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.
The development of a robust multi-cancer early detection (MCED) test would be transformational within the cancer diagnostics field, as earlier detection is vital to inhibit disease progression, improve patient prognosis and reduce mortality rates. In this large-scale spectroscopic study, we present the Dxcover® Cancer Liquid Biopsy, the world’s first infrared spectroscopy-based blood test for the early detection of multiple cancers. The full cohort (n=2094) was comprised of patients with a confirmed cancer diagnosis (n=1543), symptomatic non-cancer patients (n=460) and healthy volunteers (n=91). For the cancer versus non-cancer classification, receiver operating characteristic analysis produced a mean curve with an area under the curve (AUC) value of 0.86. When adjusted for greater sensitivity, our model achieved 90% sensitivity with 61% specificity, and yielded a sensitivity of 56% with 91% specificity when tailored for higher specificity. We also tuned for maximized sensitivity or specificity, whilst the other parameter was fixed above a minimum value of 45% for the cross-validation result, based upon performance levels of currently available clinically benchmarked tests. This resulted in a 94% sensitivity where specificity was 47%, and a 94% specificity with 48% sensitivity. The overall detection rates were 93% for stage I, 84% for stage II, 92% for stage III and 95% for stage IV for the sensitivity-tuned model. The specificity-tuned model detected 90% of non-cancer patients accurately, resulting in few false negatives (54/551). For the binary classifiers of individual cancer types against symptomatic control patients, promising AUC values were reported 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). Importantly, the detection rates did not vary significantly according to cancer stage, suggesting the technology is very effective at detecting early-stage tumors, which is a necessity for MCED tests. Citation Format: James M. Cameron, Alexandra Sala, Georgios Antoniou, Paul M. Brennan, Justin J. A. Conn, Siobhan Connal, David S. Palmer, Benjamin R. Smith, Matthew J. Baker. Multi-cancer early detection with a spectroscopic liquid biopsy platform [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5920.
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.
A rapid, low-cost, sensitive, multi-cancer early detection (MCED) test would be transformational in the diagnostics field. Earlier cancer detection can increase survival rates and quality of life of patients. An effective test must accurately identify the small proportion of patients with typically non-specific symptoms who have cancer. Such symptoms do not easily segregate by organ system, necessitating a multi-cancer approach. In this large-scale study (n = 2094 patients) we applied the Dxcover® Cancer Liquid Biopsy to differentiate cancer against non-cancer patients, 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 (FTIR) spectroscopy to analyze all macromolecules in a minute volume of patient serum, and machine learning algorithms to build a classifier of the resultant spectral profiles to detect cancer. This approach can be fine-tuned to maximize either sensitivity or specificity depending on the requirements from different healthcare systems and cancer diagnostic pathways. The cancer v asymptomatic non-cancer classification detected 64% of stage I cancers when specificity was 99% (overall sensitivity 56%). When tuned for higher sensitivity, this model identified 99% of stage I cancers (while specificity was 58%). When examining cancer against all non-cancer (including symptomatic patients), the sensitivity-tuned model enabled 90% sensitivity with 61% specificity, with detection rates of 93% for stage I, 84% for stage II, 92% for stage III and 95% for stage IV. For organ specific cancer classifiers, area under the receiver operating characteristic (ROC) curve values were calculated for all cancers: brain (0.90), breast (0.75), colorectal (0.91), kidney (0.91), lung (0.91), ovarian (0.86), pancreatic (0.85) and prostate (0.86). Cancer treatment is more effective when given earlier and this low-cost strategy can facilitate the requisite earlier diagnosis.
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