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Integrative radiomics and transcriptomics analyses reveal subtype characterization of non-small cell lung cancer

  • Oncology
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To assess whether integrative radiomics and transcriptomics analyses could provide novel insights for radiomic features’ molecular annotation and effective risk stratification in non-small cell lung cancer (NSCLC).

Methods

A total of 627 NSCLC patients from three datasets were included. Radiomics features were extracted from segmented 3-dimensional tumour volumes and were z-score normalized for further analysis. In transcriptomics level, 186 pathways and 28 types of immune cells were assessed by using the Gene Set Variation Analysis (GSVA) algorithm. NSCLC patients were categorized into subgroups based on their radiomic features and pathways enrichment scores using consensus clustering. Subgroup-specific radiomics features were used to validate clustering performance and prognostic value. Kaplan–Meier survival analysis with the log-rank test and univariable and multivariable Cox analyses were conducted to explore survival differences among the subgroups.

Results

Three radiotranscriptomics subtypes (RTSs) were identified based on the radiomics and pathways enrichment profiles. The three RTSs were characterized as having specific molecular hallmarks: RTS1 (proliferation subtype), RTS2 (metabolism subtype), and RTS3 (immune activation subtype). RTS3 showed increased infiltration of most immune cells. The RTS stratification strategy was validated in a validation cohort and showed significant prognostic value. Survival analysis demonstrated that the RTS strategy could stratify NSCLC patients according to prognosis (p = 0.009), and the RTS strategy remained an independent prognostic indicator after adjusting for other clinical parameters.

Conclusions

This radiotranscriptomics study provides a stratification strategy for NSCLC that could provide information for radiomics feature molecular annotation and prognostic prediction.

Key Points

• Radiotranscriptomics subtypes (RTSs) could be used to stratify molecularly heterogeneous patients.

• RTSs showed relationships between molecular phenotypes and radiomics features.

• The RTS algorithm could be used to identify patients with poor prognosis.

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Abbreviations

CI:

Confidence interval

DICOM:

Digital Imaging and Communications in Medicine

FPKM:

Fragments per kilobase of transcript per million mapped reads

GLCM:

Gray level cooccurrence matrix

GLDM:

Gray level dependence matrix

GLRLM:

Gray level run length matrix

GLSZM:

Gray level size zone matrix

GSVA:

Gene Set Variation Analysis

HR:

Hazard ratio

K-M:

Kaplan-Meier

NGTDM:

Neighbouring gray tone difference matrix

NSCLC:

Non-small cell lung cancer

ROI:

Region of interest

RTS:

Radiotranscriptomics subtype

TCIA:

The Cancer Imaging Archive

UMAP:

Uniform manifold approximation and projection

References

  1. Bera K, Braman N, Gupta A, Velcheti V, Madabhushi A (2022) Predicting cancer outcomes with radiomics and artificial intelligence in radiology. Nat Rev Clin Oncol 19:132–146

    Article  CAS  PubMed  Google Scholar 

  2. Lambin P, Rios-Velazquez E, Leijenaar R et al (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48:441–446

    Article  PubMed  PubMed Central  Google Scholar 

  3. Dercle L, Zhao B, Gonen M et al (2022) Early readout on overall survival of patients with melanoma treated with immunotherapy using a novel imaging analysis. JAMA Oncol 8:385–392

    Article  PubMed  PubMed Central  Google Scholar 

  4. Xu X, Zhang HL, Liu QP et al (2019) Radiomic analysis of contrast-enhanced CT predicts microvascular invasion and outcome in hepatocellular carcinoma. J Hepatol 70:1133–1144

    Article  PubMed  Google Scholar 

  5. Sun R, Limkin EJ, Vakalopoulou M et al (2018) A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study. Lancet Oncol 19:1180–1191

    Article  CAS  PubMed  Google Scholar 

  6. Xi IL, Zhao Y, Wang R et al (2020) Deep learning to distinguish benign from malignant renal lesions based on routine MR imaging. Clin Cancer Res 26:1944–1952

    Article  PubMed  Google Scholar 

  7. Feng S, Xia T, Ge Y et al (2022) Computed tomography imaging-based radiogenomics analysis reveals hypoxia patterns and immunological characteristics in ovarian cancer. Front Immunol 13:868067

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. An J, Oh M, Kim SY et al (2022) PET-based radiogenomics supports mTOR pathway targeting for hepatocellular carcinoma. Clin Cancer Res 28:1821–1831

    Article  CAS  PubMed  Google Scholar 

  9. Andre F, Ismaila N, Allison KH et al (2022) Biomarkers for adjuvant endocrine and chemotherapy in early-stage breast cancer: ASCO guideline update. J Clin Oncol 40:1816–1837

    Article  CAS  PubMed  Google Scholar 

  10. Wolf DM, Yau C, Wulfkuhle J et al (2022) Redefining breast cancer subtypes to guide treatment prioritization and maximize response: predictive biomarkers across 10 cancer therapies. Cancer Cell 40(609–623):e606

    Google Scholar 

  11. Thai AA, Solomon BJ, Sequist LV, Gainor JF, Heist RS (2021) Lung cancer. Lancet 398:535–554

    Article  PubMed  Google Scholar 

  12. Shiri I, Amini M, Nazari M et al (2022) Impact of feature harmonization on radiogenomics analysis: prediction of EGFR and KRAS mutations from non-small cell lung cancer PET/CT images. Comput Biol Med 142:105230

    Article  CAS  PubMed  Google Scholar 

  13. Yamazaki M, Yagi T, Tominaga M, Minato K, Ishikawa H (2022) Role of intratumoral and peritumoral CT radiomics for the prediction of EGFR gene mutation in primary lung cancer. Br J Radiol 95:20220374

  14. Nair JKR, Saeed UA, McDougall CC et al (2021) Radiogenomic models using machine learning techniques to predict EGFR mutations in non-small cell lung cancer. Can Assoc Radiol J 72:109–119

    Article  PubMed  Google Scholar 

  15. Clark K, Vendt B, Smith K et al (2013) The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. J Digit Imaging 26:1045–1057

    Article  PubMed  PubMed Central  Google Scholar 

  16. Aerts HJ, Velazquez ER, Leijenaar RT et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006

    Article  CAS  PubMed  Google Scholar 

  17. Aerts HJWL, Rios Velazquez E, Leijenaar RTH et al (2015) Data From NSCLC-radiomics-genomics. Cancer Imaging Arch. https://doi.org/10.7937/K9/TCIA.2015.L4FRET6Z

  18. Bakr S, Gevaert O, Echegaray S et al (2018) A radiogenomic dataset of non-small cell lung cancer. Sci Data 5:180202

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Bakr S, Olivier G, Echegaray S et al (2017) Data for NSCLC radiogenomics collection. Cancer Imaging Arch. https://doi.org/10.7937/K9/TCIA.2017.7hs46erv

  20. Aerts HJWL, Velazquez ER, Leijenaar RTH et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. https://doi.org/10.1038/ncomms5006(link)

  21. Gevaert O, Xu J, Hoang CD et al (2012) Non-small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data–methods and preliminary results. Radiology 264:387–396

    Article  PubMed  PubMed Central  Google Scholar 

  22. Ji GW, Zhu FP, Xu Q et al (2020) Radiomic features at contrast-enhanced CT predict recurrence in early stage hepatocellular carcinoma: a multi-institutional study. Radiology 294:568–579

    Article  PubMed  Google Scholar 

  23. van Griethuysen JJM, Fedorov A, Parmar C et al (2017) Computational radiomics system to decode the radiographic phenotype. Cancer Res 77:e104–e107

    Article  PubMed  PubMed Central  Google Scholar 

  24. Charoentong P, Finotello F, Angelova M et al (2017) Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade. Cell Rep 18:248–262

    Article  CAS  PubMed  Google Scholar 

  25. Xu T, Le TD, Liu L et al (2017) CancerSubtypes: an R/Bioconductor package for molecular cancer subtype identification, validation and visualization. Bioinformatics 33:3131–3133

    Article  CAS  PubMed  Google Scholar 

  26. Wang C, Ma J, Shao J et al (2022) Non-invasive measurement using deep learning algorithm based on multi-source features fusion to predict PD-L1 expression and survival in NSCLC. Front Immunol 13:828560

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Wong CW, Chaudhry A (2020) Radiogenomics of lung cancer. J Thorac Dis 12:5104–5109

  28. Rossi G, Barabino E, Fedeli A et al (2021) Radiomic detection of EGFR mutations in NSCLC. Cancer Res 81:724–731

    Article  CAS  PubMed  Google Scholar 

  29. Perez-Johnston R, Araujo-Filho JA, Connolly JG et al (2022) CT-based radiogenomic analysis of clinical stage I lung adenocarcinoma with histopathologic features and oncologic outcomes. Radiology 303:664–672

    Article  PubMed  Google Scholar 

  30. Tu W, Sun G, Fan L et al (2019) Radiomics signature: a potential and incremental predictor for EGFR mutation status in NSCLC patients, comparison with CT morphology. Lung Cancer 132:28–35

    Article  PubMed  Google Scholar 

  31. Liu D, Chen J, Ge H et al (2022) Radiogenomics to characterize the immune-related prognostic signature associated with biological functions in glioblastoma. Eur Radiol. https://doi.org/10.1007/s00330-022-09012-x

    Article  PubMed  PubMed Central  Google Scholar 

  32. Hanahan D, Weinberg RA (2011) Hallmarks of cancer: the next generation. Cell 144:646–674

    Article  CAS  PubMed  Google Scholar 

  33. Boehm KM, Aherne EA, Ellenson L et al (2022) Multimodal data integration using machine learning improves risk stratification of high-grade serous ovarian cancer. Nat Cancer 3:723–733

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Feng L, Liu Z, Li C et al (2022) Development and validation of a radiopathomics model to predict pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicentre observational study. Lancet Digit Health 4:e8–e17

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The authors acknowledge The Cancer Imaging Archive (TCIA) and Gene Expression Omnibus (GEO) databases for sharing their dataset publicly. The authors also thank the guidance of professor Sandy Napel and professor Leonard Wee.

Funding

This study has received funding by Guangxi Natural Science Foundation under Grant (No. 2020GXNSFDA238005) and Innovation Project of Guangxi Graduate Education (No. YCBZ2022077).

Author information

Author notes

  1. Peng Lin and Yi-qun Lin contributed equally to this work.

Authors and Affiliations

  1. Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, People’s Republic of China

    Peng Lin, Rui-zhi Gao, Wei-jun Wan, Yun He & Hong Yang

  2. Department of Radiology, The 909th Hospital. School of Medicine, Xiamen University, Fujian, Zhangzhou, People’s Republic of China

    Yi-qun Lin

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  1. Peng Lin
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Corresponding authors

Correspondence to Yun He or Hong Yang.

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Guarantor

The scientific guarantor of this publication is Hong Yang.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

Some study subjects or cohorts have been previously reported in Aerts HJ et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006; Bakr S et al. A radiogenomic dataset of non-small cell lung cancer. Sci Data 5:180202.

Methodology

• retrospective

• observational

• multicenter study

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Lin, P., Lin, Yq., Gao, Rz. et al. Integrative radiomics and transcriptomics analyses reveal subtype characterization of non-small cell lung cancer. Eur Radiol 33, 6414–6425 (2023). https://doi.org/10.1007/s00330-023-09503-5

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