Tumor Progression from an Evolutionary and Ecological Perspective

Transcription

1 Tumor Progression from an Evolutionary and Ecological Perspective José Costa, M.D. Department of Pathology, Yale School of Medicine

2 I. Liminary Remarks II. III. IV. The Standard Models of Tumor Progression The Montebello Model of Tumor Progression Novel Lines of Inquiry Generated by the MM V. Practical Implications

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4 The Laws of Tumor Progression Utility: Prognostication Prediction Caveat: Things do not happen on account of the laws

5 Laws Come from Capacities clinic Observation experiments in silico Capacity: a property capable of causing an effect on something else

6 A Model A fixed enough arrangement of components, with stable enough capacities, that, in the right sort of environment, will, with repeated operation, give the kind of regular behavior that we represent in the scientific laws.

7 The Standard Models of Tumor Progression 1. Foulds (1949) 2. Nowell (1976) 3. Fearon & Vogelstein (1992)

8 Foulds General Principles of Tumor Progression Rule I: Rule II: Rule III: Rule IV: Rule V: Rule VI: Independent progression of multiple tumors. Progression occurs independently in different tumors in the same animal. Independent progression of characters. Progression occurs independently in different characters in the same tumor. Progression is independent of growth. Progression occurs in latent tumor cells whose growth is arrested. Two notable corollaries of Rule III are: (i) At its first clinical manifestation a tumor may be at any stage of progression (ii) Progression is independent of the size or clinical duration of a tumor. Progression is continuous or discontinuous by gradual change or by abrupt steps. Progressions follows one of alternative paths of development. Progression does not always reach an end-point within the lifetime of the host.

9 Nowell: Clonal Evolution Underlies Progression

10 Fearon & Vogelstein: Accumulation of Mutations Drives Progression

11 The Central Role of Mutation in Cancer Models Mutation is rate limiting. Mutator phenotype generates variation. Accumulation of mutation underlies progression, culminating in metastasis.

12 Montebello Model

13 Carcinogenesis is a microevolutionary process best described in the context of metapopulation dynamics Montebello Model of Tumor Formation J.Costa, Montebello 2005

14 Prevalence of Mutations in Tissues Mouse Liver (x10-5 ) (a) Neonate 3.9 Young adult 5.4 Middle age 9.4 Senescent 12.2 Human Lymphocyte (x10-6 ) (b) y %/year >age60 a. Dolle MET, et al, Nature Genetics, 17: , b. King CM, et al, Mutation Research, 316: 79-90, 1994.

15 Ki-ras (Ex 1) mut in F-344 rats (10) 15 weeks after mutagenization

16 Prevalence of K-ras Ex1 Mutation 1.5-5x10-3 Normal Crypts Bautista and Tuttle, 1999.

17 Tuttle, Ki-ras Mut s and Location of DMH Induced Colon T s in F-344 Rat

18 Molecular Cloning and Sequencing Data During Progression WT N=158/201 Val (GTT) Ser (AGT) Arg (CGT) Cys (TGT) * * * * Val 13.9% Ser 1.49% Arg 1% Cys 1% 28/201 3/201 2/201 2/201 IC 10%-18% IC 0.03%-3.7% IC 0.03%-3% IC 0.03%-3%

19 Clones Mut Act Mut Can-TML NL (9.5) 1 (4.5%) 0.43 AD (7.7) 9 (64%) 5.0 CA (21.4%) 35 (81%) 17.5 Tarafa, 2006.

20 Michor, et al, Current Biology, April 2003

21 Gonzalez, 2002.

22 87T1 TGFbRII 2 BAX 7 Gonzalez, WT / WT WT / MUT MUT / MUT

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24 The Emergence of Variation Metapopulation: Collection of subpopulations of the same species each of which occupies a separate patch of a subdivided habitat (R. Levins, 1969).

25 Metapopulation Dynamics with Disturbance Multi-species model with disturbance P i = cp(1 i D Σ i 1 P j ) e i P i Σ i 1 1 cpp i j Spatially explicit model of habitat destruction in a multi-species metapopulation model (Tilman, et al.)

26 Agent Based Model Wildtype Proliferative Multiple mutations Apoptotic defect Susceptibility Deleterious

27 Emerging Tumor Undergoing Disturbance

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31 Simulated Mutational Load Distribution (cross-section of a virtual population)

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33 Effect on Total Mutational Load by Varying Disturbance Parameters A) Disturbance per unit time B) Frequency C) Intensity

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35 Tarafa, et al., 2006.

36 Conclusions The Montebello Model of Tumor Progression 1. Shows the importance of disturbance; 2. De-emphasizes mutation and suggests that environment acts through toxicity; 3. Supports the premise that metapopulation dynamics is useful for measuring cancer risk and early detection; 4. Is a suitable tool to simulate and design clinical prevention trials.

37 Conclusions Limitations of the Montebello Model 1. Does not take tumor population structure (e.g. T. stem cells) into account. 2. Does not explicitly include epigenetic alterations.

38 Novel Lines of Inquiry 1.What is the level of selection?: genes,cells,tissue units(e.g.crypts) 2. What dimension(s) of evolution should we consider?

39 DNA methylation stabilizes chromatin states, so that they persist as an Epigenetic memory

40 Epigenetic progenitor model for the origin of cancer A.P. Feinberg, R. Ohlsson, S Henikoff, 2006, Nature Reviews, Genetics

41 Most of the genome of higher organisms consists of non-coding DNA and repetitive DNA Text Allis, Jenuwein and Reiberg, in Epigenetics, 2007

42 Genomic coverage of all categories of CpG islands using custom DNA microarrays CLASS members probed % replication subtotal CpG islands in promoters 19,545 18, ,745 promoters without islands 5,366 5, ,158 All promoter-associated 24,911 24, ,903 Unique, noncoding 43,033 41, ,237 CpGs at interspersed repeats 214, , ,935 CpGs in tandem repeats Subtotal 283, , ,084 Total, other probes/controls 377,512

43 Genome-wide DNA methylation profiles are reproducible Hypomethylation Hypermethylation Hypomethylation Hypermethylation Separate digestion, amplification, hybridization of the same DNA

44 Hypomethylation of Chymo-Serine-Protease gene in chromosome 7 Normal Tumor

45 Hypomethylated promoters are specific recurrent events in tumors tumor margin normal

46 Alterations in methylation state of genes in tumors of the head an neck Hypermethylated (subset) Incidence Hypomethylated Incidence PAX9 (homeobox-paired) 27/27 FGF receptor-like prot-1 25/27 Epithelial V-like Antigen 26/27 VE-cadherin (cadherin 5) 24/27 CCAAT-bind. transc. factor 26/27 Aryl hydrocarbon receptor 21/27 Hox-A4 transc.factor 26/27 CDC42 small effector 2 20/27 PPARGC-1 alpha 25/27 chymotrypsin serine protease 20/27 GRAM domain protein 25/27 G-protein coupled receptor 20/27 MOX-2 (growth arrest) 24/27 Ankyrin repeat protein 19/27 deleted in bladder cancer 24/27 HERV-K (C19) Rec protein 19/27

47 Hypomethylated repetitive DNA territories in an individual tumor Normal Tumor

48 Hypomethylated territories (example, chrom. 7) recur in multiple tumors tumor margin normal

49 Hypomethylated LTR s reccur upstream of promoters: Alternative promoter?

50 Hierarchical clustering using subset of best 500 methylation probes

51 Aggregate hypomethylation of 9 morphologically normal and 27 tumor tissues Copy number alterations are more prevalent in hypomethylated tumor samples

52 Variable expressivity of coat color in the absence of genetic variation. Activation of transposons also increases the incidence of diabetes and

53 Consequences for the research infrastructure Annotated QC d samples High-throughput technologies Mathematical modeling and insilico simulation Computational Biology and IT support.

54 Expanding the Goals Treat Patient Mechanistic classification ID drugable target Prediction of response. Progress in the context of Clinical Trials Prevent in a population Measure erosion of wellness ID levels of risk Interfere with disturbance Conduct prevention trials;surrogate EP.

55 Nancy Cartwright A Dappled World, Cambridge University Press, Peter Schuster Evolutionary Dynamics, J. P. Crutchfield, P. Schuster. Oxford University Press, New York, 2003 Martin Novak Crespi and Summers Evolutionary Dynamics: Exploring the Equations of Life, Harvard University Press, Evolutionary Biology of Cancer, Trends in Ecology and Evolution 20:545, Sjöblom, et al. The Consensus Coding Sequences of Human Breast and Colorectal Cancers, Science 314: 268, Feinberg, Ohlsson, Henikoff The Epigenetic Origins of Human Cancer, Nat. Rev. Gen. 7:21, 2006.

56 Dolores Bautista Debbie Dillon Isabel Gonzalez Willard Miranker Paul Lizardi Jahn Nesland Thomas Diesboeck Ricard Solé David Tuck

57 Collaborators and support Sebastian Szpakowski, Xueguang Sun, Jill Rubinstein, Jose Lage, Andrew Dyer, Michael Krauthammer, David Tuck, Perry Miller, Hongyu Zhao, Diane Kowalski, Janet Brandsma, Clarence Sasaki, Jose Costa Research supported by the Swebelius Foundation and grants from the National Cancer Institute IMAT Program

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