Tumor Vessel Normalization via PFKFB3 Inhibition Alleviates Hypoxia and Increases Tumor Necrosis in Rectal Cancer upon Radiotherapy.

Treatment of patients with locally advanced rectal cancer (RC) is based on neoadjuvant chemoradiotherapy followed by surgery. In order to reduce the development of therapy resistance, it is necessary to further improve previous treatment approaches. Recent in vivo experimental studies suggested that the reduction of tumor hypoxia by tumor vessel normalization (TVN), through the inhibition of the glycolytic activator PFKFB3, could significantly improve tumor response to therapy. We have evaluated in vitro and in vivo the effects of the PFKFB3 inhibitor 2E-3-(3-pyridinyl)-1-(4-pyridinyl)-2-propen-1-one (3PO) on cell survival, clonogenicity, migration, invasion, and metabolism using colorectal cancer cells, patient-derived tumor organoid (PDO), and xenograft (PDX). 3PO treatment of colorectal cancer cells increased radiation-induced cell death and reduced cancer cell invasion. Moreover, gene set enrichment analysis shows that 3PO is able to alter the metabolic status of PDOs toward oxidative phosphorylation. Additionally, in vivo neoadjuvant treatment with 3PO induced TVN, alleviated tumor hypoxia, and increased tumor necrosis. Our results support PFKFB3 inhibition as a possible future neoadjuvant addition for patients with RC. SIGNIFICANCE: Novel therapies to better treat colorectal cancer are necessary to improve patient outcomes. Therefore, in this study, we evaluated the combination of a metabolic inhibitor (3PO) and standard radiotherapy in different experimental settings. We have observed that the addition of 3PO increased radiation effects, ultimately improving tumor cell response to therapy.

Adaptive digital tissue deconvolution.

MOTIVATION: The inference of cellular compositions from bulk and spatial transcriptomics data increasingly complements data analyses. Multiple computational approaches were suggested and recently, machine learning techniques were developed to systematically improve estimates. Such approaches allow to infer additional, less abundant cell types. However, they rely on training data which do not capture the full biological diversity encountered in transcriptomics analyses; data can contain cellular contributions not seen in the training data and as such, analyses can be biased or blurred. Thus, computational approaches have to deal with unknown, hidden contributions. Moreover, most methods are based on cellular archetypes which serve as a reference; e.g. a generic T-cell profile is used to infer the proportion of T-cells. It is well known that cells adapt their molecular phenotype to the environment and that pre-specified cell archetypes can distort the inference of cellular compositions. RESULTS: We propose Adaptive Digital Tissue Deconvolution (ADTD) to estimate cellular proportions of pre-selected cell types together with possibly unknown and hidden background contributions. Moreover, ADTD adapts prototypic reference profiles to the molecular environment of the cells, which further resolves cell-type specific gene regulation from bulk transcriptomics data. We verify this in simulation studies and demonstrate that ADTD improves existing approaches in estimating cellular compositions. In an application to bulk transcriptomics data from breast cancer patients, we demonstrate that ADTD provides insights into cell-type specific molecular differences between breast cancer subtypes. AVAILABILITY AND IMPLEMENTATION: A python implementation of ADTD and a tutorial are available at Gitlab and zenodo (doi:10.5281/zenodo.7548362).

CODEX: COunterfactual Deep learning for the in silico EXploration of cancer cell line perturbations.

MOTIVATION: High-throughput screens (HTS) provide a powerful tool to decipher the causal effects of chemical and genetic perturbations on cancer cell lines. Their ability to evaluate a wide spectrum of interventions, from single drugs to intricate drug combinations and CRISPR-interference, has established them as an invaluable resource for the development of novel therapeutic approaches. Nevertheless, the combinatorial complexity of potential interventions makes a comprehensive exploration intractable. Hence, prioritizing interventions for further experimental investigation becomes of utmost importance. RESULTS: We propose CODEX (COunterfactual Deep learning for the in silico EXploration of cancer cell line perturbations) as a general framework for the causal modeling of HTS data, linking perturbations to their downstream consequences. CODEX relies on a stringent causal modeling strategy based on counterfactual reasoning. As such, CODEX predicts drug-specific cellular responses, comprising cell survival and molecular alterations, and facilitates the in silico exploration of drug combinations. This is achieved for both bulk and single-cell HTS. We further show that CODEX provides a rationale to explore complex genetic modifications from CRISPR-interference in silico in single cells. AVAILABILITY AND IMPLEMENTATION: Our implementation of CODEX is publicly available at https://github.com/sschrod/CODEX. All data used in this article are publicly available.

Stable feature selection utilizing Graph Convolutional Neural Network and Layer-wise Relevance Propagation for biomarker discovery in breast cancer.

High-throughput technologies are becoming increasingly important in discovering prognostic biomarkers and in identifying novel drug targets. With Mammaprint, Oncotype DX, and many other prognostic molecular signatures breast cancer is one of the paradigmatic examples of the utility of high-throughput data to deliver prognostic biomarkers, that can be represented in a form of a rather short gene list. Such gene lists can be obtained as a set of features (genes) that are important for the decisions of a Machine Learning (ML) method applied to high-dimensional gene expression data. Several studies have identified predictive gene lists for patient prognosis in breast cancer, but these lists are unstable and have only a few genes in common. Instability of feature selection impedes biological interpretability: genes that are relevant for cancer pathology should be members of any predictive gene list obtained for the same clinical type of patients. Stability and interpretability of selected features can be improved by including information on molecular networks in ML methods. Graph Convolutional Neural Network (GCNN) is a contemporary deep learning approach applicable to gene expression data structured by a prior knowledge molecular network. Layer-wise Relevance Propagation (LRP) and SHapley Additive exPlanations (SHAP) are methods to explain individual decisions of deep learning models. We used both GCNN+LRP and GCNN+SHAP techniques to construct feature sets by aggregating individual explanations. We suggest a methodology to systematically and quantitatively analyze the stability, the impact on the classification performance, and the interpretability of the selected feature sets. We used this methodology to compare GCNN+LRP to GCNN+SHAP and to more classical ML-based feature selection approaches. Utilizing a large breast cancer gene expression dataset we show that, while feature selection with SHAP is useful in applications where selected features have to be impactful for classification performance, among all studied methods GCNN+LRP delivers the most stable (reproducible) and interpretable gene lists.

Histological assessment of cardiac amyloidosis in patients undergoing transcatheter aortic valve replacement.

AIMS: Studies have reported a strongly varying co-prevalence of aortic stenosis (AS) and cardiac amyloidosis (CA). We sought to histologically determine the co-prevalence of AS and CA in patients undergoing transcatheter aortic valve replacement (TAVR). Consequently, we aimed to derive an algorithm to identify cases in which to suspect the co-prevalence of AS and CA. METHODS AND RESULTS: In this prospective, monocentric study, endomyocardial biopsies of 162 patients undergoing TAVR between January 2017 and March 2021 at the University Medical Centre Göttingen were analysed by one pathologist blinded to clinical data using haematoxylin-eosin staining, Elastica van Gieson staining, and Congo red staining of endomyocardial biopsies. CA was identified in only eight patients (4.9%). CA patients had significantly higher N-terminal pro-brain natriuretic peptide (NT-proBNP) levels (4356.20 vs. 1938.00 ng/L, P = 0.034), a lower voltage-to-mass ratio (0.73 vs. 1.46 × 10-2 mVm2/g, P = 0.022), and lower transaortic gradients (Pmean 17.5 vs. 38.0 mmHg, P = 0.004) than AS patients. Concomitant CA was associated with a higher prevalence of post-procedural acute kidney injury (50.0% vs. 13.1%, P = 0.018) and sudden cardiac death [SCD; P (log-rank test) = 0.017]. Following propensity score matching, 184 proteins were analysed to identify serum biomarkers of concomitant CA. CA patients expressed lower levels of chymotrypsin (P = 0.018) and carboxypeptidase 1 (P = 0.027). We propose an algorithm using commonly documented parameters-stroke volume index, ejection fraction, NT-proBNP levels, posterior wall thickness, and QRS voltage-to-mass ratio-to screen for CA in AS patients, reaching a sensitivity of 66.6% with a specificity of 98.1%. CONCLUSIONS: The co-prevalence of AS and CA was lower than expected, at 4.9%. Despite excellent 1 year mortality, AS + CA patients died significantly more often from SCD. We propose a multimodal algorithm to facilitate more effective screening for CA containing parameters commonly documented during clinical routine. Proteomic biomarkers may yield additional information in the future.