Integrated immune profiling approach for immuno-oncology: Unveiling biomarkers and therapeutic targets with mass cytometry.
Bibliographic record
Abstract
e14575 Background: Over the last decade, immunotherapy has revolutionized cancer treatment, offering transformative outcomes for many patients. However, its effectiveness in treating solid tumors remains limited due to challenges such as T cell exhaustion, immunosuppressive tumor microenvironment (TME), lack of tumor-specific antigens and a complex immune landscape. Solid tumors also induce systemic immune changes that evolve dynamically with tumor progression. To improve patient outcome, it is essential to understand the interplay between localized and systemic immune responses. Methods: To address these challenges, we employed an integrative approach leveraging CyTOF and Imaging Mass Cytometry (IMC) technologies. Matched samples, including peripheral blood mononuclear cells (PBMCs), tumor-derived cells (TDCs) and formalin-fixed, paraffin-embedded (FFPE) tumor tissues from cancer patients, were analyzed. A custom CyTOF panel with over 40 markers was designed to profile immune subsets, checkpoint molecules, cytokines and transcription factors. Samples were barcoded, stained and analyzed using a CyTOF XT system. For spatial immune profiling, FFPE tumor tissues were stained with a 40-plus-marker IMC panel to characterize immune, stromal and tumor cells and assess immune activation states. Data was acquired with the Hyperion XTi Imaging System, enabling high-resolution imaging of tumor microenvironments. Results: CyTOF analysis revealed heterogeneity in T cell exhaustion and activation profiles in PBMCs and TDCs, highlighting the functional diversity of immune responses. IMC technology provided spatial insights into immune cell interactions within the TME, identifying distinct patterns of immune infiltration and congregation around tumor cells and stromal elements. Comparative analysis of CyTOF and IMC data uncovered shared and divergent phenotypes between peripheral and tumor-resident immune compartments, identifying biomarkers that link systemic immune profiles with TME dynamics. Conclusions: These findings offer invaluable insights into the immunological mechanisms driving antitumor responses. By integrating functional and spatial immune profiling, our approach identifies predictive biomarkers and potential therapeutic targets, guiding the development of personalized immunotherapies. This work highlights the importance of understanding systemic and localized immune responses to improve cancer treatment outcomes and optimize treatment strategies. For Research Use Only. Not for use in diagnostic procedures.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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 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".