1. 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

2. 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

3. 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 = +

4. 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 )

5. 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

6. 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

7. 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

8. 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

9. 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

10. 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

11. Tao Wang
12245 Montego Plz, Dallas, TX, 75230