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Prospective observational study on biomarkers of response in pancreatic ductal adenocarcinoma

Nature Medicine volume 30, pages 749–761 (2024)Cite this article

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Abstract

Adjuvant chemotherapy benefits patients with resected pancreatic ductal adenocarcinoma (PDAC), but the compromised physical state of post-operative patients can hinder compliance. Biomarkers that identify candidates for prompt adjuvant therapy are needed. In this prospective observational study, 1,171 patients with PDAC who underwent pancreatectomy were enrolled and extensively followed-up. Proteomic profiling of 191 patient samples unveiled clinically relevant functional protein modules. A proteomics-level prognostic risk model was established for PDAC, with its utility further validated using a publicly available external cohort. More importantly, through an interaction effect regression analysis leveraging both clinical and proteomic datasets, we discovered two biomarkers (NDUFB8 and CEMIP2), indicative of the overall sensitivity of patients with PDAC to adjuvant chemotherapy. The biomarkers were validated through immunohistochemistry on an internal cohort of 386 patients. Rigorous validation extended to two external multicentic cohorts—a French multicentric cohort (230 patients) and a cohort from two grade-A tertiary hospitals in China (466 patients)—enhancing the robustness and generalizability of our findings. Moreover, experimental validation through functional assays was conducted on PDAC cell lines and patient-derived organoids. In summary, our cohort-scale integration of clinical and proteomic data demonstrates the potential of proteomics-guided prognosis and biomarker-aided adjuvant chemotherapy for PDAC.

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Fig. 1: Study design overview and tumor-specific pathway hallmarks in PDAC.
Fig. 2: Proteomic landscape for PDACs and clinical relevance of functional protein modules.
Fig. 3: Establishing an optimal proteomics-level prognostic risk model using LASSO–Cox regression.
Fig. 4: Identification of two proteomic biomarkers for predicting the outcome of adjuvant chemotherapy in patients with PDAC.
Fig. 5: Independent validation of the clinical translational value of identified biomarkers in PDAC cohorts using IHC staining.

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Data availability

Raw and processed proteomic and transcriptomic data generated in this study have been deposited at the National Omics Data Encyclopedia (NODE), which can be accessed with the accession code OEP004100. In addition, resources arising from this study are accessible for interactive exploration on the project’s dedicated website (http://www.genetictargets.com/PDAC2), where IHC imaging source data are also available.

Code availability

Source codes used in the study are accessible via the tab ‘CODES & INSTRUCTIONS’ on our website (http://www.genetictargets.com/PDAC2), where users can follow detailed step-by-step instructions (http://www.genetictargets.com/PDAC2BOOKLET/index.html) to reproduce all results, including figures and tables, presented in the paper. Codes were also deposited into a public data repository at Figshare (https://doi.org/10.6084/m9.figshare.24863049). Our commitment to transparency in code and data sharing promotes reproducible research, allowing the broader clinical and research community to fully benefit from our work without any barriers.

References

  1. Quante, A. S. et al. Projections of cancer incidence and cancer-related deaths in Germany by 2020 and 2030. Cancer Med. 5, 2649–2656 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Rahib, L. et al. Projecting cancer incidence and deaths to 2030: the unexpected burden of thyroid, liver, and pancreas cancers in the United States. Cancer Res. 74, 2913–2921 (2014).

    Article  CAS  PubMed  Google Scholar 

  3. Siegel, R. L., Miller, K. D., Fuchs, H. E. & Jemal, A. Cancer Statistics, 2021. CA Cancer J. Clin. 71, 7–33 (2021).

    Article  PubMed  Google Scholar 

  4. Park, W., Chawla, A. & O’Reilly, E. M. Pancreatic cancer. JAMA 326, 851 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Conroy, T. et al. FOLFIRINOX or gemcitabine as adjuvant therapy for pancreatic cancer. N. Engl. J. Med. 379, 2395–2406 (2018).

    Article  CAS  PubMed  Google Scholar 

  6. Murphy, J. E. et al. Total neoadjuvant therapy with FOLFIRINOX followed by individualized chemoradiotherapy for borderline resectable pancreatic adenocarcinoma: a phase 2 clinical trial. JAMA Oncol. 4, 963–969 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Tempero, M. A. et al. Adjuvant nab-paclitaxel + gemcitabine in resected pancreatic ductal adenocarcinoma: results from a randomized, open-label, phase III trial. J. Clin. Oncol. 41, 2007–2019 (2023).

    Article  CAS  PubMed  Google Scholar 

  8. Sakuramoto, S. et al. Adjuvant chemotherapy for gastric cancer with S-1, an oral fluoropyrimidine. N. Engl. J. Med. 357, 1810–1820 (2007).

    Article  CAS  PubMed  Google Scholar 

  9. Bailey, P. et al. Genomic analyses identify molecular subtypes of pancreatic cancer. Nature 531, 47–52 (2016).

    Article  CAS  PubMed  Google Scholar 

  10. Collisson, E. A., Bailey, P., Chang, D. K. & Biankin, A. V. Molecular subtypes of pancreatic cancer. Nat. Rev. Gastroenterol. Hepatol. 16, 207–220 (2019).

    Article  PubMed  Google Scholar 

  11. Moffitt, R. A. et al. Virtual microdissection identifies distinct tumor- and stroma-specific subtypes of pancreatic ductal adenocarcinoma. Nat. Genet. 47, 1168–1178 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Nicolle, R. et al. A transcriptomic signature to predict adjuvant gemcitabine sensitivity in pancreatic adenocarcinoma. Ann. Oncol. 32, 250–260 (2021).

    Article  CAS  PubMed  Google Scholar 

  13. Fraunhoffer, N. et al. A transcriptomic-based tool to predict gemcitabine sensitivity in advanced pancreatic adenocarcinoma. Gastroenterology 164, 476–480 (2023).

    Article  CAS  PubMed  Google Scholar 

  14. Heredia, N. J. et al. Droplet Digital™ PCR quantitation of HER2 expression in FFPE breast cancer samples. Methods 59, S20–S23 (2013).

    Article  CAS  PubMed  Google Scholar 

  15. Clark, D. J. et al. Integrated proteogenomic characterization of clear cell renal cell carcinoma. Cell 179, 964–983 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Gillette, M. A. et al. Proteogenomic characterization reveals therapeutic vulnerabilities in lung adenocarcinoma. Cell 182, 200–225 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Satpathy, S. et al. A proteogenomic portrait of lung squamous cell carcinoma. Cell 184, 4348–4371 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Zhang, H. et al. Integrated proteogenomic characterization of human high-grade serous ovarian cancer. Cell 166, 755–765 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Cao, L. et al. Proteogenomic characterization of pancreatic ductal adenocarcinoma. Cell 184, 5031–5052 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Hyeon, D. Y. et al. Proteogenomic landscape of human pancreatic ductal adenocarcinoma in an Asian population reveals tumor cell-enriched and immune-rich subtypes. Nat. Cancer 4, 290–307 (2022).

    Article  PubMed  Google Scholar 

  21. Springfeld, C. et al. Neoadjuvant therapy for pancreatic cancer. Nat. Rev. Clin. Oncol. 20, 318–337 (2023).

    Article  PubMed  Google Scholar 

  22. Krug, K. et al. Proteogenomic landscape of breast cancer tumorigenesis and targeted therapy. Cell 183, 1436–1456(2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. He, J. et al. 2564 resected periampullary adenocarcinomas at a single institution: trends over three decades. HPB (Oxford) 16, 83–90 (2014).

    Article  PubMed  Google Scholar 

  24. Chen, Y. et al. Improvement in treatment and outcome of pancreatic ductal adenocarcinoma in north China. J. Gastrointest. Surg. 15, 1026–1034 (2011).

    Article  PubMed  Google Scholar 

  25. Cui, J. et al. Chinese Society of Clinical Oncology (CSCO): clinical guidelines for the diagnosis and treatment of pancreatic cancer. J. Natl Cancer Cent. 2, 205–215 (2022).

    Article  Google Scholar 

  26. Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Liberzon, A. et al. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst. 1, 417–425 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Sahlstrom, E., Bereza-Carlson, P., Nilsson, J., Tingstedt, B. & Andersson, B. Risk factors and outcomes for patients with pancreatic cancer undergoing surgical exploration without resection due to metastatic disease: a national cohort study. Hepatobiliary Pancreat. Dis. Int. 21, 279–284 (2022).

    Article  PubMed  Google Scholar 

  29. Gilkes, D. M., Semenza, G. L. & Wirtz, D. Hypoxia and the extracellular matrix: drivers of tumour metastasis. Nat. Rev. Cancer 14, 430–439 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Dimitrakopoulos, C. et al. Identification and validation of a biomarker signature in patients with resectable pancreatic cancer via genome-wide screening for functional genetic variants. JAMA Surg. 154, e190484 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Mueller, A. C. et al. Induction of ADAM10 by radiation therapy drives fibrosis, resistance, and epithelial-to-mesenchyal transition in pancreatic cancer. Cancer Res. 81, 3255–3269 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Xie, Z. et al. Exosome-delivered CD44v6/C1QBP complex drives pancreatic cancer liver metastasis by promoting fibrotic liver microenvironment. Gut 71, 568–579 (2022).

    Article  CAS  PubMed  Google Scholar 

  33. Yan, Y. et al. KLF4-mediated suppression of CD44 signaling negatively impacts pancreatic cancer stemness and metastasis. Cancer Res. 76, 2419–2431 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Yao, H. et al. Glypican-3 and KRT19 are markers associating with metastasis and poor prognosis of pancreatic ductal adenocarcinoma. Cancer Biomark. 17, 397–404 (2016).

    Article  CAS  PubMed  Google Scholar 

  35. Rashid, N. U. et al. Purity Independent Subtyping of Tumors (PurIST), a clinically robust, single-sample classifier for tumor subtyping in pancreatic cancer. Clin. Cancer Res. 26, 82–92 (2020).

    Article  PubMed  Google Scholar 

  36. Kammertoens, T., Schuler, T. & Blankenstein, T. Immunotherapy: target the stroma to hit the tumor. Trends Mol. Med. 11, 225–231 (2005).

    Article  CAS  PubMed  Google Scholar 

  37. Demir, I. E., Friess, H. & Ceyhan, G. O. Neural plasticity in pancreatitis and pancreatic cancer. Nat. Rev. Gastroenterol. Hepatol. 12, 649–659 (2015).

    Article  CAS  PubMed  Google Scholar 

  38. Simon, N., Friedman, J., Hastie, T. & Tibshirani, R. Regularization paths for Cox’s proportional hazards model via coordinate descent. J. Stat. Softw. 39, 1–13 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Schubert, O. T. et al. Building high-quality assay libraries for targeted analysis of SWATH MS data. Nat. Protoc. 10, 426–441 (2015).

    Article  CAS  PubMed  Google Scholar 

  40. Zhu, T. et al. DPHL: a DIA pan-human protein mass spectrometry library for robust biomarker discovery. Genomics Proteomics Bioinformatics 18, 104–119 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Polley, M. Y. et al. Statistical and practical considerations for clinical evaluation of predictive biomarkers. J. Natl Cancer Inst. 105, 1677–1683 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Piekutowska-Abramczuk, D. et al. NDUFB8 mutations cause mitochondrial complex I deficiency in individuals with Leigh-like encephalomyopathy. Am. J. Hum. Genet. 102, 460–467 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. De Angelis, J. E. et al. Tmem2 regulates embryonic Vegf signaling by controlling hyaluronic acid turnover. Dev. Cell 40, 123–136 (2017).

    Article  PubMed  Google Scholar 

  44. Schinzel, R. T. et al. The hyaluronidase, TMEM2, promotes ER homeostasis and longevity independent of the UPRER. Cell 179, 1306–1318 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Uesaka, K. et al. Adjuvant chemotherapy of S-1 versus gemcitabine for resected pancreatic cancer: a phase 3, open-label, randomised, non-inferiority trial (JASPAC 01). Lancet 388, 248–257 (2016).

    Article  CAS  PubMed  Google Scholar 

  46. Cancer Genome Atlas Research Network. Integrated genomic characterization of pancreatic ductal adenocarcinoma. Cancer Cell 32, 185–203 (2017).

  47. Collisson, E. A. et al. Subtypes of pancreatic ductal adenocarcinoma and their differing responses to therapy. Nat. Med. 17, 500–503 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Neoptolemos, J. P. et al. Comparison of adjuvant gemcitabine and capecitabine with gemcitabine monotherapy in patients with resected pancreatic cancer (ESPAC-4): a multicentre, open-label, randomised, phase 3 trial. Lancet 389, 1011–1024 (2017).

    Article  CAS  PubMed  Google Scholar 

  49. Neoptolemos, J. P. et al. Adjuvant chemotherapy with fluorouracil plus folinic acid vs gemcitabine following pancreatic cancer resection: a randomized controlled trial. JAMA 304, 1073–1081 (2010).

    Article  CAS  PubMed  Google Scholar 

  50. Oettle, H. et al. Adjuvant chemotherapy with gemcitabine and long-term outcomes among patients with resected pancreatic cancer: the CONKO-001 randomized trial. JAMA 310, 1473–1481 (2013).

    Article  CAS  PubMed  Google Scholar 

  51. O’Connell, M. J. et al. Relationship between tumor gene expression and recurrence in four independent studies of patients with stage II/III colon cancer treated with surgery alone or surgery plus adjuvant fluorouracil plus leucovorin. J. Clin. Oncol. 28, 3937–3944 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Parker, J. S. et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. J. Clin. Oncol. 27, 1160–1167 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Puleo, F. et al. Stratification of pancreatic ductal adenocarcinomas based on tumor and microenvironment features. Gastroenterology 155, 1999–2013 (2018).

    Article  PubMed  Google Scholar 

  54. Amin, M. B. et al. The Eighth Edition AJCC Cancer Staging Manual: continuing to build a bridge from a population-based to a more ‘personalized’ approach to cancer staging. CA Cancer J. Clin. 67, 93–99 (2017).

    Article  PubMed  Google Scholar 

  55. Mertins, P. et al. Reproducible workflow for multiplexed deep-scale proteome and phosphoproteome analysis of tumor tissues by liquid chromatography–mass spectrometry. Nat. Protoc. 13, 1632–1661 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Escher, C. et al. Using iRT, a normalized retention time for more targeted measurement of peptides. Proteomics 12, 1111–1121 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Marsh, A. N. et al. Skyline Batch: an intuitive user interface for batch processing with Skyline. J. Proteome Res. 21, 289–294 (2022).

    Article  CAS  PubMed  Google Scholar 

  58. Sherrod, S. D. et al. Label-free quantitation of protein modifications by pseudo selected reaction monitoring with internal reference peptides. J. Proteome Res. 11, 3467–3479 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Yu, G., Wang, L. G., Han, Y. & He, Q. Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Jiao, X. et al. DAVID-WS: a stateful web service to facilitate gene/protein list analysis. Bioinformatics 28, 1805–1806 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Szklarczyk, D. et al. The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 49, D605–D612 (2021).

    Article  CAS  PubMed  Google Scholar 

  63. Hanzelmann, S., Castelo, R. & Guinney, J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 14, 7 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  64. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  PubMed  Google Scholar 

  65. Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  67. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. COvid-19 Multi-omics Blood ATlas (COMBAT) Consortium. A blood atlas of COVID-19 defines hallmarks of disease severity and specificity. Cell 185, 916–938 (2022).

Download references

Acknowledgements

This study is supported by the National Natural Science Foundation of China (82173219 (L.J.), 32170663 (H.F.), 81770124 (T.Y.), 31970628 (S.W.), 81871906 (B.S.) and 82073326 (B.S.)); National Key Research and Development Program (2019YFA0905902 (S.W.)); the Shanghai Municipal Education Commission-Gaofeng Clinical Medicine Grant Support (RC20200037 (L.J.) and RC20210190 (T.Y.)); the Medical-Engineering Cross Foundation of Shanghai Jiao Tong University (ZH2018ZDA01 (B.S.)); the Shanghai Pilot Program for Basic Research Shanghai Jiao Tong University (21TQ1400205 (L.J.)); the Shanghai Shenkang Hospital Development Center (SHDC2020CR5008 (B.S.)); the Foundation of National Facility for Translational Medicine (Shanghai) (NRCTM(SH)-2019-03 (B.S.) and TMSK-2021-502 (X.D.)); the Science and Technology Commission of Shanghai Municipality (21430711900 (X.D.)); and the Innovative Research Team of High-level Local Universities in Shanghai (B.S.).

We thank Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, National Facility for Translational Medicine, Standardized Clinical Biobank for keeping clinical samples.

Author information

Author notes

  1. These authors contributed equally: Lingxi Jiang, Jiejie Qin, Yuting Dai, Shulin Zhao, Qian Zhan, Peng Cui.

Authors and Affiliations

  1. Department of General Surgery, Pancreatic Disease Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Lingxi Jiang, Jiejie Qin, Shulin Zhao, Qian Zhan, Lingjie Ren, Xuelong Wang, Xiaomei Tang, Minmin Shi, Fangfang Ma, Jia Liu, Xiaxing Deng, Hao Chen & Baiyong Shen

  2. Research Institute of Pancreatic Diseases, Shanghai Key Laboratory of Translational Research for Pancreatic Neoplasms, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Lingxi Jiang, Jiejie Qin, Shulin Zhao, Qian Zhan, Lingjie Ren, Xuelong Wang, Xiaomei Tang, Minmin Shi, Fangfang Ma, Jia Liu, Xiaxing Deng, Hao Chen & Baiyong Shen

  3. State Key Laboratory of Systems Medicine for Cancer, Institute of Translational Medicine, Shanghai Jiao Tong University, Shanghai, China

    Lingxi Jiang, Jiejie Qin, Shulin Zhao, Qian Zhan, Lingjie Ren, Xuelong Wang, Xiaomei Tang, Minmin Shi, Fangfang Ma, Jia Liu, Xiaxing Deng, Hao Chen & Baiyong Shen

  4. Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Centre for Translational Medicine at Shanghai, Ruijin Hospital affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Yuting Dai, Ruihong Zhang, Chenxu Gao, Yanting Zhou, Qiang Tian, Shengyue Wang, Tong Yin, Hai Fang & Saijuan Chen

  5. Burning Rock Biotech, Guangzhou, China

    Peng Cui, Shangli Cai, Guoqiang Wang & Wenchuan Xie

  6. Department of Pathology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Ting Wang & Chaofu Wang

  7. Department of Pathology, Saint-Antoine Hospital – Sorbonne Universités, Paris, France

    Magali Svrcek

  8. Department of Pathology, Pitié-Salpêtrière Hospital – Sorbonne Universités, Paris, France

    Armelle Bardier-Dupas

  9. Department of Pathology, Ambroise Paré Hospital – Université Saint Quentin en Yvelines, Paris, France

    Jean Francois Emile

  10. Department of Pancreatology, Université Paris Cité – FHU MOSAIC, Beaujon Hospital, Clichy, France

    Louis de Mestier

  11. Department of Gastroenterology, Pitié-Salpêtrière Hospital – Sorbonne Universités, Paris, France

    Jean-Baptiste Bachet

  12. Université Paris Cité, FHU MOSAIC, Centre de Recherche sur l’Inflammation (CRI), INSERM, U1149, CNRS, ERL 8252, Paris, France

    Remy Nicolle

  13. Department of Pathology, Université Paris Cité – FHU MOSAIC, Beaujon Hospital, Clichy, France

    Jerome Cros

  14. Department of Biochemistry, Hôpital Européen Georges Pompidou, Centre de Recherche des Cordeliers, INSERM UMRS1138, CNRS, Sorbonne Université, USPC, Université Paris Cité, Equipe labellisée Ligue Nationale contre le cancer, CNRS, Paris, France

    Pierre Laurent-Puig

  15. Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China

    Miaoyan Wei & Si Shi

  16. Department of Hepatobiliary Pancreatic Surgery, Changhai Hospital, Naval Medical University (Second Military Medical University), Shanghai, China

    Bin Song, Wei Jing, Shiwei Guo, Kailian Zheng, Hui Jiang, Huan Wang & Gang Jin

  17. Department of Pathology, Changhai Hospital, Naval Medical University (Second Military Medical University), Shanghai, China

    Hui Jiang

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  1. Lingxi Jiang
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  2. Jiejie Qin
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  17. Fangfang Ma
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  19. Ting Wang
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  20. Chaofu Wang
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  25. Jean-Baptiste Bachet
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  27. Jerome Cros
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  31. Wei Jing
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  34. Hui Jiang
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  38. Qiang Tian
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  39. Shengyue Wang
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  40. Si Shi
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  41. Gang Jin
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  42. Tong Yin
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  43. Hai Fang
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  44. Saijuan Chen
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  45. Baiyong Shen
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L.J., J.Q., Y.D., S.Z., T.Y., H.F., S.C. and B.S. designed the study and wrote the manuscript. L.J., J.Q., Y.D., S.Z., Q.Z., S.C., L.R., X.W. and R.Z. conducted experiments. Y.D., S.Z., C.G., X.W., P.C., G.W., W.X. and H.F. performed bioinformatics analysis. H.F., C.G. and Y.D. managed data. H.F. and J.Q. created the project website. R.Z. Y.Z. and T.Y. performed proteomics assays. M.S., X.T., T.W. and C.W. participated in pathological work. G.J., B.S., W.J., S.G., K.Z., H.J., H.W., S.S. and M.W. participated in the validation work of the SH-cohort. S.Z., M.S., A.B.-D., J.F.E., L.d.M., J.B.B., R.N., J.C. and P.L.-P. participated in the validation work of the FR-cohort. F.M., J.L., X.D. and H.C. participated in patient recruitment and sample collection. Y.D., L.J., C.G., X.W., P.C. and H.F. contributed to source codes supporting reproducible research. Q.T., Y.W., T.Y. and H.F. helped optimize the research and proofread the paper. T.Y., H.F., S.C. and B.S. supervised the study and revised the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Si Shi, Gang Jin, Tong Yin, Hai Fang, Saijuan Chen or Baiyong Shen.

Ethics declarations

Competing interests

S.C., P.C., G.W. and W.X. are employees of Burning Rock Biotech. M.S. reports personal fees and non-financial support from Bayer, Bristol-Myers Squibb, Astellas, MSD Oncology, Sanofi and Owkin, and travel or accomodation grants from Bristol-Myers Squibb, Ventana/Roche, all outside the submitted work. J.C. reports personal fees from Novartis and IPSEN outside the submitted work. P.L.-P. reports grants from Foundation Roche and Siric Carpem as well as grants and non-financial support from Ligue Nationale de lutte contre le cancer during the conduct of the study. P.L.-P. also reports personal fees from AstraZeneca, Roche, Pfizer, Amgen, Sanofi, Pierre Fabre and Eli Lilly; personal fees and non-financial support from Biocartis; grants from Bio-Rad; and grants and personal fees from Bristol Myers Squibb and Servier, all outside the submitted work. J.B.B. reports grants from Foundation Roche outside of the study. J.B.B. also reports personal fees and non-financial support from Amgen, Merck Serono and Roche as well as personal fees from AstraZeneca, Bayer, Pierre Fabre, Sanofi, Servier and Shire, all outside the submitted work. Other authors declare no potential conflicts of interest.

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Nature Medicine thanks Robert Grant, Grainne O’Kane and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Ulrike Harjes, in collaboration with the Nature Medicine team.

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Extended data

Extended Data Fig. 1 Clinical stratification of the 1,171 PDAC patients in our entire ‘RJ-cohort’.

(a) Kaplan-Meier survival curves comparing overall survival (OS) among patients with different AJCC stages. Patients are stratified into three groups according to AJCC stages. P-value is calculated using the log-rank test. (b) Kaplan-Meier survival curves comparing OS based on margin status. (c) Kaplan-Meier survival curves comparing OS among patients who received adjuvant chemotherapy. (d) Kaplan-Meier survival curves comparing OS among patients grouped by adjuvant chemotherapy status, including gemcitabine-included (Gem), Non-gemcitabine included (Non-Gem), and those who did not receive adjuvant chemotherapy (No). P-values were calculated using the log-rank test, and the hazard ratios (HR) calculated using the univariable Cox regression analysis.

Extended Data Fig. 2 Proteomic features in PDAC and validation of abundance levels of modules using an external independent cohort.

(a) Determination of soft-threshold power in WGCNA. Analysis of the scale-free index for various soft-threshold powers (β) and the mean connectivity for various soft-threshold powers. (b) Dendrogram of proteins based on the measurement of dissimilarity and identification of the 32 modules. (c) Evaluating the stability of the 32 protein modules. For each calculation process, 80% of PDAC tumors were randomly selected for the identification of protein modules, employing a sample construction workflow and determining the soft-threshold power for the initial 32 protein modules. Taking ME01 as an example, the accuracy for ME01 was calculated using the formula [Number of proteins clustered in ME01] / [Total protein number of ME01]. This calculation process was repeated 20 times (n=20 biologically independent calculations) with different random seed, consistently yielding median accuracies exceeding 90%. This underscores the robustness and stability for module identification. In the boxplot, a black line within the box marks the median. The bottom and top of the box are located at the 25th and 75th percentiles, respectively. The bars represent values that are more than 1.5 times the interquartile range from the border of each box. (d) Receiver operating characteristic (ROC) curves, illustrating the prediction accuracy of the 32 protein modules in ‘RJ-cohort 1’. A random forest algorithm was employed to build the model, with 80% PDACs used as the training cohort and the remaining 20% as the validation cohort. Area under curve (AUC) values were calculated for each module separately. (e) ROC curves depicting the prediction accuracy of the 32 modules in Cao et al cohort. The model was constructed based on ‘RJ-cohort 1’, and the Cao et al. cohort was used as the validation cohort. (f) Validation of overlay of the significantly up/down-regulated proteins (left) and genes (right) between tumors and TATs on the weighted correlation network nodes from the Cao et al cohort. (g) Spearman’s Correlation of module scores calculated by using proteomic and transcriptomic data in ‘RJ-cohort 1’. Each dot represents one module, with the red dots indicating significant positive correlation (P-value < 0.05, r > 0) between protein and RNA-seq, and the blue dots for significant negative correlations (P-value < 0.05, r < 0). Two-sided P-values were calculated. (h) Kaplan-Meier survival curves comparing OS between patient subgroups stratified by RNA-seq. P-values were calculated using the log-rank test, and hazard ratios calculated using the univariable Cox regression analysis. (i) Kaplan-Meier survival curves comparing OS between patient RNA-seq subgroups stratified by both adjuvant therapy and the high/low abundance (median cutoff) of ME11. P-values were calculated using the log-rank test, and hazard ratios calculated using the univariable Cox regression analysis.

Extended Data Fig. 3 Establishment of the LASSO-Cox regression model using proteomics data.

(a) LASSO-Cox regression coefficient selection and variable screening. The lower horizontal axis represents lambda value, and the upper horizontal axis scale represents the number of variables in the LASSO-Cox regression model. The minimum mean cross-validated error of λ is selected. (b) Cross-validation in the LASSO-Cox regression model to select the tuning parameter. The horizontal axis represents the log (lambda) value, and the vertical axis represents partial likelihood deviance. Error bars represent the means ± SD (by 10-fold cross-validation). The minimum mean 10-fold cross-validated error of λ is selected. (c) Receiver operating characteristic (ROC) curve, area under curve (AUC), and C-index of the LASSO-Cox regression model for OS in ‘RJ-cohort 1’. (d) Time-dependent ROC curve, AUC, and C-index of the LASSO-Cox regression model for OS in ‘RJ-cohort 1’. The 1-year, 2-year and 3-year OS were calculated separately. (e) Forest plots of multivariable analysis showing the LASSO score and adjuvant chemotherapy as independent prognostic factors for OS. The center of the Forest plot represents the HR of the Cox proportional hazards model, the error bars are two-sided 95% CIs (Cox regression multivariable analysis). N=190 biologically independent samples. One sample was excluded for the missing CA19-9 information. (f) Kaplan-Meier survival curves comparing OS (upper) and DFS (down) between RNA-seq LASSO score-low and -high groups in ‘RJ-cohort 1’. The LASSO score was calculated based on RNA-seq data. P-value was calculated using the log-rank test. (g) GSEA was used to identify the enriched pathways between LASSO score-high and -low groups at both the protein and RNA-seq levels. Proteins/genes were ranked by log2(FC) between score-high and -low groups. The LASSO groups were identified based on the protein-level data. In the visual representation, blue indicated down-regulated pathways in the LASSO score-high group, and red represents up-regulated pathway in the LASSO score-high group. The signed -log10(P-value) was calculated by -log10(P-value) × 1 when NES > 0, or -log10(P-value) × −1 when NES < 0. Significance was defined as P-value < 0.05 and adjusted P-value < 0.25, with the dashed line indicating P-value of 0.05. P-values were calculated based on 1,000 permutations in GSEA and subsequently adjusted using the Benjamini & Hochberg methods. Two-sided P-values were calculated. (h) Boxplot illustrating the LASSO score in different RNA-seq subtypes. The P-value was calculated using the Mann-Whitney U test. N = 28 biologically independent samples for the basal subtype and n = 157 biologically independent samples for the classical subtype. In the boxplot, a black line within the box marks the median. The bottom and top of the box are located at the 25th and 75th percentiles, respectively. The bars represent values that are more than 1.5 times the interquartile range from the border of each box. Two-sided P-values were calculated.

Extended Data Fig. 4 Enrichment analysis for patient subgroups stratified by the protein abundance of either NDUFB8 or CEMIP2.

(a) Protein-protein interaction (PPI) network (left) centered on NDUFB8 and functional enrichment (right) for NDUFB8. PPI data was obtained from the STRING database, and functional enrichment analysis was performed using DAVID. P-values were calculated from the STRING database. Two-sided P-values were calculated. (b) Violin plots displaying the protein abundance of NDUFB8 and CEMIP2 between tumors and TATs. P-values were calculated using the Mann-Whitney U test. In the boxplot, a black line within the box marks the median. The bottom and top of the box are located at the 25th and 75th percentiles, respectively. The bars represent values that are more than 1.5 times the interquartile range from the border of each box. N = 281 biologically independent samples including 191 tumors and 90 TATs. Two-sided P-values were calculated. (c-d) Kaplan-Meier survival curves comparing OS between patient subgroups stratified by gemcitabine-included (Gem), non-gemcitabine-included (non-Gem), and without adjuvant therapy (No) according to the high/low abundance (median cutoff) of NDUFB8 (c) and CEMIP2 (d). P-values were calculated using the log-rank test, and the hazard ratios calculated using the univariable Cox regression analysis. (e-f) Kaplan-Meier survival curves comparing OS between patient subgroups stratified by adjuvant chemotherapy and the high/low expression of NDUFB8 in patients with LASSO score-low (e) or -high (f) in ‘RJ-cohort 1’. P-values were calculated using the log-rank test. (g) Associations of NDUFB8 and CEMIP2 abundance with proteomic profiles. Significantly enriched pathways were calculated using GSEA. (h) The fold changes of NDUFB8 and CEMIP2 in three pancreatic cell lines (HPNE, 8988 and CFPAC) and two PDOs (T1030411 and T983610). HPNE was used as an internal control, and β-actin used as a loading control. Error bars represent the means ± SD (n = 3 biologically independent experiments). Two-sided P-values were calculated. (i) The fold change of NDUFB8 and CEMIP2 in two PDAC cells (8988 and CFPAC) and two PDOs (T1030411 and T983610) upon gene silencing. Control plasmid-transfected cells were used as an internal control, and β-actin used as a loading control. Error bars represent the means ± SD (n = 3 biologically independent experiments). Two-sided P-values were calculated.

Extended Data Fig. 5 Distinct roles of NDUFB8 and CEMIP2 in PDAC cells after chemotherapy treatments.

(a-b) Kaplan-Meier survival curves comparing OS between patient subgroups stratified by gemcitabine-included (Gem), non-gemcitabine-included (non-Gem), and without adjuvant therapy (No) according to the high/low expression of NDUFB8 (a) and CEMIP2 (b). P-values were calculated using the log-rank test. Two-sided P-values were calculated. (c-d) The relative cell viability of shCtl-, shNDUFB8-, and shCEMIP2-transfected cells was detected by XTT assay after paclitaxel plus gemcitabine treatment (A+G), single-agent 5-Fu (5-FU), and the FOLFIRINOX chemotherapy cocktail [FU+IRI+OXA: 5-FU+Irinotecan (SN-38) +Oxaliplatin] for 48h. (e) The viability of shCtl-, shNDUFB8-, and shCEMIP2-transfected PDOs was detected by 3D cell titer glo assays after A+G, 5-FU, and FU+IRI+OXA treatment for 48h. PDAC cells/PDOs without treatment were used as control. (c-e) Results represent the mean ± SD (n=3 biologically independent experiments). P-values were calculated using unpaired t-test. Two-sided P-values were calculated.

Supplementary information

Reporting Summary

Supplementary Table 1

1a. Sample information and clinical characteristics of the 1,171 patients with PDAC (RJ-cohort). Related to Fig. 1. 1b. Sample information and clinical characteristics of the 191 patients with PDAC (RJ-cohort 1). Related to Fig. 1.

Supplementary Table 2

2a. Differentially expressed proteins between tumor and TATs in PDACs. Related to Fig. 1. P values were calculated using the R package ‘limma’ and subsequently adjusted using the Benjamini–Hochberg method. Two-sided P values were calculated. 2b. Significantly enriched pathways between tumor and TATs. Enrichment analysis was performed using DAVID (https://david.ncifcrf.gov/). Related to Fig. 1. P values were calculated from the DAVID database and subsequently adjusted using the Benjamini–Hochberg method. Two-sided P values were calculated.

Supplementary Table 3

3a. The 32 functional modules identified by WGCNA. Related to Fig. 2. 3b. Scores of the 32 modules in TATs and PDACs of RJ-cohort 1. Related to Fig. 2. 3c. Statistical information between module scores and clinical features. Related to Fig. 2. 3d. Correlation between the LASSO score and the 32 module scores in RJ-cohort 1. Related to Fig. 3. 3e. Correlation between the LASSO score and the 32 module scores in the Cao et al. cohort. Related to Fig. 3. 3f. The LASSO score in RJ-cohort 2. Related to Fig. 3.

Supplementary Table 4

4. List of chemotherapy-by-biomarker interaction terms for OS. Related to Fig. 4. P values were calculated by univariable Cox proportional hazards regression. Two-sided P values were calculated.

Supplementary Table 5

5a. Sample information, clinical features and protein levels of markers in RJ-cohort 3. Related to Fig. 5. 5b. Sample information, clinical features and protein levels of markers in SH-cohort. Related to Fig. 5. 5c. Sample information, clinical features and protein levels of markers in FR-cohort. Related to Fig. 5.

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Jiang, L., Qin, J., Dai, Y. et al. Prospective observational study on biomarkers of response in pancreatic ductal adenocarcinoma. Nat Med 30, 749–761 (2024). https://doi.org/10.1038/s41591-023-02790-x

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