GENE-18. DIVERGENT CLONAL EVOLUTION OF MELANOMA BRAIN METASTASES DURING TREATMENT WITH IMMUNOTHERAPY
Bibliographic record
Abstract
Reversal of immune cell exhaustion through immune checkpoint blockade (ICB) has become a first-line approach for patients with metastatic melanoma due to success in controlling extracranial tumors. However, patients frequently experience discordant responses, with extracranial response and intracranial progression. We hypothesize that ICB exerts selective pressure leading to the clonal evolution of treatment resistant clones that ultimately culminate in disease progression. We collected a cohort of 97 patients, encompassing 312 pre- and post-immunotherapy melanoma tumors, for whole exome sequencing (WES) and included primary, extracranial, or intracranial samples. Each tumor was analyzed for somatic mutations, copy number alterations, neoantigen profile, and patient specific phylogenetic trees were constructed encompassing a tumor’s genetic subclones. Heterogeneity of the tumor microenvironment was evaluated using high multiplicity single-cell immunofluorescent staining (CycIF). Single cell sequencing was performed on fresh tissue from 4 pre-treatment and 14 post-immunotherapy melanoma brain metastases using the Smart-Seq2 protocol. WES of pre- and post-immunotherapy tumors yielded distinct patterns of clonal evolution and immunoediting within brain metastases compared to their extracranial counterparts, including mutations in B2M. In paired pre- and post-immunotherapy samples, CycIF demonstrated decreased in CD8 infiltration and increased CD45RO, FOXP3, and PD-L1 staining suggesting less cytotoxic, terminally differentiated T cells in resistant tumors. Single cell sequencing analysis of 3,974 tumor and immune cells demonstrated patient-specific tumor clustering and gene expression profiles mediating resistance to ICB. In conclusion, we document, for the first time, evidence of ongoing branched evolution during immunotherapy in brain metastases with divergence compared to systemic sites of disease. Next-generation sequencing provides novel insights into clonal evolution mediating discordant responses of intra- and extracranial sites and immunosuppressive features of the intracranial tumor microenvironment. Targeting these mechanisms of resistance provide potential therapeutic avenues for patients with progressive intracranial disease.
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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".