Systems biology aims to understand biological processes through modeling dynamic networks representing interactions between components. It analyzes multi-omics data from projects like TCGA using bioinformatics tools. TCGA collected genomic data from thousands of cancer patients across 20 tumor types to identify common pathways. Combinatorial Adaptive Resistance Therapy aims to target upregulated survival pathways and downregulated cell death pathways that cause adaptive resistance to targeted agents through rational drug combinations. Future strategies will use deep profiling of patients to define molecularly targeted drug cocktails and adapt therapies based on longitudinal molecular monitoring.

1. 시스템 생물학
단국의대 제일병원
산부인과 종양과
이인호

2. Contents
• 시스템 생물학이란
• 분석과정의 실제
• TCGA (The Cancer Genome Atlas)
• CART (Combinatorial Adaptive Resistance
Therapy)
• In the future

3. 시스템 생물학
(Systems Biology)

4. Introduction to Systems Biology
• First introduced in
1934,
• By Austrian biologist
Ludwig von
Bertalanffy,
• He applied the general
system theory to
biology.

5. Introduction to Systems Biology
• To fully understand the
functioning of cellular
processes, whole cells,
organisms, and even
organisms:
– it is not enough to simply
assign functions to
individual genes, proteins,
and other cellular
organisms,
– we need an integrated way
to look at the dynamic
networks representing the
interactions of components.

6. Introduction to Systems Biology
• What is a System:
– dynamics of its components,
– interaction of components,
– we need modeling to understand the mechanism.

7. Genomics, Proteomics & Systems
Biology
1990 1995 2000 2005 2010 2015 2020
Genomics
Proteomics
Systems Biology

8. Two ways of looking a problem
• Top down or bottom up
– Either look at the whole
organism and abstract large
portions of it
– Or try to understand each small
piece and then after
understanding every small
piece assemble into the whole
– Both are used, valid and
complement each other

9. Introduction to Systems Biology

10. So how can we meaningfully integrate
the data?

11. Systems Biology
•Integrative systems biology
Extracting biological knowledge from the
‘omics through integration
•Predictive systems biology
Predicting future of biosystem using ‘omics
knowledge, e.g. in-silico biosystems
Davidov, E.; Clish, C. B.; Oresic, M.; Zhang, X; et al. Omics: A Journal of Integrative Biology. 2004, 8, 267-288.
Clish, C. B.; Davidov, E.; Oresic, M.; Zhang, X; et al. Omics: A Journal of Integrative Biology. 2004, 8, 3-13.
is a field in biology aiming at systems level understanding of biological
processes, where a bunch of parts that are connected to one another and
work together. It attempts to create predictive models of cells, organs,
biochemical processes and complete organisms.

12. sdsddddddddddddddddddddddddd

18. Bioinformatics Tools
–Database & searching
–Computational algorithms
• Alignment
• Similarity
• Clustering
• Pattern Searching
–Structure predictions
–Statistical methods
–Data visualization

19. Programs to be required
• R program / BRB
array tools
: 분석프로그램
• Cluster 3.0
: clustering
• TreeView
• CorelDRAW
: heatmap 작성

20. R program
(http://www.r-project.org/)

21. R의 특징과 장점
• 통계 계산과 그래픽을 위한
프로그래밍 언어이자
소프트웨어 환경이다.
• 뉴질랜드 오클랜드 대학의 로스
이하카와 로버트 젠틀맨에 의해
시작되어 현재는 R 코어 팀이
개발하고 있다.
• R은 통계 소프트웨어 개발과
자료 분석에 널리 사용되고
있으며, 패키지 개발이
용이하여 통계학자들 사이에서
통계 소프트웨어 개발에 많이
쓰이고 있다.
(1) 사용료를 지불하지 않는 공개용
무료 통계 패키지이며, 프로그래밍
언어이다.
(2) 사용자의 요구에 맞추어 새로운
함수를 쉽게 생성할 수 있다.
(3) 최신 통계 기법에 의한 자료의
처리와 분석이 용이하다.
(4) "Help" 기능이 잘 갖추어 있으며,
예제를 따라하면서 쉽게 배울 수
있다.

22. 분석과정
• Normalization
• Global normalization was used to median the center the log-ratios on each array in
order to adjust for differences in labeling intensitites of the Cy3 and Cy5 dyes.
• Lowess intensity dependent normalization was used to adjust for differences in
labeling intensities of the Cy3 and Cy5 dyes. The adjusting factor varied over
intensity levels.
• Filtering
• Genes showing minimal variation across the set of arrays were excluded from the
analysis.
• Genes whose expression differed by at least 1.5 fold from the median in at least
20% of the arrays were retained.
• Class Comparison
• Genes that were differentially expressed among the two classes using a random-
variance t-test.

23. 분석과정
• Gene Ontology Analysis
• Sample paragraph #1:
• gene ontology (GO) groups of genes whose expression was
differentially regulated among the classes.
• Sample paragraph #2:
• The evaluation of which Gene Ontology classes are differentially
expressed between pre and post treatment samples was performed
using a functional class scoring analysis
• Survival Analysis
• Genes whose expression was significantly related to survival of the
patient.

24. 분석과정
• Class Prediction
• Develop models for utilizing gene expression profile to predict the class of future
samples.
• Binary Tree Prediction
• Developed a binary tree classifier for utilizing gene expression profile to predict
the class of future samples.
• Sample Cluster Reproducibility and Significance
• Hierarchical clustering to cluster the samples and used the R (reproducibility)
measure and the D (discrepancy) measure described in (11) to evaluate the
robustness of the clusters.

25. 분석과정의 실례
(R program)

27. Clinical and pathological features of
patients with ovarian cancer

28. Schematic overview of the strategy
used for constructing the prediction models
and evaluating predicted outcomes
based on gene expression signatures.

29. Data download (training set : cell lines)

30. Import data

31. Class comparison

32. Clustering

33. Data download (test set : cohorts)

34. Merge and import data

35. Class prediction (1)

36. Class prediction (2)

37. Survival analysis

38. Kaplan-Meier plots of overall survival

39. Cox proportional hazard regression
analyses of overall survival

40. TCGA
(The Cancer Genome Atlas)

41. TCGA (The Cancer Genome Atlas )
• 20 tumor types
• 500 tissues/tumor
• All genomic data
– mRNA
expression
– miRNA
expression
– DNA copy
number
– Methylation data
– Mutations
– Proteomics
– Whole Genome
Seq

42. Flow of data in TCGA
Tissue Source Site
Genome Characterization Center
Genome Sequencing Center
Sequence Read Archive
Biospecimen Core Resource
Data Coordinating Center
Genome Data Analysis Center

43. Data coordination for the Pan-Cancer
TCGA project
• Data were collected by the biospecimen
collection resource (BCR) from 12
different tumour types, characterized on
six major platforms by the genome
characterization and sequencing centers
(GCC/GSC). Datasets are deposited into
the TCGA data coordination center (DCC)
from which it is then distributed to the
Broad Institute's Firehose and Memorial
Sloan Kettering Cancer Center's cBioPortal
for various automated processing
pipelines. Analysis working groups (AWG)
conduct focused analyses on individual
tumour types. Results from the DCC,
Firehose, and AWGs were collected and
stored in Sage Bionetworks’ Synapse
system to create a “data freeze.” Genome
data analysis centers (GDACs) accessed
and deposited both data and results
through Synapse to coordinate distributed
analyses.

44. Integrated data set for the comparison
and contrast of multiple tumour types
• The Pan-Cancer project assembled data from
thousands of patients with primary tumours
occurring in different sites of the body covering
twelve tumour types including glioblastoma
multiform (GBM), lymphoblastic acute myeloid
leukemia (LAML), head and neck squamous
carcinoma (HNSC), lung adenocarcinoma (LUAD),
lung squamous carcinoma (LUSC), breast carcinoma
(BRCA), kidney renal clear cell carcinoma (KIRC),
ovarian carcinoma (OV), bladder carcinoma (BLCA),
colon adenocarcinoma (COAD), uterine cervical and
endometrial carcinoma (UCEC), and rectal
adenocarcinoma (READ).
• Six platforms of omics characterizations were
performed creating a “data stack” in which data
elements across the platforms are linked by the fact
that tissue material from the same samples were
assayed, thus maximizing the potential of
integrative analysis.
• Use of the data enables the identification of
general trends including common pathways (lower
panel) revealing master regulatory hubs activated
(red) or deactivated (blue) across different tissue
types.

45. A number of major questions in cancer biology that go
beyond the single-tumour
perspective are being addressed in the collection of
Pan-Cancer manuscripts.
• Can increases in statistical power help new driver mutations be
distinguished from the background of passenger mutations as the sample
size is increased by aggregating the 12 tumour types together?
• What tissue associations underlie the major genomic structural changes in
cancer?
• What pathways emerge as critical and potentially actionable when all
mutational events across many tissues are considered together?
• Can the increase in numbers of samples enhance the analysis of co-
occurrence and mutual exclusivity of gene aberrations and improve our
ability to distinguish drivers from passengers?
• Can molecular subtypes be delineated to disentangle tissue-specific from
tissue independent components of disease?
• Which events actionable in one tumour lineage are also actionable in
another tumour lineage, potentially increasing the range of indications for
specific targeted therapeutics?

46. The data “freeze” used by the Pan-Cancer
project
• aRPPA: Reverse-phase protein arrays measuring protein and phosphoprotein abundance.
• bDNA Methylation, DNA methylation at CpG islands.
• cCopy Number. Microarray-based measurement of copy number.
• dMutation. Samples subjected to whole-exome sequencing to determine single nucleotide and structural variants.
• emiRNA. Sequencing of microRNA.
• fExpression. RNA Sequencing and microarray gene expression.

47. ConsensusClusterPlus

49. Pathological Disease Types, Rows, and Their Relationship to the
13 Integrated Subtypes Defined by the Cluster-of-
Cluster-Assignments Method

51. Genomic Determinants of the C2-
Squamous-like COCA Subtype

52. Divergence of the Bladder Cancer Samples
across Multiple COCA Subtypes

53. Comparison of Molecular Characteristics of C2-Squamous-like, C4-
BRCA/Basal, and C9-OV Subtypes Reveals Differences in TP63
and TP53 Signaling

54. Goal
♦ Understand heterogeneity of cancer
● Different progression rate
● Different response to therapy
● Different recurrence rate
♦ Identify subtypes of cancer
● Systems-level characterization of ’omic-scale data
(gene expression, CGH, proteomics, & epigenomics, etc)
● Integration of ’omic-scale data
♦ Discover clinical relevance of subtypes
● Prognosis
● Response to certain treatment
♦ Bench to bedside
● Develop scoring system (i.e., RISK SCORE) to stratify
patients
● Rationalized clinical trial
● Personalized therapy
♦ Identify druggable targets

55. Publications

56. Rational Drug Combination with
DNA Damage Agents

57. Mechanism of PARP action
• DNA-damaging agents, such as
chemotherapeutics, radiation, or
replication errors, activate PARP,
resulting in poly(ADP-ribose)–
branched chains attached to
DNA, recruiting associated repair
proteins and cell cycle checkpoint
mediators.
• This cascade may lead to cell
cycle arrest while the cell
commits to either DNA repair or
apoptosis.
• Overactivation of PARP will lead
to NAD+ depletion and necrotic
cell death.
• PARP inhibition is thought to
impair DNA repair function,
leading to cellular dysfunction
and death, and may also affect
other PARP mediated DNA
modulating effects.
Ratnam et al, Clin Cancer Research, 2007

58. Synthetic lethality in tumors
from BRCA1 and BRCA2 mutation carriers
treated with PARP inhibitors.

59. Clinic data of PARPi
• modest clinical success as monotherapy, with
greatest impact in patients with BRCA1/2
germline mutations.
• in recurrent BRCA mutant ovarian cancer
achieved objective response rates of 41% and
26%, respectively;
• however, responses were unfortunately
transient.
• maintenance therapy in patients who achieve
a complete response (CR) to platin-based
therapy demonstrated a marked increase in
progression free survival; however, this was
not accompanied by improvement in overall
survival.
• not all individuals with defective HR respond
to PARPi and, not all patients who benefit
from PARPi have demonstrable aberrations in
HR
• This creates an urgent gap in knowledge in
elucidating the etiology of preexisting,
adaptive or acquired resistance to PARPi.
Thus, development of rational combination
therapies with PARPi that increase activity in
HR incompetent tumors and render HR
competent tumors responsive to PARPi
represents the second key goal of this
proposal.

60. No way to predict
• Although PARPi monotherapy induces
impressive responses in a subset of BRCA
mutant ovarian cancers, the response
duration varies markedly
• which patients will demonstrate benefit with
high fidelity
• which patients will have a sustained response
• which patients will progress after an initial
objective response

61. Combinatorial Adaptive Resistance
Therapy (CART)
• The CART paradigm predicts
that upregulated cell
survival and proliferation
pathways and
downregulated cell death
pathways are potential
adaptive resistance
mechanisms decreasing
efficacy of the targeted
agent.
• Upregulated survival and
DDR pathways will be
targeted in a combinatorial
fashion with the original
drug.

62. Figure7RationalStrategyforCombination
Therapy

63. Figure5SchematicofOlaparibandPI3K
inhibitorcombinationtrial

64. Evolutionary model of clonal heterogeneity
• Darwinian evolution of a heterogeneous tumor
in response to selection pressure from drug
intervention is shown. Each circle represents a
cell; g1–g3 are three cell generations. At the
time of administration of the first-line drug
(Drug 1), there are four discrete populations
with distinct genomic changes, such as somatic
mutations (represented by colored squares).
Only two of the populations survive Drug 1,
presumably due to advantages conferred by
mutations. These surviving populations
constitute the majority of the tumor (g2), which
is now resistant to Drug 1. The majority of cells
in g2 acquire new mutations as represented by
the light blue, dark blue and green squares.
Selective pressure from a second-line treatment
(Drug 2) results in a third generation (g3) that is
multi-drug resistant. Evolutionary models based
on population genetics can be used to
mathematically represent this process. Such
models can be used to assess potential
outcomes of hypothetical drug combinations or
different dosing schedules in silico.

65. The evolution of strategies and technologies for
evaluating drug combinations.
• The near future will see
the advent of cocktails
of molecularly targeted
combinations that are
rationally defined based
on deep profiling of the
patient and adapted in
response to longitudinal
molecular follow-up.
The syringe symbol
indicates cytotoxic
chemotherapy and the
target symbol indicates
molecularly targeted
therapy.

66. Central Dogma: DNA -> RNA -> Protein
Protein
RNA
DNA
transcription
translation
CCTGAGCCAACTATTGATGAA
PEPTIDE
CCUGAGCCAACUAUUGAUGAA

67. Why it’s useful
• All of the information needed to build
an organism is contained in its DNA. If
we could understand it, we would
know how life works.
– Preventing and curing diseases like
cancer (which is caused by mutations
in DNA) and inherited diseases.
– Curing infectious diseases (everything
from AIDS and malaria to the common
cold). If we understand how a
microorganism works, we can figure
out how to block it.
– Understanding genetic and
evolutionary relationships between
species
– Understanding genetic relationships
between humans. Projects exist to
understand human genetic diversity.
Also, sequencing the Neanderthal
genome.

68. DNA Sequencing

69. 경청해 주셔서 감사합니다.