Abstract 3668: Investigating a tumor suppressor role for Parkinson's susceptibility gene LRRK2 in lung cancer
Bibliographic record
Abstract
Abstract In this study we investigate a novel and previously untested research question: does a known disease susceptibility gene in Parkinson's disease (PD) also act as a tumor suppressor gene in lung cancer? Our screen of publicly available cancer patient sequence data for genome and transcriptome alterations of genes that modulate autophagy, an important tumor cell survival process, revealed a dramatic loss of gene expression of Leucine-rich repeat kinase 2 (LRRK2) in non-small cell lung tumors compared to matched normal tissue. We further confirmed reduced or absent LRRK2 protein expression in a panel of lung cancer cell lines. Activating kinase-domain mutations in LRRK2 and their effect on autophagy in neuronal cell types is under study in PD; however, the pathological role of altered LRRK2 in cancer and its effect on autophagy status and tumorigenicity is completely unexplored. Pharmacological inhibition of the LRRK2 kinase domain was recently shown to stimulate autophagy in PD cell lines, while many tumors are thought to exploit elevated autophagy to survive metabolic stress and chemotherapy. Ongoing clinical trials are testing combination autophagy inhibition with current standard of care treatments in multiple cancers. Therefore, a better understanding of pathogenic LRRK2-mediated autophagy in lung cancer could form the basis of a new approach to help identify lung cancer patients that may benefit from treatment strategies employing autophagy inhibitors. Our preliminary data indicates that LRRK2 knockdown in lung cancer cell lines leads to altered cellular phenotypes and increased protein levels of a known lung cancer gene. We are further testing whether genetic and/or pharmacological inhibition of LRRK2 facilitates tumorigenesis in standard murine models of lung cancer. Our investigation of the biological relevance and therapeutic potential of a surprising discovery linking neurodegenerative disease-associated LRRK2 and lung cancer may uncover a new tumor suppressor gene and provide a novel marker (i.e. LRRK2 loss) to aid in lung cancer detection. Citation Format: Chandra Lebovitz, Norman Chow, Wan Lam, Sharon Gorski. Investigating a tumor suppressor role for Parkinson's susceptibility gene LRRK2 in lung cancer. [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 3668.
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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) | 0.002 | 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".