Genomics and emerging biomarkers for immunotherapy of colorectal cancer

Kather, Jakob Nikolas; Halama, Niels; Jaeger, Dirk

doi:10.1016/j.semcancer.2018.02.010

Cited by 112 publications

(82 citation statements)

References 135 publications

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“…As most metastatic colorectal cancer cases are microsatellite-stable and the possibilities of immunotherapy are still limited in such tumors, it has been advised to intensify the research of the complex tumor microenvironment in association with microsatellite status. 39 As the first target of our study, we aimed to assess the inflammation in a large group of 553 consecutive, surgically treated primary colorectal carcinomas. Colorectal cancer is classically seen in aged people.…”

Section: Discussionmentioning

confidence: 99%

<p>The Association Between Inflammation, Epithelial Mesenchymal Transition and Stemness in Colorectal Carcinoma</p>

Briede¹,

Štrumfa²,

Vanags³

et al. 2020

JIR

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Background: Inflammation plays an important albeit dual role in carcinogenesis. Survival studies have highlighted the prognostic significance of peritumorous inflammation. Currently, the theoretical background allows inflammation, epithelial mesenchymal transition (EMT) and the closely associated stem cell differentiation in colorectal carcinoma (CRC) to be linked. However, there is scarce direct morphological evidence. Purpose and methods: The aim of our study was to investigate the role of inflammation in cancer growth and invasion by analyzing the association between inflammation and known morphological prognostic features of colorectal cancer, EMT, stemness and mismatch repair (MMR) protein expression. The study was designed as a retrospective morphological and immunohistochemical assessment of 553 consecutive cases of surgically treated primary CRC. Results: There were statistically significant associations between high-grade inflammation and lower pT (p = 0.002), absence of lymph node metastases (p < 0.001) and less frequent lymphatic (p = 0.003), venous (p = 0.017), arterial (p = 0.012), perineural (p = 0.001) and intraneural (p = 0.01) invasion. In contrast, Crohn's like reaction (CLR) by density of lymphoid follicles in the invasive front lacked significant differences in regard to pT, pN, tumor invasion into surrounding structures (blood or lymphatic vessels, nerves), grade or necrosis (all p > 0.05). The expression of E-cadherin, CD44 and MMR proteins yielded no statistically significant associations with peritumorous inflammation by Klintrup-Mäkinen score or the density of lymphoid follicles. Nevertheless, E-cadherin levels were significantly associated with the density of eosinophils (p = 0.007). Conclusion: High-grade peritumorous inflammation is associated with beneficial morphologic CRC features, including less frequent manifestations of invasion, and is not secondary to tissue damage and necrosis. CLR is not associated with cancer spread by pTN; this finding indirectly suggests an independent role of CLR in carcinogenesis. Further, inflammation by Klintrup-Mäkinen grade and CLR is not dependent on epithelial-mesenchymal transition and stem cell differentiation. Our study highlights the complex associations between inflammation, tumor morphology, EMT, stemness and MMR protein expression in human CRC tissues.

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Section: Discussionmentioning

confidence: 99%

<p>The Association Between Inflammation, Epithelial Mesenchymal Transition and Stemness in Colorectal Carcinoma</p>

Briede¹,

Štrumfa²,

Vanags³

et al. 2020

JIR

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“…Therefore, CRC provides a good model to explore the effect of different tumour-immune environments. Furthermore, therapy based on immune checkpoint inhibitors has been successfully applied for MSI tumours, but the efficacy is still not known for the majority of CRC cases 29 .…”

Section: Most Colorectal Cancers Are Antigen-hot and Enriched For Immmentioning

confidence: 99%

Evolutionary dynamics of neoantigens in growing tumours

Lakatos

Williams

Schenck

et al. 2019

Preprint

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Cancer evolution is driven by the acquisition of somatic mutations that provide cells with a beneficial phenotype in a changing microenvironment. However, mutations that give rise to neoantigens, novel cancer-specific peptides that elicit an immune response, are likely to be disadvantageous. Here we show how the clonal structure and immunogenotype of growing tumours is shaped by negative selection in response to neoantigenic mutations. We construct a mathematical model of neoantigen evolution in a growing tumour, and verify the model using genomic sequencing data. The model predicts that, in the absence of active immune escape mechanisms, tumours either evolve clonal neoantigens (antigen-'hot'), or have no clonally-expanded neoantigens at all (antigen-'cold'), whereas antigen-'warm' tumours (with high frequency subclonal neoantigens) form only following the evolution of immune evasion. Counterintuitively, strong negative selection for neoantigens during tumour formation leads to an increased number of antigen-warm or -hot tumours, as a consequence of selective pressure for immune escape. Further, we show that the clone size distribution under negative selection is effectively-neutral, and moreover, that stronger negative selection paradoxically leads to more neutral-like dynamics. Analysis of antigen clone sizes and immune escape in colorectal cancer exome sequencing data confirms these results.Overall, we provide and verify a mathematical framework to understand the evolutionary dynamics and clonality of neoantigens in human cancers that may inform patientspecific immunotherapy decision-making.

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“…MSI-high status was recently identified as a predictor of the efficacy of anti-PD-1/PD-L1 immunotherapy 31 , 52 . MSI status can be determined by immunohistochemistry ( staining for MLH1, MSH2, MSH6, PMS2) or genetic analyses ( polymerase chain reaction (PCR)-based analysis of MSI markers) 53 . However, such screening is systematically performed only at high-volume tertiary care centers.…”

Section: A Deep Learning Model For the Prediction Of Gene Expressionmentioning

confidence: 99%

Transcriptomic learning for digital pathology

Schmauch¹,

Romagnoni²,

Pronier³

et al. 2019

Preprint

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^,⋕ : These authors contributed equally.Deep learning methods for digital pathology analysis have proved an effective way to address multiple clinical questions, from diagnosis to prognosis and the prediction of treatment outcomes. They have also recently been used to predict gene mutations from pathology images, but no comprehensive evaluation of their potential for extracting molecular features from histology slides, has yet been performed. We propose a novel approach based on the integration of multiple data modes, and show that our deep learning model HE2RNA can be trained to predict systematically RNA-Seq profiles from whole-slide images alone, without the need for expert annotation. The model facilitates the virtual spatialization of gene expression, as validated by double-staining in an independent dataset. The results can therefore be interpreted in detail and this model opens up new opportunities for virtual staining. Finally, the transcriptomic representation learned by the model could be could be used to improve performances for other clinical tasks, particularly for small datasets. For example we studied the problem of predicting microsatellite instability from Hematoxylin & Eosin (H&E)-stained images. Greater prediction ability was achieved in such a realistic framework.

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Genomics and emerging biomarkers for immunotherapy of colorectal cancer

Cited by 112 publications

References 135 publications

<p>The Association Between Inflammation, Epithelial Mesenchymal Transition and Stemness in Colorectal Carcinoma</p>

<p>The Association Between Inflammation, Epithelial Mesenchymal Transition and Stemness in Colorectal Carcinoma</p>

Evolutionary dynamics of neoantigens in growing tumours

Transcriptomic learning for digital pathology

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