Three-component dissection of tumor cellular heterogeneity by a Bayesian Hierarchical Model Tao Wang Assistant Professor Quantitative Biomedical Research Center Department of Clinical Sciences UT Southwestern Medical Center Cancer Discovery, 2018, IF=24.373

Tumor cellular heterogeneity presents challenges and opportunities for understanding cancer Normal parenchyma Malignant tumor Immune cells Stroma cells Tumor cells Challenges: The sampled tumor tissue cannot be assumed to be pure population of the same type of cells Opportunities: non-tumor cells provide critical information regarding the biology of tumors. Need robust statistical methods to dissect the different components of cells

The core dissection problem In each patient i, we need to dissect one vector of expression (of length J) into three vectors (the basic idea) i iterates through patients j iterates through all genes ρ denotes mixing proportions for each patient eij denotes expression of gene j in patient i for T, S, N, and M T: tumor cell expression (Tumorgraft sample) N: normal cell expression (Normal tissue sample) S: stroma/immune expression (Unknown component) M: Expression of mixture of cells in bulk tumor RNA-Seq (Bulk tumor sample) 𝑒 𝑖𝑗 𝑇 𝜌 𝑖 𝑇 + 𝑒 𝑖𝑗 𝑆 𝜌 𝑖 𝑆 + 𝑒 𝑖𝑗 𝑁 𝜌 𝑖 𝑁 = 𝑒 𝑖𝑗 𝑀 𝜌 𝑖 𝑇 + 𝜌 𝑖 𝑆 + 𝜌 𝑖 𝑁 =1,0< 𝜌 𝑖 𝑇 , 𝜌 𝑖 𝑆 , 𝜌 𝑖 𝑁 <1 𝑒 𝑖𝑗 𝑇 𝜌 𝑖 𝑇 𝑒 𝑖𝑗 𝑆 𝜌 𝑖 𝑆 𝑒 𝑖𝑗 𝑁 𝜌 𝑖 𝑁 𝑒 𝑖𝑗 𝑀 = +

Technical challenge #1: Dissecting gene expression on the raw scale or log scale? Raw scale: numerical instabilities will likely crash the estimation or cause serious bias (Ahn et al, DeMix, 2013) Log scale: it will be numerically stable but the estimation will be distorted (Zhong and Liu 2012) 𝑒 𝑖𝑗 𝑇 𝜌 𝑖 𝑇 + 𝑒 𝑖𝑗 𝑆 𝜌 𝑖 𝑆 + 𝑒 𝑖𝑗 𝑁 𝜌 𝑖 𝑁 = 𝑒 𝑖𝑗 𝑀 log( 𝑒 𝑖𝑗 𝑇 ) 𝜌 𝑖 𝑇 + log⁡(𝑒 𝑖𝑗 𝑆 ) 𝜌 𝑖 𝑆 + log⁡(𝑒 𝑖𝑗 𝑁 ) 𝜌 𝑖 𝑁 = log⁡(𝑒 𝑖𝑗 𝑀 )

Technical challenge #2: The need to model all three components of cells Many current models and software confuse/mix the stroma/immune and the normal components ISOpure (Anghel et al, BMC Bioinformatics, 2015) DeMix (Ahn et al, Bioinformatics, 2013) InfiniumPurify (Zheng et al, Genome Biology, 2017) Normal cells, immune cells, stromal cells, etc 𝐵𝑢𝑙 𝑘 𝑖𝑗 = 𝜌 𝑖 𝑇𝑢𝑚𝑜 𝑟 𝑖𝑗 +(1− 𝜌 𝑖 )𝑁𝑜𝑟𝑚𝑎 𝑙 𝑖𝑗

DisHet: A Bayesian Hierarchical model for Dissecting Heterogeneous bulk tumors The DisHet model is a hybrid of a raw-scale model: the average expression of the S component across all patients a log-scale model: individual expression variation in S Dissects all three components of cells properly log( 𝑒 𝑖𝑗 𝑀 )~𝑁(log( 𝑒 𝑖𝑗 𝑇 𝜌 𝑖 𝑇 + 𝑒 𝑗 𝑆 𝜌 𝑖 𝑆 + 𝑒 𝑖𝑗 𝑁 𝜌 𝑖 𝑁 ), 𝜎 𝑗 2 Population-wise average of S (S of different patients should look similar) Individual variation in S

Real data analysis of DisHet agrees with biological expert knowledge - 𝜌 35 RCC patients, 25,000 genes Correlate estimated tumor component proportion with true proportions given by pathologists

Real data analysis agrees with biological expert knowledge – 𝑒 𝑆 Searching for enriched Gene Ontology pathways of genes that are highly activated in each component Stroma/immune component: immune-regulated pathways P values showing enrichment of pathways

Discovering a highly inflamed kidney cancer subtype using stroma-specific gene expression Inflamed Non-inflamed subtype subtype IS patients have worse survival Wang T, Lu R, Kapur P, et al. An Empirical Approach Leveraging Tumorgrafts to Dissect the Tumor Microenvironment in Renal Cell Carcinoma Identifies Missing Link to Prognostic Inflammatory Factors. Cancer Discovery. 2018.

Acknowledgements UTSW Kidney Cancer Program James Brugarolas Payal Kapur Raquibul Hannan Ivan Pedrosa Qurratulain Yousuf Mingyi Chen Alexander Filatenkov Jose Torrealba UTSW QBRC/BICF Rong Lu Ze Zhang He Zhang Min Soo Kim Danni Luo Xin Luo Jingxuan He Genentech Eric W Stawiski Zora Modrusan Steffen Durinck Somasekar Seshagiri 10

Tao Wang 972 567 2356 Tao.wang@utsouthwestern.edu 12245 Montego Plz, Dallas, TX, 75230