Computational Systems Biology of Cancer Metastasisof Cancer Metastasis

PhD project 2018PhD project 2018

Cancer metastasis: an unsolved clinical challenge

• Metastasis - the spread of cancer cells from one organ to anothercells from one organ to another -causes more than 90% of all cancer-related deaths

• Cancer cells largely spread by traveling in blood vessels in our bodyg y

• Metastasis is an extremely challenging process for cells withchallenging process for cells, with very high (> 98%) rates of attrition

What traits cells need to successfully metastasize?

• Dynamic/Adaptive changes in: C ll ll dh i Cell-cell adhesion Ability to migrate and invade E di tt k b i t Evading attacks by immune system Settling down in a new organ and colonizing it Resist multiple therapies/drugs given to patients

Thus, to restrict metastasis, we first need a dynamic and systems-level understanding of the process to identify how cells alter these multiple traits togetheridentify how cells alter these multiple traits together

A systems-level understanding means…

1 Realizing that integrating different parts can lead to novel behaviors/functions1. Realizing that integrating different parts can lead to novel behaviors/functions, i.e. whole is greater than sum of its parts

2. Being able to predict the behavior of the system in varied conditions

We can mathematically model these biological networks to achieve a

systems-level understanding, similar to

th t tt i d fthat attained for engineered systems as

shown aboveBurger et al. Front Oncol 2017

EMT/MET: An engine of metastasisM th 80% b i i ith li l C ll ibl t iti tMore than 80% cancers begin in epithelial organs. Cancer cells reversibly transition to a

mesenchymal state – a phenomenon known as Epithelial-Mesenchymal Transition (EMT) – that enables them to migrate, invade, and eventually enter blood circulation. Reverse of EMT – MET – helps them to colonize other organs after exiting circulation.p g g

Scheel & Weinberg, Semin Cancer Bio 2012

Mathematical model’s prediction Experimental validation

A systems biology approach to understanding EMTMathematical model s prediction Experimental validation

Lu*, Jolly* et al. PNAS 2013

H1975 ( lt d 2 th )H1975 (cultured over 2 months);CDH1 (E-marker), VIM (M-marker)

Stable existence of a hybrid E/M state

1. EMT is not a binary process – cells can attain an E, M, or a hybrid E/M state stably

Jolly et al. Oncotarget 2016George*, Jolly* et al. Cancer Res 2017

, , y y2. Cells with same genetic background

(isogenic) can contain multiple co-existing subpopulations (E, M, E/M)

Co-existence of phenotypes in a cell line

A generalized systems biology workflow

Steps involved in the workflow:

1. Identify core players regulating a specific biological property based on published experimental data (gene expression profiles, qPCR/Western Blot data, RNA-seq/ChIP-seq data, knockdown/overexpression experiments etc.)

2 Construct regulatory network formed among those players by putting together2. Construct regulatory network formed among those players by putting together their interconnections

3. Simulate the dynamics of regulatory network; compare with experiments

Tools and techniques usedMathematical modeling of biological regulatory networks• Mathematical modeling of biological regulatory networks

• Simulating a set of ordinary (and/or partial) differential equations

• Analyzing experimental transcriptomics/proteomics, and clinical data

Required background• Basic understanding of ordinary differential equations and nonlinear

dynamics (or will to acquire them)

(• Keen interest in pursuing interdisciplinary research (i.e. reading literature in both cancer biology and systems biology)

N t St d t f h i / h i t / th ti / i i• Note: Students from physics/chemistry/mathematics/engineering background are welcome too, provided they show interest in acquiring the relevant understanding of biology

Further reading

• Kolch, W.; Halasz, M.; Granovskaya, M.; Kholodenko, B. N. The dynamic control of signal transduction networks in cancer cells. Nat. Rev. Cancer 2015, 15 (9), 515–27. doi: 10.1038/nrc3983

• Jolly, M. K., Boareto, M.; Huang, B.; Jia, D.; Lu, M.; Ben-Jacob, E.; Onuchic, J.N.; Levine, H. Implications of the hybrid epithelial/mesenchymal phenotype in metastasis. Front. Oncol. 2015, 5, 155. doi: 10.3389/fonc.2015.00155

• Magi, S.; Iwamoto, K.; Okada-Hatakeyama, M. Current status of mathematical modeling of cancer – From the viewpoint of cancer hallmarks. Curr Opin Syst Biol 2017 2 38-47 doi: 10 1016/j coisb 2017 02 008Curr. Opin. Syst. Biol. 2017, 2, 38-47. doi: 10.1016/j.coisb.2017.02.008