Multi-Cancer Early Detection (MCED): The Next Frontier in Cancer Diagnostics

Reading time: 9 minutes

Preeti Prangya Panda

Cancer is the leading cause of mortality worldwide, with about 10 million deaths annually. Treatment outcomes are normally stage-sensitive, i.e., the sooner it is detected, the better the survival outcomes are for the patient. For instance, the current 5-year survival rate for stage I localized colorectal cancer is 92.3% while that of stage IV metastatic disease is only 18.4%. This is a clear indication that when the patient is diagnosed early, his or her prognosis is improved, and the death rate from cancers overall can decline.

Conventional screening technologies, including X-ray mammography and colonoscopy, can only be used for a small number of cancers and are further limited to single organs. In addition, such approaches differ in terms of specificity and sensitivity, which can lead to a poor prognosis. For instance, mammography detects breast cancer with a 50–80% sensitivity rate and 85–90% specificity rate, leading to both false positives and false negatives.

The development of multi-cancer early detection (MCED) tests has enhanced the ability of scientists to detect various cancers at earlier stages using a single blood sample through the analysis of cancer-derived DNA, proteins, and other biomarkers. Current clinical trials around the world are evaluating the effectiveness, safety, and viability of these treatments to ascertain their potential clinical applications. When successfully adopted, MCED tests have the potential to revolutionize cancer screening by identifying a wider spectrum of cancers¹. 

The Biological Science Behind MCED

MCED testing is an innovative approach to screening for cancer since it can target more types of cancer than conventional modalities. MCED tests would significantly reduce the burden of cancer morbidity and mortality by facilitating early detection and treatment, particularly of cancers that have no standard screening programs².

Blood-based MCED tests offer a less invasive and possibly more comprehensive way of detecting cancer at an early stage³.  MCED testing identifies blood-based biomarkers, including ctDNA mutations, methylation patterns, and cancer-related proteins, for detecting cancer. These tests take advantage of the biological fact that tumors secrete genetic material into the blood. Key features include¹:

• Cell-Free DNA (cfDNA): Tumor cells shed DNA fragments into the bloodstream, which can be detected and analyzed.
  
• Epigenetic Modifications: DNA methylation patterns specific to cancer cells can serve as biomarkers for tumor presence and type.
  
• Circulating Tumor Cells (CTCs): Rare cells that have shed from primary tumors into the bloodstream, detectable through advanced techniques.

These biomarkers provide a more comprehensive snapshot of the body’s cancer status, enabling the detection of multiple cancers simultaneously.

Technologies Enabling MCED

MCED has improved the early detection of cancer by reducing the interval between screening and specific diagnosis. This has been possible due to various advancements, such as:

Next-Generation Sequencing (NGS): 

Enables the large-scale analysis of cfDNA, revealing mutations and alterations in epigenetics in several cancer types. For instance, an MCED test uses genetic and fragmentomics characteristics to analyze plasma cell-free DNA with high sensitivity rates (87.4%), specificity rates (97.8%), and tissue-of-origin rates (82.4%). Early prospective results also indicated good early-stage cancer detection, which underscores the fact that these test will help improve early cancer detection and guide clinical practices⁴.

Machine Learning Algorithms: 

By Improving the interpretation of complex data, machine learning increases the accuracy and reliability of MCED tests. When molecular biomarkers are used together with machine learning, MCED tests could be used to identify numerous types of cancers using a single blood sample. Nevertheless, issues of accuracy, sensitivity, specificity, cost, and insurance coverage are to be overcome before these tests can be mainstreamed in clinical practice. 

For instance, machine learning improves MCED development by identifying patterns in genomic and proteomic data not found by conventional methods, where supervised machine learning models, such as random forest, gradient boosting, and support vector machine, can predict important cancer-linked biomarkers⁵. 

Liquid Biopsy Techniques: 

Beyond analytical methodologies, non-invasive methods that analyze blood samples for cancer-related biomarkers offer a less invasive alternative to traditional biopsies. MCED testing based on liquid biopsies have become a promising intervention to revolutionize the way cancer is detected and handled. 

Despite many MCED tests being under clinical trials, none of them have been clinically approved, indicating doubts about their effectiveness and their applicability to clinical practice. The discovery of biomarkers, analytical validation, and large-scale trials are the steps that compile MCED with endpoints, mainly emphasizing the reduction of late-stage cancer and cancer-specific mortality⁶.

Momentum is also building in the policy landscape, highlighted by recent initiatives such as the MCED Coverage Act, which aims to expand Medicare coverage for validated MCED tests and accelerate their integration into clinical care.

These technologies collectively enable the detection of a broad spectrum of cancers from a single blood sample.

Clinical and Translational Implications

The integration of MCED tests into clinical practice could have significant implications¹:

• Early detection for multiple cancers: The MCED tests are able to screen more than 50 different types of cancer using a single test¹, which is very effective in increasing screening efficiency in comparison to traditional organ-specific methods. They are also able to detect cancers such as pancreatic and ovarian that are not covered in normal screening programs. Early detection of these cancers before the onset of clinical symptoms can be of pivotal to patient health outcomes.
• Reduced Healthcare costs: The cost of treating advanced-stage cancer is a great burden to the available medical resources, and through MCED tests, early detection and treatment can be realized,  reducing the costs involved in treatment as well as making the screening easier. Cost-effectiveness of them, however, is dependent on such factors as accuracy, price, and compliance among patients. A study had estimated that MCED testing could save up to 260 billion a year in treatment costs in cancer because of early diagnosis¹.
• Personalized Treatment Plans: The treatment of advanced cancer is stressful and lower in quality of life, whereas the treatment of early-stage cancers is less invasive. MCED tests have the potential to reduce patient anxiety, increase autonomy, and promote preventive health behaviors. Frequent MCED testing is also raising awareness of cancer risk, which encourages people to have healthier lifestyles and attend screening programs.

Limitations and Challenges

Despite their potential, MCED tests face several challenges¹:

• False positives/negatives: Although the present MCED tests are working, they can be enhanced to facilitate the detection of stage I cancers. New biomarkers and technologies, such as cfDNA methylation, miRNAs, and high-tech sequencing, may be used to improve the sensitivity of the test. To enhance specificity, it is essential to recognize and consider false positives that can be triggered by aging, lifestyle, and inflammation.
• Need for counselling and follow-up: MCED can sometimes provide inaccurate positives or negatives, which need to be interpreted by the experts and the patient monitored. Cooperation with patient navigators is necessary to achieve the necessary education, counseling, and equal access required to translate early detection to improved survivorship. Also, the high cost and uninsurability of follow-up testing after a positive test result pose a major challenge,  highlighting the importance of clear healthcare policies in the implementation of MCED tests.
• Clinical trials challenges: Previous clinical trials have encountered some challenges in determining the optimal testing intervals and outcomes/endpoints, as these measures depend on the type, progression, and accuracy of cancer detection.. In addition, clinical trials are costly, time-consuming, require recruitment of large representative cohorts, protracted follow up and sophisticated diagnostic methods to determine the sensitivity and specificity of true and false tests.

Future Directions

MCED testing presents as an exciting addition to cancer screening procedures in the future, whereby positive results are complemented by diagnostic procedures like imaging tests or endoscopic procedures. The interpretation of MCED results is a complex task, which should be performed by specialists; thus educational programs are needed by healthcare providers, health insurers, and patients.

Another important step is to think about how MCED testing might affect people’s participation in regular cancer screening programs. 

It is also necessary to examine whether MCED testing provides good value for the money spent, considering both its benefits and costs. 

In addition, health systems need to create clear and organized procedures for diagnosing and managing cases where MCED test results come back positive, ensuring that patients receive timely and appropriate follow-up care.

Case-control studies, single-arm trials, and randomized controlled trials are the three key study designs that are applied to evaluate MCED test performance, which has been measured on factors such as sensitivity, specificity, and clinical benefit. 

• Case-control studies are essential for early stages as they help a researcher to compare results between individuals with and without cancer, helping to estimate fundamental measures such as sensitivity and specificity. 
• Single-arm trials are then used to assess the test results in real-world screening populations. It provides information about the detection rates, false positives, and feasibility of implementing the test at scale. 
• Randomized controlled trials are crucial for determining whether MCED testing yields actual clinical benefits, such as earlier-stage diagnosis or reduced cancer mortality, by comparing outcomes between screened and unscreened groups. 

Studies indicate that MCED tests have the potential to identify cancers in high-risk groups that are at an early stage with both high sensitivity and accuracy. Clinical trials are needed to further assess them in terms of their diagnostic precision, psychological effects, capability of preventing additional invasive diagnostics, and enhancement of the quality of life for those who are at high risk¹.

Conclusion

MCED marks a significant breakthrough in cancer diagnostics, offering a possibility of early detection of a number of cancers using a single blood sample. Although there are difficulties, current technological and clinical research and development is clearing the path towards the implementation of MCED tests in mainstream clinical practice. In the future, MCED tests may be established as a backbone in the personalized cancer treatment approach, resulting in better patient outcomes and survival rates.

Effective MCED tests would enhance individual cancer monitoring and early diagnosis among people at risk. The outcomes of clinical trials can indicate how MCED tests can be used in hereditary cancer syndromes and screening in general. Furthermore, the answers provided by these trials in terms of cost-efficiency, patient-reported outcomes, and overall clinical outcomes like reduced mortality will contribute to the evidence-based policy making to introduce MCED tests into  current screening processes.

Header Image Credit: arcyto – stock.adobe.com

Edited by Ian Padykula

References

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6. Xu Y, Zhu S, Xia C, Yu H, Shi S, Chen K, et al. Liquid biopsy-based multi-cancer early detection: an exploration road from evidence to implementation. Science Bulletin [Internet]. 2025 [cited 2025 Oct 11]; 70(17):2852–67. Available from: https://www.sciencedirect.com/science/article/pii/S2095927325006565.