Ten patients undergoing surgical resection for spinal tumors were selected. Samples of tumor, muscle, and bone were resected, de-identified by the treating surgeon, and then scanned with the TumorID ...technology ex vivo. This study investigates whether TumorID technology is able to differentiate three different human clinical fresh tissue specimens: spine tumor, normal muscle, and normal bone. The TumorID technology utilizes a 405 nm excitation laser to target endogenous fluorophores, thereby allowing for the detection of tissue based on emission spectra. Metabolic profiles of tumor and healthy tissue vary, namely NADH (bound and free emission peak, respectively: 487 nm, 501 nm) and FAD (emission peak: 544) are endogenous fluorophores with distinct concentrations in tumor and healthy tissue. Emission spectra analyzed consisted of 74 scans of spine tumor, 150 scans of healthy normal bone, and 111 scans of healthy normal muscle. An excitation wavelength of 405 nm was used to obtain emission spectra from tissue as previously described. Emission spectra consisted of approximately 1400 wavelength intensity pairs between 450 and 750 nm. Kruskal-Wallis tests were conducted comparing AUC distributions for each treatment group, α = 0.05. Spectral signatures varied amongst the three different tissue types. All pairwise comparisons among tissues for Free NADH were statistically significant (Tumor vs. Muscle: p = 0.0006, Tumor vs. Bone: p < 0.0001, Bone vs. Muscle: p = 0.0357). The overall comparison of tissues for FAD (506.5-581.5 nm) was also statistically significant (p < 0.0001), with two pairwise comparisons being statistically significant (Tumor vs. Muscle: p < 0.0001, Tumor vs. Bone: p = 0.0045, Bone vs. Muscle: p = 0.249). These statistically significant differences were maintained when stratifying tumor into metastatic carcinoma (N = 57) and meningioma (N = 17). TumorID differentiates tumor tissue from normal bone and normal muscle providing further clinical evidence of its efficacy as a tissue identification tool. Future studies should evaluate TumorID's ability to serve as an adjunctive tool for intraoperative assessment of surgical margins and surgical decision-making.
UDP-glucose-6-dehydrogenase (UGDH) is a cytosolic, hexameric enzyme that converts UDP-glucose to UDP-glucuronic acid (UDP-GlcUA), a key reaction in hormone and xenobiotic metabolism and in the ...production of extracellular matrix precursors. In this review, we classify UGDH as a molecular indicator of tumor progression in multiple cancer types, describe its involvement in key canonical cancer signaling pathways, and identify methods to inhibit UGDH, its substrates, and its downstream products. As such, we position UGDH as an enzyme to be exploited as a potential prognostication marker in oncology and a therapeutic target in cancer biology.
Spinal column tumors can be difficult to process for single-cell omic studies, given the heterogeneity in tissue. Here, we present a protocol for operating room-to-benchtop single-cell processing of ...clinical specimens from a prostate cancer patient. We describe steps for sample homogenization, red blood cell lysis, cryopreservation, and single-cell sequencing analysis. This protocol can be used to identify prognostic markers and therapeutic targets for patients with osseous spine metastases and better inform eligibility for clinical trials.
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•Collection and processing of human spinal column tumors•Enzymatic homogenization, red blood cell lysis, and cryopreservation of samples•Steps for single-cell RNA sequencing analysis using readily available RStudio packages
Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.
Spinal column tumors can be difficult to process for single-cell omic studies, given the heterogeneity in tissue. Here, we present a protocol for operating room-to-benchtop single-cell processing of clinical specimens from a prostate cancer patient. We describe steps for sample homogenization, red blood cell lysis, cryopreservation, and single-cell sequencing analysis. This protocol can be used to identify prognostic markers and therapeutic targets for patients with osseous spine metastases and better inform eligibility for clinical trials.
Abstract Background Prognostic indices for patients with brain metastases (BM) are needed to individualize treatment and stratify clinical trials. Two frequently used tools to estimate survival in ...patients with BM are the Recursive Partitioning Analysis (RPA) and the diagnosis-specific Graded Prognostic Assessment (DS-GPA). Given recent advances in therapies and improved survival for patients with BM, this study aims to validate and analyze these two models in a modern cohort. Methods Patients diagnosed with BM were identified via our institution’s Tumor Board meetings. Data were retrospectively collected from the date of diagnosis with BM. Concordance of the RPA and GPA was calculated using Harrell’s C index. A Cox proportional hazards model with backwards elimination was used to generate a parsimonious model predictive of survival. Results Our study consisted of 206 patients diagnosed with BM between 2010 and 2019. The RPA had a prediction performance characterized by Harrell’s C index of 0.588. The DS-GPA demonstrated a Harrell’s C index of 0.630. A Cox proportional hazards model assessing the effect of age, presence of lung or liver metastases, and ECOG performance status score of 3/4 on survival yielded a Harrell’s C index of 0.616. Revising the analysis with an uncategorized ECOG demonstrated a C index of 0.648. Conclusion We found that performance of the RPA remains unchanged from previous validation studies a decade earlier. The DS-GPA outperformed the RPA in predicting overall survival in our modern cohort. Analyzing variables shared by the RPA and DS-GPA produced a model that performed analogously to the DS-GPA.
