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
Patients with inflammatory bowel disease (IBD) colitis are at an increased risk of developing colorectal cancer and are currently recommended to undergo extensive annual or biennial colonoscopy, a costly and invasive procedure. Most surveillance colonoscopies are negative with no existing objective measures for assessing their risk of developing cancer. We have recently developed a less invasive, cost-effective and objective method to assess cancer risk by detecting the presence of colonic neoplasia via 3-dimensional (3D) nanoscale nuclear architecture mapping (nanoNAM) of normal-appearing rectal biopsies. To establish its translational relevance, we prospectively recruited 103 patients with IBD colitis undergoing surveillance colonoscopy and measured submicroscopic alterations in aberrant intrinsic nuclear architecture of epithelial cells from normal-appearing rectal biopsies with nanoNAM. The results were correlated with the histologic diagnoses from all random biopsies obtained during initial and follow-up colonoscopy within 3 years. Using nanoNAM-based structural characterization as input features into a soft margin-based ν-SVM risk classifier, we show that nanoNAM detects colonic neoplasia with AUC of 0.87 ± 0.04, sensitivity of 0.81 ± 0.09, and specificity of 0.82 ± 0.07 in the independent validation set. In addition, projecting nanoNAM features onto a 2-sphere reveals patients with low-risk and high-risk IBD colitis existing on separate hemispheres. Finally, we show that this ability to assess cancer risk translates to clinically-relevant estimation of individual-patient likelihood of being truly at risk. We demonstrate the potential of nanoNAM to identify patients with IBD at higher risk of developing cancer from normal-appearing rectum tissue, which may aid clinicians in patients with personalized IBD colitis surveillance
