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Background: Early detection of cancer is vital to improve patient survival, since earlier diagnosis and treatment maximizes the opportunity to combat or control disease progression. Specifically for colorectal cancer (CRC), the average 5-survival rate after diagnosis decreases from 91% in early-stage CRC, to as low as 15% for stage IV CRC. Furthermore, the rapid detection and subsequent removal of pre-cancerous adenomas - e.g., advanced adenomas (AA) - can improve survival rates of affected patients. Current stool-based tests, such as FIT and FIT-DNA, have poor AA sensitivities of 24% and 42%, and specificities of 95% and 87%, respectively. Recent research into liquid biopsies have shown some promise, however tests based on tumor-derived biomarkers alone have limited sensitivity, especially in early-stage disease. Methods: We initially applied the Dxcover® Cancer Liquid Biopsy for use as a multi-cancer early detection (MCED) test. The test uses Fourier transform infrared (FTIR) spectroscopy and machine learning algorithms to build a classifier of the resultant spectral profiles to detect cancer, and can be fine-tuned to maximize either sensitivity or specificity depending on the requirements of specific international healthcare systems. Additionally, we have analyzed a retrospective cohort of serum samples comprising 100 CRC, 99 advanced adenomas removed by surgical resection and 97 colonoscopy screening controls. Results: The CRC classifier from the discovery MCED dataset reported an area under the receiver operating characteristic curve (AUROC) value of 0.91, with 74% sensitivity and 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. When tuned for higher sensitivity, the model produced 97% sensitivity (49% specificity), and when tailored for greater specificity (97%) the sensitivity was 47%. In this study, we have progressed these findings to examine the ability of the technology to differentiate patients with CRC, AA and colonoscopy controls. Conclusions: Cancer treatment is more effective when given earlier and this low-cost strategy can facilitate the requisite earlier diagnosis. A rapid blood test that sensitive to AA and early-stage CRC could improve patient prognosis and ultimately reduce mortality. Citation Format: James M. Cameron, Georgios Antoniou, Paul M. Brennan, Justin J. Conn, Sharon King, Rose G. McHardy, Susan Moug, Jennifer Nobes, David S. Palmer, Alexandra Sala, Benjamin R. Smith, Judith Strachan, Craig Mowat, Matthew J. Baker. Early colorectal cancer detection with a spectroscopic liquid biopsy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6506.

To support the early detection and diagnosis of brain tumors we have developed a rapid, cost-effective and easy to use spectroscopic liquid biopsy based on the absorbance of infrared radiation. We have previously reported highly sensitive results of our approach which can discriminate patients with a recent brain tumor diagnosis and asymptomatic controls. Other liquid biopsy approaches (e.g., based on tumor genetic material) report a lower classification accuracy for early-stage tumors. In this manuscript we present an investigation into the link between brain tumor volume and liquid biopsy test performance. In a cohort of 177 patients (90 patients with high-grade glioma (glioblastoma (GBM) or anaplastic astrocytoma), or low-grade glioma (astrocytoma, oligo-astrocytoma and oligodendroglioma)) tumor volumes were calculated from magnetic resonance imaging (MRI) investigations and patients were split into two groups depending on MRI parameters (T1 with contrast enhancement or T2/FLAIR (fluid-attenuated inversion recovery)). Using attenuated total reflection (ATR)-Fourier transform infrared (FTIR) spectroscopy coupled with supervised learning methods and machine learning algorithms, 90 tumor patients were stratified against 87 control patients who displayed no symptomatic indications of cancer, and were classified as either glioma or non-glioma. Sensitivities, specificities and balanced accuracies were all greater than 88%, the area under the curve (AUC) was 0.98, and cancer patients with tumor volumes as small as 0.2 cm3 were correctly identified. Our spectroscopic liquid biopsy approach can identify gliomas that are both small and low-grade showing great promise for deployment of this technique for early detection and diagnosis. Citation Format: Alexandra Sala, Ashton G. Theakstone, Paul M. Brennan, Michael D. Jenkinson, Samantha J. Mills, Khaja Syed, Christopher Rinaldi, Yun Xu, Royston Goodacre, Holly J. Butler, David S. Palmer, Benjamin R. Smith, Matthew J. Baker. Rapid spectroscopic liquid biopsy for the early detection of brain tumors [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 5923.

Diagnostic delays impact the quality of life and survival of patients with brain tumors. Earlier and expeditious diagnoses in these patients are crucial to reducing the morbidities and mortalities associated with brain tumors. A simple, rapid blood test that can be administered easily in a primary care setting to efficiently identify symptomatic patients who are most likely to have a brain tumor would enable quicker referral to brain imaging for those who need it most. Blood serum samples from 603 patients were prospectively collected and analyzed. Patients either had non-specific symptoms that could be indicative of a brain tumor on presentation to the Emergency Department, or a new brain tumor diagnosis and referral to the neurosurgical unit, NHS Lothian, Scotland. Patient blood serum samples were analyzed using the Dxcover® Brain Cancer liquid biopsy. This technology utilizes infrared spectroscopy combined with a diagnostic algorithm to predict the presence of intracranial disease. Our liquid biopsy approach reported an area under the receiver operating characteristic curve of 0.8. The sensitivity-tuned model achieves a 96% sensitivity with 45% specificity (NPV 99.3%) and identified 100% of glioblastoma multiforme patients. When tuned for a higher specificity, the model yields sensitivity of 47% with 90% specificity (PPV 28.4%). This simple, non-invasive blood test facilitates the triage and radiographic diagnosis of brain tumor patients, while providing reassurance to healthy patients. Minimizing time to diagnosis would facilitate identification of brain tumor patients at an earlier stage, enabling more effective, less morbid surgical and adjuvant care. Citation Format: James M. Cameron, Paul M. Brennan, Georgios Antoniou, Holly J. Butler, Loren Christie, Justin J.A. Conn, Tom Curran, Ewan Gray, Mark G. Hegarty, Michael Jenkinson, Daniel Orringer, David S. Palmer, Alexandra Sala, Benjamin R. Smith, Matthew J. Baker. Clinical validation of a spectroscopic liquid biopsy for early detection of brain cancer [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 5922.

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 first method used for detection of pancreatic cancer; although, with poor positive predictive values reported, it cannot provide certain information about the presence of a pancreatic tumor or other surrounding tumors in symptomatic patients. 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 was focused on the discrimination between both cancer versus healthy control samples, and cancer versus symptomatic control samples from patients with comorbidities and/or confounding diseases. The study aimed to investigate the use of the Dxcover® cancer platform as a novel approach for pancreatic cancer detection. Various machine learning algorithms were applied to discriminate between cancer (n=100) and healthy control samples (n=100), achieving results amounting to a sensitivity of 91.0 ± 5.6%, specificity of 87.6 ± 5.8%, and accuracy of 89.3 ± 3.8% with partial least squares-discriminant analysis (PLS-DA). Moreover, an area under the curve (AUC) equal to 0.9536 was obtained through receiving operating characteristic (ROC) analysis. The same algorithms were also applied to discriminate between cancer (n=35) and symptomatic control samples (n=35) achieving a balanced sensitivity and specificity over 75% with an AUC of 0.8436. Both discriminations underwent bootstrapping validation and were proven statistically significant. Herein, we present these results and demonstrate that ATR-FTIR spectroscopic analysis of serum is a cost-effective, minimally invasive, highly sensitive and specific test for detection of pancreatic cancer. Citation Format: Alexandra Sala, James M. Cameron, Cerys A. Jenkins, Hugh Barr, Loren Christie, Justin J. Conn, Thomas (Jeff) R. Evans, Dean A. Harris, David S. Palmer, Christopher Rinaldi, Ashton G. Theakstone, Matthew J. Baker. Machine learning based detection of pancreatic tumors using the Dxcover® cancer 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 5921.

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.