Automated quantitative multiplex immunofluorescence in situ imaging identifies phospho-S6 and phospho-PRAS40 as predictive protein biomarkers for prostate cancer lethality
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Bibliographic record
Abstract
BACKGROUND: We have witnessed significant progress in gene-based approaches to cancer prognostication, promising early intervention for high-risk patients and avoidance of overtreatment for low-risk patients. However, there has been less advancement in protein-based approaches, even though perturbed protein levels and post-translational modifications are more directly linked with phenotype. Most current, gene expression-based platforms require tissue lysis resulting in loss of structural and molecular information, and hence are blind to tumor heterogeneity and morphological features. RESULTS: Here we report an automated, integrated multiplex immunofluorescence in situ imaging approach that quantitatively measures protein biomarker levels and activity states in defined intact tissue regions where the biomarkers of interest exert their phenotype. Using this approach, we confirm that four previously reported prognostic markers, PTEN, SMAD4, CCND1 and SPP1, can predict lethal outcome of human prostate cancer. Furthermore, we show that two PI3K pathway-regulated protein activities, pS6 (RPS6-phosphoserines 235/236) and pPRAS40 (AKT1S1-phosphothreonine 246), correlate with prostate cancer lethal outcome as well (individual marker hazard ratios of 2.04 and 2.03, respectively). Finally, we incorporate these 2 markers into a novel 5-marker protein signature, SMAD4, CCND1, SPP1, pS6, and pPRAS40, which is highly predictive for prostate cancer-specific death. The ability to substitute PTEN with phospho-markers demonstrates the potential of quantitative protein activity state measurements on intact tissue. CONCLUSIONS: In summary, our approach can reproducibly and simultaneously quantify and assess multiple protein levels and functional activities on intact tissue specimens. We believe it is broadly applicable to not only cancer but other diseases, and propose that it should be well suited for prognostication at early stages of pathogenesis where key signaling protein levels and activities are perturbed.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it