Proteus Project - CRISPR-Based Programmable Therapeutics for Targeted Apoptosis in Cancer Cells
Bibliographic record
Abstract
Abstract Cancer therapy faces a critical need for treatments that selectively eliminate malignant cells without harming healthy tissue. The Proteus Project addresses this challenge by engineering a programmable gene circuit that couples CRISPR-based RNA sensing with an inducible cell death mechanism. Specifically, we repurpose a novel Type III-E CRISPR-Cas system (“Craspase”) – an RNA-guided protease complex – to detect cancer-specific RNA transcripts and, in response, cleave engineered gasdermin fusion proteins to trigger cell death (1)(2). We constructed the Proteus system in Saccharomyces cerevisiae as a surrogate model, integrating components that sense an oncogenic KRAS mutation and execute targeted pyroptosis (inflammatory apoptosis-like cell death). Preliminary results demonstrate successful assembly of the Craspase– gasdermin circuit, expression of key proteins, and proof-of-concept cell killing specifically in the presence of the oncogenic RNA trigger. This work, conducted as part of the iGEM 2025 SynBio Collective, showcases a modular synthetic biology approach for precision oncology therapeutics. The ongoing study underscores the potential of CRISPR-guided proteases in in situ cancer cell ablation and sets the stage for future validation in mammalian systems.
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
The three-model screen
all 5,600 screened works →All three models called this out of scope.
CRISPR-based cancer therapeutic preprint; a biomedical engineering question.
The title indicates a biomedical CRISPR therapeutic project rather than a study of research.
CRISPR therapeutic engineering for cancer; domain biotech title with empty abstract.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".