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Multiple case-controlled studies have shown that analyzing fragmentation patterns in plasma cell-free DNA (cfDNA) can distinguish individuals with cancer from healthy controls. However, there have been few studies that investigate various types of cfDNA fragmentomics patterns in individuals with other diseases. We therefore developed a comprehensive statistic, called fragmentation signatures, that integrates the distributions of fragment positioning, fragment length, and fragment end-motifs in cfDNA. We found that individuals with venous thromboembolism, systemic lupus erythematosus, dermatomyositis, or scleroderma have cfDNA fragmentation signatures that closely resemble those found in individuals with advanced cancers. Furthermore, these signatures were highly correlated with increases in inflammatory markers in the blood. We demonstrate that these similarities in fragmentation signatures lead to high rates of false positives in individuals with autoimmune or vascular disease when evaluated using conventional binary classification approaches for multicancer earlier detection (MCED). To address this issue, we introduced a multiclass approach for MCED that integrates fragmentation signatures with protein biomarkers and achieves improved specificity in individuals with autoimmune or vascular disease while maintaining high sensitivity. Though these data put substantial limitations on the specificity of fragmentomics-based tests for cancer diagnostics, they also offer ways to improve the interpretability of such tests. Moreover, we expect these results will lead to a better understanding of the process—most likely inflammatory—from which abnormal fragmentation signatures are derived. 

Introduction The gold standard for diagnosis of active lupus nephritis (ALN), a kidney biopsy, is invasive with attendant morbidity and cannot be serially repeated. Urinary ALCAM (uALCAM) has shown high diagnostic accuracy for renal pathology activity in ALN patients. Methods Lateral flow assays (LFA) for assaying uALCAM were engineered using persistent luminescent nanoparticles, read by a smartphone. The stability and reproducibility of the assembled LFA strips and freeze-dried conjugated nanoparticles were verified, as was analyte specificity. Results The LFA tests for both un-normalized uALCAM (AUC=0.93) and urine normalizer (HVEM)-normalized uALCAM (AUC=0.91) exhibited excellent accuracies in distinguishing ALN from healthy controls. The accuracies for distinguishing ALN from all other lupus patients were 0.86 and 0.74, respectively. Conclusion Periodic monitoring of uALCAM using this easy-to-use LFA test by the patient at home could potentially accelerate early detection of renal involvement or disease flares in lupus patients, and hence reduce morbidity and mortality.