1. Genetic Mutations Associated with Histopathology Changes in Kidney Cancer
Kun Huang, PhD
Jun Cheng, PhD, Zhi Han, PhD, Qianjin Feng, PhD, Liang Cheng, MD, Jie Zhang, PhD,

2. Data Integration Integrative genomics / trans-omics approach Phenotype
Behavior, syndrome and clinical outcome (EMR)
Morphology (sub-cellular, cellular, tissue, organ)
Proteome (profiling, quantity, modification)
Transcriptome (gene expression, non-coding RNA)
Epigenetics (DNA methylation, histone modification, microRNA)
Genotype (DNA - SNV, CNV, structural variation)

3. Data Integration

4. Image Processing Pipeline Overview
Whole Slide Images 1 2
Representative Patches 3
Image
Preprocessing 4
SuperPixel
Segmentation 8
Feature Extraction 7
Cell Segmentation 6
Tissue Classification 5
LBP Features
Epithelial Features
Epithelial tissue
Stromal
Features
Stromal
tissue

5. Feature Extraction E:epithelial features; S:stromal features E-28; S-6

6. Feature Distribution
Cheng et al, Cancer Research, 2017

7. Topological Features – Kidney Cancer
Cheng et al, Bioinformatics2017

8. Dataset – clear cell renal cell carcinoma
TCGA-KIRC

9. Dataset – clear cell renal cell carcinoma
TCGA-KIRC with matched histopathology images and gene-level somatic mutation data
Characteristics
Summary
Patient No.
445
Age (years)
Range
26-90
Median 61
Gender
Female
156 (35.06%)
Male
289 (64.94%)
Death
151 (33.93%)
Stage
Stage I
216 (48.54%)
Stage II
46 (10.34%)
Stage III
112 (25.17%)
Stage IV
70 (15.73%)
Discrepancy
1 ( 0.22%)

10. Image feature extraction pipeline
Algorithm: Based on Phoulady et al., 2016 
Unsupervised and adaptive thresholding
Evaluation: for a patch of 1500×1500 pixels
True pos: 4082; Detected: 4159; False neg:168
Recall:( )/4082 = 97.77%
Precision is ( )/4159 = 95.96%.

11. Image feature extraction pipeline

12. In total 150 features were extracted
Image feature extraction pipeline
In total 150 features were extracted

13. Comparison
Six genes: VHL, PBRM1, MUC4, SETD2, BAP1, and MTOR
image features were compared between patients with and without mutations for each gene using Mann-Whitney U test.
P-values and q-values (after BH FDR correction) were reported.

14. Comparison Results
There are imaging features with p-value less than for every gene
Multiple test compensation suggested that only PBRM1 are associated with significantly different image features (64 with q value < 0.5)

15. Comparison Results
Multiple test compensation suggested that only PBRM1 are associated with significantly different image features (64 with q value < 0.5) ……

16. Comparison Results
Multiple test compensation suggested that only PBRM1 are associated with significantly different image features (64 with q value < 0.5)

17. PBRM1

18. Summary and Perspective
Quantitative phenotyping at cellular and tissue levels
Manifestation of genetic variations at cell and tissue morphology
Deeper analysis on the heterogeneity is needed
Pathway of PBRM1 for cellular morphology changes is not clear

19. Acknowledgement Lab members Collaborators NCI ITCR U01
Zhi Han, Yi Wu, Travis Johnson, Christina Yu, Tongxin Wang, Zhi Huang, Zixiao Lu, Wei Shao, Jun Cheng, Yatong Han
Collaborators
Jie Zhang, Raghu Machiraju, Jeffrey Parvin, Yufeng Yang, Liang Cheng, Zaibo Li, Anil Pawane, Michael Ostrowski, Gustavo Leone, Yunlong Liu, Lang Li, Tim Huang, Song-Hai Shi
NCI ITCR U01

20. Questions/Commons/Suggestions?