Download - Пятницкий М.А. Подбор персонализированной противоопухолевой терапии путем системно-биологического
Подбор персонализированной
противоопухолевойтерапиипутем
- системно биологического NGS-анализа данных
Михаил Пятницкий Personal Biomedicine
RCRC FBB MSU
Персонализированнаяонкология• Злокачественные опухоли – генетическое заболевание
• Каждая опухоль уникальна
• Нет универсального лекарства, часто резистентность
• Второго шанса в выборе терапии может не быть
• Что есть сейчас: панели отдельных генов
• Нужен системный подход (pathways)
• Our goal - integration of “omics” data in order to identify molecular mechanism/drugs sensitivity of individual tumor.• Рациональный подход к выбору терапии основанный на
индивидуальной модели онкогенеза• Исключить заведомо неэффективную терапию
Hepatocellular Carcinoma (HCC)
• the 6th most common malignancy worldwide & the 3rd cause of cancer related death
• 5 year survival rate is approximately 6.9%
• Treatment: surgical resection, transplantation, percutaneous ethanol injection, radiofrequency ablation, cryotherapy, chemotherapy, radiotherapy
Increased incidence of hepatocellular carcinoma in the world
General workflow
Non-tumor tissue Tumor
DNA
HCC resection material
RNA
Exome Transcriptome TranscriptomeExome
New-generation sequencing
Data integration and analysis
Genetic changes driving HCC developmentPossible pharmaceutical interventions
DNA RNA
• Sequencing and bioinformatics• Combination of several best known practices
• Functional annotation of variants• Genomic an protein annotations, functional impact
predictions, cancer, tissue specificity, pharmacogenomics• Extensive collection of >3500 pathways (signalling,
cancer-specific, drug metabolism)• Biological data integration (systems biology)• Geneset enrichment using comprehensive pathways
collection• Regulatory modules (key expression regulators)
• Expert data analysis, hypothesis generation• Molecular mechanisms elucidation – personal pathways• Therapy strategy evaluation
Sequencing and bioinformatics pipeline
Data integration, regulatory modules
• Predict regulatory entities, implicated in tumor progression from transcriptome data (Subnetwork Enrichment Analysis)
• Unite found regulators into clusters
clustering
Mutated geneRegulators of gene expression
DE genes
Aim: establish 3-layer cascade from cause to effect: mutated gene regulator differentially expressed gene
Output: explanation of observed expression changes (possible molecular mechanism)
Data integration, pathway enrichmentPathway name Types of performed analyses Affected genes
EGFR pathway in Hepatocellular Carcinoma
Differentially expressed genesEGF, ERBB2, SPP1, LPL, ABCC3, PDGFA, ERRFI1, TM4SF5, SULF1, NRG3, TAT
Genes with non-synonymous mutations ABCB1, MET, ABCG2, PCK1
Potential cancer driversABCB1, MET, PCK1
TGFB1-TGFBR1 Expression Targets
Top-20 most significant key expression regulators
MYBL2
Potential cancer drivers MET, COL1A2
Differentially expressed genesEDN1, BAX, HSPA1A, PLAU, LIF, SPP1, HAMP, IL18, FOXP3, COL1A2, LAMA3
Sorafenib pharmacodynamics [PharmGKB]
Top-20 most significant key expression regulators
VEGFR2
Potential cancer drivers VEGFR2
Genes with non-synonymous mutations VEGFR2
Differentially expressed genes PDGFRB, PIK3C2B
Hypothesis: for op2 recommend sorafenib as a drug inhibiting
VEGFR2
Expert data analysis − molecular models
• Manual curation of top prioritized and categorized• Somatic SNV, CNVs, indels• Germline events• Fusions, alternative isoforms• Differentially expressed genes • Regulatory modules• Enriched pathways
Output: set of biological hypotheses for further evaluation
Expert data analysis − therapy evaluation
• Manually curated database of variant-drug relationships• Biomarkers of sensitivity to drug therapy via literature
reviews, public databases, • Comparison of transcriptome profile to the publicly
available data on screening cancer cell lines against various drugs.• Experts propose possible pharmacological intervention
on the base of elucidated molecular models
Sample patient report. Overview
insensitivity to antigrowth signals
self sufficiency in growth signals
tissue invasion and metastasis
genome instability and mutation
evading apoptosis
sustained angiogenesis
evading immune detection
tumor promoting inflammation
reprogramming energy metabolism
Основной Основной Основной
Somatic mutations, hallmarks of cancer, drugs
Cancer Drugbank targetDrugbank targetNo drug annotationsPGX drug annotation, Drugbank targetDruggable (HCC clinical trial), Cancer Drugbank targetDruggable (HCC clinical trial)
NRAS
Somatic GermlineClassical
mutation G12V- anti-EGFR treatment
is not recommended
Sorafenib- clinical trial IDH1 (R132C), SUFU, TNC,
KRT8, NOTCH3, FCGBP, (V3994A), PLEC,…
Molecular models. Examples.
Somatic functional point mutations: RET, NRAS, MMP9, CCNA1
Key regulators of transcription
DE genes
Key regulators of transcription
Notch signaling• Somatic mutation-Notch1, Notch3• Significant regulator of transcription – Notch1• SOX9 TF downstream of Notch1 (DE, upregulated, significant regulator)
Sorafenib action
Somatic mutations-driven therapy hypothesis• Direct drugs-related somatic evidences (missense, nonsense)
Mutated gene Possible interventions Therapy type Hallmarks of cancer
RETSorafenib, Sunitinib, Vandetanib,, Cabozantinib, Regorafenib, Ponatinib Multi-targeted kinase inhibitor
insensitivity to antigrowth signals, self sufficiency in growth signals
RRM1Gemcitabine, Cladribine, Clofarabine, Fludarabine, Hydroxyurea Antineoplastic chemotherapy -
BRCA1
Carboplatin, Oxaliplatin, Cisplatin, Veliparib, Rucaparib, E7449, AZD2281, Olaparib
Platinum based chemotherapy; Poly(ADP-ribose) polymerase (PARP) -1 and -2 inhibitor genome instability and mutation
XDH Aldesleukin, Allopurinol, CisplatinAntineoplastic chemotherapy; Platinum based chemotherapy -
FLT4Sorafenib, Sunitinib, Pazopanib, Regorafenib Multi-targeted kinase inhibitor
evading apoptosis, self sufficiency in growth signals
HDAC6 Vorinostat Histone deacetylase inhibitor self sufficiency in growth signals
SULT1C4 Docetaxel, ThalidomideAnti-angiogenic and anti-mitotic chemotherapy -
BCL6Sorafenib, Sunitinib, Vandetanib, Cabozantinib, Regorafenib Multi-targeted kinase inhibitor
evading immune detection, insensitivity to antigrowth signals, tumor promoting inflammation, evading immune detection, insensitivity to antigrowth signals, tumor promoting inflammation
Therapy hypothesis on the base of closest transcriptome profile
• Source: Genomics of Drug Sensitivity in Cancer database (1200 cell lines, 130 drugs).• Gemcitabine - 2 cell lines among top 5 closest ones are
sensitive with min ln(IC50) = -9.8799• Docetaxel – 1 cell line among top 5 closest ones are sensitive
with ln(IC50)=-6.4776
Compound Additional compound
Target signaling pathway/molecular
target
Clinical/Trial Phase
Gemcitabine Docetaxel ATM,ATR,Chk1,Chk2 III
Therapy hypothesis summaryDirect evidences: somatic mutations + transcriptional evidences+ Sorafenib + Gemcitabine– EGFR inhibitors (cetuximab etc.) could be ineffective
Other drug-related evidences with prioritization:* Indels, CNVs, germline events, * Pathway-based analysis
Further experimental validation is needed for each patient.
Information should be used by physicians only!
Conclusion – main project features
• Approach to the integration of exome and transcriptome NGS data from individual patient – self-consistency• Unique data and algorithms for identification of
important molecular mechanism of tumor progression• Unique data and approaches for identification of
potentially beneficial pharmacological interventions• Individual approach – the strategy of biomedical
consulting, always updated information
Each patient has his own story!
Project team
Personalized Biomedicine• Ekaterina Kotelnikova• Mikhail Pyatnitskiy• Nikolai Mugue• Dmitriy Vinogradov• Olga Kremenetskaya• Anna Makarova
Faculty of Bioengineering and Bioinformatics, MSU• Elena Nabieva• Maria Logacheva• Anna Klepikova• Alexey Penin • Alexey Kondrashov
Blokhin Cancer Research Center, RAMS
Daria Shavochkina, Kristina Yurenko, Evgeniy Chuchuev , Ekaterina Moroz , Yuri Patyutko, Natalia Lazarevich