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)
