subjects using the reference RNA expression data and RNA expression data from biological samples obtained from the plurality of subjects, each of the plurality of MF profiles containing a gene group expression level for each gene group in the set of gene groups; and clustering the plurality of MF profiles to obtain the MF profile clusters.

In one aspect, provided herein is a method, comprising: using at least one computer hardware processor to perform: obtaining RNA expression data and/or whole exome sequencing (WES) data for a biological sample from a subject having a particular type of cancer; determining a molecular-functional (MF) profile for the subject at least in part by: determining, using the RNA expression data and reference RNA expression data, a gene group expression level for each gene group in a first set of gene groups associated with cancer malignancy and consisting of the tumor properties group; and determining, using the RNA expression data and the reference RNA expression data, a gene group expression level for each gene group in a second set of gene groups associated with cancer microenvironment and consisting of the tumor-promoting immune microenvironment group, the anti-tumor immune microenvironment group, the angiogenesis group, and the fibroblasts group; and accessing information specifying multiple MF profile clusters for the particular cancer type; identifying, from among the multiple MF profile clusters, an MF profile cluster with which to associate the MF profile for the subject, the MF profile clusters comprising: a first MF profile cluster associated with inflamed and vascularized biological samples and/or inflamed and fibroblast-enriched biological samples, a second MF profile cluster associated with inflamed and non-vascularized biological samples and/or inflamed and non-fibroblast-enriched biological samples, a third MF profile cluster associated with non-inflamed and vascularized biological samples and/or non-inflamed and fibroblast-enriched biological samples, and a fourth MF profile cluster associated with non-inflamed and non-vascularized biological samples and/or non-inflamed and non-fibroblast-enriched biological sample, wherein the MF profile clusters were generated by: determining a plurality of MF profiles for a respective plurality of subjects using the reference RNA expression data and RNA expression data from biological samples obtained from the plurality of subjects, each of the plurality of MF profiles containing a gene group expression level for each gene group in the set of gene groups; and clustering the plurality of MF profiles to obtain the MF profile clusters.

In one aspect, provided herein is at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to perform: obtaining RNA expression data and/or whole exome sequencing (WES) data for a biological sample from a subject having a particular type of cancer; determining a molecular-functional (MF) profile for the subject at least in part by: determining, using the RNA expression data and reference RNA expression data, a gene group expression level for each gene group in a first set of gene groups associated with cancer malignancy and consisting of the tumor properties group; and determining, using the RNA expression data and the reference RNA expression data, a gene group expression level for each gene group in a second set of gene groups associated with cancer microenvironment and consisting of the tumor-promoting immune microenvironment group, the anti-tumor immune microenvironment group, the angiogenesis group, and the fibroblasts group; and accessing information specifying multiple MF profile clusters for the particular cancer type; identifying, from among the multiple MF profile clusters, an MF profile cluster with which to associate the MF profile for the subject, the MF profile clusters comprising: a first MF profile cluster associated with inflamed and vascularized biological samples and/or inflamed and fibroblast-enriched biological samples, a second MF profile cluster associated with inflamed and non-vascularized biological samples and/or inflamed and non-fibroblast-enriched biological samples, a third MF profile cluster associated with non-inflamed and vascularized biological samples and/or non-inflamed and fibroblast-enriched biological samples, and a fourth MF profile cluster associated with non-inflamed and non-vascularized biological samples and/or non-inflamed and non-fibroblast-enriched biological sample, wherein the MF profile clusters were generated by: determining a plurality of MF profiles for a respective plurality of subjects using the reference RNA expression data and RNA expression data from biological samples obtained from the plurality of subjects, each of the plurality of MF profiles containing a gene group expression level for each gene group in the set of gene groups; and clustering the plurality of MF profiles to obtain the MF profile clusters.

In one aspect, provided herein is a system, comprising: at least one computer hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform: obtaining RNA expression data and/or whole exome sequencing (WES) data for a biological sample from a subject having a particular type of cancer; determining a molecular-functional (MF) profile for the subject at least in part by: determining, using the RNA expression data and reference RNA expression data, a gene group expression level for each gene group in a first set of gene groups associated with cancer malignancy and consisting of the proliferation rate group, the PI3K/AKT/mTOR signaling group, the RAS/RAF/MEK signaling group, the receptor tyrosine kinases expression group, the tumor suppressors group, the metastasis signature group, the anti-metastatic factors group, and the mutation status group; and determining, using the RNA expression data and the reference RNA expression data, a gene group expression level for each gene group in a second set of gene groups associated with cancer microenvironment and consisting of the antigen presentation group, the cytotoxic T and NK cells group, the B cells group, the anti-tumor microenvironment group, the checkpoint inhibition group, the Treg group, the MDSC group, the granulocytes group, the cancer associated fibroblasts group, the angiogenesis group, and the tumor-promotive immune group; and accessing information specifying multiple MF profile clusters for the particular cancer type; identifying, from among the multiple MF profile clusters, an MF profile cluster with which to associate the MF profile for the subject, the MF profile clusters comprising: a first MF profile cluster associated with inflamed and vascularized biological samples and/or inflamed and fibroblast-enriched biological samples, a second MF profile cluster associated with inflamed and non-vascularized biological samples and/or inflamed and non-fibroblast-enriched biological samples, a third MF profile cluster associated with non-inflamed and vascularized biological samples and/or non-inflamed and fibroblast-enriched biological samples, and a fourth MF profile cluster associated with non-inflamed and non-vascularized biological samples and/or non-inflamed and non-fibroblast-enriched biological samples, wherein the MF profile clusters were generated by: determining a plurality of MF profiles for a respective plurality of subjects using the reference RNA expression data and RNA expression data from biological samples obtained from the plurality of subjects, each of the plurality of MF profiles containing a gene group expression level for each gene group in the set of gene groups; and clustering the plurality of MF profiles to obtain the MF profile clusters.

In one aspect, provided herein is a method, comprising: using at least one computer hardware processor to perform: obtaining obtaining RNA expression data and/or whole exome sequencing (WES) data for a biological sample from a subject having a particular type of cancer; determining a molecular-functional (MF) profile for the subject at least in part by: determining, using the RNA expression data and reference RNA expression data, a gene group expression level for each gene group in a first set of gene groups associated with cancer malignancy and consisting of the proliferation rate group, the PI3K/AKT/mTOR signaling group, the RAS/RAF/MEK signaling group, the receptor tyrosine kinases expression group, the tumor suppressors group, the metastasis signature group, the anti-metastatic factors group, and the mutation status group; and determining, using the RNA expression data and the reference RNA expression data, a gene group expression level for each gene group in a second set of gene groups associated with cancer microenvironment and consisting of the antigen presentation group, the cytotoxic T and NK cells group, the B cells group, the anti-tumor microenvironment group, the checkpoint inhibition group, the Treg group, the MDSC group, the granulocytes group, the cancer associated fibroblasts group, the angiogenesis group, and the tumor-promotive immune group; and accessing information specifying multiple MF profile clusters for the particular cancer type; identifying, from among the multiple MF profile clusters, an MF profile cluster with which to associate the MF profile for the subject, the MF profile clusters comprising: a first MF profile cluster associated with inflamed and vascularized biological samples and/or inflamed and fibroblast-enriched biological samples, a second MF profile cluster associated with inflamed and non-vascularized biological samples and/or inflamed and non-fibroblast-enriched biological samples, a third MF profile cluster associated with non-inflamed and vascularized biological samples and/or non-inflamed and fibroblast-enriched biological samples, and a fourth MF profile cluster associated with non-inflamed and non-vascularized biological samples and/or non-inflamed and non-fibroblast-enriched biological samples, wherein the MF profile clusters were generated by: determining a plurality of MF profiles for a respective plurality of subjects using the reference RNA expression data and RNA expression data from biological samples obtained from the plurality of subjects, each of the plurality of MF profiles containing a gene group expression level for each gene group in the set of gene groups; and clustering the plurality of MF profiles to obtain the MF profile clusters.

In one aspect, provided herein is at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to perform: obtaining RNA expression data and/or whole exome sequencing (WES) data for a biological sample from a subject having a particular type of cancer; determining a molecular-functional (MF) profile for the subject at least in part by: determining, using the RNA expression data and reference RNA expression data, a gene group expression level for each gene group in a first set of gene groups associated with cancer malignancy and consisting of the proliferation rate group, the PI3K/AKT/mTOR signaling group, the RAS/RAF/MEK signaling group, the receptor tyrosine kinases expression group, the tumor suppressors group, the metastasis signature group, the anti-metastatic factors group, and the mutation status group; and determining, using the RNA expression data and the reference RNA expression data, a gene group expression level for each gene group in a second set of gene groups associated with cancer microenvironment and consisting of the antigen presentation group, the cytotoxic T and NK cells group, the B cells group, the anti-tumor microenvironment group, the checkpoint inhibition group, the Treg group, the MDSC group, the granulocytes group, the cancer associated fibroblasts group, the angiogenesis group, and the tumor-promotive immune group; and accessing information specifying multiple MF profile clusters for the particular cancer type; identifying, from among the multiple MF profile clusters, an MF profile cluster with which to associate the MF profile for the subject, the MF profile clusters comprising: a first MF profile cluster associated with inflamed and vascularized biological samples and/or inflamed and fibroblast-enriched biological samples, a second MF profile cluster associated with inflamed and non-vascularized biological samples and/or inflamed and non-fibroblast-enriched biological samples, a third MF profile cluster associated with non-inflamed and vascularized biological samples and/or non-inflamed and fibroblast-enriched biological samples, and a fourth MF profile cluster associated with non-inflamed and non-vascularized biological samples and/or non-inflamed and non-fibroblast-enriched biological samples, wherein the MF profile clusters were generated by: determining a plurality of MF profiles for a respective plurality of subjects using the reference RNA expression data and RNA expression data from biological samples obtained from the plurality of subjects, each of the plurality of MF profiles containing a gene group expression level for each gene group in the set of gene groups; and clustering the plurality of MF profiles to obtain the MF profile clusters.

In one aspect, provided herein is a system, comprising: at least one computer hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform: obtaining RNA expression data and/or whole exome sequencing (WES) data for a biological sample from a subject having a particular type of cancer; determining a molecular-functional (MF) profile for the subject at least in part by: determining, using the RNA expression data and reference RNA expression data, a gene group expression level for each gene group in a first set of gene groups associated with cancer malignancy and consisting of the proliferation rate group, the PI3K/AKT/mTOR signaling group, the RAS/RAF/MEK signaling group, the receptor tyrosine kinases expression group, the growth factors group, the tumor suppressors group, the metastasis signature group, the anti-metastatic factors group, and the mutation status group; and determining, using the RNA expression data and the reference RNA expression data, a gene group expression level for each gene group in a second set of gene groups associated with cancer microenvironment and consisting of the MHCI group, the MHCII group, the coactivation molecules group, the effector cells group, the NK cells group, the T cell traffic group, the T cells group, the B cells group, the M1 signatures group, the Th1 signature group, the antitumor cytokines group, the checkpoint inhibition group, the Treg group, the MDSC group, the granulocytes group, the M2 signature group, the Th2 signature group, the protumor cytokines group, the cancer associated fibroblasts group, the angiogenesis group, and the complement inhibition group; and accessing information specifying multiple MF profile clusters for the particular cancer type; identifying, from among the multiple MF profile clusters, an MF profile cluster with which to associate the MF profile for the subject, the MF profile clusters comprising: a first MF profile cluster associated with inflamed and vascularized biological samples and/or inflamed and fibroblast-enriched biological samples, a second MF profile cluster associated with inflamed and non-vascularized biological samples and/or inflamed and non-fibroblast-enriched biological samples, a third MF profile cluster associated with non-inflamed and vascularized biological samples and/or non-inflamed and fibroblast-enriched biological samples, and a fourth MF profile cluster associated with non-inflamed and non-vascularized biological samples and/or non-inflamed and non-fibroblast-enriched biological samples, wherein the MF profile clusters were generated by: determining a plurality of MF profiles for a respective plurality of subjects using the reference RNA expression data and RNA expression data from biological samples obtained from the plurality of subjects, each of the plurality of MF profiles containing a gene group expression level for each gene group in the set of gene groups; and clustering the plurality of MF profiles to obtain the MF profile clusters.

In one aspect, provided herein is a method, comprising: using at least one computer hardware processor to perform: obtaining RNA expression data and/or whole exome sequencing (WES) data for a biological sample from a subject having a particular type of cancer; determining a molecular-functional (MF) profile for the subject at least in part by: determining, using the RNA expression data and reference RNA expression data, a gene group expression level for each gene group in a first set of gene groups associated with cancer malignancy and consisting of the proliferation rate group, the PI3K/AKT/mTOR signaling group, the RAS/RAF/MEK signaling group, the receptor tyrosine kinases expression group, the growth factors group, the tumor suppressors group, the metastasis signature group, the anti-metastatic factors group, and the mutation status group; and determining, using the RNA expression data and the reference RNA expression data, a gene group expression level for each gene group in a second set of gene groups associated with cancer microenvironment and consisting of the MHCI group, the MHCII group, the coactivation molecules group, the effector cells group, the NK cells group, the T cell traffic group, the T cells group, the B cells group, the M1 signatures group, the Th1 signature group, the antitumor cytokines group, the checkpoint inhibition group, the Treg group, the MDSC group, the granulocytes group, the M2 signature group, the Th2 signature group, the protumor cytokines group, the cancer associated fibroblasts group, the angiogenesis group, and the complement inhibition group; and accessing information specifying multiple MF profile clusters for the particular cancer type; identifying, from among the multiple MF profile clusters, an MF profile cluster with which to associate the MF profile for the subject, the MF profile clusters comprising: a first MF profile cluster associated with inflamed and vascularized biological samples and/or inflamed and fibroblast-enriched biological samples, a second MF profile cluster associated with inflamed and non-vascularized biological samples and/or inflamed and non-fibroblast-enriched biological samples, a third MF profile cluster associated with non-inflamed and vascularized biological samples and/or non-inflamed and fibroblast-enriched biological samples, and a fourth MF profile cluster associated with non-inflamed and non-vascularized biological samples and/or non-inflamed and non-fibroblast-enriched biological samples, wherein the MF profile clusters were generated by: determining a plurality of MF profiles for a respective plurality of subjects using the reference RNA expression data and RNA expression data from biological samples obtained from the plurality of subjects, each of the plurality of MF profiles containing a gene group expression level for each gene group in the set of gene groups; and clustering the plurality of MF profiles to obtain the MF profile clusters.

In one aspect, provided herein is at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to perform: obtaining RNA expression data and/or whole exome sequencing (WES) data for a biological sample from a subject having a particular type of cancer; determining a molecular-functional (MF) profile for the subject at least in part by: determining, using the RNA expression data and reference RNA expression data, a gene group expression level for each gene group in a first set of gene groups associated with cancer malignancy and consisting of the proliferation rate group, the PI3K/AKT/mTOR signaling group, the RAS/RAF/MEK signaling group, the receptor tyrosine kinases expression group, the growth factors group, the tumor suppressors group, the metastasis signature group, the anti-metastatic factors group, and the mutation status group; and determining, using the RNA expression data and the reference RNA expression data, a gene group expression level for each gene group in a second set of gene groups associated with cancer microenvironment and consisting of the MHCI group, the MHCII group, the coactivation molecules group, the effector cells group, the NK cells group, the T cell traffic group, the T cells group, the B cells group, the M1 signatures group, the Th1 signature group, the antitumor cytokines group, the checkpoint inhibition group, the Treg group, the MDSC group, the granulocytes group, the M2 signature group, the Th2 signature group, the protumor cytokines group, the cancer associated fibroblasts group, the angiogenesis group, and the complement inhibition group; and accessing information specifying multiple MF profile clusters for the particular cancer type; identifying, from among the multiple MF profile clusters, an MF profile cluster with which to associate the MF profile for the subject, the MF profile clusters comprising: a first MF profile cluster associated with inflamed and vascularized biological samples and/or inflamed and fibroblast-enriched biological samples, a second MF profile cluster associated with inflamed and non-vascularized biological samples and/or inflamed and non-fibroblast-enriched biological samples, a third MF profile cluster associated with non-inflamed and vascularized biological samples and/or non-inflamed and fibroblast-enriched biological samples, and a fourth MF profile cluster associated with non-inflamed and non-vascularized biological samples and/or non-inflamed and non-fibroblast-enriched biological samples, wherein the MF profile clusters were generated by: determining a plurality of MF profiles for a respective plurality of subjects using the reference RNA expression data and RNA expression data from biological samples obtained from the plurality of subjects, each of the plurality of MF profiles containing a gene group expression level for each gene group in the set of gene groups; and clustering the plurality of MF profiles to obtain the MF profile clusters.

In one aspect, provided herein is a system, comprising: at least one computer hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform: obtaining first RNA expression data and/or first whole exome sequencing (WES) data from biological samples from a plurality of subjects; determining a respective plurality of molecular-functional (MF) profiles for the plurality of subjects at least in part by, for each of the plurality of subjects, determining, using the first RNA expression data, a respective gene group expression level for each group in a set of gene groups, the set of gene groups comprising gene groups associated with cancer malignancy and different gene groups associated with cancer microenvironment; clustering the plurality of MF profiles to obtain MF profile clusters including: a first MF profile cluster associated with inflamed and vascularized biological samples and/or inflamed and fibroblast-enriched biological samples, a second MF profile cluster associated with inflamed and non-vascularized biological samples and/or inflamed and non-fibroblast-enriched biological samples, a third MF profile cluster associated with non-inflamed and vascularized biological samples and/or non-inflamed and fibroblast-enriched biological samples, and a fourth MF profile cluster associated with non-inflamed and non-vascularized biological samples and/or non-inflamed and non-fibroblast-enriched biological samples; obtaining second RNA expression data for a biological sample from a subject; determining a molecular-functional (MF) profile for the subject at least in part by determining, using the second RNA expression data, a gene group expression level for each group in the set of gene groups; and identifying, from among the MF profile clusters, a particular MF profile cluster with which to associate the MF profile for the subject.

In one aspect, provided herein is a method, comprising: using at least one computer hardware processor to perform: obtaining first RNA expression data and/or first whole exome sequencing (WES) data from biological samples from a plurality of subjects; determining a respective plurality of molecular-functional (MF) profiles for the plurality of subjects at least in part by, for each of the plurality of subjects, determining, using the first RNA expression data, a respective gene group expression level for each group in a set of gene groups, the set of gene groups comprising gene groups associated with cancer malignancy and different gene groups associated with cancer microenvironment; clustering the plurality of MF profiles to obtain MF profile clusters including: a first MF profile cluster associated with inflamed and vascularized biological samples and/or inflamed and fibroblast-enriched biological samples, a second MF profile cluster associated with inflamed and non-vascularized biological samples and/or inflamed and non-fibroblast-enriched biological samples, a third MF profile cluster associated with non-inflamed and vascularized biological samples and/or non-inflamed and fibroblast-enriched biological samples, and a fourth MF profile cluster associated with non-inflamed and non-vascularized biological samples and/or non-inflamed and non-fibroblast-enriched biological samples; obtaining second RNA expression data for a biological sample from a subject; determining a molecular-functional (MF) profile for the subject at least in part by determining, using the second RNA expression data, a gene group expression level for each group in the set of gene groups; and identifying, from among the MF profile clusters, a particular MF profile cluster with which to associate the MF profile for the subject.

In one aspect, provided herein is at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to perform: obtaining first RNA expression data and/or first whole exome sequencing (WES) data from biological samples from a plurality of subjects; determining a respective plurality of molecular-functional (MF) profiles for the plurality of subjects at least in part by, for each of the plurality of subjects, determining, using the first RNA expression data, a respective gene group expression level for each group in a set of gene groups, the set of gene groups comprising gene groups associated with cancer malignancy and different gene groups associated with cancer microenvironment; clustering the plurality of MF profiles to obtain MF profile clusters including: a first MF profile cluster associated with inflamed and vascularized biological samples and/or inflamed and fibroblast-enriched biological samples, a second MF profile cluster associated with inflamed and non-vascularized biological samples and/or inflamed and non-fibroblast-enriched biological samples, a third MF profile cluster associated with non-inflamed and vascularized biological samples and/or non-inflamed and fibroblast-enriched biological samples, and a fourth MF profile cluster associated with non-inflamed and non-vascularized biological samples and/or non-inflamed and non-fibroblast-enriched biological samples; obtaining second RNA expression data for a biological sample from a subject; determining a molecular-functional (MF) profile for the subject at least in part by determining, using the second RNA expression data, a gene group expression level for each group in the set of gene groups; and identifying, from among the MF profile clusters, a particular MF profile cluster with which to associate the MF profile for the subject.

In one aspect, provided herein is a system, comprising: at least one computer hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform: 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 at least in part by determining, using the RNA expression data, a gene group expression level for each gene group in a set of gene groups comprising: first gene groups associated with cancer malignancy consisting of the tumor properties group; and second gene groups associated with cancer microenvironment consisting of the tumor-promoting immune microenvironment group, the anti-tumor immune microenvironment group, the angiogenesis group, and the fibroblasts group, determining a first set of visual characteristics for a first plurality of graphical user interface (GUI) elements using the gene group expression levels determined for the first gene groups; determining a second set of visual characteristics for a second plurality of GUI elements using the gene group expression levels determined for the second gene groups; generating a personalized GUI personalized to the subject, the generating comprising: generating a first GUI portion associated with cancer malignancy and containing the first plurality of GUI elements having the determined first set of visual characteristics; and generating a second GUI portion associated with cancer microenvironment and containing the second plurality of GUI elements having the determined second set of visual characteristics; and presenting the generated personalized GUI to a user.

In one aspect, provided herein is a method, comprising: using at least one computer hardware processor to perform: 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 at least in part by determining, using the RNA expression data, a gene group expression level for each gene group in a set of gene groups comprising: first gene groups associated with cancer malignancy consisting of the tumor properties group; and second gene groups associated with cancer microenvironment consisting of the tumor-promoting immune microenvironment group, the anti-tumor immune microenvironment group, the angiogenesis group, and the fibroblasts group, determining a first set of visual characteristics for a first plurality of graphical user interface (GUI) elements using the gene group expression levels determined for the first gene groups; determining a second set of visual characteristics for a second plurality of GUI elements using the gene group expression levels determined for the second gene groups; generating a personalized GUI personalized to the subject, the generating comprising: generating a first GUI portion associated with cancer malignancy and containing the first plurality of GUI elements having the determined first set of visual characteristics; and generating a second GUI portion associated with cancer microenvironment and containing the second plurality of GUI elements having the determined second set of visual characteristics; and presenting the generated personalized GUI to a user.

In one aspect, provided herein is at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to perform: 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 at least in part by determining, using the RNA expression data, a gene group expression level for each gene group in a set of gene groups comprising: first gene groups associated with cancer malignancy consisting of the tumor properties group; and second gene groups associated with cancer microenvironment consisting of the tumor-promoting immune microenvironment group, the anti-tumor immune microenvironment group, the angiogenesis group, and the fibroblasts group, determining a first set of visual characteristics for a first plurality of graphical user interface (GUI) elements using the gene group expression levels determined for the first gene groups; determining a second set of visual characteristics for a second plurality of GUI elements using the gene group expression levels determined for the second gene groups; generating a personalized GUI personalized to the subject, the generating comprising: generating a first GUI portion associated with cancer malignancy and containing the first plurality of GUI elements having the determined first set of visual characteristics; and generating a second GUI portion associated with cancer microenvironment and containing the second plurality of GUI elements having the determined second set of visual characteristics; and presenting the generated personalized GUI to a user.

In one aspect, provided herein is a system, comprising: at least one computer hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform: 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 at least in part by determining, using the RNA expression data, a gene group expression level for each gene group in a set of gene groups comprising: first gene groups associated with cancer malignancy consisting of the proliferation rate group, the PI3K/AKT/mTOR signaling group, the RAS/RAF/MEK signaling group, the receptor tyrosine kinases expression group, the tumor suppressors group, the metastasis signature group, the anti-metastatic factors group, and the mutation status group; and second gene groups associated with cancer microenvironment consisting of the cancer associated fibroblasts group, the angiogenesis group, the antigen presentation group, the cytotoxic T and NK cells group, the B cells group, the anti-tumor microenvironment group, the checkpoint inhibition group, the Treg group, the MDSC group, the granulocytes group, and the tumor-promotive immune group; determining a first set of visual characteristics for a first plurality of graphical user interface (GUI) elements using the gene group expression levels determined for the first gene groups; determining a second set of visual characteristics for a second plurality of GUI elements using the gene group expression levels determined for the second gene groups; generating a personalized GUI personalized to the subject, the generating comprising: generating a first GUI portion associated with cancer malignancy and containing the first plurality of GUI elements having the determined first set of visual characteristics; and generating a second GUI portion associated with cancer microenvironment and containing the second plurality of GUI elements having the determined second set of visual characteristics; and presenting the generated personalized GUI to a user.

In one aspect, provided herein is a method, comprising: using at least one computer hardware processor to perform: 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 at least in part by determining, using the RNA expression data, a gene group expression level for each gene group in a set of gene groups comprising: first gene groups associated with cancer malignancy consisting of the proliferation rate group, the PI3K/AKT/mTOR signaling group, the RAS/RAF/MEK signaling group, the receptor tyrosine kinases expression group, the tumor suppressors group, the metastasis signature group, the anti-metastatic factors group, and the mutation status group; and second gene groups associated with cancer microenvironment consisting of the cancer associated fibroblasts group, the angiogenesis group, the antigen presentation group, the cytotoxic T and NK cells group, the B cells group, the anti-tumor microenvironment group, the checkpoint inhibition group, the Treg group, the MDSC group, the granulocytes group, and the tumor-promotive immune group; determining a first set of visual characteristics for a first plurality of graphical user interface (GUI) elements using the gene group expression levels determined for the first gene groups; determining a second set of visual characteristics for a second plurality of GUI elements using the gene group expression levels determined for the second gene groups; generating a personalized GUI personalized to the subject, the generating comprising: generating a first GUI portion associated with cancer malignancy and containing the first plurality of GUI elements having the determined first set of visual characteristics; and generating a second GUI portion associated with cancer microenvironment and containing the second plurality of GUI elements having the determined second set of visual characteristics; and presenting the generated personalized GUI to a user.

In one aspect, provided herein is at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to perform: 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 at least in part by determining, using the RNA expression data, a gene group expression level for each gene group in a set of gene groups comprising: first gene groups associated with cancer malignancy consisting of the proliferation rate group, the PI3K/AKT/mTOR signaling group, the RAS/RAF/MEK signaling group, the receptor tyrosine kinases expression group, the tumor suppressors group, the metastasis signature group, the anti-metastatic factors group, and the mutation status group; and second gene groups associated with cancer microenvironment consisting of the cancer associated fibroblasts group, the angiogenesis group, the antigen presentation group, the cytotoxic T and NK cells group, the B cells group, the anti-tumor microenvironment group, the checkpoint inhibition group, the Treg group, the MDSC group, the granulocytes group, and the tumor-promotive immune group; determining a first set of visual characteristics for a first plurality of graphical user interface (GUI) elements using the gene group expression levels determined for the first gene groups; determining a second set of visual characteristics for a second plurality of GUI elements using the gene group expression levels determined for the second gene groups; generating a personalized GUI personalized to the subject, the generating comprising: generating a first GUI portion associated with cancer malignancy and containing the first plurality of GUI elements having the determined first set of visual characteristics; and generating a second GUI portion associated with cancer microenvironment and containing the second plurality of GUI elements having the determined second set of visual characteristics; and presenting the generated personalized GUI to a user.

In one aspect, provided herein is a system, comprising: at least one computer hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform: 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 at least in part by determining, using the RNA expression data, a gene group expression level for each gene group in a set of gene groups comprising: first gene groups associated with cancer malignancy consisting of the proliferation rate group, the PI3K/AKT/mTOR signaling group, the RAS/RAF/MEK signaling group, the receptor tyrosine kinases expression group, the growth factors group, the tumor suppressors group, the metastasis signature group, the anti-metastatic factors group, and the mutation status group; and second gene groups associated with cancer microenvironment consisting of the cancer associated fibroblasts group, the angiogenesis group, the MHCI group, the MHCII group, the coactivation molecules group, the effector cells group, the NK cells group, the T cell traffic group, the T cells group, the B cells group, the M1 signatures group, the Th1 signature group, the antitumor cytokines group, the checkpoint inhibition group, the Treg group, the MDSC group, the granulocytes group, the M2 signature group, the Th2 signature group, the protumor cytokines group, and the complement inhibition group; determining a first set of visual characteristics for a first plurality of graphical user interface (GUI) elements using the gene group expression levels determined for the first gene groups; determining a second set of visual characteristics for a second plurality of GUI elements using the gene group expression levels determined for the second gene groups; generating a personalized GUI personalized to the subject, the generating comprising: generating a first GUI portion associated with cancer malignancy and containing the first plurality of GUI elements having the determined first set of visual characteristics; and generating a second GUI portion associated with cancer microenvironment and containing the second plurality of GUI elements having the determined second set of visual characteristics; and presenting the generated personalized GUI to a user.

In one aspect, provided herein is a method, comprising: using at least one computer hardware processor to perform: 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 at least in part by determining, using the RNA expression data, a gene group expression level for each gene group in a set of gene groups comprising: first gene groups associated with cancer malignancy consisting of the proliferation rate group, the PI3K/AKT/mTOR signaling group, the RAS/RAF/MEK signaling group, the receptor tyrosine kinases expression group, the growth factors group, the tumor suppressors group, the metastasis signature group, the anti-metastatic factors group, and the mutation status group; and second gene groups associated with cancer microenvironment consisting of the cancer associated fibroblasts group, the angiogenesis group, the MHCI group, the MHCII group, the coactivation molecules group, the effector cells group, the NK cells group, the T cell traffic group, the T cells group, the B cells group, the M1 signatures group, the Th1 signature group, the antitumor cytokines group, the checkpoint inhibition group, the Treg group, the MDSC group, the granulocytes group, the M2 signature group, the Th2 signature group, the protumor cytokines group, and the complement inhibition group; determining a first set of visual characteristics for a first plurality of graphical user interface (GUI) elements using the gene group expression levels determined for the first gene groups; determining a second set of visual characteristics for a second plurality of GUI elements using the gene group expression levels determined for the second gene groups; generating a personalized GUI personalized to the subject, the generating comprising: generating a first GUI portion associated with cancer malignancy and containing the first plurality of GUI elements having the determined first set of visual characteristics; and generating a second GUI portion associated with cancer microenvironment and containing the second plurality of GUI elements having the determined second set of visual characteristics; and presenting the generated personalized GUI to a user.

In one aspect, provided herein is at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to perform: 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 at least in part by determining, using the RNA expression data, a gene group expression level for each gene group in a set of gene groups comprising: first gene groups associated with cancer malignancy consisting of the proliferation rate group, the PI3K/AKT/mTOR signaling group, the RAS/RAF/MEK signaling group, the receptor tyrosine kinases expression group, the growth factors group, the tumor suppressors group, the metastasis signature group, the anti-metastatic factors group, and the mutation status group; and second gene groups associated with cancer microenvironment consisting of the cancer associated fibroblasts group, the angiogenesis group, the MHCI group, the MHCII group, the coactivation molecules group, the effector cells group, the NK cells group, the T cell traffic group, the T cells group, the B cells group, the M1 signatures group, the Th1 signature group, the antitumor cytokines group, the checkpoint inhibition group, the Treg group, the MDSC group, the granulocytes group, the M2 signature group, the Th2 signature group, the protumor cytokines group, and the complement inhibition group; determining a first set of visual characteristics for a first plurality of graphical user interface (GUI) elements using the gene group expression levels determined for the first gene groups; determining a second set of visual characteristics for a second plurality of GUI elements using the gene group expression levels determined for the second gene groups; generating a personalized GUI personalized to the subject, the generating comprising: generating a first GUI portion associated with cancer malignancy and containing the first plurality of GUI elements having the determined first set of visual characteristics; and generating a second GUI portion associated with cancer microenvironment and containing the second plurality of GUI elements having the determined second set of visual characteristics; and presenting the generated personalized GUI to a user.

