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

• US 10,650,911 B2
• Filed: 06/12/2018
• Issued: 05/12/2020
• 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
• Primary Examiner(s): Brusca, John S
• Application Number:
  US16/006,572
• Publication Number:
  US 20180357377A1
• Time in Patent Office: 700 Days
• Field of Search: None
• 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 method, comprising:

obtaining 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 RNA expression data, a gene group expression level for each gene group in a set of gene groups, the set of gene groups comprising;

at least one first gene group associated with cancer malignancy and comprising a tumor properties group comprising at least ten genes selected from the list consisting of;

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; and

at least one second gene group associated with cancer microenvironment and comprising;

a tumor-promoting immune microenvironment group comprising at least ten genes selected from the list consisting of;

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, IDOL 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,

an anti-tumor immune microenvironment group comprising at least ten genes selected from the list consisting of;

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,

an angiogenesis group comprising at least ten genes selected from the list consisting of;

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 LAMPS, and

a fibroblasts group comprising at least ten genes selected from the list consisting of;

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

determining a first set of one or more visual characteristics for a first set of one or more graphical user interface (GUI) elements using one or more gene expression levels determined for the at least one first gene group;

determining a second set of one or more visual characteristics for a second set of GUI elements using one or more gene expression levels determined for the at least one second gene group, the second set of GUI elements including a first GUI element for the tumor-promoting immune microenvironment group, a second GUI element for the anti-tumor immune microenvironment group, a third GUI element for the angiogenesis group, and a fourth GUI element for the for fibroblasts group, the determining comprising;

determining a first size for the first GUI element using one or more gene expression levels determined for the tumor-promoting immune microenvironment group;

determining a second size for the second GUI element using one or more gene expression levels determined for the anti-tumor immune microenvironment group;

determining a third size for the third GUI element using one or more gene expression levels determined for the angiogenesis microenvironment group;

determining a fourth size for the fourth GUI element using one or more gene expression levels determined for the fibroblasts group;

determining a respective color for each of the first, second, third, and fourth GUI elements, to obtain first, second, third, and fourth colors, based on whether the corresponding gene group, in the at least one second gene group, represents an anti-tumor process or a pro-tumor process;

generating a GUI personalized to the subject, the generating comprising;

generating a first GUI portion associated with cancer malignancy and containing the first set 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 set of GUI elements having the determined second set of visual characteristics, the generating comprising;

generating the first GUI element in the second GUI portion to have the first size and the first color;

generating the second GUI element in the second GUI portion to have the second size and the second color;

generating the third GUI element in the second GUI portion to have the third size and the third color;

generating the fourth GUI element in the second GUI portion to have the first size and the fourth color;

presenting the generated GUI to a user;

identifying one or more anti-cancer therapies using the determined MF profile for the subject; and

administering to the subject the one or more anti-cancer therapies identified using the determined MF profile for the subject.

2. The method of claim 1, wherein obtaining the RNA expression data comprises performing whole transcriptome sequencing or mRNA sequencing.

3. The method of claim 1, wherein the biological sample is from a tumor or tissue known or suspected of having cancerous cells.

4. The method of claim 1, further comprising:

obtaining whole exome

sequencing (WES) data, and

based on the WES data, quantifying tumor burden (purity), identifying specific mutations, calculating the number of neoantigens, or any combination thereof.

5. The method of claim 1, further comprising:

identifying a plurality of candidate anti-cancer therapies using the MF profile for the subject.

6. The method of claim 5, further comprising:

presenting information about at least some of the plurality of candidate anti-cancer therapies to the user via the GUI.

7. The method of claim 1, wherein the one or more anti-cancer therapies comprise an anti-cancer therapeutic agent.

8. The method of claim 1, wherein the anti-cancer therapeutic agent is a small molecule, a polynucleotide, an expression vector, a subgenomic polynucleotide, a polypeptide, a peptide, a protein, a vector, or a eukaryotic cell.

9. The method of claim 1, wherein the one or more anti-cancer therapies comprise a chemotherapy, one or more antibody drug conjugates, an hormonal therapy, a viral therapy, a genetic therapy, a non-immune protein therapy, one or more antiangiogenic agents, one or more anti-cancer vaccines, a radiotherapy, a soluble receptor therapy, one or more cell-based therapies, an immunotherapy, or a targeted therapy.

10. The method of claim 7, wherein the anti-cancer therapeutic agent is selected from the group consisting of:

HGFR inhibitors, EGFR inhibitors, VEGF inhibitors, PDGF inhibitors, CXR2 inhibitors, CXCR4 inhibitors, DPP-4 inhibitors, galectin inhibitors, antifibrotic agents, LPR1 inhibitors, TGF-beta inhibitors, IL5 inhibitors, IL4 inhibitors, IL13 inhibitors, IL22 inhibitors, CSF1R inhibitors, IDO inhibitors, LPR1 inhibitors, CD25 inhibitors, GITR inhibitors, PD1 inhibitors, CTLA1 inhibitors, PDL1 inhibitors, LAG3 inhibitors, TIM3 inhibitors, vaccines, PRIMA-1 analogues, CD40 agonists, ICOS agonists, OX40 agonists, Bcl-2 inhibitors, AKT inhibitors, MYC-targeting siRNA, pan-tyrosine kinase inhibitors, CDK4/6 inhibitors, Aurora A inhibitors, vaccines, LAG3 inhibitors, and any antibody-drug conjugate.

11. The method of claim 7, wherein the anti-cancer therapeutic agent is administered orally, parenterally, by inhalation spray, topically, rectally, nasally, buccally, vaginally, or via an implanted reservoir.