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Body composition and lung cancer-associated cachexia in TRACERx

Abstract

Cancer-associated cachexia (CAC) is a major contributor to morbidity and mortality in individuals with non-small cell lung cancer. Key features of CAC include alterations in body composition and body weight. Here, we explore the association between body composition and body weight with survival and delineate potential biological processes and mediators that contribute to the development of CAC. Computed tomography-based body composition analysis of 651 individuals in the TRACERx (TRAcking non-small cell lung Cancer Evolution through therapy (Rx)) study suggested that individuals in the bottom 20th percentile of the distribution of skeletal muscle or adipose tissue area at the time of lung cancer diagnosis, had significantly shorter lung cancer-specific survival and overall survival. This finding was validated in 420 individuals in the independent Boston Lung Cancer Study. Individuals classified as having developed CAC according to one or more features at relapse encompassing loss of adipose or muscle tissue, or body mass index-adjusted weight loss were found to have distinct tumor genomic and transcriptomic profiles compared with individuals who did not develop such features. Primary non-small cell lung cancers from individuals who developed CAC were characterized by enrichment of inflammatory signaling and epithelial–mesenchymal transitional pathways, and differentially expressed genes upregulated in these tumors included cancer-testis antigen MAGEA6 and matrix metalloproteinases, such as ADAMTS3. In an exploratory proteomic analysis of circulating putative mediators of cachexia performed in a subset of 110 individuals from TRACERx, a significant association between circulating GDF15 and loss of body weight, skeletal muscle and adipose tissue was identified at relapse, supporting the potential therapeutic relevance of targeting GDF15 in the management of CAC.

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Fig. 1: Participant and sample selection.
Fig. 2: Body composition and cancer-specific survival in the TRACERx and Boston Lung Cancer Study (BLCS) studies.
Fig. 3: Survival outcomes according to changes in body composition between primary diagnosis and first relapse.
Fig. 4: Tumor genomic and transcriptomic profiles according to cancer cachexia and non-cachexia groups.
Fig. 5: Differential protein expression and associations between circulating GDF15, body composition and body weight changes, and cancer cachexia.

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Data availability

The WES and RNA-seq data (from the TRACERx study) used during this study have been deposited at the European Genome–phenome Archive (EGA), which is hosted by The European Bioinformatics Institute (EBI) and the Centre for Genomic Regulation (CRG) under the accession codes EGAS00001006494 (WES) and EGAS00001006517 (RNA-seq); access is controlled by the TRACERx data access committee. Details on how to apply for access are available on the linked page.

The Olink dataset, de-identified body composition and body weight as well as clinical outcome data from TRACERx and BLCS are provided in a Zenodo repository (https://doi.org/10.5281/zenodo.7617516), together with plasma GDF15 data from ARCHER1009.

Code availability

Code to reproduce the figures is provided in a Zenodo repository (https://doi.org/10.5281/zenodo.7617516).

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Acknowledgements

The TRACERx study (NCT01888601) is sponsored by University College London (UCL/12/0279) and has been approved by an independent Research Ethics Committee (13/LO/1546). TRACERx is funded by Cancer Research UK (C11496/A17786) and coordinated through the Cancer Research UK and UCL Cancer Trials Centre, which has a core grant from CRUK (C444/A15953). We gratefully acknowledge the participants and relatives who participated in the TRACERx study. We thank all site personnel, investigators, funders and industry partners that supported the generation of the data within this study. In particular, we acknowledge the support of Scientific Computing, the Advanced Sequencing Facility and Experimental Histopathology departments at the Francis Crick Institute. This work was also supported by the Cancer Research UK Lung Cancer Centre of Excellence, the CRUK City of London Centre Award (C7893/A26233) and the UCL Experimental Cancer Medicine Centre. This work was supported by the Francis Crick Institute that receives its core funding from Cancer Research UK (CC2041), the UK Medical Research Council (CC2041) and the Wellcome Trust (CC2041). In particular, we acknowledge the support of Scientific Computing, the Advanced Sequencing Facility and Experimental Histopathology departments at the Francis Crick Institute. We also acknowledge the help of M. Angelova, R. Bentham, E. Colliver, E. Gronroos, J. Rane and R. Zaidi at the Francis Crick Institute and UCL Cancer Institute, in proofreading the manuscript, the team at Bioxpedia (Aarhus, Denmark) in processing the plasma samples for the Olink assay, and we acknowledge the CANCAN consortium. For the purpose of open access, the author has applied a CC BY public copyright license to any author accepted manuscript version arising from this submission. This work was also supported by the Cancer Research UK Lung Cancer Centre of Excellence and the CRUK City of London Centre Award (C7893/A26233) as well as the UCL Experimental Cancer Medicine Centre. The authors thank the study participants and investigators participating in the BLCS study, which has been approved by the Committees on the use of human participants in research at MGB and the Harvard School of Public Health. The authors further acknowledge financial support from the National Institutes of Health (NIH) (D.C.: NIH (NCI) 5U01CA209414; H.A.: NIH-USA U24CA194354, NIH-USA U01CA190234, NIH-USA U01CA209414 and NIH-USA R35CA22052, R.M.: 5U01CA209414, M.R.: U01CA210171, U10CA180821 and R01CA255184), the European Union - European Research Council (H.A.: 866504) and the German Research Foundation (J.W.: 6406/2-1). The ARCHER1009 study was funded by Pfizer. Figures 2a and 3c were created with BioRender.com. O.A.S. is supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – Projektnummer 467697427. T.K. is supported by the JSPS Overseas Research Fellowships Program (202060447). M.J.-H. has received funding from CRUK, NIH National Cancer Institute, IASLC International Lung Cancer Foundation, Lung Cancer Research Foundation, Rosetrees Trust, UKI NETs, the National Institute for Health Research (NIHR) and NIHR UCLH Biomedical Research Centre. A.M.F. is supported by Stand Up To Cancer (SU2C-AACR-DT23-17). T.B.K.W. is supported by the Francis Crick Institute, which receives its core funding from Cancer Research UK (FC001169), the UK Medical Research Council (FC001169) and the Wellcome Trust (FC001169) as well as the Marie Curie ITN Project PLOIDYNET (FP7-PEOPLE-2013, 607722), Breast Cancer Research Foundation (BCRF), Royal Society Research Professorships Enhancement Award (RP/EA/180007) and the Foulkes Foundation. C.M.-R. is supported by the Rosetrees Trust (M630). M.R. is supported by the Hale Family Research Center for Pancreatic Cancer at DFCI and the Lustgarten Foundation. N.M. is a Sir Henry Dale Fellow, jointly funded by the Wellcome Trust and the Royal Society (grant number 211179/Z/18/Z) and receives funding from Cancer Research UK, Rosetrees and the NIHR BRC at University College London Hospitals and the CRUK University College London Experimental Cancer Medicine Centre. S.O.R. is supported by the Medical Research Council MRC.MC.UU.12012.1, a Wellcome Senior Investigator Award 214274/Z/18/Z and the NIHR Cambridge Biomedical Research Centre. C.S. is a Royal Society Napier Research Professor (RSRP\R\210001). C.S. is supported by the Francis Crick Institute that receives its core funding from Cancer Research UK (CC2041), the UK Medical Research Council (CC2041), and the Wellcome Trust (CC2041). For the purpose of Open Access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. C.S. is funded by Cancer Research UK (TRACERx (C11496/A17786), PEACE (C416/A21999) and CRUK Cancer Immunotherapy Catalyst Network); Cancer Research UK Lung Cancer Centre of Excellence (C11496/A30025); the Rosetrees Trust, Butterfield and Stoneygate Trusts; NovoNordisk Foundation (ID16584); Royal Society Professorship Enhancement Award (RP/EA/180007); National Institute for Health Research (NIHR) University College London Hospitals Biomedical Research Centre; the Cancer Research UK-University College London Centre; Experimental Cancer Medicine Centre; the Breast Cancer Research Foundation (US); and The Mark Foundation for Cancer Research Aspire Award (Grant 21-029-ASP). This work was supported by a Stand Up To Cancer‐LUNGevity-American Lung Association Lung Cancer Interception Dream Team Translational Research Grant (Grant Number: SU2C-AACR-DT23-17 to S.M. Dubinett and A.E. Spira). Stand Up To Cancer is a division of the Entertainment Industry Foundation. Research grants are administered by the American Association for Cancer Research, the Scientific Partner of SU2C. CS is in receipt of an ERC Advanced Grant (PROTEUS) from the European Research Council under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 835297).

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O.A.S. generated and analyzed the body composition and survival data from TRACERx, designed and conducted the genomic, transcriptomic and proteomic bioinformatic analyses and wrote the manuscript. J.W. generated and analyzed the body composition and survival data from BLCS and wrote the manuscript. M. Skrzypski generated body composition data in TRACERx. J.M.L. and T.K. assisted with data analyses and manuscript writing. F.Z. and A.C.K. assisted with data analysis. A.M.F., T.B.K.W., C.M.R., C.P., J.R.M.B., A.H., M.A.B. and M. Sokac assisted with bioinformatic analyses. S.C. and D.M.B. contributed and analyzed the GDF15 data from ARCHER1009. S.V., N.M., C.N.L., P.P., A.T. and S.W. assisted with sample collection and sample processing. N.J. assisted with statistical survival analyses. R.S. gave feedback on the analyses. C.P.B., D.C., R.M., C.B. and M.R. generated and contributed the BLCS data. N.S. and P.W. oversaw the measurements of GDF15 in TRACERx. Y.L. and N.P. contributed to the candidate cachexia gene list. K.P. and M.F.B. developed the DAFS body composition software and assisted with its implementation in TRACERx. N.M. assisted with the bioinformatic analyses. A.H. helped oversee the running of the TRACERx study and the survival analyses in the cachexia sub-study. S.O.R. guided the GDF15 data analyses and interpretation. N.J.B. assisted with the bioinformatic analyses and wrote the manuscript. H.A. supervised the analyses of the BLCS dataset. M.J.H. and C.S. jointly designed and supervised the study and helped write the manuscript. All authors reviewed and revised the manuscript.

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Correspondence to Mariam Jamal-Hanjani or Charles Swanton.

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O.A.S.: Advisory Board (AstraZeneca, AbbVie, Ascentage, Gilead, Janssen and Roche), speaker honoraria (Adaptive, AstraZeneca, AbbVie, BeiGene, Eli Lilly, Gilead, Janssen and Roche), research funding (BeiGene, AbbVie, Janssen and Roche). J.M.L. receives research funding from the NIHR. A.M.F. is a named inventor on a patent application to determine methods and systems for tumor monitoring (PCT/EP2022/077987). M.A.B. has consulted for Achilles Therapeutics. S.C.: employment (Pfizer). M.R. receives research funding from the NIH, Lustgarten Foundation, Stand Up to Cancer Foundation and the Hale Family Center for Pancreatic Cancer at DFCI. N.S.: consultancy and/or speaker honoraria for Abbott Laboratories, Afimmune, Amgen, AstraZeneca, Boehringer Ingelheim, Eli Lilly, Hanmi Pharmaceuticals, Janssen, Merck Sharp & Dohme, Novartis, Novo Nordisk, Pfizer, Roche Diagnostics and Sanofi; research grants for AstraZeneca, Boehringer Ingelheim, Roche Diagnostics and Novartis. P.W.: grant income from Roche Diagnostics, AstraZeneca, Boehringer Ingelheim and Novartis and speaker’s fees from Novo Nordisk outside the submitted work. K.P. and M.F.B. actively direct Voronoi Health Analytics Incorporated, a Canadian corporation that sells commercial licenses for the DAFS software. R.H.M.: Advisory Board (ViewRay, AstraZeneca), consulting (Varian Medical Systems, Sio Capital Management), honorarium (Novartis, Springer Nature), ownership (Health-AI) and research funding (ViewRay). N.M. has stock options in and has consulted for Achilles Therapeutics and holds a European patents relating to targeting neoantigens (PCT/EP2016/ 059401), identifying patient response to immune checkpoint blockade (PCT/ EP2016/071471), determining HLA LOH (PCT/GB2018/052004), predicting survival rates of patients with cancer (PCT/GB2020/050221). D.M.B.: employment (Pfizer). S.O.R.: consultancy (Pfizer, AstraZeneca, Northsea and Third Rock Ventures). N.J.B. is a co-inventor of a patent to identify responders to cancer treatment (PCT/GB2018/051912), and a patent to predict HRD deficiency (US10190160B2). H.J.W.J.A. is scientific advisor and shareholder of Onc.AI, Love Health, Health-AI and Sphera, and receives consulting fees from BMS and Editas Medicine (all outside the presented work). M.J.-H. is a CRUK Career Establishment Awardee and has received funding from CRUK, IASLC International Lung Cancer Foundation, Lung Cancer Research Foundation, Rosetrees Trust, UKI NETs, NIHR and NIHR UCLH Biomedical Research Centre. M.J.-H. has consulted and is a member of the Scientific Advisory Board and Steering Committee, for Achilles Therapeutics, has received speaker honoraria from Astex Pharmaceuticals, Oslo Cancer Cluster and Pfizer, and is co-inventor on a patent PCT/US2017/028013 relating to methods for lung cancer detection. C.S. acknowledges grant support from AstraZeneca, Boehringer-Ingelheim, Bristol Myers Squibb, Pfizer, Roche-Ventana, Invitae (previously Archer Dx Inc - collaboration in minimal residual disease sequencing technologies), and Ono Pharmaceutical. C.S. is an AstraZeneca Advisory Board member and Chief Investigator for the AZ MeRmaiD 1 and 2 clinical trials and is also Co-Chief Investigator of the NHS Galleri trial funded by GRAIL and a paid member of GRAIL’s Scientific Advisory Board. He receives consultant fees from Achilles Therapeutics (also SAB member), Bicycle Therapeutics (also a SAB member), Genentech, Medicxi, Roche Innovation Centre – Shanghai, Metabomed (until July 2022), and the Sarah Cannon Research Institute. C.S. had stock options in Apogen Biotechnologies and GRAIL until June 2021, and currently has stock options in Epic Bioscience, Bicycle Therapeutics, and has stock options and is co-founder of Achilles Therapeutics. C.S. is an inventor on a European patent application relating to assay technology to detect tumour recurrence (PCT/GB2017/053289), the patent has been licensed to commercial entities and under his terms of employment with C.S due a revenue share of any revenue generated from such license(s). C.S. holds patents relating to targeting neoantigens (PCT/EP2016/059401), identifying patient response to immune checkpoint blockade (PCT/EP2016/071471), determining HLA LOH (PCT/GB2018/052004), predicting survival rates of patients with cancer (PCT/GB2020/050221), identifying patients who respond to cancer treatment (PCT/GB2018/051912), US patent relating to detecting tumour mutations (PCT/US2017/28013), methods for lung cancer detection (US20190106751A1) and both a European and US patent related to identifying insertion/deletion mutation targets (PCT/GB2018/051892) and is co-inventor to a patent application to determine methods and systems for tumour monitoring (PCT/EP2022/077987). C.S. is a named inventor on a provisional patent protection related to a ctDNA detection algorithm.

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Extended data

Extended Data Fig. 1 Correlation between body composition metrics and body weight.

a Spearman’s correlation in the TRACERx cohort between subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), skeletal muscle (SKM) and body mass index (BMI) at primary diagnosis. b Spearman’s correlation in the BLCS cohort between SAT, VAT, SKM and BMI at primary diagnosis. c Spearman’s correlation between loss/gain of SAT, VAT, SKM and body weight between primary diagnosis and first relapse. d Losses and gains in cm2 of SAT (green), VAT (yellow) and SKM (red) according to BMI-adjusted weight loss grade 0 to 4 (n=146). Bracket indicates p-value from two-sided Wilcoxon test; box plots represent lower quartile, median and upper quartile, whiskers extend to a maximum of 1.5 × IQR beyond the box. Points indicate individual data points. * indicates p-value <0.05, *** indicates p-value <0.001.

Extended Data Fig. 2 Differential gene expression according to SAT, VAT, SKM and body weight loss.

tumor differential gene expression between patients with a SAT loss <20% versus ≥20%, b VAT loss <20% versus ≥20%, c Muscle loss <10% versus ≥10%, d BMI adjusted weight loss grade 0-3 versus 4, all adjusted for number of tumour regions, sex, and histology. e Overlap of differentially expressed genes (DEG) between the ≥20% SAT, ≥20% VAT, ≥10% SKM and grade 4 weight loss groups. P values from moderated two-sided t-test without adjusting for multiple testing.

Extended Data Fig. 3 TCRA scores according to CAC and non-CAC groups at diagnosis.

a Tumour TCRA scores according to CAC (n=66) and non-CAC status (n=85). b Tumour TCRA scores according to CAC and non-CAC in female patients (n= 26 and 37, respectively) and Cc male patients (n=40 and 48, respectively). d Blood TCRA scores according to CAC (n=66) and non-CAC status (n=85). e Blood TCRA scores in blood in female patients (n= 26 and 37, respectively) and Ff male patients (n=40 and 48, respectively). P-values from Wilcoxon tests are two-sided and not adjusted for multiple testing. Box plots represent lower quartile, median and upper quartile; whiskers extend to a maximum of 1.5 × IQR beyond the box. Points indicate individual data points.

Extended Data Fig. 4 Danaher scores according to CAC and non-CAC groups.

P-values are from two-sided Wilcoxon tests and not adjusted for multiple comparisons. No significant difference was observed between cachexia (CAC, n=56) and non-cachexia (non-CAC, n=77) groups after Bonferroni correction. Box plots represent lower quartile, median and upper quartile; whiskers extend to a maximum of 1.5 × IQR beyond the box. Points indicate individual data points.

Extended Data Fig. 5 Normalized plasma protein expression (NPX) of GDF15 in the cachexia versus non-cachexia groups.

GDF15 protein expression in the CAC group (cachexia, red) and the non-CAC group (non-cachexia, blue) at cancer diagnosis (n=110) (a) and cancer relapse (n=110) (b). Two-sided Wilcoxon test. Box plots represent lower quartile, median and upper quartile, whiskers extend to a maximum of 1.5 × IQR beyond the box. Points indicate outliers. Y-axis represents log10 scale.

Extended Data Fig. 6 Correlation between normalized plasma GDF15 expression (NPX) and changes in body composition and body weight.

a-d Spearman’s correlation between body composition at diagnosis (a-C, n=103) and body weight at diagnosis (d, n=104) and plasma GDF15. e-h Spearman’s correlation between body composition at relapse (e-g, n=61) and body weight at relapse (h, n=76). Grey error bands represent the 95% confidence interval of the fitted linear model. Y-axes represent log10 scales.

Extended Data Fig. 7 Plasma GDF15 levels in patients with versus without cachexia.

Plasma GDF15 levels in the CAC group (cachexia, red) and the non-CAC group (non-cachexia, blue) at cancer diagnosis (a, n=96) and cancer relapse (b, n=71). Two-sided Wilcoxon test. Box plots represent lower quartile, median and upper quartile, whiskers extend to a maximum of 1.5 × IQR beyond the box. Points indicate individual data points. Y-axes represent log10 scales.

Extended Data Fig. 8 Correlation of GDF15 plasma levels at diagnosis and at relapse with clinical features.

a, b Spearman’s correlation between age and plasma GDF15 at diagnosis (a, n=107) and relapse (b, n=79). c, d Correlation between histology and plasma GDF15 at diagnosis (c, n=107) and relapse (d, n=79), two-sided Wilcoxon test. e, f Spearman’s correlation between body mass index (BMI) and plasma GDF15 at diagnosis (e, n=107) and relapse (f, n=79). g, h Correlation between smoking status and plasma GDF15 at diagnosis (g, n=107) and relapse (h, n=79), two-sided Wilcoxon test. i Spearman’s correlation between smoking pack years and plasma GDF15 (at diagnosis n=107, at relapse n=79). j Correlation between adjuvant therapy and plasma GDF15 at relapse (n=79), two-sided Wilcoxon test. k Correlation between lung cancer stage and plasma GDF15 at diagnosis (n=107), two-sided Wilcoxon test. l Spearman’s correlation between tumour volume and plasma GDF15 at diagnosis (n=73). P-values not adjusted for multiple comparisons. Grey error bands represent the 95% confidence interval of the fitted linear model. Box plots represent lower quartile, median and upper quartile, whiskers extend to a maximum of 1.5 × IQR beyond the box. Points indicate individual data points. Y-axis represents log10 scales. NSCLC, non-small cell lung cancer; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma.

Extended Data Fig. 9 Copy number alterations of GDF15.

a Ploidy-adjusted copy number compared to transcript-per-million GDF15 gene expression, at baseline, log10 transformed, Spearman’s correlation, n=1050 regions from 348 patients. Grey error bands represent the 95% confidence interval of the fitted linear model. b Copy number events in relation to circulating GDF15 levels, at diagnosis (n=106), log10 transformed, p-values from two-sided Wilcoxon test, not adjusted for multiple testing. c Copy number events in relation to circulating GDF15 levels, at relapse (n=44), log10 transformed. P-values from two-sided Wilcoxon test, not adjusted for multiple testing. Box plots represent lower quartile, median and upper quartile; whiskers extend to a maximum of 1.5 × IQR beyond the box. Points indicate individual data points. Y-axes represent log10 scales.

Extended Data Fig. 10 Plasma GDF15 levels and BMI-adjusted weight loss in the ARCHER1009 cohort.

GDF15 levels according to BMI-adjusted weight loss category in patients (n=164) treated in the ARCHER1009 trial. P values from two-sided Wilcoxon test, not adjusted for multiple testing. Box plots represent lower quartile, median and upper quartile, whiskers extend to a maximum of 1.5 × IQR beyond the box. Points indicate individual data points. Y-axis represents log10 scale.

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Al-Sawaf, O., Weiss, J., Skrzypski, M. et al. Body composition and lung cancer-associated cachexia in TRACERx. Nat Med 29, 846–858 (2023). https://doi.org/10.1038/s41591-023-02232-8

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