Evidence for Treatment-by-Biomarker interaction for FDA-approved Oncology Drugs with Required Pharmacogenomic Biomarker Testing

Vivot, Alexandre; Boutron, Isabelle; Béraud-Chaulet, Geoffroy; Zeitoun, Jean–David; Ravaud, Philippe; Porcher, Raphaël

doi:10.1038/s41598-017-07358-7

Cited by 26 publications

(18 citation statements)

References 34 publications

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“…Biomarkers need to be evaluated in cohorts of patients that are both biomarker-positive and biomarker-negative. Inclusion of both populations, and control subjects with co-morbidities, in addition to healthy controls, can potentially improve approval rates by regulatory agencies such as the United States Food and Drug Administration (FDA) for “treatment-by-biomarker” compounds and companion diagnostic assays [193].…”

Section: Expert Commentarymentioning

confidence: 99%

Protein biomarkers for subtyping breast cancer and implications for future research

Mueller

Haymond

Davis

et al. 2018

Expert Review of Proteomics

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Breast cancer subtypes are currently defined by a combination of morphologic, genomic, and proteomic characteristics. These subtypes provide a molecular portrait of the tumor that aids diagnosis, prognosis, and treatment escalation/de-escalation options. Gene expression signatures describing intrinsic breast cancer subtypes for predicting risk of recurrence have been rapidly adopted in the clinic. Despite the use of subtype classifications, many patients develop drug resistance, breast cancer recurrence, or therapy failure. Areas covered: This review provides a summary of immunohistochemistry, reverse phase protein array, mass spectrometry, and integrative studies that are revealing differences in biological functions within and between breast cancer subtypes. We conclude with a discussion of rigor and reproducibility for proteomic-based biomarker discovery. Expert commentary: Innovations in proteomics, including implementation of assay guidelines and standards, are facilitating refinement of breast cancer subtypes. Proteomic and phosphoproteomic information distinguish biologically functional subtypes, are predictive of recurrence, and indicate likelihood of drug resistance. Actionable, activated signal transduction pathways can now be quantified and characterized. Proteomic biomarker validation in large, well-designed studies should become a public health priority to capitalize on the wealth of information gleaned from the proteome.

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Section: Expert Commentarymentioning

confidence: 99%

Protein biomarkers for subtyping breast cancer and implications for future research

Mueller

Haymond

Davis

et al. 2018

Expert Review of Proteomics

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“…Finally, targeted sample collection in current and future randomised clinical trials (eg, the development programme of etrolizumab and the selective anti‐IL23p19 antibody) could allow treatment‐by‐biomarker interaction and post hoc response prediction analyses, which may promote rapid biomarker discovery. Treatment‐by‐biomarker interaction analysis remains the most straightforward way to establish a predictive biomarker . Most importantly, scientists, clinicians and the pharmaceutical industry need to collaborate by sharing data from existing databases and biobanks.…”

Section: Discussionmentioning

confidence: 99%

Systematic review: predictive biomarkers of therapeutic response in inflammatory bowel disease—personalised medicine in its infancy

Stevens

Matheeuwsen

Lönnkvist

et al. 2018

Aliment Pharmacol Ther

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Summary Background Inflammatory bowel disease (IBD) is characterised by substantial heterogeneity in treatment response. With an expanding number of therapeutic agents, identifying optimal treatment at the patient level remains a major challenge. Aim To systematically review the available literature on predictive biomarkers of therapeutic response in IBD. Methods An electronic literature search was performed on 30 January 2018 using MEDLINE, EMBASE and the Cochrane Library. Retrospective, prospective, uncontrolled and controlled studies reporting on biomarkers predicting therapeutic response in paediatric and adult IBD populations were eligible for inclusion. The methodological quality of the included studies was assessed using the QUIPS tool. Due to anticipated heterogeneity and limited data, a qualitative, rather than quantitative, assessment was planned. Results Of the 10 638 citations identified, 92 articles met the inclusion criteria. Several potential DNA, mRNA and protein markers were evaluated as predictive biomarkers. Most studies focused on predicting response to anti‐TNF agents. Substantial between‐study heterogeneity was identified with respect to both the biomarkers studied and the definition of response. None of the included studies received a low risk of bias rating for all six domains. Currently, none of the biomarkers is sufficiently predictive for clinical use. Conclusions The search for predictive biomarkers is still in its infancy and current evidence is limited. Future research efforts should take into account the high patient heterogeneity within prospective trials with objective response assessment. Predictive models will most likely comprise a combination of several molecular markers from integrated omics‐levels and clinical characteristics.

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“…8 Anticancer agents are increasingly being combined with a biomarker to determine which patients are the most likely to benefit from the therapy. 9 This increase in complexity, coupled with the limits of human cognitive capacity, poses a major challenge for the oncology community.…”

Section: The Challengementioning

confidence: 99%

Decision Support Systems in Oncology

Walsh

Jong

Timmeren

et al. 2019

JCO Clinical Cancer Informatics

108

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Precision medicine is the future of health care: please watch the animation at https://vimeo.com/241154708. As a technology-intensive and -dependent medical discipline, oncology will be at the vanguard of this impending change. However, to bring about precision medicine, a fundamental conundrum must be solved: Human cognitive capacity, typically constrained to five variables for decision making in the context of the increasing number of available biomarkers and therapeutic options, is a limiting factor to the realization of precision medicine. Given this level of complexity and the restriction of human decision making, current methods are untenable. A solution to this challenge is multifactorial decision support systems (DSSs), continuously learning artificial intelligence platforms that integrate all available data—clinical, imaging, biologic, genetic, cost—to produce validated predictive models. DSSs compare the personalized probable outcomes—toxicity, tumor control, quality of life, cost effectiveness—of various care pathway decisions to ensure optimal efficacy and economy. DSSs can be integrated into the workflows both strategically (at the multidisciplinary tumor board level to support treatment choice, eg, surgery or radiotherapy) and tactically (at the specialist level to support treatment technique, eg, prostate spacer or not). In some countries, the reimbursement of certain treatments, such as proton therapy, is already conditional on the basis that a DSS is used. DSSs have many stakeholders—clinicians, medical directors, medical insurers, patient advocacy groups—and are a natural consequence of big data in health care. Here, we provide an overview of DSSs, their challenges, opportunities, and capacity to improve clinical decision making, with an emphasis on the utility in oncology.

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Evidence for Treatment-by-Biomarker interaction for FDA-approved Oncology Drugs with Required Pharmacogenomic Biomarker Testing

Cited by 26 publications

References 34 publications

Protein biomarkers for subtyping breast cancer and implications for future research

Protein biomarkers for subtyping breast cancer and implications for future research

Systematic review: predictive biomarkers of therapeutic response in inflammatory bowel disease—personalised medicine in its infancy

Decision Support Systems in Oncology

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