Integrative genomics identifies SHPRH as a tumor suppressor gene in lung adenocarcinoma that regulates DNA damage response
Bibliographic record
Abstract
BACKGROUND: Identification of driver mutations and development of targeted therapies has considerably improved outcomes for lung cancer patients. However, significant limitations remain with the lack of identified drivers in a large subset of patients. Here, we aimed to assess the genomic landscape of lung adenocarcinomas (LUADs) from individuals without a history of tobacco use to reveal new genetic drivers of lung cancer. METHODS: Integrative genomic analyses combining whole-exome sequencing, copy number, and mutational information for 83 LUAD tumors was performed and validated using external datasets to identify genetic variants with a predicted functional consequence and assess association with clinical outcomes. LUAD cell lines with alteration of identified candidates were used to functionally characterize tumor suppressive potential using a conditional expression system both in vitro and in vivo. RESULTS: We identified 21 genes with evidence of positive selection, including 12 novel candidates that have yet to be characterized in LUAD. In particular, SNF2 Histone Linker PHD RING Helicase (SHPRH) was identified due to its frequency of biallelic disruption and location within the familial susceptibility locus on chromosome arm 6q. We found that low SHPRH mRNA expression is associated with poor survival outcomes in LUAD patients. Furthermore, we showed that re-expression of SHPRH in LUAD cell lines with inactivating alterations for SHPRH reduces their in vitro colony formation and tumor burden in vivo. Finally, we explored the biological pathways associated SHPRH inactivation and found an association with the tolerance of LUAD cells to DNA damage. CONCLUSIONS: These data suggest that SHPRH is a tumor suppressor gene in LUAD, whereby its expression is associated with more favorable patient outcomes, reduced tumor and mutational burden, and may serve as a predictor of response to DNA damage. Thus, further exploration into the role of SHPRH in LUAD development may make it a valuable biomarker for predicting LUAD risk and prognosis.
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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".