A novel xenonucleic acid-mediated molecular clamping technology for early colorectal cancer screening

Innovative PNA-LB mediated allele-specific LAMP for KRAS mutation profiling on a compact lab-on-a-disc device

Tailored healthcare, an approach focused on individual patients, requires integrating emerging interdisciplinary technologies to develop accurate and user-friendly diagnostic tools. KRAS mutations, prevalent in various common cancers, are crucial determinants in selecting patients for novel KRAS inhibitor therapies. This study presents a novel state-of-the-art Lab-on-a-Disc system utilizing peptide nucleic acids-loop backward (PNA-LB) mediated allele-specific loop-mediated isothermal amplification (LAMP) for detecting the frequent G12D KRAS mutation, signifying its superiority over alternative mutation detection approaches. The designed Lab-on-a-Disc system demonstrated exceptional preclinical and technical precision, accuracy, and versatility. By applying varying cutoff values to PNA- LB LAMP reactions, the assay's sensitivity and specificity were increased by 80 % and 90 %, respectively. The device's key advantages include a robust microfluidic Lab-on-a-Disc design, precise rotary control, and a cutting-edge induction heating module. These features enable multiplexing of LAMP reactions with high reproducibility and repeatability, with CV% values less than 3.5 % and 5.5 %, respectively. The device offers several methods for accurate endpoint result detection, including naked-eye observation, RGB image analysis using Python code, and time of fluorescence (Tf) values. Preclinical specificity and sensitivity, assessed using different cutoffs for Eva-Green fluorescence Tf values and pH-sensitive dyes, demonstrated comparable performance to the best standard methods. Overall, this study represents a significant step towards tailoring treatment strategies for cancer patients through precise and efficient mutation detection technologies.

A Highly Sensitive XNA-Based RT-qPCR Assay for the Identification of ALK, RET, and ROS1 Fusions in Lung Cancer

Lung cancer is often triggered by genetic alterations that result in the expression of oncogenic tyrosine kinases. Specifically, ALK, RET, and ROS1 chimeric receptor tyrosine kinases are observed in approximately 5-7%, 1-2%, and 1-2% of NSCLC patients, respectively. The presence of these fusion genes determines the response to tyrosine kinase inhibitors. Thus, accurate detection of these gene fusions is essential in cancer research and precision oncology. To address this need, we have developed a multiplexed RT-qPCR assay using xeno nucleic acid (XNA) molecular clamping technology to detect lung cancer fusions. This assay can quantitatively detect thirteen ALK, seven ROS1, and seven RET gene fusions in FFPE samples. The sensitivity of the assay was established at a limit of detection of 50 copies of the synthetic template. Our assay has successfully identified all fusion transcripts using 50 ng of RNA from both reference FFPE samples and cell lines. After validation, a total of 77 lung cancer patient FFPE samples were tested, demonstrating the effectiveness of the XNA-based fusion gene assay with clinical samples. Importantly, this assay is adaptable to highly degraded RNA samples with low input amounts. Future steps involve expanding the testing to include a broader range of clinical samples as well as cell-free RNAs to further validate its applicability and reliability.

Circulating Nucleic Acids in Colorectal Cancer: Diagnostic and Prognostic Value

Colorectal cancer (CRC) is the third most prevalent cancer in the world and the fourth leading cause of cancer-related mortality. DNA (cfDNA/ctDNA) and RNA (cfRNA/ctRNA) in the blood are promising noninvasive biomarkers for molecular profiling, screening, diagnosis, treatment management, and prognosis of CRC. Technological advancements that enable precise detection of both genetic and epigenetic abnormalities, even in minute quantities in circulation, can overcome some of these challenges. This review focuses on testing for circulating nucleic acids in the circulation as a noninvasive method for CRC detection, monitoring, detection of minimal residual disease, and patient management. In addition, the benefits and drawbacks of various diagnostic techniques and associated bioinformatics tools have been detailed.

Staging of colorectal cancer using lipid biomarkers and machine learning

INTRODUCTION

Colorectal cancer (CRC) is the third most commonly diagnosed cancer worldwide. Alteration in lipid metabolism and chemokine expression are considered hallmark characteristics of malignant progression and metastasis of CRC. Validated diagnostic and prognostic biomarkers are urgently needed to define molecular heterogeneous CRC clinical stages and subtypes, as liver dominant metastasis has poor survival outcomes.

OBJECTIVES

The aim of this study was to integrate lipid changes, concentrations of chemokines, such as platelet factor 4 and interleukin 8, and gene marker status measured in plasma samples, with clinical features from patients at different CRC stages or who had progressed to stage-IV colorectal liver metastasis (CLM).

METHODS

High-resolution liquid chromatography-mass spectrometry (HR-LC-MS) was used to determine the levels of candidate lipid biomarkers in each CRC patient's preoperative plasma samples and combined with chemokine, gene and clinical data. Machine learning models were then trained using known clinical outcomes to select biomarker combinations that best classify CRC stage and group.

RESULTS

Bayesian neural net and multilinear regression-machine learning identified candidate biomarkers that classify CRC (stages I-III), CLM patients and control subjects (cancer-free or patients with polyps/diverticulitis), showing that integrating specific lipid signatures and chemokines (platelet factor-4 and interluken-8; IL-8) can improve prognostic accuracy. Gene marker status could contribute to disease prediction, but requires ubiquitous testing in clinical cohorts.

CONCLUSION

Our findings demonstrate that correlating multiple disease related features with lipid changes could improve CRC prognosis. The identified signatures could be used as reference biomarkers to predict CRC prognosis and classify stages, and monitor therapeutic intervention.

Application and development of noninvasive biomarkers for colorectal cancer screening: a systematic review

BACKGROUND

Colorectal cancer (CRC) is the second most common cause of cancer-related death (9.4% of the 9.9 million cancer deaths). However, CRC develops slowly, and early detection and intervention can effectively improve the survival rate and quality of life. Although colonoscopy can detect and diagnose CRC, it is unsuitable for CRC screening in average-risk populations. Some commercial kits based on DNA mutation or methylation are approved for screening, but the low sensitivity for advanced adenoma or early-stage CRC would limit the applications.

MAIN RESULTS

Recently, researchers have focused on developing noninvasive or minimally invasive, easily accessible biomarkers with higher sensitivity and accuracy for CRC screening. Numerous reports describe advances in biomarkers, including DNA mutations and methylation, mRNA and miRNA, gut microbes, and metabolites, as well as low-throughput multiomics panels. In small cohorts, the specificity and sensitivity improved when fecal immunochemical testing combined with other biomarkers; further verification in large cohorts is expected. In addition, the continuous improvement of laboratory technology has also improved the sensitivity of detection technology, such as PCR, and the application of CRISPR/Cas technology. Besides, artificial intelligence has extensively promoted the mining of biomarkers. Machine learning was performed to construct a diagnosis model for CRC screening based on the cfDNA fragment features from whole-genome sequencing data. In another study, multiomics markers, including cfDNA, epigenetic, and protein signals, were also discovered by machine learning. Finally, advancements in sensor technology promote the applicability of volatile organic compounds in CRC early detection.

CONCLUSION

Here, the authors review advances in early detection and screening of CRC based on different biomarker types. Most studies reported optimistic findings based on preliminary research, and prospective clinical studies are ongoing. These promising biomarkers are expected to more accurately identify early-stage patients with CRC and be applied in the future.