3034 Background: A rapid, low-cost, sensitive, multi-cancer early detection (MCED) test would be transformational in the diagnostics field. Earlier cancer detection and instigation of treatment can increase survival rates. An effective test must accurately identify the small proportion of patients with typically non-specific symptoms who actually have cancer. Such symptoms don’t easily segregate by organ system, necessitating a multi-cancer approach. Methods: In this large-scale study ( n = 2094 patients) we applied the Dxcover Cancer Liquid Biopsy to differentiate cancer against non-cancer, as well as organ specific tests to identify cancers of the brain, breast, colorectal, kidney, lung, ovary, pancreas, and prostate. The test uses Fourier transform infrared spectroscopy to analyze all macromolecules in a minute volume of patient serum, and machine learning to build a classifier of the resultant spectral profiles for calling the likelihood of cancer. Results: For the overall cancer classification, our model achieved 90% sensitivity with 61% specificity when tuned for sensitivity, with detection rates of 93% for stage I, 84% for stage II, 92% for stage III and 95% for stage IV. We also tuned for maximum sensitivity or specificity, whilst the other statistic was fixed above a minimum value of 45%. This resulted in 94% sensitivity with 47% specificity, and 94% specificity with 48% sensitivity, respectively. For organ specific cancer classifiers area under the curve values were calculated for all cancers: brain (0.90), breast (0.74), colorectal (0.91), kidney (0.91), lung (0.90), ovarian (0.85), pancreatic (0.81) and prostate (0.85). Conclusions: Cancer treatment is often more effective when given earlier and this low-cost strategy can facilitate the requisite earlier diagnosis. With further development, the Dxcover MCED test could have a significant impact on early detection of cancer, which is vital in the quest for improved survival and quality of life.
e15627 Background: Earlier diagnosis and treatment of colorectal cancer (CRC) maximizes the opportunity to combat or control disease progression. The average 5-year survival rate after diagnosis decreases from 91% in early-stage CRC, to as low as 15% for stage IV CRC. Furthermore, rapid detection and removal of pre-cancerous adenomas – e.g., advanced adenomas (AA) – can significantly improve survival rates of affected patients. However, current stool-based tests have inadequate AA sensitivities, such as FIT (24%) and FIT-DNA (42%). Liquid biopsies have great potential to supplement FIT tests and improve diagnostic pathways, but current blood tests based on tumor-derived biomarkers also have limited sensitivity for AA detection. Methods: The Dxcover® Cancer Liquid Biopsy is based on Fourier-transform infrared (FTIR) spectroscopy applied to serum from a standard blood sample. The spectral data are collected and analyzed using pattern recognition and machine learning algorithms to detect disease-specific signatures. We initially examined test performance for the detection of multiple cancer types. Additionally, we have analyzed a retrospective cohort of samples comprising 100 CRC, 99 AA removed by surgical resection and 97 colonoscopy screening controls. Results: The CRC classifier from the discovery multi-cancer dataset resulted in 74% sensitivity with 91% specificity when differentiating CRC and non-cancer, which surpasses the targets set by the Centers for Medicare & Medicaid Services (CMS) for coverage of CRC tests. Receiver operating characteristic (ROC) analysis reported an area under the curve (AUC) value of 0.91. The machine learning algorithms can be fine-tuned to maximize either sensitivity or specificity depending on the requirements of different patient pathways. When tuned for higher sensitivity, the model produced 97% sensitivity (49% specificity), and when tailored for greater specificity (97%) the sensitivity was 47%. We have now progressed these findings to examine the ability of the technology to differentiate patients with CRC, AA and colonoscopy controls. Furthermore, we assess whether combining spectral data with additional clinical information, such as biomarker data and patient demographics, can enhance test performance. Conclusions: A rapid blood test that is sensitive to AA and early-stage CRC could improve patient prognosis and reduce cancer burden. With further development, this liquid biopsy could have a profound impact on the earlier detection of CRC.
e16275 Background: Pancreatic cancer is the 7th most deadly cancer worldwide with over 460,000 victims per year. In the current diagnostic pathway, carbohydrate antigen (CA) 19-9 serum test is the blood assessment used for detection of pancreatic cancer; although, with poor positive predictive values reported, it is not specific for pancreatic tumors as its levels can be raised in symptomatic patients with other benign comorbidities and/or because of other tumors in the surrounding area. Attenuated total reflection–Fourier transform infrared spectroscopy (ATR-FTIR) has demonstrated exceptional potential in human blood serum analysis for cancer diagnostics and its implementation in the clinical environment could represent a significant step forward in the early detection of pancreatic cancer. This proof-of-concept study aimed to investigate the use of the Dxcover cancer liquid biopsy as a novel approach for pancreatic cancer detection. Methods: The study was focused on the discrimination between both cancer (n = 100) versus healthy control samples (n = 100), and cancer (n = 35) versus symptomatic non-malignant control samples (n = 35) from patients with comorbidities and/or confounding diseases. Various machine learning algorithms were applied to discriminate between the classes: random forest (RF), partial least squares-discriminant analysis (PLS-DA) and support vector machine (SVM). Receiver operating characteristic (ROC) analysis was also employed to evaluate the classes’ degree of diagnostic separability. Permutation tests were performed for each classification in order to ensure their statistical significance. Results: Cancer (n = 100) and healthy control samples (n = 100) were distinguished with excellent results, achieving results up to a sensitivity of 91.0 %, specificity of 87.6 %, and accuracy of 89.3 % with PLS-DA. Moreover, an area under the curve (AUC) equal to 0.954 was obtained through ROC analysis. When discriminating between cancer (n = 35) and symptomatic control samples (n = 35), accuracy values were recorded in the range between 70.6 and 77.3 % with ROC analysis showing a balanced sensitivity and specificity over 75 % with an AUC of 0.844. Both discriminations were proven statistically significant. Conclusions: Pancreatic cancer detection in early stages would be key in improving prognosis and survival rates of patients, through targeted earlier surgery and treatments. The Dxcover cancer liquid biopsy could represent a powerful tool in the clinical environment as an easy-to-use, minimally invasive and reliable spectroscopic blood test for detection of pancreatic cancer.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.