SYSTEMS AND METHODS FOR GENERATING, VISUALIZING AND CLASSIFYING MOLECULAR FUNCTIONAL PROFILES

• US 20190332744A9
• Filed: 06/12/2018
• Published: 10/31/2019
• Est. Priority Date: 06/13/2017
• Status: Active Grant

• Current Assignee: Bostongene Corporation
• Original Assignee: Bostongene Corporation
• Inventors: Bagaev, Alexander, Frenkel, Feliks, Kotlov, Nikita, Ataullakhanov, Ravshan
• Granted Patent: US 10,650,911 B2
• Time in Patent Office: Days
• CPC Class Codes: C12Q 1/6886 for cancer immunoassay for cancer G01N33/574
• CPC Class Codes: C12Q 2600/156 Polymorphic or mutational markers
• CPC Class Codes: C12Q 2600/158 Expression markers
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06F 17/18 for evaluating statistical data , e.g. average values, frequency distributions, probability functions, regression analysis forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04
• CPC Class Codes: G16B 20/00 ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
• CPC Class Codes: G16B 25/10 Gene or protein expression profiling; Expression-ratio estimation or normalisation
• CPC Class Codes: G16B 30/00 ICT specially adapted for sequence analysis involving nucleotides or amino acids
• CPC Class Codes: G16B 40/00 ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• CPC Class Codes: G16B 40/20 Supervised data analysis
• CPC Class Codes: G16B 40/30 Unsupervised data analysis
• CPC Class Codes: G16B 45/00 ICT specially adapted for bioinformatics-related data visualisation, e.g. displaying of maps or networks
• CPC Class Codes: G16B 5/00 ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
• CPC Class Codes: G16B 5/20 Probabilistic models
• CPC Class Codes: G16B 50/00 ICT programming tools or database systems specially adapted for bioinformatics
• CPC Class Codes: G16B 50/30 Data warehousing; Computing architectures
• CPC Class Codes: G16H 10/20 for electronic clinical trials or questionnaires
• CPC Class Codes: G16H 20/00 ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
• CPC Class Codes: G16H 20/10 relating to drugs or medications, e.g. for ensuring correct administration to patients
• CPC Class Codes: G16H 20/40 relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
• CPC Class Codes: G16H 50/20 : for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 : for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16H 50/50 : for simulation or modelling of medical disorders
• CPC Class Codes: G16H 50/70 : for mining of medical data, e.g. analysing previous cases of other patients
• CPC Class Codes: G16H 70/20 : relating to practices or guidelines

Abstract

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

Claims

1. 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.

2. The system of claim 1,

wherein determining the first set of visual characteristics for the first plurality of GUI elements determining sizes for each of the first plurality of GUI elements using the gene expression levels determined for the first gene groups;

and

wherein determining the second set of visual characteristics for the first plurality of GUI elements determining sizes for each of the second plurality of GUI elements using the gene expression levels determined for the second gene groups.

3. The system of claim 2,

wherein determining the MF profile for the subject comprises determining the gene expression levels for each of the first gene groups using a gene set enrichment analysis (GSEA) technique;

and

wherein determining the MF profile for the subject comprises determining the gene expression levels for each of the second gene groups using the gene set enrichment analysis (GSEA) technique.

4. The system of claim 1, wherein the gene groups associated with cancer malignancy comprise at least three genes from each of the following groups:

the tumor properties group;

MKI67, ESCO2, CETN3, CDK2, CCND1, CCNE1, AURKA, AURKB, CDK4, CDK6, PRC1, E2F1, MYBL2, BUB1, PLK1, CCNB1, MCM2, MCM6, PIK3CA, PIK3CB, PIK3CG, PIK3CD, AKT1, MTOR, PTEN, PRKCA, AKT2, AKT3, BRAF, FNTA, FNTB, MAP2K1, MAP2K2, MKNK1, MKNK2, ALK, AXL, KIT, EGFR, ERBB2, FLT3, MET, NTRK1, FGFR1, FGFR2, FGFR3, ERBB4, ERBB3, BCR-ABL, PDGFRA, PDGFRB, NGF, CSF3, CSF2, FGF7, IGF1, IGF2, IL7, FGF2, TP53, SIK1, PTEN, DCN, MTAP, AIM2, RB1, ESRP1, CTSL, HOXA1, SMARCA4, SNAI2, TWIST1, NEDD9, PAPPA, HPSE, KISS1, ADGRG1, BRMS1, TCF21, CDH1, PCDH10, NCAM1, MITF, APC, ARID1A, ATM, ATRX, BAP1, BRAF, BRCA2, CDH1, CDKN2A, CTCF, CTNNB1, DNMT3A, EGFR, FBXW7, FLT3, GATA3, HRAS, IDH1, KRAS, MAP3K1, MTOR, NAV3, NCOR1, NF1, NOTCH1, NPM1, NRAS, PBRM1, PIK3CA, PIK3R1, PTEN, RB1, RUNX1, SETD2, STAG2, TAF1, TP53, and VHL.

5. The system of claim 1, wherein determining the MF portrait comprises:

determining the gene group expression level for the tumor properties group using the gene expression level obtained from the RNA sequence data for at least three genes in the tumor properties group.

6. The system of claim 1, wherein the gene groups associated with cancer microenvironment comprise at least three genes from each of the following groups:

the anti-tumor immune microenvironment group;

HLA-A, HLA-B, HLA-C, B2M, TAP1, TAP2, HLA-DRA, HLA-DRB1, HLA-DOB, HLA-DPB2, HLA-DMA, HLA-DOA, HLA-DPA1, HLA-DPB1, HLA-DMB, HLA-DQB1, HLA-DQA1, HLA-DRB5, HLA-DQA2, HLA-DQB2, HLA-DRB6, CD80, CD86, CD40, CD83, TNFRSF4, ICOSLG, CD28, IFNG, GZMA, GZMB, PRF1, LCK, GZMK, ZAP70, GNLY, FASLG, TBX21, EOMES, CD8A, CD8B, NKG7, CD160, CD244, NCR1, KLRC2, KLRK1, CD226, GZMH, GNLY, IFNG, KIR2DL4, KIR2DS1, KIR2DS2, KIR2DS3, KIR2DS4, KIR2DS5, CXCL9, CXCL10, CXCR3, CX3CL1, CCR7, CXCL11, CCL21, CCL2, CCL3, CCL4, CCL5, EOMES, TBX21, ITK, CD3D, CD3E, CD3G, TRAC, TRBC1, TRBC2, LCK, UBASH3A, TRAT1, CD19, MS4A1, TNFRSF13C, CD27, CD24, CR2, TNFRSF17, TNFRSF13B, CD22, CD79A, CD79B, BLK, NOS2, IL12A, IL12B, IL23A, TNF, IL1B, SOCS3, IFNG, IL2, CD40LG, IL15, CD27, TBX21, LTA, IL21, HMGB1, TNF, IFNB1, IFNA2, CCL3, TNFSF10, and FASLG; and

the tumor-promoting immune microenvironment group;

PDCD1, CD274, CTLA4, LAG3, PDCD1LG2, BTLA, HAVCR2, VSIR, CXCL12, TGFB1, TGFB2, TGFB3, FOXP3, CTLA4, IL10, TNFRSF1B, CCL17, CXCR4, CCR4, CCL22, CCL1, CCL2, CCL5, CXCL13, CCL28, IDO1, ARG1, IL4R, IL10, TGFB1, TGFB2, TGFB3, NOS2, CYBB, CXCR4, CD33, CXCL1, CXCL5, CCL2, CCL4, CCL8, CCR2, CCL3, CCL5, CSF1, CXCL8, CXCL8, CXCL2, CXCL1, CCL11, CCL24, KITLG, CCL5, CXCL5, CCR3, CCL26, PRG2, EPX, RNASE2, RNASE3, IL5RA, GATA1, SIGLEC8, PRG3, CMA1, TPSAB1, MS4A2, CPA3, IL4, IL5, IL13, SIGLEC8, MPO, ELANE, PRTN3, CTSG, IL10, VEGFA, TGFB1, IDO1, PTGES, MRC1, CSF1, LRP1, ARG1, PTGS1, MSR1, CD163, CSF1R, IL4, IL5, IL13, IL10, IL25, GATA3, IL10, TGFB1, TGFB2, TGFB3, IL22, MIF, CFD, CFI, CD55, CD46, and CR1;

the fibroblasts group;

LGALS1, COL1A1, COL1A2, COL4A1, COL5A1, TGFB1, TGFB2, TGFB3, ACTA2, FGF2, FAP, LRP1, CD248, COL6A1, COL6A2, and COL6A3; and

the angiogenesis group;

VEGFA, VEGFB, VEGFC, PDGFC, CXCL8, CXCR2, FLT1, PIGF, CXCL5, KDR, ANGPT1, ANGPT2, TEK, VWF, CDH5, NOS3, KDR, VCAM1, MMRN1, LDHA, HIF1A, EPAS1, CA9, SPP1, LOX, SLC2A1, and LAMP3.

7. The system of claim 6, wherein determining the MF portrait comprises:

determining the gene group expression level for the anti-tumor immune microenvironment group using the gene expression level obtained from the RNA sequence data for at least three genes in the anti-tumor immune microenvironment group;

determining the gene group expression level for the tumor-promoting immune microenvironment group using the gene expression level obtained from the RNA sequence data for at least three genes in the tumor-promoting immune microenvironment group;

determining the gene group expression level for the fibroblasts group using the gene expression level obtained from the RNA sequence data for at least three genes in the fibroblasts group; and

determining the gene group expression level for the angiogenesis group using the gene expression level obtained from the RNA sequence data for at least three genes in the angiogenesis group.

8. The system of claim 1, wherein obtaining the RNA expression data is performed using whole transcriptome sequencing or mRNA sequencing.

9. The system of claim 1, wherein each of the biological samples is from a tumor or tissue known or suspected of having cancerous cells.

10. The system of claim 1, wherein the WES data is used to quantify tumor burden (purity), identify specific mutations, and/or to calculate the number of neoantigens.