Immunoassay based bioanalytical measurements are widely used in a variety of biomedical research and clinical settings. In these settings they are assumed to faithfully represent the experimental conditions being tested and the sample groups being compared. Although significant technical advances have been made in improving sensitivity and quality of the measurements, currently no metrics exist that objectively quantify the fidelity of the measured analytes with respect to noise associated with the specific assay. Here we introduce ratio of cross-coefficient-of-variation (rxCOV), a fidelity metric for objectively assessing immunoassay analyte measurement quality when comparing its differential expression between different sample groups or experimental conditions. We derive the metric from first principles and establish its feasibility and applicability using simulated and experimental data. We show that rxCOV assesses fidelity independent of statistical significance,and importantly, identifies when latter is meaningful. We also discuss its importance in the contextof averaging experimental replicates for increasing signal to noise ratio. Finally, we demonstrate its application in a Lynch Syndrome case study. We conclude by discussing its applicability to multiplexed immunoassays, other biosensing assays, and to paired and unpaired data. We anticipate rxCOV to be adopted as a simple and easy-to-use fidelity metric for performing robust and reproducible biomedical research.
Background Aims: Nanoscale nuclear architecture mapping (nanoNAM), an optical coherence tomography-derived approach, is capable of detecting with nanoscale sensitivity, structural alterations in the chromatin of epithelial cell nuclei at risk for malignant transformation. Since these alterations predate the development of dysplasia, we aimed to utilize nanoNAM to identify patients with Barrett’s esophagus (BE) who progress to high grade dysplasia (HGD) or esophageal adenocarcinoma (EAC). Methods: This is a nested case control study of 46 BE patients of which 21 progressed to HGD/EAC over 3.7 (± 2.37) years (cases/progressors) and 25 patients who did not progress over 6.3 (± 3.1) years (controls/non-progressors). The archived formalin-fixed paraffin-embedded (FFPE) tissue blocks collected as part of standard clinical care at index endoscopy were used. The nanoNAM imaging was performed on a 5μm FFPE section and each nucleus was mapped to a three-dimensional (3D) depth-resolved optical path difference (3D-drOPD) nuclear representation, quantifying nanoscale-sensitive alterations in the 3D nuclear architecture of the cell. Using 3D-drOPD representation of each nucleus, twelve patient-level nanoNAM features summarizing the alterations in intrinsic nuclear architecture were computed. A risk prediction model was built incorporating nanoNAM features and clinical features. Results: A statistically significant differential-shift was observed in the drOPD cumulative distributions between progressors and non-progressors. Of the twelve nanoNAM features, six – mean-max, mean-mean, mean-median, entropy-median, entropy-entropy, entropy-skewness – showed statistically significant difference between cases and controls. NanoNAM features based prediction model identified progression in independent validation sets, with area under the receiver operating characteristic (auROC) of 80.8% ± 0.35% standard error (s.e.), with an increase to 82.54% ± 0.46% (s.e.), when combined with length of BE segment. Conclusions: NanoNAM can serve as an adjunct to histopathological evaluation of BE patients and aid in risk stratification.
Poor translatability of animal disease models has hampered the development of new inflammatory bowel disorder (IBD) therapeutics. We describe a preclinical, ex vivo system using freshly obtained and well-characterized human colorectal tissue from patients with ulcerative colitis (UC) and healthy control (HC) participants to test potential therapeutics for efficacy and target engagement, using the JAK/STAT inhibitor tofacitinib (TOFA) as a model therapeutic. Colorectal biopsies from HC participants and patients with UC were cultured and stimulated with multiple mitogens ± TOFA. Soluble biomarkers were detected using a 29-analyte multiplex ELISA. Target engagement in CD3+CD4+ and CD3+CD8+ T-cells was determined by flow cytometry in peripheral blood mononuclear cells (PBMCs) and isolated mucosal mononuclear cells (MMCs) following the activation of STAT1/3 phosphorylation. Data were analyzed using linear mixed-effects modeling, t test, and analysis of variance. Biomarker selection was performed using penalized and Bayesian logistic regression modeling, with results visualized using uniform manifold approximation and projection. Under baseline conditions, 27 of 29 biomarkers from patients with UC were increased versus HC participants. Explant stimulation increased biomarker release magnitude, expanding the dynamic range for efficacy and target engagement studies. Logistic regression analyses identified the most representative UC baseline and stimulated biomarkers. TOFA inhibited biomarkers dependent on JAK/STAT signaling. STAT1/3 phosphorylation in T-cells revealed compartmental differences between PBMCs and MMCs. Immunogen stimulation increases biomarker release in similar patterns for HC participants and patients with UC, while enhancing the dynamic range for pharmacological effects. This work demonstrates the power of ex vivo human colorectal tissue as preclinical tools for evaluating target engagement and downstream effects of new IBD therapeutic agents.
Background & aims: The assessment of therapeutic response after neoadjuvant treatment and pancreatectomy for pancreatic ductal adenocarcinoma (PDAC) has been an ongoing challenge. Several limitations have been encountered when employing current grading systems for residual tumor. Considering endoscopic ultrasound (EUS) represents a sensitive imaging technique for PDAC, differences in tumor size between preoperative EUS and postoperative pathology after neoadjuvant therapy were hypothesized to represent an improved marker of treatment response. Methods: For 340 treatment-naive and 365 neoadjuvant-treated PDACs, EUS and pathologic findings were analyzed and correlated with patient overall survival (OS). A separate group of 200 neoadjuvant-treated PDACs served as a validation cohort for further analysis. Results: Among treatment-naive PDACs, there was a moderate concordance between EUS imaging and postoperative pathology for tumor size (r = 0.726, P < .001) and AJCC 8th edition T-stage (r = 0.586, P < .001). In the setting of neoadjuvant therapy, a decrease in T-stage correlated with improved 3-year OS rates (50% vs 31%, P < .001). Through recursive partitioning, a cutoff of ≥ 47% tumor size reduction was also found to be associated with improved OS (67% vs 32%, P < .001). Improved OS using ≥ 47% threshold was validated using a separate cohort of neoadjuvant-treated PDACs (72% vs 36%, P < .001). By multivariate analysis, a reduction in tumor size by ≥ 47% was an independent prognostic factor for improved OS (P = .007). Conclusions: The difference in tumor size between preoperative EUS imaging and postoperative pathology among neoadjuvant-treated PDAC patients is an important prognostic indicator and may guide subsequent chemotherapeutic management.
An unmet clinical need in solid tumor cancers is the ability to harness the intrinsic spatial information in primary tumors that can be exploited to optimize prognostics, diagnostics and therapeutic strategies for precision medicine. Here, we develop a transformational spatial analytics computational and systems biology platform (SpAn) that predicts clinical outcomes and captures emergent spatial biology that can potentially inform therapeutic strategies. We apply SpAn to primary tumor tissue samples from a cohort of 432 chemo-naïve colorectal cancer (CRC) patients iteratively labeled with a highly multiplexed (hyperplexed) panel of 55 fluorescently tagged antibodies. We show that SpAn predicts the 5-year risk of CRC recurrence with a mean AUROC of 88.5% (SE of 0.1%), significantly better than current state-of-the-art methods. Additionally, SpAn infers the emergent network biology of tumor microenvironment spatial domains revealing a spatially-mediated role of CRC consensus molecular subtype features with the potential to inform precision medicine
