Top ten research priorities for detecting cancer early

Badrick, Ellena; Cresswell, Katharine; Ellis, Patricia; Renehan, Andrew G.; Crosbie, Emma; Crosbie, Philip; Hall, Peter; O’Flynn, Helena; Martin, Richard M; Leighton, James L.; Brown, Lindsey; Makin, David A.; Morris, Rebecca; Thorpe, Emma; Dickinson, Joanne Clare; Buckley, Genevieve

doi:10.1016/s2468-2667(19)30185-9

Cited by 49 publications

(55 citation statements)

References 3 publications

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“…"What simple, non-invasive, painless, costeffective, and convenient tests can be used to detect cancer early?" ranked first in the top ten research priorities for early cancer detection by the James Lind Alliance partnership representing patients, carers, and clinician groups (29). A similar study exploring unmet research needs in EC, found "which women with abnormal bleeding require urgent specialist referral and which can be safely reassured" to be second most important priority (30).…”

Section: The Diagnosis Of Endometrial Cancermentioning

confidence: 99%

Urinary Biomarkers and Their Potential for the Non-Invasive Detection of Endometrial Cancer

et al. 2020

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Endometrial cancer is the most common malignancy of the female genital tract and its incidence is rising in parallel with the mounting prevalence of obesity. Early diagnosis has great potential to improve outcomes as treatment can be curative, especially for early stage disease. Current tests and procedures for diagnosis are limited by insufficient accuracy in some and unacceptable levels of invasiveness and discomfort in others. There has, therefore, been a growing interest in the search for sensitive and specific biomarkers for endometrial cancer detection based on non-invasive sampling methodologies. Urine, the prototype non-invasive sample, is attractive for biomarker discovery as it is easily accessible and can be collected repeatedly and in quantity. Identification of urinary biomarkers for endometrial cancer detection relies on the excretion of systemic biomarkers by the kidneys or urinary contamination by biomarkers shed from the uterus. In this review, we present the current standing of the search for endometrial cancer urinary biomarkers based on cytology, genomic, transcriptomic, proteomic, and metabolomic platforms. We summarize the biomarker candidates and highlight the challenges inherent in urinary biomarker discovery. We review the various technologies with promise for biomarker detection and assess these novel approaches for endometrial cancer biomarker research.

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Section: The Diagnosis Of Endometrial Cancermentioning

confidence: 99%

Urinary Biomarkers and Their Potential for the Non-Invasive Detection of Endometrial Cancer

et al. 2020

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“…Blood is an ideal source of biomarkers as it is readily available and its use is likely to be acceptable to both clinicians and patients [ 70 ]. “Can a blood test be used to detect some or all cancers?” ranked second in our recently completed James Lind Alliance Priority Setting Partnership for Detecting Cancer Early, confirming the appeal of a “cancer blood test” to both clinicians and patients [ 15 ]. Several blood-based metabolites have been suggested as potential EC biomarkers and can be broadly classified into amino acids and their derivatives including biogenic amines, acylcarnitines, free fatty acids, phospholipids, sphingolipids, hexoses and hormone metabolites, among others ( Table 1 ) [ 71 , 72 ].…”

Section: Endometrial Cancer Metabolomic Biomarkers In Biofluidsmentioning

confidence: 88%

“…The ideal test should be simple, robust and accurate. “What simple, non-invasive, painless and convenient tests can be used to detect cancer early?” ranked as the most important research priority for the early detection of cancer in the UK-focused research gap analysis led by the James Lind Alliance partnership, representing patients, carers and health professionals [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Metabolomic Biomarkers for Detection, Prognosis and Identifying Recurrence in Endometrial Cancer

et al. 2020

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Metabolic reprogramming is increasingly recognised as one of the defining hallmarks of tumorigenesis. There is compelling evidence to suggest that endometrial cancer develops and progresses in the context of profound metabolic dysfunction. Whilst the incidence of endometrial cancer continues to rise in parallel with the global epidemic of obesity, there are, as yet, no validated biomarkers that can aid risk prediction, early detection, prognostic evaluation or surveillance. Advances in high-throughput technologies have, in recent times, shown promise for biomarker discovery based on genomic, transcriptomic, proteomic and metabolomic platforms. Metabolomics, the large-scale study of metabolites, deals with the downstream products of the other omics technologies and thus best reflects the human phenotype. This review aims to provide a summary and critical synthesis of the existing literature with the ultimate goal of identifying the most promising metabolite biomarkers that can augment current endometrial cancer diagnostic, prognostic and recurrence surveillance strategies. Identified metabolites and their biochemical pathways are discussed in the context of what we know about endometrial carcinogenesis and their potential clinical utility is evaluated. Finally, we underscore the challenges inherent in metabolomic biomarker discovery and validation and provide fresh perspectives and directions for future endometrial cancer biomarker research.

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“…The work presented in this paper addresses the top three priority areas identified by Badrick et al (2019), including: a simple, non-invasive, painless and convenient test to detect cancer early; a blood test to detect some or all cancers early that can be included into routine care; and a test that is easily accessible to General Practice. 10 We report the development and validation of a set of machine learning algorithms to provide a calibrated risk probability of cancer (a score between zero and one, higher values indicating greater risk of cancer) for triaging symptomatic patients. A calibrated risk probability has a variety of clinical uses.…”

Section: Introductionmentioning

confidence: 99%

Development and validation of multivariable machine learning algorithms to predict risk of cancer in symptomatic patients referred urgently from primary care

Savage

Messenger

Neal

et al. 2020

Preprint

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Background: Urgent Suspected Cancer (2WW) referrals have been successful in improving early cancer detection but are increasingly a major burden on NHS services. This has been exacerbated by the COVID-19 pandemic. Method: We developed and validated tests to assess the risk of any cancer for 2WW patients. The tests use routine blood measurements (FBC, U&E, LFTs, tumour markers), combining them using machine learning and statistical modelling. Algorithms were developed and validated for nine 2WW pathways using retrospective data from 371,799 referrals to Leeds Teaching Hospitals Trust (development set 2011-16, validation set 2017-19). A minimum set of blood measurements were required for inclusion, and missing data were modelled internally by the algorithms. Findings: We present results for two clinical use-cases. In use-case 1, the algorithms correctly identify 20% of patients who do not have cancer and may not need an urgent 2WW referral. In use-case 2, they identify 90% of cancer cases with a high probability of cancer that could be prioritised for review. Interpretation: Combining a panel of widely available blood markers produces effective blood tests for cancer for NHS 2WW patients. The tests are cost-effective, can be deployed rapidly to any NHS pathology laboratory with no additional hardware requirements, and are of particular value during the COVID-19 pandemic.

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Top ten research priorities for detecting cancer early

Cited by 49 publications

References 3 publications

Urinary Biomarkers and Their Potential for the Non-Invasive Detection of Endometrial Cancer

Urinary Biomarkers and Their Potential for the Non-Invasive Detection of Endometrial Cancer

Metabolomic Biomarkers for Detection, Prognosis and Identifying Recurrence in Endometrial Cancer

Development and validation of multivariable machine learning algorithms to predict risk of cancer in symptomatic patients referred urgently from primary care

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