1. Tracing the Innate Genetic Evolution and Spatial Heterogeneity in Treatment Naïve Lung Cancer Lesions
Jihye Kim
Translational Bioinformatics and Cancer Systems Biology Laboratory
Division of Medical Oncology, Dept. Medicine, School of Medicine,
University of Colorado Anschutz Medical Campus

2. Backgrounds
Personalized molecular targeted therapies in lung cancer is based on that “oncogenic driver mutations” are homogeneous throughout and between legions in a single patient
However, cancers are composed of heterogeneous cell populations in terms of somatic mutations and dysregulated signaling pathways.
Tumor heterogeneity may have impacts on cancer evolution and therapeutic resistance, a well as on biopsy strategies.
There is no comprehensive study that analyzed treatment naïve inter-tumor heterogeneity focusing on intra-thoracic AND extra- thoracic lesions.

3. Previous studies of tumor heterogeneity
Ovaries vs Omentum/Peritoneum in treatment-naïve
Multiple lesions in early-stage primary lung adenocarcinomas
Multiple intrathoracic lesions from NSCLCs .

4. Treatment-naïve tumor heterogeneity in Lung Cancer
Our studies are about treatment- naïve inter-tumor heterogeneity focusing on intra and extra thoracic lesions, especially for patients with advanced-stage disease.

5. Samples and Methods
Samples : Tumor specimens and non-cancer tissues from treatment-naïve autopsied lung cancer patients
RNA-seq of 5 patients with multiple different metastasis sites
2 adenocarcinomas (AC), 2 squamous cell carcinomas (SQ), 1 small cell lung cancer (SCLC)
Each patient has normal and primary
Methods :
Differential Expression
Pathway analysis with GSEA/KEGG
Mutation Detection
Phylogenetic tree with mutations
Dr. Kenichi Suda
NFE2L2
KIF5B-RET
[AC]
[SQ]

6. Clustering Analysis : Gene expression excluding normal samples unsupervised, by all genes (FPKM >1)

7. KEGG Pathway analysis AC, non-smoker SQ, heavy smoker Primary
Pleura_Mediastinal
Liver
Lung_LLL
Pleura_Visceral
AC, non-smoker
SQ, heavy smoker

8. Metastatic lesions vs. Primary tumors in NSCLCs
Comparison of upregulated or downregulated pathways between metastatic lesions vs. primary tumors in NSCLCs
Pleural metastases vs. primary tumors
Lung metastases vs. primary tumors
Distant metastases vs. primary tumors
(exclude lung metastases)
Lymph node metastases vs. primary tumors
By GSEA with KEGG pathways with Norm P < 0.05

9. Metastatic lesions vs. Primary tumors in NSCLCs
Comparison of upregulated or downregulated pathways between metastatic lesions vs. primary tumors in NSCLCs
Pleural metastases vs. primary tumors
Lung metastases vs. primary tumors
Distant metastases vs. primary tumors
(exclude lung metastases)
Lymph node metastases vs. primary tumors
By GSEA with KEGG pathways with Norm P < 0.05

10. # of mutations detected
Mutation Detection
TCGA, Nature 2012
# of mutations detected
Total
Trunk
Metastatic Trunk
AC, non-smoker
350
51 (KIF5B-RET) 77
AC, smoker
362
81 (ERBB2) 18
SQ, heavy smoker
272
7 (PPL) 5
SQ, smoker
442
53 (NFE2L2) 11
SCLC, heavy smoker
207
2 (UBE2C)
(Somatic / Non-synonymous / Detrimental / Total read counts > 20 / Mutant read counts > 5 and > 2%)

11. AC, non-smoker :
By NJ method, implemented in R, phangorn package

12. SQ, heavy-smoker :
By NJ method, implemented in R, phangorn package

13. SCLC, heavy-smoker :
SCLC, heavy-smoker
KEGG Pathway of SCLC

14. Conclusions and Discussions
Complexity of genomic alterations in lung cancers and potentially targetable genetic aberrations
not able to evaluate tumor heterogeneity which would be clinically important
Similarity and the heterogeneity between primary and metastatic lesions in lung cancer patients
Gene expression clustered by patient and by tumor cell type, not by lesion location
Tumor heterogeneity by pathways or somatic mutations were correlated with smoking- status
Should be cautious when we use a metastatic lesion as a surrogate for the primary tumor
In metastatic sites, especially in pleural metastases, immune-related pathways are downregulated compared with primary lesions, NSCLC
may have a direct impact on the efficacy of treatment, especially for immunotherapeutic agents

15. Acknowledgements Hirsch Lab Tan Lab Kenichi Suda * Aik Choon Tan
Christopher J. Rivard
Leslie Rozeboom
Fred R. Hirsch
Japan Team
Isao Murakami
Tetsuya Mitsudomi
Tan Lab
Aik Choon Tan
Hyunmin Kim
Jennifer Hintzsche
Brian Jackson
Ilyssa Summer
Kelsey Nassar
Minjae Yoo