Skip to main content

Advertisement

SpringerLink
Log in

The landscape of objective response rate of anti-PD-1/L1 monotherapy across 31 types of cancer: a system review and novel biomarker investigating

  • Research
  • Published:
Cancer Immunology, Immunotherapy Aims and scope Submit manuscript

Abstract

Background

Immune checkpoint inhibitors (ICIs) have dramatically changed the landscape of cancer treatment. However, only a few patients respond to ICI treatment. Thus, uncovering clinically accessible ICI biomarkers would help identify which patients will respond well to ICI treatment. A comprehensive objective response rate (ORR) data of anti-PD-1/PD-L1 monotherapy in pan-cancer would offer the original data to explore the new biomarkers for ICIs.

Methods

We systematically searched PubMed, Cochrane, and Embase for clinical trials on July 1, 2021, limited to the years 2017–2021, from which we obtained studies centering around anti–PD-1/PD-L1 monotherapy. Finally, 121 out of 3099 publications and 143 ORR data were included. All of the 31 tumor types/subtypes can be found in the TCGA database. The gene expression profiles and mutation data were downloaded from TCGA. A comprehensive genome-wide screening of ORR highly correlated mutations among 31 cancers was conducted by Pearson correlation analysis based on the TCGA database.

Results

According to the ORR, we classified 31 types of cancer into high, medium, and low response types. Further analysis uncovered that “high response” cancers had more T cell infiltration, more neoantigens, and less M2 macrophage infiltration. A panel of 28 biomarkers reviewed from recent articles were investigated with ORR. We also found the TMB as a traditional biomarker had a high correlation coefficient with ORR in pan-cancer, however, the correlation between ITH and ORR was low across pan-cancer. Moreover, we primarily identified 1044 ORR highly correlated mutations through a comprehensive screening of TCGA data, among which USH2A, ZFHX4 and PLCO mutations were found to be highly correlated to strengthened tumor immunogenicity and inflamed antitumor immunity, as well as improved outcomes for ICIs treatment among multiple immunotherapy cohorts.

Conclusion

Our study provides comprehensive data on ORR of anti-PD-1/PD-L1 monotherapy across 31 tumor types/subtypes and an essential reference of ORR to explore new biomarkers. We also screened out a list of 1044 immune response related genes and we showed that USH2A, ZFHX4 and PLCO mutations may act as good biomarkers for predicting patient response to anti-PD-1/PD-L1 ICIs.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Data availability

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

Abbreviations

CYT score:

Cytolytic activity score

ICIs:

Immune checkpoint inhibitors

IRRG:

Immune response related genes

ITH:

Intratumoral heterogeneity

NSCLC:

Non-small cell lung cancer

ORR:

Objective response rate

OS:

Overall survival

PD-1/PD-L1:

Protein programmed cell death protein 1/protein programmed death receptor ligand-1

PFS:

Progression-free survival

TIL:

Tumor-infiltrating lymphocyte

TMB:

Tumor mutational burden

References

  1. Siegel RL, Miller KD, Fuchs HE, Jemal A (2021) Cancer statistics. CA a Cancer J Clin 71:7–33. https://doi.org/10.3322/caac.21654

    Article  Google Scholar 

  2. Ribas A, Wolchok JD (2018) Cancer immunotherapy using checkpoint blockade. Science 359:1350–1355. https://doi.org/10.1126/science.aar4060

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Zhou X, Yao Z, Bai H et al (2021) Treatment-related adverse events of PD-1 and PD-L1 inhibitor-based combination therapies in clinical trials: a systematic review and meta-analysis. Lancet Oncol 22:1265–1274. https://doi.org/10.1016/S1470-2045(21)00333-8

    Article  CAS  PubMed  Google Scholar 

  4. Samstein RM, Lee CH, Shoushtari AN et al (2019) Tumor mutational load predicts survival after immunotherapy across multiple cancer types. Nat Genet 51:202–206. https://doi.org/10.1038/s41588-018-0312-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Litchfield K, Reading JL, Puttick C et al (2021) Meta-analysis of tumor- and T cell-intrinsic mechanisms of sensitization to checkpoint inhibition. Cell. 184:596–614. https://doi.org/10.1016/j.cell.2021.01.002

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Miao D, Margolis CA, Vokes NI et al (2018) Genomic correlates of response to immune checkpoint blockade in microsatellite-stable solid tumors. Nat Genet 50:1271–1281. https://doi.org/10.1038/s41588-018-0200-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Morad G, Helmink BA, Sharma P, Wargo JA (2021) Hallmarks of response, resistance, and toxicity to immune checkpoint blockade. Cell 184:5309–5337. https://doi.org/10.1016/j.cell.2021.09.020

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Liu D, Schilling B, Liu D et al (2019) Integrative molecular and clinical modeling of clinical outcomes to PD1 blockade in patients with metastatic melanoma. Nat Med 25:1916–1927. https://doi.org/10.1038/s41591-019-0654-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Riaz N, Havel JJ, Makarov V et al (2017) Tumor and microenvironment evolution during immunotherapy with nivolumab. Cell. 171:934–49. https://doi.org/10.1016/j.cell.2017.09.028

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Snyder A, Makarov V, Merghoub T et al (2014) Genetic basis for clinical response to CTLA-4 blockade in melanoma. N Engl J Med 371:2189–2199. https://doi.org/10.1056/NEJMoa1406498

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Van Allen EM, Miao D, Schilling B et al (2015) Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Science 350:207–211. https://doi.org/10.1126/science.aad0095

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Hellmann MD, Nathanson T, Rizvi H et al (2018) Genomic features of response to combination immunotherapy in patients with advanced non-small-cell lung cancer. Cancer Cell 33:843–52. https://doi.org/10.1016/j.ccell.2018.03.018

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Rizvi NA, Hellmann MD, Snyder A et al (2015) Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science 348:124–128. https://doi.org/10.1126/science.aaa1348

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Yarchoan M, Hopkins A, Jaffee EM (2017) Tumor mutational burden and response rate to PD-1 inhibition. N Engl J Med 377:2500–2501. https://doi.org/10.1056/NEJMc1713444

    Article  PubMed  PubMed Central  Google Scholar 

  15. Hoadley KA, Yau C, Hinoue T et al (2018) Cell-of-origin patterns dominate the molecular classification of 10,000 tumors from 33 types of cancer. Cell 173:291–304. https://doi.org/10.1016/j.cell.2018.03.022

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Liu Y, Sethi NS, Hinoue T et al (2018) Comparative molecular analysis of gastrointestinal adenocarcinomas. Cancer Cell. 33:721–35. https://doi.org/10.1016/j.ccell.2018.03.010

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Tumeh PC, Harview CL, Yearley JH et al (2014) PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature 515:568–571. https://doi.org/10.1038/nature13954

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Chow MT, Ozga AJ, Servis RL, Frederick DT, Lo JA, Fisher DE, Freeman GJ, Boland GM, Luster AD (2019) Intratumoral activity of the CXCR3 chemokine system is required for the efficacy of anti-PD-1 therapy. Immunity. 50:1498–512. https://doi.org/10.1016/j.immuni.2019.04.010

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Gibney GT, Weiner LM, Atkins MB (2016) Predictive biomarkers for checkpoint inhibitor-based immunotherapy. Lancet Oncol 17:e542–e551. https://doi.org/10.1016/S1470-2045(16)30406-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Chen L, Diao L, Yang Y et al (2018) CD38-mediated immunosuppression as a mechanism of tumor cell escape from PD-1/PD-L1 blockade. Cancer Discov 8:1156–1175. https://doi.org/10.1158/2159-8290.CD-17-1033

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Koyama S, Akbay EA, Li YY et al (2016) Adaptive resistance to therapeutic PD-1 blockade is associated with upregulation of alternative immune checkpoints. Nat Commun 7:10501. https://doi.org/10.1038/ncomms10501

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Huang L, Malu S, McKenzie JA et al (2018) The RNA-binding Protein MEX3B mediates resistance to cancer immunotherapy by downregulating HLA-A expression. Clin Cancer Res 24:3366–3376. https://doi.org/10.1158/1078-0432.CCR-17-2483

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Wolf Y, Bartok O, Patkar S et al (2019) UVB-induced tumor heterogeneity diminishes immune response in melanoma. Cell 179:219–35. https://doi.org/10.1016/j.cell.2019.08.032

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Wang L, Saci A, Szabo PM et al (2018) EMT- and stroma-related gene expression and resistance to PD-1 blockade in urothelial cancer. Nat Commun 9:3503. https://doi.org/10.1038/s41467-018-05992-x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Ayers M, Lunceford J, Nebozhyn M et al (2017) IFN-gamma-related mRNA profile predicts clinical response to PD-1 blockade. J Clin Invest 127:2930–2940. https://doi.org/10.1172/JCI91190

    Article  PubMed  PubMed Central  Google Scholar 

  26. Mariathasan S, Turley SJ, Nickles D et al (2018) TGFbeta attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells. Nature 554:544–548. https://doi.org/10.1038/nature25501

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Pabla S, Conroy JM, Nesline MK et al (2019) Proliferative potential and resistance to immune checkpoint blockade in lung cancer patients. J Immunother Cancer 7:27. https://doi.org/10.1186/s40425-019-0506-3

    Article  PubMed  PubMed Central  Google Scholar 

  28. Messina JL, Fenstermacher DA, Eschrich S et al (2012) 12-Chemokine gene signature identifies lymph node-like structures in melanoma: potential for patient selection for immunotherapy? Sci Rep 2:765. https://doi.org/10.1038/srep00765

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Tokunaga R, Nakagawa S, Sakamoto Y et al (2020) 12-Chemokine signature, a predictor of tumor recurrence in colorectal cancer. Int J Cancer 147:532–541. https://doi.org/10.1002/ijc.32982

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. McDermott DF, Huseni MA, Atkins MB et al (2018) Clinical activity and molecular correlates of response to atezolizumab alone or in combination with bevacizumab versus sunitinib in renal cell carcinoma. Nat Med 24:749–757. https://doi.org/10.1038/s41591-018-0053-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Fehrenbacher L, Spira A, Ballinger M et al (2016) Atezolizumab versus docetaxel for patients with previously treated non-small-cell lung cancer (POPLAR): a multicentre, open-label, phase 2 randomised controlled trial. Lancet 387:1837–1846. https://doi.org/10.1016/S0140-6736(16)00587-0

    Article  CAS  PubMed  Google Scholar 

  32. Rooney MS, Shukla SA, Wu CJ, Getz G, Hacohen N (2015) Molecular and genetic properties of tumors associated with local immune cytolytic activity. Cell 160:48–61. https://doi.org/10.1016/j.cell.2014.12.033

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Shin DS, Zaretsky JM, Escuin-Ordinas H et al (2017) Primary resistance to PD-1 blockade mediated by JAK1/2 mutations. Cancer Discov 7:188–201. https://doi.org/10.1158/2159-8290.CD-16-1223

    Article  CAS  PubMed  Google Scholar 

  34. Riaz N, Havel JJ, Kendall SM, Makarov V, Walsh LA, Desrichard A, Weinhold N, Chan TA (2016) Recurrent SERPINB3 and SERPINB4 mutations in patients who respond to anti-CTLA4 immunotherapy. Nat Genet 48:1327–1329. https://doi.org/10.1038/ng.3677

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Conway JR, Kofman E, Mo SS, Elmarakeby H, Van Allen E (2018) Genomics of response to immune checkpoint therapies for cancer: implications for precision medicine. Genome Med 10:93. https://doi.org/10.1186/s13073-018-0605-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Shrestha R, Nabavi N, Lin YY et al (2019) BAP1 haploinsufficiency predicts a distinct immunogenic class of malignant peritoneal mesothelioma. Genome Med 11:8. https://doi.org/10.1186/s13073-019-0620-3

    Article  PubMed  PubMed Central  Google Scholar 

  37. Peng W, Chen JQ, Liu C et al (2016) Loss of PTEN promotes resistance to T Cell-mediated immunotherapy. Cancer Discov 6:202–216. https://doi.org/10.1158/2159-8290.CD-15-0283

    Article  CAS  PubMed  Google Scholar 

  38. Aredo JV, Padda SK, Kunder CA, Han SS, Neal JW, Shrager JB, Wakelee HA (2019) Impact of KRAS mutation subtype and concurrent pathogenic mutations on non-small cell lung cancer outcomes. Lung Cancer 133:144–150. https://doi.org/10.1016/j.lungcan.2019.05.015

    Article  PubMed  Google Scholar 

  39. Martin TD, Patel RS, Cook DR, Choi MY, Patil A, Liang AC, Li MZ, Haigis KM, Elledge SJ (2021) The adaptive immune system is a major driver of selection for tumor suppressor gene inactivation. Science 373:1327–1335. https://doi.org/10.1126/science.abg5784

    Article  CAS  PubMed  Google Scholar 

  40. Gettinger S, Choi J, Hastings K et al (2017) Impaired HLA class I antigen processing and presentation as a mechanism of acquired resistance to immune checkpoint inhibitors in lung cancer. Cancer Discov 7:1420–1435. https://doi.org/10.1158/2159-8290.CD-17-0593

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Castro A, Ozturk K, Pyke RM, Xian S, Zanetti M, Carter H (2019) Elevated neoantigen levels in tumors with somatic mutations in the HLA-A, HLA-B, HLA-C and B2M genes. BMC Med Genom 12:107. https://doi.org/10.1186/s12920-019-0544-1

    Article  CAS  Google Scholar 

  42. Colaprico A, Silva TC, Olsen C et al (2016) TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data. Nucleic Acids Res 44:e71. https://doi.org/10.1093/nar/gkv1507

    Article  CAS  PubMed  Google Scholar 

  43. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK (2015) Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 43:e47. https://doi.org/10.1093/nar/gkv007

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Sen T, Rodriguez BL, Chen L et al (2019) Targeting DNA damage response promotes antitumor immunity through STING-mediated T-cell activation in small cell lung cancer. Cancer Discov 9:646–661. https://doi.org/10.1158/2159-8290.CD-18-1020

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Eroglu Z, Zaretsky JM, Hu-Lieskovan S et al (2018) High response rate to PD-1 blockade in desmoplastic melanomas. Nature 553:347–350. https://doi.org/10.1038/nature25187

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Wang G, Chow RD, Zhu L et al (2020) CRISPR-GEMM pooled mutagenic screening identifies KMT2D as a major modulator of immune checkpoint blockade. Cancer Discov 10:1912–1933. https://doi.org/10.1158/2159-8290.CD-19-1448

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Xie X, Tang Y, Sheng J, Shu P, Zhu X, Cai X, Zhao C, Wang L, Huang X (2021) Titin mutation is associated with tumor mutation burden and promotes antitumor immunity in lung squamous cell carcinoma. Front Cell Dev Biol. 9:761758. https://doi.org/10.3389/fcell.2021.761758

    Article  PubMed  PubMed Central  Google Scholar 

  48. Li X, Pasche B, Zhang W, Chen K (2018) Association of MUC16 mutation with tumor mutation load and outcomes in patients with gastric cancer. JAMA Oncol 4:1691–1698. https://doi.org/10.1001/jamaoncol.2018.2805

    Article  PubMed  PubMed Central  Google Scholar 

  49. Brown LC, Tucker MD, Sedhom R et al (2021) LRP1B mutations are associated with favorable outcomes to immune checkpoint inhibitors across multiple cancer types. J Immunother Cancer. https://doi.org/10.1136/jitc-2020-001792

    Article  PubMed  PubMed Central  Google Scholar 

  50. Li P, Xiao J, Zhou B, Wei J, Luo J, Chen W (2020) SYNE1 mutation may enhance the response to immune checkpoint blockade therapy in clear cell renal cell carcinoma patients. Aging (Albany NY) 12:19316–24. https://doi.org/10.18632/aging.103781

    Article  CAS  PubMed  Google Scholar 

  51. Jhunjhunwala S, Hammer C, Delamarre L (2021) Antigen presentation in cancer: insights into tumour immunogenicity and immune evasion. Nat Rev Cancer 21:298–312. https://doi.org/10.1038/s41568-021-00339-z

    Article  CAS  PubMed  Google Scholar 

  52. Bockorny B, Semenisty V, Macarulla T et al (2020) BL-8040, a CXCR4 antagonist, in combination with pembrolizumab and chemotherapy for pancreatic cancer: the COMBAT trial. Nat Med 26:878–885. https://doi.org/10.1038/s41591-020-0880-x

    Article  CAS  PubMed  Google Scholar 

  53. Lussier DM, Alspach E, Ward JP et al (2021) Radiation-induced neoantigens broaden the immunotherapeutic window of cancers with low mutational loads. Proc Natl Acad Sci U S A 118:e2102611118. https://doi.org/10.1073/pnas.2102611118

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Zhu X, Cao Y, Liu W, Ju X, Zhao X, Jiang L, Ye Y, Jin G, Zhang H (2021) Stereotactic body radiotherapy plus pembrolizumab and trametinib versus stereotactic body radiotherapy plus gemcitabine for locally recurrent pancreatic cancer after surgical resection: an open-label, randomised, controlled, phase 2 trial. Lancet Oncol 22:1093–1102. https://doi.org/10.1016/S1470-2045(21)00286-2

    Article  CAS  PubMed  Google Scholar 

  55. Zhang Y, Chen H, Mo H et al (2021) Single-cell analyses reveal key immune cell subsets associated with response to PD-L1 blockade in triple-negative breast cancer. Cancer Cell. https://doi.org/10.1016/j.ccell.2021.09.010

    Article  PubMed  PubMed Central  Google Scholar 

  56. Anderson NR, Minutolo NG, Gill S, Klichinsky M (2021) Macrophage-based approaches for cancer immunotherapy. Cancer Res 81:1201–1208. https://doi.org/10.1158/0008-5472.CAN-20-2990

    Article  CAS  PubMed  Google Scholar 

  57. Li J, Wang W, Zhang Y et al (2020) Epigenetic driver mutations in ARID1A shape cancer immune phenotype and immunotherapy. J Clin Invest 130:2712–2726. https://doi.org/10.1172/JCI134402

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Jia Q, Wang J, He N, He J, Zhu B (2019) Titin mutation associated with responsiveness to checkpoint blockades in solid tumors. JCI Insight. https://doi.org/10.1172/jci.insight.127901

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

None.

Funding

This work was funded by the Public Welfare Technology Application Research Project of Zhejiang Province (No. LGF22H200013) and the Zhejiang Provincial Medical and Health Science and Technology Project (No. 2022KY1228).

Author information

Author notes

  1. Yize Mao, Hui Xie, Minyi Lv, Qiuxia Yang and Zeyu Shuang have authors contribute equally.

Authors and Affiliations

  1. State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China

    Yize Mao, Hui Xie, Qiuxia Yang, Zeyu Shuang & Shengping Li

  2. Department of Pancreatobiliary Surgery, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China

    Yize Mao & Shengping Li

  3. Department of Medical Imaging Center, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China

    Hui Xie & Qiuxia Yang

  4. Department of Colorectal Surgery, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, Guangdong Institute of Gastroenterology, Supported By National Key Clinical Discipline, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510655, Guangdong Province, China

    Minyi Lv & Feng Gao

  5. Department of Breast Oncology, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China

    Zeyu Shuang

  6. National Clinical Research Centre for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China

    Lina Zhu

  7. Department of Clinical Laboratory, Haining People’s Hospital, Jiaxing, China

    Wei Wang

Authors
  1. Yize Mao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hui Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Minyi Lv
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Qiuxia Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zeyu Shuang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Feng Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Shengping Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Lina Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Wei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization, Methodology, Formal analysis: YM, WW, HX, ML, SL, LZ, FG. Software, Data Curation: YM, WW, HX, ML. Writing- Original draft preparation: YM, HX, ML, QY, ZS. Writing–Reviewing and Editing: YM, HX, ML, QY, ZS, LZ, FG, SL, WW. Visualization, Supervision: YM, WW, SL, LZ and FG. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Shengping Li, Lina Zhu or Wei Wang.

Ethics declarations

Conflict of interest

The authors have no conflict of interests to declare.

Consent for publication

Not applicable.

Ethics approval and consent to participate

All the genetic data and patient cohort information we used were publicly available datasets.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mao, Y., Xie, H., Lv, M. et al. The landscape of objective response rate of anti-PD-1/L1 monotherapy across 31 types of cancer: a system review and novel biomarker investigating. Cancer Immunol Immunother 72, 2483–2498 (2023). https://doi.org/10.1007/s00262-023-03441-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00262-023-03441-3

Keywords

Search

Navigation