Photoimmunotherapy Retains Its Anti-Tumor Efficacy with Increasing Stromal Content in Heterotypic Pancreatic Cancer Spheroids.

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease characterized by increased levels of desmoplasia that contribute to reduced drug delivery and poor treatment outcomes. In PDAC, the stromal content can account for up to 90% of the total tumor volume. The complex interplay between stromal components, including pancreatic cancer-associated fibroblasts (PCAFs), and PDAC cells in the tumor microenvironment has a significant impact on the prognoses and thus needs to be recapitulated in vitro when evaluating various treatment strategies. This study is a systematic evaluation of photodynamic therapy (PDT) in 3D heterotypic coculture models of PDAC with varying ratios of patient-derived PCAFs that simulate heterogeneous PDAC tumors with increasing stromal content. The efficacy of antibody-targeted PDT (photoimmunotherapy; PIT) using cetuximab (a clinically approved anti-EGFR antibody) photoimmunoconjugates (PICs) of a benzoporphyrin derivative (BPD) is contrasted with that of liposomal BPD (Visudyne), which is currently in clinical trials for PDT of PDAC. We demonstrate that both Visudyne-PDT and PIT were effective in heterotypic PDAC 3D spheroids with a low stromal content. However, as the stromal content increases above 50% in the 3D spheroids, the efficacy of Visudyne-PDT is reduced by up to 10-fold, while PIT retains its efficacy. PIT was found to be 10-, 19-, and 14-fold more phototoxic in spheroids with 50, 75, and 90% PCAFs, respectively, as compared to Visudyne-PDT. This marked difference in efficacy is attributed to the ability of PICs to penetrate and distribute homogeneously within spheroids with a higher stromal content and the mechanistically different modes of action of the two formulations. This study thus demonstrates how the stromal content in PDAC spheroids directly impacts their responsiveness to PDT and proposes PIT to be a highly suited treatment option for desmoplastic tumors with particularly high degrees of stromal content.

An Artificial Intelligence Approach to Detect Visual Field Progression in Glaucoma Based on Spatial Pattern Analysis.

Purpose: To detect visual field (VF) progression by analyzing spatial pattern changes. Methods: We selected 12,217 eyes from 7360 patients with at least five reliable 24-2 VFs and 5 years of follow-up with an interval of at least 6 months. VFs were decomposed into 16 archetype patterns previously derived by artificial intelligence techniques. Linear regressions were applied to the 16 archetype weights of VF series over time. We defined progression as the decrease rate of the normal archetype or any increase rate of the 15 VF defect archetypes to be outside normal limits. The archetype method was compared with mean deviation (MD) slope, Advanced Glaucoma Intervention Study (AGIS) scoring, Collaborative Initial Glaucoma Treatment Study (CIGTS) scoring, and the permutation of pointwise linear regression (PoPLR), and was validated by a subset of VFs assessed by three glaucoma specialists. Results: In the method development cohort of 11,817 eyes, the archetype method agreed more with MD slope (kappa: 0.37) and PoPLR (0.33) than AGIS (0.12) and CIGTS (0.22). The most frequently progressed patterns included decreased normal pattern (63.7%), and increased nasal steps (16.4%), altitudinal loss (15.9%), superior-peripheral defect (12.1%), paracentral/central defects (10.5%), and near total loss (10.4%). In the clinical validation cohort of 397 eyes with 27.5% of confirmed progression, the agreement (kappa) and accuracy (mean of hit rate and correct rejection rate) of the archetype method (0.51 and 0.77) significantly (P < 0.001 for all) outperformed AGIS (0.06 and 0.52), CIGTS (0.24 and 0.59), MD slope (0.21 and 0.59), and PoPLR (0.26 and 0.60). Conclusions: The archetype method can inform clinicians of VF progression patterns.