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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.

Background Diagnostic delays impact the quality of life and survival of patients with brain tumors. Earlier and expeditious diagnoses in these patients are crucial to reduce 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. Methods 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. Results 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 a sensitivity of 47% with 90% specificity (PPV 28.4%). Conclusions 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 the identification of brain tumor patients at an earlier stage, enabling more effective, less morbid surgical and adjuvant care.

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.

Early diagnosis of brain tumours is challenging and a major unmet need. Patients with brain tumours most often present with non-specific symptoms more commonly associated with less serious diagnoses, making it difficult to determine which patients to prioritize for brain imaging. Delays in diagnosis affect timely access to treatment, with potential impacts on quality of life and survival. A test to help identify which patients with non-specific symptoms are most likely to have a brain tumour at an earlier stage would dramatically impact on patients by prioritizing demand on diagnostic imaging facilities. This clinical feasibility study of brain tumour early diagnosis was aimed at determining the accuracy of our novel spectroscopic liquid biopsy test for the triage of patients with non-specific symptoms that might be indicative of a brain tumour, for brain imaging. Patients with a suspected brain tumour based on assessment of their symptoms in primary care can be referred for open access CT scanning. Blood samples were prospectively obtained from 385 of such patients, or patients with a new brain tumour diagnosis. Samples were analysed using our spectroscopic liquid biopsy test to predict presence of disease, blinded to the brain imaging findings. The results were compared to the patient’s index brain imaging delivered as per standard care. Our test predicted the presence of glioblastoma, the most common and aggressive brain tumour, with 91% sensitivity, and all brain tumours with 81% sensitivity, and 80% specificity. Negative predictive value was 95% and positive predictive value 45%. The reported levels of diagnostic accuracy presented here have the potential to improve current symptom-based referral guidelines, and streamline assessment and diagnosis of symptomatic patients with a suspected brain tumour., Brennan et al. show a rapid, low-cost blood test can identify which patients with suspected brain tumour to prioritize for diagnostic imaging, reducing time to diagnosis. The test is more than 90% sensitive for the most aggressive tumours. Earlier diagnosis can provide health and economic benefits., Graphical Abstract Graphical Abstract

Objectives An early economic evaluation to inform the translation into clinical practice of a spectroscopic liquid biopsy for the detection of brain cancer. Two specific aims are (1) to update an existing economic model with results from a prospective study of diagnostic accuracy and (2) to explore the potential of brain tumor-type predictions to affect patient outcomes and healthcare costs. Methods A cost-effectiveness analysis from a UK NHS perspective of the use of spectroscopic liquid biopsy in primary and secondary care settings, as well as a cost–consequence analysis of the addition of tumor-type predictions was conducted. Decision tree models were constructed to represent simplified diagnostic pathways. Test diagnostic accuracy parameters were based on a prospective validation study. Four price points (GBP 50-200, EUR 57-228) for the test were considered. Results In both settings, the use of liquid biopsy produced QALY gains. In primary care, at test costs below GBP 100 (EUR 114), testing was cost saving. At GBP 100 (EUR 114) per test, the ICER was GBP 13,279 (EUR 15,145), whereas at GBP 200 (EUR 228), the ICER was GBP 78,300 (EUR 89,301). In secondary care, the ICER ranged from GBP 11,360 (EUR 12,956) to GBP 43,870 (EUR 50,034) across the range of test costs. Conclusions The results demonstrate the potential for the technology to be cost-effective in both primary and secondary care settings. Additional studies of test use in routine primary care practice are needed to resolve the remaining issues of uncertainty—prevalence in this patient population and referral behavior.

Patients living with brain tumours have the highest average years of life lost of any cancer, ultimately reducing average life expectancy by 20 years. Diagnosis depends on brain imaging and most often confirmatory tissue biopsy for histology. The majority of patients experience non-specific symptoms, such as headache, and may be reviewed in primary care on multiple occasions before diagnosis is made. Sixty-two per cent of patients are diagnosed on brain imaging performed when they deteriorate and present to the emergency department. Histological diagnosis from invasive surgical biopsy is necessary prior to definitive treatment, because imaging techniques alone have difficulty in distinguishing between several types of brain cancer. However, surgery itself does not necessarily control tumour growth, and risks morbidity for the patient. Due to their similar features on brain scans, glioblastoma, primary central nervous system lymphoma and brain metastases have been known to cause radiological confusion. Non-invasive tests that support stratification of tumour subtype would enhance early personalisation of treatment selection and reduce the delay and risks associated with surgery for many patients. Techniques involving vibrational spectroscopy, such as attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy, have previously demonstrated analytical capabilities for cancer diagnostics. In this study, infrared spectra from 641 blood serum samples obtained from brain cancer and control patients have been collected. Firstly, we highlight the capability of ATR-FTIR to distinguish between healthy controls and brain cancer at sensitivities and specificities above 90%, before defining subtle differences in protein secondary structures between patient groups through Amide I deconvolution. We successfully differentiate several types of brain lesions (glioblastoma, meningioma, primary central nervous system lymphoma and metastasis) with balanced accuracies &gt, 80%. A reliable blood serum test capable of stratifying brain tumours in secondary care could potentially avoid surgery and speed up the time to definitive therapy, which would be of great value for both neurologists and patients.

Over a third of brain tumour patients visit their general practitioner more than five times prior to diagnosis in the UK, leading to 62% of patients being diagnosed as emergency presentations. Unfortunately, symptoms are non-specific to brain tumours, and the majority of these patients complain of headaches on multiple occasions before being referred to a neurologist. As there are currently no methods in place for the early detection of brain cancer, the affected patients' average life expectancy is reduced by 20 years. These statistics indicate that the current pathway is ineffective, and there is a vast need for a rapid diagnostic test. Attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy is sensitive to the hallmarks of cancer, as it analyses the full range of macromolecular classes. The combination of serum spectroscopy and advanced data analysis has previously been shown to rapidly and objectively distinguish brain tumour severity. Recently, a novel high-throughput ATR accessory has been developed, which could be cost-effective to the National Health Service in the UK, and valuable for clinical translation. In this study, 765 blood serum samples have been collected from healthy controls and patients diagnosed with various types of brain cancer, contributing to one of the largest spectroscopic studies to date. Three robust machine learning techniques - random forest, partial least squares-discriminant analysis and support vector machine - have all provided promising results. The novel high-throughput technology has been validated by separating brain cancer and non-cancer with balanced accuracies of 90% which is comparable to the traditional fixed diamond crystal methodology. Furthermore, the differentiation of brain tumour type could be useful for neurologists, as some are difficult to distinguish through medical imaging alone. For example, the highly aggressive glioblastoma multiforme and primary cerebral lymphoma can appear similar on magnetic resonance imaging (MRI) scans, thus are often misdiagnosed. Here, we report the ability of infrared spectroscopy to distinguish between glioblastoma and lymphoma patients, at a sensitivity and specificity of 90.1% and 86.3%, respectively. A reliable serum diagnostic test could avoid the need for surgery and speed up time to definitive chemotherapy and radiotherapy.

Non-specific symptoms, as well as the lack of a cost-effective test to triage patients in primary care, has resulted in increased time-to-diagnosis and a poor prognosis for brain cancer patients. A rapid, cost-effective, triage test could significantly improve this patient pathway. A blood test using attenuated total reflection (ATR)-Fourier transform infrared (FTIR) spectroscopy for the detection of brain cancer, alongside machine learning technology, is advancing towards clinical translation. However, whilst the methodology is simple and does not require extensive sample preparation, the throughput of such an approach is limited. Here we describe the development of instrumentation for the analysis of serum that is able to differentiate cancer and control patients at a sensitivity and specificity of 93.2% and 92.8%. Furthermore, preliminary data from the first prospective clinical validation study of its kind are presented, demonstrating how this innovative technology can triage patients and allow rapid access to imaging., Diagnosing brain cancer is frequently difficult and requires specialist equipment. Here, the authors develop their previous attenuated total reflectance-Fourier transform infrared spectroscopy method and incoporate the use of disposable silicon wafers for diagnosing brain cancer using serum samples.

Simple Summary Gliomas represent the vast majority of primary brain tumours and are of significant medical importance due to the poor clinical course of affected patients. The isocitrate dehydrogenase 1 (IDH1) mutation is associated with improved prognosis, compared to patients with IDH1-wildtype lesions of the same stage. In this proof-of-concept study, Fourier transform infrared spectroscopy was used to determine the IDH1 molecular status in fixed glioma sections. Classification algorithms successfully distinguished the two IDH1 classes with high accuracies (>80%). Knowledge of the IDH1 status would be beneficial, as maximum resection may be preferred in patients with IDH1-mutant gliomas, whilst a more limited resection can be best for IDH1-wildtype gliomas. Furthermore, we examined blood serum in an attempt to identify the biomolecular alterations caused by the IDH1 mutation. Non-invasive approaches that can detect the molecular status may guide some patients to an alternative treatment prior to surgery. Abstract Mutations in the isocitrate dehydrogenase 1 (IDH1) gene are found in a high proportion of diffuse gliomas. The presence of the IDH1 mutation is a valuable diagnostic, prognostic and predictive biomarker for the management of patients with glial tumours. Techniques involving vibrational spectroscopy, e.g., Fourier transform infrared (FTIR) spectroscopy, have previously demonstrated analytical capabilities for cancer detection, and have the potential to contribute to diagnostics. The implementation of FTIR microspectroscopy during surgical biopsy could present a fast, label-free method for molecular genetic classification. For example, the rapid determination of IDH1 status in a patient with a glioma diagnosis could inform intra-operative decision-making between alternative surgical strategies. In this study, we utilized synchrotron-based FTIR microanalysis to probe tissue microarray sections from 79 glioma patients, and distinguished the positive class (IDH1-mutated) from the IDH1-wildtype glioma, with a sensitivity and specificity of 82.4% and 83.4%, respectively. We also examined the ability of attenuated total reflection (ATR)-FTIR spectroscopy in detecting the biomolecular events and global epigenetic and metabolic changes associated with mutations in the IDH1 enzyme, in blood serum samples collected from an additional 72 brain tumour patients. Centrifugal filtration enhanced the diagnostic ability of the classification models, with balanced accuracies up to ~69%. Identification of the molecular status from blood serum prior to biopsy could further direct some patients to alternative treatment strategies.

Objectives To determine the potential costs and health benefits of a serum-based spectroscopic triage tool for brain tumours, which could be developed to reduce diagnostic delays in the current clinical pathway. Design A model-based health pre-trial economic assessment. Decision tree models were constructed based on simplified diagnostic pathways. Models were populated with parameters identified from rapid reviews of the literature and clinical expert opinion. Setting Explored as a test in both primary and secondary care (neuroimaging) in the UK health service, as well as application to the USA. Participants Calculations based on an initial cohort of 10 000 patients. In primary care, it is estimated that the volume of tests would approach 75 000 per annum. The volume of tests in secondary care is estimated at 53 000 per annum. Main outcome measures The primary outcome measure was quality-adjusted life-years (QALY), which were employed to derive incremental cost-effectiveness ratios (ICER) in a cost-effectiveness analysis. Results Results indicate that using a blood-based spectroscopic test in both scenarios has the potential to be highly cost-effective in a health technology assessment agency decision-making process, as ICERs were well below standard threshold values of £20 000–£30 000 per QALY. This test may be cost-effective in both scenarios with test sensitivities and specificities as low as 80%; however, the price of the test would need to be lower (less than approximately £40). Conclusion Use of this test as triage tool in primary care has the potential to be both more effective and cost saving for the health service. In secondary care, this test would also be deemed more effective than the current diagnostic pathway.

It has recently been demonstrated that a spectroscopic test using blood-serum is able to effectively identify brain tumours in patients with sensitivities and specificities as high as 91.5% and 83% respectively. The approach employs Attenuated Total Reflectance - Fourier Transform Infrared (ATR-FTIR) spectroscopy to derive a disease specific spectroscopic signature, that using pattern recognition and machine learning algorithms is able to identify brain tumour cases. The ability to identify brain cancer cases based on serum samples alone raises the possibility of systematic screening prior to investigation with more expensive (MRI/CT imaging) and invasive (biopsy) tests. However, current methodology is currently limited to single patient analysis due to limitations in standard instrumentation. Here, we investigate the performance of a novel ATR-FTIR spectroscopic approach that allows automated, disposable, and multi-patient sampling. Our results indicate that this optimised approach is able to stratify brain tumour patients at performances equivalent to previous approaches, with sensitivities and specificities as high as 94.56% and 87.65% respectively. In advance of any prospective clinical study results being available, a cost-effectiveness analysis (CEA) was conducted to calculate the effects on health outcomes and health service costs of introducing this technology as a triage tool in primary and secondary care. Results indicate that using a serum spectroscopy test for brain tumours in both scenarios may be considered highly cost-effective in an HTA agency decision making process. Incremental cost-effectiveness ratios were well below standard threshold values of £20,000 to £30,000 per quality adjusted life year (QALY) gained used in the UK, and similar thresholds used internationally. In primary care, the test has the potential to be both more effective and cost saving for the health service.