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Record W2899858004 · doi:10.1093/neuonc/noy148.444

GENE-18. DIVERGENT CLONAL EVOLUTION OF MELANOMA BRAIN METASTASES DURING TREATMENT WITH IMMUNOTHERAPY

2018· article· en· W2899858004 on OpenAlexaff
Christopher Alvarez‐Breckenridge, Benjamin Izar, Jackson H. Stocking, Matt Lastrapes, Naema Nayyar, Corey M. Gill, Mia Bertalan, Alexander Kaplan, Devin McCabe, Douglas B. Johnson, Craig Horbinski, Rasheed Zakaria, Farshad Nassiri, Gelareh Zadeh, David E. Fisher, Maria Martinez‐Lage, Mario L. Suvà, Ryan J. Sullivan, Daniel P. Cahill, Scott L. Carter, Priscilla K. Brastianos

Bibliographic record

VenueNeuro-Oncology · 2018
Typearticle
Languageen
FieldMedicine
TopicBrain Metastases and Treatment
Canadian institutionsUniversity Health NetworkToronto Western HospitalPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsImmunotherapyMelanomaCD8Cancer researchCDKN2AImmune checkpointTumor microenvironmentBiologyExome sequencingSomatic evolution in cancerImmune systemMedicineImmunologyCancerGeneMutationInternal medicineGenetics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.025
GPT teacher head0.301
Teacher spread0.276 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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