RIT1M90I Is a Driver of Lung Adenocarcinoma Tumorigenesis and Resistance to Targeted Therapy
Bibliographic record
Abstract
RIT1 is a RAS-family guanosine triphosphatase that is mutated in 2.4% and amplified in up to 14% of patients with lung adenocarcinoma. Yet the oncogenic potential of RIT1 in the lungs has not been fully established. Consequently, patients with RIT1 alterations are considered "oncogene-negative" and are not eligible for any targeted therapy in the clinic. The role of RIT1 in cancer has been historically understudied due to the lack of in vitro and in vivo models harboring RIT1 alterations. In this study, we generated a murine model of RIT1M90I-mutant lung cancer. RIT1M90I expression induced tumorigenesis in the lungs, and the tumors displayed histopathologic features similar to lung adenocarcinoma in humans. An unbiased chemical compound screen leveraging this model revealed a sensitivity to inhibitors of the MAPK, PI3K, and cholesterol biosynthesis pathways in RIT1-mutant cell lines. The SHP2 inhibitor, migoprotafib, in combination with other MAPK pathway-targeted therapies, effectively suppressed the growth of RIT1-mutant cells ex vivo and in vivo. Finally, RIT1M90I drove resistance to the KRASG12C inhibitor, divarasib, and the combination with migoprotafib reverted this phenotype. Together, these data show that RIT1M90I is a bona fide oncogenic driver of lung cancer and a mediator of targeted therapy resistance as a co-occurring mutation and suggest that patients with RIT1-altered cancer may benefit from combination treatments with an SHP2 inhibitor. SIGNIFICANCE: Development of a mouse model of RIT1M90I-altered non-small cell lung cancer reveals that RIT1M90I is a driver of lung tumorigenesis and that RIT1-mutated tumors are sensitive to MAPK pathway inhibitors. See related commentary by Wu and Vaishnavi, p. 3186 See related article by Mozzarelli et al., p. 3196.
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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".