[18F]Fluciclatide PET as a biomarker of response to combination therapy of pazopanib and paclitaxel in platinum-resistant/refractory ovarian cancer

Physically informed deep neural networks for metabolite-corrected plasma input function estimation in dynamic PET imaging

INTRODUCTION

We propose a novel approach for the non-invasive quantification of dynamic PET imaging data, focusing on the arterial input function (AIF) without the need for invasive arterial cannulation.

METHODS

Our method utilizes a combination of three-dimensional depth-wise separable convolutional layers and a physically informed deep neural network to incorporatea priori knowledge about the AIF's functional form and shape, enabling precise predictions of the concentrations of [11C]PBR28 in whole blood and the free tracer in metabolite-corrected plasma.

RESULTS

We found a robust linear correlation between our model's predicted AIF curves and those obtained through traditional, invasive measurements. We achieved an average cross-validated Pearson correlation of 0.86 for whole blood and 0.89 for parent plasma curves. Moreover, our method's ability to estimate the volumes of distribution across several key brain regions - without significant differences between the use of predicted versus actual AIFs in a two-tissue compartmental model - successfully captures the intrinsic variability related to sex, the binding affinity of the translocator protein (18 kDa), and age.

CONCLUSIONS

These results not only validate our method's accuracy and reliability but also establish a foundation for a streamlined, non-invasive approach to dynamic PET data quantification. By offering a precise and less invasive alternative to traditional quantification methods, our technique holds significant promise for expanding the applicability of PET imaging across a wider range of tracers, thereby enhancing its utility in both clinical research and diagnostic settings.

Current trends in the characterization and monitoring of vascular response to cancer therapy

Tumor vascular physiology is an important determinant of disease progression as well as the therapeutic outcome of cancer treatment. Angiogenesis or the lack of it provides crucial information about the tumor's blood supply and therefore can be used as an index for cancer growth and progression. While standalone anti-angiogenic therapy demonstrated limited therapeutic benefits, its combination with chemotherapeutic agents improved the overall survival of cancer patients. This could be attributed to the effect of vascular normalization, a dynamic process that temporarily reverts abnormal vasculature to the normal phenotype maximizing the delivery and intratumor distribution of chemotherapeutic agents. Longitudinal monitoring of vascular changes following antiangiogenic therapy can indicate an optimal window for drug administration and estimate the potential outcome of treatment. This review primarily focuses on the status of various imaging modalities used for the longitudinal characterization of vascular changes before and after anti-angiogenic therapies and their clinical prospects.

Dynomics: A Novel and Promising Approach for Improved Breast Cancer Prognosis Prediction

Traditional imaging techniques for breast cancer (BC) diagnosis and prediction, such as X-rays and magnetic resonance imaging (MRI), demonstrate varying sensitivity and specificity due to clinical and technological factors. Consequently, positron emission tomography (PET), capable of detecting abnormal metabolic activity, has emerged as a more effective tool, providing critical quantitative and qualitative tumor-related metabolic information. This study leverages a public clinical dataset of dynamic 18F-Fluorothymidine (FLT) PET scans from BC patients, extending conventional static radiomics methods to the time domain-termed as 'Dynomics'. Radiomic features were extracted from both static and dynamic PET images on lesion and reference tissue masks. The extracted features were used to train an XGBoost model for classifying tumor versus reference tissue and complete versus partial responders to neoadjuvant chemotherapy. The results underscored the superiority of dynamic and static radiomics over standard PET imaging, achieving accuracy of 94% in tumor tissue classification. Notably, in predicting BC prognosis, dynomics delivered the highest performance, achieving accuracy of 86%, thereby outperforming both static radiomics and standard PET data. This study illustrates the enhanced clinical utility of dynomics in yielding more precise and reliable information for BC diagnosis and prognosis, paving the way for improved treatment strategies.

Anti-angiogenic therapy in ovarian cancer: Current understandings and prospects of precision medicine

Ovarian cancer (OC) remains the most fatal disease of gynecologic malignant tumors. Angiogenesis refers to the development of new vessels from pre-existing ones, which is responsible for supplying nutrients and removing metabolic waste. Although not yet completely understood, tumor vascularization is orchestrated by multiple secreted factors and signaling pathways. The most central proangiogenic signal, vascular endothelial growth factor (VEGF)/VEGFR signaling, is also the primary target of initial clinical anti-angiogenic effort. However, the efficiency of therapy has so far been modest due to the low response rate and rapidly emerging acquiring resistance. This review focused on the current understanding of the in-depth mechanisms of tumor angiogenesis, together with the newest reports of clinical trial outcomes and resistance mechanism of anti-angiogenic agents in OC. We also emphatically summarized and analyzed previously reported biomarkers and predictive models to describe the prospect of precision therapy of anti-angiogenic drugs in OC.

Prognostic Value of an Integrin-Based Signature in Hepatocellular Carcinoma and the Identification of Immunological Role of LIMS2

Objective

Evidence proves that integrins affect almost every step of hepatocellular carcinoma (HCC) progression. The current study aimed at constructing an integrin-based signature for prognostic prediction of HCC.

Methods

TCGA-LIHC and ICGC-LIRI-JP cohorts were retrospectively analyzed. Integrin genes were analyzed via univariate Cox regression, followed by generation of a prognostic signature with LASSO approach. Independent factors were input into the nomogram. WGCNA was adopted to select this signature-specific genes. Gene Ontology (GO) enrichment together with Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted to explore the function of the dysregulated genes. The abundance of tumor microenvironment components was estimated with diverse popular computational methods. The relative importance of genes from this signature was estimated through random-forest method.

Results

Eight integrin genes (ADAM15, CDC42, DAB2, ITGB1BP1, ITGB5, KIF14, LIMS2, and SELP) were adopted to define an integrin-based signature. Each patient was assigned the riskScore. High-riskScore subpopulation exhibited worse overall survival, with satisfying prediction efficacy. Also, the integrin-based signature was independent of routine clinicopathological parameters. The nomogram (comprising integrin-based signature, and stage) accurately inferred prognostic outcome, with the excellent net benefit. Genes with the strongest positive interaction to low-riskScore were primarily linked to biosynthetic, metabolic, and catabolic processes and immune pathways; those with the strongest association with high-riskScore were principally associated with diverse tumorigenic signaling. The integrin-based signature was strongly linked with tumor microenvironment components. Among the genes from this signature, LIMS2 possessed the highest importance, and its expression was proven through immunohistochemical staining.

Conclusion

Altogether, our study defined a quantitative integrin-based signature that reliably assessed HCC prognosis and tumor microenvironment features, which possessed the potential as a tool for prognostic prediction.

Spatiotemporal learning of dynamic positron emission tomography data improves diagnostic accuracy in breast cancer

Positron emission tomography (PET) can reveal metabolic activity in a voxelwise manner. PET analysis is commonly performed in a static manner by analyzing the standardized uptake value (SUV) obtained from the plateau region of PET acquisitions. A dynamic PET acquisition can provide a map of the spatiotemporal concentration of the tracer in vivo, hence conveying information about radiotracer delivery to tissue, its interaction with the target and washout. Therefore, tissue-specific biochemical properties are embedded in the shape of time activity curves (TACs), which are generally used for kinetic analysis. Conventionally, TACs are employed along with information about blood plasma activity concentration, i.e., the arterial input function (AIF), and specific compartmental models to obtain a full quantitative analysis of PET data. The main drawback of this approach is the need for invasive procedures requiring arterial blood sample collection during the whole PET scan. In this paper, we address the challenge of improving PET diagnostic accuracy through an alternative approach based on the analysis of time signal intensity patterns. Specifically, we demonstrate the diagnostic potential of tissue TACs provided by dynamic PET acquisition using various deep learning models. Our framework is shown to outperform the discriminative potential of classical SUV analysis, hence paving the way for more accurate PET-based lesion discrimination without additional acquisition time or invasive procedures. Clinical Relevance- The diagnostic accuracy of dynamic PET data exploited by deep-learning based time signal intensity pattern analysis is superior to that of static SUV imaging.

Integrins regulate stemness in solid tumor: an emerging therapeutic target

Integrins are the adhesion molecules and transmembrane receptors that consist of α and β subunits. After binding to extracellular matrix components, integrins trigger intracellular signaling and regulate a wide spectrum of cellular functions, including cell survival, proliferation, differentiation and migration. Since the pattern of integrins expression is a key determinant of cell behavior in response to microenvironmental cues, deregulation of integrins caused by various mechanisms has been causally linked to cancer development and progression in several solid tumor types. In this review, we discuss the integrin signalosome with a highlight of a few key pro-oncogenic pathways elicited by integrins, and uncover the mutational and transcriptomic landscape of integrin-encoding genes across human cancers. In addition, we focus on the integrin-mediated control of cancer stem cell and tumor stemness in general, such as tumor initiation, epithelial plasticity, organotropic metastasis and drug resistance. With insights into how integrins contribute to the stem-like functions, we now gain better understanding of the integrin signalosome, which will greatly assist novel therapeutic development and more precise clinical decisions.

Molecular Imaging of Angiogenesis in Oncology: Current Preclinical and Clinical Status

Angiogenesis is an active process, regulating new vessel growth, and is crucial for the survival and growth of tumours next to other complex factors in the tumour microenvironment. We present possible molecular imaging approaches for tumour vascularisation and vitality, focusing on radiopharmaceuticals (tracers). Molecular imaging in general has become an integrated part of cancer therapy, by bringing relevant insights on tumour angiogenic status. After a structured PubMed search, the resulting publication list was screened for oncology related publications in animals and humans, disregarding any cardiovascular findings. The tracers identified can be subdivided into direct targeting of angiogenesis (i.e., vascular endothelial growth factor, laminin, and fibronectin) and indirect targeting (i.e., glucose metabolism, hypoxia, and matrix metallo-proteases, PSMA). Presenting pre-clinical and clinical data of most tracers proposed in the literature, the indirect targeting agents are not 1:1 correlated with angiogenesis factors but do have a strong prognostic power in a clinical setting, while direct targeting agents show most potential and specificity for assessing tumour vascularisation and vitality. Within the direct agents, the combination of multiple targeting tracers into one agent (multimers) seems most promising. This review demonstrates the present clinical applicability of indirect agents, but also the need for more extensive research in the field of direct targeting of angiogenesis in oncology. Although there is currently no direct tracer that can be singled out, the RGD tracer family seems to show the highest potential therefore we expect one of them to enter the clinical routine.

Spatial heterogeneity of radiolabeled choline positron emission tomography in tumors of patients with non-small cell lung cancer: first-in-patient evaluation of [18F]fluoromethyl-(1,2-2H4)-choline

Purpose: The spatio-molecular distribution of choline and its metabolites in tumors is highly heterogeneous. Due to regulation of choline metabolism by hypoxic transcriptional signaling and other survival factors, we envisage that detection of such heterogeneity in patient tumors could provide the basis for advanced localized therapy. However, non-invasive methods to assess this phenomenon in patients are limited. We investigated such heterogeneity in Non-Small Cell Lung Cancer (NSCLC) with [18F]fluoromethyl-(1,2-2H4) choline ([18F]D4-FCH) and positron emission tomography/computed tomography (PET/CT). Experimental design: [18F]D4-FCH (300.5±72.9MBq [147.60-363.6MBq]) was administered intravenously to 17 newly diagnosed NSCLC patients. PET/CT scans were acquired concurrently with radioactive blood sampling to permit mathematical modelling of blood-tissue transcellular rate constants. Comparisons were made with biopsy-derived choline kinase-α (CHKα) expression and diagnostic [18F]fluorodeoxyglucose ([18F]FDG) scans. Results: Oxidation of [18F]D4-FCH to [18F]D4-fluorobetaine was suppressed (48.58±0.31% parent at 60 min) likely due to the deuterium isotope effect embodied within the design of the radiotracer. Early (5 min) and late (60 min) images showed specific uptake of tracer in all 51 lesions (tumors, lymph nodes and metastases) from 17 patients analyzed. [18F]D4-FCH-derived uptake (SUV60max) in index primary lesions (n=17) ranged between 2.87-10.13; lower than that of [18F]FDG PET [6.89-22.64]. Mathematical modelling demonstrated net irreversible uptake of [18F]D4-FCH at steady-state, and parametric mapping of the entire tumor showed large intratumorally heterogeneity in radiotracer retention, which is likely to have influenced correlations with biopsy-derived CHKα expression. Conclusions: [18F]D4-FCH is detectable in NSCLC with large intratumorally heterogeneity, which could be exploited in the future for targeting localized therapy.