Scoring PD-L1 Expression in Urothelial Carcinoma: An International Multi-Institutional Study on Comparison of Manual and Artificial Intelligence Measurement Model (AIM-PD-L1) Pathology Assessments.

Assessing programmed death ligand 1 (PD-L1) expression on tumor cells (TCs) using Food and Drug Administration-approved, validated immunoassays can guide the use of immune checkpoint inhibitor (ICI) therapy in cancer treatment. However, substantial interobserver variability has been reported using these immunoassays. Artificial intelligence (AI) has the potential to accurately measure biomarker expression in tissue samples, but its reliability and comparability to standard manual scoring remain to be evaluated. This multinational study sought to compare the %TC scoring of PD-L1 expression in advanced urothelial carcinoma, assessed by either an AI Measurement Model (AIM-PD-L1) or expert pathologists. The concordance among pathologists and between pathologists and AIM-PD-L1 was determined. The positivity rate of ≥ 1%TC PD-L1 was between 20-30% for 8/10 pathologists, and the degree of agreement and scoring distribution for among pathologists and between pathologists and AIM-PD-L1 was similar both scored as a continuous variable or using the pre-defined cutoff. Numerically higher score variation was observed with the 22C3 assay than with the 28-8 assay. A 2-h training module on the 28-8 assay did not significantly impact manual assessment. Cases exhibiting significantly higher variability in the assessment of PD-L1 expression (mean absolute deviation > 10) were found to have patterns of PD-L1 staining that were more challenging to interpret. An improved understanding of sources of manual scoring variability can be applied to PD-L1 expression analysis in the clinical setting. In the future, the application of AI algorithms could serve as a valuable reference guide for pathologists while scoring PD-L1.

Multi omics analysis of fibrotic kidneys in two mouse models.

Kidney fibrosis represents an urgent unmet clinical need due to the lack of effective therapies and an inadequate understanding of the molecular pathogenesis. We have generated a comprehensive and combined multi-omics dataset (proteomics, mRNA and small RNA transcriptomics) of fibrotic kidneys that is searchable through a user-friendly web application: http://hbcreports.med.harvard.edu/fmm/ . Two commonly used mouse models were utilized: a reversible chemical-induced injury model (folic acid (FA) induced nephropathy) and an irreversible surgically-induced fibrosis model (unilateral ureteral obstruction (UUO)). mRNA and small RNA sequencing, as well as 10-plex tandem mass tag (TMT) proteomics were performed with kidney samples from different time points over the course of fibrosis development. The bioinformatics workflow used to process, technically validate, and combine the single omics data will be described. In summary, we present temporal multi-omics data from fibrotic mouse kidneys that are accessible through an interrogation tool (Mouse Kidney Fibromics browser) to provide a searchable transcriptome and proteome for kidney fibrosis researchers.

Insight into the RNA Exosome Complex Through Modeling Pontocerebellar Hypoplasia Type 1b Disease Mutations in Yeast.

Pontocerebellar hypoplasia type 1b (PCH1b) is an autosomal recessive disorder that causes cerebellar hypoplasia and spinal motor neuron degeneration, leading to mortality in early childhood. PCH1b is caused by mutations in the RNA exosome subunit gene, EXOSC3 The RNA exosome is an evolutionarily conserved complex, consisting of nine different core subunits, and one or two 3'-5' exoribonuclease subunits, that mediates several RNA degradation and processing steps. The goal of this study is to assess the functional consequences of the amino acid substitutions that have been identified in EXOSC3 in PCH1b patients. To analyze these EXOSC3 substitutions, we generated the corresponding amino acid substitutions in the Saccharomyces cerevisiae ortholog of EXOSC3, Rrp40 We find that the rrp40 variants corresponding to EXOSC3-G31A and -D132A do not affect yeast function when expressed as the sole copy of the essential Rrp40 protein. In contrast, the rrp40-W195R variant, corresponding to EXOSC3-W238R in PCH1b patients, impacts cell growth and RNA exosome function when expressed as the sole copy of Rrp40 The rrp40-W195R protein is unstable, and does not associate efficiently with the RNA exosome in cells that also express wild-type Rrp40 Consistent with these findings in yeast, the levels of mouse EXOSC3 variants are reduced compared to wild-type EXOSC3 in a neuronal cell line. These data suggest that cells possess a mechanism for optimal assembly of functional RNA exosome complex that can discriminate between wild-type and variant exosome subunits. Budding yeast can therefore serve as a useful tool to understand the molecular defects in the RNA exosome caused by PCH1b-associated amino acid substitutions in EXOSC3, and potentially extending to disease-associated substitutions in other exosome subunits.

Posttranslational marks control architectural and functional plasticity of the nuclear pore complex basket.

The nuclear pore complex (NPC) serves as both the unique gate between the nucleus and the cytoplasm and a major platform that coordinates nucleocytoplasmic exchanges, gene expression, and genome integrity. To understand how the NPC integrates these functional constraints, we dissected here the posttranslational modifications of the nuclear basket protein Nup60 and analyzed how they intervene to control the plasticity of the NPC. Combined approaches highlight the role of monoubiquitylation in regulating the association dynamics of Nup60 and its partner, Nup2, with the NPC through an interaction with Nup84, a component of the Y complex. Although major nuclear transport routes are not regulated by Nup60 modifications, monoubiquitylation of Nup60 is stimulated upon genotoxic stress and regulates the DNA-damage response and telomere repair. Together, these data reveal an original mechanism contributing to the plasticity of the NPC at a molecular-organization and functional level.