Citations

Abstract BACKGROUND CODEX (CO-Detection by indEXing) imaging has emerged as a pivotal tool in cellular biology, providing unprecedented insights into cellular distributions and interactions within the tissue microenvironment, crucial for advancing computational pathology. Despite its advantages, CODEX is susceptible to stitching errors during the merging of image tiles, which can significantly distort spatial analysis by introducing artifacts that obscure true cellular architecture. METHODS Addressing this challenge, we introduce CISD (CODEX Image Stitch Detection), a novel, user-friendly algorithm optimized for the detection of stitching artifacts in large-scale CODEX datasets. Developed and validated using a comprehensive dataset from 7 matched glioblastoma patients—encompassing over 2800 images across 29 immune protein markers—CISD utilizes a combination of image preprocessing, statistical measures, and thresholding to detect stitching errors. RESULTS CISD demonstrates robust performance, achieving a median Area Under Curve (AUC) value in the gold standard dataset where 73% of markers exceed 70% AUC, and 17% surpass 90%. Further validation shows that in a secondary dataset, 45% of markers maintain AUC values over 70% with 38% exceeding 90%, validating the algorithm’s consistent efficacy across varied datasets. CONCLUSION CISD’s significance lies not only in its computational achievements but also in its accessibility, facilitating high-quality analysis of extensive datasets without complex pattern recognition requirements. This democratizes high-precision computational pathology, enabling more researchers to accurately analyze spatial data. By reducing technical barriers, CISD sets a new benchmark in CODEX imaging, enhancing data reliability and accelerating research discoveries in cellular biology. This innovation stands to substantially refine spatial analysis in digital pathology, ensuring dependable interpretation of complex biological data.

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.