Due to rapid development of high-throughput sequencing and biotechnology, it has brought new opportunities and challenges in developing efficient computational methods for exploring personalized genomics data of cancer patients. Because of the high-dimension and small sample size characteristics of these personalized genomics data, it is difficult for excavating effective information by using traditional statistical methods. In the past few years, network control methods have been proposed to solve networked system with high-dimension and small sample size. Researchers have made progress in the design and optimization of network control principles. However, there are few studies comprehensively surveying network control methods to analyze the biomolecular network data of individual patients. To address this problem, here we comprehensively surveyed complex network control methods on personalized omics data for understanding tumor heterogeneity in precision medicine of individual patients with cancer.

Although patients with light chain amyloidosis (AL) may present with co-deposition of amyloid and immune complexes (ICs) in renal biopsies, data on clinical characteristics and prognostic value of renal IC deposition are limited. A total of 73 patients with AL amyloidosis who were newly diagnosed by renal biopsy in Xijing Hospital (Xi’an, China) were divided into two groups (IC and non-IC groups). As a result, renal IC deposition was found in 26% of patients. Patients with IC deposition were associated with more urinary protein excretion and lower serum albumin. Notably, patients in the non-IC group achieved higher hematological overall response rate (81.5% vs. 47.4%, p = 0.007) and ≥VGPR rate (75.9% vs. 39.8%, p = 0.004) compared with those in IC group. Renal response rate was also higher in the non-IC group (63% vs. 31.6%, p = 0.031). With the median follow-up time of 19 months, a significantly worse overall survival was observed in patients with the IC group as compared with those without renal IC deposition in the Kaplan–Meier analysis (p = 0.036). Further multivariate analysis demonstrated that renal immune complex deposition was associated with worse overall survival in patients with AL amyloidosis (HR 5.927, 95% CI 2.148–16.356, p = 0.001).

BackgroundEmerging evidence revealed that gut microbial dysbiosis is implicated in the development of plasma cell dyscrasias and amyloid deposition diseases, but no data are available on the relationship between gut microbiota and immunoglobulin light chain (AL) amyloidosis.MethodsTo characterize the gut microbiota in patients with AL amyloidosis, we collected fecal samples from patients with AL amyloidosis (n=27) and age-, gender-, and BMI-matched healthy controls (n=27), and conducted 16S rRNA MiSeq sequencing and amplicon sequence variants (ASV)-based analysis.ResultsThere were significant differences in gut microbial communities between the two groups. At the phylum level, the abundance of Actinobacteriota and Verrucomicrobiota was significantly higher, while Bacteroidota reduced remarkably in patients with AL amyloidosis. At the genus level, 17 genera, including Bifidobacterium, Akkermansia, and Streptococcus were enriched, while only 4 genera including Faecalibacterium, Tyzzerella, Pseudomonas, and Anaerostignum decreased evidently in patients with AL amyloidosis. Notably, 5 optimal ASV-based microbial markers were identified as the diagnostic model of AL amyloidosis and the AUC value of the train set and the test set was 0.8549 (95% CI 0.7310-0.9789) and 0.8025 (95% CI 0.5771-1), respectively. With a median follow-up of 19.0 months, further subgroup analysis also demonstrated some key gut microbial markers were related to disease severity, treatment response, and even prognosis of patients with AL amyloidosis.ConclusionsFor the first time, we demonstrated the alterations of gut microbiota in AL amyloidosis and successfully established and validated the microbial-based diagnostic model, which boosted more studies about microbe-based strategies for diagnosis and treatment in patients with AL amyloidosis in the future.