Abstract 2972: Development and characterization of synthetic antibodies with a human framework that directly link metabolism to immune system in cancer cells
Bibliographic record
Abstract
Abstract Deregulated cellular energetics and evasion of immune system are two hallmark features of cancer cells. In fact, increased uptake of glucose and glutamine by cancer cells elevates flux of a minor glucose pathway known as the hexosamine biosynthetic pathway (HBP), resulting in alteration of glycosylation. Addition of sugar moieties to proteins plays an important role in sensing cellular metabolism, growth factors, signaling molecules, nutrient flux and stress. Since many of these processes are de-regulated in tumorigenesis, global alterations in glycosylation via HBP flux are likely to play an important role in cancer. We hypothesize that cancer cells can “sense” stress through the HBP pathway, and, consequently, reprogram the cell surface and adapt to tougher conditions. Neo-epitopes on the cell surface that emerge from tuning the HBP pathway activity may be prime candidates as readouts that reliably report the status of metabolic reprogramming. In order to advance the understanding of metabolic reprogramming from the perspective of the cell surface, we combined genetics and advanced protein engineering using phage-displayed synthetic human antibody libraries to generate recombinant antibodies (rAbs) that respond to changes in the activity of the HBP. Our method for discovery of surface and nutrient-associated protein sensors (SNAPS) has allowed us to identify multiple human synthetic rAbs whose activity is potentiated under different metabolic states. More specifically, these rAbs recognize cell surface features that discriminate cancer cells with increased HBP flux and effectively recruit components of the immune system to initiate tumor cell lysis. Our findings suggest that the nutrient-dependent cell surface epitopes may have functional roles in defining cancer cell identity and may link multiple cancer hallmarks. Citation Format: Jelena Tomic, Megan McLaughlin, Ron Geyer, Sachdev Sidhu, Jason Moffat. Development and characterization of synthetic antibodies with a human framework that directly link metabolism to immune system in cancer cells. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2972.
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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) | 0.001 | 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) | 0.002 | 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".