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Published in: Cancer Cell International 1/2021

Open Access 01-12-2021 | Colorectal Cancer | Primary research

A deep learning quantified stroma-immune score to predict survival of patients with stage II–III colorectal cancer

Authors: Zeyan Xu, Yong Li, Yingyi Wang, Shenyan Zhang, Yanqi Huang, Su Yao, Chu Han, Xipeng Pan, Zhenwei Shi, Yun Mao, Yao Xu, Xiaomei Huang, Huan Lin, Xin Chen, Changhong Liang, Zhenhui Li, Ke Zhao, Qingling Zhang, Zaiyi Liu

Published in: Cancer Cell International | Issue 1/2021

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Abstract

Background

Profound heterogeneity in prognosis has been observed in colorectal cancer (CRC) patients with intermediate levels of disease (stage II–III), advocating the identification of valuable biomarkers that could improve the prognostic stratification. This study aims to develop a deep learning-based pipeline for fully automatic quantification of immune infiltration within the stroma region on immunohistochemical (IHC) whole-slide images (WSIs) and further analyze its prognostic value in CRC.

Methods

Patients from two independent cohorts were divided into three groups: the development group (N = 200), the internal (N = 134), and the external validation group (N = 90). We trained a convolutional neural network for tissue classification of CD3 and CD8 stained WSIs. A scoring system, named stroma-immune score, was established by quantifying the density of CD3+ and CD8+ T-cells infiltration in the stroma region.

Results

Patients with higher stroma-immune scores had much longer survival. In the development group, 5-year survival rates of the low and high scores were 55.7% and 80.8% (hazard ratio [HR] for high vs. low 0.39, 95% confidence interval [CI] 0.24–0.63, P < 0.001). These results were confirmed in the internal and external validation groups with 5-year survival rates of low and high scores were 57.1% and 78.8%, 63.9% and 88.9%, respectively (internal: HR for high vs. low 0.49, 95% CI 0.28–0.88, P = 0.017; external: HR for high vs. low 0.35, 95% CI 0.15–0.83, P = 0.018). The combination of stroma-immune score and tumor-node-metastasis (TNM) stage showed better discrimination ability for survival prediction than using the TNM stage alone.

Conclusions

We proposed a stroma-immune score via a deep learning-based pipeline to quantify CD3+ and CD8+ T-cells densities within the stroma region on WSIs of CRC and further predict survival.
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Literature
1.
go back to reference Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68:394–424.CrossRef Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68:394–424.CrossRef
2.
go back to reference Edge SB, Compton CC. The American Joint Committee on Cancer: the 7th edition of the AJCC cancer staging manual and the future of TNM. Ann Surg Oncol. 2010;17:1471–4.CrossRef Edge SB, Compton CC. The American Joint Committee on Cancer: the 7th edition of the AJCC cancer staging manual and the future of TNM. Ann Surg Oncol. 2010;17:1471–4.CrossRef
3.
go back to reference Nagtegaal ID, Quirke P, Schmoll H-J. Has the new TNM classification for colorectal cancer improved care? Nat Rev Clin Oncol. 2012;9:119–23.CrossRef Nagtegaal ID, Quirke P, Schmoll H-J. Has the new TNM classification for colorectal cancer improved care? Nat Rev Clin Oncol. 2012;9:119–23.CrossRef
4.
go back to reference Dekker E, Tanis PJ, Vleugels JLA, Kasi PM, Wallace MB. Colorectal cancer. Lancet. 2019;394:1467–80.CrossRef Dekker E, Tanis PJ, Vleugels JLA, Kasi PM, Wallace MB. Colorectal cancer. Lancet. 2019;394:1467–80.CrossRef
5.
go back to reference Pagès F, Mlecnik B, Marliot F, Bindea G, Ou F-S, Bifulco C, et al. International validation of the consensus Immunoscore for the classification of colon cancer: a prognostic and accuracy study. Lancet. 2018;391:2128–39.CrossRef Pagès F, Mlecnik B, Marliot F, Bindea G, Ou F-S, Bifulco C, et al. International validation of the consensus Immunoscore for the classification of colon cancer: a prognostic and accuracy study. Lancet. 2018;391:2128–39.CrossRef
6.
go back to reference Trabelsi M, Farah F, Zouari B, Jaafoura MH, Kharrat M. An Immunoscore system based on CD3+ and CD8+ infiltrating lymphocytes densities to predict the outcome of patients with colorectal adenocarcinoma. OTT. 2019;12:8663–73.CrossRef Trabelsi M, Farah F, Zouari B, Jaafoura MH, Kharrat M. An Immunoscore system based on CD3+ and CD8+ infiltrating lymphocytes densities to predict the outcome of patients with colorectal adenocarcinoma. OTT. 2019;12:8663–73.CrossRef
7.
go back to reference Yoo S-Y, Park HE, Kim JH, Wen X, Jeong S, Cho N-Y, et al. Whole-slide image analysis reveals quantitative landscape of tumor–immune microenvironment in colorectal cancers. Clin Cancer Res. 2020;26:870–81.CrossRef Yoo S-Y, Park HE, Kim JH, Wen X, Jeong S, Cho N-Y, et al. Whole-slide image analysis reveals quantitative landscape of tumor–immune microenvironment in colorectal cancers. Clin Cancer Res. 2020;26:870–81.CrossRef
8.
go back to reference Kather JN, Krisam J, Charoentong P, Luedde T, Herpel E, Weis C-A, et al. Predicting survival from colorectal cancer histology slides using deep learning: a retrospective multicenter study. PLoS Med. 2019;16:e1002730.CrossRef Kather JN, Krisam J, Charoentong P, Luedde T, Herpel E, Weis C-A, et al. Predicting survival from colorectal cancer histology slides using deep learning: a retrospective multicenter study. PLoS Med. 2019;16:e1002730.CrossRef
9.
go back to reference Martin B, Banner BM, Schäfer E-M, Mayr P, Anthuber M, Schenkirsch G, et al. Tumor proportion in colon cancer: results from a semiautomatic image analysis approach. Virchows Arch. 2020;477:185–93.CrossRef Martin B, Banner BM, Schäfer E-M, Mayr P, Anthuber M, Schenkirsch G, et al. Tumor proportion in colon cancer: results from a semiautomatic image analysis approach. Virchows Arch. 2020;477:185–93.CrossRef
10.
go back to reference Zhao K, Li Z, Yao S, Wang Y, Wu X, Xu Z, et al. Artificial intelligence quantified tumour-stroma ratio is an independent predictor for overall survival in resectable colorectal cancer. EBioMedicine. 2020;61:103054.CrossRef Zhao K, Li Z, Yao S, Wang Y, Wu X, Xu Z, et al. Artificial intelligence quantified tumour-stroma ratio is an independent predictor for overall survival in resectable colorectal cancer. EBioMedicine. 2020;61:103054.CrossRef
11.
go back to reference Bera K, Schalper KA, Rimm DL, Velcheti V, Madabhushi A. Artificial intelligence in digital pathology—new tools for diagnosis and precision oncology. Nat Rev Clin Oncol. 2019;16:703–15.CrossRef Bera K, Schalper KA, Rimm DL, Velcheti V, Madabhushi A. Artificial intelligence in digital pathology—new tools for diagnosis and precision oncology. Nat Rev Clin Oncol. 2019;16:703–15.CrossRef
12.
go back to reference Nearchou IP, Lillard K, Gavriel CG, Ueno H, Harrison DJ, Caie PD. Automated analysis of lymphocytic infiltration, tumor budding, and their spatial relationship improves prognostic accuracy in colorectal cancer. Cancer Immunol Res. 2019;7:609–20.CrossRef Nearchou IP, Lillard K, Gavriel CG, Ueno H, Harrison DJ, Caie PD. Automated analysis of lymphocytic infiltration, tumor budding, and their spatial relationship improves prognostic accuracy in colorectal cancer. Cancer Immunol Res. 2019;7:609–20.CrossRef
13.
go back to reference Nearchou IP, Gwyther BM, Georgiakakis ECT, Gavriel CG, Lillard K, Kajiwara Y, et al. Spatial immune profiling of the colorectal tumor microenvironment predicts good outcome in stage II patients. npj Digit Med. 2020;3:71.CrossRef Nearchou IP, Gwyther BM, Georgiakakis ECT, Gavriel CG, Lillard K, Kajiwara Y, et al. Spatial immune profiling of the colorectal tumor microenvironment predicts good outcome in stage II patients. npj Digit Med. 2020;3:71.CrossRef
16.
go back to reference Hunt RJ. Percent agreement, Pearson’s correlation, and Kappa as measures of inter-examiner reliability. J Dent Res. 1986;65:128–30.CrossRef Hunt RJ. Percent agreement, Pearson’s correlation, and Kappa as measures of inter-examiner reliability. J Dent Res. 1986;65:128–30.CrossRef
18.
go back to reference Saleh R, Sasidharan Nair V, Toor SM, Taha RZ, Murshed K, Al-Dhaheri M, et al. Differential gene expression of tumor-infiltrating CD8+ T cells in advanced versus early-stage colorectal cancer and identification of a gene signature of poor prognosis. J Immunother Cancer. 2020;8:e001294.CrossRef Saleh R, Sasidharan Nair V, Toor SM, Taha RZ, Murshed K, Al-Dhaheri M, et al. Differential gene expression of tumor-infiltrating CD8+ T cells in advanced versus early-stage colorectal cancer and identification of a gene signature of poor prognosis. J Immunother Cancer. 2020;8:e001294.CrossRef
19.
go back to reference Idos GE, Kwok J, Bonthala N, Kysh L, Gruber SB, Qu C. The prognostic implications of tumor infiltrating lymphocytes in colorectal cancer: a systematic review and meta-analysis. Sci Rep. 2020;10:3360.CrossRef Idos GE, Kwok J, Bonthala N, Kysh L, Gruber SB, Qu C. The prognostic implications of tumor infiltrating lymphocytes in colorectal cancer: a systematic review and meta-analysis. Sci Rep. 2020;10:3360.CrossRef
20.
go back to reference Kumar S, Singh SK, Rana B, Rana A. Tumor-infiltrating CD8+ T cell antitumor efficacy and exhaustion: molecular insights. Drug Discov Today. 2021;26:951–67.CrossRef Kumar S, Singh SK, Rana B, Rana A. Tumor-infiltrating CD8+ T cell antitumor efficacy and exhaustion: molecular insights. Drug Discov Today. 2021;26:951–67.CrossRef
21.
go back to reference Sangaletti S, Chiodoni C, Tripodo C, Colombo MP. The good and bad of targeting cancer-associated extracellular matrix. Curr Opin Pharmacol. 2017;35:75–82.CrossRef Sangaletti S, Chiodoni C, Tripodo C, Colombo MP. The good and bad of targeting cancer-associated extracellular matrix. Curr Opin Pharmacol. 2017;35:75–82.CrossRef
22.
go back to reference Joyce JA, Fearon DT. T cell exclusion, immune privilege, and the tumor microenvironment. Science. 2015;348:74–80.CrossRef Joyce JA, Fearon DT. T cell exclusion, immune privilege, and the tumor microenvironment. Science. 2015;348:74–80.CrossRef
23.
go back to reference Lu P, Weaver VM, Werb Z. The extracellular matrix: a dynamic niche in cancer progression. J Cell Biol. 2012;196:395–406.CrossRef Lu P, Weaver VM, Werb Z. The extracellular matrix: a dynamic niche in cancer progression. J Cell Biol. 2012;196:395–406.CrossRef
24.
go back to reference Failmezger H, Muralidhar S, Rullan A, de Andrea CE, Sahai E, Yuan Y. Topological tumor graphs: a graph-based spatial model to infer stromal recruitment for immunosuppression in melanoma histology. Cancer Res. 2020;80:1199–209.CrossRef Failmezger H, Muralidhar S, Rullan A, de Andrea CE, Sahai E, Yuan Y. Topological tumor graphs: a graph-based spatial model to infer stromal recruitment for immunosuppression in melanoma histology. Cancer Res. 2020;80:1199–209.CrossRef
25.
go back to reference Reichling C, Taieb J, Derangere V, Klopfenstein Q, Le Malicot K, Gornet J-M, et al. Artificial intelligence-guided tissue analysis combined with immune infiltrate assessment predicts stage III colon cancer outcomes in PETACC08 study. Gut. 2020;69:681–90.CrossRef Reichling C, Taieb J, Derangere V, Klopfenstein Q, Le Malicot K, Gornet J-M, et al. Artificial intelligence-guided tissue analysis combined with immune infiltrate assessment predicts stage III colon cancer outcomes in PETACC08 study. Gut. 2020;69:681–90.CrossRef
26.
go back to reference van Pelt GW, Sandberg TP, Morreau H, Gelderblom H, van Krieken JHJM, Tollenaar RAEM, et al. The tumour-stroma ratio in colon cancer: the biological role and its prognostic impact. Histopathology. 2018;73:197–206.CrossRef van Pelt GW, Sandberg TP, Morreau H, Gelderblom H, van Krieken JHJM, Tollenaar RAEM, et al. The tumour-stroma ratio in colon cancer: the biological role and its prognostic impact. Histopathology. 2018;73:197–206.CrossRef
27.
go back to reference Chu QD, Zhou M, Medeiros KL, Peddi P, Kavanaugh M, Wu X-C. Poor survival in stage IIB/C (T4N0) compared to stage IIIA (T1–2 N1, T1N2a) colon cancer persists even after adjusting for adequate lymph nodes retrieved and receipt of adjuvant chemotherapy. BMC Cancer. 2016;16:460.CrossRef Chu QD, Zhou M, Medeiros KL, Peddi P, Kavanaugh M, Wu X-C. Poor survival in stage IIB/C (T4N0) compared to stage IIIA (T1–2 N1, T1N2a) colon cancer persists even after adjusting for adequate lymph nodes retrieved and receipt of adjuvant chemotherapy. BMC Cancer. 2016;16:460.CrossRef
28.
go back to reference Giraldo NA, Sanchez-Salas R, Peske JD, Vano Y, Becht E, Petitprez F, et al. The clinical role of the TME in solid cancer. Br J Cancer. 2019;120:45–53.CrossRef Giraldo NA, Sanchez-Salas R, Peske JD, Vano Y, Becht E, Petitprez F, et al. The clinical role of the TME in solid cancer. Br J Cancer. 2019;120:45–53.CrossRef
29.
go back to reference West NP, Dattani M, McShane P, Hutchins G, Grabsch J, Mueller W, et al. The proportion of tumour cells is an independent predictor for survival in colorectal cancer patients. Br J Cancer. 2010;102:1519–23.CrossRef West NP, Dattani M, McShane P, Hutchins G, Grabsch J, Mueller W, et al. The proportion of tumour cells is an independent predictor for survival in colorectal cancer patients. Br J Cancer. 2010;102:1519–23.CrossRef
30.
go back to reference van Pelt GW, Krol JA, Lips IM, Peters FP, van Klaveren D, Boonstra JJ, et al. The value of tumor-stroma ratio as predictor of pathologic response after neoadjuvant chemoradiotherapy in esophageal cancer. Clin Transl Radiat Oncol. 2020;20:39–44.CrossRef van Pelt GW, Krol JA, Lips IM, Peters FP, van Klaveren D, Boonstra JJ, et al. The value of tumor-stroma ratio as predictor of pathologic response after neoadjuvant chemoradiotherapy in esophageal cancer. Clin Transl Radiat Oncol. 2020;20:39–44.CrossRef
Metadata
Title
A deep learning quantified stroma-immune score to predict survival of patients with stage II–III colorectal cancer
Authors
Zeyan Xu
Yong Li
Yingyi Wang
Shenyan Zhang
Yanqi Huang
Su Yao
Chu Han
Xipeng Pan
Zhenwei Shi
Yun Mao
Yao Xu
Xiaomei Huang
Huan Lin
Xin Chen
Changhong Liang
Zhenhui Li
Ke Zhao
Qingling Zhang
Zaiyi Liu
Publication date
01-12-2021
Publisher
BioMed Central
Published in
Cancer Cell International / Issue 1/2021
Electronic ISSN: 1475-2867
DOI
https://doi.org/10.1186/s12935-021-02297-w

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