Neuropathy is a feature more frequently observed in pancreatic ductal adenocarcinoma (PDAC) than other tumors. Schwann cells, the most prevalent cell type in peripheral nerves, migrate toward tumor cells and associate with poor prognosis in PDAC. To unveil the effects of Schwann cells on the neuro-stroma niche, here we perform single-cell RNA-sequencing and microarray-based spatial transcriptome analysis of PDAC tissues. Results suggest that Schwann cells may drive tumor cells and cancer-associated fibroblasts (CAFs) to more malignant subtypes: basal-like and inflammatory CAFs (iCAFs), respectively. Moreover, in vitro and in vivo assays demonstrate that Schwann cells enhance the proliferation and migration of PDAC cells via Midkine signaling and promote the switch of CAFs to iCAFs via interleukin-1α. Culture of tumor cells and CAFs with Schwann cells conditioned medium accelerates PDAC progression. Thus, we reveal that Schwann cells induce malignant subtypes of tumor cells and CAFs in the PDAC milieu.

Single-cell and spatial transcriptome sequencing, two recently optimized transcriptome sequencing methods, are increasingly used to study cancer and related diseases. Cell annotation, particularly for malignant cell annotation, is essential and crucial for in-depth analyses in these studies. However, current algorithms lack accuracy and generalization, making it difficult to consistently and rapidly infer malignant cells from pan-cancer data. To address this issue, we present Cancer-Finder, a domain generalization-based deep-learning algorithm that can rapidly identify malignant cells in single-cell data with an average accuracy of 95.16%. More importantly, by replacing the single-cell training data with spatial transcriptomic datasets, Cancer-Finder can accurately identify malignant spots on spatial slides. Applying Cancer-Finder to 5 clear cell renal cell carcinoma spatial transcriptomic samples, Cancer-Finder demonstrates a good ability to identify malignant spots and identifies a gene signature consisting of 10 genes that are significantly co-localized and enriched at the tumor-normal interface and have a strong correlation with the prognosis of clear cell renal cell carcinoma patients. In conclusion, Cancer-Finder is an efficient and extensible tool for malignant cell annotation.

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Background: Translocation-associated renal cell carcinoma involving ALK (ALK-tRCC) is a rare subtype of adult renal cell carcinoma (RCC) reported in recent years. It was recognized as a group of emerging /provisional RCC in the latest World Health Organization’s classification (2016). Case presentation: A new Chinese case of ALK-tRCC was reported. The patient was a 58-year-old man with a tumor in kidney. The tumor was composed of sheets of large cells with abundant eosinophilic cytoplasm and indistinct cell borders but conspicuous intracytoplasmic vacuoles. The nuclei were enlarged with a nucleolar of grade 4. Immunohistochemically, tumor cells were diffusely positive for PAX8, keratin (AE1/AE3), epithelial membrane antigen (EMA) and CK7. Fluorescent in situ hybridization (FISH) showed a rearrangement of ALK in tumor cells. Conclusion: ALK-tRCC is a rare subtype of adult RCC. Its diagnosis is very difficult because the histological spectrum is very wide. We suggested that RCCs should be screened for ALK expression by immunohistochemistry (IHC) for the patient might benefit from ALK inhibitors therapy.

Thyroid fine needle aspiration biopsy (FNAB) remains indeterminate in 16%-24% of the cases. Molecular testing could improve the diagnostic accuracy of FNAB. This study examined the gene mutation profile of patients with thyroid nodules and analyzed the diagnostic ability of molecular testing for thyroid nodules using a self-developed 18-gene test. Between January 2019 and August 2021, 513 samples (414 FNABs and 99 formalin-fixed paraffin-embedded (FFPE) specimens) underwent molecular testing at Ruijin Hospital. Sensitivity (Sen), specificity (Spe), positive predictive value (PPV), negative predictive value (NPV), and accuracy were calculated. There were 457 mutations in 428 samples. The rates of BRAF, RAS, TERT promoter, RET/PTC, and NTRK3 fusion mutations were 73.3% (n=335), 9.6% (n=44), 2.8% (n=13), 4.8% (n=22), and 0.4% (n=2), respectively. The diagnostic ability of cytology and molecular testing were evaluated in Bethesda II and V-VI samples. For cytology alone, Sen, Spe, PPV, NPV, and accuracy were 100%, 25.0%, 97.4%, 100%, and 97.4%; these numbers were 87.5%, 50.0%, 98.0%, 12.5%, and 86.2% when considering positive mutation, and 87.5%, 75.0%, 99.0%, 17.6%, and 87.1% when considering positive cytology or and positive mutation. In Bethesda III-IV nodules, when relying solely on the presence of pathogenic mutations for diagnosis, Sen, Spe, PPV, NPV, and AC were 76.2%, 66.7%, 94.1%, 26.8%, and 75.0%, respectively. It might be necessary to analyze the molecular mechanisms of disease development at the genetic level to predict patients with malignant nodules more accurately in different risk strata and develop rational treatment strategies and definite management plans.