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Molecular Diagnostics

Predicting response to immune checkpoint blockade in NSCLC with tumour-only RNA-seq

British Journal of Cancer volume 128, pages 1148–1154 (2023)Cite this article

Subjects

Abstract

Background

Targeted RNA sequencing (RNA-seq) from FFPE specimens is used clinically in cancer for its ability to estimate gene expression and to detect fusions. Using a cohort of NSCLC patients, we sought to determine whether targeted RNA-seq could be used to measure tumour mutational burden (TMB) and the expression of immune-cell-restricted genes from FFPE specimens and whether these could predict response to immune checkpoint blockade.

Methods

Using The Cancer Genome Atlas LUAD dataset, we developed a method for determining TMB from tumour-only RNA-seq and showed a correlation with DNA sequencing derived TMB calculated from tumour/normal sample pairs (Spearman correlation = 0.79, 95% CI [0.73, 0.83]. We applied this method to targeted sequencing data from our patient cohort and validated these results against TMB estimates obtained using an orthogonal assay (Spearman correlation = 0.49, 95% CI [0.24, 0.68]).

Results

We observed that the RNA measure of TMB was significantly higher in responders to immune blockade treatment (P = 0.028) and that it was predictive of response (AUC = 0.640 with 95% CI [0.493, 0.786]). By contrast, the expression of immune-cell-restricted genes was uncorrelated with patient outcome.

Conclusion

TMB calculated from targeted RNA sequencing has a similar diagnostic ability to TMB generated from targeted DNA sequencing.

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Fig. 1: Filtering variants from tumour-only RNA-seq to compute RNA TMB.
Fig. 2: RNA TMB versus DNA TMB for the lung cancer cohort.
Fig. 3: Predictive ability of TMB derived from RNA-seq.

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Data availability

The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request.

Code availability

R code that implements the variant filtering strategies, performs differential expression analysis and generates all figures contained in this manuscript, along with all relevant data are available on Figshare at https://doi.org/10.6084/m9.figshare.21332646.

References

  1. Paez JG, Janne PA, Lee JC, Tracy S, Greulich H, Gabriel S, et al. EGFR mutations in lung cancer: correlation with clinical response to gefitinib therapy. Science. 2004;304:1497–500.

    Article  CAS  PubMed  Google Scholar 

  2. Recondo G, Facchinetti F, Olaussen KA, Besse B, Friboulet L. Making the first move in EGFR-driven or ALK-driven NSCLC: first-generation or next-generation TKI? Nat Rev Clin Oncol. 2018;15:694–708.

    Article  CAS  PubMed  Google Scholar 

  3. Paik PK, Drilon A, Fan PD, Yu H, Rekhtman N, Ginsberg MS, et al. Response to MET inhibitors in patients with stage IV lung adenocarcinomas harboring MET mutations causing exon 14 skipping. Cancer Discov. 2015;5:842–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Drilon A, Clark JW, Weiss J, Ou SI, Camidge DR, Solomon BJ, et al. Antitumor activity of crizotinib in lung cancers harboring a MET exon 14 alteration. Nat Med. 2020;26:47–51.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Drilon A, Hu ZI, Lai GGY, Tan DSW. Targeting RET-driven cancers: lessons from evolving preclinical and clinical landscapes. Nat Rev Clin Oncol. 2018;15:151–67.

    Article  CAS  PubMed  Google Scholar 

  6. Takeuchi K, Soda M, Togashi Y, Suzuki R, Sakata S, Hatano S, et al. RET, ROS1 and ALK fusions in lung cancer. Nat Med. 2012;18:378–81.

    Article  CAS  PubMed  Google Scholar 

  7. Remon J, Pignataro D, Novello S, Passiglia F. Current treatment and future challenges in ROS1- and ALK-rearranged advanced non-small cell lung cancer. Cancer Treat Rev. 2021;95:102178.

    Article  CAS  PubMed  Google Scholar 

  8. Majewski IJ, Mittempergher L, Davidson NM, Bosma A, Willems SM, Horlings HM, et al. Identification of recurrent FGFR3 fusion genes in lung cancer through kinome-centred RNA sequencing. J Pathol. 2013;230:270–6.

    Article  CAS  PubMed  Google Scholar 

  9. Reguart N, Teixido C, Gimenez-Capitan A, Pare L, Galvan P, Viteri S, et al. Identification of ALK, ROS1, and RET fusions by a multiplexed mRNA-based assay in formalin-fixed, paraffin-embedded samples from advanced non-small-cell lung cancer patients. Clin Chem. 2017;63:751–60.

    Article  CAS  PubMed  Google Scholar 

  10. McLeer-Florin A, Duruisseaux M, Pinsolle J, Dubourd S, Mondet J, Phillips Houlbracq M, et al. ALK fusion variants detection by targeted RNA-next generation sequencing and clinical responses to crizotinib in ALK-positive non-small cell lung cancer. Lung cancer. 2018;116:15–24.

    Article  PubMed  Google Scholar 

  11. Reck M, Rodriguez-Abreu D, Robinson AG, Hui R, Csoszi T, Fulop A, et al. Pembrolizumab versus chemotherapy for PD-L1-positive non-small-cell lung cancer. N Engl J Med. 2016;375:1823–33.

    Article  CAS  PubMed  Google Scholar 

  12. Hendry S, Byrne DJ, Wright GM, Young RJ, Sturrock S, Cooper WA, et al. Comparison of four PD-L1 immunohistochemical assays in lung cancer. J Thorac Oncol. 2018;13:367–76.

    Article  PubMed  Google Scholar 

  13. Cooper WA, Russell PA, Cherian M, Duhig EE, Godbolt D, Jessup PJ, et al. Intra- and interobserver reproducibility assessment of PD-L1 biomarker in non-small cell lung cancer. Clin Cancer Res. 2017;23:4569–77.

    Article  CAS  PubMed  Google Scholar 

  14. Butter R, t Hart NA, Hooijer GKJ, Monkhorst K, Speel EJ, Theunissen P, et al. Multicentre study on the consistency of PD-L1 immunohistochemistry as predictive test for immunotherapy in non-small cell lung cancer. J Clin Pathol. 2020;73:423–30.

    Article  CAS  PubMed  Google Scholar 

  15. Yarchoan M, Hopkins A, Jaffee EM. Tumor mutational burden and response rate to PD-1 inhibition. N Engl J Med. 2017;377:2500–1.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Marabelle A, Fakih M, Lopez J, Shah M, Shapira-Frommer R, Nakagawa K, et al. Association of tumour mutational burden with outcomes in patients with advanced solid tumours treated with pembrolizumab: prospective biomarker analysis of the multicohort, open-label, phase 2 KEYNOTE-158 study. Lancet Oncol. 2020;21:1353–65.

    Article  CAS  PubMed  Google Scholar 

  17. Ready N, Hellmann MD, Awad MM, Otterson GA, Gutierrez M, Gainor JF, et al. First-line nivolumab plus ipilimumab in advanced non-small-cell lung cancer (CheckMate 568): outcomes by programmed death ligand 1 and tumor mutational burden as biomarkers. J Clin Oncol. 2019;37:992–1000.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Hellmann MD, Paz-Ares L, Bernabe Caro R, Zurawski B, Kim SW, Carcereny Costa E, et al. Nivolumab plus ipilimumab in advanced non-small-cell lung cancer. N Engl J Med. 2019;381:2020–31.

    Article  CAS  PubMed  Google Scholar 

  19. Stenzinger A, Endris V, Budczies J, Merkelbach-Bruse S, Kazdal D, Dietmaier W, et al. Harmonization and standardization of panel-based tumor mutational burden measurement: real-world results and recommendations of the quality in pathology study. J Thorac Oncol. 2020;15:1177–89.

    Article  CAS  PubMed  Google Scholar 

  20. Strickler JH, Hanks BA, Khasraw M. Tumor mutational burden as a predictor of immunotherapy response: is more always better? Clin Cancer Res. 2021;27:1236–41.

    Article  CAS  PubMed  Google Scholar 

  21. Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 2011;17:3.

    Article  Google Scholar 

  22. Kim D, Langmead B, Salzberg SL. HISAT: a fast spliced aligner with low memory requirements. Nat Methods. 2015;12:357–60.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Van der Auwera GA, Carneiro MO, Hartl C, Poplin R, Del Angel G, Levy-Moonshine A, et al. From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline. Current protocols in bioinformatics/editoral board. Andreas D Baxevanis [et al.] 2013;43:11 0 1–0 33.

    Google Scholar 

  24. DiGuardo MA, Davila JI, Jackson RA, Nair AA, Fadra N, Minn KT, et al. RNA-seq reveals differences in expressed tumor mutation burden in colorectal and endometrial cancers with and without defective DNA-mismatch repair. J Mol Diagn. 2021;23:555–64.

    Article  CAS  PubMed  Google Scholar 

  25. Jessen E, Liu Y, Davila J, Kocher JP, Wang C. Determining mutational burden and signature using RNA-seq from tumor-only samples. BMC Med Genomics. 2021;14:65.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Zhao C, Jiang T, Hyun Ju J, Zhang S, Tao J, Fu Y, et al. TruSight oncology 500: enabling comprehensive genomic profiling and biomarker reporting with targeted sequencing. bioRxiv. [Preprint] 2020. https://www.biorxiv.org/content/10.1101/2020.10.21.349100v1.

  27. Genomes Project C, Auton A, Brooks LD, Durbin RM, Garrison EP, Kang HM, et al. A global reference for human genetic variation. Nature. 2015;526:68–74.

    Article  Google Scholar 

  28. Wang C, Davila JI, Baheti S, Bhagwate AV, Wang X, Kocher JP, et al. RVboost: RNA-seq variants prioritization using a boosting method. Bioinformatics. 2014;30:3414–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Neums L, Suenaga S, Beyerlein P, Anders S, Koestler D, Mariani A, et al. VaDiR: an integrated approach to variant detection in RNA. Gigascience. 2018;7:gix122.

  30. Karczewski KJ, Francioli LC, Tiao G, Cummings BB, Alfoldi J, Wang Q, et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature. 2020;581:434–43.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Chang H, Sasson A, Srinivasan S, Golhar R, Greenawalt DM, Geese WJ, et al. Bioinformatic methods and bridging of assay results for reliable tumor mutational burden assessment in non-small-cell lung cancer. Mol diagnosis Ther. 2019;23:507–20.

    Article  CAS  Google Scholar 

  32. Merino DM, McShane LM, Fabrizio D, Funari V, Chen SJ, White JR, et al. Establishing guidelines to harmonize tumor mutational burden (TMB): in silico assessment of variation in TMB quantification across diagnostic platforms: phase I of the Friends of Cancer Research TMB Harmonization Project. J Immunother Cancer. 2020;8:e000147.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Fancello L, Gandini S, Pelicci PG, Mazzarella L. Tumor mutational burden quantification from targeted gene panels: major advancements and challenges. J Immunother Cancer. 2019;7:183.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Risso D, Schwartz K, Sherlock G, Dudoit S. GC-content normalization for RNA-Seq data. BMC Bioinforma. 2011;12:480.

    Article  CAS  Google Scholar 

  35. Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26:139–40.

    Article  CAS  PubMed  Google Scholar 

  36. Team RC. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2020.

  37. Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinforma. 2011;12:77.

    Article  Google Scholar 

  38. Schloerke B, Cook D, Larmarange J, Briatte F, Marbach M, Thoen E, et al. GGally: Extension to ‘ggplot2’. 2021; R package version; 2.

  39. Simko TWaV. R package “corrplot”: visualization of a correlation matrix. 2017. https://bmjopenrespres.bmj.com/content/4/1/e000250#ref-22andelsewhere.

  40. Galvano A, Gristina V, Malapelle U, Pisapia P, Pepe F, Barraco N, et al. The prognostic impact of tumor mutational burden (TMB) in the first-line management of advanced non-oncogene addicted non-small-cell lung cancer (NSCLC): a systematic review and meta-analysis of randomized controlled trials. ESMO Open. 2021;6:100124.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Mosele F, Remon J, Mateo J, Westphalen CB, Barlesi F, Lolkema MP, et al. Recommendations for the use of next-generation sequencing (NGS) for patients with metastatic cancers: a report from the ESMO Precision Medicine Working Group. Annal Oncol. 2020;31:1491–505.

  42. Cohen D, Hondelink LM, Solleveld-Westerink N, Uljee SM, Ruano D, Cleton-Jansen AM, et al. Optimizing mutation and fusion detection in NSCLC by sequential DNA and RNA sequencing. J Thorac Oncol. 2020;15:1000–14.

    Article  CAS  PubMed  Google Scholar 

  43. Pestinger V, Smith M, Sillo T, Findlay JM, Laes JF, Martin G, et al. Use of an integrated pan-cancer oncology enrichment next-generation sequencing assay to measure tumour mutational burden and detect clinically actionable variants. Mol diagnosis Ther. 2020;24:339–49.

    Article  CAS  Google Scholar 

  44. Sorokin M, Gorelyshev A, Efimov V, Zotova E, Zolotovskaia M, Rabushko E, et al. RNA sequencing data for FFPE tumor blocks can be used for robust estimation of tumor mutation burden in individual biosamples. Front Oncol. 2021;11:732644.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Alborelli I, Leonards K, Rothschild SI, Leuenberger LP, Savic Prince S, Mertz KD, et al. Tumor mutational burden assessed by targeted NGS predicts clinical benefit from immune checkpoint inhibitors in non-small cell lung cancer. J Pathol. 2020;250:19–29.

    Article  CAS  PubMed  Google Scholar 

  46. Benayed R, Offin M, Mullaney K, Sukhadia P, Rios K, Desmeules P, et al. High yield of RNA sequencing for targetable kinase fusions in lung adenocarcinomas with no mitogenic driver alteration detected by DNA sequencing and low tumor mutation burden. Clin Cancer Res. 2019;25:4712–22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Li MM, Datto M, Duncavage EJ, Kulkarni S, Lindeman NI, Roy S, et al. Standards and guidelines for the interpretation and reporting of sequence variants in cancer: a Joint Consensus Recommendation of the Association for Molecular Pathology, American Society of Clinical Oncology, and College of American Pathologists. J Mol Diagn: JMD. 2017;19:4–23.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Linette GP, Carreno BM. Tumor-infiltrating lymphocytes in the checkpoint inhibitor era. Curr Hematol Malig Rep. 2019;14:286–91.

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank all the patients who participated in this study. We acknowledge the cooperation of the Melbourne Health Shared Pathology Service. We thank Ann Officer and Marliese Alexander for the study coordination. We thank Dr Michael Christie for the pathology review. Some results shown here are based on data generated and made publicly available by the TCGA Research Network (http://cancergenome.nih.gov/).

Funding

This work was funded by Bristol Myers Squibb.

Author information

Author notes

  1. These authors contributed equally: John F. Markham, Andrew P. Fellowes.

Authors and Affiliations

  1. Peter MacCallum Cancer Centre, 305 Grattan Street, Parkville, VIC, 3000, Australia

    John F. Markham, Andrew P. Fellowes, Thomas Green, Jose Luis Leal, Roxane Legaie, Tom John, Ben Solomon & Stephen B. Fox

  2. Department of Pathology, Peter MacCallum Cancer Centre, Parkville, VIC, Australia

    John F. Markham, Andrew P. Fellowes, Thomas Green, Roxane Legaie & Stephen B. Fox

  3. Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia

    John F. Markham, Jose Luis Leal, Tom John, Ben Solomon & Stephen B. Fox

  4. Murdoch Children’s Research Institute, Flemington Road, Parkville, VIC, 3052, Australia

    Darren Cullerne

  5. Southern Blood and Cancer Service, Te Whatu Ora Southern, Dunedin, New Zealand

    Tessa Morris

  6. Mercy Cancer Care, Mercy Hospital, Dunedin, New Zealand

    Tessa Morris

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Contributions

JFM and APF designed the work, played important roles in interpreting the results, and drafted and revised the manuscript. RL and DC played important roles in interpreting the results and revising the manuscript. TG acquired the laboratory data and drafted the manuscript. TM acquired the clinical data. JLL, TJ, BS and SBF played important roles in interpreting the results and revising the manuscript. All authors approved the final version of this manuscript and are accountable for all aspects of the work.

Corresponding author

Correspondence to Andrew P. Fellowes.

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Competing interests

SF is on advisory boards for AstraZeneca, Pfizer, Merck, Bayer, GSK, Roche, Janssen, Novartis, and Thermo Fisher. Honoraria are paid to Peter MacCallum Cancer Centre. BS is on advisory boards/receives honoraria for Roche/Genentech, Pfizer, Novartis, AstraZeneca, Merck, Bristol Myers Squibb, Amgen, BeiGene, Janssen and Lilly.

Ethics approval and consent to participate

All patients gave written informed consent for their tissue samples and medical history to be used in this research. This research was approved by the Peter MacCallum Cancer Centre Human Research Ethics Committee (HREC ref HREC/17/PMCC/42). This study was performed in accordance with the Declaration of Helsinki.

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Markham, J.F., Fellowes, A.P., Green, T. et al. Predicting response to immune checkpoint blockade in NSCLC with tumour-only RNA-seq. Br J Cancer 128, 1148–1154 (2023). https://doi.org/10.1038/s41416-022-02105-w

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