Booth Id:
CBIO049
Category:
Computational Biology and Bioinformatics
Year:
2017
Finalist Names:
Miller, Michael
Abstract:
Unlike homogeneous cellular bodies, cancer is best understood as a diverse
community of heterogeneous cells that interacts through complex ecological interactions.
Intratumor interactions, such as competition and cooperation, occur constantly within
malignant tumors and are analogous to interactions between organisms in an ecosystem.
Accounting for these interactions in conjunction with genetic mutations currently poses the
greatest obstacle to effective cancer treatment. However, due to ongoing advances in single
cell technology, genetic sequences and phenotypic parameters of individual cancer cells
can now be captured precisely, providing researchers with access to unprecedented data.
Utilizing the breakthroughs of single cell sequencing, a two-phase approach was created to
effectively interpret single cell data collected while targeting both tumor heterogeneity and
complex ecological interactions. Previously, to combat tumor heterogeneity, a
computational model was constructed that employs statistical analysis through t-tests and
ANOVA to effectively detect driver mutations for cancer development. In addition to
refining this model, a mathematical approach was developed to analyze ecological
interactions present within tumors. The approach employs ordinary differential equations
to analyze the allometric relationship between measurable phenotypic factors of
intratumor cells, enabling the quantification of ecological interactions among cell
subpopulations. Ultimately, this two-phase approach can help dually identify intratumor
cell populations most conducive to tumor growth and genetic mutations through which
cooperative ecological relationships promote overall tumor progression, laying the
foundation for personalized cancer intervention, such as gene editing.