A novel protein-based prognostic signature improves risk stratification to guide clinical management in early-stage lung adenocarcinoma patients

Journal of Pathology

Volumn 245, Issue 4, 2018, Pages 421-432

  Martínez Terroba, Elena ^a,b   Behrens, Carmen ^c   de Miguel, Fernando J ^a,d   Agorreta, Jackeline ^a,b,e,f   Monsó, Eduard ^g,h   Millares, Laura ^g,h   Sainz, Cristina ^a,f   Mesa Guzman, Miguel ^e,i   Pérez Gracia, José Luis ^e,f,i   Lozano, María Dolores ^e,f,i   Zulueta, Javier J ^e,f,i   Pio, Ruben ^a,d,e,f   Wistuba, Ignacio I ^c,j   Montuenga, Luis M ^a,b,e,f   Pajares, María J ^a,b,e,f  

^a UNIVERSITY OF NAVARRA   (Spain)

^b UNIVERSITY OF NAVARRA   (Spain)

^c UNIVERSITY OF TEXAS MD ANDERSON CANCER CENTER   (United States)

^d UNIVERSITY OF NAVARRA   (Spain)

^e IdiSNA Navarra Institute for Health Research   (Spain)

^f Autonomous University of Madrid Instituto de Investigación Hospital Universitario La Paz   (Spain)

^g Hospital Parc Tauli   (Spain)

^h INSTITUTO DE SALUD CARLOS III   (Spain)

ⁱ UNIVERSITY CLINIC OF NAVARRA   (Spain)

^j UNIVERSITY OF TEXAS   (United States)

more..

Author keywords

adenocarcinoma; clinical utility; disease free survival; immunohistochemistry; overall survival; signature

Indexed keywords

BRCA1 PROTEIN; GLUCOSE TRANSPORTER 1; QKI PROTEIN; RNA BINDING PROTEIN; UNCLASSIFIED DRUG; BRCA1 PROTEIN, HUMAN; QKI PROTEIN, HUMAN; SLC2A1 PROTEIN, HUMAN; TUMOR MARKER;

ADJUVANT CHEMOTHERAPY; ADULT; AGED; ANTIBODY SPECIFICITY; ANTIGEN RETRIEVAL; ARTICLE; CANCER PATIENT; CANCER PROGNOSIS; CANCER RECURRENCE; CANCER RISK; CANCER STAGING; CANCER SURVIVAL; CANCER THERAPY; CELLULAR DISTRIBUTION; DISEASE FREE SURVIVAL; EARLY CANCER; FEMALE; GENE EXPRESSION; HIGH RISK POPULATION; HUMAN; HUMAN CELL; IMMUNOHISTOCHEMISTRY; LUNG ADENOCARCINOMA; MAJOR CLINICAL STUDY; MALE; MOLECULAR WEIGHT; OVERALL SURVIVAL; PRACTICE GUIDELINE; PREDICTION; PRIORITY JOURNAL; RISK FACTOR; SURVIVAL RATE; TISSUE MICROARRAY; WESTERN BLOTTING; WORLD HEALTH ORGANIZATION; A-549 CELL LINE; CHEMISTRY; CLINICAL DECISION MAKING; CLINICAL TRIAL; DECISION SUPPORT SYSTEM; DISEASE EXACERBATION; GENETICS; LUNG TUMOR; MIDDLE AGED; MORTALITY; MULTICENTER STUDY; PREDICTIVE VALUE; REPRODUCIBILITY; RISK ASSESSMENT; SPAIN; TEXAS; TIME FACTOR; TUMOR RECURRENCE; VALIDATION STUDY;

A549 CELLS; ADENOCARCINOMA OF LUNG; AGED; BIOMARKERS, TUMOR; BRCA1 PROTEIN; CLINICAL DECISION-MAKING; DECISION SUPPORT TECHNIQUES; DISEASE PROGRESSION; DISEASE-FREE SURVIVAL; FEMALE; GLUCOSE TRANSPORTER TYPE 1; HUMANS; IMMUNOHISTOCHEMISTRY; LUNG NEOPLASMS; MALE; MIDDLE AGED; NEOPLASM RECURRENCE, LOCAL; NEOPLASM STAGING; PREDICTIVE VALUE OF TESTS; REPRODUCIBILITY OF RESULTS; RISK ASSESSMENT; RISK FACTORS; RNA-BINDING PROTEINS; SPAIN; TEXAS; TIME FACTORS;

EID: 85049949735     PISSN: 00223417     EISSN: 10969896     Source Type: Journal    
DOI: 10.1002/path.5096     Document Type: Article

References (50)

1. Siegel RL, Miller KD, Jemal A. Cancer Statistics, 2017. CA Cancer J Clin 2017; 67: 7–30
2. Uramoto H, Tanaka F. Recurrence after surgery in patients with NSCLC. Transl Lung Cancer Res 2014; 3: 242–249
3. Bradbury P, Sivajohanathan D, Chan A, et al. Postoperative adjuvant systemic therapy in completely resected non-small-cell lung cancer: a systematic review. Clin Lung Cancer 2017; 18: 259–273
4. Shieh Y, Bohnenkamp M. Low-dose computed tomography for lung cancer screening: clinical and coding considerations. Chest 2017; 152: 204–209
5. National Lung Screening Trial Research Team, Aberle DR, Adams AM, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med 2011; 365: 395–409
6. Vargas AJ, Harris CC. Biomarker development in the precision medicine era: lung cancer as a case study. Nat Rev Cancer 2016; 16: 525–537
7. Feldman R, Kim ES. Prognostic and predictive biomarkers post curative intent therapy. Ann Transl Med 2017; 5: 374–387
8. Kratz JR, He J, Van Den Eeden SK, et al. A practical molecular assay to predict survival in resected non-squamous, non-small-cell lung cancer: development and international validation studies. Lancet 2012; 379: 823–832
9. Wistuba II, Behrens C, Lombardi F, et al. Validation of a proliferation-based expression signature as prognostic marker in early stage lung adenocarcinoma. Clin Cancer Res 2013; 19: 6261–6271
10. Rakha E, Pajares MJ, Ilie M, et al. Stratification of resectable lung adenocarcinoma by molecular and pathological risk estimators. Eur J Cancer 2015; 51: 1897–1903
11. Tang H, Wang S, Xiao G, et al. Comprehensive evaluation of published gene expression prognostic signatures for biomarker-based lung cancer clinical studies. Ann Oncol 2017; 28: 733–740
12. Phillips T, Simmons P, Inzunza HD, et al. Development of an automated PD-L1 immunohistochemistry (IHC) assay for non-small cell lung cancer. Appl Immunohistochem Mol Morphol 2015; 23: 541–549
13. Wolff AC, Hammond ME, Hicks DG, et al. Recommendations for human epidermal growth factor receptor 2 testing in breast cancer: American Society of Clinical Oncology/College of American Pathologists clinical practice guideline update. J Clin Oncol 2013; 31: 3997–4013
14. Monsó E, Montuenga LM, Sánchez de Cos J, et al. Biological marker analysis as part of the CIBERES-RTIC Cancer-SEPAR strategic project on lung cancer. Arch Bronconeumol 2015; 51: 462–467
15. Travis WD, World Health Organization, International Agency for Research on Cancer, et al. (eds). Pathology and Genetics of Tumours of the Lung, Pleura, Thymus and Heart. Lyon: IARC Press, 2004
16. Goldstraw P, Chansky K, Crowley J, et al. The IASLC lung cancer staging project: proposals for revision of the TNM Stage Groupings in the forthcoming (eighth) edition of the TNM Classification for Lung Cancer. J Thorac Oncol 2016; 11: 39–51
17. Altman DG, McShane LM, Sauerbrei W, et al. Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK): explanation and elaboration. PLoS Med 2012; 9: e1001216
18. Bild AH, Yao G, Chang JT, et al. Oncogenic pathway signatures in human cancers as a guide to targeted therapies. Nature 2006; 439: 353–357
19. Lee ES, Son DS, Kim SH, et al. Prediction of recurrence-free survival in postoperative non-small cell lung cancer patients by using an integrated model of clinical information and gene expression. Clin Cancer Res 2008; 14: 7397–7404
20. Director's Challenge Consortium for the Molecular Classification of Lung Adenocarcinoma, Shedden K, Taylor JM, et al. Gene expression-based survival prediction in lung adenocarcinoma: a multi-site, blinded validation study. Nat Med 2008; 14: 822–827
21. Okayama H, Kohno T, Ishii Y, et al. Identification of genes upregulated in ALK-positive and EGFR/KRAS/ALK-negative lung adenocarcinomas. Cancer Res 2012; 72: 100–111
22. Tomida S, Takeuchi T, Shimada Y, et al. Relapse-related molecular signature in lung adenocarcinomas identifies patients with dismal prognosis. J Clin Oncol 2009; 27: 2793–2799
23. Wilkerson MD, Yin X, Walter V, et al. Differential pathogenesis of lung adenocarcinoma subtypes involving sequence mutations, copy number, chromosomal instability, and methylation. PLoS One 2012; 7: e36530
24. Larsen JE, Pavey SJ, Bowman R, et al. Gene expression of lung squamous cell carcinoma reflects mode of lymph node involvement. Eur Respir J 2007; 30: 21–25
25. Raponi M, Zhang Y, Yu J, et al. Gene expression signatures for predicting prognosis of squamous cell and adenocarcinomas of the lung. Cancer Res 2006; 66: 7466–7472
26. Wilkerson MD, Yin X, Hoadley KA, et al. Lung squamous cell carcinoma mRNA expression subtypes are reproducible, clinically important, and correspond to normal cell types. Clin Cancer Res 2010; 16: 4864–4875
27. Pajares MJ, Agorreta J, Larrayoz M, et al. Expression of tumor-derived vascular endothelial growth factor and its receptors is associated with outcome in early squamous cell carcinoma of the lung. J Clin Oncol 2012; 30: 1129–1136
28. Collins GS, Reitsma JB, Altman DG, et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (tripod): the tripod statement. Ann Intern Med 2015; 162: 55–63
29. Harrell F, Lee K, Mark D. Tutorial in biostatistics multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med 1996; 15: 361–387
30. Harrell FE, Jr, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med 1996; 15: 361–387
31. Smith GC, Seaman SR, Wood AM, et al. Correcting for optimistic prediction in small data sets. Am J Epidemiol 2014; 180: 318–324
32. Ezponda T, Pajares MJ, Agorreta J, et al. The oncoprotein SF2/ASF promotes non-small cell lung cancer survival by enhancing survivin expression. Clin Cancer Res 2010; 16: 4113–4125
33. Grbesa I, Pajares MJ, Martinez-Terroba E, et al. Expression of sirtuin 1 and 2 is associated with poor prognosis in non-small cell lung cancer patients. PLoS One 2015; 10: e0124670
34. de Miguel FJ, Pajares MJ, Martinez-Terroba E, et al. A large-scale analysis of alternative splicing reveals a key role of QKI in lung cancer. Mol Oncol 2016; 10: 1437–1449
35. Valles I, Pajares MJ, Segura V, et al. Identification of novel deregulated RNA metabolism-related genes in non-small cell lung cancer. PLoS One 2012; 7: e42086
36. Roepman P, Jassem J, Smit EF, et al. An immune response enriched 72-gene prognostic profile for early-stage non-small-cell lung cancer. Clin Cancer Res 2009; 15: 284–290
37. Lu Y, Lemon W, Liu PY, et al. A gene expression signature predicts survival of patients with stage I non-small cell lung cancer. PLoS Med 2006; 3: e467
38. Xie Y, Xiao G, Coombes KR, et al. Robust gene expression signature from formalin-fixed paraffin-embedded samples predicts prognosis of non-small-cell lung cancer patients. Clin Cancer Res 2011; 17: 5705–5714
39. Beer DG, Kardia SL, Huang CC, et al. Gene-expression profiles predict survival of patients with lung adenocarcinoma. Nat Med 2002; 8: 816–824
40. Sun Z, Wigle DA, Yang P. Non-overlapping and non-cell-type-specific gene expression signatures predict lung cancer survival. J Clin Oncol 2008; 26: 877–883
41. Bianchi F, Nuciforo P, Vecchi M, et al. Survival prediction of stage I lung adenocarcinomas by expression of 10 genes. J Clin Invest 2007; 117: 3436–3644
42. Grinberg M, Djureinovic D. Reaching the limits of prognostication in non-small cell lung cancer: an optimized biomarker panel fails to outperform clinical parameters. Mod Pathol 2017; 30: 964–977
43. Subramanian J, Simon R. Gene expression-based prognostic signatures in lung cancer: ready for clinical use? J Natl Cancer Inst 2010; 102: 464–474
44. Barron CC, Bilan PJ, Tsakiridis T, et al. Facilitative glucose transporters: implications for cancer detection, prognosis and treatment. Metabolism 2016; 65: 124–139
45. Hardy RJ, Loushin CL, Friedrich VL, Jr, et al. Neural cell type-specific expression of QKI proteins is altered in quakingviable mutant mice. J Neurosci 1996; 16: 7941–7949
46. Welcsh PL, King M-C. BRCA1 and BRCA2 and the genetics of breast and ovarian cancer. Hum Mol Genet 2001; 10: 705–713
47. Rosell R, Skrzypski M, Jassem E, et al. BRCA1: a novel prognostic factor in resected non-small-cell lung cancer. PLoS One 2007; 2: e1129
48. Pirker R, Filipits M. Personalized treatment of advanced non-small-cell lung cancer in routine clinical practice. Cancer Metastasis Rev 2016; 35: 141–150
49. Sheffield BS. Immunohistochemistry as a practical tool in molecular pathology. Arch Pathol Lab Med 2016; 140: 766–769
50. Zhu C-Q, Ding K, Strumpf D, et al. Prognostic and predictive gene signature for adjuvant chemotherapy in resected non-small-cell lung cancer. J Clin Oncol 2010; 28: 4417–4424