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