International validation of the consensus Immunoscore for the classification of colon cancer: a prognostic and accuracy study

Pagès, Franck; Mlecnik, Bernhard; Marliot, Florence; Bindea, Gabriela; Ou, Fang Shu; Bifulco, Carlo; Lugli, Alessandro; Zlobec, Inti; Rau, Tilman T.; Berger, Martin D.; Nagtegaal, Irıs D.; Vink‐Börger, Elisa; Hartmann, Arndt; Geppert, Carol; Kolwelter, Julie; Merkel, Susanne; Grützmann, Robert; Eynde, Marc Van den; Jouret‐Mourin, Anne; Kartheuser, Alex; Diller, Leonard; Remue, Christophe; Wang, Julia Y.; Bavi, Prashant; Roehrl, Michael H. A.; Ohashi, Pamela S.; Nguyen, Linh T.; Han, SeongJun; MacGregor, Heather L.; Hafezi-Bakhtiari, Sara; Wouters, Bradly G.; Masucci, Giuseppe; Andersson, Emilia; Závadová, Eva; Vočka, Michal; Špaček, Jan; Petruželka, Luboš; Konopásek, B; Dundr, Pavel; Skálová, Helena; Němejcová, Kristýna; Botti, Gerardo; Tatangelo, Fabiana; Delrio, Paolo; Ciliberto, Gennaro; Maio, Michele; Laghi, Luigi; Grizzi, Fabio; Fredriksen, Tessa; Buttard, Bénédicte; Angelova, Mihaela; Vasaturo, Angela; Maby, Pauline; Church, S.; Angell, Helen K.; Lafontaine, Lucie; Bruni, Daniela; Sissy, Carine El; Haicheur, Nacilla; Kirilovsky, Amos; Berger, Anne; Lagorce, Christine; Meyers, Jeffrey P.; Paustian, Christopher; Feng, Zipei; Ballesteros-Merino, Carmen; Dijkstra, Jeroen R.; Water, Carlijn van de; Vliet, Shannon van; Knijn, Nikki; Mușină, Ana Maria; Scripcariu, Dragoş Viorel; Popivanova, Boryana Konstantinova; Xu, Mingli; Fujita, Toshio; Hazama, Shoichi; Suzuki, Nobuaki; Nagano, Hiroaki; Okuno, Kiyotaka; Torigoe, Toshihiko; Sato, Noriyuki; Furuhata, Tomohisa; Takemasa, Ichiro; Itoh, Kyogo; Patel, Prabhu S.; Vora, Hemangini H.; Shah, Birva; Patel, Jayendrakumar B.; Rajvik, Kruti N.; Pandya, Shashank; Shukla, Shilin N.; Wang, Yili; Zhang, Guanjun; Kawakami, Yutaka; Marincola, Francesco M.; Ascierto, Paolo A.; Sargent, Daniel J.; Fox, Bernard A.; Galon, Jérôme

doi:10.1016/s0140-6736(18)30789-x

Cited by 1,529 publications

(1,383 citation statements)

References 31 publications

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“…Disease recurrence after surgery was increased in patients with a low immunoscore compared to a high immunoscore: After 5 years, 72% of the I0 patients had experienced relapse compared to 5% of the I4 patients. These findings were confirmed by a larger study recently on colorectal cancer patients in varying stages of the disease (Pagès et al 2018). Also for other cancer types, such as gastric cancer and melanoma, the immunoscore has been a good predictor of the immunotherapy success (Jiang et al 2018; Galon et al 2017).…”

Section: Influence Of the Tumour Micro-environmentsupporting

confidence: 75%

“…It has been found useful for a.o. colorectal cancer, gastric cancer and melanoma (Pagès et al 2018; Jiang et al 2018; Galon et al 2017), and therefore, can function as a possible biomarker for ACT success. Additionally, CTL effector phenotypes can be assessed by interferon-γ ELISPOT since it can measure how responsive CTLs are to specific epitopes (Schaefer et al 2012).…”

Section: Resultsmentioning

confidence: 99%

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Immune biomarkers for predicting response to adoptive cell transfer as cancer treatment

Belzen

Keşmir

2018

Immunogenetics

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Adoptive cell transfer (ACT) is a form of personalised immunotherapy which has shown promising results in metastasised cancer. For this treatment, autologous T lymphocytes are selected and stimulated in vitro before re-administration in large numbers. However, only a fraction of patients benefit from ACT, and it is not yet known what biomarkers can predict treatment outcome. In this review, we describe what tumour characteristics are associated with response to ACT. Based on the current knowledge, the best candidate biomarker for a good anti-tumour response seems to be a large number of neoantigens with a homogeneous distribution across the tumour in combination with sufficient MHC-I expression level. Additionally, it is necessary to be able to isolate a diverse population of T cells reactive to these neoantigens from tumour tissue or peripheral blood. Additional promising candidate biomarkers shared with other cancer immunotherapies are a large number of tumour-infiltrating cytotoxic and memory T cells, normal levels of glycolysis, and a pro-inflammatory cytokine profile within the tumour. Intense research in this field will hopefully result in identification of more biomarkers for cancers with low mutational load.

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Section: Influence Of the Tumour Micro-environmentsupporting

confidence: 75%

Section: Resultsmentioning

confidence: 99%

Immune biomarkers for predicting response to adoptive cell transfer as cancer treatment

Belzen

Keşmir

2018

Immunogenetics

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“…Therefore, accurate assessment of patient relapse risk is essential for improving personalized cancer care. To date, studies on the prognostic role of the density of CD3+ and CD8+ lymphocytes in the central- and peri-tumoral areas represented by intensity of IHC staining have gained increased attention [4, 27, 28], and this method has been validated through both a single-centre cohort study [5] and international multi-centre validation in localized colon cancer [6]. However, the assessment of only CD3+ and CD8+ lymphocytes cannot comprehensively reflect the local immune status.…”

Section: Discussionmentioning

confidence: 99%

Immune cell infiltration as a biomarker for the diagnosis and prognosis of stage I–III colon cancer

Zhou

Zhang

Zeng

et al. 2018

Cancer Immunol Immunother

214

183

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Tumour-infiltrating immune cells are a source of important prognostic information for patients with resectable colon cancer. We developed a novel immune model based on systematic assessments of the immune landscape inferred from bulk tumor transcriptomes of stage I–III colon cancer patients. The “Cell type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT)” algorithm was used to estimate the fraction of 22 immune cell types from six microarray public datasets. The random forest method and least absolute shrinkage and selection operator model were then used to establish immunoscores for diagnosis and prognosis. By comparing immune cell compositions in samples of 870 colon cancer patients and 70 normal controls, we constructed a diagnostic model, designated the diagnostic immune risk score (dIRS), that showed high specificity and sensitivity in both the training [area under the curve (AUC) = 0.98, p < 0.001] and validation (AUC 0.96, p < 0.001) sets. We also established a prognostic immune risk score (pIRS) that was found to be an independent prognostic factor for relapse-free survival in every series (training: HR 2.23; validation: HR 1.65; entire: HR 2.01; p < 0.001 for all), which showed better prognostic value than TNM stage. In addition, integration of the pIRS with clinical characteristics in a composite nomogram showed improved accuracy of relapse risk prediction, providing a higher net benefit than TNM stage, with well-fitted calibration curves. The proposed dIRS and pIRS models represent promising novel signatures for the diagnosis and prognosis prediction of colon cancer.Electronic supplementary materialThe online version of this article (10.1007/s00262-018-2289-7) contains supplementary material, which is available to authorized users.

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“…In a subpopulation of patients with UICC stage II cancer, the Immunoscore was also able to perform a risk stratification for recurrence at 5 years (hazard ratio for high versus low Immunoscore 0.33, 95% CI 0.21–0.52; p < 0.0001). In conclusion, the authors state that the results support the implementation of the consensus Immunoscore as a new component of a TNM-Immune classification of cancer [48]. Indeed, the results are promising and support the introduction in the evaluation of colon cancer.…”

Section: Colorectal Cancermentioning

confidence: 66%

“…The Immunoscore could be a new approach to classify cancer, designated as TNM-Immune [45-47]. In Mai 2018 the results of the international validation study of the consensus Immunoscore for the classification of colon cancer were published [48]. Tissue samples from 2,681 patients were included in the analyses (training set: 700 patients; internal validation set: 636 patients; external validation set: 1,345 patients).…”

Section: Colorectal Cancermentioning

confidence: 99%

Immunologic Biomarkers and Biomarkers for Immunotherapies in Gastrointestinal Cancer

Martin¹,

Märkl²

2019

Visc Med

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Gastrointestinal (GI) cancers contribute significantly to the worldwide cancer burden. Pathologic evaluation is indispensable for the estimation of prognosis and therapeutic strategy. At present, immunotherapies are evolving into efficient therapeutic approaches, which are accompanied by the need for biomarkers to predict therapy response. In colorectal cancers, the only predictive biomarker for Food and Drug Administration-approved immunotherapy is the mismatch repair status. Besides, pathogenic polymerase epsilon mutations, tumor mutational burden, neoantigen load, and features of the immune contexture could soon find their way into clinical routine. Furthermore, in colorectal cancer, the Immunoscore, which is defined by the amount of CD3+ and CD8+ T-cells in the tumor center as well as at the infiltrative margin, might supplement the TNM system in the future (as TNM-Immune). This immunologic biomarker was shown to be impressively prognostic and predictive in colorectal cancer. In conclusion, there is increasing evidence of immunologic as well as predictive biomarkers for immunotherapies in GI cancers. Nevertheless, future progress is necessary for the variety of current advances to be implemented in clinical care.

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International validation of the consensus Immunoscore for the classification of colon cancer: a prognostic and accuracy study

Cited by 1,529 publications

References 31 publications

Immune biomarkers for predicting response to adoptive cell transfer as cancer treatment

Immune biomarkers for predicting response to adoptive cell transfer as cancer treatment

Immune cell infiltration as a biomarker for the diagnosis and prognosis of stage I–III colon cancer

Immunologic Biomarkers and Biomarkers for Immunotherapies in Gastrointestinal Cancer

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