cancer models Flashcards
step wise model of cancer transformation
Mutation 1 - removes a negative regulator of cell cycle
Mutation 2 - activates a positive regulator of cell cycle
Mutation 3 - inhibits cell death
Additive effects of TME
Transformed cancer cells
what should a good cancer model have
- relevance to human biology
- predictive accuracy: do treatments translate
- scalability: how many conditions can you test
- reproducibility - should be easily reporducible
- integrates heterogenity - how complex is the system
- mechanic insight - how reductionist can it be
cell lines
cultured cancer cells, taken from a patient and grown in a lab
cell lines pros
- Continuous and unlimited supply of cells
- Consistent genetic and phenotype chatacteristics
- Ease of handling
- Can be modified easily and excellent Techniques to study molecular interactions
- Ability to maintain cells under controlled conditions
- Cost effectiveness
- Automated process
Can be grown in different conditions
cell lines negatives
- Risk of genetic drift and mutation
- Artificial environment
- Possible contamination issues
- Lose relevant in vivo characteristics
- Ethical concerns
- Homogenous cultures
- No gradients or 3D or TME
organoids
3D cultures of cells that mimic the structure and function of an organ
organoids pros
- continuous and unlimited supply of cells
- consistent genetic characteristics
- ease of handling
- can be modified easily
- ability to maintain cells under controlled conditions
- cost effectiveness
- gradients and heterogenous
-can be grown in different conditions
organoids negs
– risk of genetic drift and mutation
- artificial environment
- possible contamination issues
- lose relevant in vivo characteristics
- ethical concerns
- homogenous cultures
- less reproducible
- harder to study mechanisms
xenografts
use cells (cell lines) from patients implanted into a mouse
xenografts positives
- closer in vivo tumour biology
- study more tumour host interactions
- better drug discovery and testing
- lots of cancer cell line variants
- can study metastasis
xenografts negatives
- limited generalisability to humans
- no host immune system
- difference in TME
- ethical concerns
- limited reproducibility between animal models
- uses homogenous cell lines
- expensive
patient derived xenografts
use cells from a patients tumour that are implanted into a mouse, with the cells being maintained by serial passage in the mouse
tumour induced mouse models
genetically engineered mice models
- genetic models: tissue modified to express oncogenes
- spontaneous models: uses cancer causing agents to develop cancers
tumour induced mouse pros
- Close in vivo tumour biology
- Study tumour host interactions
- Potential in drug discovery and testing
- Controlled changes to genetic background
- Ability to study tumour progression and metastasis
- Develops naturally time and TME
tumour-induced mouse negs
- not scalable
- time takes months and months
- difference in TME
- ethical concerns
- limited reproducibility between different animal models
- high cost and technical expertise required
zebrafish models
use Zebrafish, which can be genetically modified to develop tumours
zebra fish pros
- rapid development
- high throughput screening
- transparent embryos for visualising tumour growth
- crossing models rapid
- inexpensive
-conservation of gene function across species
zabra fish negs
- differences in TME
- ethical concerns
- limited reproducibility between different animal models
- technical expertise required
- limited understanding of fish tumours and immune system
- few examples of drug discoveries
PDX pros
- closes representation of humour tumour
- patient specific tumour - host interactions
- personalised medicine
- availability of human tumour specimens
- ability to test multiple drugs and treatments
- drug response is the most predictive
PDX negs
- patient specific
- no host immune system
- differences in tumour microenvironment
- ethical concerns
- high cost and technical expertise required
- slow and limited material
- limited mechanistic insight
omics based models
use large scale data sets of different omics data, such as genomics, transcriptomics, proteomics and metabalomics, to understand the underlying mechanisms of cancer development, progression and drug resistance.
omics pros
- high throughput analysis
- ability to study complex biological systems
- integration of multiple layers of information
- increased data accuracy
- ease of data generation
- non bias
- ethical
omics negs
- limited functional analysis
- basically no mechanical insight
- large amounts of sample material
- problem of data integration and interpretation
- limited understanding of the causal relationship between changes in molecules and biological processes
- can not stand alone
- expensive
