Molecular cytometry identifies a wide range of translationally relevant markers in tumor and peripheral immune cells of lung cancer patients
Bibliographic record
Abstract
Abstract Many immunotherapy drugs, used singly or in combination, are emerging. To realize precision oncology, and better design clinical trials, in-depth immune profiling is key. Many new technologies are available; the most promising simultaneously analyzes >102 proteins and 400 mRNA cell-by-cell (molecular cytometry). Using this technology, we deeply profiled TIL, from freshly resected lung tissue, and PBMC collected at surgery (n=10). Antibody staining was robust, with all canonical cell populations at expected frequency. We identified markers uniquely upregulated in TIL, including CXCR6, CD39, CD26, CD69, CD103, and RGS. We also asked what markers were uniquely enriched in PD1-TIL, in order to find other drug targets for patients who fail Nivolumab. We found many molecules upregulated in PD1-TIL, including CD326, CD98, LGALS3, TIM3, CD54, CD235ab, CXCL8, CD141, and CD117. We further identified precise combinations of these markers that inform design of combination immunotherapy. We also characterized immune landscapes in metastatic disease vs. localized adenocarcinoma, finding drug targets and tumor-immune interactions are different across these settings. For example, T-cell activation markers are downregulated in metastatic tissue, e.g., HLADR (p = 1.5E-16), CD69 (p=2.9e-10), and CD38 (p=3.2e-16). CD103 was also highly downregulated (p=1.9E-14). Notably, in metastatic disease various myeloid proteins are elevated, including CD206 (p=0.002), CD32 (p=0.02), and CD61 (p=0.006). We also characterized the degree of immune exhaustion, using ratios of ZBED2:LGALS in cells. In summary, we demonstrate the utility of molecular cytometry for providing unique (and translationally-relevant) insight into lung cancer.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".