Nanotechnology and machine learning enable circulating tumor cells as a reliable biomarker for radiotherapy responses of gastrointestinal cancer patients.

A highly sensitive, circulating tumor cell (CTC)-based liquid biopsy was used to monitor gastrointestinal cancer patients during treatment to determine if CTC abundance was predictive of disease recurrence. The approach used a combination of biomimetic cell rolling on recombinant E-selectin and dendrimer-mediated multivalent immunocapture at the nanoscale to purify CTCs from peripheral blood mononuclear cells. Due to the exceptionally high numbers of CTCs captured, a machine learning algorithm approach was developed to efficiently and reliably quantify abundance of immunocytochemically-labeled cells. A convolutional neural network and logistic regression model achieved 82.9% true-positive identification of CTCs with a false positive rate below 0.1% on a validation set. The approach was then used to quantify CTC abundance in peripheral blood samples from 27 subjects before, during, and following treatments. Samples drawn from the patients either prior to receiving radiotherapy or early in chemotherapy had a median 50 CTC ml-1 whole blood (range 0.6-541.6). We found that the CTC counts drawn 3 months post treatment were predictive of disease progression (p = .045). This approach to quantifying CTC abundance may be a clinically impactful in the timely determination of gastrointestinal cancer progression or response to treatment

Cytochalasin B Treatment and Osmotic Pressure Enhance the Production of Extracellular Vesicles (EVs) with Improved Drug Loading Capacity

Extracellular vesicles (EVs) have been highlighted as novel drug carriers due to their unique structural properties and intrinsic features, including high stability, biocompatibility, and cell-targeting properties. Although many efforts have been made to harness these features to develop a clinically effective EV-based therapeutic system, the clinical translation of EV-based nano-drugs is hindered by their low yield and loading capacity. Herein, we present an engineering strategy that enables upscaled EV production with increased loading capacity through the secretion of EVs from cells via cytochalasin-B (CB) treatment and reduction of EV intravesicular contents through hypo-osmotic stimulation. CB (10 µg/mL) promotes cells to extrude EVs, producing ~three-fold more particles than through natural EV secretion. When CB is induced in hypotonic conditions (223 mOsm/kg), the produced EVs (hypo-CIMVs) exhibit ~68% less intravesicular protein, giving 3.4-fold enhanced drug loading capacity compared to naturally secreted EVs. By loading doxorubicin (DOX) into hypo-CIMVs, we found that hypo-CIMVs efficiently deliver their drug cargos to their target and induce up to ~1.5-fold more cell death than the free DOX. Thus, our EV engineering offers the potential for leveraging EVs as an effective drug delivery vehicle for cancer treatment

Enhanced detection of cell-free DNA (cfDNA) enables its use as a reliable biomarker for diagnosis and prognosis of gastric cancer.

Although circulating cell-free DNA (cfDNA) is a promising biomarker for the diagnosis and prognosis of various tumors, clinical correlation of cfDNA with gastric cancer has not been fully understood. To address this, we developed a highly sensitive cfDNA capture system by integrating polydopamine (PDA) and silica. PDA-silica hybrids incorporated different molecular interactions to a single system, enhancing cfDNA capture by 1.34-fold compared to the conventional silica-based approach (p = 0.001), which was confirmed using cell culture supernatants. A clinical study using human plasma samples revealed that the diagnostic accuracy of the new system to be superior than the commercially available cfDNA kit, as well as other serum antigen tests. Among the cancer patients, plasma cfDNA levels exhibited a good correlation with the size of a tumor. cfDNA was also predicative of distant metastasis, as the median cfDNA levels of metastatic cancer patients were ~60-fold higher than those without metastasis (p = 0.008). Furthermore, high concordance between tissue biopsy and cfDNA genomic analysis was found, as HER2 expression in cfDNA demonstrated an area under ROC curve (AUC) of 0.976 (p <0.001) for detecting patients with HER2-positive tumors. The new system also revealed high prognostic capability of cfDNA, as the concentration of cfDNA was highly associated with the survival outcomes. Our novel technology demonstrates the potential to achieve efficient detection of cfDNA that may serve as a reliable biomarker for gastric tumor

Machine-Learning-Based Clinical Biomarker Using Cell-Free DNA for Hepatocellular Carcinoma (HCC)

(1) Background: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related death worldwide. Although various serum enzymes have been utilized for the diagnosis and prognosis of HCC, the currently available biomarkers lack the sensitivity needed to detect HCC at early stages and accurately predict treatment responses. (2) Methods: We utilized our highly sensitive cell-free DNA (cfDNA) detection system, in combination with a machine learning algorithm, to provide a platform for improved diagnosis and prognosis of HCC. (3) Results: cfDNA, specifically alpha-fetoprotein (AFP) expression in captured cfDNA, demonstrated the highest accuracy for diagnosing malignancies among the serum/plasma biomarkers used in this study, including AFP, aspartate aminotransferase, alanine aminotransferase, albumin, alkaline phosphatase, and bilirubin. The diagnostic/prognostic capability of cfDNA was further improved by establishing a cfDNA score (cfDHCC), which integrated the total plasma cfDNA levels and cfAFP-DNA expression into a single score using machine learning algorithms. (4) Conclusion: The cfDHCC score demonstrated significantly improved accuracy in determining the pathological features of HCC and predicting patients&rsquo; survival outcomes compared to the other biomarkers. The results presented herein reveal that our cfDNA capture/analysis platform is a promising approach to effectively utilize cfDNA as a biomarker for the diagnosis and prognosis of HCC

Programmed Death 1 and Cytotoxic T-Lymphocyte-Associated Protein 4 Gene Expression in Peripheral Blood Mononuclear Cells Can Serve as Prognostic Biomarkers for Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a highly aggressive form of liver cancer with poor prognosis. The lack of reliable biomarkers for early detection and accurate diagnosis and prognosis poses a significant challenge to its effective clinical management. In this study, we investigated the diagnostic and prognostic potential of programmed cell death protein 1 (PD-1) and cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) expression in peripheral blood mononuclear cells (PBMCs) in HCC. PD-1 and CTLA-4 gene expression was analyzed comparatively using PBMCs collected from HCC patients and healthy individuals. The results revealed higher PD-1 gene expression levels in patients with multifocal tumors, lymphatic invasion, or distant metastasis than those in their control counterparts. However, conventional serum biomarkers of liver function do not exhibit similar correlations. In conclusion, PD-1 gene expression is associated with OS and PFS and CTLA-4 gene expression is associated with OS, whereas the serum biomarkers analyzed in this study show no significant correlation with survival in HCC. Hence, PD-1 and CTLA-4 expressed in PBMCs are considered potential prognostic biomarkers for patients with HCC that can facilitate prediction of malignancy, response to currently available HCC treatments, and overall survival