Metabolomic differentiation of tumor core versus edge in glioma

OBJECTIVE

Gliomas exhibit high intratumor and interpatient heterogeneity. Recently, it has been shown that the microenvironment and phenotype differ significantly between the glioma core (inner) and edge (infiltrating) regions. This proof-of-concept study differentiates metabolic signatures associated with these regions, with the potential for prognosis and targeted therapy that could improve surgical outcomes.

METHODS

Paired glioma core and infiltrating edge samples were obtained from 27 patients after craniotomy. Liquid-liquid metabolite extraction was performed on the samples and metabolomic data were obtained via 2D liquid chromatography-mass spectrometry/mass spectrometry. To gauge the potential of metabolomics to identify clinically relevant predictors of survival from tumor core versus edge tissues, a boosted generalized linear machine learning model was used to predict metabolomic profiles associated with O6-methylguanine DNA methyltransferase (MGMT) promoter methylation.

RESULTS

A panel of 66 (of 168) metabolites was found to significantly differ between glioma core and edge regions (p ≤ 0.05). Top metabolites with significantly different relative abundances included DL-alanine, creatine, cystathionine, nicotinamide, and D-pantothenic acid. Significant metabolic pathways identified by quantitative enrichment analysis included glycerophospholipid metabolism; butanoate metabolism; cysteine and methionine metabolism; glycine, serine, alanine, and threonine metabolism; purine metabolism; nicotinate and nicotinamide metabolism; and pantothenate and coenzyme A biosynthesis. The machine learning model using 4 key metabolites each within core and edge tissue specimens predicted MGMT promoter methylation status, with AUROCEdge = 0.960 and AUROCCore = 0.941. Top metabolites associated with MGMT status in the core samples included hydroxyhexanoycarnitine, spermine, succinic anhydride, and pantothenic acid, and in the edge samples metabolites included 5-cytidine monophosphate, pantothenic acid, itaconic acid, and uridine.

CONCLUSIONS

Key metabolic differences are identified between core and edge tissue in glioma and, furthermore, demonstrate the potential for machine learning to provide insight into potential prognostic and therapeutic targets.

Deciphering tumor ecosystems at super resolution from spatial transcriptomics with TESLA

Cell populations in the tumor microenvironment (TME), including their abundance, composition, and spatial location, are critical determinants of patient response to therapy. Recent advances in spatial transcriptomics (ST) have enabled the comprehensive characterization of gene expression in the TME. However, popular ST platforms, such as Visium, only measure expression in low-resolution spots and have large tissue areas that are not covered by any spots, which limits their usefulness in studying the detailed structure of TME. Here, we present TESLA, a machine learning framework for tissue annotation with pixel-level resolution in ST. TESLA integrates histological information with gene expression to annotate heterogeneous immune and tumor cells directly on the histology image. TESLA further detects unique TME features such as tertiary lymphoid structures, which represents a promising avenue for understanding the spatial architecture of the TME. Although we mainly illustrated the applications in cancer, TESLA can also be applied to other diseases.

Identification of ALDH1A3 as a Viable Therapeutic Target in Breast Cancer Metastasis-Initiating Cells

The development of efficacious therapies targeting metastatic spread of breast cancer to the brain represents an unmet clinical need. Accordingly, an improved understanding of the molecular underpinnings of central nervous system spread and progression of breast cancer brain metastases (BCBM) is required. In this study, the clinical burden of disease in BCBM was investigated, as well as the role of aldehyde dehydrogenase 1A3 (ALDH1A3) in the metastatic cascade leading to BCBM development. Initial analysis of clinical survival trends for breast cancer and BCBM determined improvement of breast cancer survival rates; however, this has failed to positively affect the prognostic milestones of triple-negative breast cancer (TNBC) brain metastases (BM). ALDH1A3 and a representative epithelial-mesenchymal transition (EMT) gene signature (mesenchymal markers, CD44 or Vimentin) were compared in tumors derived from BM, lung metastases (LM), or bone metastases (BoM) of patients as well as mice after injection of TNBC cells. Selective elevation of the EMT signature and ALDH1A3 were observed in BM, unlike LM and BoM, especially in the tumor edge. Furthermore, ALDH1A3 was determined to play a role in BCBM establishment via regulation of circulating tumor cell adhesion and migration phases in the BCBM cascade. Validation through genetic and pharmacologic inhibition of ALDH1A3 via lentiviral shRNA knockdown and a novel small-molecule inhibitor demonstrated selective inhibition of BCBM formation with prolonged survival of tumor-bearing mice. Given the survival benefits via targeting ALDH1A3, it may prove an effective therapeutic strategy for BCBM prevention and/or treatment.