Abstract 5075: An integrated <i>in silico</i> and proteomic approach towards the identification of novel cancer biomarkers based on tissue specificity
Bibliographic record
Abstract
Abstract Serological biomarkers represent a non-invasive and cost-effective means to aid in clinical management of cancer patients, particularly in areas of disease detection, prognosis, monitoring and therapeutic stratification. Several notable biomarkers currently in clinical use are proteins that demonstrate high tissue specificity to a single tissue, such as prostate specific antigen (PSA) for prostate cancer and human chorionic gonadotropin (hCG) for testicular cancer, the former being highly expressed only in the prostate and the latter in the placenta. Recent advances in high-throughput technologies (e.g. high-content microarray chips, serial analysis of gene expression platforms, thorough expressed sequence tag annotation) has enabled creation of publicly available gene and/or protein databases which describe the expression of thousands of genes (and/or proteins) in multiple tissues. Furthermore, over the past five years, our laboratory has characterized the proteomes of over 35 cell lines from numerous cancer sites as well as relevant biological fluids using a 2D-LC-MS/MS approach, identifying between 1000-4000 proteins per cancer site. In this respect, the present study represents an integrative analysis of existing databases in combination with our in-house generated proteomic datasets, in pursuit of highly tissue specific proteins, which could be further explored as cancer biomarkers and/or therapeutic targets. Through stringent bioinformatic mining of the Human Protein Atlas, BioGPS, Unigene, TiGER, TiSGeD and other related databases, a list of proteins highly specific to normal lung, colon, prostate, testis and ovaries was generated. Interestingly, only a few proteins from each tissue were commonly reported as highly specific across all of these databases and these were selected for further analysis. Subsequent integration and comparison to our in-house generated proteomes: prostate cancer (PC3, LNCaP, 22Rv1 and seminal plasma from prostate cancer patients; 1958 proteins), lung cancer (H1688, H23, H460, and H520; 1856 proteins), ovarian cancer (HTB75, TOV-112D, TOV-21G and RMUG-S; 1123 proteins) and colon cancer (HCT116, LoVo, LS174T, LS180, SW1116, SW480 and SW620; 2124 proteins), helped to corroborate our candidate list. Three, 4, 5, 10 and 7 highly specific proteins based on the applied criteria were prioritized for colon, testis, ovaries, prostate and lung, respectively. The identification of known biomarkers within these lists (e.g. PSA, KLK2, MSMB) warrants the analysis of the remaining proteins in serum, as potential cancer biomarkers and/or therapeutic targets. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 5075. doi:10.1158/1538-7445.AM2011-5075
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".