Molecular subtyping of head and neck cancer - Clinical applicability and correlations with morphological characteristics

AIM: We aimed to evaluate the applicability of a customized NanoString panel for molecular subtyping of recurrent or metastatic head and neck squamous cell carcinoma (R/M-HNSCC). Additionally, histological analyses were conducted, correlated with the molecular subtypes and tested for their prognostic value. MATERIAL AND METHODS: We conducted molecular subtyping of R/M-HNSCC according to the molecular subtypes defined by Keck et al. For molecular analyses a 231 gene customized NanoString panel (the most accurately subtype defining genes, based on previous analyses) was applied to tumor samples from R/M-HNSCC patients that were treated in the CeFCiD trial (AIO/IAG-KHT trial 1108). A total of 130 samples from 95 patients were available for sequencing, of which 80 samples from 67 patients passed quality controls and were included in histological analyses. H&E stained slides were evaluated regarding distinct morphological patterns (e.g. tumor budding, nuclear size, stroma content). RESULTS: Determination of molecular subtypes led to classification of tumor samples as basal (n = 46, 45 %), inflamed/mesenchymal (n = 31, 30 %) and classical (n = 26, 25 %). Expression levels of Amphiregulin (AREG) were significantly higher for the basal and classical subtypes compared to the mesenchymal subtype. While molecular subtypes did not have an impact on survival, high levels of tumor budding were associated with poor outcomes. No correlation was found between molecular subtypes and histological characteristics. CONCLUSIONS: Utilizing the 231-gene NanoString panel we were able to determine the molecular subtype of R/M-HNSCC samples by the use of FFPE material. The value to stratify for different treatment options remains to be explored in the future. The prognostic value of tumor budding was underscored in this clinically well annotated cohort

Tumour budding in oral squamous cell carcinoma : a meta-analysis

Background: Tumour budding has been reported as a promising prognostic marker in many cancers. This meta-analysis assessed the prognostic value of tumour budding in oral squamous cell carcinoma (OSCC). Methods: We searched OvidMedline, PubMed, Scopus and Web of Science for articles that studied tumour budding in OSCC. We used reporting recommendations for tumour marker (REMARK) criteria to evaluate the quality of studies eligible for meta-analysis. Results: A total of 16 studies evaluated the prognostic value of tumour budding in OSCC. The meta-analysis showed that tumour budding was significantly associated with lymph node metastasis (odds ratio = 7.08, 95% CI = 1.75-28.73), disease-free survival (hazard ratio = 1.83, 95% CI = 1.34-2.50) and overall survival (hazard ratio = 1.88, 95% CI = 1.25-2.82). Conclusions: Tumour budding is a simple and reliable prognostic marker for OSCC. Evaluation of tumour budding could facilitate personalised management of OSCC.Peer reviewe

Transformer-based biomarker prediction from colorectal cancer histology: A large-scale multicentric study.

Deep learning (DL) can accelerate the prediction of prognostic biomarkers from routine pathology slides in colorectal cancer (CRC). However, current approaches rely on convolutional neural networks (CNNs) and have mostly been validated on small patient cohorts. Here, we develop a new transformer-based pipeline for end-to-end biomarker prediction from pathology slides by combining a pre-trained transformer encoder with a transformer network for patch aggregation. Our transformer-based approach substantially improves the performance, generalizability, data efficiency, and interpretability as compared with current state-of-the-art algorithms. After training and evaluating on a large multicenter cohort of over 13,000 patients from 16 colorectal cancer cohorts, we achieve a sensitivity of 0.99 with a negative predictive value of over 0.99 for prediction of microsatellite instability (MSI) on surgical resection specimens. We demonstrate that resection specimen-only training reaches clinical-grade performance on endoscopic biopsy tissue, solving a long-standing diagnostic problem

Simulation of seismic wave propagation in porous rocks considering the exploration and the monitoring of geological reservoirs

Die Modellierung von seismischen Wellen wird in der Geophysik immer wichtiger. Die Fluide, welche beispielweise den Porenraum von Reservoirgesteine oder Böden füllen, und die Wechselwirkung dieser Fluide untereinander und mit dem Gestein müssen berücksichtigt werden um die seismische Wellenausbreitung in solchen Medien genau zu beschreiben. Das Ziel dieser Arbeit ist die konsistente Herleitung einer Theorie der seismischen Wellenausbreitung in porösen Medien, die von zwei nicht-mischbaren Fluiden gesättigt werden, sowie die numerische Lösung dieser hergeleiteten Wellengleichung. Die Theorie basiert auf Biot's Theorie der Poroelastizität. Der Code zur numerischen Lösung ist in das Softwarepaket NEXD eingebettet, welches die nodale diskontinuierliche Galerkin-Methode verwendet um die Wellengleichung in 1D, 2D oder 3D zu lösen. Die vorliegende Arbeit kann zum Beispiel bei der Exploration und dem Monitoring von geologischen Reservoiren eingesetzt werden.Modelling the propagation of seismic waves in porous media gets more and more popular in the seismological community. The fluid content of, for example, reservoir rocks or soils, and the interaction between the fluids and the rock has to be taken into account to accurately describe seismic wave propagation through such porous media. The aim of this work is the presentation of the consistent derivation of a theory for seismic wave propagation in porous media saturated by two immiscible fluids and the accompanying numerical solution for the derived wave equation. The theory is based on Biot's theory of poroelasticity. The poroelastic solver is integrated into the larger software package NEXD that uses the nodal discontinuous Galerkin method to solve wave equations in 1D, 2D, or 3D. This work can be applied to various scientific questions in, for example, exploration and monitoring of hydrocarbon or geothermal reservoirs as well as CO2 storage sites

S5CL: Unifying fully-supervised, self-supervised, and semi-supervised learning through hierarchical contrastive learning.

In computational pathology, we often face a scarcity of annotations and a large amount of unlabeled data. One method for dealing with this is semi-supervised learning which is commonly split into a self-supervised pretext task and a subsequent model fine-tuning. Here, we compress this two-stage training into one by introducing S5CL, a unified framework for fully-supervised, self-supervised, and semi-supervised learning. With three contrastive losses defined for labeled, unlabeled, and pseudo-labeled images, S5CL can learn feature representations that reflect the hierarchy of distance relationships: similar images and augmentations are embedded the closest, followed by different looking images of the same class, while images from separate classes have the largest distance. Moreover, S5CL allows us to flexibly combine these losses to adapt to different scenarios. Evaluations of our framework on two public histopathological datasets show strong improvements in the case of sparse labels: for a H &E-stained colorectal cancer dataset, the accuracy increases by up to 9 % compared to supervised cross-entropy loss; for a highly imbalanced dataset of single white blood cells from leukemia patient blood smears, the F1-score increases by up to 6 % (Code: https://github.com/manuel-tran/s5cl )

Local attention graph-based transformer for multi-target genetic alteration prediction.

Classical multiple instance learning (MIL) methods are often based on the identical and independent distributed assumption between instances, hence neglecting the potentially rich contextual information beyond individual entities. On the other hand, Transformers with global self-attention modules have been proposed to model the interdependencies among all instances. However, in this paper we question: Is global relation modeling using self-attention necessary, or can we appropriately restrict self-attention calculations to local regimes in large-scale whole slide images (WSIs)? We propose a general-purpose local attention graph-based Transformer for MIL (LA-MIL), introducing an inductive bias by explicitly contextualizing instances in adaptive local regimes of arbitrary size. Additionally, an efficiently adapted loss function enables our approach to learn expressive WSI embeddings for the joint analysis of multiple biomarkers. We demonstrate that LA-MIL achieves state-of-the-art results in mutation prediction for gastrointestinal cancer, outperforming existing models on important biomarkers such as microsatellite instability for colorectal cancer. Our findings suggest that local self-attention sufficiently models dependencies on par with global modules. Our LA-MIL implementation is available at https://github.com/agentdr1/LA_MIL