Computational tumor stroma reaction evaluation led to novel prognosis-associated fibrosis and molecular signature discoveries in high-grade serous ovarian carcinoma
BackgroundAs one of the key criteria to differentiate benign vs. malignant tumors in ovarian and other solid cancers, tumor-stroma reaction (TSR) is long observed by pathologists and has been found correlated with patient prognosis. However, paucity of study aims to overcome subjective bias or autom...
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2022-09-01
|
| Series: | Frontiers in Medicine |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2022.994467/full |
| _version_ | 1828738246336577536 |
|---|---|
| author | Jun Jiang Burak Tekin Lin Yuan Sebastian Armasu Stacey J. Winham Ellen L. Goode Hongfang Liu Yajue Huang Ruifeng Guo Chen Wang |
| author_facet | Jun Jiang Burak Tekin Lin Yuan Sebastian Armasu Stacey J. Winham Ellen L. Goode Hongfang Liu Yajue Huang Ruifeng Guo Chen Wang |
| author_sort | Jun Jiang |
| collection | DOAJ |
| description | BackgroundAs one of the key criteria to differentiate benign vs. malignant tumors in ovarian and other solid cancers, tumor-stroma reaction (TSR) is long observed by pathologists and has been found correlated with patient prognosis. However, paucity of study aims to overcome subjective bias or automate TSR evaluation for enabling association analysis to a large cohort.Materials and methodsServing as positive and negative sets of TSR studies, H&E slides of primary tumors of high-grade serous ovarian carcinoma (HGSOC) (n = 291) and serous borderline ovarian tumor (SBOT) (n = 15) were digitally scanned. Three pathologist-defined quantification criteria were used to characterize the extents of TSR. Scores for each criterion were annotated (0/1/2 as none-low/intermediate/high) in the training set consisting of 18,265 H&E patches. Serial of deep learning (DL) models were trained to identify tumor vs. stroma regions and predict TSR scores. After cross-validation and independent validations, the trained models were generalized to the entire HGSOC cohort and correlated with clinical characteristics. In a subset of cases tumor transcriptomes were available, gene- and pathway-level association studies were conducted with TSR scores.ResultsThe trained models accurately identified the tumor stroma tissue regions and predicted TSR scores. Within tumor stroma interface region, TSR fibrosis scores were strongly associated with patient prognosis. Cancer signaling aberrations associated 14 KEGG pathways were also found positively correlated with TSR-fibrosis score.ConclusionWith the aid of DL, TSR evaluation could be generalized to large cohort to enable prognostic association analysis and facilitate discovering novel gene and pathways associated with disease progress. |
| first_indexed | 2024-04-12T23:59:47Z |
| format | Article |
| id | doaj.art-0aec973806a7429cb62a15fb1591291a |
| institution | Directory Open Access Journal |
| issn | 2296-858X |
| language | English |
| last_indexed | 2024-04-12T23:59:47Z |
| publishDate | 2022-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj.art-0aec973806a7429cb62a15fb1591291a2022-12-22T03:11:23ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2022-09-01910.3389/fmed.2022.994467994467Computational tumor stroma reaction evaluation led to novel prognosis-associated fibrosis and molecular signature discoveries in high-grade serous ovarian carcinomaJun Jiang0Burak Tekin1Lin Yuan2Sebastian Armasu3Stacey J. Winham4Ellen L. Goode5Hongfang Liu6Yajue Huang7Ruifeng Guo8Chen Wang9Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United StatesDepartment of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, United StatesPathology Center, Shanghai General Hospital, Shanghai, ChinaDepartment of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United StatesDepartment of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United StatesDepartment of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United StatesDepartment of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, United StatesDepartment of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, United StatesDepartment of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, United StatesDepartment of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United StatesBackgroundAs one of the key criteria to differentiate benign vs. malignant tumors in ovarian and other solid cancers, tumor-stroma reaction (TSR) is long observed by pathologists and has been found correlated with patient prognosis. However, paucity of study aims to overcome subjective bias or automate TSR evaluation for enabling association analysis to a large cohort.Materials and methodsServing as positive and negative sets of TSR studies, H&E slides of primary tumors of high-grade serous ovarian carcinoma (HGSOC) (n = 291) and serous borderline ovarian tumor (SBOT) (n = 15) were digitally scanned. Three pathologist-defined quantification criteria were used to characterize the extents of TSR. Scores for each criterion were annotated (0/1/2 as none-low/intermediate/high) in the training set consisting of 18,265 H&E patches. Serial of deep learning (DL) models were trained to identify tumor vs. stroma regions and predict TSR scores. After cross-validation and independent validations, the trained models were generalized to the entire HGSOC cohort and correlated with clinical characteristics. In a subset of cases tumor transcriptomes were available, gene- and pathway-level association studies were conducted with TSR scores.ResultsThe trained models accurately identified the tumor stroma tissue regions and predicted TSR scores. Within tumor stroma interface region, TSR fibrosis scores were strongly associated with patient prognosis. Cancer signaling aberrations associated 14 KEGG pathways were also found positively correlated with TSR-fibrosis score.ConclusionWith the aid of DL, TSR evaluation could be generalized to large cohort to enable prognostic association analysis and facilitate discovering novel gene and pathways associated with disease progress.https://www.frontiersin.org/articles/10.3389/fmed.2022.994467/fulltumor-stroma reactionhigh-grade serous ovarian carcinomadigital pathologyprognostic fibrosismolecular signature |
| spellingShingle | Jun Jiang Burak Tekin Lin Yuan Sebastian Armasu Stacey J. Winham Ellen L. Goode Hongfang Liu Yajue Huang Ruifeng Guo Chen Wang Computational tumor stroma reaction evaluation led to novel prognosis-associated fibrosis and molecular signature discoveries in high-grade serous ovarian carcinoma Frontiers in Medicine tumor-stroma reaction high-grade serous ovarian carcinoma digital pathology prognostic fibrosis molecular signature |
| title | Computational tumor stroma reaction evaluation led to novel prognosis-associated fibrosis and molecular signature discoveries in high-grade serous ovarian carcinoma |
| title_full | Computational tumor stroma reaction evaluation led to novel prognosis-associated fibrosis and molecular signature discoveries in high-grade serous ovarian carcinoma |
| title_fullStr | Computational tumor stroma reaction evaluation led to novel prognosis-associated fibrosis and molecular signature discoveries in high-grade serous ovarian carcinoma |
| title_full_unstemmed | Computational tumor stroma reaction evaluation led to novel prognosis-associated fibrosis and molecular signature discoveries in high-grade serous ovarian carcinoma |
| title_short | Computational tumor stroma reaction evaluation led to novel prognosis-associated fibrosis and molecular signature discoveries in high-grade serous ovarian carcinoma |
| title_sort | computational tumor stroma reaction evaluation led to novel prognosis associated fibrosis and molecular signature discoveries in high grade serous ovarian carcinoma |
| topic | tumor-stroma reaction high-grade serous ovarian carcinoma digital pathology prognostic fibrosis molecular signature |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2022.994467/full |
| work_keys_str_mv | AT junjiang computationaltumorstromareactionevaluationledtonovelprognosisassociatedfibrosisandmolecularsignaturediscoveriesinhighgradeserousovariancarcinoma AT buraktekin computationaltumorstromareactionevaluationledtonovelprognosisassociatedfibrosisandmolecularsignaturediscoveriesinhighgradeserousovariancarcinoma AT linyuan computationaltumorstromareactionevaluationledtonovelprognosisassociatedfibrosisandmolecularsignaturediscoveriesinhighgradeserousovariancarcinoma AT sebastianarmasu computationaltumorstromareactionevaluationledtonovelprognosisassociatedfibrosisandmolecularsignaturediscoveriesinhighgradeserousovariancarcinoma AT staceyjwinham computationaltumorstromareactionevaluationledtonovelprognosisassociatedfibrosisandmolecularsignaturediscoveriesinhighgradeserousovariancarcinoma AT ellenlgoode computationaltumorstromareactionevaluationledtonovelprognosisassociatedfibrosisandmolecularsignaturediscoveriesinhighgradeserousovariancarcinoma AT hongfangliu computationaltumorstromareactionevaluationledtonovelprognosisassociatedfibrosisandmolecularsignaturediscoveriesinhighgradeserousovariancarcinoma AT yajuehuang computationaltumorstromareactionevaluationledtonovelprognosisassociatedfibrosisandmolecularsignaturediscoveriesinhighgradeserousovariancarcinoma AT ruifengguo computationaltumorstromareactionevaluationledtonovelprognosisassociatedfibrosisandmolecularsignaturediscoveriesinhighgradeserousovariancarcinoma AT chenwang computationaltumorstromareactionevaluationledtonovelprognosisassociatedfibrosisandmolecularsignaturediscoveriesinhighgradeserousovariancarcinoma |