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Theranostics 2020; 10(23):10838-10848. doi:10.7150/thno.50283 This issue Cite

Research Paper

Tumor immune profiles noninvasively estimated by FDG PET with deep learning correlate with immunotherapy response in lung adenocarcinoma

Changhee Park1#, Kwon Joong Na2#, Hongyoon Choi3✉, Chan-Young Ock1✉, Seunggyun Ha4, Miso Kim1, Samina Park2, Bhumsuk Keam1,5, Tae Min Kim1,5, Jin Chul Paeng3, In Kyu Park2, Chang Hyun Kang2, Dong-Wan Kim1,5, Gi-Jeong Cheon3,5, Keon Wook Kang3,5, Young Tae Kim2,5, Dae Seog Heo1,5

1. Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
2. Department of Thoracic and Cardiovascular Surgery, Seoul National University Hospital, Seoul, Republic of Korea.
3. Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
4. Division of Nuclear Medicine, Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
5. Cancer Research Institute, Seoul National University, Seoul, Republic of Korea.
#These authors contributed equally to this work.

✉ Corresponding authors: Hongyoon Choi, MD, PhD, Department of Nuclear Medicine, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, Republic of Korea, 03080. Phone: +82-2-2072-3347; E-mail: chy1000com; and, Chan-Young Ock, MD, More

Citation:
Park C, Na KJ, Choi H, Ock CY, Ha S, Kim M, Park S, Keam B, Kim TM, Paeng JC, Park IK, Kang CH, Kim DW, Cheon GJ, Kang KW, Kim YT, Heo DS. Tumor immune profiles noninvasively estimated by FDG PET with deep learning correlate with immunotherapy response in lung adenocarcinoma. Theranostics 2020; 10(23):10838-10848. doi:10.7150/thno.50283. https://www.thno.org/v10p10838.htm
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Abstract

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Rationale: The clinical application of biomarkers reflecting tumor immune microenvironment is hurdled by the invasiveness of obtaining tissues despite its importance in immunotherapy. We developed a deep learning-based biomarker which noninvasively estimates a tumor immune profile with fluorodeoxyglucose positron emission tomography (FDG-PET) in lung adenocarcinoma (LUAD).

Methods: A deep learning model to predict cytolytic activity score (CytAct) using semi-automatically segmented tumors on FDG-PET trained by a publicly available dataset paired with tissue RNA sequencing (n = 93). This model was validated in two independent cohorts of LUAD: SNUH (n = 43) and The Cancer Genome Atlas (TCGA) cohort (n = 16). The model was applied to the immune checkpoint blockade (ICB) cohort, which consists of patients with metastatic LUAD who underwent ICB treatment (n = 29).

Results: The predicted CytAct showed a positive correlation with CytAct of RNA sequencing in validation cohorts (Spearman rho = 0.32, p = 0.04 in SNUH cohort; spearman rho = 0.47, p = 0.07 in TCGA cohort). In ICB cohort, the higher predicted CytAct of individual lesion was associated with more decrement in tumor size after ICB treatment (Spearman rho = -0.54, p < 0.001). Higher minimum predicted CytAct in each patient associated with significantly prolonged progression free survival and overall survival (Hazard ratio 0.25, p = 0.001 and 0.18, p = 0.004, respectively). In patients with multiple lesions, ICB responders had significantly lower variance of predicted CytActs (p = 0.005).

Conclusion: The deep learning model that predicts CytAct using FDG-PET of LUAD was validated in independent cohorts. Our approach may be used to noninvasively assess an immune profile and predict outcomes of LUAD patients treated with ICB.

Keywords: Immunotherapy, tumor microenvironment, fluorodeoxyglucose positron emission tomography, deep learning, gene expression profile

Citation styles

APA
Park, C., Na, K.J., Choi, H., Ock, C.Y., Ha, S., Kim, M., Park, S., Keam, B., Kim, T.M., Paeng, J.C., Park, I.K., Kang, C.H., Kim, D.W., Cheon, G.J., Kang, K.W., Kim, Y.T., Heo, D.S. (2020). Tumor immune profiles noninvasively estimated by FDG PET with deep learning correlate with immunotherapy response in lung adenocarcinoma. Theranostics, 10(23), 10838-10848. https://doi.org/10.7150/thno.50283.

ACS
Park, C.; Na, K.J.; Choi, H.; Ock, C.Y.; Ha, S.; Kim, M.; Park, S.; Keam, B.; Kim, T.M.; Paeng, J.C.; Park, I.K.; Kang, C.H.; Kim, D.W.; Cheon, G.J.; Kang, K.W.; Kim, Y.T.; Heo, D.S. Tumor immune profiles noninvasively estimated by FDG PET with deep learning correlate with immunotherapy response in lung adenocarcinoma. Theranostics 2020, 10 (23), 10838-10848. DOI: 10.7150/thno.50283.

NLM
Park C, Na KJ, Choi H, Ock CY, Ha S, Kim M, Park S, Keam B, Kim TM, Paeng JC, Park IK, Kang CH, Kim DW, Cheon GJ, Kang KW, Kim YT, Heo DS. Tumor immune profiles noninvasively estimated by FDG PET with deep learning correlate with immunotherapy response in lung adenocarcinoma. Theranostics 2020; 10(23):10838-10848. doi:10.7150/thno.50283. https://www.thno.org/v10p10838.htm

CSE
Park C, Na KJ, Choi H, Ock CY, Ha S, Kim M, Park S, Keam B, Kim TM, Paeng JC, Park IK, Kang CH, Kim DW, Cheon GJ, Kang KW, Kim YT, Heo DS. 2020. Tumor immune profiles noninvasively estimated by FDG PET with deep learning correlate with immunotherapy response in lung adenocarcinoma. Theranostics. 10(23):10838-10848.

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