The document discusses developing a personalized cancer treatment approach through integrating multi-omics data from next-generation sequencing to identify molecular mechanisms and potential drug sensitivities of individual tumors. It presents a workflow that sequences tumor and non-tumor tissue, analyzes the exome, transcriptome and other omics data using bioinformatics, and integrates the data to generate hypotheses about a patient's cancer development and possible targeted pharmaceutical interventions. The goal is to rationally select therapies based on an individual tumor's oncogenesis model and avoid ineffective treatments.

1. Подбор персонализированной
противоопухолевой терапии путем
системно-биологического анализа
NGS-данных
Михаил Пятницкий
Personal Biomedicine
RCRC FBB MSU

2. Персонализированная онкология
• 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

3. 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

4. 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 development
Possible pharmaceutical interventions
DNA RNA

5. • 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

6. Data integration, regulatory modules
• Predict regulatory entities,
implicated in tumor progression
from transcriptome data
(Subnetwork Enrichment
Analysis)
• Unite found regulators into
clusters
Mutated gene
Regulators 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)

7. Data integration, pathway enrichment
Pathway name Types of performed analyses Affected genes
EGFR pathway in
Hepatocellular
Carcinoma
Differentially expressed genes
EGF, ERBB2, SPP1, LPL, ABCC3, PDGFA,
ERRFI1, TM4SF5, SULF1, NRG3, TAT
Genes with non-synonymous mutations ABCB1, MET, ABCG2, PCK1
Potential cancer drivers
ABCB1, MET, PCK1
TGFB1-TGFBR1
Expression Targets
Top-20 most significant key expression
regulators
MYBL2
Potential cancer drivers MET, COL1A2
Differentially expressed genes
EDN1, 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

8. 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

9. 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

10. Sample patient report. Overview
ОсновнойОсновнойОсновнойОсновнойОсновнойОсновнойОсновнойОсновнойОсновной
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
Somatic mutations, hallmarks of cancer, drugs
Cancer Drugbank target
Drugbank target
No drug annotations
PGX drug annotation, Drugbank target
Druggable (HCC clinical trial), Cancer
Drugbank target
Druggable (HCC clinical trial)
NRAS
Somatic Germline
Classical
mutation G12V-
anti-EGFR
treatment
is not
recommended
Sorafenib-
clinical trial
IDH1 (R132C), SUFU, TNC,
KRT8, NOTCH3, FCGBP,
(V3994A), PLEC,…

11. 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

12. Somatic mutations-driven therapy
hypothesis
• Direct drugs-related somatic evidences (missense, nonsense)
Mutated gene Possible interventions Therapy type Hallmarks of cancer
RET
Sorafenib, Sunitinib, Vandetanib,,
Cabozantinib, Regorafenib, Ponatinib Multi-targeted kinase inhibitor
insensitivity to antigrowth signals, self
sufficiency in growth signals
RRM1
Gemcitabine, 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, Cisplatin
Antineoplastic chemotherapy;
Platinum based chemotherapy -
FLT4
Sorafenib, 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, Thalidomide
Anti-angiogenic and anti-mitotic
chemotherapy -
BCL6
Sorafenib, 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

13. 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

14. Therapy hypothesis summary
Direct 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!

15. 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!

16. 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