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Li-Fraumeni syndrome (inhereted p53 mutation). Germ Line p53 Mutations in a Familial Syndrome of Breast Cancer, Sarcomas, and Other Neoplasms. Malkin ...
The genetics of the p53 pathway, Apoptosis and Cancer Therapy

Alexei Vazquez

Carciogenesis and Cancer Treatment Carciogenesis Normal

Environmental Factors

Treatment Cancer

Genetic background Copy number variations Single Nucleotide Polymorphisms (SNPs)?

% Survival

Somatic mutations Amplifications/deletions Epigenetic alterations Chromosomal alterations

Years

Outline

• Basic concepts – Cell, cell duplication, DNA replication – DNA replication mistakes – Human genome, germline and somatic cells, sexual reproduction – Genetic alterations

• Case studies – Genetic alterations affecting the response to cancer treatment (focus on the p53 pathway) – Finding optimal drug combinations as a minimal hitting set problem

Outline

• Basic concepts – Cell, cell duplication, DNA replication – DNA replication mistakes – Human genome, germline and somatic cells, sexual reproduction – Genetic alterations

• Case studies – Genetic alterations affecting the response to cancer treatment (focus on the p53 pathway) – Finding optimal drug combinations as a minimal hitting set problem

Cell

= DNA + machinery to replicate DNA

DNA

A

T G

C

...ACCGTAGTCGGTTTTAACGTTTTTACCCACACACAGATCGAT...

Cell duplication / DNA replication

Cell duplication / DNA replication

DNA alterations ACGTGGGTCCAAGTAGT

reference sequence

ACGTGGGTCTAAGTAGT ACGTGGGTCCAAGTAGT

mutation

CAA ACGTGGGTCGTAGT ACGTGGGTCCAAGTAGT

deletion

ACGTGGGTCCCAACAAGTAGT ACGTGGGTCCAAGTAGT

insertion

ACGTGGGTCCAAGTAGTACGTGGGTCCAAGTAGT ACGTGGGTCCAAGTAGT

duplication

Human genome Chromosome

Human Cell

Nucleus

Germline and Somatic cells

= somatic cells + ovum cells

= somatic cells + sperm cells

somatic cells + ovum cells

somatic cells + sperm cells

somatic cells + sperm cells OR somatic cells + ovum cells

somatic cells + ovum cells

somatic cells + sperm cells

somatic cells + ovum cells

somatic alteration non inheritable

somatic cells + sperm cells

somatic cells + sperm cells OR somatic cells + ovum cells

germline alteration inheritable

somatic cells + ovum cells

somatic cells + sperm cells

Note: genetics = heredity + variation

Genetic alterations / polymorphisms ACGTGGGTCCAAGTAGT

ACGTGGGTCTAAGTAGT ACGTGGGTCCAAGTAGT

mutation

Single Nucleotide Polymorphism (SNP)

CAA ACGTGGGTCGTAGT ACGTGGGTCCAAGTAGT

deletion

(CNV)

ACGTGGGTCCAAGTAGTACGTGGGTCCAAGTAGT ACGTGGGTCCAAGTAGT

ACGTGGGTCCCAACAAGTAGT ACGTGGGTCCAAGTAGT

Copy Number Variation

insertion

duplication

Needles in a haystack

• ~56,000,000 SNPs [NCBI dbSNP] • Direct experimental testing of all SNPs is unfeasible. • How do we find which SNPs are functional?

Location matters gene region: intergenic region

regulatory sequences protein coding sequences

...ACCGTAGTCGGTTTTAACGTTTTTACCCACACACAGATCGAT...

SNPx, C/T

ACGTGGGTCTAAGTAGT ACGTGGGTCCAAGTAGT Probably harmless Non-functional SNP

SNPy, C/T

ACGTGGGTCTAAGTAGT ACGTGGGTCCAAGTAGT Potentially harmful: Altered protein levels Altered protein structure and function Disease risk (Cancer, Diabetes, etc.) Functional SNP

Genetic epidemiology

Study of the genetic factors in determining health and disease, and the interplay of those genetic factors with environmental factors. – Family studies – Population studies • Genome wide association studies

Genetic epidemiology

• Simple – mono-genic – genetic alterations with low frequency in the population – high penetrance (a mutation carrier will with high probability manifest the disease)

• Complex – poly-genic – genetic alterations with a significant frequency in the population – small penetrance

Outline

• Basic concepts – Cell, cell duplication, DNA replication – DNA replication mistakes – Human genome, germline and somatic cells, sexual reproduction – Genetic alterations

• Case studies – Genetic alterations affecting the response to cancer treatment (focus on the p53 pathway) – Finding optimal drug combinations as a minimal hitting set problem

p53: the guardian of the genome

Weinberg, The Biology of Cancer (© Garland Science 2007)

p53 mutation prevalence

Weinberg, The Biology of Cancer (© Garland Science 2007)

Li-Fraumeni syndrome (inhereted p53 mutation)

Germ Line p53 Mutations in a Familial Syndrome of Breast Cancer, Sarcomas, and Other Neoplasms Malkin, et al, Science 250 1233-1238 (1990)

p53 pathway

What about when p53 is not mutated?

Finding functional SNPs in the p53 pathway

~100,000 compounds

60 cell lines

Response

Tissue of origin Somatic mutations on 20 genes Amplifications/deletions ~100K SNP chip SNP example: ACGTGGGTCTAAGTAGT ACGTGGGTCCAAGTAGT

Finding functional SNPs in the p53 pathway Genotype=genetic variations + somatic alterations Cell line Cell1 Cell2 Cell3

Tissue breast breast lung

p53 status wt/wt wt/mt wt/wt

SNP1 AA AG GG

SNP2 CC CT CT

. . .

SNP3 AC AC AA

. . .

? Phenotype=drug response Cell line Cell1 Cell2 Cell3

Drug1

Drug2 10.2 5.4 5.6

Drug3 4.3 5.8 7.5

Drug4 6.3 8.2 4.9

. . .

Drug5 5.7 8.9 4.5

6.4 5.4 9.3

. . .

GI50, drug concentration necessary to achieve 50% growth inhibition.

One SNP at the time gene X, SNP Y, alleles G and T GI50, drug concentration necessary to achieve 50% growth inhibition.

Chemical agent “functional”

“non-functional”

ACGTGGGTCTAAGTAGT ACGTGGGTCCAAGTAGT ACGTGGGTCGAAGTAGT ACGTGGGTCTAAGTAGT

. . .

. . .

Drug 1 Drug 2 Drug 3

One SNP at the time

SNP 3 SNP 2 SNP 1

Probability to obtain s or more significant SNP-drug hits after testing one SNP and M drugs, given that a total of S significant SNP-drug hits were observed after testing N SNPs and M drugs.

P MHT s =∑Mk=s Hypergeometric  k , M ; S , NM 

One SNP at the time

Several SNPs at once Atributes: cancer type, somatic mutations, SNPs. Drug

Cell

Stress response

Several SNPs at once

-logGI50 of cell line i for drug j

Other contributions, noise.

X ij =Y j∑mk=1 a ik Z k W ij Typical -logGI50 for drug j.

Contribution of attribute k to the -logGI50 of drug j.

Several SNPs at once

SNP:rs6734469-GG

SNP:rs6734469-AA

Tested: Reference:

264 SNPs residing in genes in the p53 pathway 10,000 sets of 264 SNPs selected at random from the 100K available

p53

rs6734469

YWHAQ

Chemotherapeutics

YWHAQ

S

rs1027154

p53

PIAS1

PIAS1

Senescence P

p53 rs2727567 rs319227 rs319217

p53

AMPK

PP2A

p53

P

p53

P

MDM2 CCNG1

rs2069347

Apoptosis Cell Cycle Arrest

p53

PP2A

p53 Stress Response

p53 SNP309

Finding optimal drug combinations

~100,000 compounds

60 cell lines

Tissue of origin Somatic mutations on 20 genes Amplifications/deletions ~100K SNP chip

Response

Finding optimal drug combinations

NCI60 cell lines

Connecting strains and drugs

Finding optimal drug combinations

sets

set elements

Find a minimal cocktail of drugs such that each strain respond well to at least one drug

Drugs degree distribution

A heuristic algorithm sequentially covering drugs with highest degree should work well

A hitting set of the NCI60 (size 3)

It is a minimal hitting set

Incomplete data

edge density

Including constraints

Do not work well when used together

Conclusions

Personalized medicine

Non-personalized medicine

Acknowledgments •

NCI60 analysis – Gareth Bond, Elisabeth Bond, Lukasz Grochola (Oxford University) – Arnold Levine (IAS)



Soft tissue sarcomas – Lukasz Grochola (Oxford University) – Peter Wurl (Malteser St Franziskus Hospital, Flensburg) – Helge Taubert (Martin-Luther-University Halle-Wittenberg) – Thomas Muller (German Red Cross Blood Transfusion Service NSTOB)



Breast cancer – Kim Hirshfield (Cancer Institute of New Jersey)



– Diptee Kulkarni (Cancer Institute of New Jersey) Transcription factor binding sites/miRNA – Jun Song (IAS)