Multi Cancer Early Detection by Using Circulating Tumor DNA—The Galleri Test. Reply to Klein et al. The Promise of Multicancer Early Detection. Comment on “Pons-Belda et al. Can Circulating Tumor DNA Support a Successful Screening Test for Early Cancer Detection? The Grail Paradigm. Diagnostics 2021, 11, 2171”

The Unholy Grail of cancer screening: or is it just about the Benjamins?

The biotechnology company Grail developed a non-invasive blood test (Galleri test) which is claimed to detect 50 types of cancer at early and potentially curable stages. The initially promising results from prospective studies, and the anticipated financial success of Grail led the sequencing giant Illumina to purchase Grail for $8 billion (2021). Following this event, Grail collaborated with the UK National Health System to further clarify the test's capability, in a 3-year prospective trial, along with the standard of care. At the end of the first year, UK-NHS announced that they will stop the trial due to unsatisfactory clinical performance and until they analyze the data for the first year (which already enrolled 140,000 participants). Legal and financial issues between the interested parties are currently in flux. The Grail case is reminiscent of the multi-billion Theranos scandal, which sent a few people to jail and led to company dissolution. We previously expressed concerns about the sensitivity and specificity of the Galleri test. In this Perspective, we revisit the hyped technology, and we provide new suggestions on the use of this test.

Use of ctDNA in early breast cancer: analytical validity and clinical potential

Circulating free tumor DNA (ctDNA) analysis is gaining popularity in precision oncology, particularly in metastatic breast cancer, as it provides non-invasive, real-time tumor information to complement tissue biopsies, allowing for tailored treatment strategies and improved patient selection in clinical trials. Its use in early breast cancer has been limited so far, due to the relatively low sensitivity of available techniques in a setting characterized by lower levels of ctDNA shedding. However, advances in sequencing and bioinformatics, as well as the use of methylome profiles, have led to an increasing interest in the application of ctDNA analysis in early breast cancer, from screening to curative treatment evaluation and minimal residual disease (MRD) detection. With multiple prospective clinical trials in this setting, ctDNA evaluation may become useful in clinical practice. This article reviews the data regarding the analytical validity of the currently available tests for ctDNA detection and the clinical potential of ctDNA analysis in early breast cancer.

Current Challenges of Methylation-Based Liquid Biopsies in Cancer Diagnostics

In current clinical practice, effective cancer testing and screening paradigms are limited to specific types of cancer, exhibiting varying efficiency, acceptance, and adherence. Cell-free DNA (cfDNA) methylation profiling holds promise in providing information about the presence of malignity regardless of its type and location while leveraging blood-based liquid biopsies as a method to obtain analytical samples. However, technical difficulties, costs and challenges resulting from biological variations, tumor heterogeneity, and exogenous factors persist. This method exploits the mechanisms behind cfDNA release but faces issues like fragmentation, low concentrations, and high background noise. This review explores cfDNA methylation's origins, means of detection, and profiling for cancer diagnostics. The critical evaluation of currently available multi-cancer early detection methods (MCEDs) as well as tests targeting single genes, emphasizing their potential and limits to refine strategies for early cancer detection, are explained. The current methodology limitations, workflows, comparisons of clinically approved liquid biopsy-based methylation tests for cancer, their utilization in companion diagnostics as well as the biological limitations of the epigenetics approach are discussed, aiming to help healthcare providers as well as researchers to orient themselves in this increasingly complex and evolving field of diagnostics.

Liquid biopsy: New opportunities for precision medicine in hepatocellular carcinoma care

Liquid biopsy, specifically the analysis of circulating tumor DNA (ctDNA), offers a non-invasive approach for hepatocellular carcinoma (HCC) diagnosis and management. However, its implementation in the clinical setting is difficult due to challenges such as low ctDNA yield and difficulty in understanding the mutation signals from background noise. This review highlights the crucial role of artificial intelligence (AI) in addressing these limitations and in improving discoveries in the field of liquid biopsy for HCC care. Combining AI with liquid biopsy data can offer a promising future for the discovery of novel biomarkers and an AI-powered clinical decision support system (CDSS) can turn liquid biopsy into an important tool for personalized management of HCC. Despite the current challenges, the integration of AI shows promise to significantly improve patient outcomes and revolutionize the field of oncology.

Precision Population Cancer Medicine in Cancer of the Uterine Cervix: A Potential Roadmap to Eradicate Cervical Cancer

With the success of the Human Genome Project, the era of genomic medicine (GM) was born. Later on, as GM made progress, there was a feeling of exhilaration that GM could help resolve many disease processes. It also led to the conviction that personalized medicine was possible, and a relatively synonymous word, precision medicine (PM), was coined. However, the influence of environmental factors and social determinants of diseases was only partially given their due importance in the definition of PM, although more recently, this has been recognized. With the rapid advances in GM, big data, data mining, wearable devices for health monitoring, telemedicine, etc., PM can be more easily extended to population-level health care in disease management, prevention, early screening, and so on.and the term precision population medicine (PPM) more aptly describes it. PPM's potential in cancer care was posited earlier,and the current authors planned a series of cancer disease-specific follow-up articles. These papers are mainly aimed at helping emerging students in health sciences (medicine, pharmacy, nursing, dentistry, public health, population health), healthcare management (health-focused business administration, nonprofit administration, public institutional administration, etc.), and policy-making (e.g., political science), although not exclusively. This first disease-specific report focuses on the cancer of the uterine cervix (CC). It describes how recent breakthroughs can be leveraged as force multipliers to improve outcomes in CC - by improving early detection, better screening for CC, potential GM-based interventions during the stage of persistent Human papillomavirus (HPV) infection and treatment interventions - especially among the disadvantaged and resource-scarce populations. This work is a tiny step in our attempts to improve outcomes in CC and ultimately eradicate CC from the face of the earth.

Global serum profiling: an opportunity for earlier cancer detection

The advances in cancer research achieved in the last 50 years have been remarkable and have provided a deeper knowledge of this disease in many of its conceptual and biochemical aspects. From viewing a tumor as a 'simple' aggregate of mutant cells and focusing on detecting key cell changes leading to the tumorigenesis, the understanding of cancer has broadened to consider it as a complex organ interacting with its close and far surroundings through tumor and non-tumor cells, metabolic mechanisms, and immune processes. Metabolism and the immune system have been linked to tumorigenesis and malignancy progression along with cancer-specific genetic mutations. However, most technologies developed to overcome the barriers to earlier detection are focused solely on genetic information. The concept of cancer as a complex organ has led to research on other analytical techniques, with the quest of finding a more sensitive and cost-effective comprehensive approach. Furthermore, artificial intelligence has gained broader consensus in the oncology community as a powerful tool with the potential to revolutionize cancer diagnosis for physicians. We herein explore the relevance of the concept of cancer as a complex organ interacting with the bodily surroundings, and focus on promising emerging technologies seeking to diagnose cancer earlier, such as liquid biopsies. We highlight the importance of a comprehensive approach to encompass all the tumor and non-tumor derived information salient to earlier cancer detection.

Cell-Free Nucleic Acids: Physico-Chemical Properties, Analytical Considerations, and Clinical Applications

Human body fluids are rich sources of cell-free nuclear material, which exhibits unique characteristics [...].

A hybrid cancer prediction based on multi-omics data and reinforcement learning state action reward state action (SARSA)

These days, the ratio of cancer diseases among patients has been growing day by day. Recently, many cancer cases have been reported in different clinical hospitals. Many machine learning algorithms have been suggested in the literature to predict cancer diseases with the same class types based on trained and test data. However, there are many research rooms available for further research. In this paper, the studies look into the different types of cancer by analyzing, classifying, and processing the multi-omics dataset in a fog cloud network. Based on SARSA on-policy and multi-omics workload learning, made possible by reinforcement learning, the study made new hybrid cancer detection schemes. It consists of different layers, such as clinical data collection via laboratories and tool processes (biopsy, colonoscopy, and mammography) at the distributed omics-based clinics in the network. The study considers the different cancer classes such as carcinomas, sarcomas, leukemias, and lymphomas with their types in work and processes them using the multi-omics distributed clinics in work. In order to solve the problem, the study presents omics cancer workload reinforcement learning state action reward state action "SARSA" (OCWLS) schemes, which are made up of an on-policy learning scheme on different parameters like states, actions, timestamps, reward, accuracy, and processing time constraints. The goal is to process multiple cancer classes and workload feature matching while reducing the time it takes to process in clinical hospitals that are spread out. Simulation results show that OCWLS is better than other machine learning methods regarding+ processing time, extracting features from multiple classes of cancer, and matching in the system.

C. elegans as a Powerful Tool for Cancer Screening

Regular cancer screening is critical for early cancer detection. Cancer screening tends to be burdensome, invasive, and expensive, especially for a comprehensive multi-organ check. Improving the rate and effectiveness of routine cancer screenings remain a challenge in health care. Multi-cancer early detection (MCED) is an exciting concept and a potentially effective solution for addressing current issues with routine cancer screening. In recent years, several technologies have matured for MCED, such as identifying cell-free tumor DNA in blood or using organisms such as Caenorhabditis elegans as a tool for early cancer detection. In Japan, N-NOSE is a commercially available multi-cancer detection test based on the chemotaxis of C. elegans using a urine sample showing 87.5% sensitivity and 90.2% specificity. In this review, we focus on using C. elegans as a powerful biosensor for universal cancer screening. We review N-NOSE clinical research results, spotlighting it as an effective primary cancer screening test.