Eleftherios Diamandis

Differences in tumors related to location, tissue type, and histological subtype have been well documented for decades. Tumors are also molecularly very diverse. In this short review we describe the current classification schemes for tumor heterogeneity. We enlist the various drivers of tumor heterogeneity generation and comment on their clinical significance. New molecular techniques promise to assess tumor heterogeneity at affordable cost, so that these techniques can soon enter the clinic. While tumor heterogeneity currently represents a major unfavorable barrier in the field of oncology, it may also be a key in revolutionizing cancer diagnosis and treatment. Information regarding tumor heterogeneity has the potential to provide more thorough prognostic information, guide more efficacious combination treatment regimens, and lead to the development of novel therapeutic strategies and identification of new …

Background: Gliomas are aggressive malignant tumors, with poor prognosis. There is an unmet need for the discovery of new, non-invasive biomarkers for differential diagnosis, prognosis, and management of brain tumors. Our objective is to validate four plasma biomarkers–glial fibrillary acidic protein (GFAP), neurofilament light (NEFL), matrix metalloprotease 3 (MMP3) and fatty acid binding protein 4 (FABP4)–and compare them with established brain tumor molecular markers and survival.Methods: Our cohort consisted of patients with benign and malignant brain tumors (GBM= 77, Astrocytomas= 26, Oligodendrogliomas= 23, Secondary tumors= 35, Meningiomas= 70, Schwannomas= 15, Pituitary adenomas= 15, Normal individuals= 30). For measurements, we used ultrasensitive electrochemiluminescence multiplexed immunoassays.Results: High plasma GFAP concentration was associated with GBM, low GFAP and high FABP4 were associated with meningiomas, and low GFAP and low FABP4 were associated with astrocytomas and oligodendrogliomas. Several prognostic genetic alterations were significantly associated with plasma biomarker levels. We found no independent associations between plasma GFAP, NEFL, FABP4 and MMP3, and overall survival. The candidate biomarkers could not reliably discriminate GBM from primary or secondary CNS lymphomas.Conclusions: GFAP, NEFL, FABP4 and MMP3 are useful for differential diagnosis and prognosis, and are associated with molecular changes in gliomas.

Introduction Despite well-established clinical criteria for diagnosis of SOS/VOD following allogeneic HCT, there is a lack of established diagnostic protein biomarkers. Methods Prospective samples were collected from patients with very severe SOS/VOD at diagnosis and days+ 3,+ 7,+ 14, and+ 30 post-initiation of defibrotide. Samples from age-matched controls with no VOD were collected at day+ 14,+ 30,+ 60,+ 90 and+ 180 following allogeneic HCT. Serum samples were analyzed for 2925 protein levels by antibody-based proximity extension assay (PEA). Mean differences in the log-transformed abundance values were compared using t-tests in a volcano plot. Results Five patients with very severe SOS/VOD and five control patients were compared. Ten proteins were identified that showed a statistically significant and log-transformed 3-fold increase in concentration. They were CALCA, CCL20, GPR37, IGFBP4, IL1RL1, SLC39A14, SPINK4, FABP3, MYL3, and CHCHD10. Four different proteins, namely CD83, LAIR2, CD7, and HEM6 showed a significant decrease with defibrotide treatment. SOS/VOD resolved in 80%(n= 4) of patients, while one patient deceased due to SOS/VOD. Conclusion PEA technology identified 10 proteins that were significantly elevated in patients with very severe SOS/VOD. Prospective studies in a larger cohort using this technology may be able to conclusively identify diagnostic protein biomarkers for SOS/VOD.

The letter discusses the accuracy of the automated chat tool Chatbot GPT, which uses artificial intelligence to answer questions. The author submitted two questions to the chatbot and found that while the output was mostly accurate, there were some inaccuracies in the information provided. In one case, the chatbot attributed advancements in prostate specific antigen assays to the author, when in fact they were made by other scientists. In another case, the chatbot credited the author with achievements in space exploration that were actually made by someone else with a similar name. The author concludes that Chatbot GPT should be used with caution due to its potential for inaccuracy and misleading information.[Extracted from the article]Copyright of Clinical Chemistry & Laboratory Medicine is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the …

In this commentary, we address a paper published by Johnson et al. by assessing the robustness of their method to discover diagnostic biomarkers in Alzheimer’s disease (AD). In addition, we examine how these newly discovered and previously discovered biomarkers, can play a role in assisting patients with AD and those at risk for developing AD, with an emphasis on the translational hurdles that accompany such discoveries.

BackgroundMultiple antigens, autoantibodies (AAb), and antigen-autoantibody (Ag-AAb) complexes were compared for their ability to complement CA125 for early detection of ovarian cancer.MethodsTwenty six biomarkers were measured in a single panel of sera from women with early stage (I-II) ovarian cancers (n = 64), late stage (III-IV) ovarian cancers (186), benign pelvic masses (200) and from healthy controls (502), and then split randomly (50:50) into a training set to identify the most promising classifier and a validation set to compare its performance to CA125 alone.ResultsEight biomarkers detected ≥ 8% of early stage cases at 98% specificity. A four-biomarker panel including CA125, HE4, HE4 Ag-AAb and osteopontin detected 75% of early stage cancers in the validation set from among healthy controls compared to 62% with CA125 alone (p = 0.003) at 98% specificity. The same panel increased …

Hundreds of papers have already been published discussing the use and misuse of the journal impact factor (JIF)[1–5]. Simply, the JIF represents the average number of citations received by a journal over a specified period of time, and it is calculated every year by the organization Clarivate Analytics (London, England). The calculation involves dividing the total citations received by a journal by the number of citable items (papers published) over a set period, usually 2 years. Despite numerous concerns [3], the JIF supposedly represents the impact (quality) of the journal, and it is frequently used by editors, authors and administrators alike, as one component that could influence journal marketing, promotions, winning of awards, grants etc.[6, 7]. Apart from the number of citations, numerous other indices are associated with the quality of a journal, which are not captured by the JIF. Here, I propose a complementary …

Certain demyelinating disorders, such as neuromyelitis optica spectrum disorder (NMOSD) and myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) exhibit serum autoantibodies against aquaporin-4 (αAQP4) and myelin oligodendrocyte glycoprotein (αMOG). The variability of the autoantibody presentation warrants further research into subtyping each case. To elucidate the relationship between astroglial and neuronal protein concentrations in the peripheral circulation with occurrence of these autoantibodies, 86 serum samples were analyzed using immunoassays. The protein concentration of glial fibrillary acidic protein (GFAP), neurofilament light chain (NFL) and tau protein was measured in 3 groups of subcategories of suspected NMOSD: αAQP4 positive (n = 20), αMOG positive (n = 32) and αMOG/αAQP4 seronegative (n = 34). Kruskal-Wallis analysis, univariate predictor analysis, and multivariate logistic regression with ROC curves were performed. GFAP and NFL concentrations were significantly elevated in the αAQP4 positive group (p = 0.003; p = 0.042, respectively), and tau was elevated in the αMOG/αAQP4 seronegative group (p < 0.001). A logistic regression model to classify serostatus was able to separate αAQP4 seropositivity using GFAP + tau, and αMOG seropositivity using tau. The areas under the ROC curves (AUCs) were 0.77 and 0.72, respectively. Finally, a combined seropositivity versus negative status logistic regression model was generated, with AUC = 0.80. The 3 markers can univariately and multivariately classify with moderate accuracy the samples with seropositivity and …