From netrin‐1‐targeted SPECT/CT to internal radiotherapy for management of advanced solid tumors
Bibliographic record
Abstract
Abstract Targeted radionuclide therapy is a revolutionary tool for the treatment of highly spread metastatic cancers. Most current approaches rely on the use of vectors to deliver radionuclides to tumor cells, targeting membrane‐bound cancer‐specific moieties. Here, we report the embryonic navigation cue netrin‐1 as an unanticipated target for vectorized radiotherapy. While netrin‐1, known to be re‐expressed in tumoral cells to promote cancer progression, is usually characterized as a diffusible ligand, we demonstrate here that netrin‐1 is actually poorly diffusible and bound to the extracellular matrix. A therapeutic anti‐netrin‐1 monoclonal antibody (NP137) has been preclinically developed and was tested in various clinical trials showing an excellent safety profile. In order to provide a companion test detecting netrin‐1 in solid tumors and allowing the selection of therapy‐eligible patients, we used the clinical‐grade NP137 agent and developed an indium‐111‐NODAGA‐NP137 single photon emission computed tomography (SPECT) contrast agent. NP137‐ 111 In provided specific detection of netrin‐1‐positive tumors with an excellent signal‐to‐noise ratio using SPECT/CT imaging in different mouse models. The high specificity and strong affinity of NP137 paved the way for the generation of lutetium‐177‐DOTA‐NP137, a novel vectorized radiotherapy, which specifically accumulated in netrin‐1‐positive tumors. We demonstrate here, using tumor cell‐engrafted mouse models and a genetically engineered mouse model, that a single systemic injection of NP137‐ 177 Lu provides important antitumor effects and prolonged mouse survival. Together, these data support the view that NP137‐ 111 In and NP137‐ 177 Lu may represent original and unexplored imaging and therapeutic tools against advanced solid cancers.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".