Histology-Based Radiomics for [18F]FDG PET Identifies Tissue Heterogeneity in Pancreatic Cancer.

Radiomics features can reveal hidden patterns in a tumor but usually lack an underlying biologic rationale. In this work, we aimed to investigate whether there is a correlation between radiomics features extracted from [18F]FDG PET images and histologic expression patterns of a glycolytic marker, monocarboxylate transporter-4 (MCT4), in pancreatic cancer. Methods: A cohort of pancreatic ductal adenocarcinoma patients (n = 29) for whom both tumor cross sections and [18F]FDG PET/CT scans were available was used to develop an [18F]FDG PET radiomics signature. By using immunohistochemistry for MCT4, we computed density maps of MCT4 expression and extracted pathomics features. Cluster analysis identified 2 subgroups with distinct MCT4 expression patterns. From corresponding [18F]FDG PET scans, radiomics features that associate with the predefined MCT4 subgroups were identified. Results: Complex heat map visualization showed that the MCT4-high/heterogeneous subgroup was correlating with a higher MCT4 expression level and local variation. This pattern linked to a specific [18F]FDG PET signature, characterized by a higher SUVmean and SUVmax and second-order radiomics features, correlating with local variation. This MCT4-based [18F]FDG PET signature of 7 radiomics features demonstrated prognostic value in an independent cohort of pancreatic cancer patients (n = 71) and identified patients with worse survival. Conclusion: Our cross-modal pipeline allows the development of PET scan signatures based on immunohistochemical analysis of markers of a particular biologic feature, here demonstrated on pancreatic cancer using intratumoral MCT4 expression levels to select [18F]FDG PET radiomics features. This study demonstrated the potential of radiomics scores to noninvasively capture intratumoral marker heterogeneity and identify a subset of pancreatic ductal adenocarcinoma patients with a poor prognosis.

MOXD1 is a lineage-specific gene and a tumor suppressor in neuroblastoma.

Neuroblastoma is a childhood developmental cancer; however, its embryonic origins remain poorly understood. Moreover, in-depth studies of early tumor-driving events are limited because of the lack of appropriate models. Herein, we analyzed RNA sequencing data obtained from human neuroblastoma samples and found that loss of expression of trunk neural crest-enriched gene MOXD1 associates with advanced disease and worse outcome. Further, by using single-cell RNA sequencing data of human neuroblastoma cells and fetal adrenal glands and creating in vivo models of zebrafish, chick, and mouse, we show that MOXD1 is a determinate of tumor development. In addition, we found that MOXD1 expression is highly conserved and restricted to mesenchymal neuroblastoma cells and Schwann cell precursors during healthy development. Our findings identify MOXD1 as a lineage-restricted tumor-suppressor gene in neuroblastoma, potentiating further stratification of these tumors and development of novel therapeutic interventions.

Cytoplasmic HIF-2α as tissue biomarker to identify metastatic sympathetic paraganglioma.

Pheochromocytomas (PCCs) and paragangliomas (PGLs) are rare neuroendocrine tumors. PGLs can further be divided into sympathetic (sPGLs) and head-and-neck (HN-PGLs). There are virtually no treatment options, and no cure, for metastatic PCCs and PGLs (PPGLs). Here, we composed a tissue microarray (TMA) consisting of 149 PPGLs, reflecting clinical features, presenting as a useful resource. Mutations in the pseudohypoxic marker HIF-2α correlate to an aggressive tumor phenotype. We show that HIF-2α localized to the cytoplasm in PPGLs. This subcompartmentalized protein expression differed between tumor subtypes, and strongly correlated to proliferation. Half of all sPGLs were metastatic at time of diagnosis. Cytoplasmic HIF-2α was strongly expressed in metastatic sPGLs and predicted poor outcome in this subgroup. We propose that higher cytoplasmic HIF-2α expression could serve as a useful clinical marker to differentiate paragangliomas from pheochromocytomas, and may help predict outcome in sPGL patients.

Functional noninvasive detection of glycolytic pancreatic ductal adenocarcinoma.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) lacks effective treatment options beyond chemotherapy. Although molecular subtypes such as classical and QM (quasi-mesenchymal)/basal-like with transcriptome-based distinct signatures have been identified, deduced therapeutic strategies and targets remain elusive. Gene expression data show enrichment of glycolytic genes in the more aggressive and therapy-resistant QM subtype. However, whether the glycolytic transcripts are translated into functional glycolysis that could further be explored for metabolic targeting in QM subtype is still not known. METHODS: We used different patient-derived PDAC model systems (conventional and primary patient-derived cells, patient-derived xenografts (PDX), and patient samples) and performed transcriptional and functional metabolic analysis. These included RNAseq and Illumina HT12 bead array, in vitro Seahorse metabolic flux assays and metabolic drug targeting, and in vivo hyperpolarized [1-13C]pyruvate and [1-13C]lactate magnetic resonance spectroscopy (HP-MRS) in PDAC xenografts. RESULTS: We found that glycolytic metabolic dependencies are not unambiguously functionally exposed in all QM PDACs. Metabolic analysis demonstrated functional metabolic heterogeneity in patient-derived primary cells and less so in conventional cell lines independent of molecular subtype. Importantly, we observed that the glycolytic product lactate is actively imported into the PDAC cells and used in mitochondrial oxidation in both classical and QM PDAC cells, although more actively in the QM cell lines. By using HP-MRS, we were able to noninvasively identify highly glycolytic PDAC xenografts by detecting the last glycolytic enzymatic step and prominent intra-tumoral [1-13C]pyruvate and [1-13C]lactate interconversion in vivo. CONCLUSION: Our study adds functional metabolic phenotyping to transcriptome-based analysis and proposes a functional approach to identify highly glycolytic PDACs as candidates for antimetabolic therapeutic avenues.