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

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Presentation transcript:

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,

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)

Data Integration

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

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

Feature Distribution Cheng et al, Cancer Research, 2017

Topological Features – Kidney Cancer Cheng et al, Bioinformatics2017

Dataset – clear cell renal cell carcinoma TCGA-KIRC

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%)

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:(4159-168)/4082 = 97.77% Precision is (4159-168)/4159 = 95.96%.

Image feature extraction pipeline

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

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.

Comparison Results There are imaging features with p-value less than 0.05 for every gene Multiple test compensation suggested that only PBRM1 are associated with significantly different image features (64 with q value < 0.5)

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

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

PBRM1

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

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

Questions/Commons/Suggestions?