Inflammation in the tumor-adjacent lung as a predictor of clinical outcome in lung adenocarcinoma

Dolgalev, Igor; Zhou, Hua; Murrell, Nina; Le, Hortense; Sakellaropoulos, Theodore; Coudray, Nicolas; Zhu, Kelsey; Vasudevaraja, Varshini; Yeaton, Anna; Goparaju, Chandra; Li, Yonghua; Sulaiman, Imran; Tsay, Jun-Chieh J.; Meyn, Peter; Mohamed, Hussein; Sydney, Iris; Shiomi, Tomoe; Ramaswami, Sitharam; Narula, Navneet; Kulicke, Ruth; Davis, Fred P.; Stransky, Nicolas; Smolen, Gromoslaw A.; Cheng, Wei-Yi; Cai, James; Punekar, Salman; Velcheti, Vamsidhar; Sterman, Daniel H.; Poirier, J. T.; Neel, Ben; Wong, Kwok-Kin; Chiriboga, Luis; Heguy, Adriana; Papagiannakopoulos, Thales; Nadorp, Bettina; Snuderl, Matija; Segal, Leopoldo N.; Moreira, Andre L.; Pass, Harvey I.; Tsirigos, Aristotelis

Inflammation in the tumor-adjacent lung as a predictor of clinical outcome in lung adenocarcinoma

Igor Dolgalev, Hua Zhou, Nina Murrell, Hortense Le, Theodore Sakellaropoulos, Nicolas Coudray, Kelsey Zhu, Varshini Vasudevaraja, Anna Yeaton, Chandra Goparaju, Yonghua Li, Imran Sulaiman, Jun-Chieh J. Tsay, Peter Meyn, Hussein Mohamed, Iris Sydney, Tomoe Shiomi, Sitharam Ramaswami, Navneet Narula, Ruth Kulicke, Fred P. Davis, Nicolas Stransky, Gromoslaw A. Smolen, Wei-Yi Cheng, James Cai, Salman Punekar, Vamsidhar Velcheti, Daniel H. Sterman, J. T. Poirier, Ben Neel, Kwok-Kin Wong, Luis Chiriboga, Adriana Heguy, Thales Papagiannakopoulos, Bettina Nadorp, Matija Snuderl, Leopoldo N. Segal, Andre L. Moreira, Harvey I. Pass () and Aristotelis Tsirigos ()
Additional contact information
Igor Dolgalev: NYU Grossman School of Medicine
Hua Zhou: NYU Grossman School of Medicine
Nina Murrell: NYU Grossman School of Medicine
Hortense Le: NYU Grossman School of Medicine
Theodore Sakellaropoulos: NYU Grossman School of Medicine
Nicolas Coudray: NYU Grossman School of Medicine
Kelsey Zhu: NYU Grossman School of Medicine
Varshini Vasudevaraja: NYU Grossman School of Medicine
Anna Yeaton: The Optical Profiling Platform at The Broad Institute of MIT And Harvard
Chandra Goparaju: NYU Grossman School of Medicine
Yonghua Li: NYU Grossman School of Medicine
Imran Sulaiman: NYU Grossman School of Medicine
Jun-Chieh J. Tsay: NYU Grossman School of Medicine
Peter Meyn: NYU Grossman School of Medicine
Hussein Mohamed: NYU Grossman School of Medicine
Iris Sydney: NYU Grossman School of Medicine
Tomoe Shiomi: NYU Grossman School of Medicine
Sitharam Ramaswami: NYU Grossman School of Medicine
Navneet Narula: NYU Grossman School of Medicine
Ruth Kulicke: Celsius Therapeutics, Cambridge
Fred P. Davis: Celsius Therapeutics, Cambridge
Nicolas Stransky: Celsius Therapeutics, Cambridge
Gromoslaw A. Smolen: Celsius Therapeutics, Cambridge
Wei-Yi Cheng: Roche Innovation Center New York
James Cai: Roche Innovation Center New York
Salman Punekar: New York University Langone Health
Vamsidhar Velcheti: New York University Langone Health
Daniel H. Sterman: NYU Grossman School of Medicine
J. T. Poirier: New York University Langone Health
Ben Neel: New York University Langone Health
Kwok-Kin Wong: New York University Langone Health
Luis Chiriboga: NYU Grossman School of Medicine
Adriana Heguy: NYU Grossman School of Medicine
Thales Papagiannakopoulos: NYU Grossman School of Medicine
Bettina Nadorp: NYU Grossman School of Medicine
Matija Snuderl: NYU Grossman School of Medicine
Leopoldo N. Segal: NYU Grossman School of Medicine
Andre L. Moreira: NYU Grossman School of Medicine
Harvey I. Pass: NYU Grossman School of Medicine
Aristotelis Tsirigos: NYU Grossman School of Medicine

Nature Communications, 2023, vol. 14, issue 1, 1-16

Abstract: Abstract Approximately 30% of early-stage lung adenocarcinoma patients present with disease progression after successful surgical resection. Despite efforts of mapping the genetic landscape, there has been limited success in discovering predictive biomarkers of disease outcomes. Here we performed a systematic multi-omic assessment of 143 tumors and matched tumor-adjacent, histologically-normal lung tissue with long-term patient follow-up. Through histologic, mutational, and transcriptomic profiling of tumor and adjacent-normal tissue, we identified an inflammatory gene signature in tumor-adjacent tissue as the strongest clinical predictor of disease progression. Single-cell transcriptomic analysis demonstrated the progression-associated inflammatory signature was expressed in both immune and non-immune cells, and cell type-specific profiling in monocytes further improved outcome predictions. Additional analyses of tumor-adjacent transcriptomic data from The Cancer Genome Atlas validated the association of the inflammatory signature with worse outcomes across cancers. Collectively, our study suggests that molecular profiling of tumor-adjacent tissue can identify patients at high risk for disease progression.

Date: 2023
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DOI: 10.1038/s41467-023-42327-x

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