Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ASCO Annual Meeting 2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

2024 ASCO Annual Meeting

Keep up to date with all the top stories and latest evidence with our hub page, including official congress videos and expert takeaways.
ADVANCED SEARCH
Log in
Top
Discover Oncology
Published in: Discover Oncology 1/2023

Open Access 01-12-2023 | NSCLC | Research

Depiction of neuroendocrine features associated with immunotherapy response using a novel one-class predictor in lung adenocarcinoma

Authors: Hao Liu, Yan Han, Zhantao Liu, Liping Gao, Tienan Yi, Yuandong Yu, Yu Wang, Ping Qu, Longchao Xiang, Yong Li

Published in: Discover Oncology | Issue 1/2023

Login to get access

Abstract

Background

Tumours with no evidence of neuroendocrine transformation histologically but harbouring neuroendocrine features are collectively referred to as non-small cell lung cancer (NSCLC) with neuroendocrine differentiation (NED). Investigating the mechanisms underlying NED is conducive to designing appropriate treatment options for NSCLC patients.

Methods

In the present study, we integrated multiple lung cancer datasets to identify neuroendocrine features using a one-class logistic regression (OCLR) machine learning algorithm trained on small cell lung cancer (SCLC) cells, a pulmonary neuroendocrine cell type, based on the transcriptome of NSCLC and named the NED index (NEDI). Single-sample gene set enrichment analysis, pathway enrichment analysis, ESTIMATE algorithm analysis, and unsupervised subclass mapping (SubMap) were performed to assess the altered pathways and immune characteristics of lung cancer samples with different NEDI values.

Results

We developed and validated a novel one-class predictor based on the expression values of 13,279 mRNAs to quantitatively evaluate neuroendocrine features in NSCLC. We observed that a higher NEDI correlated with better prognosis in patients with LUAD. In addition, we observed that a higher NEDI was significantly associated with reduced immune cell infiltration and immune effector molecule expression. Furthermore, we found that etoposide-based chemotherapy might be more effective in the treatment of LUAD with high NEDI values. Moreover, we noted that tumours with low NEDI values had better responses to immunotherapy than those with high NEDI values.

Conclusions

Our findings improve the understanding of NED and provide a useful strategy for applying NEDI-based risk stratification to guide decision-making in the treatment of LUAD.
Appendix
Available only for authorised users
Literature
1.
Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71:209–49.PubMedCrossRef
2.
3.
Passaro A, Janne PA, Mok T, Peters S. Overcoming therapy resistance in EGFR-mutant lung cancer. Nat Cancer. 2021;2:377–91.PubMedCrossRef
4.
Quintanal-Villalonga A, Taniguchi H, Zhan YA, Hasan MM, Chavan SS, Meng F, Uddin F, Manoj P, Donoghue MTA, Won HH, et al. Multiomic analysis of lung tumors defines pathways activated in neuroendocrine transformation. Cancer Discov. 2021;11:3028–47.PubMedPubMedCentralCrossRef
5.
Slodkowska J. The value of immunohistochemical identification of neuroendocrine differentiation in non small cell lung carcinoma. Rocz Akad Med Bialymst. 1997;42(Suppl 1):23–7.PubMed
6.
Graziano SL, Mazid R, Newman N, Tatum A, Oler A, Mortimer JA, Gullo JJ, DiFino SM, Scalzo AJ. The use of neuroendocrine immunoperoxidase markers to predict chemotherapy response in patients with non-small-cell lung cancer. J Clin Oncol. 1989;7:1398–406.PubMedCrossRef
7.
Skov BG, Sorensen JB, Hirsch FR, Larsson LI, Hansen HH. Prognostic impact of histologic demonstration of chromogranin A and neuron specific enolase in pulmonary adenocarcinoma. Ann Oncol. 1991;2:355–60.PubMedCrossRef
8.
Slodkowska J, Zych J, Szturmowicz M, Demkow U, Rowinska-Zakrzewska E, Roszkowski-Sliz K. Neuroendocrine phenotype of non-small cell lung carcinoma: immunohistological evaluation and biochemical study. Int J Biol Markers. 2005;20:217–26.PubMedCrossRef
9.
Liu Y, Xu J, Jiao Y, Hu Y, Yi C, Li Q, Tong Z, Wang X, Hu L, Xiao Q, et al. Neuroendocrine differentiation is a prognostic factor for stage II poorly differentiated colorectal cancer. Biomed Res Int. 2014;2014: 789575.PubMedPubMedCentral
10.
Conroy JM, Pabla S, Nesline MK, Glenn ST, Papanicolau-Sengos A, Burgher B, Andreas J, Giamo V, Wang Y, Lenzo FL, et al. Next generation sequencing of PD-L1 for predicting response to immune checkpoint inhibitors. J Immunother Cancer. 2019;7:18.PubMedPubMedCentralCrossRef
11.
Sorokin M, Ignatev K, Poddubskaya E, Vladimirova U, Gaifullin N, Lantsov D, Garazha A, Allina D, Suntsova M, Barbara V, Buzdin A. RNA sequencing in comparison to immunohistochemistry for measuring cancer biomarkers in breast cancer and lung cancer specimens. Biomedicines. 2020;8:114.PubMedPubMedCentralCrossRef
12.
Brunet JP, Tamayo P, Golub TR, Mesirov JP. Metagenes and molecular pattern discovery using matrix factorization. Proc Natl Acad Sci U S A. 2004;101:4164–9.PubMedPubMedCentralCrossRef
13.
Sokolov A, Paull EO, Stuart JM. One-class detection of cell states in tumor subtypes. Pac Symp Biocomput. 2016;21:405–16.PubMedPubMedCentral
14.
Malta TM, Sokolov A, Gentles AJ, Burzykowski T, Poisson L, Weinstein JN, Kaminska B, Huelsken J, Omberg L, Gevaert O, et al. Machine learning identifies stemness features associated with oncogenic dedifferentiation. Cell. 2018;173(338–354): e315.
15.
16.
Ghandi M, Huang FW, Jane-Valbuena J, Kryukov GV, Lo CC, McDonald ER 3rd, Barretina J, Gelfand ET, Bielski CM, Li H, et al. Next-generation characterization of the Cancer Cell Line Encyclopedia. Nature. 2019;569:503–8.PubMedPubMedCentralCrossRef
17.
Gillette MA, Satpathy S, Cao S, Dhanasekaran SM, Vasaikar SV, Krug K, Petralia F, Li Y, Liang WW, Reva B, et al. Proteogenomic characterization reveals therapeutic vulnerabilities in lung adenocarcinoma. Cell. 2020;182(200–225): e235.
18.
Satpathy S, Krug K, Jean Beltran PM, Savage SR, Petralia F, Kumar-Sinha C, Dou Y, Reva B, Kane MH, Avanessian SC, et al. A proteogenomic portrait of lung squamous cell carcinoma. Cell. 2021;184(4348–4371): e4340.
19.
George J, Lim JS, Jang SJ, Cun Y, Ozretic L, Kong G, Leenders F, Lu X, Fernandez-Cuesta L, Bosco G, et al. Comprehensive genomic profiles of small cell lung cancer. Nature. 2015;524:47–53.PubMedPubMedCentralCrossRef
20.
21.
Karlsson A, Jonsson M, Lauss M, Brunnstrom H, Jonsson P, Borg A, Jonsson G, Ringner M, Planck M, Staaf J. Genome-wide DNA methylation analysis of lung carcinoma reveals one neuroendocrine and four adenocarcinoma epitypes associated with patient outcome. Clin Cancer Res. 2014;20:6127–40.PubMedCrossRef
22.
Laddha SV, da Silva EM, Robzyk K, Untch BR, Ke H, Rekhtman N, Poirier JT, Travis WD, Tang LH, Chan CS. Integrative genomic characterization identifies molecular subtypes of lung carcinoids. Cancer Res. 2019;79:4339–47.PubMedPubMedCentralCrossRef
23.
Niederst MJ, Sequist LV, Poirier JT, Mermel CH, Lockerman EL, Garcia AR, Katayama R, Costa C, Ross KN, Moran T, et al. RB loss in resistant EGFR mutant lung adenocarcinomas that transform to small-cell lung cancer. Nat Commun. 2015;6:6377.PubMedCrossRef
24.
Fujiwara T, Hiramatsu M, Isagawa T, Ninomiya H, Inamura K, Ishikawa S, Ushijima M, Matsuura M, Jones MH, Shimane M, et al. ASCL1-coexpression profiling but not single gene expression profiling defines lung adenocarcinomas of neuroendocrine nature with poor prognosis. Lung Cancer. 2012;75:119–25.PubMedCrossRef
25.
Balko JM, Potti A, Saunders C, Stromberg A, Haura EB, Black EP. Gene expression patterns that predict sensitivity to epidermal growth factor receptor tyrosine kinase inhibitors in lung cancer cell lines and human lung tumors. BMC Genomics. 2006;7:289.PubMedPubMedCentralCrossRef
26.
Okayama H, Kohno T, Ishii Y, Shimada Y, Shiraishi K, Iwakawa R, Furuta K, Tsuta K, Shibata T, Yamamoto S, et al. Identification of genes upregulated in ALK-positive and EGFR/KRAS/ALK-negative lung adenocarcinomas. Cancer Res. 2012;72:100–11.PubMedCrossRef
27.
Der SD, Sykes J, Pintilie M, Zhu CQ, Strumpf D, Liu N, Jurisica I, Shepherd FA, Tsao MS. Validation of a histology-independent prognostic gene signature for early-stage, non-small-cell lung cancer including stage IA patients. J Thorac Oncol. 2014;9:59–64.PubMedCrossRef
28.
Schabath MB, Welsh EA, Fulp WJ, Chen L, Teer JK, Thompson ZJ, Engel BE, Xie M, Berglund AE, Creelan BC, et al. Differential association of STK11 and TP53 with KRAS mutation-associated gene expression, proliferation and immune surveillance in lung adenocarcinoma. Oncogene. 2016;35:3209–16.PubMedCrossRef
29.
Riaz N, Havel JJ, Makarov V, Desrichard A, Urba WJ, Sims JS, Hodi FS, Martin-Algarra S, Mandal R, Sharfman WH, et al. Tumor and microenvironment evolution during immunotherapy with nivolumab. Cell. 2017;171(934–949): e916.
30.
Tomida S, Takeuchi T, Shimada Y, Arima C, Matsuo K, Mitsudomi T, Yatabe Y, Takahashi T. Relapse-related molecular signature in lung adenocarcinomas identifies patients with dismal prognosis. J Clin Oncol. 2009;27:2793–9.PubMedCrossRef
31.
Cho JW, Hong MH, Ha SJ, Kim YJ, Cho BC, Lee I, Kim HR. Genome-wide identification of differentially methylated promoters and enhancers associated with response to anti-PD-1 therapy in non-small cell lung cancer. Exp Mol Med. 2020;52:1550–63.PubMedPubMedCentralCrossRef
32.
Hight SK, Mootz A, Kollipara RK, McMillan E, Yenerall P, Otaki Y, Li LS, Avila K, Peyton M, Rodriguez-Canales J, et al. An in vivo functional genomics screen of nuclear receptors and their co-regulators identifies FOXA1 as an essential gene in lung tumorigenesis. Neoplasia. 2020;22:294–310.PubMedPubMedCentralCrossRef
33.
Jung H, Kim HS, Kim JY, Sun JM, Ahn JS, Ahn MJ, Park K, Esteller M, Lee SH, Choi JK. DNA methylation loss promotes immune evasion of tumours with high mutation and copy number load. Nat Commun. 2019;10:4278.PubMedPubMedCentralCrossRef
34.
Song LR, Weng JC, Li CB, Huo XL, Li H, Hao SY, Wu Z, Wang L, Li D, Zhang JT. Prognostic and predictive value of an immune infiltration signature in diffuse lower-grade gliomas. JCI Insight. 2020;5:e133811.PubMedPubMedCentralCrossRef
35.
Mayakonda A, Lin DC, Assenov Y, Plass C, Koeffler HP. Maftools: efficient and comprehensive analysis of somatic variants in cancer. Genome Res. 2018;28:1747–56.PubMedPubMedCentralCrossRef
36.
Leiserson MD, Wu HT, Vandin F, Raphael BJ. CoMEt: a statistical approach to identify combinations of mutually exclusive alterations in cancer. Genome Biol. 2015;16:160.PubMedPubMedCentralCrossRef
37.
38.
Yoshihara K, Shahmoradgoli M, Martinez E, Vegesna R, Kim H, Torres-Garcia W, Trevino V, Shen H, Laird PW, Levine DA, et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun. 2013;4:2612.PubMedCrossRef
39.
Charoentong P, Finotello F, Angelova M, Mayer C, Efremova M, Rieder D, Hackl H, Trajanoski Z. Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade. Cell Rep. 2017;18:248–62.PubMedCrossRef
40.
Reich M, Liefeld T, Gould J, Lerner J, Tamayo P, Mesirov JP. GenePattern 2.0. Nat Genet. 2006;38:500–1.PubMedCrossRef
41.
Hoshida Y, Brunet JP, Tamayo P, Golub TR, Mesirov JP. Subclass mapping: identifying common subtypes in independent disease data sets. PLoS ONE. 2007;2: e1195.PubMedPubMedCentralCrossRef
42.
Geeleher P, Cox N, Huang RS. pRRophetic: an R package for prediction of clinical chemotherapeutic response from tumor gene expression levels. PLoS ONE. 2014;9: e107468.PubMedPubMedCentralCrossRef
43.
Lamb J, Crawford ED, Peck D, Modell JW, Blat IC, Wrobel MJ, Lerner J, Brunet JP, Subramanian A, Ross KN, et al. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science. 2006;313:1929–35.PubMedCrossRef
44.
45.
Nunez E, Steyerberg EW, Nunez J. Regression modeling strategies. Rev Esp Cardiol. 2011;64:501–7.PubMed
46.
Gerds TA, Cai T, Schumacher M. The performance of risk prediction models. Biom J. 2008;50:457–79.PubMedCrossRef
47.
48.
Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43: e47.PubMedPubMedCentralCrossRef
49.
Jones MH, Virtanen C, Honjoh D, Miyoshi T, Satoh Y, Okumura S, Nakagawa K, Nomura H, Ishikawa Y. Two prognostically significant subtypes of high-grade lung neuroendocrine tumours independent of small-cell and large-cell neuroendocrine carcinomas identified by gene expression profiles. Lancet. 2004;363:775–81.PubMedCrossRef
50.
Cancer Genome Atlas Research N: Comprehensive molecular profiling of lung adenocarcinoma. Nature 2014, 511:543–550.
51.
Oxnard GR, Hu Y, Mileham KF, Husain H, Costa DB, Tracy P, Feeney N, Sholl LM, Dahlberg SE, Redig AJ, et al. Assessment of resistance mechanisms and clinical implications in patients with EGFR T790M-positive lung cancer and acquired resistance to osimertinib. JAMA Oncol. 2018;4:1527–34.PubMedCrossRef
52.
53.
Howe MC, Chapman A, Kerr K, Dougal M, Anderson H, Hasleton PS. Neuroendocrine differentiation in non-small cell lung cancer and its relation to prognosis and therapy. Histopathology. 2005;46:195–201.PubMedCrossRef
54.
Tirosh I, Izar B, Prakadan SM, Wadsworth MH 2nd, Treacy D, Trombetta JJ, Rotem A, Rodman C, Lian C, Murphy G, et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science. 2016;352:189–96.PubMedPubMedCentralCrossRef
55.
Senbabaoglu Y, Gejman RS, Winer AG, Liu M, Van Allen EM, de Velasco G, Miao D, Ostrovnaya I, Drill E, Luna A, et al. Tumor immune microenvironment characterization in clear cell renal cell carcinoma identifies prognostic and immunotherapeutically relevant messenger RNA signatures. Genome Biol. 2016;17:231.PubMedPubMedCentralCrossRef
56.
Roh W, Chen PL, Reuben A, Spencer CN, Prieto PA, Miller JP, Gopalakrishnan V, Wang F, Cooper ZA, Reddy SM, et al. Integrated molecular analysis of tumor biopsies on sequential CTLA-4 and PD-1 blockade reveals markers of response and resistance. Sci Transl Med. 2017. https://​doi.​org/​10.​1126/​scitranslmed.​aah3560.
57.
Auslander N, Zhang G, Lee JS, Frederick DT, Miao B, Moll T, Tian T, Wei Z, Madan S, Sullivan RJ, et al. Robust prediction of response to immune checkpoint blockade therapy in metastatic melanoma. Nat Med. 2018;24:1545–9.PubMedPubMedCentralCrossRef
58.
Hugo W, Zaretsky JM, Sun L, Song C, Moreno BH, Hu-Lieskovan S, Berent-Maoz B, Pang J, Chmielowski B, Cherry G, et al. Genomic and transcriptomic features of response to anti-PD-1 therapy in metastatic melanoma. Cell. 2016;165:35–44.PubMedPubMedCentralCrossRef
59.
Tibshirani R. The lasso method for variable selection in the Cox model. Stat Med. 1997;16:385–95.PubMedCrossRef
60.
Schleusener JT, Tazelaar HD, Jung SH, Cha SS, Cera PJ, Myers JL, Creagan ET, Goldberg RM, Marschke RF Jr. Neuroendocrine differentiation is an independent prognostic factor in chemotherapy-treated nonsmall cell lung carcinoma. Cancer. 1996;77:1284–91.PubMedCrossRef
61.
Gajra A, Tatum AH, Newman N, Gamble GP, Lichtenstein S, Rooney MT, Graziano SL. The predictive value of neuroendocrine markers and p53 for response to chemotherapy and survival in patients with advanced non-small cell lung cancer. Lung Cancer. 2002;36:159–65.PubMedCrossRef
62.
Sha D, Jin Z, Budczies J, Kluck K, Stenzinger A, Sinicrope FA. Tumor mutational burden as a predictive biomarker in solid tumors. Cancer Discov. 2020;10:1808–25.PubMedPubMedCentralCrossRef
63.
Ge Y, Gomez NC, Adam RC, Nikolova M, Yang H, Verma A, Lu CP, Polak L, Yuan S, Elemento O, Fuchs E. Stem cell lineage infidelity drives wound repair and cancer. Cell. 2017;169(636–650): e614.
64.
Borisov N, Shabalina I, Tkachev V, Sorokin M, Garazha A, Pulin A, Eremin II, Buzdin A. Shambhala: a platform-agnostic data harmonizer for gene expression data. BMC Bioinformatics. 2019;20:66.PubMedPubMedCentralCrossRef
65.
Borisov N, Suntsova M, Sorokin M, Garazha A, Kovalchuk O, Aliper A, Ilnitskaya E, Lezhnina K, Korzinkin M, Tkachev V, et al. Data aggregation at the level of molecular pathways improves stability of experimental transcriptomic and proteomic data. Cell Cycle. 2017;16:1810–23.PubMedPubMedCentralCrossRef
66.
Suntsova M, Gaifullin N, Allina D, Reshetun A, Li X, Mendeleeva L, Surin V, Sergeeva A, Spirin P, Prassolov V, et al. Atlas of RNA sequencing profiles for normal human tissues. Sci Data. 2019;6:36.PubMedPubMedCentralCrossRef
Metadata
Title
Depiction of neuroendocrine features associated with immunotherapy response using a novel one-class predictor in lung adenocarcinoma
Authors
Hao Liu
Yan Han
Zhantao Liu
Liping Gao
Tienan Yi
Yuandong Yu
Yu Wang
Ping Qu
Longchao Xiang
Yong Li
Publication date
01-12-2023
Publisher
Springer US
Published in
Discover Oncology / Issue 1/2023
Print ISSN: 1868-8497
Electronic ISSN: 2730-6011
DOI
https://doi.org/10.1007/s12672-023-00693-4

Other articles of this Issue 1/2023

Discover Oncology 1/2023 Go to the issue

Research

Evaluation of predictive and prognostic value of androgen receptor expression in breast cancer subtypes treated with neoadjuvant chemotherapy

Research

Targeting XPO6 inhibits prostate cancer progression and enhances the suppressive efficacy of docetaxel

Correction

Correction: MUC13 promotes the development of esophageal cancer by upregulating the expression of o-glycan process-related molecules

Research

Association between testosterone and cancers risk in women: a two-sample Mendelian randomization study

Perspective

NRF2 and STAT3: friends or foes in carcinogenesis?

Research

RUNX1-IT1 favors breast cancer carcinogenesis through regulation of IGF2BP1/GPX4 axis
Live Webinar | 27-06-2024 | 18:00 (CEST)

Keynote webinar | Spotlight on medication adherence

Reserve your place

Live: Thursday 27th June 2024, 18:00-19:30 (CEST)

WHO estimates that half of all patients worldwide are non-adherent to their prescribed medication. The consequences of poor adherence can be catastrophic, on both the individual and population level.

Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.

Prof. Kevin Dolgin
Prof. Florian Limbourg
Prof. Anoop Chauhan
Developed by: Springer Medicine
Obesity Clinical Trial Summary

At a glance: The STEP trials

Read more

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.

Developed by: Springer Medicine
Image Credits