A synthetic notch (synNotch) system linking intratumoral immune-cancer cell communication to a synthetic blood biomarker assay
Bibliographic record
Abstract
Introduction: Cellular immunotherapy has greatly improved cancer treatment in recent years. For instance, chimeric antigen receptor (CAR) T cell therapy has been proven highly effective in treating hematological malignancies, and many CAR cell designs are being explored for solid tumors. However, many questions remain why responses differ across patients and some tumor types are resistant. Improved and relatively inexpensive ways to monitor these cells could provide some answers. Clinically, blood tests are regularly used to monitor these therapies, but blood signals often do not reflect the activity of immune cells within the tumor(s). Here, using the synthetic Notch (synNotch) receptor that tethers antigen binding to customized transgene expression, we linked intratumoral immune-cancer cell communication to a simple secreted reporter blood test. Specifically, we engineered immune cells with a CD19-targeted synNotch receptor and demonstrated that binding to CD19 on cancer cells in vivo resulted in the production of secreted embryonic alkaline phosphatase (SEAP) at levels that are readily detected in the blood. Methods and Results: Jurkat T cells were engineered via sequential lentiviral transduction of two components: an anti-CD19 synNotch receptor and a synNotch response element encoding SEAP. Co-culture of engineered cells with CD19 + , but not CD19 − , Nalm6 cells, resulted in significantly elevated SEAP in media. Nod-scid-gamma (NSG) mice were subcutaneously injected with either CD19 + or CD19 − Nalm6 cells. Intratumoral injection of engineered T cells (1x10 7 ) resulted in significantly elevated blood SEAP activity in mice bearing CD19 + tumors (n = 7), but not CD19 − tumors (n = 5). Discussion: Our synNotch reporter system allows for the monitoring of antigen-dependent intratumoral immune-cancer cell interactions through a simple and convenient blood test. Continued development of this system for different target antigens of interest should provide a broadly applicable platform for improved monitoring of many cell-based immunotherapies during their initial development and clinical translation, ultimately improving our understanding of design considerations and patient-specific responses.
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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".