Antibody-Based Proteomics Analysis of Tumor Cell Signaling Pathways
Bibliographic record
Abstract
The promise of personalized medicine is ultimately contingent on the successful identification of specific biomarkers for diseases and therapeutic modalities that can compensate for the molecular lesions that underlie these diseases. In the case of cancer, more than two decades of research have demonstrated the critical roles of a relatively small subset of proteins that are encoded by oncogenes and tumor suppressor genes. The gain of function of perhaps just a few oncoproteins and the loss of function of only a small number of tumor suppressor proteins in the right combinations may culminate in full neoplastic transformation. However, there may well be billions of such genetic change combinations so that every cancer patient has a unique form of the disease. Presently, just under half of cancer patients die from their disease within 5 years so there is a pressing need for development of new diagnostics and treatments. Like many chronic diseases associated with aging, cancer is a systems disorder. Most of the known oncogenes and tumor suppressor genes specify protein kinases, their regulators, or their target substrates. The human genome encodes at least 515 protein kinases (the kineome) [1, 2] and 140 protein phosphatases [3], which catalyze the reversible phosphorylation of over a third of all proteins at more than 1,000,000 sites (the phosphoproteome) [4]. Many of these phosphorylation events play key roles in the regulation of cell proliferation and survival. The phosphoproteome represents a relatively untapped source of potential biomarkers, and phosphoproteomics profiling should be extremely insightful for analysis of signaling pathways [5]. Our current knowledge of the composition and architecture of cell signaling systems is still extremely rudimentary. To elucidate these molecular communications webs, specific information is required concerning the spatial and temporal expression and activity of thousands of individual proteins in the nearly 200 different cell types in the organs and tissues of the human body. One of the major challenges of this decade will be the elucidation of these regulatory networks and the development of technologies to track their protein components in tumor biopsies and bodily fluids for cancer diagnostics. Although cancer is commonly viewed as a genetics disease, its successful treatment will require the knowledge of malfunctioning signal transduction at the protein level and the application of small molecule drugs. A very powerful arsenal of protein kinase inhibitors is being developed by the pharmaceutical industry, which is now spending about a third of their annual research and development budgets on this class of enzymes [6]. We predict that within the next 10 years, most of the new drugs in clinical trials and entering the market place will be protein kinase inhibitors. One reason for this is because the industry is currently focused on only a few dozen of the protein kinases, and over 90% of them still remain to be explored for their therapeutic potential [4]. Another impetus is that over 400 other diseases have been linked to defective kinase signaling. Consequently, there will be an increasing demand to track signal transduction proteins in the near future.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".