免疫检查点抑制剂疗效相关预测模型的研究进展

陈瑛瑶; 储香玲; 俞昕; 苏春霞

doi:10.19401/j.cnki.1007-3639.2023.01.007

您当前的位置：

首页 >

文章列表页 >

免疫检查点抑制剂疗效相关预测模型的研究进展

综述 | 更新时间：2025-12-31

- 免疫检查点抑制剂疗效相关预测模型的研究进展
- Advances in models predicting efficacy of immune checkpoint inhibitors
- 中国癌症杂志 2023年33卷第1期页码：61-70
- 作者机构：
  
  同济大学附属上海市肺科医院肿瘤科，上海 200433
- 作者简介：
  
  [ "陈瑛瑶（ORCID: 0000-0002-2804-0851），博士在读。" ]
  苏春霞（ORCID: 0000-0003-1632-9487），博士，主任医师、教授。
- 基金信息：
- DOI：10.19401/j.cnki.1007-3639.2023.01.007
  中图分类号： R730.51
- 收稿：2022-08-24，
  
  修回：2022-10-24，
  
  纸质出版：2023-01-30
- 稿件说明：
移动端阅览
陈瑛瑶, 储香玲, 俞昕, 等. 免疫检查点抑制剂疗效相关预测模型的研究进展[J]. 中国癌症杂志, 2023,33(1):61-70.

Yingyao CHEN, Xiangling CHU, Xin YU, et al. Advances in models predicting efficacy of immune checkpoint inhibitors[J]. China Oncology, 2023, 33(1): 61-70.
陈瑛瑶, 储香玲, 俞昕, 等. 免疫检查点抑制剂疗效相关预测模型的研究进展[J]. 中国癌症杂志, 2023,33(1):61-70. DOI： 10.19401/j.cnki.1007-3639.2023.01.007.

Yingyao CHEN, Xiangling CHU, Xin YU, et al. Advances in models predicting efficacy of immune checkpoint inhibitors[J]. China Oncology, 2023, 33(1): 61-70. DOI： 10.19401/j.cnki.1007-3639.2023.01.007.

摘要

免疫检查点抑制剂（immune checkpoint inhibitor，ICI）的应用让肿瘤治疗取得了新突破，但不同患者接受免疫治疗后疗效差异较大，仅部分患者能够从中获益。通过检测一些生物标志物可以预测ICI的疗效，如程序性死亡[蛋白]配体-1（programmed death ligand-1，PD-L1）及肿瘤突变负荷（tumor mutation burden，TMB）等。除此之外，目前已有多项研究基于肿瘤患者的基因组学、转录组学或影像组学等数据，筛选多个生物标志物并建立免疫治疗效果相关预测模型。这类模型具备严谨的建立及验证流程，能够纳入更多肿瘤免疫相关变量，有助于提高对ICI疗效的预测能力。本文就肿瘤免疫治疗效果相关预测模型进行综述，以期为免疫治疗获益人群的筛选提供新思路。

Abstract

The use of immune checkpoint inhibitor (ICI) has revolutionized the treatment among patients with various types of tumors. However

only some patients can benefit from ICI. The identification of predictive markers of response to treatment in patients is required

such as programmed death ligand-1 (PD-L1) and tumor mutation burden (TMB). Besides

there have been numerous studies using sequence and radiomics data based on large populations to explore the factors related to the efficacy

and to establish the prediction model. This kind of model has a rigorous establishment and validation process

can include more tumor immune related variables

and is helpful to improve the prediction ability of the efficacy of ICI. This paper reviewed the establishment of immunotherapy prediction models and provided new thoughts pertaining to screening the potential beneficiaries from immunotherapy.

关键词

Keywords

references

RIBAS A , WOLCHOK J D . Cancer immunotherapy using checkpoint blockade [J ] . Science , 2018 , 359 ( 6382 ): 1350 - 1355 . DOI: 10.1126/science.aar4060 http://doi.org/10.1126/science.aar4060

VADDEPALLY R K , KHAREL P , PANDEY R , et al . Review of indications of FDA-approved immune checkpoint inhibitors per NCCN guidelines with the level of evidence [J ] . Cancers (Basel) , 2020 , 12 ( 3 ): 738 . DOI: 10.3390/cancers12030738 http://doi.org/10.3390/cancers12030738 https://www.mdpi.com/2072-6694/12/3/738 https://www.mdpi.com/2072-6694/12/3/738

ZHANG Y Y , ZHANG Z M . The history and advances in cancer immunotherapy: understanding the characteristics of tumor-infiltrating immune cells and their therapeutic implications [J ] . Cell Mol Immunol , 2020 , 17 ( 8 ): 807 - 821 . DOI: 10.1038/s41423-020-0488-6 http://doi.org/10.1038/s41423-020-0488-6

ZHU S M , ZHANG T , ZHENG L , et al . Combination strategies to maximize the benefits of cancer immunotherapy [J ] . J Hematol Oncol , 2021 , 14 ( 1 ): 156 . DOI: 10.1186/s13045-021-01164-5 http://doi.org/10.1186/s13045-021-01164-5 https://doi.org/10.1186/s13045-021-01164-5 https://doi.org/10.1186/s13045-021-01164-5

GALLUZZI L , HUMEAU J , BUQUÉ A , et al . Immunostimulation with chemotherapy in the era of immune checkpoint inhibitors [J ] . Nat Rev Clin Oncol , 2020 , 17 ( 12 ): 725 - 741 . DOI: 10.1038/s41571-020-0413-z http://doi.org/10.1038/s41571-020-0413-z https://doi.org/10.1038/s41571-020-0413-z https://doi.org/10.1038/s41571-020-0413-z

ROBERT C , RIBAS A , SCHACHTER J , et al . Pembrolizumab versus ipilimumab in advanced melanoma (KEYNOTE-006): post-hoc 5-year results from an open-label, multicentre, randomised, controlled, phase 3 study [J ] . Lancet Oncol , 2019 , 20 ( 9 ): 1239 - 1251 . DOI: S1470-2045(19)30388-2 http://doi.org/S1470-2045(19)30388-2

O’DONNELL J S , TENG M W L , SMYTH M J . Cancer immunoediting and resistance to T cell-based immunotherapy [J ] . Nat Rev Clin Oncol , 2019 , 16 ( 3 ): 151 - 167 . DOI: 10.1038/s41571-018-0142-8 http://doi.org/10.1038/s41571-018-0142-8

KALBASI A , RIBAS A . Tumour-intrinsic resistance to immune checkpoint blockade [J ] . Nat Rev Immunol , 2020 , 20 ( 1 ): 25 - 39 . DOI: 10.1038/s41577-019-0218-4 http://doi.org/10.1038/s41577-019-0218-4

WANG Y , TONG Z , ZHANG W H , et al . FDA-approved and emerging next generation predictive biomarkers for immune checkpoint inhibitors in cancer patients [J ] . Front Oncol , 2021 , 11 : 683419 . DOI: 10.3389/fonc.2021.683419 http://doi.org/10.3389/fonc.2021.683419 https://www.frontiersin.org/articles/10.3389/fonc.2021.683419/full https://www.frontiersin.org/articles/10.3389/fonc.2021.683419/full

ZHOU C B , ZHOU Y L , FANG J Y . Gut microbiota in cancer immune response and immunotherapy [J ] . Trends Cancer , 2021 , 7 ( 7 ): 647 - 660 . DOI: 10.1016/j.trecan.2021.01.010 http://doi.org/10.1016/j.trecan.2021.01.010 https://linkinghub.elsevier.com/retrieve/pii/S240580332100039X https://linkinghub.elsevier.com/retrieve/pii/S240580332100039X

AHN S , CHUNG Y R , SEO A N , et al . Changes and prognostic values of tumor-infiltrating lymphocyte subsets after primary systemic therapy in breast cancer [J ] . PLoS One , 2020 , 15 ( 5 ): e0233037 . DOI: 10.1371/journal.pone.0233037 http://doi.org/10.1371/journal.pone.0233037 https://dx.plos.org/10.1371/journal.pone.0233037 https://dx.plos.org/10.1371/journal.pone.0233037

ZHANG Q , LUO J , WU S , et al . Prognostic and predictive impact of circulating tumor DNA in patients with advanced cancers treated with immune checkpoint blockade [J ] . Cancer Discov , 2020 , 10 ( 12 ): 1842 - 1853 . DOI: 10.1158/2159-8290.CD-20-0047 http://doi.org/10.1158/2159-8290.CD-20-0047

DOROSHOW D B , BHALLA S , BEASLEY M B , et al . PD-L1 as a biomarker of response to immune-checkpoint inhibitors [J ] . Nat Rev Clin Oncol , 2021 , 18 ( 6 ): 345 - 362 . DOI: 10.1038/s41571-021-00473-5 http://doi.org/10.1038/s41571-021-00473-5

ZOUEIN J , KESROUANI C , KOURIE H R . PD-L1 expression as a predictive biomarker for immune checkpoint inhibitors: between a dream and a nightmare [J ] . Immunotherapy , 2021 , 13 ( 12 ): 1053 - 1065 . DOI: 10.2217/imt-2020-0336 http://doi.org/10.2217/imt-2020-0336

COLLINS G S , REITSMA J B , ALTMAN D G , et al . Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement [J ] . J Clin Epidemiol , 2015 , 68 ( 2 ): 134 - 143 . DOI: 10.1016/j.jclinepi.2014.11.010 http://doi.org/10.1016/j.jclinepi.2014.11.010

STEYERBERG E W , MOONS K G M , VAN DER WINDT D A , et al . Prognosis Research Strategy (PROGRESS) 3: prognostic model research [J ] . PLoS Med , 2013 , 10 ( 2 ): e1001381 . DOI: 10.1371/journal.pmed.1001381 http://doi.org/10.1371/journal.pmed.1001381 https://dx.plos.org/10.1371/journal.pmed.1001381 https://dx.plos.org/10.1371/journal.pmed.1001381

GRAY E P , TEARE M D , STEVENS J , et al . Risk prediction models for lung cancer: a systematic review [J ] . Clin Lung Cancer , 2016 , 17 ( 2 ): 95 - 106 . DOI: 10.1016/j.cllc.2015.11.007 http://doi.org/10.1016/j.cllc.2015.11.007

KAMATH P S , WIESNER R H , MALINCHOC M , et al . A model to predict survival in patients with end-stage liver disease [J ] . Hepatology , 2001 , 33 ( 2 ): 464 - 470 . DOI: 10.1053/jhep.2001.22172 http://doi.org/10.1053/jhep.2001.22172

BAI X , WU D H , MA S C , et al . Development and validation of a genomic mutation signature to predict response to PD-1 inhibitors in non-squamous NSCLC: a multicohort study [J ] . J Immunother Cancer , 2020 , 8 ( 1 ): e000381 . DOI: 10.1136/jitc-2019-000381 http://doi.org/10.1136/jitc-2019-000381 https://jitc.bmj.com/lookup/doi/10.1136/jitc-2019-000381 https://jitc.bmj.com/lookup/doi/10.1136/jitc-2019-000381

CHOWELL D , YOO S K , VALERO C , et al . Improved prediction of immune checkpoint blockade efficacy across multiple cancer types [J ] . Nat Biotechnol , 2022 , 40 ( 4 ): 499 - 506 . DOI: 10.1038/s41587-021-01070-8 http://doi.org/10.1038/s41587-021-01070-8 https://doi.org/10.1038/s41587-021-01070-8 https://doi.org/10.1038/s41587-021-01070-8

DAI Y F , QIANG W J , LIN K Q , et al . An immune-related gene signature for predicting survival and immunotherapy efficacy in hepatocellular carcinoma [J ] . Cancer Immunol Immunother , 2021 , 70 ( 4 ): 967 - 979 . DOI: 10.1007/s00262-020-02743-0 http://doi.org/10.1007/s00262-020-02743-0

GU X Y , GUAN J , XU J , et al . Model based on five tumour immune microenvironment-related genes for predicting hepatocellular carcinoma immunotherapy outcomes [J ] . J Transl Med , 2021 , 19 ( 1 ): 26 . DOI: 10.1186/s12967-020-02691-4 http://doi.org/10.1186/s12967-020-02691-4

YI M , LI A P , ZHOU L H , et al . Immune signature-based risk stratification and prediction of immune checkpoint inhibitor’s efficacy for lung adenocarcinoma [J ] . Cancer Immunol Immunother , 2021 , 70 ( 6 ): 1705 - 1719 . DOI: 10.1007/s00262-020-02817-z http://doi.org/10.1007/s00262-020-02817-z https://doi.org/10.1007/s00262-020-02817-z https://doi.org/10.1007/s00262-020-02817-z

HONG S , ZHANG Y M , CAO M M , et al . Hypoxic characteristic genes predict response to immunotherapy for urothelial carcinoma [J ] . Front Cell Dev Biol , 2021 , 9 : 762478 . DOI: 10.3389/fcell.2021.762478 http://doi.org/10.3389/fcell.2021.762478 https://www.frontiersin.org/articles/10.3389/fcell.2021.762478/full https://www.frontiersin.org/articles/10.3389/fcell.2021.762478/full

SUN Z L , JING C Y , GUO X D , et al . Comprehensive analysis of the immune infiltrates of pyroptosis in kidney renal clear cell carcinoma [J ] . Front Oncol , 2021 , 11 : 716854 . DOI: 10.3389/fonc.2021.716854 http://doi.org/10.3389/fonc.2021.716854 https://www.frontiersin.org/articles/10.3389/fonc.2021.716854/full https://www.frontiersin.org/articles/10.3389/fonc.2021.716854/full

ZHANG C Q , ZHANG Z H , SUN N , et al . Identification of a costimulatory molecule-based signature for predicting prognosis risk and immunotherapy response in patients with lung adenocarcinoma [J ] . Oncoimmunology , 2020 , 9 ( 1 ): 1824641 . DOI: 10.1080/2162402X.2020.1824641 http://doi.org/10.1080/2162402X.2020.1824641 https://www.tandfonline.com/doi/full/10.1080/2162402X.2020.1824641 https://www.tandfonline.com/doi/full/10.1080/2162402X.2020.1824641

XIANG R S , RONG Y P , GE Y H , et al . Cell differentiation trajectory predicts patient potential immunotherapy response and prognosis in gastric cancer [J ] . Aging (Albany NY) , 2021 , 13 ( 4 ): 5928 - 5945 .

LEADER A M , GROUT J A , MAIER B B , et al . Single-cell analysis of human non-small cell lung cancer lesions refines tumor classification and patient stratification [J ] . Cancer Cell , 2021 , 39 ( 12 ): 1594 - 1609 .e12. DOI: 10.1016/j.ccell.2021.10.009 http://doi.org/10.1016/j.ccell.2021.10.009

LU M , WU K H , TRUDEAU S , et al . A genomic signature for accurate classification and prediction of clinical outcomes in cancer patients treated with immune checkpoint blockade immunotherapy [J ] . Sci Rep , 2020 , 10 ( 1 ): 20575 . DOI: 10.1038/s41598-020-77653-3 http://doi.org/10.1038/s41598-020-77653-3

GAO L , XUE J , LIU X M , et al . A risk model based on autophagy-related lncRNAs for predicting prognosis and efficacy of immunotherapy and chemotherapy in gastric cancer patients [J ] . Aging (Albany NY) , 2021 , 13 ( 23 ): 25453 - 25465 .

NABET B Y , ESFAHANI M S , MODING E J , et al . Noninvasive early identification of therapeutic benefit from immune checkpoint inhibition [J ] . Cell , 2020 , 183 ( 2 ): 363 - 376.e13 . DOI: 10.1016/j.cell.2020.09.001 http://doi.org/10.1016/j.cell.2020.09.001

WEI C , WANG M Y , GAO Q L , et al . Dynamic peripheral blood immune cell markers for predicting the response of patients with metastatic cancer to immune checkpoint inhibitors [J ] . Cancer Immunol Immunother , 2023 : 72 ( 1 ): 23 - 37 . DOI: 10.1007/s00262-022-03221-5 http://doi.org/10.1007/s00262-022-03221-5 https://doi.org/10.1007/s00262-022-03221-5 https://doi.org/10.1007/s00262-022-03221-5

NEWELL F , PIRES DA SILVA I , JOHANSSON P A , et al . Multiomic profiling of checkpoint inhibitor-treated melanoma: identifying predictors of response and resistance, and markers of biological discordance [J ] . Cancer Cell , 2022 , 40 ( 1 ): 88 - 102.e7 . DOI: 10.1016/j.ccell.2021.11.012 http://doi.org/10.1016/j.ccell.2021.11.012 https://linkinghub.elsevier.com/retrieve/pii/S1535610821006127 https://linkinghub.elsevier.com/retrieve/pii/S1535610821006127

HE B X , DONG D , SHE Y L , et al . Predicting response to immunotherapy in advanced non-small cell lung cancer using tumor mutational burden radiomic biomarker [J ] . J Immunother Cancer , 2020 , 8 ( 2 ): e000550 . DOI: 10.1136/jitc-2020-000550 http://doi.org/10.1136/jitc-2020-000550 https://jitc.bmj.com/lookup/doi/10.1136/jitc-2020-000550 https://jitc.bmj.com/lookup/doi/10.1136/jitc-2020-000550

MU W , JIANG L , SHI Y , et al . Non-invasive measurement of PD-L1 status and prediction of immunotherapy response using deep learning of PET/CT images [J ] . J Immunother Cancer , 2021 , 9 ( 6 ): e002118 . DOI: 10.1136/jitc-2020-002118 http://doi.org/10.1136/jitc-2020-002118 https://jitc.bmj.com/lookup/doi/10.1136/jitc-2020-002118 https://jitc.bmj.com/lookup/doi/10.1136/jitc-2020-002118

TREBESCHI S , DRAGO S G , BIRKBAK N J , et al . Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers [J ] . Ann Oncol , 2019 , 30 ( 6 ): 998 - 1004 . DOI: S0923-7534(19)31202-5 http://doi.org/S0923-7534(19)31202-5

LIU C , GONG J , YU H , et al . A CT-based radiomics approach to predict nivolumab response in advanced non-small cell lung cancer [J ] . Front Oncol , 2021 , 11 : 544339 . DOI: 10.3389/fonc.2021.544339 http://doi.org/10.3389/fonc.2021.544339 https://www.frontiersin.org/articles/10.3389/fonc.2021.544339/full https://www.frontiersin.org/articles/10.3389/fonc.2021.544339/full

VAIDYA P , BERA K , PATIL P D , et al . Novel, non-invasive imaging approach to identify patients with advanced non-small cell lung cancer at risk of hyperprogressive disease with immune checkpoint blockade [J ] . J Immunother Cancer , 2020 , 8 ( 2 ): e001343 . DOI: 10.1136/jitc-2020-001343 http://doi.org/10.1136/jitc-2020-001343 https://jitc.bmj.com/lookup/doi/10.1136/jitc-2020-001343 https://jitc.bmj.com/lookup/doi/10.1136/jitc-2020-001343

YANG Y , YANG J C , SHEN L , et al . A multi-omics-based serial deep learning approach to predict clinical outcomes of single-agent anti-PD-1/PD-L1 immunotherapy in advanced stage non-small cell lung cancer [J ] . Am J Transl Res , 2021 , 13 ( 2 ): 743 - 756 .

YANG B , ZHOU L , ZHONG J , et al . Combination of computed tomography imaging-based radiomics and clinicopathological characteristics for predicting the clinical benefits of immune checkpoint inhibitors in lung cancer [J ] . Respir Res , 2021 , 22 ( 1 ): 189 . DOI: 10.1186/s12931-021-01780-2 http://doi.org/10.1186/s12931-021-01780-2 https://doi.org/10.1186/s12931-021-01780-2 https://doi.org/10.1186/s12931-021-01780-2

DERCLE L , ZHAO B S , GÖNEN M , et al . Early readout on overall survival of patients with melanoma treated with immunotherapy using a novel imaging analysis [J ] . JAMA Oncol , 2022 , 8 ( 3 ): 385 - 392 . DOI: 10.1001/jamaoncol.2021.6818 http://doi.org/10.1001/jamaoncol.2021.6818

NARDONE V , TINI P , PASTINA P , et al . Radiomics predicts survival of patients with advanced non-small cell lung cancer undergoing PD-1 blockade using Nivolumab [J ] . Oncol Lett , 2020 , 19 ( 2 ): 1559 - 1566 . DOI: 10.3892/ol.2019.11220 http://doi.org/10.3892/ol.2019.11220

HINATA M , USHIKU T . Detecting immunotherapy-sensitive subtype in gastric cancer using histologic image-based deep learning [J ] . Sci Rep , 2021 , 11 ( 1 ): 22636 . DOI: 10.1038/s41598-021-02168-4 http://doi.org/10.1038/s41598-021-02168-4

BUTNER J D , ELGANAINY D , WANG C X , et al . Mathematical prediction of clinical outcomes in advanced cancer patients treated with checkpoint inhibitor immunotherapy [J ] . Sci Adv , 2020 , 6 ( 18 ): eaay6298 . DOI: 10.1126/sciadv.aay6298 http://doi.org/10.1126/sciadv.aay6298 https://www.science.org/doi/10.1126/sciadv.aay6298 https://www.science.org/doi/10.1126/sciadv.aay6298

JI R R , CHASALOW S D , WANG L S , et al . An immune-active tumor microenvironment favors clinical response to ipilimumab [J ] . Cancer Immunol Immunother , 2012 , 61 ( 7 ): 1019 - 1031 . DOI: 10.1007/s00262-011-1172-6 http://doi.org/10.1007/s00262-011-1172-6 http://link.springer.com/10.1007/s00262-011-1172-6 http://link.springer.com/10.1007/s00262-011-1172-6

JOHNSON D B , ESTRADA M V , SALGADO R , et al . Melanoma-specific MHC-Ⅱ expression represents a tumour-autonomous phenotype and predicts response to anti-PD-1/PD-L1 therapy [J ] . Nat Commun , 2016 , 7 : 10582 . DOI: 10.1038/ncomms10582 http://doi.org/10.1038/ncomms10582 https://doi.org/10.1038/ncomms10582 https://doi.org/10.1038/ncomms10582

CHEN Q Y , ZHANG L , MO X K , et al . Current status and quality of radiomic studies for predicting immunotherapy response and outcome in patients with non-small cell lung cancer: a systematic review and meta-analysis [J ] . Eur J Nucl Med Mol Imaging , 2021 , 49 ( 1 ): 345 - 360 . DOI: 10.1007/s00259-021-05509-7 http://doi.org/10.1007/s00259-021-05509-7 https://doi.org/10.1007/s00259-021-05509-7 https://doi.org/10.1007/s00259-021-05509-7

WALSH R J , SOO R A . Resistance to immune checkpoint inhibitors in non-small cell lung cancer: biomarkers and therapeutic strategies [J ] . Ther Adv Med Oncol , 2020 , 12 : 1758835920937902.

BAGCHI S , YUAN R , ENGLEMAN E G . Immune checkpoint inhibitors for the treatment of cancer: clinical impact and mechanisms of response and resistance [J ] . Annu Rev Pathol , 2021 , 16 : 223 - 249 . DOI: 10.1146/annurev-pathol-042020-042741 http://doi.org/10.1146/annurev-pathol-042020-042741

HOGG S J , BEAVIS P A , DAWSON M A , et al . Targeting the epigenetic regulation of antitumour immunity [J ] . Nat Rev Drug Discov , 2020 , 19 ( 11 ): 776 - 800 . DOI: 10.1038/s41573-020-0077-5 http://doi.org/10.1038/s41573-020-0077-5 https://doi.org/10.1038/s41573-020-0077-5 https://doi.org/10.1038/s41573-020-0077-5

CHEN J F , WU P , XIA R , et al . STAT3-induced lncRNA HAGLROS overexpression contributes to the malignant progression of gastric cancer cells via mTOR signal-mediated inhibition of autophagy [J ] . Mol Cancer , 2018 , 17 ( 1 ): 6 . DOI: 10.1186/s12943-017-0756-y http://doi.org/10.1186/s12943-017-0756-y https://doi.org/10.1186/s12943-017-0756-y https://doi.org/10.1186/s12943-017-0756-y

KILGOUR E , ROTHWELL D G , BRADY G , et al . Liquid biopsy-based biomarkers of treatment response and resistance [J ] . Cancer Cell , 2020 , 37 ( 4 ): 485 - 495 . DOI: S1535-6108(20)30152-5 http://doi.org/S1535-6108(20)30152-5

DERCLE L , CONNORS D E , TANG Y , et al . Vol-PACT: a foundation for the NIH public-private partnership that supports sharing of clinical trial data for the development of improved imaging biomarkers in oncology [J ] . JCO Clin Cancer Inform , 2018 , 2 : 1 - 12 . DOI: 10.1200/CCI.17.00137 http://doi.org/10.1200/CCI.17.00137

SUN X Q , HU B . Mathematical modeling and computational prediction of cancer drug resistance [J ] . Brief Bioinform , 2018 , 19 ( 6 ): 1382 - 1399 . DOI: 10.1093/bib/bbx065 http://doi.org/10.1093/bib/bbx065

POLDRACK R A , HUCKINS G , VAROQUAUX G . Establishment of best practices for evidence for prediction: a review [J ] . JAMA Psychiatry , 2020 , 77 ( 5 ): 534 - 540 . DOI: 10.1001/jamapsychiatry.2019.3671 http://doi.org/10.1001/jamapsychiatry.2019.3671

BONNETT L J , SNELL K I E , COLLINS G S , et al . Guide to presenting clinical prediction models for use in clinical settings [J ] . BMJ , 2019 , 365 : l737 .

CHENG M L , PECTASIDES E , HANNA G J , et al . Circulating tumor DNA in advanced solid tumors: clinical relevance and future directions [J ] . CA Cancer J Clin , 2021 , 71 ( 2 ): 176 - 190 . DOI: 10.3322/caac.21650 http://doi.org/10.3322/caac.21650 https://onlinelibrary.wiley.com/doi/10.3322/caac.21650 https://onlinelibrary.wiley.com/doi/10.3322/caac.21650

IGNATIADIS M , SLEDGE G W , JEFFREY S S . Liquid biopsy enters the clinic-implementation issues and future challenges [J ] . Nat Rev Clin Oncol , 2021 , 18 ( 5 ): 297 - 312 . DOI: 10.1038/s41571-020-00457-x http://doi.org/10.1038/s41571-020-00457-x https://doi.org/10.1038/s41571-020-00457-x https://doi.org/10.1038/s41571-020-00457-x

HORVAT T Z , ADEL N G , DANG T O , et al . Immune-related adverse events, need for systemic immunosuppression, and effects on survival and time to treatment failure in patients with melanoma treated with ipilimumab at memorial Sloan Kettering cancer center [J ] . J Clin Oncol , 2015 , 33 ( 28 ): 3193 - 3198 . DOI: 10.1200/JCO.2015.60.8448 http://doi.org/10.1200/JCO.2015.60.8448

浏览量

3290

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

联合检测LDHA和PD-L1在晚期胃癌PD-1抑制剂疗效预测及预后评估中的价值

卡瑞利珠单抗联合化疗及靶向治疗在复发、转移及初治晚期宫颈癌中的疗效与安全性分析：一项回顾性队列研究

靶向治疗与免疫治疗在放射性碘难治性分化型甲状腺癌中的应用与前景

高龄与Ⅱ期结直肠癌患者预后不良的相关性研究

基于免疫微环境特征的曲妥珠单抗与免疫治疗联合应用预测模型