Lack of predictive tools for conventional and targeted cancer therapy: Barriers to biomarker development and clinical translation

Batis, Nikolaos; Brooks, Jill; Payne, Karl; Sharma, Neil; Nankivell, Paul; Mehanna, Hisham

doi:10.1016/j.addr.2021.113854

Cited by 19 publications

(9 citation statements)

References 84 publications

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“…Biomarkers predictive of immunotherapy response have great potential to improve treatment selection, effectiveness and cost-bene t, however they have to date not transitioned from discovery to routine clinical application. This failure of translation has been attributed to various factors, including poorly designed and underpowered studies, lack of robust validation studies, and a lack of standardised protocols across various settings (22). Furthermore, a poor biomarker (not sensitive or speci c enough) would potentialy lead to misclassi cation and patients might miss out on an effective therapy if misclassi ed as a non-responder.…”

Section: Discussionmentioning

confidence: 99%

Perspectives of health professionals and patients on implementation of a predictive model of response to immunotherapies in advanced melanoma

Salam

Gide

Cust

et al. 2023

Preprint

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Background Immunotherapies have significantly improved the overall survival for patients with advanced melanoma. However, almost half of such patients either do not respond to the therapy or develop resistance to it, subjecting patients to ineffective treatments and unnecessary costs. Predictive biomarker testing can ensure that the patient receives the most effective therapy thereby reducing costs and toxicities. This study was conducted prior to and alongside a clinical validation study of routine predictive biomarker testing for patients with advanced melanoma to gain an insight into the factors associated with successful implementation of this intervention. Methods We conducted semi-structured interviews (n=25) with health professionals and patients guided by the EPIS (Exploration, Preparation, Implementation, and Sustainment) framework to understand enablers and barriers of implementation. Data analysis involved inductive and deductive thematic analysis using the Consolidated Framework for Implementation Research (CFIR). Results Health providers and patients consistently reported ‘clinical utility of predictive biomarker test’ as a major enabler, recognising that an effective test would assist in identifying likely non-responders and consequently avoid the side effects and other costs of ineffective treatment. Trust in data scientists, adaptability of the test platform, pre-existing organisational infrastructure, and supportive organisational implementation culture were also identified as factors that would support implementation. Lack of validated predictive biomarkers, resources and costs required to implement the test, and health providers’ knowledge, beliefs and concerns around the test were the principal factors that would impede implementation. Conclusion This study identifies factors influencing implementation of biomarkers as predictors of treatment response to immunotherapy for melanoma and potential strategies to overcome barriers impeding their transition from discovery to the clinic.

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Section: Discussionmentioning

confidence: 99%

Perspectives of health professionals and patients on implementation of a predictive model of response to immunotherapies in advanced melanoma

Salam

Gide

Cust

et al. 2023

Preprint

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“…Biomarkers to predict tumor response to classical chemotherapy or endocrine therapy are few, despite being the most extensively used to treat cancer patients [ 142 , 143 ]. Traditional examples of cancer predictive biomarkers are pharmacogenetic-based ones, such as germline variants on TPMT or TYMS genes that estimate the effectiveness/toxicity of treatment with mercaptopurine for leukemia or with fluorouracil for colon, bladder, and gastric carcinoma, respectively (reviewed by Reference [ 144 ]).…”

Section: Clinical Applications Of Cancer Biomarkers: Examplesmentioning

confidence: 99%

“…Therefore, targeted therapies only work in a subset of cancers, those that have the specific alteration for which the therapy was designed. It is important, if not essential, to perform a biomarker assay to identify those individuals who will benefit from therapy in order to increase efficacy and diminish costs [ 142 ]. These kinds of assays are often called companion diagnostics and are usually approved by the regulatory agencies in conjunction with the drug they are paired with.…”

Section: Clinical Applications Of Cancer Biomarkers: Examplesmentioning

confidence: 99%

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Molecular Biomarkers in Cancer

2022

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Molecular cancer biomarkers are any measurable molecular indicator of risk of cancer, occurrence of cancer, or patient outcome. They may include germline or somatic genetic variants, epigenetic signatures, transcriptional changes, and proteomic signatures. These indicators are based on biomolecules, such as nucleic acids and proteins, that can be detected in samples obtained from tissues through tumor biopsy or, more easily and non-invasively, from blood (or serum or plasma), saliva, buccal swabs, stool, urine, etc. Detection technologies have advanced tremendously over the last decades, including techniques such as next-generation sequencing, nanotechnology, or methods to study circulating tumor DNA/RNA or exosomes. Clinical applications of biomarkers are extensive. They can be used as tools for cancer risk assessment, screening and early detection of cancer, accurate diagnosis, patient prognosis, prediction of response to therapy, and cancer surveillance and monitoring response. Therefore, they can help to optimize making decisions in clinical practice. Moreover, precision oncology is needed for newly developed targeted therapies, as they are functional only in patients with specific cancer genetic mutations, and biomarkers are the tools used for the identification of these subsets of patients. Improvement in the field of cancer biomarkers is, however, needed to overcome the scientific challenge of developing new biomarkers with greater sensitivity, specificity, and positive predictive value.

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“…However, in the current treatment, neoadjuvant chemotherapy before surgery has been considered the preferred strategy for operable or non-operable BC ( 3 ). Recurrence or metastasis may occur in these treated BC patients, but we lack effective and reliable predictive biomarkers to guide risk stratification before treatment ( 4 , 5 ). However, these studies’ results are still inconsistent in the efficacy of risk estimation among various tumors, and it is difficult to find accurate estimates of BC diagnosis and metastasis.…”

Section: Introductionmentioning

confidence: 99%

Prognostic Evaluation of Metastasis-Related Lymphocyte/Monocyte Ratio in Stage Ⅰ-Ⅲ Breast Cancer Receiving Chemotherapy

et al. 2022

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PurposeThis study aims to clarify the prognostic significance of metastasis-related indicators in peripheral blood in stage I-III breast cancer (BC).MethodsThe clinicopathological data of 938 breast cancer patients and 509 benign breast disease patients were retrospectively analyzed, and fasting blood samples were collected before treatment. Univariate and multivariate regression analyses were used to evaluate factors related to metastasis risk and prognosis. The Kaplan-Meier method was used to generate survival curves, and the log-rank test was used to measure differences in survival between groups.ResultsUse the cut-off value (3.433) of LMR, the logistic regression analysis revealed that high carbohydrate antigen 153 (CA153), carbohydrate antigen 125 (CA125), carcinoembryonic antigen (CEA), killer T cell level, and low lymphocyte to monocyte ratio (LMR) level were significantly associated with BC distant metastasis. In contrast, LMR>=3.433 (HR: 0.409, 95%CI: 0.193–0.867, P = 0.020), Th/Tc ratio >=1.946 (HR: 0.378, 95% CI: 0.158–0.904, P =0.029) is regarded as a protective factor in the multivariate cox analyses. LMR is an independent prognostic factor for DFS in HER2-negative BC patients.ConclusionPeripheral blood parameters play an important role in predicting distant metastasis and prognosis of BC patients. As a potential marker, LMR can predict the metastasis and prognosis of patients with stage I-III BC.

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Lack of predictive tools for conventional and targeted cancer therapy: Barriers to biomarker development and clinical translation

Abstract: Graphical abstract

Cited by 19 publications

References 84 publications

Perspectives of health professionals and patients on implementation of a predictive model of response to immunotherapies in advanced melanoma

Perspectives of health professionals and patients on implementation of a predictive model of response to immunotherapies in advanced melanoma

Molecular Biomarkers in Cancer

Prognostic Evaluation of Metastasis-Related Lymphocyte/Monocyte Ratio in Stage Ⅰ-Ⅲ Breast Cancer Receiving Chemotherapy

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