903O A prospective study of a multi-cancer early detection blood test

Blood-based multi-cancer detection: A state-of-the-art update

The early detection of cancer is a key goal of the National Cancer Plan formally released by the National Institutes of Health's (NIH) National Cancer Institute (NCI) in April 2023. To support this effort, many laboratories and vendors are developing multi-cancer detection (MCD) assays that interrogate blood and other bodily fluids for cancer-related biomarkers, most commonly circulating tumor DNA (ctDNA). While this approach holds promise for non-invasively detecting early signals of multiple different cancers and potentially reducing cancer-related mortality, there is a dearth of prospective clinical data to inform the deployment of MCD assays for cancer screening in the general adult population. In this review we highlight differing technologies that underpin various MCD assays in clinical development, the importance of achieving adequate performance specifications for MCD assays, ongoing clinical studies investigating the utility of MCD assays in cancer screening and detection, and efforts by the NCI's Division of Cancer Prevention (DCP) to establish a network infrastructure that has the capacity to comprehensively address the scientific and logistical challenges of evaluating blood-based MCD approaches and other cancer screening tools.

Multi-Cancer Early Detection: The New Frontier in Cancer Early Detection

The new generation of cancer early detection tests holds remarkable promise for revolutionizing and changing the paradigm of cancer early detection. Dozens of cancer early detection tests are being developed and evaluated. Some are already commercialized and available for use, most as a complement to and not in place of existing recommended cancer screening tests. This review evaluates existing single- and multi-cancer early detection tests (MCEDs), discussing their performance characteristics including sensitivity, specificity, positive and negative predictive values, and accuracy. It also critically looks at the potential harms that could result from these tests, including false positive and negative results, the risk of overdiagnosis and overtreatment, psychological and economic harms, and the risk of widening cancer inequities. We also review the large-scale, population-based studies that are being launched in the United States and United Kingdom to determine the impact of MCEDs on clinically relevant outcomes and implications for current practice.

Emerging Strategies in Lung Cancer Screening: Blood and Beyond

BACKGROUND

Although low dose computed tomography (LDCT)-based lung cancer screening (LCS) can decrease lung cancer-related mortality among high-risk individuals, it remains an imperfect and substantially underutilized process. LDCT-based LCS may result in false-positive findings, which can lead to invasive procedures and potential morbidity. Conversely, current guidelines may fail to capture at-risk individuals, particularly those from under-represented minority populations. To address these limitations, numerous biomarkers have emerged to complement LDCT and improve early lung cancer detection.

CONTENT

This review focuses primarily on blood-based biomarkers, including protein, microRNAs, circulating DNA, and methylated DNA panels, in current clinical development for LCS. We also examine other emerging biomarkers-utilizing airway epithelia, exhaled breath, sputum, and urine-under investigation. We highlight challenges and limitations of biomarker testing, as well as recent strategies to integrate molecular strategies with imaging technologies.

SUMMARY

Multiple biomarkers are under active investigation for LCS, either to improve risk-stratification after nodule detection or to optimize risk-based patient selection for LDCT-based screening. Results from ongoing and future clinical trials will elucidate the clinical utility of biomarkers in the LCS paradigm.

Predictive Performance of Cell-Free Nucleic Acid-Based Multi-Cancer Early Detection Tests: A Systematic Review

BACKGROUND

Cancer-screening tests that can detect multiple cancer types, or multi-cancer early detection (MCED) tests, have emerged recently as a potential new tool in decreasing cancer morbidity and mortality. Most MCED assays are based on detecting cell-free tumor DNA (CF-DNA) in the blood. MCEDs offer the potential for screening for cancer organ sites with high mortality, both with and without recommended screening. However, their clinical utility has not been established. Before clinical utility can be established, the clinical validity of MCEDs, i.e., their ability to predict cancer status, must be demonstrated. In this study we performed a systematic review of the predictive ability for cancer of cell-free-nucleic acid-based MCED tests.

CONTENT

We searched PubMed for relevant publications from January 2017 to February 2023, using MeSH terms related to multi-cancer detection, circulating DNA, and related concepts. Of 1811 publications assessed, 61 were reviewed in depth and 20 are included in this review. For almost all studies, the cancer cases were assessed at time of diagnosis. Most studies reported specificity (generally 95% or higher) and overall sensitivity (73% median). The median number of cancer types assessed per assay was 5. Many studies also reported sensitivity by stage and/or cancer type. Sensitivity generally increased with stage.

SUMMARY

To date, relatively few published studies have assessed the clinical validity of MCED tests. Most used cancer cases assessed at diagnosis, with generally high specificity and variable sensitivity depending on cancer type and stage. The next steps should be testing in the intended-use population, i.e., asymptomatic persons.

Protein profile pattern analysis: A multifarious, in vitro diagnosis technique for universal screening

Universal health care is attracting increased attention nowadays, because of the large increase in population all over the world, and a similar increase in life expectancy, leading to an increase in the incidence of non-communicable (various cancers, coronary diseases, neurological and old-age-related diseases) and communicable diseases/pandemics like SARS-COVID 19. This has led to an immediate need for a healthcare technology that should be cost-effective and accessible to all. A technology being considered as a possible one at present is liquid biopsy, which looks for markers in readily available samples like body fluids which can be accessed non- or minimally- invasive manner. Two approaches are being tried now towards this objective. The first involves the identification of suitable, specific markers for each condition, using established methods like various Mass Spectroscopy techniques (Surface-Enhanced Laser Desorption/Ionization Mass Spectroscopy (SELDI-MS), Matrix-Assisted Laser Desorption/Ionization (MALDI-MS), etc., immunoassays (Enzyme-Linked Immunoassay (ELISA), Proximity Extension Assays, etc.) and separation methods like 2-Dimensional Polyacrylamide Gel Electrophoresis (2-D PAGE), Sodium Dodecyl-Sulfate Polyacrylamide Gel Electrophoresis (SDS-PAGE), Capillary Electrophoresis (CE), etc. In the second approach, no attempt is made the identification of specific markers; rather an efficient separation method like High-Performance Liquid Chromatography/ Ultra-High-Performance Liquid Chromatography (HPLC/UPLC) is used to separate the protein markers, and a profile of the protein pattern is recorded, which is analysed by Artificial Intelligence (AI)/Machine Learning (MI) methods to derive characteristic patterns and use them for identifying the disease condition. The present report gives a summary of the current status of these two approaches and compares the two in the use of their suitability for universal healthcare.

Cell-free DNA approaches for cancer early detection and interception

Rapid advancements in the area of early cancer detection have brought us closer to achieving the goals of finding cancer early enough to treat or cure it, while avoiding harms of overdiagnosis. We evaluate progress in the development of early cancer detection tests in the context of the current principles for cancer screening. We review cell-free DNA (cfDNA)-based approaches using mutations, methylation, or fragmentomes for early cancer detection. Lastly, we discuss the challenges in demonstrating clinical utility of these tests before integration into routine clinical care.

Genomic approaches to cancer and minimal residual disease detection using circulating tumor DNA

Liquid biopsies using cell-free circulating tumor DNA (ctDNA) are being used frequently in both research and clinical settings. ctDNA can be used to identify actionable mutations to personalize systemic therapy, detect post-treatment minimal residual disease (MRD), and predict responses to immunotherapy. ctDNA can also be isolated from a range of different biofluids, with the possibility of detecting locoregional MRD with increased sensitivity if sampling more proximally than blood plasma. However, ctDNA detection remains challenging in early-stage and post-treatment MRD settings where ctDNA levels are minuscule giving a high risk for false negative results, which is balanced with the risk of false positive results from clonal hematopoiesis. To address these challenges, researchers have developed ever-more elegant approaches to lower the limit of detection (LOD) of ctDNA assays toward the part-per-million range and boost assay sensitivity and specificity by reducing sources of low-level technical and biological noise, and by harnessing specific genomic and epigenomic features of ctDNA. In this review, we highlight a range of modern assays for ctDNA analysis, including advancements made to improve the signal-to-noise ratio. We further highlight the challenge of detecting ultra-rare tumor-associated variants, overcoming which will improve the sensitivity of post-treatment MRD detection and open a new frontier of personalized adjuvant treatment decision-making.

Analytical validation of a multi-cancer early detection test with cancer signal origin using a cell-free DNA-based targeted methylation assay

The analytical validation is reported for a targeted methylation-based cell-free DNA multi-cancer early detection test designed to detect cancer and predict the cancer signal origin (tissue of origin). A machine-learning classifier was used to analyze the methylation patterns of >105 genomic targets covering >1 million methylation sites. Analytical sensitivity (limit of detection [95% probability]) was characterized with respect to tumor content by expected variant allele frequency and was determined to be 0.07%-0.17% across five tumor cases and 0.51% for the lymphoid neoplasm case. Test specificity was 99.3% (95% confidence interval, 98.6-99.7%). In the reproducibility and repeatability study, results were consistent in 31/34 (91.2%) pairs with cancer and 17/17 (100%) pairs without cancer; between runs, results were concordant for 129/133 (97.0%) cancer and 37/37 (100%) non-cancer sample pairs. Across 3- to 100-ng input levels of cell-free DNA, cancer was detected in 157/182 (86.3%) cancer samples but not in any of the 62 non-cancer samples. In input titration tests, cancer signal origin was correctly predicted in all tumor samples detected as cancer. No cross-contamination events were observed. No potential interferent (hemoglobin, bilirubin, triglycerides, genomic DNA) affected performance. The results of this analytical validation study support continued clinical development of a targeted methylation cell-free DNA multi-cancer early detection test.

Performance of a targeted methylation-based multi-cancer early detection test by race and ethnicity

Disparities in cancer screening and outcomes based on factors such as sex, socioeconomic status, and race and ethnicity in the United States are well documented. A blood-based multi-cancer early detection (MCED) test that detects a shared cancer signal across multiple cancer types and also predicts the cancer signal origin was developed and validated in the Circulating Cell-free Genome Atlas study (CCGA; NCT02889978). CCGA is a prospective, multicenter, case-control, observational study with longitudinal follow-up (overall N = 15,254). In this pre-specified, exploratory, descriptive analysis, test performance was evaluated among racial and ethnic groups. Overall, 4077 participants comprised the independent validation set with confirmed cancer status (cancer: n = 2823; non-cancer: n = 1254). Participants were stratified into the following racial/ethnic groups: Black (non-Hispanic), Hispanic (all races), Other (non-Hispanic), Other/unknown and White (non-Hispanic). Cancer and non-cancer participants were predominantly White (n = 2316, 82.0% and n = 996, 79.4%, respectively). Across groups, specificity for cancer signal detection ranged from 98.1% [n = 103; 95% CI: 93.2-99.5%] to 100% [n = 85; 95% CI: 95.7-100.0%]. The sensitivity for cancer signal detection across groups ranged from 43.9% [n = 57; 95% CI: 31.8-56.7%] to 63.0% [n = 192; 95% CI: 56.0-69.5%] and generally increased with clinical stage. The MCED test had consistently high specificity and similar sensitivity across racial and ethnic groups, though results are limited by sample size for some groups. Results support the broad applicability of this MCED test and clinical implementation on a population scale as a complement to standard screening.

Molecular Monitoring in Endometrial Cancer-Ready for Prime Time?

SUMMARY

Efforts are under way to define the role of minimally invasive strategies for molecular monitoring and risk stratification in endometrial cancer. A recent publication aims to define the association between circulating tumor DNA level and disease stage in patients with newly diagnosed endometrial cancer and determine whether sequencing of longitudinal cell-free DNA samples can be used for disease monitoring and detection of progression or recurrence. These results accelerate the current knowledge of molecular follow-up in endometrial cancer. See related article by Ashley et al., p. 410.

Potential Impact of Preoperative Circulating Biomarkers on Individual Escalating/de-Escalating Strategies in Early Breast Cancer

The research on non-invasive circulating biomarkers to guide clinical decision is in wide expansion, including the earliest disease settings. Several new intensification/de-intensification strategies are approaching clinical practice, personalizing the treatment for each patient. Moreover, liquid biopsy is revealing its potential with multiple techniques and studies available on circulating biomarkers in the preoperative phase. Inflammatory circulating cells, circulating tumor cells (CTCs), cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), and other biological biomarkers are improving the armamentarium for treatment selection. Defining the escalation and de-escalation of treatments is a mainstay of personalized medicine in early breast cancer. In this review, we delineate the studies investigating the possible application of these non-invasive tools to give a more enlightened approach to escalating/de-escalating strategies in early breast cancer.