A Bayesian Approach to Determine the Composition of Heterogeneous Cancer Tissue

Cancer Tissue Heterogeneity is an important consideration in cancer research as it can give insights into the causes and progression of cancer. It is known to play a significant role in cancer cell survival, growth and metastasis. Determining the compositional breakup of a heterogeneous cancer tissue can also help address the therapeutic challenges posed by heterogeneity. This necessitates a low cost, scalable algorithm to address the challenge of accurate estimation of the composition of a heterogeneous cancer tissue. In this paper, we propose an algorithm to tackle this problem by utilizing the data of accurate, but high cost, single cell line cell-by-cell observation methods in low cost ensemble observation method for heterogeneous cancer cell mixtures to obtain their composition in a Bayesian framework. The algorithm is analyzed and validated using synthetic data and experimental data obtained from mixtures of cancer cell lines.