Gene Expression Signatures Predicting Survival and Chemotherapy Benefit in Patients with Resected Non-small-Cell Lung Cancer
Bibliographic record
Abstract
Adjuvant cisplatin-based chemotherapy is standard use in adenocarcinomas and squamous cell lung cancer patients with stage IIA, IIB or IIIA disease who have undergone complete surgical resection [[1]Kris M.G. Gaspar L.E. Chaft J.E. et al.Adjuvant systemic therapy and adjuvant radiation therapy for stage I to IIIA completely resected non-small-cell lung cancers: American society of clinical oncology/cancer care ontario clinical practice guideline update.J Clin Oncol. 2017; 35: 2960-2974Crossref PubMed Scopus (210) Google Scholar]. Adjuvant chemotherapy has proven to be efficacious in a total of 12,473 patients (median age, 64 (57–70) years) when started up to 4 months postoperatively in patients who recovered slowly from surgery. A Cox model identified the lowest mortality risk when chemotherapy was started 50 days postoperatively [[2]Salazar M.C. Rosen J.E. Wang Z. et al.Association of delayed adjuvant chemotherapy with survival after lung cancer surgery.JAMA Oncol. 2017; 3: 610-619Crossref PubMed Scopus (110) Google Scholar]. Guo et al., in the current EbioMedicine issue, have reported a 7-gene signature predicting survival in NSCLC patients. In the Case Western Reserve University (CWRU) patient cohort, the 30 month survival rate was less than 40% in the high-risk patients who did not receive chemotherapy, while the 30 months survival rate was 100% in patients receiving adjuvant chemotherapy [[3]Guo N. Dowlati A. Raese A.R. et al.A predictive 7-gene assay and prognostic protein biomarkers for non-small cell lung cancer.2018Summary Full Text Full Text PDF Scopus (12) Google Scholar]. In a validation set, the 5-year survival rate was 70.9% in high-risk patients who received adjuvant chemotherapy, whereas it was only 45.8% in high-risk patients who did not receive adjuvant chemotherapy. Conversely, no benefit of chemotherapy was obtained in patients deemed low-risk. The 7-gene signature of Guo et al. is akin to the 14-gene signature developed by David Jablons in resected lung adenocarcinomas [[4]Woodard G.A. Wang S.X. Kratz J.R. et al.Adjuvant chemotherapy guided by molecular profiling and improved outcomes in early stage, non-small-cell lung cancer.Clin Lung Cancer. 2018; 19: 58-64Summary Full Text Full Text PDF PubMed Scopus (25) Google Scholar]. In the Jablons study, the 5-year disease-free survival rate was 91.7% in high-risk patients who received adjuvant chemotherapy, whereas it was only 48.9% in high-risk patients who did not receive adjuvant chemotherapy. Untreated molecular low-risk patients had a 5-year disease-free survival rate of 93.8% [[4]Woodard G.A. Wang S.X. Kratz J.R. et al.Adjuvant chemotherapy guided by molecular profiling and improved outcomes in early stage, non-small-cell lung cancer.Clin Lung Cancer. 2018; 19: 58-64Summary Full Text Full Text PDF PubMed Scopus (25) Google Scholar]. Intriguingly, another study reported that for low-risk patients, adjuvant chemotherapy could be detrimental for survival [[5]Chen T. Chen L. Prediction of clinical outcome for all stages and multiple cell types of non-small cell lung Cancer in five countries using lung cancer prognostic index.EBioMedicine. 2014; 1: 156-166Summary Full Text Full Text PDF PubMed Scopus (7) Google Scholar]. This finding posit that chemotherapy can be nefarious for resected NSCLC patients and, furthermore, experiments support that chemotherapy induces tumor cells expressing the actin protein, mammalian-enabled (MENA), to migrate and, despite decreasing tumor size, it increases the risk of metastatic dissemination [[6]Karagiannis G.S. Pastoriza J.M. Wang Y. et al.Neoadjuvant chemotherapy induces breast cancer metastasis through a TMEM-mediated mechanism.Sci Transl Med. 2017; 9Crossref PubMed Scopus (279) Google Scholar] (Fig. 1). Moreover, chemotherapy can induce hypoxia, causing enhanced expression of tumor cell surface markers, such as CD47, that functions as a ligand for signal regulatory protein-α (SIRPα), a protein expressed on macrophage and dendritic cells, and CD73, causing immune evasion [[7]Samanta D. Park Y. Ni X. et al.Chemotherapy induces enrichment of CD47(+)/CD73(+)/PDL1(+) immune evasive triple-negative breast cancer cells.Proc Natl Acad Sci U S A. 2018; 115: E1239-E1248Crossref PubMed Scopus (180) Google Scholar] (Fig. 1). Guo et al. highlight that CD27, as part of the 7-gene signature, induces NF-kB activation and could be a potential target for immunotherapy [[3]Guo N. Dowlati A. Raese A.R. et al.A predictive 7-gene assay and prognostic protein biomarkers for non-small cell lung cancer.2018Summary Full Text Full Text PDF Scopus (12) Google Scholar]. The findings are of interest and can pave the way to develop new theranostic models for PD-1 blockade in early resected NSCLC [[8]Forde P.M. Chaft J.E. Smith K.N. et al.Neoadjuvant PD-1 Blockade in Resectable Lung Cancer.N Engl J Med. 2018; 378: 1976-1986Crossref PubMed Scopus (1049) Google Scholar]. The 5-year survival rates range from 67% for patients with T1 N0 disease to 23% for T1-3 N2 disease [[1]Kris M.G. Gaspar L.E. Chaft J.E. et al.Adjuvant systemic therapy and adjuvant radiation therapy for stage I to IIIA completely resected non-small-cell lung cancers: American society of clinical oncology/cancer care ontario clinical practice guideline update.J Clin Oncol. 2017; 35: 2960-2974Crossref PubMed Scopus (210) Google Scholar]. Gene expression signatures for predicting metastasis and survival in early NSCLC has been numerously reported in adenocarcinoma, and, to a lesser extent, in squamous cell carcinoma of the lung. Despite a lack of commonality of many genes identified between the published prognostic signatures, numerous gene expression signatures occupy overlapping prognostic space and were able to predict outcome in early NSCLC [[9]Rosell R. Taron M. Jablons D. Lung Cancer metastasis.in: Psaila B. Welch D.R. Lyden D. Cancer metastasis: Biologic basis and therapeutics. Cambridge University Press, Cambridge2011: 369-381Crossref Google Scholar]. The Guo et al. predictive assay based on mRNA expression should be pondered and adapted to the evolving concepts in NSCLC, taking into account the crossover in sex incidences, the accumulation of somatic mutations, known as drivers, and, possibly, other genomic alterations [[3]Guo N. Dowlati A. Raese A.R. et al.A predictive 7-gene assay and prognostic protein biomarkers for non-small cell lung cancer.2018Summary Full Text Full Text PDF Scopus (12) Google Scholar]. Adenosine deaminases acting on RNA (ADARs) can induce A-to-I RNA editing where genomically encoded adenosines are transformed and recognized as guanosines in the RNA sequence, creating an inner transcriptome diversity that, unlike DNA mutations, does not leave traces on the genome [[10]Paz-Yaacov N. Bazak L. Buchumenski I. et al.Elevated RNA editing activity is a major contributor to transcriptomic diversity in tumors.Cell Rep. 2015; 13: 267-276Summary Full Text Full Text PDF PubMed Scopus (210) Google Scholar]. Further clarification on the potentially harmful effect of chemotherapy in promoting metastasis via blood vessel tumor cell migration warrants investigation, as it has been shown that MENA is differentially spliced in streaming disseminating tumor cells, showing the splicing pattern of MENAINV-high and MENA11a-low [[6]Karagiannis G.S. Pastoriza J.M. Wang Y. et al.Neoadjuvant chemotherapy induces breast cancer metastasis through a TMEM-mediated mechanism.Sci Transl Med. 2017; 9Crossref PubMed Scopus (279) Google Scholar] (Fig. 1). In short, somatic mutations, RNA editing and the role of macrophages should be kept in mind, in addition to gene transcription, for further optimization of adjuvant and neoadjuvant therapies, including anti-PD-1 and anti-PD-L1 blockade. The authors declare no conflict of interest. Work in Dr. Rosell's laboratory is partially supported by a grant from a Marie Skłodowska-Curie Innovative Training Networks European Grant (ELBA No 765492).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".