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Molecular Diagnostics

Radiomics predicts risk of cachexia in advanced NSCLC patients treated with immune checkpoint inhibitors

British Journal of Cancer volume 125, pages 229–239 (2021)Cite this article

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Abstract

Background

Approximately 50% of cancer patients eventually develop a syndrome of prolonged weight loss (cachexia), which may contribute to primary resistance to immune checkpoint inhibitors (ICI). This study utilised radiomics analysis of 18F-FDG-PET/CT images to predict risk of cachexia that can be subsequently associated with clinical outcomes among advanced non-small cell lung cancer (NSCLC) patients treated with ICI.

Methods

Baseline (pre-therapy) PET/CT images and clinical data were retrospectively curated from 210 ICI-treated NSCLC patients from two institutions. A radiomics signature was developed to predict the cachexia with PET/CT images, which was further used to predict durable clinical benefit (DCB), progression-free survival (PFS) and overall survival (OS) following ICI.

Results

The radiomics signature predicted risk of cachexia with areas under receiver operating characteristics curves (AUCs) ≥ 0.74 in the training, test, and external test cohorts. Further, the radiomics signature could identify patients with DCB from ICI with AUCs≥0.66 in these three cohorts. PFS and OS were significantly shorter among patients with higher radiomics-based cachexia probability in all three cohorts, especially among those potentially immunotherapy sensitive patients with PD-L1-positive status (p < 0.05).

Conclusions

PET/CT radiomics analysis has the potential to predict the probability of developing cachexia before the start of ICI, triggering aggressive monitoring to improve potential to achieve more clinical benefit.

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Fig. 1: Study design, which contains two main phases.
Fig. 2: Radiomics signatures of NSCLC patients and their diagnostic performance in various cohorts.
Fig. 3: Radiomics Nomograms.
Fig. 4: Prognostic ability of RS in various cohorts.

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References

  1. Fearon, K., Arends, J. & Baracos, V. Understanding the mechanisms and treatment options in cancer cachexia. Nat. Rev. Clin. Oncol. 10, 90–99 (2013).

    Article  CAS  PubMed  Google Scholar 

  2. Vagnildhaug, O. M., Balstad, T. R., Almberg, S. S., Brunelli, C., Knudsen, A. K., Kaasa, S. et al. A cross-sectional study examining the prevalence of cachexia and areas of unmet need in patients with cancer. Support Care Cancer 26, 1871–1880 (2018).

    Article  PubMed  Google Scholar 

  3. Tisdale, M. J. Cachexia in cancer patients. Nat. Rev. Cancer 2, 862–871 (2002).

    Article  CAS  PubMed  Google Scholar 

  4. Fearon, K., Strasser, F., Anker, S. D., Bosaeus, I., Bruera, E., Fainsinger, R. L. et al. Definition and classification of cancer cachexia: an international consensus. Lancet Oncol. 12, 489–495 (2011).

    Article  PubMed  Google Scholar 

  5. Muscaritoli, M., Bossola, M., Aversa, Z., Bellantone, R. & Fanelli, F. R. Prevention and treatment of cancer cachexia: new insights into an old problem. Eur. J. Cancer 42, 31–41 (2006).

    Article  CAS  PubMed  Google Scholar 

  6. Baracos, V. E., Martin, L., Korc, M., Guttridge, D. C. & Fearon, K. C. Cancer-associated cachexia. Nat. Rev. Dis. Prim. 4, 1–18 (2018).

    Google Scholar 

  7. Young, R. P., Hopkins, R. J., Christmas, T., Black, P. N., Metcalf, P. & Gamble, G. D. COPD prevalence is increased in lung cancer, independent of age, sex and smoking history. Eur. Respir. J. 34, 380–386 (2009).

    Article  CAS  PubMed  Google Scholar 

  8. Wagner, P. Possible mechanisms underlying the development of cachexia in COPD. Eur. Respir. J. 31, 492–501 (2008).

    Article  CAS  PubMed  Google Scholar 

  9. Jo, H., Horinouchi, H., Yagishita, S., Shinno, Y., Okuma, Y., Yoshida, T. et al. Impact of cachexia in advanced NSCLC patients treated with PD-1 inhibitor. J. Clin. Oncol. 38(5_suppl), 44 (2020).

    Article  Google Scholar 

  10. Reck, M., Rodríguez-Abreu, D., Robinson, A. G., Hui, R., Csőszi, T., Fülöp, A. et al. Pembrolizumab versus chemotherapy for PD-L1–positive non–small-cell lung cancer. N. Engl. J. Med. 375, 1823–1833 (2016).

    Article  CAS  PubMed  Google Scholar 

  11. Brocco, D., Di Marino, P. & Grassandonia, A. From cachexia to obesity: the role of host metabolism in cancer immunotherapy. Curr. Opin. Support Palliat. Care 13, 305–310 (2019).

    Article  PubMed  Google Scholar 

  12. Turner, D. C., Kondic, A. G., Anderson, K. M., Robinson, A. G., Garon, E. B., Riess, J. W. et al. Pembrolizumab exposure–response assessments challenged by association of cancer cachexia and catabolic clearance. Clin. Cancer Res. 24, 5841–5849 (2018).

    Article  CAS  PubMed  Google Scholar 

  13. Laird, B. & Fallon, M. Treating cancer cachexia; an evolving landscape. Ann. Oncol. 28, 2055–2056 (2017).

    Article  CAS  PubMed  Google Scholar 

  14. Blum, D., Stene, G. B., Solheim, T. S., Fayers, P., Hjermstad, M. J., Baracos, V. E. et al. Validation of the consensus-definition for cancer cachexia and evaluation of a classification model—a study based on data from an international multicentre project (EPCRC-CSA). Ann. Oncol. 25, 1635–1642 (2014).

    Article  CAS  PubMed  Google Scholar 

  15. van der Meij, B. S., Schoonbeek, C. P., Smit, E. F., Muscaritoli, M., van Leeuwen, P. A. & Langius, J. A. Pre-cachexia and cachexia at diagnosis of stage III non-small-cell lung carcinoma: an exploratory study comparing two consensus-based frameworks. Br. J. Nutr. 109, 2231–2239 (2013).

    Article  PubMed  CAS  Google Scholar 

  16. Vigano, A. A. L., Morais, J. A., Ciutto, L., Rosenthall, L., di Tomasso, J., Khan, S. et al. Use of routinely available clinical, nutritional, and functional criteria to classify cachexia in advanced cancer patients. Clin. Nutr. 36, 1378–1390 (2017).

    Article  PubMed  Google Scholar 

  17. Schmidt, S. F., Rohm, M., Herzig, S. & Diaz, M. B. Cancer cachexia: more than skeletal muscle wasting. Trends Cancer 4, 849–860 (2018).

    Article  CAS  PubMed  Google Scholar 

  18. Tisdale, M. J. Are tumoral factors responsible for host tissue wasting in cancer cachexia? Future Oncol. 6, 503–513 (2010).

    Article  CAS  PubMed  Google Scholar 

  19. Devlin, M. J., Cloutier, A. M., Thomas, N. A., Panus, D. A., Lotinun, S., Pinz, I. et al. Caloric restriction leads to high marrow adiposity and low bone mass in growing mice. J. Bone Min. Res. 25, 2078–2088 (2010).

    Article  Google Scholar 

  20. Barreto, R., Mandili, G., Witzmann, F. A., Novelli, F., Zimmers, T. A. & Bonetto, A. Cancer and chemotherapy contribute to muscle loss by activating common signaling pathways. Front. Physiol. 7, 472 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Hiensch, A. E., Bolam, K. A., Mijwel, S., Jeneson, J. A., Huitema, A. D., Kranenburg, O. et al. Doxorubicin‐induced skeletal muscle atrophy: elucidating the underlying molecular pathways. Acta Physiol. 229, e13400 (2020).

    Article  CAS  Google Scholar 

  22. Damrauer, J. S., Stadler, M. E., Acharyya, S., Baldwin, A. S., Couch, M. E. & Guttridge, D. C. Chemotherapy-induced muscle wasting: association with NF-κB and cancer cachexia. Eur. J. Transl. Myol. 28, 7590 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Braun, T. P., Szumowski, M., Levasseur, P. R., Grossberg, A. J., Zhu, X., Agarwal, A. et al. Muscle atrophy in response to cytotoxic chemotherapy is dependent on intact glucocorticoid signaling in skeletal muscle. PLoS ONE 9, e106489 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Courtois, A., Nusgens, B. V., Hustinx, R., Namur, G., Gomez, P., Somja, J. et al. 18F-FDG uptake assessed by PET/CT in abdominal aortic aneurysms is associated with cellular and molecular alterations prefacing wall deterioration and rupture. J. Nucl. Med. 54, 1740–1747 (2013).

    Article  CAS  PubMed  Google Scholar 

  25. Shozushima, M., Tsutsumi, R., Terasaki, K., Sato, S., Nakamura, R. & Sakamaki, K. Augmentation effects of lymphocyte activation by antigen-presenting macrophages on FDG uptake. Ann. Nucl. Med. 17, 555–560 (2003).

    Article  PubMed  Google Scholar 

  26. Aerts, H. J., Velazquez, E. R., Leijenaar, R. T., Parmar, C., Grossmann, P., Carvalho, S. et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat. Commun. 5, 1–9 (2014).

    Google Scholar 

  27. Mu, W., Jiang, L., Zhang, J., Shi, Y., Gray, J. E., Tunali, I. et al. Non-invasive decision support for NSCLC treatment using PET/CT radiomics. Nat. Commun. 11, 5228 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Mu, W., Tunali, I., Qi, J., Schabath, M. B. & Gillies, R. J. Radiomics of 18F fluorodeoxyglucose PET/CT images predicts severe immune-related adverse events in patients with NSCLC. Radio. Artif. Intell. 2, e190063 (2020).

    Article  Google Scholar 

  29. Mu, W., Tunali, I., Gray, J. E., Qi, J., Schabath, M. B. & Gillies, R. J. Radiomics of 18 F-FDG PET/CT images predicts clinical benefit of advanced NSCLC patients to checkpoint blockade immunotherapy. Eur. J. Nucl. Med. Mol. Imaging 47, 1168–1182 (2020).

    Article  CAS  PubMed  Google Scholar 

  30. Zhang, J., Zhao, X., Zhao, Y., Zhang, J., Zhang, Z., Wang, J. et al. Value of pre-therapy 18 F-FDG PET/CT radiomics in predicting EGFR mutation status in patients with non-small cell lung cancer. Eur. J. Nucl. Med. Mol. Imaging 47, 1137–1146 (2020).

    Article  CAS  PubMed  Google Scholar 

  31. Jiang, M., Sun, D., Guo, Y., Guo, Y., Xiao, J., Wang, L. et al. Assessing PD-L1 expression level by radiomic features from PET/CT in nonsmall cell lung cancer patients: an initial result. Acad. Radio. 27, 171–179 (2020).

    Article  Google Scholar 

  32. Martin, L., Birdsell, L., MacDonald, N., Reiman, T., Clandinin, M. T., McCargar, L. J. et al. Cancer cachexia in the age of obesity: skeletal muscle depletion is a powerful prognostic factor, independent of body mass index. J. Clin. Oncol. 31, 1539–1547 (2013).

    Article  PubMed  Google Scholar 

  33. Mu, W., Chen, Z., Shen, W., Yang, F., Liang, Y., Dai, R. et al. A segmentation algorithm for quantitative analysis of heterogeneous tumors of the cervix with 18 F-FDG PET/CT. IEEE Trans. Biomed. Eng. 62, 2465–2479 (2015).

    Article  PubMed  Google Scholar 

  34. Otsu, N. A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cyber. Syst. 9, 62–66 (1979).

    Article  Google Scholar 

  35. Zwanenburg, A., Vallières, M., Abdalah, M. A., Aerts, H. J., Andrearczyk, V., Apte, A. et al. The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiol 295, 328–338 (2020).

    Article  Google Scholar 

  36. Friedman, J., Hastie, T. & Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33, 1 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Lambin, P., Leijenaar, R. T., Deist, T. M., Peerlings, J., De Jong, E. E., Van Timmeren, J. et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat. Rev. Clin. Oncol. 14, 749–762 (2017).

  38. Huang, Y.-q, Liang, C.-h, He, L., Tian, J., Liang, C.-s, Chen, X. et al. Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer. J. Clin. Oncol. 34, 2157–2164 (2016).

    Article  PubMed  Google Scholar 

  39. DeLong, E., DeLong, D. & Clarke-Pearson, D. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44, 837–845 (1988).

    Article  CAS  PubMed  Google Scholar 

  40. Park, J. E., Kim, D., Kim, H. S., Park, S. Y., Kim, J. Y., Cho, S. J. et al. Quality of science and reporting of radiomics in oncologic studies: room for improvement according to radiomics quality score and TRIPOD statement. Eur. Radiol. 30, 523–536 (2020).

    Article  PubMed  Google Scholar 

  41. Vanhoutte, G., van de Wiel, M., Wouters, K., Sels, M., Bartolomeeussen, L., De Keersmaecker, S. et al. Cachexia in cancer: what is in the definition? BMJ Open Gastroenterol. 3, e000097 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Vagnildhaug, O. M., Brunelli, C., Hjermstad, M. J., Strasser, F., Baracos, V., Wilcock, A. et al. A prospective study examining cachexia predictors in patients with incurable cancer. BMC Palliat. Care 18, 1–10 (2019).

    Article  Google Scholar 

  43. Santiloni Cury, S., De Moraes, D., Paccielli Freire, P., De Oliveira, G., Venâncio Pereira Marques, D., Javier Fernandez, G. et al. Tumor transcriptome reveals high expression of IL-8 in non-small cell lung cancer patients with low pectoralis muscle area and reduced survival. Cancers 11, 1251 (2019).

    Article  PubMed Central  CAS  Google Scholar 

  44. de Jong, E., Sanders, K., Deist, T., van Elmpt, W., Jochems, A., van Timmeren, J. et al. Can radiomics help to predict skeletal muscle response to chemotherapy in stage IV non-small cell lung cancer? Eur. J. Cancer 120, 107–113 (2019).

    Article  PubMed  CAS  Google Scholar 

  45. Curtis, S. J., Sinkevicius, K. W., Li, D., Lau, A. N., Roach, R. R., Zamponi, R. et al. Primary tumor genotype is an important determinant in identification of lung cancer propagating cells. Cell Stem Cell 7, 127–133 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Laughney, A. M., Hu, J., Campbell, N. R., Bakhoum, S. F., Setty, M., Lavallée, V. P. et al. Regenerative lineages and immune-mediated pruning in lung cancer metastasis. Nat. Med. 26, 259–269 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Couch, M., Lai, V., Cannon, T., Guttridge, D., Zanation, A., George, J. et al. Cancer cachexia syndrome in head and neck cancer patients: Part I. Diagnosis, impact on quality of life and survival, and treatment. Head. Neck 29, 401–411 (2007).

    Article  PubMed  Google Scholar 

  48. Tisdale, M. J. Molecular pathways leading to cancer cachexia. Physiol 20, 340–348 (2005).

    Article  CAS  Google Scholar 

  49. Stephens, K. E., Ishizaka, A., Larrick, J. W. & Raffin, T. A. Tumor necrosis factor causes increased pulmonary permeability and edema. Am. Rev. Respir. Dis. 137, 1364–1370 (1988).

    Article  CAS  PubMed  Google Scholar 

  50. Bashir, U., Siddique, M. M., Mclean, E., Goh, V. & Cook, G. J. Imaging heterogeneity in lung cancer: techniques, applications, and challenges. Am. J. Roentgenol. 207, 534–543 (2016).

    Article  Google Scholar 

  51. Khaddour, K., Gomez-Perez, S. L., Jain, N., Patel, J. D. & Boumber, Y. Obesity, sarcopenia, and outcomes in non-small cell lung cancer patients treated with immune checkpoint inhibitors and tyrosine kinase inhibitors. Front. Oncol. 10, 576314 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Cheng, B., Ren, Y., Cao, H. & Chen, J. Discovery of novel resorcinol diphenyl ether-based PROTAC-like molecules as dual inhibitors and degraders of PD-L1. Eur. J. Med. Chem. 199, 112377 (2020).

    Article  CAS  PubMed  Google Scholar 

  53. Borghaei, H., Langer, C. J., Paz-Ares, L., Rodríguez-Abreu, D., Halmos, B., Garassino, M. C. et al. Pembrolizumab plus chemotherapy versus chemotherapy alone in patients with advanced non–small cell lung cancer without tumor PD-L1 expression: a pooled analysis of 3 randomized controlled trials. Cancer 126, 4867–4877 (2020).

    Article  CAS  PubMed  Google Scholar 

  54. Jiang, Y., Wang, H., Wu, J., Chen, C., Yuan, Q., Huang, W. et al. Noninvasive imaging evaluation of tumor immune microenvironment to predict outcomes in gastric cancer. Ann. Oncol. 31, 760–768 (2020).

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

We sincerely thank orthopaedist (ret.) Yi Wang (wangyi999888@163.com) for the skeletal muscle identification, and Jin Qi from H. Lee Moffitt Cancer Center & Research Institute for her kind help in refining the tumour segmentation.

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Author notes

  1. These authors jointly supervised this work: Matthew B. Schabath, Robert J. Gillies.

Authors and Affiliations

  1. Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA

    Wei Mu, Christopher J. Whelan & Robert J. Gillies

  2. James A. Haley Veterans’ Hospital, Tampa, FL, USA

    Evangelia Katsoulakis

  3. Department of Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA

    Kenneth L. Gage

  4. Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA

    Matthew B. Schabath

  5. Department of Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA

    Matthew B. Schabath

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Contributions

The authors meet criteria for authorship as recommended by the International Committee of Medical Journal Editors. W.M., C.J.W., K.L.G., M.B.S. and R.J.G. contributed to the conception and design of the work; W.M. designed the model and the computational framework and analysed the data; E.K. and W.M. collected the image and clinical data; M.B.S. and R.J.G. supervised the study; M.B.S. and R.J.G. revised the work critical for important intellectual content. All authors contributed to the production of the final manuscript.

Corresponding authors

Correspondence to Matthew B. Schabath or Robert J. Gillies.

Ethics declarations

Ethical approval and consent to participate

All procedures performed in studies involving human participants were in accordance with the ethical standards of the Institutional Review Board at the University of South Florida (USF) and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. The requirement for informed consent was waived because of the retrospective nature of the study.

Data availability

The PET/CT imaging data and clinical information are not publicly available for patient privacy purposes but are available from the corresponding authors upon reasonable request (R.J.G. and M.B.S.). The remaining data are available within the Article and Supplementary Information.

Competing interests

R.J.G. declared a potential conflict with HealthMyne, Inc [Investor, Board of Advisors]. Contents of this research do not represent the views of the Department of Veterans Affairs or the United States Government. The remaining authors declare no competing interests.

Funding information

This study was funded by U.S. Public Health Service research grant U01 CA143062 and R01 CA190105 (awarded to R.J.G.).

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Mu, W., Katsoulakis, E., Whelan, C.J. et al. Radiomics predicts risk of cachexia in advanced NSCLC patients treated with immune checkpoint inhibitors. Br J Cancer 125, 229–239 (2021). https://doi.org/10.1038/s41416-021-01375-0

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