Bhat, Krishna

Tumor-educated platelets (TEPs) are a potential method of liquid biopsy for the diagnosis and monitoring of cancer. However, the mechanism underlying tumor education of platelets is not known, and transcripts associated with TEPs are often not tumor-associated transcripts. We demonstrated that direct tumor transfer of transcripts to circulating platelets is an unlikely source of the TEP signal. We used CDSeq, a latent Dirichlet allocation algorithm, to deconvolute the TEP signal in blood samples from patients with glioblastoma. We demonstrated that a substantial proportion of transcripts in the platelet transcriptome are derived from nonplatelet cells, and the use of this algorithm allows the removal of contaminant transcripts. Furthermore, we used the results of this algorithm to demonstrate that TEPs represent a subset of more activated platelets, which also contain transcripts normally associated with nonplatelet inflammatory cells, suggesting that these inflammatory cells, possibly in the tumor microenvironment, transfer transcripts to platelets that are then found in circulation. Our analysis suggests a useful and efficient method of processing TEP transcriptomic data to enable the isolation of a unique TEP signal associated with specific tumors.

Purpose The clinical significance of epidermal growth factor receptor variant III (EGFRvIII) expression in glioblastoma multiforme (GBM) and its relationship with other key molecular markers are not clear. We sought to evaluate the clinical significance of GBM subtypes as defined by EGFRvIII status. Patients and Methods The expression of EGFRvIII was assessed by immunohistochemistry in 649 patients with newly diagnosed GBM. These data were then examined in conjunction with the expression of phospho-intermediates (in a subset of these patients) of downstream AKT and Ras pathways and YKL-40 as well as with known clinical risk factors, including the Radiation Therapy Oncology Group's recursive partitioning analysis (RTOG-RPA) class. Results The RTOG-RPA class was highly predictive of survival in EGFRvIII-negative patients but much less predictive in EGFRvIII-positive patients. These findings were seen in both an initial test set (n = 268) and a larger validation set (n = 381). Similarly, activation of the AKT/MAPK pathways and YKL-40 positivity were predictive of poor outcome in EGFRvIII-negative patients but not in EGFRvIII-positive patients. Pair-wise combinations of markers identified EGFRvIII and YKL-40 as prognostically important. In particular, outcome in patients with EGFRvIII-negative/YKL-40–negative tumors was significantly better than the outcome in patients with the other three combinations of these two markers. Conclusion Established prognostic factors in GBM were not predictive of outcome in the EGFRvIII-positive subset, although this requires confirmation in independent data sets. GBMs negative for both EGFRvIII and YKL-40 show less aggressive behavior.