DELFMUT: duplex sequencing-oriented depth estimation model for stable detection of low-frequency mutations.

Duplex sequencing technology has been widely used in the detection of low-frequency mutations in circulating tumor deoxyribonucleic acid (DNA), but how to determine the sequencing depth and other experimental parameters to ensure the stable detection of low-frequency mutations is still an urgent problem to be solved. The mutation detection rules of duplex sequencing constrain not only the number of mutated templates but also the number of mutation-supportive reads corresponding to each forward and reverse strand of the mutated templates. To tackle this problem, we proposed a Depth Estimation model for stable detection of Low-Frequency MUTations in duplex sequencing (DELFMUT), which models the identity correspondence and quantitative relationships between templates and reads using the zero-truncated negative binomial distribution without considering the sequences composed of bases. The results of DELFMUT were verified by real duplex sequencing data. In the case of known mutation frequency and mutation detection rule, DELFMUT can recommend the combinations of DNA input and sequencing depth to guarantee the stable detection of mutations, and it has a great application value in guiding the experimental parameter setting of duplex sequencing technology.

SHR-A1811 (antibody-drug conjugate) in advanced HER2-mutant non-small cell lung cancer: a multicenter, open-label, phase 1/2 study.

A dose-escalation and expansion, phase 1/2 study (ClinicalTrials.gov, NCT04818333) was conducted to assess the novel antibody-drug conjugate SHR-A1811 in pretreated HER2-altered advanced non-small cell lung cancer (NSCLC). Here, we report results from the phase 1 portion. Patients who had previously failed or were intolerant to platinum-based chemotherapy were enrolled and received SHR-A1811 intravenously at doses of 3.2 to 8.0 mg/kg every 3 weeks. Dose escalation followed a Bayesian logistic regression model that included overdose control, with subsequent selection of tolerable levels for dose expansion. Overall, 63 patients were enrolled, including 43 receiving a recommended dose for expansion of 4.8 mg/kg. All patients had HER2-mutant disease. Dose-limiting toxicity occurred in one patient in the 8.0 mg/kg dose cohort. Grade ≥ 3 treatment-related adverse events occurred in 29 (46.0%) patients. One patient in the 6.4 mg/kg cohort died due to interstitial lung disease. As of April 11, 2023, the 4.8 mg/kg cohort showed an objective response rate of 41.9% (95% CI 27.0-57.9), and a disease control rate of 95.3% (95% CI 84.2-99.4). The median duration of response was 13.7 months, with 13 of 18 responses ongoing. The median progression-free survival was 8.4 months (95% CI 7.1-15.0). SHR-A1811 demonstrated favourable safety and clinically meaningful efficacy in pretreated advanced HER2-mutant NSCLC.

LDHB contributes to the regulation of lactate levels and basal insulin secretion in human pancreatic ß cells.

Using 13C6 glucose labeling coupled to gas chromatography-mass spectrometry and 2D 1H-13C heteronuclear single quantum coherence NMR spectroscopy, we have obtained a comparative high-resolution map of glucose fate underpinning ß cell function. In both mouse and human islets, the contribution of glucose to the tricarboxylic acid (TCA) cycle is similar. Pyruvate fueling of the TCA cycle is primarily mediated by the activity of pyruvate dehydrogenase, with lower flux through pyruvate carboxylase. While the conversion of pyruvate to lactate by lactate dehydrogenase (LDH) can be detected in islets of both species, lactate accumulation is 6-fold higher in human islets. Human islets express LDH, with low-moderate LDHA expression and ß cell-specific LDHB expression. LDHB inhibition amplifies LDHA-dependent lactate generation in mouse and human ß cells and increases basal insulin release. Lastly, cis-instrument Mendelian randomization shows that low LDHB expression levels correlate with elevated fasting insulin in humans. Thus, LDHB limits lactate generation in ß cells to maintain appropriate insulin release.

Modeling of senescence-related chemoresistance in ovarian cancer using data analysis and patient-derived organoids.

Background: Ovarian cancer (OC) is a malignant tumor associated with poor prognosis owing to its susceptibility to chemoresistance. Cellular senescence, an irreversible biological state, is intricately linked to chemoresistance in cancer treatment. We developed a senescence-related gene signature for prognostic prediction and evaluated personalized treatment in patients with OC. Methods: We acquired the clinical and RNA-seq data of OC patients from The Cancer Genome Atlas and identified a senescence-related prognostic gene set through differential and cox regression analysis in distinct chemotherapy response groups. A prognostic senescence-related signature was developed and validated by OC patient-derived-organoids (PDOs). We leveraged gene set enrichment analysis (GSEA) and ESTIMATE to unravel the potential functions and immune landscape of the model. Moreover, we explored the correlation between risk scores and potential chemotherapeutic agents. After confirming the congruence between organoids and tumor tissues through immunohistochemistry, we measured the IC50 of cisplatin in PDOs using the ATP activity assay, categorized by resistance and sensitivity to the drug. We also investigated the expression patterns of model genes across different groups. Results: We got 2740 differentially expressed genes between two chemotherapy response groups including 43 senescence-related genes. Model prognostic genes were yielded through univariate cox analysis, and multifactorial cox analysis. Our work culminated in a senescence-related prognostic model based on the expression of SGK1 and VEGFA. Simultaneously, we successfully constructed and propagated three OC PDOs for drug screening. PCR and WB from PDOs affirmed consistent expression trends as those of our model genes derived from comprehensive data analysis. Specifically, SGK1 exhibited heightened expression in cisplatin-resistant OC organoids, while VEGFA manifested elevated expression in the sensitive group (P<0.05). Intriguingly, GSEA results unveiled the enrichment of model genes in the PPAR signaling pathway, pivotal regulator in chemoresistance and tumorigenesis. This revelation prompted the identification of potential beneficial drugs for patients with a high-risk score, including gemcitabine, dabrafenib, epirubicin, oxaliplatin, olaparib, teniposide, ribociclib, topotecan, venetoclax. Conclusion: Through the formulation of a senescence-related signature comprising SGK1 and VEGFA, we established a promising tool for prognosticating chemotherapy reactions, predicting outcomes, and steering therapeutic strategies. Patients with high VEGFA and low SGK1 expression levels exhibit heightened sensitivity to chemotherapy.