Abstract 2256: Identifying the molecular signature of melanoma metastasis via a personalized quantitative proteomics approach
Bibliographic record
Abstract
Abstract Melanoma metastasis is the primary cause of skin cancer deaths. Unfortunately, the biology of melanoma metastasis remains elusive, although several pathways, including epithelial-to-mesenchymal transition (EMT), have been suggested as underlying mechanisms of tumor progression and immunosuppression. No reliable single or multiple prognostic markers currently exist in the clinic to predict chances of melanoma metastasis, resistance to therapy, and recurrence after treatment. Microarrays have been the primary means for large-scale expression analyses of genes implicated in cancer progression. However, recent advents in high throughput mass spectrometry allow for investigating cancer development directly at the protein level. In this study, we applied a quantitative proteomics technique, Stable Isotope Labeling with Amino acids in Cell culture (SILAC), to compare the proteomes of primary and metastatic cancer cells derived from the same melanoma patient. We hypothesized that quantitative proteomic profiling of primary and metastatic cell lines from the same patient will: 1) reflect differences in their metastatic potential by identifying differentially expressed proteins and pathways, 2) uncover new protein candidates involved in cancer progression-related processes, such as EMT, and 3) yield promising prognostic biomarkers and therapuetic targets. A “bottom-up” proteomics approach and a two-dimensional (strong cation exchange followed by reversed-phase) LC-MS/MS strategy on an LTQ-Orbitrap were utilized to perform 3 different pair-wise comparisons of the expressions of extracellular and membrane-bound proteins in the conditioned media of melanoma cell lines from 3 different patients (3 primary cells labelled with ‘light’ arginine and lysine and their corresponding ‘heavy’- labelled metastatic counterparts). We identified over 1000 proteins from each pair of primary and metastatic cell conditioned media, several of which were previously reported as either up-regulated or down-regulated in melanoma, confirming the validity of our approach. In addition, we identified candidate proteins that have not been correlated previously with melanoma metastasis. Candidate proteins were selected based on the quantification ratio (>2-fold change) between the two conditions and the consistency among triplicates within each condition. Many of the identified proteins may shed light on the molecular mechanisms underlying melanoma metastasis and may open novel areas for further research geared towards finding effective personalized prognostic and therapeutic approaches to manage this devastating cancer. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 2256.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".