Exploring tumor-extrinsic and intrinsic influences on lung adenocarcinoma progression to uncover novel prognostic features and therapeutic targets
Bibliographic record
Abstract
Lung cancer remains the leading cause of cancer-related death worldwide, with only 1 in 4 patients surviving past 5 years of their diagnosis. The poor survival rate is largely due to late diagnoses as well as a lack of efficient therapies. Indeed, despite having revolutionized the field of clinical oncology, 60% of patients with non-small cell lung cancer (NSCLC) will fail to respond to immunotherapies and an even larger proportion will fail to develop a durable response. Therefore, discovering novel therapeutic targets and gaining a deeper understanding of lung cancer biology are critical for improving patient prognoses. Given that about 85% of lung cancers are attributable to cigarette smoking, we first investigated how smoking influences the tumor microenvironment (TME) using image mass cytometry. By analyzing tumor cores from over 400 lung adenocarcinoma patients, we found that smoking status has very little impact on the cellular composition of tumors but has significant effects on the spatial organization of immune cells within the TME. Notably, mast cells were the only immune cell type significantly increased in never smokers compared to active smokers. Mast cell abundance correlated with better survival outcomes, and their interactions with cytotoxic T cells were more frequent in never smokers, suggesting a protective immune landscape influenced by smoking status. Building upon our investigation into how cigarette smoke alters the TME, we next explored genes associated with survival outcomes in lung cancer, particularly focusing on those involved in extracellular matrix (ECM) remodeling and inflammation, directly impacted by cigarette smoke exposure. Analyzing publicly available mRNA sequencing data, we prioritized genes that showed elevated expression correlating with poor survival outcomes that could become actionable targets. This approach led us to investigate heparanase (HPSE) as a gene already linked to cancer progression but remained understudied in lung cancer. HPSE is the sole mammalian enzyme capable of cleaving heparan sulfate chains at the sugar moiety level, which are ubiquitous components of the ECM and serve as a ligand sink for a variety of chemokines, cytokines, and growth factors. Expression of HPSE by cancer cells allows them to take advantage of the physiological role of these sugar chains by loosening the ECM and accessing bio-available growth factors. Functional dissection of HPSE in pre-clinical models demonstrated that reducing its expression led to a less aggressive tumor phenotype both in vitro and in vivo. Decreased Hpse expression resulted in significant changes within the tumor immune microenvironment, including a shift toward an anti-tumor immune response. These findings suggest that HPSE not only serves as a putative driver of lung cancer progression but also represents a potential therapeutic target. Targeting HPSE could disrupt the pro-tumorigenic immune landscape typical of lung cancer, which could enhance the efficacy of existing immunotherapies. Our research underscores the importance of considering smoking status when studying the TME and its impact on lung cancer progression. We shine light on the underappreciated role of mast cells in orchestrating an anti-tumor response, especially in never smokers. Further, we provide a rationale for targeting ECM remodeling enzymes as a therapeutic strategy, such as HPSE. This integrated approach enhances our understanding of lung cancer biology and opens avenues for developing more effective treatments and predictive biomarkers, ultimately aiming to improve survival outcomes for patients, especially those with a history of smoking. Given that approximately 30% of men and 6% of women continue to smoke, lung cancer will remain a significant healthcare burden for the foreseeable future. Advancing our knowledge of how cigarette smoke affects tumor biology is paramount to providing better care for these patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".