Universal cancer screening: revolutionary, rational, and realizable

Frameshift mutations in peripheral blood as a biomarker for surveillance of Lynch syndrome

BACKGROUND

Lynch syndrome is a hereditary cancer predisposition syndrome caused by germline mutations in DNA mismatch repair genes, which lead to high microsatellite instability and frameshift mutations at coding mononucleotide repeats in the genome. Recurrent frameshift mutations in these regions are thought to play a central role in the increased risk of various cancers, but no biomarkers are currently available for the surveillance of high microsatellite instability-associated cancers.

METHODS

A frameshift mutation-based biomarker panel was developed and validated by targeted next-generation sequencing of supernatant DNA from cultured high microsatellite instability colorectal cancer cells. This panel supported selection of 122 frameshift mutation targets as potential biomarkers. This biomarker panel was then tested using matched tumor, adjacent normal tissue, and buffy coat samples (53 samples) and blood-derived cell-free DNA (cfDNA) (38 samples) obtained from 45 high microsatellite instability and mismatch repair-deficient patients. We also sequenced cfDNA from 84 healthy participants to assess background noise.

RESULTS

Recurrent frameshift mutations at coding mononucleotide repeats were detectable not only in tumors but also in cfDNA from high microsatellite instability and mismatch repair-deficient patients, including a Lynch syndrome carrier, with a varying range of target detection (up to 85.2%), whereas they were virtually undetectable in healthy participants. Receiver operating characteristic curve analysis showed high sensitivity and specificity (area under the curve = 0.94) of the investigated panel.

CONCLUSIONS

We demonstrated that frameshift mutations can be detected in cfDNA from high microsatellite instability and mismatch repair-deficient patients and asymptomatic carriers. The 122-target frameshift mutation panel described here has promise as a tool for improved surveillance of high microsatellite instability and mismatch repair-deficient patients, with the potential to reduce the frequency of invasive screening methods for this high-cancer-risk cohort.

del Hierro A, H-Vázquez J, Cuesta-Sancho S, Bernardo D. State-of-the-art cytometry in the search of novel biomarkers in digestive cancers

Despite that colorectal and liver cancer are among the most prevalent tumours in the world, the identification of non-invasive biomarkers to aid on their diagnose and subsequent prognosis is a current unmet need that would diminish both their incidence and mortality rates. In this context, conventional flow cytometry has been widely used in the screening of biomarkers with clinical utility in other malignant processes like leukaemia or lymphoma. Therefore, in this review, we will focus on how advanced cytometry panels covering over 40 parameters can be applied on the study of the immune system from patients with colorectal and hepatocellular carcinoma and how that can be used on the search of novel biomarkers to aid or diagnose, prognosis, and even predict clinical response to different treatments. In addition, these multiparametric and unbiased approaches can also provide novel insights into the specific immunopathogenic mechanisms governing these malignant diseases, hence potentially unravelling novel targets to perform immunotherapy or identify novel mechanisms, rendering the development of novel treatments. As a consequence, computational cytometry approaches are an emerging methodology for the early detection and predicting therapies for gastrointestinal cancers.

A multi-cancer early detection blood test using machine learning detects early-stage cancers lacking USPSTF-recommended screening

US Preventive Services Task Force (USPSTF) guidelines recommend single-cancer screening for select cancers (e.g., breast, cervical, colorectal, lung). Advances in genome sequencing and machine learning have facilitated the development of blood-based multi-cancer early detection (MCED) tests intended to complement single-cancer screening. MCED tests can interrogate circulating cell-free DNA to detect a shared cancer signal across multiple tumor types. We report real-world experience with an MCED test that detected cancer signals in three individuals subsequently diagnosed with cancers of the ovary, kidney, and head/neck that lack USPSTF-recommended screening. These cases illustrate the potential of MCED tests to detect early-stage cancers amenable to cure.

An Exploratory Analysis of the Cost-Effectiveness of a Multi-cancer Early Detection Blood Test Compared with Standard of Care Screening in Ontario, Canada

BACKGROUND

Determining whether multi-cancer early detection (MCED) tests are cost effective is important in deciding whether they should be included in the clinical path of cancer care, especially for cancers where screening tools do not exist.

RESEARCH OBJECTIVE

The main objective of this study is to determine the cost effectiveness of including a MCED screening regimen together with existing provincial screening protocols for selected cancers that are prevalent in Ontario, Canada, among average risk persons aged 50-75 years. The selected cancers include breast, colorectal, lung, esophageal, liver, pancreatic, stomach, and ovarian.

METHODS

Cost effectiveness was estimated from a provincial Ministry of Health perspective. A state-transition Markov model representing the decision path of both the proposed and existing screening strategies along the natural history of the selected types of cancers was implemented. The incremental cost-effectiveness ratio (ICER) was calculated using data from available literature and the guidelines published by the Canadian Agency for Drugs and Technologies in Health (CADTH) for conducting a cost-effectiveness analysis, which included a discount rate of 1.5% applied to all costs and outcomes. Costs were also converted to 2022 Canadian dollars. To test the robustness of the model, both univariate and probabilistic sensitivity analyses were conducted.

RESULTS

MCED screening resulted in more diagnosed cases of each type of cancer, even at an earlier stage of disease. This was also associated with fewer related deaths compared with standard of care. Notwithstanding, the analysis revealed that the MCED intervention was not cost effective [ICER: CAD$143,369 per quality-adjusted life year (QALY)], given a willingness to pay (WTP) threshold of $100,000 per QALY. The probabilistic sensitivity analyses revealed that the MCED intervention strategy was preferred to standard of care no more than 2% of the time at this WTP for both males and females. The model was most sensitive to the cost of MCED screening, and the levels of specificity of the MCED and colorectal cancer screening tests.

CONCLUSION

The main contribution of the study is to present and execute a methodological approach that can be adopted to test the cost effectiveness of an MCED tool in the Canadian setting. The model is also sufficiently generic that it could be adapted to other jurisdictions, and with consideration for increasing the WTP threshold beyond the common $100,000 per QALY limit, given the life-threatening nature of cancer, to ensure that MCED interventions are cost-effective.

Blood-based screening for HPV-associated cancers

Background

HPV-associated oropharyngeal cancer (HPV+OPSCC) is the most common HPV-associated cancer in the United States yet unlike cervical cancer lacks a screening test. HPV+OPSCCs are presumed to start developing 10-15 years prior to clinical diagnosis. Circulating tumor HPV DNA (ctHPVDNA) is a sensitive and specific biomarker for HPV+OPSCC. Taken together, blood-based screening for HPV+OPSCC may be feasible years prior to diagnosis.

Methods

We developed an HPV whole genome sequencing assay, HPV-DeepSeek, with 99% sensitivity and specificity at clinical diagnosis. 28 plasma samples from HPV+OPSCC patients collected 1.3-10.8 years prior to diagnosis along with 1:1 age and gender-matched controls were run on HPV-DeepSeek and an HPV serology assay.

Results

22/28 (79%) of cases and 0/28 controls screened positive for HPV+OPSCC with 100% detection within four years of diagnosis and a maximum lead time of 7.8 years. We next applied a machine learning model classifying 27/28 cases (96%) with 100% detection within 10 years. Plasma-based PIK3CA gene mutations, viral genome integration events and HPV serology were used to orthogonally validate cancer detection with 68% (19/28) of the cohort having multiple cancer signals detected. Molecular fingerprinting of HPV genomes was performed across patients demonstrating that each viral genome was unique, ruling out contamination. In patients with tumor blocks from diagnosis (15/28), molecular fingerprinting was performed within patients confirming the same viral genome across time.

Conclusions

We demonstrate accurate blood-based detection of HPV-associated cancers with lead times up to 10 years before clinical cancer diagnosis and in doing so, highlight the enormous potential of ctDNA-based cancer screening.

Advancing equitable and personalized cancer care: Novel applications and priorities of artificial intelligence for fairness and inclusivity in the patient care workflow

Patient care workflows are highly multimodal and intertwined: the intersection of data outputs provided from different disciplines and in different formats remains one of the main challenges of modern oncology. Artificial Intelligence (AI) has the potential to revolutionize the current clinical practice of oncology owing to advancements in digitalization, database expansion, computational technologies, and algorithmic innovations that facilitate discernment of complex relationships in multimodal data. Within oncology, radiation therapy (RT) represents an increasingly complex working procedure, involving many labor-intensive and operator-dependent tasks. In this context, AI has gained momentum as a powerful tool to standardize treatment performance and reduce inter-observer variability in a time-efficient manner. This review explores the hurdles associated with the development, implementation, and maintenance of AI platforms and highlights current measures in place to address them. In examining AI's role in oncology workflows, we underscore that a thorough and critical consideration of these challenges is the only way to ensure equitable and unbiased care delivery, ultimately serving patients' survival and quality of life.

A non-invasive method for concurrent detection of multiple early-stage cancers in women

Untargeted serum metabolomics was combined with machine learning-powered data analytics to develop a test for the concurrent detection of multiple cancers in women. A total of fifteen cancers were tested where the resulting metabolome data was sequentially analysed using two separate algorithms. The first algorithm successfully identified all the cancer-positive samples with an overall accuracy of > 99%. This result was particularly significant given that the samples tested were predominantly from early-stage cancers. Samples identified as cancer-positive were next analysed using a multi-class algorithm, which then enabled accurate discernment of the tissue of origin for the individual samples. Integration of serum metabolomics with appropriate data analytical tools, therefore, provides a powerful screening platform for early-stage cancers.

Evaluating Stacked Methylation Markers for Blood-Based Multicancer Detection

The ability to detect several types of cancer using a non-invasive, blood-based test holds the potential to revolutionize oncology screening. We mined tumor methylation array data from the Cancer Genome Atlas (TCGA) covering 14 cancer types and identified two novel, broadly-occurring methylation markers at TLX1 and GALR1. To evaluate their performance as a generalized blood-based screening approach, along with our previously reported methylation biomarker, ZNF154, we rigorously assessed each marker individually or combined. Utilizing TCGA methylation data and applying logistic regression models within each individual cancer type, we found that the three-marker combination significantly increased the average area under the ROC curve (AUC) across the 14 tumor types compared to single markers (p = 1.158 × 10-10; Friedman test). Furthermore, we simulated dilutions of tumor DNA into healthy blood cell DNA and demonstrated increased AUC of combined markers across all dilution levels. Finally, we evaluated assay performance in bisulfite sequenced DNA from patient tumors and plasma, including early-stage samples. When combining all three markers, the assay correctly identified nine out of nine lung cancer plasma samples. In patient plasma from hepatocellular carcinoma, ZNF154 alone yielded the highest combined sensitivity and specificity values averaging 68% and 72%, whereas multiple markers could achieve higher sensitivity or specificity, but not both. Altogether, this study presents a comprehensive pipeline for the identification, testing, and validation of multi-cancer methylation biomarkers with a considerable potential for detecting a broad range of cancer types in patient blood samples.

Targeting altered glycosylation in secreted tumor glycoproteins for broad cancer detection

There is an urgent need to develop new tumor biomarkers for early cancer detection, but the variability of tumor-derived antigens has been a limitation. Here we demonstrate a novel anti-Tn antibody microarray platform to detect Tn+ glycoproteins, a near universal antigen in carcinoma-derived glycoproteins, for broad detection of cancer. The platform uses a specific recombinant IgG1 to the Tn antigen (CD175) as a capture reagent and a recombinant IgM to the Tn antigen as a detecting reagent. These reagents were validated by immunohistochemistry in recognizing the Tn antigen using hundreds of human tumor specimens. Using this approach, we could detect Tn+ glycoproteins at subnanogram levels using cell lines and culture media, serum, and stool samples from mice engineered to express the Tn antigen in intestinal epithelial cells. The development of a general cancer detection platform using recombinant antibodies for detection of altered tumor glycoproteins expressing a unique antigen could have a significant impact on cancer detection and monitoring.

Dying To Find Out: The Cost of Time at the Dawn of the Multicancer Early Detection Era

Cancer is a significant burden worldwide that adversely impacts life expectancy, quality of life, health care costs, and workforce productivity. Although currently recommended screening tests for individual cancers reduce mortality, they detect only a minority of all cancers and sacrifice specificity for high sensitivity, resulting in a high cumulative rate of false positives. Blood-based multicancer early detection tests (MCED) based on next-generation sequencing (NGS) and other technologies hold promise for broadening the number of cancer types detected in screened populations and hope for reducing cancer mortality. The promise of this new technology to improve cancer detection rates and make screening more efficient at the population level demands the development of novel trial designs that accelerate clinical adoption. Carefully designed clinical trials are needed to address these issues.

Perspectives of private payers on multicancer early-detection tests: informing research, implementation, and policy

Emerging blood-based multicancer early-detection (MCED) tests may redefine cancer screening, reduce mortality, and address health disparities if their benefit is demonstrated. U.S. payers' coverage policies will impact MCED test adoption and access; thus, their perspectives must be understood. We examined views, coverage barriers, and evidentiary needs for MCED from 19 private payers collectively covering 150 000 000 enrollees. Most saw an MCED test's potential merit for cancers without current screening (84%), but fewer saw its merit for cancers with existing screening (37%). The largest coverage barriers were inclusion of cancers without demonstrated benefits of early diagnosis (73%), a high false-negative rate (53%), and lack of care protocols for MCED-detected but unconfirmed cancers (53%). The majority (58%) would not require mortality evidence and would accept surrogate endpoints. Most payers (64%) would accept rigorous real-world evidence in the absence of a large randomized controlled trial. The majority (74%) did not expect MCED to reduce disparities due to potential harm from overtreatment resulting from an MCED and barriers to downstream care. Payers' perspectives and evidentiary needs may inform MCED test developers, researchers producing evidence, and health systems framing MCED screening programs. Private payers should be stakeholders of a national MCED policy and equity agenda.

Shifting the Cancer Screening Paradigm: The Rising Potential of Blood-Based Multi-Cancer Early Detection Tests

Cancer remains a leading cause of death worldwide, partly owing to late detection which entails limited and often ineffective therapeutic options. Most cancers lack validated screening procedures, and the ones available disclose several drawbacks, leading to low patient compliance and unnecessary workups, adding up the costs to healthcare systems. Hence, there is a great need for innovative, accurate, and minimally invasive tools for early cancer detection. In recent years, multi-cancer early detection (MCED) tests emerged as a promising screening tool, combining molecular analysis of tumor-related markers present in body fluids with artificial intelligence to simultaneously detect a variety of cancers and further discriminate the underlying cancer type. Herein, we aim to provide a highlight of the variety of strategies currently under development concerning MCED, as well as the major factors which are preventing clinical implementation. Although MCED tests depict great potential for clinical application, large-scale clinical validation studies are still lacking.

Label-Free Electrochemical Biosensor Platforms for Cancer Diagnosis: Recent Achievements and Challenges

With its fatal effects, cancer is still one of the most important diseases of today's world. The underlying fact behind this scenario is most probably due to its late diagnosis. That is why the necessity for the detection of different cancer types is obvious. Cancer studies including cancer diagnosis and therapy have been one of the most laborious tasks. Since its early detection significantly affects the following therapy steps, cancer diagnosis is very important. Despite researchers' best efforts, the accurate and rapid diagnosis of cancer is still challenging and difficult to investigate. It is known that electrochemical techniques have been successfully adapted into the cancer diagnosis field. Electrochemical sensor platforms that are brought together with the excellent selectivity of biosensing elements, such as nucleic acids, aptamers or antibodies, have put forth very successful outputs. One of the remarkable achievements of these biomolecule-attached sensors is their lack of need for additional labeling steps, which bring extra burdens such as interference effects or demanding modification protocols. In this review, we aim to outline label-free cancer diagnosis platforms that use electrochemical methods to acquire signals. The classification of the sensing platforms is generally presented according to their recognition element, and the most recent achievements by using these attractive sensing substrates are described in detail. In addition, the current challenges are discussed.

Liquid biopsies: the future of cancer early detection

Cancer is a worldwide pandemic. The burden it imposes grows steadily on a global scale causing emotional, physical, and financial strains on individuals, families, and health care systems. Despite being the second leading cause of death worldwide, many cancers do not have screening programs and many people with a high risk of developing cancer fail to follow the advised medical screening regime due to the nature of the available screening tests and other challenges with compliance. Moreover, many liquid biopsy strategies being developed for early detection of cancer lack the sensitivity required to detect early-stage cancers. Early detection is key for improved quality of life, survival, and to reduce the financial burden of cancer treatments which are greater at later stage detection. This review examines the current liquid biopsy market, focusing in particular on the strengths and drawbacks of techniques in achieving early cancer detection. We explore the clinical utility of liquid biopsy technologies for the earlier detection of solid cancers, with a focus on how a combination of various spectroscopic and -omic methodologies may pave the way for more efficient cancer diagnostics.

Clinical validation of a ctDNA-Based Assay for Multi-Cancer Detection: An Interim Report from a Vietnamese Longitudinal Prospective Cohort Study of 2795 Participants

The SPOT-MAS assay "Screening for the Presence Of Tumor by Methylation And Size" detects the five most common cancers in Vietnam by evaluating circulating tumor DNA in the blood. Here, we validated its performance in a prospective multi-center clinical trial, K-DETEK. Our analysis of 2795 participants from 14 sites across Vietnam demonstrates its ability to detect cancers in asymptomatic individuals with a positive predictive value of 60%, with 83.3% accuracy in detecting tumor location. We present a case report to support further using SPOT-MAS as a complementary method to achieve early cancer detection and provide the opportunity for early treatment.

Cancer Serum Atlas-Supported Precise Pan-Targeted Proteomics Enable Multicancer Detection

The wide dynamic range of serum proteome restrained discovery of clinically interested proteins in large cohort studies. Herein, we presented a high-sensitivity, high-throughput, and precise pan-targeted serum proteomic strategy for highly efficient cancer serum proteomic research and biomarker discovery. We constructed a resource of over 2000 cancer-secreted proteins, and the standard MS assays and spectra of at least one synthetic unique peptide per protein were acquired and documented (Cancer Serum Atlas, www.cancerserumatlas.com). Then, the standard peptide-anchored parallel reaction monitoring (SPA-PRM) method was developed with support of the Cancer Serum Atlas, achieving precise quantification of cancer-secreted proteins with high throughput and sensitivity. We directly quantified 325 cancer-related serum proteins in 288 serums of four cancer types (liver, stomach, lung, breast) and controls with the pan-targeted strategy and discovered considerable potential biomarker benefits for early detection of cancer. Finally, a proteomic-based multicancer detection model was built, demonstrating high sensitivity (87.2%) and specificity (100%), with 73.8% localization accuracy for an independent test set. In conclusion, the Cancer Serum Atlas provides a wide range of potential biomarkers that serve as targets and standard assays for systematic and highly efficient serological studies of cancer. The Cancer Serum Atlas-supported pan-targeted proteomic strategy enables highly efficient biomarker discovery and multicancer detection and thus can be a powerful tool for liquid biopsy.

Evaluation of cell-free DNA approaches for multi-cancer early detection

In the Circulating Cell-free Genome Atlas (NCT02889978) substudy 1, we evaluate several approaches for a circulating cell-free DNA (cfDNA)-based multi-cancer early detection (MCED) test by defining clinical limit of detection (LOD) based on circulating tumor allele fraction (cTAF), enabling performance comparisons. Among 10 machine-learning classifiers trained on the same samples and independently validated, when evaluated at 98% specificity, those using whole-genome (WG) methylation, single nucleotide variants with paired white blood cell background removal, and combined scores from classifiers evaluated in this study show the highest cancer signal detection sensitivities. Compared with clinical stage and tumor type, cTAF is a more significant predictor of classifier performance and may more closely reflect tumor biology. Clinical LODs mirror relative sensitivities for all approaches. The WG methylation feature best predicts cancer signal origin. WG methylation is the most promising technology for MCED and informs development of a targeted methylation MCED test.

The Potential Value-Based Price of a Multi-Cancer Early Detection Genomic Blood Test to Complement Current Single Cancer Screening in the USA

BACKGROUND

Multi-cancer early detection (MCED) testing could increase detection of cancer at early stages, when survival outcomes are better and treatment costs are lower, but is expected to increase screening costs. This study modeled an MCED test for 19 solid cancers in a US population and estimated the potential value-based price (the maximum price to meet a given willingness to pay) of the MCED test plus current single cancer screening (usual care) compared to usual care alone from a third-party payer perspective over a lifetime horizon.

METHODS

A hybrid cohort-level state-transition and decision-tree model was developed to estimate the clinical and economic outcomes of annual MCED testing between age 50 and 79 years. The impact on time and stage of diagnosis was computed using an interception modeling approach, with the consequences of cancer modeled based on stage at diagnosis. The model parameters were mainly sourced from the literature, including a published case-control study to inform MCED test performance. All costs were inflated to 2021 US dollars.

RESULTS

Multi-cancer early detection testing shifted cancer diagnoses to earlier stages, with a 53% reduction in stage IV cancer diagnoses, resulting in longer overall survival compared with usual care. Addition of MCED decreased per cancer treatment costs by $5421 and resulted in a gain of 0.13 and 0.38 quality-adjusted life-years across all individuals in the screening program and those diagnosed with cancer, respectively. At a willingness-to-pay threshold of $100,000 per quality-adjusted life-year gained, the potential value-based price of an MCED test was estimated at $1196. The projected survival of individuals diagnosed with cancer and the number of cancers detected at an earlier stage by MCED had the greatest impact on outcomes.

CONCLUSIONS

An MCED test with high specificity would potentially improve long-term health outcomes and reduce cancer treatment costs, resulting in a value-based price of $1196 at a $100,000/quality-adjusted life-year willingness-to-pay threshold.

Multicancer Early Detection Technologies: A Review Informed by Past Cancer Screening Studies

More than 75% of cancer-related deaths occur from cancers for which we do not screen. New screening liquid biopsies may help fill these clinical gaps, although evidence of benefit still needs to be assessed. Which lessons can we learn from previous efforts to guide those of the future? Screening trials for ovarian, prostate, pancreatic, and esophageal cancers are revisited to assess the evidence, which has been limited by small effect sizes, short duration of early-stage disease relative to screening frequency, study design, and confounding factors. Randomized controlled trials (RCT) to show mortality reduction have required millions of screening-years, two-decade durations, and been susceptible to external confounding. Future RCTs with late-stage incidence as a surrogate endpoint could substantially reduce these challenges, and clinical studies demonstrating safety and effectiveness of screening in high-risk populations may enable extrapolation to broader average-risk populations. Multicancer early detection tests provide an opportunity to advance these practical study designs. Conditional approvals based on RCTs with surrogate endpoints, contingent upon real world evidence generation and continuation of trials to definitive endpoints, may lower practical barriers to innovation in cancer screening and enable greater progress.

A Fluorescent “Turn-On” Clutch Probe for Plasma Cell-Free DNA Identification from Lung Cancer Patients

Early diagnosis of cancer is of paramount significance for the therapeutic intervention of cancers. Although the detection of circulating cell-free DNA (cfDNA) has emerged as a promising, minimally invasive approach for early cancer diagnosis, there is an urgent need to develop a highly sensitive and rapid method to precisely identify plasma cfDNA from clinical samples. Herein, we report a robust fluorescent “turn-on” clutch probe based on non-emissive QDs-Ru complexes to rapidly recognize EGFR gene mutation in plasma cfDNA from lung cancer patients. In this system, the initially quenched emission of QDs is recovered while the red emission of Ru(II) complexes is switched on. This is because the Ru(II) complexes can specifically intercalate into the double-stranded DNA (dsDNA) to form Ru-dsDNA complexes and simultaneously liberate free QDs from the QDs-Ru complexes, which leads to the occurrence of an overlaid red fluorescence. In short, the fluorescent “turn-on” clutch probe offers a specific, rapid, and sensitive paradigm for the recognition of plasma cfDNA biomarkers from clinical samples, providing a convenient and low-cost approach for the early diagnosis of cancer and other gene-mutated diseases.

Assessment of extracellular vesicle isolation methods from human stool supernatant

Extracellular vesicles (EVs) are of growing interest due to their potential diagnostic, disease surveillance, and therapeutic applications. While several studies have evaluated EV isolation methods in various biofluids, there are few if any data on these techniques when applied to stool. The latter is an ideal biospecimen for studying EVs and colorectal cancer (CRC) because the release of tumour markers by luminal exfoliation into stool occurs earlier than vascular invasion. Since EV release is a conserved mechanism, bacteria in stool contribute to the overall EV population. In this study, we assessed five EV separation methods (ultracentrifugation [UC], precipitation [EQ‐O, EQ‐TC], size exclusion chromatography [SEC], and ultrafiltration [UF]) for total recovery, reproducibility, purity, RNA composition, and protein expression in stool supernatant. CD63, TSG101, and ompA proteins were present in EV fractions from all methods except UC. Human (18s) and bacterial (16s) rRNA was detected in stool EV preparations. Enzymatic treatment prior to extraction is necessary to avoid non‐vesicular RNA contamination. Ultrafiltration had the highest recovery, RNA, and protein yield. After assessing purity further, SEC was the isolation method of choice. These findings serve as the groundwork for future studies that use high throughput omics technologies to investigate the potential of stool‐derived EVs as a source for novel biomarkers for early CRC detection.

Artificial Intelligence in Oncology: Current Capabilities, Future Opportunities, and Ethical Considerations

The promise of highly personalized oncology care using artificial intelligence (AI) technologies has been forecasted since the emergence of the field. Cumulative advances across the science are bringing this promise to realization, including refinement of machine learning- and deep learning algorithms; expansion in the depth and variety of databases, including multiomics; and the decreased cost of massively parallelized computational power. Examples of successful clinical applications of AI can be found throughout the cancer continuum and in multidisciplinary practice, with computer vision-assisted image analysis in particular having several U.S. Food and Drug Administration-approved uses. Techniques with emerging clinical utility include whole blood multicancer detection from deep sequencing, virtual biopsies, natural language processing to infer health trajectories from medical notes, and advanced clinical decision support systems that combine genomics and clinomics. Substantial issues have delayed broad adoption, with data transparency and interpretability suffering from AI's "black box" mechanism, and intrinsic bias against underrepresented persons limiting the reproducibility of AI models and perpetuating health care disparities. Midfuture projections of AI maturation involve increasing a model's complexity by using multimodal data elements to better approximate an organic system. Far-future positing includes living databases that accumulate all aspects of a person's health into discrete data elements; this will fuel highly convoluted modeling that can tailor treatment selection, dose determination, surveillance modality and schedule, and more. The field of AI has had a historical dichotomy between its proponents and detractors. The successful development of recent applications, and continued investment in prospective validation that defines their impact on multilevel outcomes, has established a momentum of accelerated progress.

A Novel Blood-Based microRNA Diagnostic Model with High Accuracy for Multi-Cancer Early Detection

Early detection is critical to reduce cancer deaths as treating early stage cancers is more likely to be successful. However, patients with early stage diseases are often asymptomatic and thus less likely to be diagnosed. Here, we utilized four microarray datasets with a standardized platform to investigate comprehensive microRNA expression profiles from 7536 serum samples. A 4-miRNA diagnostic model was developed from the lung cancer training set (n = 416, 208 lung cancer patients and 208 non-cancer participants). The model showed 99% sensitivity and specificity in the lung cancer validation set (n = 3328, 1358 cancer patients and 1970 non-cancer participants); and the sensitivity remained to be >99% for patients with stage 1 disease. When applied to the additional combined dataset of 3792 participants including 2038 cancer patients across 12 different cancer types and 1754 independent non-cancer controls, the model demonstrated high sensitivities ranging from 83.2 to 100% for biliary tract, bladder, colorectal, esophageal, gastric, glioma, liver, pancreatic, and prostate cancers, and showed reasonable sensitivities of 68.2 and 72.0% for ovarian cancer and sarcoma, respectively, while maintaining 99.3% specificity. Our study provided a proof-of-concept data in demonstrating that the 4-miRNA model has the potential to be developed into a simple, inexpensive and noninvasive blood test for early detection of multiple cancers with high accuracy.

Multicancer Screening Tests: Anticipating And Addressing Considerations For Payer Coverage And Patient Access

There is a tremendous public health need to identify potentially lethal cancers at earlier stages, when there is a greater chance for improved survival. Although in the US there are currently screening recommendations for only five cancers (breast, colorectal, cervical, lung, and prostate), new tests can screen for up to fifty cancers simultaneously based on a simple blood draw. However, these multicancer screening tests (also called "liquid biopsy" tests) will also present challenges to payers because of intrinsic features of the tests and the complexity of payer coverage assessments for screening tests. We describe these considerations while also offering potential solutions that can inform payers' decision making if these tests prove to be beneficial.

Racial/Ethnic Differences in Cancer Diagnosed after Metastasis: Absolute Burden and Deaths Potentially Avoidable through Earlier Detection

BACKGROUND

Racial/ethnic disparities in cancer mortality are well described and are partly attributable to later stage of diagnosis. It is unclear to what extent reductions in the incidence of late-stage cancer could narrow these relative and absolute disparities.

METHODS

We obtained stage- and cancer-specific incidence and survival data from the Surveillance, Epidemiology, and End Results Program for persons ages 50 to 79 years between 2006 and 2015. For eight hypothetical cohorts of 100,000 persons defined by race/ethnicity and sex, we estimated cancer-related deaths if cancers diagnosed at stage IV were detected earlier, by assigning them outcomes of earlier stages.

RESULTS

We observed a 3-fold difference in the absolute burden of stage IV cancer between the group with the highest rate (non-Hispanic Black males, 337 per 100,000) and the lowest rate (non-Hispanic Asian/Pacific Islander females, 117 per 100,000). Assuming all stage IV cancers were diagnosed at stage III, 32-80 fewer cancer-related deaths would be expected across subgroups, a relative reduction of 13%-14%. Assuming one third of metastatic cancers were diagnosed at each earlier stage (I, II, and III), 52-126 fewer cancer-related deaths would be expected across subgroups, a relative reduction of 21%-23%.

CONCLUSIONS

Across population subgroups, non-Hispanic Black males have the highest burden of stage IV cancer and would have the most deaths averted from improved detection of cancer before metastasis.

IMPACT

Detecting cancer before metastasis could meaningfully reduce deaths in all populations, but especially in non-Hispanic Black populations. See related commentary by Loomans-Kropp et al., p. 512.

Multicancer early detection: International summit to Clarify the Roadmap

To expedite the development and application of potentially transformative multicancer early detection assays, an international summit was hosted by the Mayo Clinic Cancer Center, the American Cancer Society, and the Union for International Cancer Control on World Cancer Day 2020 at the historic Mayowood Mansion in Rochester, Minnesota. Insights shared during this unique summit have been formulated into a series of expert‐authored commentaries, which accompany this introductory article.

Multicancer early detection test: Preclinical, translational, and clinical evidence-generation plan and provocative questions

Minimally invasive molecular biomarkers have been applied to the early detection of multiple cancers in large scale case-control and cohort studies. These demonstrations of feasibility herald the potential for permanent transformation of current cancer screening paradigms. This commentary discusses the major opportunities and challenges facing the preclinical development and clinical validation of multicancer early detection test strategies. From a diverse set of early detection research perspectives, the authors recommend specific approaches and highlight important questions for future investigation.

Evaluating the impact of multicancer early detection testing on health and economic outcomes: Toward a decision modeling strategy

Emerging data provide initial support for the concept that a single, minimally invasive liquid biopsy test, performed in conjunction with confirmatory radiologic or other diagnostic testing, when indicated, could be deployed on a broad scale to screen individuals for multiple types of cancer. Ideally, such a test could do this in a way that yields a clinically important percentage of true-positive indications of cancer while minimizing false-positive signals. Modern decision modeling approaches can and should be deployed to investigate the health and economic consequences of such multicancer early detection (MCED) testing within defined at-risk populations. In this paper, through small-scale analyses involving 3 hypothetical MCED-detectible cancers, the authors illustrate the potential for MCED testing to be cost-effective, along with the pivotal role of test-induced stage shift on results. The time is ripe for additional, prospective investigations of the clinical value of MCED testing, the benefits versus the risks for screened populations, and the overall projected impact on health outcomes and costs over time.

New genomic technologies for multi-cancer early detection: Rethinking the scope of cancer screening

Cancers other than breast, colorectal, cervical, and lung do not have guideline-recommended screening. New multi-cancer early detection (MCED) tests-using a single blood sample-have been developed based on circulating cell-free DNA (cfDNA) or other analytes. In this commentary, we review the current evidence on these tests, provide several major considerations for new MCED tests, and outline how their evaluation will need to differ from that established for traditional single-cancer screening tests.

A non-invasive method for concurrent detection of early-stage women-specific cancers

We integrated untargeted serum metabolomics using high-resolution mass spectrometry with data analysis using machine learning algorithms to accurately detect early stages of the women specific cancers of breast, endometrium, cervix, and ovary across diverse age-groups and ethnicities. A two-step approach was employed wherein cancer-positive samples were first identified as a group. A second multi-class algorithm then helped to distinguish between the individual cancers of the group. The approach yielded high detection sensitivity and specificity, highlighting its utility for the development of multi-cancer detection tests especially for early-stage cancers.

Estimating the population health impact of a multi-cancer early detection genomic blood test to complement existing screening in the US and UK

BACKGROUND

Multi-cancer early detection (MCED) next-generation-sequencing blood tests represent a potential paradigm shift in screening.

METHODS

We estimated the impact of screening in the US and UK. We used country-specific parameters for uptake, and test-specific sensitivity and false-positive rates for current screening: breast, colorectal, cervical and lung (US only) cancers. For the MCED test, we used cancer-specific sensitivities by stage. Outcomes included the true-positive:false-positive (TP:FP) ratio; and the cost of diagnostic investigations among screen positives, per cancer detected (Diagcost). Outcomes were estimated for recommended screening only, and then when giving the MCED test to anyone without cancer detected by current screening plus similarly aged adults ineligible for recommended screening.

RESULTS

In the US, current screening detects an estimated 189,498 breast, cervical, colorectal and lung cancers. An MCED test with 25-100% uptake detects an additional 105,526-422,105 cancers (multiple types). The estimated TP:FP (Diagcost) was 1.43 ($89,042) with current screening but only 1:1.8 ($7060) using an MCED test. For the UK the corresponding estimates were 1:18 (£10,452) for current screening, and 1:1.6 (£2175) using an MCED test.

CONCLUSIONS

Adding an MCED blood test to recommended screening can potentially be an efficient strategy. Ongoing randomised studies are required for full efficacy and cost-effectiveness evaluations.

Complex Age- and Cancer-Related Changes in Human Blood Transcriptome-Implications for Pan-Cancer Diagnostics

Early cancer detection is the key to a positive clinical outcome. While a number of early diagnostics methods exist in clinics today, they tend to be invasive and limited to a few cancer types. Thus, a clear need exists for non-invasive diagnostics methods that can be used to detect the presence of cancer of any type. Liquid biopsy based on analysis of molecular components of peripheral blood has shown significant promise in such pan-cancer diagnostics; however, existing methods based on this approach require improvements, especially in sensitivity of early-stage cancer detection. The improvement would likely require diagnostics assays based on multiple different types of biomarkers and, thus, calls for identification of novel types of cancer-related biomarkers that can be used in liquid biopsy. Whole-blood transcriptome, especially its non-coding component, represents an obvious yet under-explored biomarker for pan-cancer detection. In this study, we show that whole transcriptome analysis using RNA-seq could indeed serve as a viable biomarker for pan-cancer detection. Furthermore, a class of long non-coding (lnc) RNAs, very long intergenic non-coding (vlinc) RNAs, demonstrated superior performance compared with protein-coding mRNAs. Finally, we show that age and presence of non-blood cancers change transcriptome in similar, yet not identical, directions and explore implications of this observation for pan-cancer diagnostics.

IgM and IgA augmented autoantibody signatures improve early-stage detection of colorectal cancer prior to nodal and distant spread

Objectives

Tumor‐associated autoantibodies (AAbs) in individuals with cancer can precede clinical diagnosis by several months to years. The objective of this study was to determine whether the primary immune response in form of IgM and gut mucosa‐associated IgA can aid IgG AAbs in the detection of early‐stage colorectal cancer (CRC).

Methods

We developed a novel protein array comprising 492 antigens seropositive in CRC. The array was used to profile IgG, IgM and IgA antibody signatures in 99 CRC patients and 99 sex‐ and age‐matched non‐cancer controls. A receiver operating curve (ROC), Kaplan–Meier survival analysis and univariate and multivariate Cox regression analyses were conducted.

Results

We identified a panel of 16 multi‐isotype AAbs with a cumulative sensitivity of 91% and specificity of 74% (AUC 0.90, 95% CI: 0.850–0.940) across all CRC stages. IgM and IgG isotypes were conversely associated with disease stage with IgM contributing significantly to improved stage I and II sensitivity of 96% at 78% specificity (AUC 0.928, 95% CI: 0.884–0.973). A single identified IgA AAb reached an overall sensitivity of 5% at 99% specificity (AUC 0.520, 95% CI: 0.440–0.601) balanced across all CRC stages. Kaplan–Meier analysis revealed that se33‐1 (ZNF638) IgG AAbs were associated with reduced 5‐year overall survival (log‐rank test, P = 0.012), whereas cumulative IgM isotype signatures were associated with improved 5‐year overall survival (log‐rank test, P = 0.024).

Conclusion

IgM AAbs are associated with early‐stage colorectal cancer. Combining IgG, IgM and IgA AAbs is a novel strategy to improve early diagnosis of cancers.

EpiPanGI Dx: A Cell-free DNA Methylation Fingerprint for the Early Detection of Gastrointestinal Cancers

PURPOSE

DNA methylation alterations have emerged as front-runners in cell-free DNA (cfDNA) biomarker development. However, much effort to date has focused on single cancers. In this context, gastrointestinal (GI) cancers constitute the second leading cause of cancer-related deaths worldwide; yet there is no blood-based assay for the early detection and population screening of GI cancers.

EXPERIMENTAL DESIGN

Herein, we performed a genome-wide DNA methylation analysis of multiple GI cancers to develop a pan-GI diagnostic assay. By analyzing DNA methylation data from 1,781 tumor and adjacent normal tissues, we first identified differentially methylated regions (DMR) between individual GI cancers and adjacent normal, as well as across GI cancers. We next prioritized a list of 67,832 tissue DMRs by incorporating all significant DMRs across various GI cancers to design a custom, targeted bisulfite sequencing platform. We subsequently validated these tissue-specific DMRs in 300 cfDNA specimens and applied machine learning algorithms to develop three distinct categories of DMR panels RESULTS: We identified three distinct DMR panels: (i) cancer-specific biomarker panels with AUC values of 0.98 (colorectal cancer), 0.98 (hepatocellular carcinoma), 0.94 (esophageal squamous cell carcinoma), 0.90 (gastric cancer), 0.90 (esophageal adenocarcinoma), and 0.85 (pancreatic ductal adenocarcinoma); (ii) a pan-GI panel that detected all GI cancers with an AUC of 0.88; and (iii) a multi-cancer (tissue of origin) prediction panel, EpiPanGI Dx, with a prediction accuracy of 0.85-0.95 for most GI cancers.

CONCLUSIONS

Using a novel biomarker discovery approach, we provide the first evidence for a cfDNA methylation assay that offers robust diagnostic accuracy for GI cancers.

Intraindividual Long-term Immune Marker Stability in Plasma Samples Collected in Median 9.4 Years Apart in 304 Adult Cancer-free Individuals

BACKGROUND

Changes in immune marker levels in the blood could be used to improve the early detection of tumor-associated inflammatory processes. To increase predictiveness and utility in cancer detection, intraindividual long-term stability in cancer-free individuals is critical for biomarker candidates as to facilitate the detection of deviation from the norm.

METHODS

We assessed intraindividual long-term stability for 19 immune markers (IL10, IL13, TNFα, CXCL13, MCP-3, MIP-1α, MIP-1β, fractalkine, VEGF, FGF-2, TGFα, sIL2Rα, sIL6R, sVEGF-R2, sTNF-R1, sTNF-R2, sCD23, sCD27, and sCD30) in 304 cancer-free individuals. Repeated blood samples were collected up to 20 years apart. Intraindividual reproducibility was assessed by calculating intraclass correlation coefficients (ICC) using a linear mixed model.

RESULTS

ICCs indicated fair to good reproducibility (ICCs ≥ 0.40 and < 0.75) for 17 of 19 investigated immune markers, including IL10, IL13, TNFα, CXCL13, MCP-3, MIP-1α, MIP-1β, fractalkine, VEGF, FGF-2, TGFα, sIL2Rα, sIL6R, sTNF-R1, sTNF-R2, sCD27, and sCD30. Reproducibility was strong (ICC ≥ 0.75) for sCD23, while reproducibility was poor (ICC < 0.40) for sVEGF-R2. Using a more stringent criterion for reproducibility (ICC ≥ 0.55), we observed either acceptable or better reproducibility for IL10, IL13, CXCL13, MCP-3, MIP-1α, MIP-1β, VEGF, FGF-2, sTNF-R1, sCD23, sCD27, and sCD30.

CONCLUSIONS

IL10, IL13, CXCL13, MCP-3, MIP-1α, MIP-1β, VEGF, FGF-2, sTNF-R1, sCD23, sCD27, and sCD30 displayed ICCs consistent with intraindividual long-term stability in cancer-free individuals.

IMPACT

Our data support using these markers in prospective longitudinal studies seeking early cancer detection biomarkers.

Prognostic Significance of Blood-Based Multi-cancer Detection in Plasma Cell-Free DNA

PURPOSE

We recently reported the development of a cell-free DNA (cfDNA) targeted methylation (TM)-based sequencing approach for a multi-cancer early detection (MCED) test that includes cancer signal origin prediction. Here, we evaluated the prognostic significance of cancer detection by the MCED test using longitudinal follow-up data.

EXPERIMENTAL DESIGN

As part of a Circulating Cell-free Genome Atlas (CCGA) substudy, plasma cfDNA samples were sequenced using a TM approach, and machine learning classifiers predicted cancer status and cancer signal origin. Overall survival (OS) of cancer participants in the first 3 years of follow-up was evaluated in relation to cancer detection by the MCED test and clinical characteristics.

RESULTS

Cancers not detected by the MCED test had significantly better OS (P < 0.0001) than cancers detected, even after accounting for other covariates, including clinical stage and method of clinical diagnosis (i.e., standard-of-care screening or clinical presentation with signs/symptoms). Additionally, cancers not detected by the MCED test had better OS than was expected when data were adjusted for age, stage, and cancer type from the Surveillance, Epidemiology, and End Results (SEER) program. In cancers with current screening options, the MCED test also differentiated more aggressive cancers from less aggressive cancers (P < 0.0001).

CONCLUSIONS

Cancer detection by the MCED test was prognostic beyond clinical stage and method of diagnosis. Cancers not detected by the MCED test had better prognosis than cancers detected and SEER-based expected survival. Cancer detection and prognosis may be linked by the underlying biological factor of tumor fraction in cfDNA.

DNA Methylation Markers for Detection of Cholangiocarcinoma: Discovery, Validation, and Clinical Testing in Biliary Brushings and Plasma

Cholangiocarcinoma (CCA) has poor prognosis due to late-stage, symptomatic presentation. Altered DNA methylation markers may improve diagnosis of CCA. Reduced-representation bisulfite sequencing was performed on DNA extracted from frozen CCA tissues and matched to adjacent benign biliary epithelia or liver parenchyma. Methylated DNA markers (MDMs) identified from sequenced differentially methylated regions were selected for biological validation on DNA from independent formalin-fixed, paraffin-embedded CCA tumors and adjacent hepatobiliary control tissues using methylation-specific polymerase chain reaction. Selected MDMs were then blindly assayed on DNA extracted from independent archival biliary brushing specimens, including 12 perihilar cholangiocarcinoma, 4 distal cholangiocarcinoma cases, and 18 controls. Next, MDMs were blindly assayed on plasma DNA from patients with extrahepatic CCA (eCCA), including 54 perihilar CCA and 5 distal CCA cases and 95 healthy and 22 primary sclerosing cholangitis controls, balanced for age and sex. From more than 3,600 MDMs discovered in frozen tissues, 39 were tested in independent samples. In the clinical pilot of 16 MDMs on cytology brushings, methylated EMX1 (empty spiracles homeobox 1) had an area under the curve (AUC) of 0.98 (95% confidence interval [CI], 0.95-1.0). In the clinical pilot on plasma, a cross-validated recursive partitioning tree prediction model from nine MDMs was accurate for de novo eCCA (AUC, 0.88 [0.81-0.95]) but not for primary sclerosing cholangitis-associated eCCA (AUC, 0.54 [0.35-0.73]). Conclusion: Next-generation DNA sequencing yielded highly discriminant methylation markers for CCA. Confirmation of these findings in independent tissues, cytology brushings, and plasma supports further development of DNA methylation to augment diagnosis of CCA.

Feasibly of CD24/CD11b as a Screening Test for Hematological Malignancies

An estimated 1.24 million blood cancer cases occur annually worldwide, accounting for approximately 6% of all cancer cases. Currently, there are no standardized hematology cancer screening tests that are recommended for the general population. CD24 is a mucin-like cell surface molecule and P-selectin ligand, which plays a significant role in the maturation of B-lymphocytes and was found to be overexpressed in a number of hematological malignancies. Our primary aim was to assess the sensitivity and specificity of the CD24/CD11b-based blood test for the detection of hematological malignancies. Our cohort included 488 subjects with positive hematological cancer diagnosis (n = 122) and healthy subjects (n = 366). CD24/CD11b expression in peripheral blood leukocytes (PBLs) obtained from blood samples of participants was analyzed by flow cytometry. Our results demonstrated that the average levels of CD24/CD11b in healthy patients (21.7 ± 9.0) were statistically significantly lower compared to levels of CD24/CD11b in cancer patients (29.5 ± 18.7, p < 0.001). The highest levels of CD24/CD11b were found in multiple myeloma (39.1 ± 23.6), followed by chronic myeloid leukemia (33.0 ± 13.7) and non-Hodgkin lymphoma (32.3 ± 13.3). The test had an overall sensitivity for hematologic cancers of 78.5% (95% CI, 70.7-86.3%) and specificity of 80.2% (95% CI, 76.1-84.3%). In conclusion, our findings indicate the feasibility of a CD24/CD11b-based blood test as a screening test of hematological malignancies.

The PATHFINDER Study: Assessment of the Implementation of an Investigational Multi-Cancer Early Detection Test into Clinical Practice

To examine the extent of the evaluation required to achieve diagnostic resolution and the test performance characteristics of a targeted methylation cell-free DNA (cfDNA)-based multi-cancer early detection (MCED) test, ~6200 participants ≥50 years with (cohort A) or without (cohort B) ≥1 of 3 additional specific cancer risk factors will be enrolled in PATHFINDER (NCT04241796), a prospective, longitudinal, interventional, multi-center study. Plasma cfDNA from blood samples will be analyzed to detect abnormally methylated DNA associated with cancer (i.e., cancer "signal") and a cancer signal origin (i.e., tissue of origin). Participants with a "signal detected" will undergo further diagnostic evaluation per guiding physician discretion; those with a "signal not detected" will be advised to continue guideline-recommended screening. The primary objective will be to assess the number and types of subsequent diagnostic tests needed for diagnostic resolution. Based on microsimulations (using estimates of cancer incidence and dwell times) of the typical risk profiles of anticipated participants, the median (95% CI) number of participants with a "signal detected" result is expected to be 106 (87-128). Subsequent diagnostic evaluation is expected to detect 52 (39-67) cancers. The positive predictive value of the MCED test is expected to be 49% (39-58%). PATHFINDER will evaluate the integration of a cfDNA-based MCED test into existing clinical cancer diagnostic pathways. The study design of PATHFINDER is described here.

Clinical validation of a targeted methylation-based multi-cancer early detection test using an independent validation set

BACKGROUND

A multi-cancer early detection (MCED) test used to complement existing screening could increase the number of cancers detected through population screening, potentially improving clinical outcomes. The Circulating Cell-free Genome Atlas study (CCGA; NCT02889978) was a prospective, case-controlled, observational study and demonstrated that a blood-based MCED test utilizing cell-free DNA (cfDNA) sequencing in combination with machine learning could detect cancer signals across multiple cancer types and predict cancer signal origin (CSO) with high accuracy. The objective of this third and final CCGA substudy was to validate an MCED test version further refined for use as a screening tool.

PATIENTS AND METHODS

This pre-specified substudy included 4077 participants in an independent validation set (cancer: n = 2823; non-cancer: n = 1254, non-cancer status confirmed at year-one follow-up). Specificity, sensitivity, and CSO prediction accuracy were measured.

RESULTS

Specificity for cancer signal detection was 99.5% [95% confidence interval (CI): 99.0% to 99.8%]. Overall sensitivity for cancer signal detection was 51.5% (49.6% to 53.3%); sensitivity increased with stage [stage I: 16.8% (14.5% to 19.5%), stage II: 40.4% (36.8% to 44.1%), stage III: 77.0% (73.4% to 80.3%), stage IV: 90.1% (87.5% to 92.2%)]. Stage I-III sensitivity was 67.6% (64.4% to 70.6%) in 12 pre-specified cancers that account for approximately two-thirds of annual USA cancer deaths and was 40.7% (38.7% to 42.9%) in all cancers. Cancer signals were detected across >50 cancer types. Overall accuracy of CSO prediction in true positives was 88.7% (87.0% to 90.2%).

CONCLUSION

In this pre-specified, large-scale, clinical validation substudy, the MCED test demonstrated high specificity and accuracy of CSO prediction and detected cancer signals across a wide diversity of cancers. These results support the feasibility of this blood-based MCED test as a complement to existing single-cancer screening tests.

CLINICAL TRIAL NUMBER

NCT02889978.

Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review

ABSTRACT

Despite considerable research efforts, pancreatic cancer is associated with a dire prognosis and a 5-year survival rate of only 10%. Early symptoms of the disease are mostly nonspecific. The premise of improved survival through early detection is that more individuals will benefit from potentially curative treatment. Artificial intelligence (AI) methodology has emerged as a successful tool for risk stratification and identification in general health care. In response to the maturity of AI, Kenner Family Research Fund conducted the 2020 AI and Early Detection of Pancreatic Cancer Virtual Summit (www.pdac-virtualsummit.org) in conjunction with the American Pancreatic Association, with a focus on the potential of AI to advance early detection efforts in this disease. This comprehensive presummit article was prepared based on information provided by each of the interdisciplinary participants on one of the 5 following topics: Progress, Problems, and Prospects for Early Detection; AI and Machine Learning; AI and Pancreatic Cancer-Current Efforts; Collaborative Opportunities; and Moving Forward-Reflections from Government, Industry, and Advocacy. The outcome from the robust Summit conversations, to be presented in a future white paper, indicate that significant progress must be the result of strategic collaboration among investigators and institutions from multidisciplinary backgrounds, supported by committed funders.

High Detection Rates of Pancreatic Cancer Across Stages by Plasma Assay of Novel Methylated DNA Markers and CA19-9

PURPOSE

We have previously identified tissue methylated DNA markers (MDMs) associated with pancreatic ductal adenocarcinoma (PDAC). In this case-control study, we aimed to assess the diagnostic performance of plasma MDMs for PDAC.

EXPERIMENTAL DESIGN

Thirteen MDMs (GRIN2D, CD1D, ZNF781, FER1L4, RYR2, CLEC11A, AK055957, LRRC4, GH05J042948, HOXA1, PRKCB, SHISA9, and NTRK3) were identified on the basis of selection criteria applied to results of prior tissue experiments and assays were optimized in plasma. Next, 340 plasma samples (170 PDAC cases and 170 controls) were assayed using target enrichment long-probe quantitative amplified signal method. Initially, 120 advanced-stage PDAC cases and 120 healthy controls were used to train a prediction algorithm at 97.5% specificity using random forest modeling. Subsequently, the locked algorithm derived from the training set was applied to an independent blinded test set of 50 early-stage PDAC cases and 50 controls. Finally, data from all 340 patients were combined, and cross-validated.

RESULTS

The cross-validated area under the receiver operating characteristic curve (AUC) for the training set was 0.93 (0.89-0.96) for the MDM panel alone, 0.91 (95% confidence interval, 0.87-0.96) for carbohydrate antigen 19-9 (CA19-9) alone, and 0.99 (0.98-1) for the combined MDM-CA19-9 panel. In the test set of early-stage PDAC, the AUC for MDMs alone was 0.84 (0.76-0.92), CA19-9 alone was 0.87 (0.79-0.94), and combined MDM-CA19-9 panel was 0.90 (0.84-0.97) significantly better compared with either MDMs alone or CA19-9 alone (P = 0.0382 and 0.0490, respectively). At a preset specificity of 97.5%, the sensitivity for the combined panel in the test set was 80% (28%-99%) for stage I disease and 82% (68%-92%) for stage II disease. Using the combined datasets, the cross-validated AUC was 0.9 (0.86-0.94) for the MDM panel alone and 0.89 for CA19-9 alone (0.84-0.93) versus 0.97 (0.94-0.99) for the combined MDM-CA19-9 panel (P ≤ 0.0001). Overall, cross-validated sensitivity of MDM-CA19-9 panel was 92% (83%-98%), with an observed specificity of 92% at the preset specificity of 97.5%.

CONCLUSIONS

Plasma MDMs in combination with CA19-9 detect PDAC with significantly higher accuracy compared with either biomarker individually.

Transforming the landscape of early cancer detection using blood tests-Commentary on current methodologies and future prospects

Early cancer detection should lead to an overall stage shift, less-intensive treatments and better patient outcomes. Current recommended screening programmes are limited to a handful of individual cancers. A multi-cancer early detection test that simultaneously detects and localises multiple cancers could reduce the morbidity and mortality associated with cancer.