TMOD-18. FUNCTIONAL DISSECTION OF RECURRENT COPY-NUMBER-ALTERATIONS INFORMS MECHANISMS OF LOW-GRADE-GLIOMA PROGRESSION
Bibliographic record
Abstract
Abstract Low-grade gliomas (LGG) are slowly growing brain cancers that ultimately undergo a malignant progression to lethal secondary glioblastomas with a dismal prognosis. Despite intensive research, very little progress has been made in our understanding of this transformation. Hence, outside of known alternations in well-characterized driver genes CDKN2A, PTEN and PDGFRA, genetic drivers that underlie LGG transformation are largely unknown. Contrary to other types of cancer, LGGs exhibit very few driver mutations (30-40 mutations) as they progress. Interestingly, LGGs also exhibit a highly prevalent and heterogenous pattern of copy-number-alterations (CNAs) suggesting that they might be driven by changes in gene dosages that result from widespread chromosomal instability observed within these tumors. Through analysis of the human Mayo Clinic and TCGA LGG datasets (~1500 patients), we have identified eleven highly recurrent CNAs affecting LGG patients, many of which correlate with patient survival. We hypothesized that these CNAs contain driver genes that confer a growth advantage resulting in the highly recurrent CNA pattern and prognostic associations. To investigate the tumor suppressive potential of the ~1800 genes located in recurrently deleted regions and the oncogenic potential of the ~1200 genes located in recurrently amplified regions, our lab developed a novel in vivo ‘CRISPR-Knock Out and Activation Linked Assay’ (CRISPR-KOALA). Stereotaxic delivery of arm-level CNA lentiviral gRNA libraries targeting the mouse orthologs of genes in CNAs into the brain of our animal model (rs557mut;IDHmut;P53KO;AtrxKO;Cas9GFP: Yanchus et. al Science 2022) resulted in rapid formation of tumors compared to animals injected with control libraries. Deconvoluting the screen revealed enrichment of immune modulators such as Them6 as well as a modulator of NMDA receptors (Grina) implicating immune system and glutamate dysregulation in LGG malignant transformation. Taken together, our study provides insights into mechanisms underlying LGG progression which may serve to inform intervention strategies for this lethal 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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".