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Press Releases

Take a look at a selection of our recent media coverage:

NMR-based metabolomic biomarkers help identify cancer in patients with non-specific symptoms

11th January 2022

NMR-based metabolomic biomarkers based on specific patterns can assist in the diagnosis of cancer in patients with non-specific symptoms

NMR-based metabolomic biomarkers which identify specific disease patterns, can be used to assist in the diagnosis of cancer in patients who present with non-specific signs and symptoms and even distinguish between those with and without metastatic disease. This was an important finding by a team from the Department of Oncology, University of Oxford, United Kingdom.

The earlier most cancers are detected, the better the prognosis. For example, colorectal cancer, when identified at stage 1 has a 97.7% survival which falls to only 43.9% if detected at stage 4. Although there are often classic symptoms and signs of a possible cancer, e.g. palpable abnormalities such as a breast lump or haematuria, diagnosis becomes more difficult where the has non-specific symptoms such as fatigue.

One potential solution to the diagnosis of a cancer in patients who have non-specific signs and symptoms is metabolomics which can rapidly supply information on thousands of molecules and hence serve as a biofluid-based diagnostic method. The technique aims to comprehensively identify endogenous metabolites in biological systems, providing a complete biochemical phenotype of a cell, tissue, or whole organism, using established analytical techniques such as nuclear magnetic resonance spectroscopy (NMR) or gas chromatography-mass spectrometry (GC-MS). Using an NMR-based metabolomic approach, the Oxford team had previously and successfully detected tumours at the micro metastatic stage based on analysis of urine metabolomics.

For the present study, they hypothesised that biomarkers within the blood metabolome could identify patients referred from primary care with suspected cancer but largely non-specific symptoms, or those deemed to be at ‘low risk, but not no-risk’. In other words, the researchers felt that they would be ale to distinguish between those with and without a cancer and even to identify patients with metastatic disease.

They recruited patients aged 40 years and over who were not referred under the specific ‘2-week wait’ cancer specific pathway and those with one of the following symptoms: unexplained weight loss, severe unexplained fatigue, persistent nausea or appetite loss, new atypical pain, an unexplained laboratory finding or finally, where the primary care physician had a suspicion (i.e., ‘gut feeling’) of cancer. Prior to the metabolomics analysis, patients were randomised into a modelling set and an independent test set which was used to determine the ability of the models to classify new patients.

Blood samples were collected and analysed by NMR-based metabolomics and receiver operator characteristic curves were constructed and the area under the curves (AUC) examined.

Findings

A total of 284 patients with a mean age of 68 years (57% male) were included in the analysis. The most common reasons for referral were weight loss (64%), ‘gut feeling’ of the referring physician (63%), unexplained laboratory results (37%), fatigue (29%), non-specific pain (28%) and nausea/appetite loss (27%). On average, referred patients had at least two of these symptoms.

For distinguishing between patients who were unwell with the above non-specific symptoms and those with a solid tumour diagnosis, the modelling plasma metabolome had an AUC of 0.91 and showed a sensitivity of 94% (95% CI 73 – 99) and a specificity of 82% (95% CI 75 – 87) at detecting cancer.

For the identification set, the AUC was 0.83 giving a sensitivity of 71% and a specificity of 70%. In addition, the model showed a sensitivity of 94% and a specificity of 88% for distinguishing between metastatic and non-metastatic disease.

Interestingly, the authors also examined whether the metabolomics model could identify early-stage cancers before conventional imaging and found that this was possible for 2 out of 5 patients.

Although a preliminary study, the authors concluded that NMR-based metabolomics represented a sensitive and specific means for the identification of solid organ tumours in patients with non-specific symptoms, who have been traditionally hard to diagnosis. They called for the technique to be tested in a larger cohort of patients.

Citation

Larkin JR et al. Metabolomic Biomarkers in Blood Samples Identify Cancers in a Mixed Population of Patients with Nonspecific Symptoms Clin Cancer Res 2022

FDA approves AI software to aid detection of prostate cancer

28th September 2021

AI software designed to identify an area on a prostate biopsy image with a high likelihood of cancer has received FDA approval.

Prostate cancer is the second most common cancer in men, with 1.3 million new cases recorded in 2018. Confirmation of a prostate cancer diagnosis can only be achieved via biopsy and subsequent examination of digitalised slides of the biopsy. Now, the first artificial intelligence (AI) software for in vitro diagnostic detection cancer in prostate biopsies has been approved by the FDA in the US. The software is designed to identify an area of interest on the prostate biopsy image with the highest likelihood of harbouring cancer. This alerts the pathologist if the area of concern has not been noticed on their initial review and thus can assist them in their overall assessment of the biopsy slides.

The AI system approved is Paige Prostate and it is anticipated to increase the number of identified prostate biopsy samples with cancerous tissue and ultimately save lives. The FDA approval was based on a study of Paige Prostate undertaken with three pathologists. In the study, which was conducted in two phases, each pathologist was required to assess 232 anonymised whole slide images and asked to dichotomise these as either cancerous or benign, with only 93 slides (40%) that were in fact cancerous. In the first phase, the pathologists assessed the scans alone, whereas in the second phase, 4-weeks later, the same scans were reviewed but this time using the AI software, Paige Prostate.

Findings

In the study, the Paige Prostate software alone, had a sensitivity for detecting cancer of 96% and a specificity of 98%. Without the use of Paige Prostate, the pathologists averaged a sensitivity of 74% but with the addition of the AI software, their average sensitivity increased significantly to 90% (p < 0.001). Addition of Paige Prostate mainly improved pathologists’ detection of grade 1 to 3 cancers. However, despite a greater sensitivity from the use of Paige Prostate, there was no significant difference in specificity (p = 0.327) since this was already high at an average of 97% without Paige Prostate.

Source. FDA Press release September 2021