Abstract 2168: Longitudinal analysis of diffuse glioma reveals cell state dynamics at recurrence associated with changes in genetics and the microenvironment
Bibliographic record
Abstract
Abstract Diffuse glioma is characterized by a poor prognosis and a universal resistance to therapy, though the evolutionary processes behind this resistance remain unclear. The Glioma Longitudinal Analysis (GLASS) Consortium has previously demonstrated that therapy-induced selective pressures shape the genetic evolution of glioma in a stochastic manner. However, single-cell studies have revealed that malignant glioma cells are highly plastic and transition their cell state in response to diverse challenges, including changes in the microenvironment and the administration of standard-of-care therapy. To interrogate the factors driving therapy resistance in diffuse glioma, we collected and analyzed RNA- and/or DNA-sequencing data from temporally separated tumor pairs of over 300 adult patients with IDH-wild-type or IDH-mutant glioma. In a subset of these tumor pairs, we additionally performed multiplex immunofluorescence to capture the spatial relationship between tumor cells and their microenvironment. Recurrent tumors exhibited diverse changes that were attributable to changes in histological features, somatic alterations, and microenvironment interactions. IDH-wild-type tumors overall were more invasive at recurrence and exhibited increased expression of neuronal signaling programs that reflected a possible role for neuronal interactions in promoting glioma progression. In contrast, recurrent IDH-mutant tumors exhibited a significant increase in proliferative expression programs that correlated with discrete genetic changes. Hypermutation and acquired CDKN2A homozygous deletions associated with an increase in proliferating stem-like malignant cells at recurrence in both glioma subtypes, reflecting active tumor expansion. A transition to the mesenchymal phenotype was associated with the presence of a specific myeloid cell state defined by unique ligand-receptor interactions with malignant cells, providing opportunities to target this transition through therapy. Collectively, our results uncover recurrence-associated changes in genetics and the microenvironment that can be targeted to shape disease progression following initial diagnosis. Citation Format: Frederick S. Varn, Kevin C. Johnson, Jan Martinek, Jason T. Huse, MacLean P. Nasrallah, Pieter Wesseling, Lee A. Cooper, Tathiane M. Malta, Taylor E. Wade, Thais S. Sabedot, Daniel J. Brat, Peter V. Gould, Adelheid Wöehrer, Kenneth Aldape, Azzam Ismail, Floris P. Barthel, Hoon Kim, Emre Kocakavuk, Nazia Ahmed, Kieron White, Santhosh Sivajothi, Indrani Datta, Jill S. Barnholtz-Sloan, Spyridon Bakas, Fulvio D'Angelo, Hui K. Gan, Luciano Garofano, Mustafa Khasraw, Simona Migliozzi, D. Ryan Ormond, Sun Ha Paek, Erwin G. Van Meir, Annemiek M. Walenkamp, Colin Watts, Michael Weller, Tobias Weiss, Karolina Palucka, Lucy F. Stead, Laila M. Poisson, Houtan Noushmehr, Antonio Iavarone, Roel G. Verhaak, The GLASS Consortium. Longitudinal analysis of diffuse glioma reveals cell state dynamics at recurrence associated with changes in genetics and the microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2168.
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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".