Multimodal analysis of methylomics and fragmentomics in plasma cell-free DNA for multi-cancer early detection and localization.

Despite their promise, circulating tumor DNA (ctDNA)-based assays for multi-cancer early detection face challenges in test performance, due mostly to the limited abundance of ctDNA and its inherent variability. To address these challenges, published assays to date demanded a very high-depth sequencing, resulting in an elevated price of test. Herein, we developed a multimodal assay called SPOT-MAS (screening for the presence of tumor by methylation and size) to simultaneously profile methylomics, fragmentomics, copy number, and end motifs in a single workflow using targeted and shallow genome-wide sequencing (~0.55×) of cell-free DNA. We applied SPOT-MAS to 738 non-metastatic patients with breast, colorectal, gastric, lung, and liver cancer, and 1550 healthy controls. We then employed machine learning to extract multiple cancer and tissue-specific signatures for detecting and locating cancer. SPOT-MAS successfully detected the five cancer types with a sensitivity of 72.4% at 97.0% specificity. The sensitivities for detecting early-stage cancers were 73.9% and 62.3% for stages I and II, respectively, increasing to 88.3% for non-metastatic stage IIIA. For tumor-of-origin, our assay achieved an accuracy of 0.7. Our study demonstrates comparable performance to other ctDNA-based assays while requiring significantly lower sequencing depth, making it economically feasible for population-wide screening.

Fragment length profiles of cancer mutations enhance detection of circulating tumor DNA in patients with early-stage hepatocellular carcinoma.

BACKGROUND: Late detection of hepatocellular carcinoma (HCC) results in an overall 5-year survival rate of less than 16%. Liquid biopsy (LB) assays based on detecting circulating tumor DNA (ctDNA) might provide an opportunity to detect HCC early noninvasively. Increasing evidence indicates that ctDNA detection using mutation-based assays is significantly challenged by the abundance of white blood cell-derived mutations, non-tumor tissue-derived somatic mutations in plasma, and the mutational tumor heterogeneity. METHODS: Here, we employed concurrent analysis of cancer-related mutations, and their fragment length profiles to differentiate mutations from different sources. To distinguish persons with HCC (PwHCC) from healthy participants, we built a classification model using three fragmentomic features of ctDNA through deep sequencing of thirteen genes associated with HCC. RESULTS: Our model achieved an area under the curve (AUC) of 0.88, a sensitivity of 89%, and a specificity of 82% in the discovery cohort consisting of 55 PwHCC and 55 healthy participants. In an independent validation cohort of 54 PwHCC and 53 healthy participants, the established model achieved comparable classification performance with an AUC of 0.86 and yielded a sensitivity and specificity of 81%. CONCLUSIONS: Our study provides a rationale for subsequent clinical evaluation of our assay performance in a large-scale prospective study.

Synergistic effect of miscible cellulose-based microparticles and pH modulators on the bioavailability of a weakly basic drug and its metabolites.

This study aimed to evaluate the miscibility of cellulose derivatives to improve the release rate and stability of microparticles containing the weakly basic drug itraconazole (ITZ). We also investigated the effect of some organic acids on the microenvironmental pH (pHm) and the release rate of ITZ from the cellulose-based microparticles. The synergistic effect of cellulose-based microparticles and pHm modulators on the bioavailability of ITZ compared with the reference product was investigated in a rabbit model. Differential scanning calorimetry and Fourier-transform infrared spectroscopy (FTIR) analysis showed that ITZ, hydroxypropyl methylcellulose, and hydroxypropyl methylcellulose phthalate were miscible at a ratio of 1.5:3:1 (w/w/w), and the stability of the microparticles was maintained for 6 months under accelerated conditions. In addition, X-ray diffraction, FTIR, and scanning electron microscopy were used to characterize the properties of the microparticles. Through the titration technique and determination of pHm, the combination of fumaric acid and maleic acid (1:2, w/w) was found to be the most effective pHm modulator for microparticles. The integration of cellulose-based microparticles and pHm modulators showed a synergistic effect on the flux and relative bioavailability of ITZ and its active metabolite OH-ITZ (182.60 % and 217.67 %, respectively) when compared with the reference product.

Circulating DNA methylation profile improves the accuracy of serum biomarkers for the detection of nonmetastatic hepatocellular carcinoma.

Aim: This study exploited hepatocellular carcinoma (HCC)-specific circulating DNA methylation profiles to improve the accuracy of a current screening assay for HCC patients in high-risk populations. Methods: Differentially methylated regions in cell-free DNA between 58 nonmetastatic HCC and 121 high-risk patients with liver cirrhosis or chronic hepatitis were identified and used to train machine learning classifiers. Results: The model could distinguish HCC from high-risk non-HCC patients in a validation cohort, with an area under the curve of 0.84. Combining these markers with the three serum biomarkers (AFP, lectin-reactive AFP, des-γ-carboxy prothrombin) in a commercial test, µTASWako®, achieved an area under the curve of 0.87 and sensitivity of 68.8% at 95.8% specificity. Conclusion: HCC-specific circulating DNA methylation markers may be added to the available assay to improve the early detection of HCC.

The early detection of liver cancer in high-risk populations can help people with the disease have a higher chance of survival and better quality of life. However, this is still a healthcare challenge. Current commercial blood tests measuring protein signatures in the blood have low accuracy due to increased levels of these proteins being detected in both liver cancer patients and patients with chronic liver diseases. In this study, we identified a set of signatures in DNA released by cancer cells into the bloodstream and used them as biomarkers to distinguish liver cancer patients from high-risk patients. We also demonstrated that adding those signatures to a commercial blood test currently used in clinics could improve the accuracy in detecting liver cancer patients.

Effective Decompression of JPEG Document Images.

This paper concentrates on developing an effective approach for decompressing JPEG document images. Our main goal is targeted to time-critical applications, especially to those situated on mobile network infrastructures. To this aim, the proposed approach is designed to work either in the transform domain or image spatial plane. Specifically, the image blocks are first classified into smooth blocks (e.g., background and uniform regions) and non-smooth blocks (e.g., text, graphics, and line-drawings). Next, the smooth blocks are fully decoded in the transform domain by minimizing the total block boundary variation, which is very efficient to compute. For decoding non-smooth blocks, a novel text model is presented that accounts for the specifics of document content. In addition, an efficient optimization algorithm is introduced to reconstruct the non-smooth blocks. The proposed approach has been validated by extensive experiments, demonstrating a significant improvement of visual quality, assuming that document images have been encoded at low bit rates and thus are subject to severe distortion.