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肿瘤生物学与转化医学 Cancer Biology & Translation Medicine. 陈志南 Zhi-Nan Chen. 第四军医大学 znchen@fmmu.edu.cn. Global Action Against Cancer. 1 Lung. Global (Year). 7 Esopha- geal. 2 Breast. 7.6 million Deaths. 10.9 million New Cases. Cancer. 3 Colon. 6 Cervical. 5 Liver. 4 Gastric.

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slide1

肿瘤生物学与转化医学

Cancer Biology & Translation Medicine

陈志南

Zhi-Nan Chen

第四军医大学znchen@fmmu.edu.cn

slide2

Global Action Against Cancer

1

Lung

Global (Year)

7

Esopha-

geal

2

Breast

7.6 million

Deaths

10.9 million

New Cases

Cancer

3

Colon

6

Cervical

5

Liver

4

Gastric

Update Edition 2005

slide3

Cancer

Against Cancer in China

Incidence 2,000,000

Mortality 1,500,000

1

Lung

8

Nasopharyn-

geal

2

Liver

Liver Cancer

Mortality/100,000

Countryside 26.93(1st)

7

Breast

3

Gastric

City 24.41 (2nd)

Cancer

6

Cervical

4

Esopha-

geal

5

Colon

Disease Control Division,

Ministry of Health of PRC, 2008

slide4

The third time investigation of death causes:

cancer ranks second with a mortality rate of 22.32%.

——Ministry of Health, April 2008

slide5

Cancer Cell: Contributing Factors

增生

  • Inherited susceptibility
  • Chemical carcinogens
  • Radiation
  • Infectious agents
  • DNA repair system
  • General health (diet, stress, etc)
  • Immune system

异常

Cancer Etiology

Cancer: Three Characteristics

Multiple Carcinogen

slide6

Cell

Dysplasia

genomic DNA

transcription

Carcinoma

in situ

post-transcription

translation

Primary cancer

post translation

Metastasis

(2nd Cancer)

Tumor

Initiation

Development

Invasion

Metastasis

Multiple Stages

slide7

Loss of genomic integrity and imbalance of molecules are mechanism for the cancer incidence

Multiple Gene Mutation

EMBO reports 1, 2, 115-119, 2000

slide9

Breast cancer 11 Samples

Colon cancer 11 Samples

removing errors, normal variants

Sequencing: 13,023 genes

Cancer Genome Atlas

Mining the cancer genome

Cancer Molecular

Balance & Mutation

1149 mutation genes

(Individual tumors: average 90)

(Significant frequency: 189 genes)

(Significant frequency:average 11 per tumor)

Tobias Sjoblom, et al. Science 314: 268-274, 2006

slide10

Cancer Genome Atlas

Mutational evolution in a lobular breast tumour profiled at single nucleotide resolution

Sohrab P. Shah, Ryan D. Morin, Jaswinder Khattra, Leah Prentice, Trevor Pugh, Angela Burleigh, Allen Delaney, Karen Gelmon, Ryan Guliany, Janine Senz, Christian Steidl, Robert A. Holt, Steven Jones, Mark Sun, Gillian Leung, Richard Moore, Tesa Severson, Greg A. Taylor, Andrew E. Teschendorff, Kane Tse, Gulisa Turashvili, Richard Varhol, René L. Warren, Peter Watson, Yongjun Zhao, Carlos Caldas, David Huntsman, Martin Hirst, Marco A. Marra & Samuel Aparicio

Recent advances in next generation sequencing 1, 2, 3, 4 have made it possible to precisely characterize all somatic coding mutations that occur during the development and progression of individual cancers. We found 32 somatic non-synonymous coding mutations present in the metastasis. Five of the 32 mutations (in ABCB11, HAUS3, SLC24A4, SNX4 and PALB2) were prevalent in the DNA of the primary tumour removed at diagnosis 9 years earlier, six (in KIF1C, USP28, MYH8, MORC1, KIAA1468 and RNASEH2A) were present at lower frequencies (1–13%), 19 were not detected in the primary tumour, and two were undetermined.

Sohrab P. Shah, et al. Nature 461: 809-813, 2009

slide11

Primary tumor

(Cell escape)

Invasion

Travel

Intravasation

Transport

Extravasation

Migration

Growth

(2nd tumor)

Invasion & Metastasis

— Main Death Causes

slide12

Adhesion Movement

A little long cell

Need protease

Mesenchymal cell-like movement

Form of pseudopodium

Rho/ROCKsignal

A little circle cell

A little rely on

protease

Amoeba-like movement

Myosin strong

Contraction

Rac/WAVE2 signal

Cell. 2008 Oct 31; 135 (3): 510-523

slide13

R

Control of cell cycle

G1: DNA pre-synthesis

S: DNAsynthesis

G2: DNApost- synthesis

M: Cell division

GO: Oncogenes

STOP: Tumor superessor

G1→S→G2→M

slide14

Matrix Degradation —— MMPs

Matrix degradation:

Structural base of tumor invasion & metastasis

Epithelial lining cells

Transformed epithelial cells

Tumor fb

MMP-1, 2, 3, 11, 14

Tissue

Martrix

Tumor fb

Transformed epithelial cells

MMP-7, 13(9)

Epithelial cells of

tumor angiogenesis

MMP-1, 2, 14

slide15

Angiogenesis

  • Tumor>2-3mm: Need vessels
  • Key Molecular: MMPs, VEGF, bFGF, PDGF
  • Tumor vessels density is a marker for early diagnosis and prognosis

Nature Rev Cancer, 4,2004

slide16

Cancer: Wounds that fail to heal

The Chain of Inflammation & Cancer

Tumor Development

Nature, 420, 2002

slide17

Bacterial

H pylori

幽门螺杆菌

Virus

EB, HCV

Parasite

flukes, schistosomes

吸虫,血吸虫

Chronic inflammation

Cancer

Chemial irritis

PMA 佛波酯

Nondigestible Particles

asbestos, silica

石棉纤维,矽

Strong association: inflammation and cancer

slide18

DNA repair

DNA repair

P53、Rb

Oxidative Stress氧化应激

Chronic inflammation

ROS

活性氧

RNS

活性氮

Oxidized DNA nucleosides

氧化脱氧核苷酸

Peroxynitrite

过氧亚硝基

Aldehydes醛

DNA damage

DNA mutation

slide19

Inflammatory cytokines & Oncogenes

Tumor inflammation environment

Cancer Letters, 267, 2008

slide20

Signal Transduction

Blood vessel

Cytokines

Chemokines

Macrophage

CypA

CD147

Hypoxia

ROS

CypA

PI3K

Erk1/2

P38

HIF

Proliferation

Anti-apoptosis

Angiogenesis

Reported

Cancer cell

Our study

Not confirmed

the seven hallmarks of cancer and their links to tumor metabolism
The Seven Hallmarks of Cancer and Their Links to Tumor Metabolism

增生

环路

凋亡

血管

生成

免疫

逃避

侵袭转移

抵抗

抑瘤

无限

增殖

Tumor Metabolism

Cancer Cell. 13: 472-482, 2008

slide22

Intratumoral hypoxia and metabolic symbiosis

Anaerobic glycolysis

or Aerobic stromal cell

J Clin Invest. 118 (12): 3835-3837, 2008

molecular mechanisms of cancer specific metabolic reprogramming
Molecular Mechanisms of Cancer-Specific Metabolic Reprogramming

葡萄糖转运

糖原分解

乳酸产生

氧化磷酸化降低

脂类合成

氧化抑制

slide24

Cancer Biomarker

-- A Systems Approach

  • 高危评估 Risk assessment
  • 早期诊断 Noninvasive screening for early-stage disease
  • 检测定位 Detection and localization
  • 预后判断 Disease stratification and prognosis
  • 治疗反应 Response to therapy
  • 复发监测 Screening for disease recurrence

Leland H. Hartwell, et al. Nat Biotech, 24 (8), 2006

slide25

Seperation and identification of mixture sample

Ab array, Cell array, Tissue array, Co-IP (pull-down), Biosence, etc.

Comparative proteomics

2DE, 2DELC-MS

Validation peptides sequence of protein

MALDI-MS, SELDI-MS, LC-MSMS, ESI-MS (m/z)

Analysis of the databases

How Identified Cancer Biomarker?

  • Biomarker Discovery:
  • Expression Mapping (Modification mapping)
  • Functional proteomics: interaction of the proteins
  • Epitope mapping (active core)
slide26

System Biology Approaches “-omic” Technologies

(Preclinical or Clinical Utilization)

MALDI-MS/MS

2D Gels- MS

NMR

GC-MS

LC-MS

FT-IR

Microarray

SAGE

Amplichip Cyp 450 Test

Global SNP Arrays

Trends Biotechnol. 23 (11), 544-546, 2005

slide27

What is an Ideal Cancer Biomarker?

  • Screening a healthy population or a high risk population for the presence of cancer
  • Making a diagnosis of cancer or of a specific type of cancer
  • Determining the prognosis in a patient
  • Monitoring the course in a patient in remission or while receiving surgery, radiation, chemotherapy, or biotherapy
  • Screening a healthy population or a high risk population for the presence of cancer
  • Making a diagnosis of cancer or of a specific type of cancer
  • Determining the prognosis in a patient
  • Monitoring the course in a patient in remission or while receiving surgery, radiation, chemotherapy, or biotherapy
slide28

Application of Cancer Biomarker

  • Identification and diagnosis
    • Individuals affected with disease
    • People who may be at risk but do not yet exhibit symptoms
  • Monitor progress of disease
  • Monitor effects of treatment
  • Remission
  • Follow-up
    • Cancers found in early stage: low morbidity and recurrence rates
    • Cancers identified in late stage: high recurrence and mortality rates
slide29

AFP = alpha fetoprotein

CEA = carcinogenic embryonic antigen

CA 15-3 = carbohydrate antigen 15-3

CA 19-9 = carbohydrate antigen 19-9

CA 125 = carbohydrate antigen 125

PSA = free prostate specific antigen + prostate specific antigen - alpha(1)antichymotrypsin complex

PSAF = free prostate specific antigen

PSAC = prostate specific antigen - alpha(1)antichymotrypsin complex

PAP = prostatic acid phosphatase

hTG = human thyroglobulin

hCGb = human chorionic gonadotropin beta

Ferr = Ferritin

NSE = neuron specific enolase

IL-2 = interleukin 2

IL-6 = interleukin 6

A2M = alpha 2 macroglobulin

B2M = beta 2 microglobulin

Cancer Biomarker and Types of Cancer: statistically significant association between a particular cancer and the associated cancer marker (s)

slide30

Problems of Cancer Biomarker

  • No cancer biomarker is absolutely specific
  • No cancer biomarker test is free of false negatives
  • No cancer biomarker test is free of false positives
  • No cancer biomarker is absolutely specific
  • No cancer biomarker test is free of false negatives
  • No cancer biomarker test is free of false positives
slide32

This has empowered more direct means of target identification, e.g. by expanding protein databases and enabling the mapping of novel cancer-associated genes (Venter et al. 2001).

Antibody Based Cancer Biomarkers

  • Human genome is 2.91-billion base pairs in length (1990)
  • There are about 25,000 genes exist in the human genome (42% have an unknown function)
  • Approximately 12,000 genes that appear to have the capacity to make secreted proteins, all the genes have been determined the entire nucleotide sequences (3141 genes locus on the first chromosome)
  • At May 2006, the first chromosome sequences were completed, at least 1,000 new genes were found. This is the end of 16 year’s Human Genomic Plan.
slide33

Antibody Based Cancer Biomarkers

  • Complete genome sequences have provided a plethora of potential drug targets. But the hard task of finding their weak spots is just beginning (about 5000 genes can be used as drug target)
  • A challenging new development in the field of drug-target discovery is systems biology, or the recognition that genes, or better the gene products, are part of, and function, in large complex networks.

Nature, 428, 225-231, 2004.

slide35

转化医学概念

  • EA. Zerhouni,NIH路线图计划(NIH Roadmap),2003
  • 将医学生物学基础研究成果迅速有效的转化为可在临床实际应用的理论、技术、方法和药物
  • 基础研究→临床应用,实验室成果→产业化
  • 在实验室到病房(Bench to Bedside, B2B)之间架起一条快速通道
  • 双向、开放:
    • 基础研究提供新疗法、新药物
    • 临床研究者对疾病的进程和特性提供反馈意见
  • “驱动临床研究引擎的激发器”
slide36

转化医学发展现状

  • 美国已经在38所大学建立了转化医学研究中心,在2012年以前将会达到60个以上,NIH每年资助经费达5亿美元
  • 英国已投入4.5亿英镑用于转化医学研究,并启动世界上首个转化医学合作研究中心
  • 欧洲共同体为转化医学计划投入60亿欧元
  • Science Translational Medicine、Journal of Translational Medicine和Translational Research三本国际性专业杂志
slide37

中国的转化医学

  • 转化医学战略研讨会
    • 2009,中国工程院
    • 2010,中国科学院
  • 转化医学中心
    • 中南大学
    • 上海交通大学
    • 同济大学
  • 成果转化率:25%,商品化:<15%
slide38

转化医学与4P医学

Predictive Medicine – 预测医学

Preventive Medicine – 预防医学

Personalized Medicine – 个体化医学

Participate Medicine – 参与医学

slide39

Prophylactic Surgery

Chemoprevention

Screening

RISK

Lifestyle changes

Average Moderate High Very High

Personalized Prevention & Early Detection

slide40

Personalized Medicine – 个体化治疗

  • No “one size fits all” drug
  • Most drugs work for 30% to 70% of patients
  • Multiple factors determine drug responses
  • Phamacogenetics is essential for individualized therapy
slide41

Personalized Medicine – 个体化治疗

Herceptin, the first marketed personalized medicine, was approved using a coordinated drug/diagnostic approval process that will become more common.

slide42

Molecular Docking

HAb18G & Its Antibodies

Our Study

HAb18G/CD147 I Set

HAb18G/CD147& HAb18, 6H8, 5A12 Antibodies

slide43

Our Study

Crystal Structure of HAb18G/CD147

C2

I

Xiao-Ling Yu, Zhi-Nan Chen, J Biol Chem, 283 (26), 2008

National patent: 200710018514.X

PCT patent: PCT/CN2007/003034 PDB ID: 3B5H

slide44

Our Study

Tissue Atlas - HAb18G/CD147

HAb18G/CD147

slide45

Our Study

Tissue Atlas - HAb18G/CD147

HAb18G/CD147

slide46

Our Study

Tissue Atlas - HAb18G/CD147

Fetal

Normal

Cancer

Liver

Lung

Shao-Hui Hu, Zhi-Nan Chen, et al. Proteomics, 7 (13), 2007

slide47

Our Study

Tissue Atlas - HAb18G/CD147

Yu Li, Zhi-Nan Chen, et al. Histopathology, 2009

slide48

Iodine (131I) Metuximab Injection

(LICARTINTM)

Our Study

Cancer Biol Ther 5 (7), 2006

slide49

Iodine (131I) Metuximab Injection

(LICARTINTM)

Our Study

82.50%

recurrence rate

30.42↓

61.88%

57.09%

survival rate

20.62↑

26.67%

P=0.0174

P=0.0289

87.82%

Control group

Treatment group

AFP

44.08%

60 cases HCC (III, IV stage)

P=0.0016

Anti-recurrence Treatment after Liver Transplantation

Hepatology, 45 (2): 269-276, 2007

slide50

Iodine (131I) Metuximab Injection

(LICARTINTM)

Our Study

  • 四期临床(截至21010年4月累计使用1500支)
  • 北京利卡汀治疗基地,原子能研究院401医院
  • 上海利卡汀临床研究中心,东方肝胆医院
  • 广州利卡汀治疗基地,广州军区458医院
    • 北京佑安医院 解放军总医院 武警总医院
    • 北大肿瘤医院 上海中山医院 上海东方肝胆医院
    • 中山大学肿瘤医院 浙江大学一院、二院
    • 浙江省肿瘤医院 四川大学华西医院 华中附属协和医院
    • 湖南省医院 中南大学湘雅医院 福建医大第一医院
    • 四军大唐都医院 郑州大学一附院 河南省医院
    • 中国医大一附院、二附院 福建医大协和医院