Advances in models predicting efficacy of immune checkpoint inhibitors

doi:10.19401/j.cnki.1007-3639.2023.01.007

Abstract

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.

Key words: Immunotherapy, Efficacy, Prognosis, Prediction model

CLC Number:

R730.51

CHEN Yingyao, CHU Xiangling, YU Xin, SU Chunxia. Advances in models predicting efficacy of immune checkpoint inhibitors[J]. China Oncology, 2023, 33(1): 61-70.

Figures/Tables 2

Tab. 1

Summary of ICI efficacy-related prediction models"

Author	Model type	Sample size	Data source	Research description	Validate
Bai, et al^[19]	Statistical model	NSCLC (n=316)	Tumor tissue	Developed a gene mutation marker model to predict the response of NSCLC patients to PD-1 inhibitor therapy	Internal+external
Chowell, et al^[20]	Statistical model	NSCLC (n=538), melanoma (n=186), other (n=755)	Tumor tissue peripheral blood	Built a prognostic model based on genomics, clinical and demographic characteristics to predict the survival of patients receiving immunotherapy	Internal
Gu, et al^[21]	Statistical model	HCC (n=365)	TCGA	Established a five-gene-based prognostic model based on the tumour immune microenvironment that can predict immunotherapy efficacy in HCC patients	Internal+external
Dai, et al^[22]	Statistical model	HCC (n=365)	TCGA	Constructed an immune related gene based prognostic index, which can better predict the efficacy of immunotherapy for HCC patients	External
Yi, et al^[23]	Statistical model	LUAD (n=497)	TCGA	Established a prognostic risk score model based on 17 immune related genes. Patients with low scores had a good prognosis after immunotherapy	Internal+external
Hong, et al^[24]	Statistical model	Urothelial carcinoma(n=348)	EGA (EGAS#0000 1002556)	Built a risk stratification system based on 4 genes to predict the therapeutic response of ICIS in metastatic urothelial carcinoma	External
Sun, et al^[25]	Statistical model	Kidney renal clear cell carcinoma (n=539)	TCGA	Established a prognostic risk scoring model based on six genes related to pyroptosis	Internal+external
Zhang, et al^[26]	Statistical model	LUAD (n=1549)	TCGA	Conducted a comprehensive costimulatory molecule landscape analysis of patients with LUAD and built a model for prognosis and immunotherapy response prediction	Internal+external
Xiang, et al^[27]	Statistical model	GC (n=6)	GEO (GSE112302)	Generate a prognostic risk score signature based on GC differentiation-related genes to predict overall survival	External
Leader, et al^[28]	Statistical model	NSCLC (n=27)	Tumor tissue	Identified a cellular module called lung cancer activation model which is correlated with NSCLC response to immunotherapy	External
Lu, et al^[29]	Statistical model	UCEC (n=488)	TCGA	Established a miRNA based diagnostic model and a prognostic model that can predict the prognosis and therapeutic responseof UCEC patients	Internal
Gao, et al^[30]	Statistical model	GC (n=397)	TCGA	Built a prognostic model based on nine autophagy related lncRNA pairs for GC which can predict the efficacy of immunotherapy and chemotherapy	Internal
Nabet, et al^[31]	Statistical model	NSCLC (n=99)	Peripheral blood	Built two predictive models through circulating tumor DNA and peripheral CD8 T cell levels	Internal+external
Wei, et al^[32]	Statistical model	NSCLC (n=30), RCC (n=22), other (n=68)	Peripheral blood	Built a model based on dynamic peripheral blood immune cell markers monitoring	Internal
Newell, et al^[33]	Statistical model	Melanoma (n=77)	Tumor tissue	Built a multivariable model combining the TMB and IFNg-related gene expression and robustly predicts response to immunotherapy	Internal+external
He, et al^[34]	Statistical model	NSCLC (n=327)	CT	Developed an individual non-invasive biomarker that could distinguish high-TMB from low-TMB	Internal
Mu, et al^[35]	Statistical model	NSCLC (n=697)	18F-FDG PET/CT	Identified an effective and stable deeply learned score to measure PD-L1 expression status non-invasively	Internal+external
Trebesch, et al^[36]	Statistical model	NSCLC (n=123), melanoma (n=80)	Contrast-enhanced CT	Provide a predictive machine learning model that could be used within the context of lesion response to treatment, patient treatment response, and response pattern characterization	Internal+external
Liu, et al^[37]	Statistical model	NSCLC (n=46)	CT	Built a novel CT-based radiomics model that has the ability to predict the progression probability for patients with advanced NSCLC receiving nivolumab therapy	Internal
Vaidya, et al^[38]	Statistical model	NSCLC (n=109)	CT	Developed a radiomic model using the integration of intratumoral and novel peritumoral texture and vessel tortuosity metrics	Internal+external
Yang, et al^[39]	Statistical model	NSCLC (n=200)	CT	Offer a model which use multidimensional serial information with inputs from pretreatment clinical data, laboratory tests, and radiomics combined with deep learning	Internal
Yang, et al^[40]	Statistical model	NSCLC (n=149)	CT	Established a radiomics model by combining CT image features and clinicopathological factors to predict the prognosis of immunotherapy	Internal
Dercle, et al^[41]	Statistical model	Melanoma (n=575)	CT	Offer a prognostic model based on CT images at baseline and on first follow-up	Internal
Nardone, et al^[42]	Statistical model	NSCLC (n=59)	CT	Built a model based on texture analysis to select NSCLC patients who may benefit by Nivolumab treatment	External
Hinata, et al^[43]	Statistical model	GC (n=408)	Tumor tissue	Constructed a deep learning model based on tumor histopathological images to effectively stratify GC patients receiving immunotherapy	Internal+external
Butner, et al^[44]	Mathe matical model			Present a mechanistic mathematical model of ICIs therapy to address the oncological need for early, broadly applicable readouts of patient response to immunotherapy

Tab. 1

Fig. 1

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