Impact of LAG-3/FGL1 pathway on immune evasive contexture and clinical outcomes in advanced urothelial carcinoma.

BACKGROUND: Anti-programmed death-1 (PD-1)/anti-PD-ligand-1 (PD-L1) pathway inhibition is a standard regimen for advanced urothelial carcinoma (UC); however, its limited efficacy has been reflected in reported medium response rates. This study explored the role of next-generation coinhibitory receptors (IRs; lymphocyte activation gene 3 (LAG-3), T-cell immunoglobulin and mucin domain 3 (TIM-3), and T-cell immunoreceptor with Ig and ITIM domains (TIGIT)) and their ligands (LGs) in the response to PD-(L)1 blockade therapy and the oncological outcomes in patients with UC. METHODS: We investigated metastatic UC cases who underwent PD-(L)1 therapy (cohort 1: n=348, cohort 2: n=89, and cohort 4: n=29) or advanced UC cases involving surgery (cohort 3: n=293 and cohort 5: n=90). We assessed the mRNA expression profiles and corresponding clinical information regarding IRs and LGs using cohorts 1, 2, and 3. Additionally, we elucidated the spatial features of these targeted markers using multiplex immunohistochemistry (mIHC) on formalin-fixed paraffin-embedded samples from cohorts 4 and 5. Survival, differential expressed gene, and Gene Set Enrichment analyses were performed. For mIHC, quantitative analyses were also performed to correlate immune and tumor cell densities with patient survival. RESULTS: LAG-3 expression was strongly associated with the responsiveness of PD-(L)1 blockade compared with the expression of TIM-3 and TIGIT. In tumors with high LAG-3 levels, the increased expression of fibrinogen-like protein 1 (FGL1) had a significantly negative effect on the response to PD-(L)1 blockade and overall survival. Moreover, high FGL1 levels were associated with elevated CD4+ regulatory T-cell gene signatures and the upregulation of CD39 and neuropilin-1, with both indicating CD8+ T-cell exhaustion. mIHC analyses revealed that patients with stromal CD8+LAG-3+cellshigh-tumor FGL1+cellshigh exhibited a significant negative correlation with survival rates compared with those with stromal CD8+LAG-3+cellshigh-tumor FGL1+cellslow. CONCLUSIONS: LAG-3 expression and high FGL1 coexpression are important predictive factors of adverse oncological outcomes related to the presence of immunosuppressive contextures. These findings are hypothesis-generating, warranting further mechanistic and clinical studies aimed to evaluate LAG-3/FGL1 blockade in UC.

Raman Multi-Omic Snapshot and Statistical Validation of Structural Differences between Herpes Simplex Type I and Epstein-Barr Viruses.

Raman spectroscopy was applied to study the structural differences between herpes simplex virus Type I (HSV-1) and Epstein-Barr virus (EBV). Raman spectra were first collected with statistical validity on clusters of the respective virions and analyzed according to principal component analysis (PCA). Then, average spectra were computed and a machine-learning approach applied to deconvolute them into sub-band components in order to perform comparative analyses. The Raman results revealed marked structural differences between the two viral strains, which could mainly be traced back to the massive presence of carbohydrates in the glycoproteins of EBV virions. Clear differences could also be recorded for selected tyrosine and tryptophan Raman bands sensitive to pH at the virion/environment interface. According to the observed spectral differences, Raman signatures of known biomolecules were interpreted to link structural differences with the viral functions of the two strains. The present study confirms the unique ability of Raman spectroscopy for answering structural questions at the molecular level in virology and, despite the structural complexity of viral structures, its capacity to readily and reliably differentiate between different virus types and strains.

Raman Fingerprints of SARS-CoV-2 Omicron Subvariants: Molecular Roots of Virological Characteristics and Evolutionary Directions.

The latest RNA genomic mutation of SARS-CoV-2 virus, termed the Omicron variant, has generated a stream of highly contagious and antibody-resistant strains, which in turn led to classifying Omicron as a variant of concern. We systematically collected Raman spectra from six Omicron subvariants available in Japan (i.e., BA.1.18, BA.2, BA.4, BA.5, XE, and BA.2.75) and applied machine-learning algorithms to decrypt their structural characteristics at the molecular scale. Unique Raman fingerprints of sulfur-containing amino acid rotamers, RNA purines and pyrimidines, tyrosine phenol ring configurations, and secondary protein structures clearly differentiated the six Omicron subvariants. These spectral characteristics, which were linked to infectiousness, transmissibility, and propensity for immune evasion, revealed evolutionary motifs to be compared with the outputs of genomic studies. The availability of a Raman "metabolomic snapshot", which was then translated into a barcode to enable a prompt subvariant identification, opened the way to rationalize in real-time SARS-CoV-2 activity and variability. As a proof of concept, we applied the Raman barcode procedure to a nasal swab sample retrieved from a SARS-CoV-2 patient and identified its Omicron subvariant by coupling a commercially available magnetic bead technology with our newly developed Raman analyses.

Cold-induced vasodilation response in a Japanese cohort: insights from cold-water immersion and genome-wide association studies.

BACKGROUND: Cold-induced vasodilation (CIVD) occurs after blood vessels in the skin are constricted due to local cold exposure. Although many CIVD studies have been conducted, the underlying molecular mechanisms are yet to be clarified. Therefore, we explored genetic variants associated with CIVD response using the largest-scale dataset reported to date in a CIVD study involving wavelet analysis; thus, the findings improve our understanding of the molecular mechanisms that regulate the CIVD response. METHODS: We performed wavelet analysis of three skin blood flow signals [endothelial nitric oxide (eNO)-independent, eNO-dependent, and neurogenic activities] during finger cold-water immersion at 5 °C in 94 Japanese young adults. Additionally, we conducted genome-wide association studies of CIVD using saliva samples collected from the participants. RESULTS: We found that the mean wavelet amplitudes of eNO-independent and neurogenic activities significantly increased and decreased prior to CIVD, respectively. Our results also implied that as many as ~ 10% of the Japanese subjects did not show an apparent CIVD response. Our genome-wide association studies of CIVD using ~ 4,040,000 imputed data found no apparent CIVD-related genetic variants; however, we identified 10 genetic variants, including 2 functional genes (COL4A2 and PRLR) that are associated with notable blunted eNO-independent and neurogenic activity responses in individuals without CIVD response during local cold exposure. CONCLUSIONS: Our findings indicate that individuals without CIVD response differentiated by genotypes with COL4A2 and PRLR genetic variants exhibited notable blunted eNO-independent and neurogenic activity responses during local cold exposure.

Deep learning-based predictions of clear and eosinophilic phenotypes in clear cell renal cell carcinoma.

We have recently shown that histological phenotypes focusing on clear and eosinophilic cytoplasm in clear cell renal cell carcinoma (ccRCC) correlated with prognosis and the response to angiogenesis inhibition and checkpoint blockade. This study aims to objectively show the diagnostic utility of clear or eosinophilic phenotypes of ccRCC by developing an artificial intelligence (AI) model using the TCGA-ccRCC dataset and to demonstrate if the clear or eosinophilic predicted phenotypes correlate with pathological factors and gene signatures associated with angiogenesis and cancer immunity. Before the development of the AI model, histological evaluation using hematoxylin and eosin whole-slide images of the TCGA-ccRCC cohort (n = 435) was performed by a urologic pathologist. The AI model was developed as follows. First, the highest-grade area on each whole slide image was captured for image processing. Second, the selected regions were cropped into tiles. Third, the AI model was trained using transfer learning on a deep convolutional neural network, and clear or eosinophilic predictions were scaled as AI scores. Next, we verified the AI model using a validation cohort (n = 95). Finally, we evaluated the accuracy of the prognostic predictions of the AI model and revealed that the AI model detected clear and eosinophilic phenotypes with high accuracy. The AI model stratified the patients' outcomes, and the predicted eosinophilic phenotypes correlated with adverse clinicopathological characteristics and high immune-related gene signatures. In conclusion, the AI-based histologic subclassification accurately predicted clear or eosinophilic phenotypes of ccRCC, allowing for consistently reproducible stratification for prognostic and therapeutic stratification.

OLIG2 is an in vivo bookmarking transcription factor in the developing neural tube in mouse.

Cells possess intrinsic features that are inheritable via epigenetic regulation, such as DNA methylation and histone modification. These inheritable features maintain a unique gene expression pattern, underlying cellular memory. Because of the degradation or displacement of mitotic chromosomes, most transcription factors do not contribute to cellular memory. However, accumulating in vitro evidence indicates that some transcription factors can be retained in mitotic chromosomes called as bookmarking. Such transcription factors may contribute to a novel third mechanism of cellular memory. Since most findings of transcription factor bookmarking have been reported in vitro, little is currently known in vivo. In the neural tube of mouse embryos, we discovered that OLIG2, a basic helix loop helix (bHLH) transcription factor that regulates proliferation of neural progenitors and the cell fate of motoneurons and oligodendrocytes, binds to chromatin through every cell cycle including M-phase. OLIG2 chromosomal localization coincides with mitotic cell features such as the phosphorylation of histone H3, KI67, and nuclear membrane breakdown. Chromosomal localization of OLIG2 is regulated by an N-terminus triple serine motif. Photobleaching analysis revealed slow OLIG2 mobility, suggesting a high affinity of OLIG2 to DNA. In Olig2 N-terminal deletion mutant mice, motoneurons and oligodendrocyte progenitor numbers are reduced in the neural tube, suggesting that the bookmarking regulatory domain is important for OLIG2 function. We conclude that OLIG2 is a de novo in vivo bookmarking transcription factor. Our results demonstrate the presence of in vivo bookmarking in a living organism and illustrate a novel function of transcription factors.

PBRM1 Immunohistochemical Expression Profile Correlates with Histomorphological Features and Endothelial Expression of Tumor Vasculature for Clear Cell Renal Cell Carcinoma.

Loss of the polybromo-1 (PBRM1) protein has been expected as a possible biomarker for clear cell renal cell carcinoma (ccRCC). There is little knowledge about how PBRM1 immunohistochemical expression correlates with the histomorphological features of ccRCC and the endothelial expression of tumor vasculature. The present study evaluates the association of architectural patterns with the PBRM1 expression of cancer cells using a cohort of 425 patients with nonmetastatic ccRCC. Furthermore, we separately assessed the PBRM1 expression of the endothelial cells and evaluated the correlation between the expression of cancer cells and endothelial cells. PBRM1 loss in cancer cells was observed in 148 (34.8%) patients. In the correlation analysis between architectural patterns and PBRM1 expression, macrocyst/microcystic, tubular/acinar, and compact/small nested were positively correlated with PBRM1 expression, whereas alveolar/large nested, thick trabecular/insular, papillary/pseudopapillary, solid sheets, and sarcomatoid/rhabdoid were negatively correlated with PBRM1 expression. PBRM1 expression in vascular endothelial cells correlated with the expression of cancer cells (correlation coefficient = 0.834, p < 0.001). PBRM1 loss in both cancer and endothelial cells was associated with a lower recurrence-free survival rate (p < 0.001). Our PBRM1 expression profile indicated that PBRM1 expression in both cancer and endothelial cells may be regulated in an orchestrated manner.

Histologic-Based Tumor-Associated Immune Cells Status in Clear Cell Renal Cell Carcinoma Correlates with Gene Signatures Related to Cancer Immunity and Clinical Outcomes.

The three-tier immunophenotype (desert, excluded, and inflamed) and the four-tier immunophenotype (cold, immunosuppressed, excluded, and hot) have been linked to prognosis and immunotherapy response. This study aims to evaluate whether immunophenotypes of clear cell renal cell carcinoma, identified on hematoxylin and eosin-stained slides, correlate with gene expression signatures related to cancer immunity, and clinical outcomes. We evaluated tumor-associated immune cells (TAICs) status using three methodologies: three-tier immunophenotype based on the location of TAICs, four-tier immunophenotype considering both the location and degree of TAICs and inflammation score focusing only on the degree of TAICs, using a localized clear cell renal cell carcinoma cohort (n = 436) and The Cancer Genome Atlas (TCGA)-KIRC cohort (n = 162). We evaluated the association of the TAICs status assessed by three methodologies with CD8 and PD-L1 immunohistochemistry and immune gene expression signatures by TCGA RNA-sequencing data. All three methodologies correlated with immunohistochemical and immune gene expression signatures. The inflammation score and the four-tier immunophenotype showed similarly higher accuracy in predicting recurrence-free survival and overall survival compared to the three-tier immunophenotype. In conclusion, a simple histologic assessment of TIACs may predict clinical outcomes and immunotherapy responses.

Development and validation of a vascularity-based architectural classification for clear cell renal cell carcinoma: correlation with conventional pathological prognostic factors, gene expression patterns, and clinical outcomes.

The prognostic significance of an architectural grading system for clear cell renal cell carcinoma (ccRCC) has recently been demonstrated. The present study aimed to establish a vascularity-based architectural classification using the cohort of 436 patients with localized ccRCC who underwent extirpative surgery and correlated the findings with conventional pathologic factors, gene expression, and prognosis. First, we assessed architectural patterns in the highest-grade area on hematoxylin and eosin-stained slides, then separately evaluated our surrogate score for vascularity. We grouped nine architectural patterns into three categories based on the vascular network score. "Vascularity-based architectural classification" was defined: category 1: characterized by enrichment of the vascular network, including compact/small nested, macrocyst/microcystic, and tubular/acinar patterns; category 2: characterized by a widely spaced-out vascular network, including alveolar/large nested, thick trabecular/insular, papillary/pseudopapillary patterns; category 3: characterized by scattered vascularity without a vascular network, including solid sheets, rhabdoid and sarcomatoid patterns. Adverse pathological prognostic factors such as TNM stage, WHO/ISUP grade, and necrosis were significantly associated with category 3, followed by category 2 (all p < 0.001). We successfully validated the classification using The Cancer Genome Atlas (TCGA) cohort (n = 162), and RNA-sequencing data available from TCGA showed that the angiogenesis gene signature was significantly enriched in category 1 compared to categories 2 and 3, whereas the immune gene signature was significantly enriched in category 3 compared to categories 1 and 2. In univariate analysis, vascularity-based architectural classification showed the best accuracy in pathological prognostic factors for predicting recurrence-free survival (c-index = 0.786). The predictive accuracy of our model which integrated WHO/ISUP grade, necrosis, TNM stage, and vascularity-based architectural classification was greater than conventional risk models (c-index = 0.871 vs. 0.755-0.843). Our findings suggest that the vascularity-based architectural classification is prognostically useful and may help stratify patients appropriately for management based on their likelihood of post-surgical recurrence.