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Enregistrement W2748571563 · doi:10.1373/jalm.2016.021824

Half-Century of Cancer Biomarkers: Lessons from the Past and Projections for the Future

2017· article· en· W2748571563 sur OpenAlexaff
Panagiota S. Filippou, Eleftherios P. Diamandis

Notice bibliographique

RevueThe Journal of Applied Laboratory Medicine · 2017
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueCancer Research and Treatments
Établissements canadiensMount Sinai HospitalUniversity of TorontoUniversity Health Network
Organismes subventionnairesnon disponible
Mots-clésCancerMedicineInternal medicine

Résumé

récupéré en direct d'OpenAlex

A handful of cancer biomarkers are used widely in clinical practice, mainly for aiding in diagnosis and for monitoring therapy. These biomarkers include α-fetoprotein (AFP),4 carcinoembryonic antigen (CEA), prostate-specific antigen, and the carbohydrate antigens CA125, CA19.9, and CA15.3. Other, more specialized cancer biomarkers are used less frequently. Molecular/genomic markers will not be discussed here. An examination of the history of these biomarkers reveals that they were discovered in the mid-1960s and 1970s (AFP, CEA) or in the 1980s, thanks to the monoclonal antibody technology revolution. The success of these markers in clinical practice sparked interest to discover new cancer biomarkers that could be applied for population screening, early diagnosis, prediction of therapeutic response, monitoring therapy, etc. However, for almost 40 years, no major cancer biomarkers have been discovered and entered the clinic, despite spectacular advances in biology, medicine, genomics, proteomics, and other omic technologies. One wonders as to why this field has not progressed to expectations. The few biomarkers that obtained Food and Drug Administration approval in the last 40 years are mainly used for very specific and restricted clinical applications and are mainly genomic markers. The lack of new cancer biomarkers in the clinic is in stark contrast to the number of relevant publications, describing supposedly fantastic biomarkers for various malignancies. What happened to all these biomarkers and why have they not been clinically used? Writing on this issue in other forums, we identified 3 reasons for the failure of most cancer biomarkers to reach the clinic (1, 2). Scientific fraud (very rare). Discovery of cancer biomarkers that show statistical differences between the comparison groups but have poor characteristics such as sensitivity, specificity, and predictive value and are thus clinically useless. False discovery, which means discovery of cancer biomarkers that in the initial publication showed much promise, but failed on subsequent validation. The last category is relevant to the highly discussed issue of irreproducibility of scientific publications. It was recently realized that many scientific papers, even in the highest impact journals, fail to reproduce for various reasons, as described elsewhere (3). This issue was highlighted many years ago by us and others (4). The situation with cancer biomarkers is different when considering molecular changes such as mutations, copy number variations, deletions, insertions, etc. The advent of next-generation sequencing has revealed that a myriad of genomic changes may be related to cancer aggressiveness, progression, and response to therapy. While some of these molecular changes have clear clinical value, such as selecting targeted therapies, there is still an issue as to which molecular changes are clinically actionable (drivers) and which ones are bystanders (passengers). As more cancer genomes are being sequenced, the strongest molecular markers will likely guide future targeted therapies, something that is now known as “precision medicine.” Is there any hope that we will witness a renaissance of the classic serum circulating cancer biomarkers in the future? Or should we accept that the best performing biomarkers have already been discovered and there is nothing much to expect? Recently, we suggested that it may be possible to use the hundreds of published cancer biomarkers with poor clinical performance (e.g., low sensitivity) in isolated cases, thus introducing the concept of personalized cancer biomarkers. We speculated that although many new biomarkers exhibit very low sensitivity (at high specificity) for cancer diagnosis and monitoring, it may be possible that these biomarkers may serve a clinical purpose in small groups of patients for whom these markers are altered in the circulation (5). In one of our latest iterations of the idea of personalized biomarkers, we suggested that newly diagnosed patients may submit their serum to centralized laboratories that will screen for hundreds or thousands of biomarkers simultaneously, to identify some that may have clinical value for these specific patients (e.g., for monitoring success of therapy). In essence, our suggestion is similar to organ transplantation, whereby the donor and recipient are HLA-typed so that the most compatible organ can be selected, to avoid rejection. Our initial suggestion (5) had some limitations, especially the technology that could be used to screen quickly and relatively cheaply thousands of molecules simultaneously, in small sample volumes, to identify the ones that are probably most useful. However, recent technological developments, such as mass spectrometry or simultaneous multiplex ELISAs, may alleviate this problem. For example, it is now possible to quantify every human protein without the use of a specific reagent, such as an antibody, by using mass spectrometry and selected reaction monitoring assays. While this technology is still not sensitive enough for this application, future developments may make this a feasible approach. Additionally, there are now companies that expanded on the original Luminex multiparametric assay, to analysis of thousands of serum proteins simultaneously, using quantitative micro-ELISAs (e.g., see www.raybiotech.com). We envision, then, that the current stagnation with serological cancer biomarker discovery may be lifted by new approaches that will be based on personalized cancer biomarkers. These biomarkers could be identified after an initial screen of patient serum as described above. We caution that this approach needs experimental verification for its effectiveness. Validation of New Cancer Biomarkers: A Position Statement from the European Group on Tumor Markers Michael J. Duffy, Catharine M. Sturgeon, György Sölétormos, Vivian Barak, et al. Clin Chem. 2015;61:809–20 α-fetoprotein carcinoembryonic antigen.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,019
score de la tête « metaresearch » (Gemma)0,023
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,019
Score d'incertitude au seuil0,101

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0190,023
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0030,002
Études des sciences et des technologies0,0020,010
Communication savante0,0070,017
Science ouverte0,0020,004
Intégrité de la recherche0,0080,012
Charge utile insuffisante (le modèle a refusé de juger)0,0080,002

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,019
Tête enseignante GPT0,341
Écart entre enseignants0,322 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreSynthèse

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations4
Publié2017
Routes d'admission1
Résumé présentoui

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