Abstract 4166: Molecular diagnosis of lung tumors with a history of pancreatic cancer: Spatial profiling-based differential markers and functional insights into invasive mucinous adenocarcinoma

Abstract Background: Differentiating pancreatic cancer lung metastasis (PCLM) from primary lung cancer, particularly invasive mucinous adenocarcinoma (IMA), sometimes be difficult due to their pathological similarities such as mucin production. This study aimed to identify diagnostic markers for distinguishing IMA from PCLM and to elucidate the molecular pathogenesis of mucinous lung adenocarcinoma using spatial multi-omics analyses. Methods: We performed spatially resolved transcriptomics by GeoMx digital spatial profiler on surgically resected lung tumors, from nine patients with a history of pancreatic cancer and three patients of IMA without such history. Multiple regions of interest (ROIs) were selected in tumor components from each sample. Based on GeoMx results, validation was performed by immunohistochemistry (IHC) on tissue specimens. Subsequently, functional analysis was conducted using the lung adenocarcinoma cell line. Results: By spatial transcriptomics profiling, positive UGT2B15 and negative S100A4 were suggested as candidate markers for distinguishing IMA from PCLM. The expression status of these proteins were confirmed by IHC on tissue specimens. Transcription factor analyses suggested that HNF1A was involved in UGT2B15 expression in the IMA, and functional analyses using lung cancer cell lines also demonstrated these associations. Conclusion: We identified UGT2B15 and S100A4 as potential candidate markers for distinguishing IMA from PCLM. The involvement of the transcription factor HNF1A in the expression of UGT2B15 in the IMA was suggested. Citation Format: Ryunosuke Fujii, Kousei Ishimura, Kazuhiko Shien, Shuta Tomida, Kenta Manabe, Shunsuke Mori, Kazuya Hisamatsu, Ryota Fujiwara, Atsushi Matsuoka, Ryo Yoshichika, Kazuhiro Okada, Yuma Fukumoto, Haruchika Yamamoto, Kumi Nakajima, Shin Tanaka, Hidejiro Torigoe, Ken Suzawa, Kentaroh Miyoshi, Mikio Okazaki, Seiichiro Sugimoto, Hirofumi Inoue, Kosei Takagi, Hidetaka Yamamoto, Shinichi Toyooka. Molecular diagnosis of lung tumors with a history of pancreatic cancer: Spatial profiling-based differential markers and functional insights into invasive mucinous adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 4166.

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Journal
Cancer Research
Published
2026-04-03
DOI
https://doi.org/10.1158/1538-7445.am2026-4166
Primary Topic
Single-cell and spatial transcriptomics
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article
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article

Abstract 4166: Molecular diagnosis of lung tumors with a history of pancreatic cancer: Spatial profiling-based differential markers and functional insights into invasive mucinous adenocarcinoma

Kazuhiko Shien, Ryo Yoshichika, Mikio Okazaki, Shunsuke Mori et al.
Cancer Research
Single-cell and spatial transcriptomics
article

Abstract 4166: Molecular diagnosis of lung tumors with a history of pancreatic cancer: Spatial profiling-based differential markers and functional insights into invasive mucinous adenocarcinoma

Kazuhiko Shien, Ryo Yoshichika, Mikio Okazaki, Shunsuke Mori, Shinichi Toyooka, Haruchika Yamamoto, Hidetaka Yamamoto, Ryota Fujiwara, Kazuhiro Okada, Kosei Takagi, Yuma Fukumoto, Hirofumi Inoue, Ryunosuke Fujii, Kenta Manabe, Shin Tanaka, Seiichiro Sugimoto, Kentaroh Miyoshi, Kousei Ishimura, Ken Suzawa, Kazuya Hisamatsu, Shuta Tomida, Atsushi Matsuoka, Hidejiro Torigoe
article en

Abstract

Abstract Background: Differentiating pancreatic cancer lung metastasis (PCLM) from primary lung cancer, particularly invasive mucinous adenocarcinoma (IMA), sometimes be difficult due to their pathological similarities such as mucin production. This study aimed to identify diagnostic markers for distinguishing IMA from PCLM and to elucidate the molecular pathogenesis of mucinous lung adenocarcinoma using spatial multi-omics analyses. Methods: We performed spatially resolved transcriptomics by GeoMx digital spatial profiler on surgically resected lung tumors, from nine patients with a history of pancreatic cancer and three patients of IMA without such history. Multiple regions of interest (ROIs) were selected in tumor components from each sample. Based on GeoMx results, validation was performed by immunohistochemistry (IHC) on tissue specimens. Subsequently, functional analysis was conducted using the lung adenocarcinoma cell line. Results: By spatial transcriptomics profiling, positive UGT2B15 and negative S100A4 were suggested as candidate markers for distinguishing IMA from PCLM. The expression status of these proteins were confirmed by IHC on tissue specimens. Transcription factor analyses suggested that HNF1A was involved in UGT2B15 expression in the IMA, and functional analyses using lung cancer cell lines also demonstrated these associations. Conclusion: We identified UGT2B15 and S100A4 as potential candidate markers for distinguishing IMA from PCLM. The involvement of the transcription factor HNF1A in the expression of UGT2B15 in the IMA was suggested. Citation Format: Ryunosuke Fujii, Kousei Ishimura, Kazuhiko Shien, Shuta Tomida, Kenta Manabe, Shunsuke Mori, Kazuya Hisamatsu, Ryota Fujiwara, Atsushi Matsuoka, Ryo Yoshichika, Kazuhiro Okada, Yuma Fukumoto, Haruchika Yamamoto, Kumi Nakajima, Shin Tanaka, Hidejiro Torigoe, Ken Suzawa, Kentaroh Miyoshi, Mikio Okazaki, Seiichiro Sugimoto, Hirofumi Inoue, Kosei Takagi, Hidetaka Yamamoto, Shinichi Toyooka. Molecular diagnosis of lung tumors with a history of pancreatic cancer: Spatial profiling-based differential markers and functional insights into invasive mucinous adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 4166.

Cancer ResearchVol. 86(7_Supplement)
Okayama University (JP), Okayama University Hospital (JP)
Good health and well-being
Openalex Percentile: Top 27%
Single-cell and spatial transcriptomics
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