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Fragmentomics of urinary cell-free DNA in nuclease knockout mouse models.

ABSTRACT: Urinary cell-free DNA (ucfDNA) is a potential biomarker for bladder cancer detection. However, the biological characteristics of ucfDNA are not well understood. We explored the roles of deoxyribonuclease 1 (DNASE1) and deoxyribonuclease 1-like 3 (DNASE1L3) in the fragmentation of ucfDNA using mouse models. The deletion of Dnase1 in mice (Dnase1-/-) caused aberrations in ucfDNA fragmentation, including a 24-fold increase in DNA concentration, and a 3-fold enrichment of long DNA molecules, with a relative decrease of fragments with thymine ends and reduction of jaggedness (i.e., the presence of single-stranded protruding ends). In contrast, such changes were not observed in mice with Dnase1l3 deletion (Dnase1l3-/-). These results suggested that DNASE1 was an important nuclease contributing to the ucfDNA fragmentation. Western blot analysis revealed that the concentration of DNASE1 protein was higher in urine than DNASE1L3. The native-polyacrylamide gel electrophoresis zymogram showed that DNASE1 activity in urine was higher than that in plasma. Furthermore, the proportion of ucfDNA fragment ends within DNase I hypersensitive sites (DHSs) was significantly increased in Dnase1-deficient mice. In humans, patients with bladder cancer had lower proportions of ucfDNA fragment ends within the DHSs when compared with participants without bladder cancer. The area under the curve (AUC) for differentiating patients with and without bladder cancer was 0.83, suggesting the analysis of ucfDNA fragmentation in the DHSs may have potential for bladder cancer detection. This work revealed the intrinsic links between the nucleases in urine and ucfDNA fragmentomics.

SUBMITTER: Chen M

PROVIDER: S-EPMC9258866 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Early detection can benefit cancer patients with more effective treatments and better prognosis, but existing early screening tests are limited, especially for multi-cancer detection. This study investigated the most prevalent and lethal cancer types, including primary liver cancer (PLC), colorectal adenocarcinoma (CRC), and lung adenocarcinoma (LUAD). Leveraging the emerging cell-free DNA (cfDNA) fragmentomics, we developed a robust machine learning model for multi-cancer early detection. 1,214 participants, including 381 PLC, 298 CRC, 292 LUAD patients, and 243 healthy volunteers, were enrolled. The majority of patients (N = 971) were at early stages (stage 0, N = 34; stage I, N = 799). The participants were randomly divided into a training cohort and a test cohort in a 1:1 ratio while maintaining the ratio for the major histology subtypes. An ensemble stacked machine learning approach was developed using multiple plasma cfDNA fragmentomic features. The model was trained solely in the training cohort and then evaluated in the test cohort. Our model showed an Area Under the Curve (AUC) of 0.983 for differentiating cancer patients from healthy individuals. At 95.0% specificity, the sensitivity of detecting all cancer reached 95.5%, while 100%, 94.6%, and 90.4% for PLC, CRC, and LUAD, individually. The cancer origin model demonstrated an overall 93.1% accuracy for predicting cancer origin in the test cohort (97.4%, 94.3%, and 85.6% for PLC, CRC, and LUAD, respectively). Our model sensitivity is consistently high for early-stage and small-size tumors. Furthermore, its detection and origin classification power remained superior when reducing sequencing depth to 1× (cancer detection: ≥ 91.5% sensitivity at 95.0% specificity; cancer origin: ≥ 91.6% accuracy). In conclusion, we have incorporated plasma cfDNA fragmentomics into the ensemble stacked model and established an ultrasensitive assay for multi-cancer early detection, shedding light on developing cancer early screening in clinical practice.

Project description:BACKGROUND:The present study sought to identify a panel of DNA markers for noninvasive diagnosis using cell-free DNA (cfDNA) from urine supernatant or cellular DNA from urine sediments of hematuria patients. A panel of 48 bladder cancer-specific genes was selected. A next-generation sequencing-based assay with a cfDNA barcode-enabled single-molecule test was employed. Mutation profiles of blood, urine, and tumor sample from 16 bladder cancer patients were compared. Next, urinary cellular DNA and cfDNA were prospectively collected from 125 patients (92 bladder cancer cases and 33 controls) and analyzed using the 48-gene panel. The individual gene markers and combinations of markers were validated according to the pathology results. The mean areas under the receiver operating characteristic (ROC) curves (AUCs) obtained with the various modeling approaches were calculated and compared. RESULTS:This pilot study of 16 bladder cancer patients demonstrated that gene mutations in urine supernatant and sediments had better concordance with cancer tissue as compared with plasma. Logistic analyses suggested two powerful combinations of genes for genetic diagnostic modeling: five genes for urine supernatant (TERT, FGFR3, TP53, PIK3CA, and KRAS) and seven genes for urine sediments (TERT, FGFR3, TP53, HRAS, PIK3CA, KRAS, and ERBB2). The accuracy of the five-gene panel and the seven-gene panel in the validation cohort yielded AUCs of 0.94 [95% confidence interval (CI) 0.91-0.97] and 0.91 (95% CI 0.86-0.96), respectively. With the addition of age and gender, the diagnostic power of the urine supernatant five-gene model and the urine sediment seven-gene model improved as the revised AUCs were 0.9656 (95% CI 0.9368-0.9944) and 0.9587 (95% CI 0.9291-0.9883). CONCLUSIONS:cfDNA from urine bears great diagnostic potential. A five-gene panel for urine supernatant and a seven-gene panel for urine sediments are promising options for identifying bladder cancer in hematuria patients.

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Fragmentomics of urinary cell-free DNA in nuclease knockout mouse models.

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