Abstract 2694: An anatomic proteomic atlas of human glioblastoma
Bibliographic record
Abstract
Abstract Glioblastoma (GBM) is an aggressive brain tumor with an expected survival of under 15 months despite spirited multimodal therapy. This grim outlook has remained virtually unchanged over the last 40 years and thus necessitates alternative research and therapeutic development strategies. Historically, traditional models of cancer biology have largely considered GBM tissue to be a homogenous mass. Emerging studies however now suggests that the interaction of tumor cells with various normal components of the brain and immune system play an important role in helping cancer grow and develop resistance to therapies. Better understanding of these hallmark features of GBM, collectively known as the “tumor microenvironment” (TME), could lead to the development of new and more effective therapies. In light of this, there is a renewed interest in revisiting our theories of cancer and preserving the microscopic anatomy of GBM in our molecular profiling efforts. Here we leverage laser capture microdissection (LCM) and mass spectrometry-based proteomics, in order to generate a detailed map of the distribution of proteins within tissues across a large number of patients. Specifically, we will isolate, and profile well-understood microscopic components of GBM: cellular tumor (CT), microvascular proliferation (MVP), infiltrating tumor (IT), palisading cells around necrosis (PAN) and normal brain tissue (LE). All of this data will be provided in an online-based GBM atlas as a publicly available resource. This atlas and the associated database for clinical and genomic data will serve as a useful platform for developing therapeutics and testing novel hypotheses related to GBM biology. Citation Format: K.H. Brian Lam, Ugljesa Djuric, Ihor Batruch, Maxime Richer, Phedias Diamandis. An anatomic proteomic atlas of human glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2694.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".