Citations

The current study establishes functional links between endoplasmic reticulum (ER) stress and cellular senescence per se in the specific context of aging human lung fibroblasts. Recognizing that the process of aging per se is complex, modulated by the myriad of lifelong and environmental exposures, it is striking to note that chronic ER stress may play a crucial role in the establishment and maintenance of cellular senescence in lung fibroblasts.

ABSTRACT Phage therapy has not been established in the clinical routine, in part due to uncertainties concerning efficacy and immunogenicity. Here, three rabbits were immunized against staphylococcal phage K to assess viral potency in the presence of immunized serum. Three rabbits received weekly intramuscular injections of ~10 10±1 pfu/mL phage K. Phage K-specific IgG formation was measured by an enzyme-linked immunosorbent assay (ELISA); phage inactivation was assessed by calculating K-rates. Using transmission electron microscopy (TEM) and immunogold labeling, antibody binding to phage K was visualized. This was numerically assessed by objective imaging analysis comparing the relative distances of each gold particle to the nearest phage head and tail structure. Immunization led to a strong IgG response, plateauing 7 days after the last phage injection. There was no significant correlation between K-rate and antibody titer over time. TEM showed IgG binding to the head structure of phage K. Image analysis showed a significant reduction in relative distances between antibodies and phage head structures when comparing samples from day 0 and day 28 ( P < 0.0001). These results suggest that while individual serum analysis for antibodies against therapeutic phage bears consideration prior to and with prolonged therapy, during phage application, the formation of specific antibodies against phage may only partially explain decreased phage potency in the presence of immunized serum. Instead, other factors may contribute to an individual’s “humoral receptiveness” to phage therapy. Future investigations should be directed toward the identification of the humoral factors that have the most significant predictive value on phage potency in vivo .

Abstract Deep learning may detect biologically important signals embedded in tumor morphologic features that confer distinct prognoses. Tumor morphologic features were quantified to enhance patient risk stratification within DNA mismatch repair (MMR) groups using deep learning. Using a quantitative segmentation algorithm (QuantCRC) that identifies 15 distinct morphologic features, we analyzed 402 resected stage III colon carcinomas [191 deficient (d)-MMR; 189 proficient (p)-MMR] from participants in a phase III trial of FOLFOX-based adjuvant chemotherapy. Results were validated in an independent cohort (176 d-MMR; 1,094 p-MMR). Association of morphologic features with clinicopathologic variables, MMR, KRAS, BRAFV600E, and time-to-recurrence (TTR) was determined. Multivariable Cox proportional hazards models were developed to predict TTR. Tumor morphologic features differed significantly by MMR status. Cancers with p-MMR had more immature desmoplastic stroma. Tumors with d-MMR had increased inflammatory stroma, epithelial tumor-infiltrating lymphocytes (TIL), high-grade histology, mucin, and signet ring cells. Stromal subtype did not differ by BRAFV600E or KRAS status. In p-MMR tumors, multivariable analysis identified tumor-stroma ratio (TSR) as the strongest feature associated with TTR [HRadj 2.02; 95% confidence interval (CI), 1.14–3.57; P = 0.018; 3-year recurrence: 40.2% vs. 20.4%; Q1 vs. Q2–4]. Among d-MMR tumors, extent of inflammatory stroma (continuous HRadj 0.98; 95% CI, 0.96–0.99; P = 0.028; 3-year recurrence: 13.3% vs. 33.4%, Q4 vs. Q1) and N stage were the most robust prognostically. Association of TSR with TTR was independently validated. In conclusion, QuantCRC can quantify morphologic differences within MMR groups in routine tumor sections to determine their relative contributions to patient prognosis, and may elucidate relevant pathophysiologic mechanisms driving prognosis. Significance: A deep learning algorithm can quantify tumor morphologic features that may reflect underlying mechanisms driving prognosis within MMR groups. TSR was the most robust morphologic feature associated with TTR in p-MMR colon cancers. Extent of inflammatory stroma and N stage were the strongest prognostic features in d-MMR tumors. TIL density was not independently prognostic in either MMR group.