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.

Noninvasive detection of any-stage cancer using free glycosaminoglycans.

Cancer mortality is exacerbated by late-stage diagnosis. Liquid biopsies based on genomic biomarkers can noninvasively diagnose cancers. However, validation studies have reported ~10% sensitivity to detect stage I cancer in a screening population and specific types, such as brain or genitourinary tumors, remain undetectable. We investigated urine and plasma free glycosaminoglycan profiles (GAGomes) as tumor metabolism biomarkers for multi-cancer early detection (MCED) of 14 cancer types using 2,064 samples from 1,260 cancer or healthy subjects. We observed widespread cancer-specific changes in biofluidic GAGomes recapitulated in an in vivo cancer progression model. We developed three machine learning models based on urine (Nurine = 220 cancer vs. 360 healthy) and plasma (Nplasma = 517 vs. 425) GAGomes that can detect any cancer with an area under the receiver operating characteristic curve of 0.83-0.93 with up to 62% sensitivity to stage I disease at 95% specificity. Undetected patients had a 39 to 50% lower risk of death. GAGomes predicted the putative cancer location with 89% accuracy. In a validation study on a screening-like population requiring ≥ 99% specificity, combined GAGomes predicted any cancer type with poor prognosis within 18 months with 43% sensitivity (21% in stage I; N = 121 and 49 cases). Overall, GAGomes appeared to be powerful MCED metabolic biomarkers, potentially doubling the number of stage I cancers detectable using genomic biomarkers.

TOTEM: a multi-cancer detection and localization approach using circulating tumor DNA methylation markers.

BACKGROUND: Detection of cancer and identification of tumor origin at an early stage improve the survival and prognosis of patients. Herein, we proposed a plasma cfDNA-based approach called TOTEM to detect and trace the cancer signal origin (CSO) through methylation markers. METHODS: We performed enzymatic conversion-based targeted methylation sequencing on plasma cfDNA samples collected from a clinical cohort of 500 healthy controls and 733 cancer patients with seven types of cancer (breast, colorectum, esophagus, stomach, liver, lung, and pancreas) and randomly divided these samples into a training cohort and a testing cohort. An independent validation cohort of 143 healthy controls, 79 liver cancer patients and 100 stomach cancer patients were recruited to validate the generalizability of our approach. RESULTS: A total of 57 multi-cancer diagnostic markers and 873 CSO markers were selected for model development. The binary diagnostic model achieved an area under the curve (AUC) of 0.907, 0.908 and 0.868 in the training, testing and independent validation cohorts, respectively. With a training specificity of 98%, the specificities in the testing and independent validation cohorts were 100% and 98.6%, respectively. Overall sensitivity across all cancer stages was 65.5%, 67.3% and 55.9% in the training, testing and independent validation cohorts, respectively. Early-stage (I and II) sensitivity was 50.3% and 45.7% in the training and testing cohorts, respectively. For cancer patients correctly identified by the binary classifier, the top 1 and top 2 CSO accuracies were 77.7% and 86.5% in the testing cohort (n = 148) and 76.0% and 84.0% in the independent validation cohort (n = 100). Notably, performance was maintained with only 21 diagnostic and 214 CSO markers, achieving a training AUC of 0.865, a testing AUC of 0.866, and an integrated top 2 accuracy of 83.1% in the testing cohort. CONCLUSIONS: TOTEM demonstrates promising potential for accurate multi-cancer detection and localization by profiling plasma methylation markers. The real-world clinical performance of our approach needs to be investigated in a much larger prospective cohort.

Ultra-short cell-free DNA fragments enhance cancer early detection in a multi-analyte blood test combining mutation, protein and fragmentomics.

OBJECTIVES: Cancer morbidity and mortality can be reduced if the cancer is detected early. Cell-free DNA (cfDNA) fragmentomics emerged as a novel epigenetic biomarker for early cancer detection, however, it is still at its infancy and requires technical improvement. We sought to apply a single-strand DNA sequencing technology, for measuring genetic and fragmentomic features of cfDNA and evaluate the performance in detecting multiple cancers. METHODS: Blood samples of 364 patients from six cancer types (colorectal, esophageal, gastric, liver, lung, and ovarian cancers) and 675 healthy individuals were included in this study. Circulating tumor DNA mutations, cfDNA fragmentomic features and a set of protein biomarkers were assayed. Sensitivity and specificity were reported by cancer types and stages. RESULTS: Circular Ligation Amplification and sequencing (CLAmp-seq), a single-strand DNA sequencing technology, yielded a population of ultra-short fragments (<100 bp) than double-strand DNA preparation protocols and reveals a more significant size difference between cancer and healthy cfDNA fragments (25.84 bp vs. 16.05 bp). Analysis of the subnucleosomal peaks in ultra-short cfDNA fragments indicates that these peaks are regulatory element "footprints" and correlates with gene expression and cancer stages. At 98 % specificity, a prediction model using ctDNA mutations alone showed an overall sensitivity of 46 %; sensitivity reaches 60 % when protein is added, sensitivity further increases to 66 % when fragmentomics is also integrated. More improvements observed for samples representing earlier cancer stages than later ones. CONCLUSIONS: These results suggest synergistic properties of protein, genetic and fragmentomics features in the identification of early-stage cancers.

Unintrusive multi-cancer detection by circulating cell-free DNA methylation sequencing (THUNDER): development and independent validation studies.

BACKGROUND: Early detection of cancer offers the opportunity to identify candidates when curative treatments are achievable. The THUNDER study (THe UNintrusive Detection of EaRly-stage cancers, NCT04820868) aimed to evaluate the performance of enhanced linear-splinter amplification sequencing, a previously described cell-free DNA (cfDNA) methylation-based technology, in the early detection and localization of six types of cancers in the colorectum, esophagus, liver, lung, ovary, and pancreas. PATIENTS AND METHODS: A customized panel of 161 984 CpG sites was constructed and validated by public and in-house (cancer: n = 249; non-cancer: n = 288) methylome data, respectively. The cfDNA samples from 1693 participants (cancer: n = 735; non-cancer: n = 958) were retrospectively collected to train and validate two multi-cancer detection blood test (MCDBT-1/2) models for different clinical scenarios. The models were validated on a prospective and independent cohort of age-matched 1010 participants (cancer: n = 505; non-cancer: n = 505). Simulation using the cancer incidence in China was applied to infer stage shift and survival benefits to demonstrate the potential utility of the models in the real world. RESULTS: MCDBT-1 yielded a sensitivity of 69.1% (64.8%-73.3%), a specificity of 98.9% (97.6%-99.7%), and tissue origin accuracy of 83.2% (78.7%-87.1%) in the independent validation set. For early-stage (I-III) patients, the sensitivity of MCDBT-1 was 59.8% (54.4%-65.0%). In the real-world simulation, MCDBT-1 achieved a sensitivity of 70.6% in detecting the six cancers, thus decreasing late-stage incidence by 38.7%-46.4%, and increasing 5-year survival rate by 33.1%-40.4%, respectively. In parallel, MCDBT-2 was generated at a slightly low specificity of 95.1% (92.8%-96.9%) but a higher sensitivity of 75.1% (71.9%-79.8%) than MCDBT-1 for populations at relatively high risk of cancers, and also had ideal performance. CONCLUSION: In this large-scale clinical validation study, MCDBT-1/2 models showed high sensitivity, specificity, and accuracy of predicted origin in detecting six types of cancers.

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.

The aggregate value of cancer screenings in the United States: full potential value and value considering adherence.

BACKGROUND: Although cancer mortality has been decreasing since 1991, many cancers are still not detected until later stages with poorer outcomes. Screening for early-stage cancer can save lives because treatments are generally more effective at earlier than later stages of disease. Evidence of the aggregate benefits of guideline-recommended single-site cancer screenings has been limited. This article assesses the benefits in terms of life-years gained and associated value from major cancer screening technologies in the United States. METHODS: A mathematical model was built to estimate the aggregate benefits of screenings for breast, colorectal, cervical, and lung cancer over time since the start of US Preventive Services Task Force (USPSTF) recommendations. For each type, the full potential benefits under perfect adherence and the benefits considering reported adherence rates were estimated. The effectiveness of each screening technology was abstracted from published literature on the life-years gained per screened individual. The number of individuals eligible for screening per year was estimated using US Census data matched to the USPSTF recommendations, which changed over time. Adherence rates to screening protocols were based on the National Health Interview Survey results with extrapolation. RESULTS: Since initial USPSTF recommendations, up to 417 million people were eligible for cancer screening. Assuming perfect adherence to screening recommendations, the life-years gained from screenings are estimated to be 15.5-21.3 million (2.2-4.9, 1.4-3.6, 11.4-12.3, and 0.5 million for breast, colorectal, cervical, and lung cancer, respectively). At reported adherence rates, combined screening has saved 12.2-16.2 million life-years since the introduction of USPSTF recommendations, ~ 75% of potential with perfect adherence. These benefits translate into a value of $8.2-$11.3 trillion at full potential and $6.5-$8.6 trillion considering current adherence. Therefore, single-site screening could have saved an additional 3.2-5.1 million life-years, equating to $1.7-$2.7 trillion, with perfect adherence. CONCLUSIONS: Although gaps persist between the full potential benefit and benefits considering adherence, existing cancer screening technologies have offered significant value to the US population. Technologies and policy interventions that can improve adherence and/or expand the number of cancer types tested will provide significantly more value and save significantly more patient lives.

Letter to the Editor: An ultra-sensitive assay using cell-free DNA fragmentomics for multi-cancer early detection.

Early detection can benefit cancer patients with more effective treatments and better prognosis, but existing early screening tests are limited, especially for multi-cancer detection. This study investigated the most prevalent and lethal cancer types, including primary liver cancer (PLC), colorectal adenocarcinoma (CRC), and lung adenocarcinoma (LUAD). Leveraging the emerging cell-free DNA (cfDNA) fragmentomics, we developed a robust machine learning model for multi-cancer early detection. 1,214 participants, including 381 PLC, 298 CRC, 292 LUAD patients, and 243 healthy volunteers, were enrolled. The majority of patients (N = 971) were at early stages (stage 0, N = 34; stage I, N = 799). The participants were randomly divided into a training cohort and a test cohort in a 1:1 ratio while maintaining the ratio for the major histology subtypes. An ensemble stacked machine learning approach was developed using multiple plasma cfDNA fragmentomic features. The model was trained solely in the training cohort and then evaluated in the test cohort. Our model showed an Area Under the Curve (AUC) of 0.983 for differentiating cancer patients from healthy individuals. At 95.0% specificity, the sensitivity of detecting all cancer reached 95.5%, while 100%, 94.6%, and 90.4% for PLC, CRC, and LUAD, individually. The cancer origin model demonstrated an overall 93.1% accuracy for predicting cancer origin in the test cohort (97.4%, 94.3%, and 85.6% for PLC, CRC, and LUAD, respectively). Our model sensitivity is consistently high for early-stage and small-size tumors. Furthermore, its detection and origin classification power remained superior when reducing sequencing depth to 1× (cancer detection: ≥ 91.5% sensitivity at 95.0% specificity; cancer origin: ≥ 91.6% accuracy). In conclusion, we have incorporated plasma cfDNA fragmentomics into the ensemble stacked model and established an ultrasensitive assay for multi-cancer early detection, shedding light on developing cancer early screening in clinical practice.

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.

Integrating Artificial Intelligence for Advancing Multiple-Cancer Early Detection via Serum Biomarkers: A Narrative Review.

The concept and policies of multicancer early detection (MCED) have gained significant attention from governments worldwide in recent years. In the era of burgeoning artificial intelligence (AI) technology, the integration of MCED with AI has become a prevailing trend, giving rise to a plethora of MCED AI products. However, due to the heterogeneity of both the detection targets and the AI technologies, the overall diversity of MCED AI products remains considerable. The types of detection targets encompass protein biomarkers, cell-free DNA, or combinations of these biomarkers. In the development of AI models, different model training approaches are employed, including datasets of case-control studies or real-world cancer screening datasets. Various validation techniques, such as cross-validation, location-wise validation, and time-wise validation, are used. All of the factors show significant impacts on the predictive efficacy of MCED AIs. After the completion of AI model development, deploying the MCED AIs in clinical practice presents numerous challenges, including presenting the predictive reports, identifying the potential locations and types of tumors, and addressing cancer-related information, such as clinical follow-up and treatment. This study reviews several mature MCED AI products currently available in the market, detecting their composing factors from serum biomarker detection, MCED AI training/validation, and the clinical application. This review illuminates the challenges encountered by existing MCED AI products across these stages, offering insights into the continued development and obstacles within the field of MCED AI.

Helping patients understand multi-cancer early detection tests: a scoping review.

Multi-cancer early detection tests are emerging as a revolutionary technology for the early detection of dozens of cancers from a single blood sample, including cancers without proven screening methods. However, they also come with challenges, including false-positive and false-negative results. To help patients make informed decisions, patient education materials are crucial. A review of available materials reveals that, while some materials provide understandable and actionable information, most lack a balanced presentation of the current benefits and risks of multi-cancer early detection testing. The dynamic nature of this field necessitates continuous updates to educational materials, incorporating current evidence and uncertainties.

Multi-cancer early detection tests hold promise for cancer screening. Patient education materials need to present balanced information for informed decision-making, writes @jasonvassy. #MCED.

Circulating tumour DNA analysis for early detection of lung cancer: a systematic review.

Background: Circulating tumor DNA (ctDNA) analysis has been applied in cancer diagnostics including lung cancer. Specifically for the early detection purpose, various modalities of ctDNA analysis have demonstrated their potentials. Such analyses have showed diverse performance across different studies. Methods: We performed a systematic review of original studies published before 1 January 2023. Studies that evaluated ctDNA alone and in combination with other biomarkers for early detection of lung cancer were included. Results: The systematic review analysis included 56 original studies that were aimed for early detection of lung cancer. There were 39 studies for lung cancer only and 17 for pan-cancer early detection. Cancer and control cases included were heterogenous across studies. Different molecular features of ctDNA have been evaluated, including 7 studies on cell-free DNA concentration, 17 on mutation, 29 on methylation, 5 on hydroxymethylation and 8 on fragmentation patterns. Among these 56 studies, 17 have utilised different combinations of the above-mentioned ctDNA features and/or circulation protein markers. For all the modalities, lower sensitivities were reported for the detection of early-stage cancer. Conclusions: The systematic review suggested the clinical utility of ctDNA analysis for early detection of lung cancer, alone or in combination with other biomarkers. Future validation with standardised testing protocols would help integration into clinical care.

Cancer Screening: Present Recommendations, the Development of Multi-Cancer Early Development Tests, and the Prospect of Universal Cancer Screening.

Cancer continues to pose a considerable challenge to global health. In the search for innovative strategies to combat this complex enemy, the concept of universal cancer screening has emerged as a promising avenue for early detection and prevention. In contrast to targeted approaches that focus on specific populations or high-risk individuals, universal screening seeks to cast a wide net to detect incipient malignancies in different demographic groups. This paradigm shift in cancer care underscores the importance of comprehensive screening programs that go beyond conventional boundaries. As our understanding of the complex molecular and genetic basis of cancer deepens, the need to develop comprehensive screening methods becomes increasingly apparent. In this article, we look at the rationale and potential benefits of universal cancer screening.

Prediction of methylation status using WGS data of plasma cfDNA for multi-cancer early detection (MCED).

BACKGROUND: Cell-free DNA (cfDNA) contains a large amount of molecular information that can be used for multi-cancer early detection (MCED), including changes in epigenetic status of cfDNA, such as cfDNA fragmentation profile. The fragmentation of cfDNA is non-random and may be related to cfDNA methylation. This study provides clinical evidence for the feasibility of inferring cfDNA methylation levels based on cfDNA fragmentation patterns. We performed whole-genome bisulfite sequencing and whole-genome sequencing (WGS) on both healthy individuals and cancer patients. Using the information of whole-genome methylation levels, we investigated cytosine-phosphate-guanine (CpG) cleavage profile and validated the method of predicting the methylation level of individual CpG sites using WGS data. RESULTS: We conducted CpG cleavage profile biomarker analysis on data from both healthy individuals and cancer patients. We obtained unique or shared potential biomarkers for each group and built models accordingly. The modeling results proved the feasibility to predict the methylation status of single CpG sites in cfDNA using cleavage profile model from WGS data. CONCLUSION: By combining cfDNA cleavage profile of CpG sites with machine learning algorithms, we have identified specific CpG cleavage profile as biomarkers to predict the methylation status of individual CpG sites. Therefore, methylation profile, a widely used epigenetic biomarker, can be obtained from a single WGS assay for MCED.

Clinical performance and utility: A microsimulation model to inform the design of screening trials for a multi-cancer early detection test.

OBJECTIVES: Designing cancer screening trials for multi-cancer early detection (MCED) tests presents a significant methodology challenge, as natural histories of cell-free DNA-shedding cancers are not yet known. A microsimulation model was developed to project the performance and utility of an MCED test in cancer screening trials. METHODS: Individual natural history of preclinical progression through cancer stages for 23 cancer classes was simulated by a stage-transition model under a broad range of cancer latency parameters. Cancer incidences and stage distributions at clinical presentation in simulated trials were set to match the data from Surveillance, Epidemiology, and End Results program. One or multiple rounds of annual screening using a targeted methylation-based MCED test (GalleriⓇ) was conducted to detect preclinical cancers. Mortality benefit of early detection was simulated by a stage-shift model. RESULTS: In simulated trials, accounting for healthy volunteer effect and varying test sensitivity, positive predictive value in the prevalence screening round reached 48% to 61% in 6 natural history scenarios. After 3 rounds of annual screening, the cumulative proportions of stage I/II cancers increased by approximately 9% to 14%, the incidence of stage IV cancers was reduced by 37% to 46%, the reduction of stages III and IV cancer incidences was 9% to 24%, and the reduction of mortality reached 13% to 16%. Greater reductions of late-stage cancers and cancer mortality were achieved by five rounds of MCED screening. CONCLUSIONS: Simulation results guide trial design and suggest that adding this MCED test to routine screening in the United States may shift cancer detection to earlier stages, and potentially save lives.

A non-invasive screening method using Caenorhabditis elegans for early detection of multiple cancer types: A prospective clinical study.

Cancer is the second leading cause of death worldwide, according to the World Health Organization, surpassed only by cardiovascular diseases. Early identification and intervention can significantly improve outcomes. However, finding a universal, non-invasive, economical, and precise method for early cancer detection remains a significant challenge. This study explores the efficacy of an innovative cancer detection test, N-NOSE, leveraging a Caenorhabditis elegans olfactory assay on urine samples across a diverse patient group exceeding 1600 individuals diagnosed with various cancers, with samples from the Shikoku Cancer Center (Ehime, Japan) under approved ethical standards. Current cancer screening techniques often require invasive procedures, can be painful or complex, with poor performance, and might be prohibitively costly, limiting accessibility for many. N-NOSE addresses these challenges head-on by offering a test based on urine analysis, eliminating the need for invasive methods, and being more affordable with higher performance at early stages than extensive blood tests or comprehensive body scans for cancer detection. In this study, N-NOSE demonstrated a capability to accurately identify upwards of 20 cancer types, achieving detection sensitivities between 60 and 90 %, including initial-stage cancers. The findings robustly advocate for N-NOSE's potential as a revolutionary, cost-effective, and minimally invasive strategy for broad-spectrum early cancer detection. It is also particularly significant in low- and middle-income countries with limited access to advanced cancer diagnostic methods, which may contribute to the improved outcome of affected individuals.

Perceptions of multi-cancer early detection tests among communities facing barriers to health care.

Marginalized racial and ethnic groups and rural and lower income communities experience significant cancer inequities. Blood-based multi-cancer early detection tests (MCEDs) provide a simple and less invasive method to screen for multiple cancers at a single access point and may be an important strategy to reduce cancer inequities. In this qualitative study, we explored barriers and facilitators to MCED adoption among communities facing health care access barriers in Alaska, California, and Oregon. We used reflexive thematic analysis to analyze general barriers to cancer screening, MCED-specific barriers, facilitators of MCED adoption, and MCED communication strategies. We found barriers and facilitators to MCED adoption across 4 levels of the social-ecological model: (1) individual, (2) interpersonal, (3) health care system, and (4) societal. These included adverse psychological impacts, positive perceptions of MCEDs, information and knowledge about cancer screening, the quality of the patient-provider relationship, a lack of health care system trustworthiness, logistical accessibility, patient supports, and financial accessibility. Optimal MCED communication strategies included information spread through the medical environment and the community. These findings underscore the importance of understanding and addressing the multilevel factors that may influence MCED adoption among communities facing health care access barriers to advance health equity.

Physicians' perception on using a multi-cancer early detection blood test to reduce disparities in cancer screening.

 INTRODUCTION: Cancer causes significant morbidity and mortality in the United States. It is the second most common cause of death in the United States, after heart disease. African Americans are disproportionately affected by malignancy, with overall higher death rates compared to other racial and ethnic groups. Screening tests can identify early stage malignancy allowing for timely intervention. However, African Americans less frequently undergo cancer screening. Advancement in genomic technology has led to the identification of signals for cancer in the blood. This has resulted in the development of multi-cancer early detection (MCED) tests which evaluate for circulating cell-free DNA (cfDNA). This study evaluated physicians' perception of the use of a multi-cancer early detection test (MCED). METHODS: An anonymous, 29 question survey was administered to African American / Black physicians and medical students. Survey participants were identified through the National Medical Association and other professional organizations that included primarily African American physicians. Surveys were excluded from analysis if respondent was non-African American / Black or was not a physician or medical student. The survey collected physician demographics, percentage of African American / Black patients in their practice, patient barriers to screening, potential use of MCED tests and factors influencing decision to recommend testing. Descriptive statistics were generated. Additional analysis was performed using Chi-Square with statistical significance set at p-value <0.05. The survey was pilot tested for reliability and validity. RESULTS: 1196 (681 female, 515 male) physicians and medical students completed the survey. 95.8 % were physicians who were or had been in clinical practice. Fifty-three percent of physicians reported that >40 % of their patients were African American / Black. Barriers to cancer screening included lack of understanding of the importance (33.8 %), lack of or limited insurance coverage (23.5 %), socioeconomic factors unrelated to insurance coverage (16.2 %), fear of cancer (8.8 %), history of racism and bias in the health care system (7.4 %) with 8.8 % reporting 'other' and 1.5 % reporting no perceived barriers. There was a significant difference (p<0.03) in the rate that physicians' perceived racism and bias in the health care system as barrier for cancer screening in African American / Black patients when compared to other patients. Most physicians and medical students indicated that a MCED test would benefit all patients (86.8 %), would encourage further cancer screening tests (83.8 %), and would be beneficial for minority and under-represented patients regardless of socioeconomics or health care access (83.8 %). Seventy-five percent of survey respondents indicated that a MCED test would be beneficial in promoting further cancer screening and early detection in African American / Black patients. Factors that would impact the ordering of an MCED test included scientific evidence and test validity (63.2 %), efficiency, accessibility, ease of ordering and ease of receiving results (11.8 %), insurance coverage (13.2 %) and 'other factors' (11.8 %). DISCUSSION: This is one of the largest surveys to assess physicians' perceptions about MCED testing and is the first study to evaluate the perspectives of African American physicians. It offers insight about physician acceptance and potential incorporation of MCED into clinical practice. It is important that a multifaceted approach is employed to improve cancer outcomes and reduce disparities in survival. MCED tests, a relatively new advancement in genomic technology, have the potential to be an important component in cancer screening strategies.

Addressing positive multi-cancer early detection tests in head and neck Surgery: Experience with head and neck work up for high-risk referrals.

OBJECTIVES: Blood-based multi-cancer early detection (MCED) tests are now commercially available. However, there are currently no consensus guidelines available for head and neck cancer (HNC) providers to direct work up or surveillance for patients with a positive MCED test. We seek to describe cases of patients with positive MCED tests suggesting HNC and provide insights for their evaluation. METHODS: Retrospective chart review of patients referred to Otolaryngology with an MCED result suggesting HNC. Patients enrolled in prospective MCED clinical trials were excluded. Cancer diagnoses were confirmed via frozen-section pathology. RESULTS: Five patients were included (mean age: 69.2 years, range 50-87; 4 male) with MCED-identified-high-risk for HNC or lymphoma. Only patient was symptomatic. After physical exam and follow-up head and neck imaging, circulating tumor HPV DNA testing, two patients were diagnosed with p16 + oropharyngeal squamous cell carcinomas and underwent appropriate therapy. A third patient had no evidence of head and neck cancer but was diagnosed with sarcoma of the thigh. The remaining two patients had no evidence of malignancy after in-depth workup. CONCLUSIONS: In this retrospective study, 2 of 5 patients referred to Otolaryngology with a positive MCED result were diagnosed with HPV + oropharyngeal squamous cell carcinoma. We recommend that positive HNC MCED work up include thorough head and neck examination with flexible laryngoscopy and focused CT or MRI imaging. Given the potential for inaccurate MCED tissue of origin classification, PET/CT may be useful in specific situations. For a patient with no cancer identified, development of clear guidelines is warranted.

Clinical validation of a blood-based liquid biopsy test integrating cell-free DNA quantification and next-generation sequencing for cancer screening in dogs.

OBJECTIVE: To validate the performance of a novel, integrated test for canine cancer screening that combines cell-free DNA quantification with next-generation sequencing (NGS) analysis. SAMPLE: Retrospective data from a total of 1,947 cancer-diagnosed and presumably cancer-free dogs were used to validate test performance for the detection of 7 predefined cancer types (lymphoma, hemangiosarcoma, osteosarcoma, leukemia, histiocytic sarcoma, primary lung tumors, and urothelial carcinoma), using independent training and testing sets. METHODS: Cell-free DNA quantification data from all samples were analyzed using a proprietary machine learning algorithm to determine a Cancer Probability Index (High, Moderate, or Low). High and Low Probability of Cancer were final result classifications. Moderate cases were additionally analyzed by NGS to arrive at a final classification of High Probability of Cancer (Cancer Signal Detected) or Low Probability of Cancer (Cancer Signal Not Detected). RESULTS: Of the 595 dogs in the testing set, 89% (n = 530) received a High or Low Probability result based on the machine learning algorithm; 11% (65) were Moderate Probability, and NGS results were used to assign a final classification. Overall, 87 of 122 dogs with the 7 predefined cancer types were classified as High Probability and 467 of 473 presumably cancer-free dogs were classified as Low Probability, corresponding to a sensitivity of 71.3% for the predefined cancer types at a specificity of 98.7%. CLINICAL RELEVANCE: This integrated test offers a novel option to screen for cancer types that may be difficult to detect by physical examination at a dog's wellness visit.