modeling spatial ecology in clear cell renal cell carcinomayoungchul kim jamie teer du dongliang...
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Modeling Spatial Ecology in Clear Cell Renal Cell
Carcinoma
Brandon Manley, MD
Nicholas Chakiryan, MDMoffitt Cancer Center
Multiplexed Immunofluorescence Technology
Technology growing faster than our comprehension?• Predicted to become a $2 billion global market by the year 2023• More markers = better??• Clinical relevance? • Coinciding with the use of these multiplexing technologies has
been the development of analytic software to help researchers digest the wealth of information these experiments provide (HALO, Oncotopix, HistoCAT)
• Description of IF data and use of digital image analysis largely similar to traditional IHC analysis:
• Positive cells• Cell density
• Growing interest in spatial analysis of cells in microenvironment• Limited tools/pipelines (complex and multidimensional)
C. MoranSeguraJ. Nguyen
How Math can help us understand these complex immune relationships
• Examples of how such models can aid in the study of complex systems include those used in weather forecasting and building better aircrafts.
• These models can allow researchers to simulate microenvironmental effects that are difficult or costly to emulate in a biological system.
• These models also allow for expansion of time scales (days, years) that are not possible using traditional in vivo experiments.
• With the advent of larger and more complex experiments, appropriate mathematical and computational modeling approaches have been required to help conduct and maximize information from these investigations.
Density vs. Distribution
⁼Density
≠Distribution
H&E Multiplex IHC (CD68, CD163, CD206) Cartesian Coordinates
Workflow
Cartesian Coordinates Point Pattern Plot
Workflow
Workflow
Novel Metrics for Spatial Heterogeneity
Empiric Histograms
Cell to Cell Distance
Freq
uenc
y
Greg KimmelPhilipp Altrock
Probability Density Function
Kolmogorov-Smirnov (KS)
Cramér–von Mises (CVM)
Novel Metrics for Spatial Heterogeneity
Novel Metrics Applied
Associations with Clinical Outcomes
Clinical Outcomes
Associations with Clinical Outcomes
• 39 patients with metastatic ccRCC (41 specimens) • 19 primary and 22 metastatic samples
• All patients received immunotherapy
Y. Kim
Associations with Clinical OutcomesOVERALL SURVIVAL, primary tumor specimens
Associations with Clinical OutcomesOVERALL SURVIVAL, primary tumor specimens
Associations with Clinical OutcomesOVERALL SURVIVAL FROM IMMUNOTHERAPY, primary tumor specimens
Associations with Clinical OutcomesOVERALL SURVIVAL FROM IMMUNOTHERAPY, primary tumor specimens
Associations with Clinical OutcomesOVERALL SURVIVAL FROM TARGETED THERAPY, primary tumor specimens
Associations with Clinical OutcomesOVERALL SURVIVAL FROM TARGETED THERAPY, primary tumor specimens
Conclusions
• Novel methodology to describe spatial heterogeneity in the tumor-immune microenvironment
• Clustering of tumor-associated macrophages associated with improved OS and OS from IT
• Clustering of stroma cells within the tumor specimen associated with improved OS from TT
• Further study required to confirm findings and validate novel metrics
Thank You!GU TeamEsther Katende Julio Pow-SangPhillipe SpiessWade SextonScott GilbertMichael PochRoger LiJad ChahoudRohit JainJingsong ZhangAli Hajiran Ahmet AydinLogan ZempKaren Irby
IMOPhilipp Altrock Gregory Kimmel Meghan Farell-Fairbanks
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Pathology/Tissue CoreJasreman Dhillon Natasha Francis Michelle Fournier Theresa Boyle Warren GloriaYamila Caraballo-PerezLuiza DiasShawn Brass
Molecular Oncology/Tumor BiologyJohn Koomen Liang Wang Eric Haura
Genomics CoreSean Yoder
Bioinformatics/BiostatisticsYoungchul Kim Jamie Teer Du Dongliang Jose LabordeChia-Ho ChengEric WelshWenyi FanJun-Min ZhouPaul Stewart
TCCErin Siegel Shelley Tworoger Kristie Newsome Kylie Baggett Genesis BlancoAmanda BloomerCDSCMargaret Penichet Vonetta WilliamsKavita GhiaAQUA Lab/Immunofluorescence Carlos Morgan-SeguraJonathan NguyenImmunologyJames Mulé IRB/GrantsAngela Reagan Sarah Majirsky Izaura Spence
