Development and validation of a prognostic and predictive 32-gene signature for gastric cancer

Jae Ho Cheong; Sam C. Wang; Sunho Park; Matthew R. Porembka; Alana L. Christie; Hyunki Kim; Hyo Song Kim; Hong Zhu; Woo Jin Hyung; Sung Hoon Noh; Bo Hu; Changjin Hong; John D. Karalis; In Ho Kim; Sung Hak Lee; Tae Hyun Hwang

doi:10.1038/s41467-022-28437-y

Development and validation of a prognostic and predictive 32-gene signature for gastric cancer

Jae Ho Cheong, Sam C. Wang, Sunho Park, Matthew R. Porembka, Alana L. Christie, Hyunki Kim, Hyo Song Kim, Hong Zhu, Woo Jin Hyung, Sung Hoon Noh, Bo Hu, Changjin Hong, John D. Karalis, In Ho Kim, Sung Hak Lee, Tae Hyun Hwang

Department of Surgery

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Abstract

Genomic profiling can provide prognostic and predictive information to guide clinical care. Biomarkers that reliably predict patient response to chemotherapy and immune checkpoint inhibition in gastric cancer are lacking. In this retrospective analysis, we use our machine learning algorithm NTriPath to identify a gastric-cancer specific 32-gene signature. Using unsupervised clustering on expression levels of these 32 genes in tumors from 567 patients, we identify four molecular subtypes that are prognostic for survival. We then built a support vector machine with linear kernel to generate a risk score that is prognostic for five-year overall survival and validate the risk score using three independent datasets. We also find that the molecular subtypes predict response to adjuvant 5-fluorouracil and platinum therapy after gastrectomy and to immune checkpoint inhibitors in patients with metastatic or recurrent disease. In sum, we show that the 32-gene signature is a promising prognostic and predictive biomarker to guide the clinical care of gastric cancer patients and should be validated using large patient cohorts in a prospective manner.

Original language	English
Article number	774
Journal	Nature communications
Volume	13
Issue number	1
DOIs	https://doi.org/10.1038/s41467-022-28437-y
Publication status	Published - 2022 Dec

Bibliographical note

Funding Information:
This research was supported by a grant from the Korean Health Technology R&D Project through the Korean Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant No. HI13C2162). SCW is a Disease Oriented Clinical Scholar at UT Southwestern and supported by the NCI/NIH (K08 CA222611). MRP is a Dedman Family Scholar in Clinical Care. JDK holds a Physician-Scientist Institutional Award from the Burroughs Wellcome Fund (award no. 1018897).

Publisher Copyright:
© 2022, The Author(s).

All Science Journal Classification (ASJC) codes

Chemistry(all)
Biochemistry, Genetics and Molecular Biology(all)
Physics and Astronomy(all)

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10.1038/s41467-022-28437-y

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Cheong, J. H., Wang, S. C., Park, S., Porembka, M. R., Christie, A. L., Kim, H., Kim, H. S., Zhu, H., Hyung, W. J., Noh, S. H., Hu, B., Hong, C., Karalis, J. D., Kim, I. H., Lee, S. H., & Hwang, T. H. (2022). Development and validation of a prognostic and predictive 32-gene signature for gastric cancer. Nature communications, 13(1), [774]. https://doi.org/10.1038/s41467-022-28437-y

Cheong, Jae Ho ; Wang, Sam C. ; Park, Sunho ; Porembka, Matthew R. ; Christie, Alana L. ; Kim, Hyunki ; Kim, Hyo Song ; Zhu, Hong ; Hyung, Woo Jin ; Noh, Sung Hoon ; Hu, Bo ; Hong, Changjin ; Karalis, John D. ; Kim, In Ho ; Lee, Sung Hak ; Hwang, Tae Hyun. / Development and validation of a prognostic and predictive 32-gene signature for gastric cancer. In: Nature communications. 2022 ; Vol. 13, No. 1.

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title = "Development and validation of a prognostic and predictive 32-gene signature for gastric cancer",

abstract = "Genomic profiling can provide prognostic and predictive information to guide clinical care. Biomarkers that reliably predict patient response to chemotherapy and immune checkpoint inhibition in gastric cancer are lacking. In this retrospective analysis, we use our machine learning algorithm NTriPath to identify a gastric-cancer specific 32-gene signature. Using unsupervised clustering on expression levels of these 32 genes in tumors from 567 patients, we identify four molecular subtypes that are prognostic for survival. We then built a support vector machine with linear kernel to generate a risk score that is prognostic for five-year overall survival and validate the risk score using three independent datasets. We also find that the molecular subtypes predict response to adjuvant 5-fluorouracil and platinum therapy after gastrectomy and to immune checkpoint inhibitors in patients with metastatic or recurrent disease. In sum, we show that the 32-gene signature is a promising prognostic and predictive biomarker to guide the clinical care of gastric cancer patients and should be validated using large patient cohorts in a prospective manner.",

author = "Cheong, {Jae Ho} and Wang, {Sam C.} and Sunho Park and Porembka, {Matthew R.} and Christie, {Alana L.} and Hyunki Kim and Kim, {Hyo Song} and Hong Zhu and Hyung, {Woo Jin} and Noh, {Sung Hoon} and Bo Hu and Changjin Hong and Karalis, {John D.} and Kim, {In Ho} and Lee, {Sung Hak} and Hwang, {Tae Hyun}",

note = "Funding Information: This research was supported by a grant from the Korean Health Technology R&D Project through the Korean Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant No. HI13C2162). SCW is a Disease Oriented Clinical Scholar at UT Southwestern and supported by the NCI/NIH (K08 CA222611). MRP is a Dedman Family Scholar in Clinical Care. JDK holds a Physician-Scientist Institutional Award from the Burroughs Wellcome Fund (award no. 1018897). Publisher Copyright: {\textcopyright} 2022, The Author(s).",

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Development and validation of a prognostic and predictive 32-gene signature for gastric cancer. / Cheong, Jae Ho; Wang, Sam C.; Park, Sunho; Porembka, Matthew R.; Christie, Alana L.; Kim, Hyunki; Kim, Hyo Song; Zhu, Hong; Hyung, Woo Jin; Noh, Sung Hoon; Hu, Bo; Hong, Changjin; Karalis, John D.; Kim, In Ho; Lee, Sung Hak; Hwang, Tae Hyun.

In: Nature communications, Vol. 13, No. 1, 774, 12.2022.

Research output: Contribution to journal › Article › peer-review

TY - JOUR

T1 - Development and validation of a prognostic and predictive 32-gene signature for gastric cancer

AU - Cheong, Jae Ho

AU - Wang, Sam C.

AU - Park, Sunho

AU - Porembka, Matthew R.

AU - Christie, Alana L.

AU - Kim, Hyunki

AU - Kim, Hyo Song

AU - Zhu, Hong

AU - Hyung, Woo Jin

AU - Noh, Sung Hoon

AU - Hu, Bo

AU - Hong, Changjin

AU - Karalis, John D.

AU - Kim, In Ho

AU - Lee, Sung Hak

AU - Hwang, Tae Hyun

N1 - Funding Information: This research was supported by a grant from the Korean Health Technology R&D Project through the Korean Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant No. HI13C2162). SCW is a Disease Oriented Clinical Scholar at UT Southwestern and supported by the NCI/NIH (K08 CA222611). MRP is a Dedman Family Scholar in Clinical Care. JDK holds a Physician-Scientist Institutional Award from the Burroughs Wellcome Fund (award no. 1018897). Publisher Copyright: © 2022, The Author(s).

PY - 2022/12

Y1 - 2022/12

N2 - Genomic profiling can provide prognostic and predictive information to guide clinical care. Biomarkers that reliably predict patient response to chemotherapy and immune checkpoint inhibition in gastric cancer are lacking. In this retrospective analysis, we use our machine learning algorithm NTriPath to identify a gastric-cancer specific 32-gene signature. Using unsupervised clustering on expression levels of these 32 genes in tumors from 567 patients, we identify four molecular subtypes that are prognostic for survival. We then built a support vector machine with linear kernel to generate a risk score that is prognostic for five-year overall survival and validate the risk score using three independent datasets. We also find that the molecular subtypes predict response to adjuvant 5-fluorouracil and platinum therapy after gastrectomy and to immune checkpoint inhibitors in patients with metastatic or recurrent disease. In sum, we show that the 32-gene signature is a promising prognostic and predictive biomarker to guide the clinical care of gastric cancer patients and should be validated using large patient cohorts in a prospective manner.

AB - Genomic profiling can provide prognostic and predictive information to guide clinical care. Biomarkers that reliably predict patient response to chemotherapy and immune checkpoint inhibition in gastric cancer are lacking. In this retrospective analysis, we use our machine learning algorithm NTriPath to identify a gastric-cancer specific 32-gene signature. Using unsupervised clustering on expression levels of these 32 genes in tumors from 567 patients, we identify four molecular subtypes that are prognostic for survival. We then built a support vector machine with linear kernel to generate a risk score that is prognostic for five-year overall survival and validate the risk score using three independent datasets. We also find that the molecular subtypes predict response to adjuvant 5-fluorouracil and platinum therapy after gastrectomy and to immune checkpoint inhibitors in patients with metastatic or recurrent disease. In sum, we show that the 32-gene signature is a promising prognostic and predictive biomarker to guide the clinical care of gastric cancer patients and should be validated using large patient cohorts in a prospective manner.

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VL - 13

JO - Nature Communications

JF - Nature Communications

SN - 2041-1723

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ER -

Development and validation of a prognostic and predictive 32-gene signature for gastric cancer

Abstract

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