Multiomics profiling of longitudinal melanoma specimens unravels molecular mechanisms of resistance to sequential targeted and cancer immunotherapies.
Bibliographic record
Abstract
e22015 Background: We evaluated spatially-resolved protein profiling of longitudinal tumor specimens derived from two patients with metastatic melanoma who progressed on sequential therapies including targeted therapy (TT) and immune checkpoint blockade (ICB) therapy targeting T cell-surface antigens (CTLA-4 and PD-1). The purpose of this study was to identify molecular determinants of resistance to sequential TT and ICB therapies. Methods: We performed multiplexed and multidimensional spatial protein profiling using NanoString’s GeoMx Digital Spatial Profiling (DSP) platform and single-cell level imaging analysis with Cyclic Immunofluorescence (CycIF) to simultaneously determine dynamic changes in tumor intrinsic signaling pathways and immune response in the tumor microenvironment (TME). Results: The first patient presented with a BRAFV600E-positive brain metastasis. This patient was sequentially treated with ipilimumab (Ipi), the combination of dabrafenib and trametinib, and pembrolizumab (Pembro) and progressed despite of high expression of CD56 NK cell after all treatments. In addition, CycIF analysis revealed drug resistance in a subpopulation of cells that had continued activation of mTOR (pS6) and EGFR pathways. The second patient presented with dermal metastases on the flank with NRASQ61K mutation. This patient was sequentially treated with Pembo, Talimogene Laherparepvec, the combination of Ipi plus nivolumab, and two different investigational agents combined with Pembro. This patient displayed stable disease (SD) on Pembro but eventually progressed on the subsequent therapies. DSP analysis demonstrated CD68/CD40 myeloid cell infiltrates as well as HLA-DR and CD44 in the TME after the last treatment. CycIF analysis revealed dynamic changes in tumoral characteristics including DNA damage and proliferation during treatment. Furthermore, the analysis suggested that there might be a resistant subpopulation in the last tumor biopsy, which is in line with progression of the disease. Conclusions: In this study, we conducted detailed analyses on serial specimens from two patients to precisely define the spatial distribution of immune responses and cancer signaling pathways. The findings propose that concurrent proteomics analysis and immune monitoring of longitudinal tumor biopsies can be informative in clinical evaluation in order to identify salvage therapies to overcome drug resistance.
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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 | 0.000 | 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) | 0.001 | 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".