Identification of 15 T Cell Restricted Genes Evaluates T Cell Infiltration of Human Healthy Tissues and Cancers and Shows Prognostic and Predictive Potential

Cari, Luigi; Rosa, Francesca De; Petrillo, Maria Grazia; Migliorati, Graziella; Nocentini, Giuseppe; Riccardi, Carlo

doi:10.3390/ijms20205242

Cited by 8 publications

(9 citation statements)

References 45 publications

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“…The microarray data are normalized by Genevestigator suite at two levels: Robust Multi-array Average (RMA) within experiments (through the Bioconductor package “affy” and a customized version of the package “affyExtensions”) and trimmed mean adjustment to a target for normalization between datasets determined by calculating the mean of all the expression values in an experiment (across all samples) after excluding the top and the bottom 5%. Normalization makes array data comparable, despite having been obtained in different laboratories 44 , 45 .…”

Section: Methodsmentioning

confidence: 99%

Detection of urinary miRNAs for diagnosis of clear cell renal cell carcinoma

Cochetti

Cari

Nocentini

et al. 2020

Sci Rep

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The lack of symptoms at the early stages of clear cell renal cell carcinoma (ccRCC) allows the tumour to metastasize, leading to a dramatic reduction in patient survival. Therefore, we studied and set up a method based on urinary microRNAs (miRNAs) for the diagnosis of ccRCC. First, miRNA expression in ccRCC specimens and kidney tissues from healthy subjects (HSs) was investigated through analysis of data banks and validated by comparing expression of miRNAs in ccRCC and adjacent non-cancerous kidney tissue specimens by RT-qPCR. Subsequently, we developed an algorithm to establish which miRNAs are more likely to be found in the urine of ccRCC patients that indicated miR-122, miR-1271, and miR-15b as potential interesting markers. The evaluation of their levels and three internal controls in the urine of 13 patients and 14 HSs resulted in the development of a score (7p-urinary score) to evaluate the presence of ccRCC in patients. The resulting area under the Receiver Operating Characteristic (ROC) curve, sensitivity, and specificity were equal to 0.96, 100% (95% CI 75–100%), and 86% (95% CI 57–98%), respectively. In conclusion, our study provides a proof of concept that combining the expression values of some urinary miRNAs might be useful in the diagnosis of ccRCC.

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Section: Methodsmentioning

confidence: 99%

Detection of urinary miRNAs for diagnosis of clear cell renal cell carcinoma

Cochetti

Cari

Nocentini

et al. 2020

Sci Rep

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“…Supervised correlation analysis using fixed gene sets can also be used to test if mones encode morphological features associated with a biological phenotype. We tested whether mones encode immune infiltration in pan-GI (COAD, READ, and STAD) and pan-LUNG (LUAD and LUSC) cancers, with immune related gene sets taken from 31 and 32 (see “ Methods ” and Supplementary File 9 ).…”

Section: Resultsmentioning

confidence: 99%

Deep learning features encode interpretable morphologies within histological images

pour

White

Park

et al. 2022

Sci Rep

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Convolutional neural networks (CNNs) are revolutionizing digital pathology by enabling machine learning-based classification of a variety of phenotypes from hematoxylin and eosin (H&E) whole slide images (WSIs), but the interpretation of CNNs remains difficult. Most studies have considered interpretability in a post hoc fashion, e.g. by presenting example regions with strongly predicted class labels. However, such an approach does not explain the biological features that contribute to correct predictions. To address this problem, here we investigate the interpretability of H&E-derived CNN features (the feature weights in the final layer of a transfer-learning-based architecture). While many studies have incorporated CNN features into predictive models, there has been little empirical study of their properties. We show such features can be construed as abstract morphological genes (“mones”) with strong independent associations to biological phenotypes. Many mones are specific to individual cancer types, while others are found in multiple cancers especially from related tissue types. We also observe that mone-mone correlations are strong and robustly preserved across related cancers. Importantly, linear mone-based classifiers can very accurately separate 38 distinct classes (19 tumor types and their adjacent normals, AUC = $$97.1\% \pm 2.8\%$$ 97.1 % ± 2.8 % for each class prediction), and linear classifiers are also highly effective for universal tumor detection (AUC = $$99.2\% \pm 0.12\%$$ 99.2 % ± 0.12 % ). This linearity provides evidence that individual mones or correlated mone clusters may be associated with interpretable histopathological features or other patient characteristics. In particular, the statistical similarity of mones to gene expression values allows integrative mone analysis via expression-based bioinformatics approaches. We observe strong correlations between individual mones and individual gene expression values, notably mones associated with collagen gene expression in ovarian cancer. Mone-expression comparisons also indicate that immunoglobulin expression can be identified using mones in colon adenocarcinoma and that immune activity can be identified across multiple cancer types, and we verify these findings by expert histopathological review. Our work demonstrates that mones provide a morphological H&E decomposition that can be effectively associated with diverse phenotypes, analogous to the interpretability of transcription via gene expression values. Our work also demonstrates mones can be interpreted without using a classifier as a proxy.

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“…Die Applikation von Immunsuppressiva stellt keine Kontraindikation für eine AIT dar, vielmehr stellt sich die Frage, ob eine Wirksamkeit gegeben ist. Immunologische Daten zum Einfluss von Glukokortikosteroiden auf eine Toleranzinduktion sind divergent und zeigen sowohl mögliche positive (beispielsweise Stimulation von regulatorischen T‑Zellen und IL[Interleukin]-10-Sekretion) [ 19 , 21 , 64 ] als auch negative Einflüsse (beispielsweise Hemmung dendritischer Zellen, regulatorischer T‑Zellen, Stimulation von Th2-Zellen) [ 15 , 27 ]. Klinische Studiendaten liegen hierzu in Bezug auf die AIT nicht vor.…”

Section: Allergenimmuntherapie Und Medikamenteneinnahmeunclassified

Allergen-Immuntherapie: Facts und FAQs

Mortasawi¹,

Pfützner²

2021

Hautarzt

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Die Allergenimmuntherapie (AIT) ist – neben der oft nicht suffizient durchzuführenden Allergenmeidung – die einzige kausale Therapie Ig(Immunglobulin)E-vermittelter Allergien gegen Aeroallergene und Hymenopterengifte. Sie kann je nach Allergen als subkutane Injektion (subkutane Immuntherapie [SCIT]) oder über eine sublinguale Applikation (sublinguale Immuntherapie [SLIT]) erfolgen, kürzlich wurde zudem auch ein Verfahren zur oralen Immuntherapie zur Behandlung der Nahrungsmittelallergie zugelassen. Neben der korrekten Indikationsstellung (positive Anamnese und Diagnostik einer IgE-vermittelten Allergie, Allergenkarenz nicht ausreichend möglich) sind mögliche Kontraindikationen und Risikofaktoren zu beachten. Zudem kann es unter einer AIT zu – potenziell auch lebensgefährlichen – Nebenwirkungen kommen. Im Folgenden sollen häufig gestellte Fragen und Fakten zur Entscheidungsfindung für die Durchführung und zum Risikomanagement der AIT beleuchtet und unter Berücksichtigung der aktuellen Datenlage diskutiert werden.

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Identification of 15 T Cell Restricted Genes Evaluates T Cell Infiltration of Human Healthy Tissues and Cancers and Shows Prognostic and Predictive Potential

Cited by 8 publications

References 45 publications

Detection of urinary miRNAs for diagnosis of clear cell renal cell carcinoma

Detection of urinary miRNAs for diagnosis of clear cell renal cell carcinoma

Deep learning features encode interpretable morphologies within histological images

Allergen-Immuntherapie: Facts und FAQs

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