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Enregistrement W1974240359 · doi:10.1373/clinchem.2004.032177

How Are We Going to Discover New Cancer Biomarkers? A Proteomic Approach for Bladder Cancer

2004· letter· en· W1974240359 sur OpenAlexaff
Eleftherios P. Diamandis

Notice bibliographique

RevueClinical Chemistry · 2004
Typeletter
Langueen
DomaineChemistry
ThématiqueAdvanced Proteomics Techniques and Applications
Établissements canadiensUniversity of TorontoMount Sinai Hospital
Organismes subventionnairesnon disponible
Mots-clésCancerCancer biomarkersDiseaseMedicineBiomarker discoveryPopulationBiomarkerBladder cancerMonoclonal antibodyComputational biologyIdentification (biology)BioinformaticsImmunologyProteomicsBiologyInternal medicineAntibodyGeneGenetics

Résumé

récupéré en direct d'OpenAlex

A handful of cancer biomarkers are currently used routinely for population screening, disease diagnosis, prognosis, monitoring of therapy, and prediction of therapeutic response. Unfortunately, most of these biomarkers suffer from low sensitivity, specificity, and predictive value, particularly when applied to rare diseases in population screening programs. Thus, for the classic cancer biomarkers much is left to be desired in terms of clinical applicability. We need new cancer biomarkers that will further enhance our ability to diagnose, prognose, and predict therapeutic response in many cancer types. Because biomarkers can be analyzed relatively noninvasively and economically, it is worth investing in discovering more biomarkers in the future. The completion of the Human Genome Project has raised expectations that the knowledge of all genes and proteins will lead to identification of many candidate biomarkers for cancer and other diseases. These predictions still need to be realized. The prevailing view among specialists is that the most powerful single cancer biomarkers may have already been discovered. Likely, in the future we will discover biomarkers that are less sensitive or specific but could be used in panels, in combination with powerful bioinformatic tools, to devise diagnostic algorithms with improved sensitivity and specificity. These efforts are currently in progress (1). Most of the currently used cancer biomarkers were discovered after development of novel analytical techniques such as immunologic assays and the monoclonal antibody technology. Animals were immunized with extracts from tumors or cancer cell lines, followed by screening of hybridomas for monoclonal antibodies that recognize “cancer-associated” antigens. More recently, and with the completion of the Human Genome Project, many researchers have hypothesized that the best cancer biomarkers will likely be secreted proteins (2). Approximately 20–25% of all cell proteins are secreted. However, this is not an absolute requirement because many classic cancer biomarkers, such as carcinoembryonic antigen (CEA) and Her2/neu, are bound to cell membranes, but their extracellular domains can be found, through shedding, in the circulation. Other groups are using bioinformatics such as digital differential display and in silico Northern blotting to compare gene expression between healthy and cancerous tissues to identify overexpressed genes (3). Although one of the prevailing hypotheses in new biomarker discovery is that the most promising biomarkers should be overproduced proteins, this is not generally true for some of the best-known cancer biomarkers, such as prostate-specific antigen (PSA) (4). Overexpressed genes are now identified experimentally by use of microarrays. Some of these genes have been proposed as candidate cancer biomarkers (5)(6). Despite this reasonable hypothesis, very few cancer biomarkers have been discovered by use of this approach. Another approach, followed by our group, is based on the hypothesis that if a molecule is already a known cancer biomarker, members of the same family of genes/proteins may also constitute novel biomarkers. We have since shown that kallikreins, a group of serine proteases with high homology at both the DNA and protein level (this family includes PSA), are candidate biomarkers for ovarian, prostate, and breast cancers (7). A novel approach that has been introduced recently is the use of proteomic technologies for new biomarker discovery (8). In fact, serum proteomic patterns, identified by the so-called surface-enhanced laser desorption/ionization-time-of-flight mass spectrometry (SELDI-TOF) technology, have been proposed as highly accurate predictors for presence of cancer, and they are currently under clinical evaluation (9). The same technology has been used to identify molecules that may constitute novel biomarkers (9). Aspects of this technology have been criticized recently (10). Some cancers are more amenable than others to early diagnosis by biochemical testing. For example, it has previously been suggested that certain biological fluids, such as sputum, urine, pancreatic juice, and cerebrospinal fluid, may be the fluids of choice for early diagnosis of lung, bladder, pancreatic, and brain tumors. Indeed, one would expect that biochemical testing of urine should be able to diagnose early bladder carcinoma because candidate informative molecules could be excreted into the urine during cancer development. Proteomic profiling of urine has been suggested as a diagnostic test for bladder carcinoma (11). In addition, many other biochemical molecules or genetic markers have been discovered that could be used to diagnose bladder carcinoma with fair sensitivity and specificity. Such molecules (or methods) include, but are not limited to, the following (the approximate diagnostic sensitivities and specificities are in parentheses): BTA stat (68%; 66%); BTA-TRAK (71%; 62%); NMP22 (64%; 71%); telomerase (74%; 89%); HA-HAase (91%; 86%); Immunocyt (68%; 79%); F/FDP (68%; 86%); multicolor fluorescence in situ hybridization assays (84%; 90%); cytokeratins (76%; 84%); metalloproteinases (60%; 80%); and p53 mutation (32%; 100%) (12)(13). The most common noninvasive test, however, is voided urine cytology (VUC), which has a sensitivity of ∼50% and a specificity of 97% (12). This test has higher sensitivity for higher grade tumors. Bladder cancer is very common, ranking second only to prostate cancer for cancers of the urinary tract. Approximately 54 000 new cases of bladder cancer are diagnosed and ∼12 000 people die from this disease every year in the United States alone. Most patients are diagnosed with superficial tumors, which can be completely resected. However, two-thirds of these patients will experience recurrence within 5 years, and almost 90% will have a recurrence by 15 years. Early diagnosis leads to better clinical outcomes, underscoring the importance of finding new ways for screening the general population. Currently, potential bladder tumor markers can be used in various clinical scenarios, including (14): Serial testing for earlier detection of recurrence; Complementary testing to urine cytology to improve the detection rate; Providing a less expensive and more objective alternative to the urine cytology test; and Directing the cytoscopic evaluation of patient follow-up. The gold standard for the detection of urothelial neoplasia is cytologic examination of urothelial cells from voided urine, urinary bladder washings, and urinary tract brushing specimens in combination with cystoscopic examination (12)(13). Because cystoscopy is an invasive procedure and urinary cytology suffers from low sensitivity and specificity, particularly for lower grade tumors, it is desirable to identify novel biomarkers for this cancer. Biochemical testing of urine is a noninvasive and less expensive procedure for diagnosing and monitoring this disease. Because none of the markers mentioned above has sufficient sensitivity and specificity, the quest for identifying additional bladder cancer biomarkers continues. In this issue of Clinical Chemistry, Kageyama et al. (15) propose proteomic analysis of urine as a new way to identify bladder cancer biomarkers. Previously, Celis et al. (16) used two-dimensional gel electrophoresis and developed a comprehensive database for bladder cancer profiles of both transitional and squamous cell carcinomas. Through their studies, Kageyama et al. (15) were able to identify a potential tumor marker, calreticulin, which is found in the urine of patients with bladder carcinoma. The authors used a differential display method of bladder cancer vs healthy urothelial tissue and mass spectrometry to identify proteins that are increased in cancer tissue. In addition to calreticulin, an endoplasmic reticulum chaperone, they found nine other candidate proteins that could constitute new biomarkers for bladder carcinoma. The authors confirmed their data with quantitative Western blot analysis, immunoprecipitation, and immunohistochemistry. Their reported sensitivity and specificity were 73% and 86%, respectively, similar to the values reported for other biochemical bladder markers (see above). However, the diagnostic accuracy of their test was vulnerable to urinary tract infections (15). The main question surrounding bladder cancer and urinary biomarkers is how these molecules can be used in clinical practice. Clearly, these tests are not useful for population screening because of their low sensitivity and specificity. In addition, none of the available tests is sufficiently accurate to replace cystoscopy in the investigation of a patient with a possible bladder tumor. VUC has relatively low sensitivity, especially for low-grade tumors, but it is currently the most specific test for bladder carcinoma. Consequently, when VUC is positive, it indicates a high-risk tumor that requires definitive treatment. VUC is currently used for monitoring of patients with known high-risk disease, and positive cytology with negative cystoscopy may indicate malignancy of the prostate or upper urinary tract. Currently, patients with bladder cancer are followed up with frequent cystoscopic examinations. Cystoscopy is invasive and expensive, and a urinary bladder test that is simple, quick, and inexpensive could be invaluable in this clinical scenario. Current guidelines suggest that low-risk patients should be surveyed once a year with cystoscopy and high-risk patients at 3-month intervals. Currently, cystoscopy is always combined with VUC. Because, as mentioned earlier, new urinary bladder tests such as BTA or NMP22 could detect lower-grade disease recurrence with higher sensitivity than VUC, it could be worthwhile to consider including one or more of these tests in the routine follow-up of patients with bladder carcinoma. However, large prospective studies will be necessary to test the clinical utility of these assays against cytology. Such trials could show the value of these new tests in reducing the frequency of cystoscopy and in contributing to the earlier and more sensitive detection of disease recurrence, leading to earlier therapeutic interventions and, fortunately, to improved clinical outcomes. In conclusion, bladder cancer biomarkers have proliferated more than any other class of cancer markers over the last 10 years. We now have at hand a multitude of molecules that can be measured with automated, inexpensive, quantitative assays in urine. These markers may aid in the monitoring of patients with bladder carcinoma and have the potential to reduce the number of follow-up cystoscopies, thus reducing healthcare costs and patient discomfort and, at the same time, detecting relapsing disease more effectively than VUC. It is time to test these new possibilities with prospective clinical trials.

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)0,003
score de la tête « metaresearch » (Gemma)0,003
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: aucune
GenreSignal candidat: Commentaire · Signal consensuel: aucune
Score de désaccord entre enseignants0,003
Score d'incertitude au seuil0,017

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,003
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0030,001
Études des sciences et des technologies0,0010,002
Communication savante0,0030,006
Science ouverte0,0010,001
Intégrité de la recherche0,0020,004
Charge utile insuffisante (le modèle a refusé de juger)0,0020,001

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 Opus0,073
Tête enseignante GPT0,374
Écart entre enseignants0,302 · 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'étudeExpérimental (laboratoire)
Domainenon disponible
GenreCommentaire

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

En bref

Citations40
Publié2004
Routes d'admission1
Résumé présentoui

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