Details

Autor(en) / Beteiligte

• Yoo, Seung-Yeon
• Park, Hye Eun
• Kim, Jung Ho
• Wen, Xianyu
• Jeong, Seorin
• Cho, Nam-Yun
• Gwon, Hwang Gwan
• Kim, Kwangsoo
• Lee, Hye Seung
• Jeong, Seung-Yong
• Park, Kyu Joo
• Han, Sae-Won
• Kim, Tae-You
• Bae, Jeong Mo
• Kang, Gyeong Hoon

Titel

Whole-Slide Image Analysis Reveals Quantitative Landscape of Tumor-Immune Microenvironment in Colorectal Cancers

Ist Teil von

• Clinical cancer research, 2020-02, Vol.26 (4), p.870-881

Ort / Verlag

United States

Erscheinungsjahr

2020

Quelle

Free E-Journal (出版社公開部分のみ）

Beschreibungen/Notizen

• Despite the well-known prognostic value of the tumor-immune microenvironment (TIME) in colorectal cancers, objective and readily applicable methods for quantifying tumor-infiltrating lymphocytes (TIL) and the tumor-stroma ratio (TSR) are not yet available. We established an open-source software-based analytic pipeline for quantifying TILs and the TSR from whole-slide images obtained after CD3 and CD8 IHC staining. Using a random forest classifier, the method separately quantified intraepithelial TILs (iTIL) and stromal TILs (sTIL). We applied this method to discovery and validation cohorts of 578 and 283 stage III or high-risk stage II colorectal cancers patients, respectively, who were subjected to curative surgical resection and oxlaliplatin-based adjuvant chemotherapy. Automatic quantification of iTILs and sTILs showed a moderate concordance with that obtained after visual inspection by a pathologist. The K-means-based consensus clustering of 197 TIME parameters that showed robustness against interobserver variations caused colorectal cancers to be grouped into five distinctive subgroups, reminiscent of those for consensus molecular subtypes (CMS1-4 and mixed/intermediate group). In accordance with the original CMS report, the CMS4-like subgroup (cluster 4) was significantly associated with a worse 5-year relapse-free survival and proved to be an independent prognostic factor. The clinicopathologic and prognostic features of the TIME subgroups have been validated in an independent validation cohort. Machine-learning-based image analysis can be useful for extracting quantitative information about the TIME, using whole-slide histopathologic images. This information can classify colorectal cancers into clinicopathologically relevant subgroups without performing a molecular analysis of the tumors.