Computational biology of cancer cell pathways Modelling of cancer cell function and response to therapy

Signalling pathways Signal from outside cell Signal from inside cell Source: Biocarta database

Epidermal growth factor (EGF) signalling Signal from outside cell Gene expression

Signal from outside or inside cell Signal transmitted to genome Changes in gene expression = cell response to signal Information about cell’s environment and internal state is coupled to gene expression Signalling pathways Receptors E.g., kinases Transcription factors

Mutations in cancer Point mutations - changes in protein sequence or control sequences Genome instability: Loss or gain of single genes or chromosome portions (many genes) Abnormal amounts of proteins Abnormal function, e.g always ‘switched on’ or inactivated Signalling pathways in cancer cells

Mutations in cancer Point mutations - changes in protein sequence or control sequences Genome instability: Loss or gain of single genes or chromosome portions (many genes) Abnormal amounts of proteins Abnormal function, e.g always ‘switched on’ or inactivated Changes in information processing underpin hallmarks of cancer Signalling pathways in cancer cells

Epidermal growth factor receptor (EGFR) Overexpression of EGFR is common in many solid tumours Correlates with increased metastasis, decreased survival and a poor prognosis Protects malignant tumour cells from the cytotoxic effects of chemotherapy and radiotherapy, making these treatments less effective EGFR is the target for several new anticancer therapies

EGFR-targeted therapy cell surface portion binds epidermal growth factor intracellular tyrosine kinase transmit signal by phosphorylation CELL MEMBRANE

EGFR-targeted therapy Small molecule inhibitors Therapeutic antibody: Cetuximab (colorectal cancer)

Inhibition of EGFR Both types of inhibitors block signalling from the EGF receptor Inhibition limits tumour growth, dissemination, angiogenesis Reduces resistance to chemotherapy and radiotherapy Aids the induction of cell death (apoptosis)

Not a linear pathway, but a complex network Genome

Growth factor (EGF) Receptor tyrosine kinase PLCRasPI3K PKCMAPKPKB/Akt TFsFunctional targets CELL GROWTH AND PROLIFERATION ERK Cross-activation by other pathways Not a linear pathway, but a complex network

Signal processing by the entire network and mutations or expression changes in signal proteins can limit response to therapy or cause side effects. Not a linear pathway, but a complex network

Complexity needs to be modelled in the computer Computer models of pathways need biochemical kinetic data for every connection. Enable one to simulate –the change in concentration or –activation (eg. phosphorylation) of the proteins of the pathway over time. Modelling gives information on how signals are processed.

The effect of the number of active EGFR molecules on ERK activation EGFR PLCRasPI3K PKCMAPKPKB/Akt TFsFunctional targets CELL GROWTH AND PROLIFERATION ERK

The effect of the number of active EGFR molecules on ERK activation Schoeberl et al., 2002, Nat. Biotech. 20: ,000 active receptors 50,000 active receptors = Inhibition by one order of magnitude EGFR PLCRasPI3K PKCMAPKPKB/Akt TFsFunctional targets CELL GROWTH AND PROLIFERATION ERK

The effect of active EGFR number on ERK activation 500,000 active receptors 50,000 active receptors Can this be achieved by receptor inactivation alone?

The effect of active EGFR number on ERK activation … Or, what might happen if ERK is overexpressed? Several proteins in the pathway are abnormally expressed?

The effect of active EGFR number on ERK activation 50,000 active receptors with normal levels of ERK or ERK overexpression and cross-activation

Computer models of pathways Integration of complex knowledge –Biological processes are mediated by pathways in health and disease –Modelling aids integrative understanding of relationships between physiology and clinical observations and the molecular level Analysis and simulation of signal processing in cancer cells –Effects of mutations and abnormal gene expression –Discovery of new targets for therapy: key modulators of pathway function –Effects of therapeutic inhibitors –Possible side effects

Effects of abnormal gene expression Roughly 90% of human cancers are epithelial in origin and exhibit a large number of changes in the structure and function of the genome. Abnormal expression levels can be observed for a a large number of genes. This complexity might be the reason for the clinical diversity of tumours (even with similar histology). A comprehensive analysis of the multiple genetic alterations present is required for an understanding of abnormal signal processing in cancer and differences between tumours.

Gene expression analysis The use of expression microarrays enables the large- scale analysis of mRNA expression (expression profiling) in tumour samples. Expression profiling can be used to simultaneously assess the expression of the entire human genome. mRNA concentration is used as a surrogate for protein conc. - protein concentrations may be hypothetically inferred.

ER-positive breast tumour subtype has a distinct microarray expression profile ER = oestrogen receptor Example: Gene expression profiles from breast cancer patient samples

Role of ER and EGFR in anti-oestrogen therapy Response to tamoxifen is dependent on ER expression. Overexpression of EGFR is associated with tamoxifen resistance - EGFR as a target for therapy.

Differences in expression of EGFR and other proteins in the network between patients? May account for different responses to therapy with EGFR inhibitors. ER-positive tumours genes EGFR EGFR as a target for therapy

Modelling of individual response Gene expression of EGFR network genes in each individual tumour Approximation of relative changes in protein expression Input in computer model Model signal processing in different tumours in response to EGFR inhibition Hypothesis generation re. response Validate with clinical response Caveat: may be not directly comparable – direct measurement of protein concentration

Summary Molecular changes in cancer are highly complex. They affect signal processing in pathways and networks. Changes in signal processing underpin hallmarks of cancer. Computer modelling gives information on how signals are processed. Modelling aids fundamental understanding of cancer, discovery of new targets for therapy, prediction of effects of therapy and possible side effects.