Cohort • Typical stage: I-IIIa NSCLC • Patients: n = ~150
Immune cell imaging • Multiplex immunofluorescence
Genetics • RNA-sequencing Shared Nearest Neighbors Approach and Interactive Browser for Network Analysis of a Comprehensive Non–Small-Cell Lung Cancer Data Set Immune cell profiling • Flow cytometry
Proteomics • Reverse phase protein array
• Whole exome sequencing • T-cell receptor sequencing
b
I mmunogenomi C pr O filing of N on-small cell lung cancer Project (ICON)
Stephanie T. Schmidt, Neal Akhave, Ryan E. Knightly, Alexandre Reuben, Natalie Vokes, Jianhua Zhang, Jun Li, Junya Fujimoto, Lauren A. Byers, Beatriz Sanchez-Espiridion, Lixia Diao, Jing Wang, Lorenzo Federico, Marie- Andree Forget, Daniel J. McGrail, Annikka Weissferdt, Shiaw-Yih Lin, Younghee Lee, Erika Suzuki, Jeffrey J. Kovacs, Carmen Behrens, Ignacio I. Wistuba, Andrew Futreal, Ara Vaporciyan, Boris Sepesi, John V. Heymach, Chantale Bernatchez, Cara Haymaker, Tina Cascone, Jianjun Zhang, Christopher A. Bristow, Timothy P. Heffernan, Marcelo V. Negrao*, Don L. Gibbons*, and the ICON team * Equal contribution Schmidt, et al. JCO Clinical Cancer Informatics no. 6 (2022) e2200040. Presenter (STS) has no disclosures.
RNA-seq WES TCR-seq mIF Flow cytometry RPPA
Treatment: ∎ Chemotherapy ∎ None ∎ Other Stage: ∎ 0 ∎ 1 ∎ 2 ∎ 3 ∎ 4
Histology: ∎ Adenocarcinoma ∎ Squamous cell ∎ Small cell ∎ Other Availability: ∎ Present ∎ Absent
Shared Nearest Neighbors (SNN) approach for multi-platform networks
Rank Gene A Gene B … 1 Protein 1 Protein 2
Qualifying pairs: A - B , B - C , B - E , C - D , D - E
2 Protein 2 Protein 4 3 Protein 3 Protein 1 4 Protein 4 Protein 6 5 Protein 5 Protein 5 … …
…
MD ANDERSON CANCER CENTER
Compile SNN matrix A B C D E …
Build network
Calculate overlap score
ICON data network highlights new connections based on interplay between integrated platforms Selection of SNN with RNA-based edges Improved performance over SNN, RNA alone SNN platforms provide more edges per node Overlaps in network edges from each platform shown Community detection identifies modules 10 top-level and 93 mid-level modules with 10+ genes
SNN (Flow, RPPA) only
Network of highest- level phenomena
SNN (Flow, RPPA) + RNA
Intermediate phenomena
RNA only
Individual genes at deepest layer
MD ANDERSON CANCER CENTER
Schmidt, et al. JCO Clinical Cancer Informatics no. 6 (2022) e2200040.
ICON data browser enables interactive network exploration for insights into tumor characteristics of interest
Score distributions for highlighted modules relevant to selected features
Network panel (top) and plotting/modeling panel (bottom) of the ICON data browser
Recurrence in Stage 2+, non-LUSC tumors
TP53 oncogenotype in LUSC tumors
To learn more, please see our recent publication in JCO CCI.
Ongoing: Expansion of approach to NEOSTAR to enable multi-cohort, multi-platform integration
MD ANDERSON CANCER CENTER
Schmidt, et al. JCO Clinical Cancer Informatics no. 6 (2022) e2200040.
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