MétaCan
Menu
Back to cohort
Record W2105017286 · doi:10.1093/jnci/djh056

Analysis of Serum Proteomic Patterns for Early Cancer Diagnosis: Drawing Attention to Potential Problems

2004· article· en· W2105017286 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJNCI Journal of the National Cancer Institute · 2004
Typearticle
Languageen
FieldChemistry
TopicAdvanced Proteomics Techniques and Applications
Canadian institutionsMount Sinai Hospital
Fundersnot available
KeywordsMedicineBiobankProstate cancerProteomicsCancerBioinformaticsInternal medicineBiology

Abstract

fetched live from OpenAlex

In a recent update (1) of already impressive data (2), it was reported that the use of proteomic patterns in serum to diagnose ovarian and prostate cancers can achieve perfect diagnostic sensitivity and specificity. A diagnostic sensitivity and specificity of 100% is unprecedented for any tumor marker known to date and, if reproducible, this finding could have a major impact on the way we diagnose cancer in the future. Over the last 2 years, results reported by several groups (2– 6) have suggested that such proteomic patterns, particularly those generated by surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) mass spectrometry, may facilitate the early diagnosis of various cancers, including those of the ovary, prostate, breast, and bladder. SELDI-TOF proteomic profiling technology has been reviewed (7,8). The impressive results reported with this new technology were welcomed by scientists, the popular press, the public, and even by politicians (9). Although there has been little published criticism of this methodology (10–12), serious skepticism about its utility has been expressed publicly at various scientific meetings. Many investigators and clinicians have adopted a wait-andsee approach pending the outcomes of prospective clinical studies using this technology which are starting now but will require years to complete. Here, I summarize some shortcomings of this technology for the purpose of stimulating further discussion and research. Considering what is known about tumor markers, the mechanisms by which they are released into the circulation, their abundance in biologic fluids, their metabolism and excretion, and their dynamic relationships within the host, it is unlikely that proteomic profiling by SELDI-TOF methods will be a useful approach for the diagnosis of cancer. Moreover, it is conceivable that published data may, in fact, be biased by artifacts related to the nature of the clinical samples used, the mass spectrometry instrument, and/or the bioinformatic analysis. In a recent meta-analysis (12) of prostate cancer proteomic data from four papers by three different research groups, I pointed out that the discriminatory peaks (i.e., peaks representing molecules that appear or disappear during cancer progression, or whose amounts differ in cancerous versus noncancerous tissue) identified in the four papers were very different, even in the two papers published by the same group using the same experimental data but different bioinformatic tools (12,13). These data are summarized in Table 1. These discrepancies suggest that serum proteomic patterns obtained by the SELDITOF technique may not be reproducible and that the discriminatory peaks are not consistent either within a group or among groups of investigators for the same type of cancer, even when the general analytical methods or datasets are the same. Furthermore, the reported diagnostic sensitivities and specificities of prostate cancer diagnosis based on SELDI-TOF technology differ substantially among the four reports. Another rather surprising phenomenon associated with these data is that serum proteins that are known to distinguish patients with benign conditions from patients with malignancies (e.g., prostatespecific antigen in prostate cancer) were not identified by this new technology, raising serious questions about its analytic sensitivity. After reviewing the serum levels of known tumor markers for various malignancies and the current sensitivity of mass spectrometers, I have concluded that the SELDI-TOF technology that is currently used for serum analysis is not capable of detecting any serum component at concentrations of less than 1 g/mL (12). This range of concentrations is approximately 1000-fold higher than the concentrations of known tumor markers in the circulation (12). This analysis led me to conclude that the serum discriminatory peaks identified by mass spectrometry very likely represent high-abundance molecules that were unlikely to have been released into the circulation by very small tumors or their microenvironments. I suggested that the discriminatory peaks may instead represent acute-phase reactants (i.e., molecules whose serum concentrations are increased in patients with acute or chronic inflammatory conditions) or other proteins or protein fragments that are released into the circulation by large organs, such as the liver, in response to the presence of a tumor or cancer epiphenomena, such as infection, inflammation, or malnutrition (10,11). Alternatively, some of these proteomic changes may represent artifacts of sample collection, storage, or pretreatment, patient selection, or other idiosyncrasies. Little effort has been made to positively identify at least some of the molecules that comprise the discriminatory peaks to understand their origins and why their levels are altered. Indeed, in the few cases where such peaks were identified, some were composed of high-abundance molecules released by the liver and others were composed of acute-phase reactants (Table 2) (16 –18). At the 2003 annual meeting of the American Association for Cancer Research, Zhang et al. (16) reported the identities of three discriminatory peaks in ovarian cancer: apolipoprotein A1, transthyretin (pre-albumin) fragment, and inter-alpha-trypsin inhibitor. Others identified haptoglobinsubunit for ovarian cancer (17) and vitamin

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.713
Threshold uncertainty score0.391

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.030
GPT teacher head0.336
Teacher spread0.305 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it