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Frontiers of Medicine

ISSN 2095-0217

ISSN 2095-0225(Online)

CN 11-5983/R

邮发代号 80-967

2019 Impact Factor: 3.421

Frontiers of Medicine  2023, Vol. 17 Issue (4): 617-648   https://doi.org/10.1007/s11684-023-1015-9
  本期目录
Heterogeneity of the tumor immune microenvironment and clinical interventions
Zheng Jin1,2,3,4, Qin Zhou1,2,5, Jia-Nan Cheng1,2(), Qingzhu Jia1,2(), Bo Zhu1,2()
1. Department of Oncology, Xinqiao Hospital, Army Medical University, Chongqing 400037, China
2. Key Laboratory of Tumor Immunotherapy, Chongqing 400037, China
3. Research Institute, GloriousMed Clinical Laboratory (Shanghai) Co. Ltd., Shanghai 201318, China
4. Institute of Life Sciences, Chongqing Medical University, Chongqing 400016, China
5. School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing 400054, China
 全文: PDF(2711 KB)   HTML
Abstract

The tumor immune microenvironment (TIME) is broadly composed of various immune cells, and its heterogeneity is characterized by both immune cells and stromal cells. During the course of tumor formation and progression and anti-tumor treatment, the composition of the TIME becomes heterogeneous. Such immunological heterogeneity is not only present between populations but also exists on temporal and spatial scales. Owing to the existence of TIME, clinical outcomes can differ when a similar treatment strategy is provided to patients. Therefore, a comprehensive assessment of TIME heterogeneity is essential for developing precise and effective therapies. Facilitated by advanced technologies, it is possible to understand the complexity and diversity of the TIME and its influence on therapy responses. In this review, we discuss the potential reasons for TIME heterogeneity and the current approaches used to explore it. We also summarize clinical intervention strategies based on associated mechanisms or targets to control immunological heterogeneity.

Key wordstumor immune heterogeneity    clinical intervention    tumor microenvironment
收稿日期: 2023-02-15      出版日期: 2023-10-12
Corresponding Author(s): Jia-Nan Cheng,Qingzhu Jia,Bo Zhu   
 引用本文:   
. [J]. Frontiers of Medicine, 2023, 17(4): 617-648.
Zheng Jin, Qin Zhou, Jia-Nan Cheng, Qingzhu Jia, Bo Zhu. Heterogeneity of the tumor immune microenvironment and clinical interventions. Front. Med., 2023, 17(4): 617-648.
 链接本文:  
https://academic.hep.com.cn/fmd/CN/10.1007/s11684-023-1015-9
https://academic.hep.com.cn/fmd/CN/Y2023/V17/I4/617
Fig.1  
TechnologyPlatformCharacterizationApplication
Liquid biopsyqPCRFast turnaround time
Low sensitivity
Streamlined workflow on validated platform
Identification of therapeutic targets
Exploration of resistance mechanisms
Dynamic monitoring of therapies
Stratification and therapeutic intervention
dPCRHigh sensitivity
Cost efficient
Limited targets detection
Mass spectrometryEnabling to detect low frequency mutations
High multiplex capability
Low input DNA requirement
NGSHigh sensitivity and specificity
High throughout
Expensive and time-consuming
scRNA-seqDroplet-basedHigh throughput
High dropout rates
Low sequencing cost per cell
Enabling to detect rare cell types
Composition annotation of TIME
Identification of cell-cell communications
Lineage tracing of specific cell population
Detection of potential factors in both disease progression and response to immune checkpoint inhibitors
Plate-basedLow throughput
Low dropout rates
High sequencing cost per cell
Broad spectrum of gene expression
Spatial transcriptomicsIn situ hybridization- or image-basedHigh efficiency
Strong signal
Both fresh-frozen and FFPE samples
Subcellular resolution
Limited gene detection
Exploration of spatial heterogeneity of TIME
Providing new insights of special spatial structures in TIME
Supporting clinical diagnosis
Identification of prognosis associated factors
NGS-basedMatched downstream analysis tools
High throughput
Robust and high-sensitivity
Multi-scale field of views
Digital pathologyWSI and AIHigh resolution
Storage and management efficient
Multiple images with different magnifications at once
Time-consuming for model-construction
Enhancing the understanding of TIME
Evaluation of immune cell interactions
Identification of genomic features
Prediction of objective response to therapies
Tab.1  
Fig.2  
ApplicationTumorInput parametersNo. of slidesNo. of patientsExt. validationAUC (95% CI)PMID
Assessing tumor-infiltrating CD8 cells and response to immunotherapySolid tumors78 radiomic features, 5 location features, 1 technical featureNot reported254Yes0.67 (0.57–0.77)30120041
Prediction of response to PD-(L)1 blockade in NSCLCNSCLC10 genomic features, 72 radiomic features, 2 pathology featuresNot reported247Yes0.80 (0.74–0.86)36038778
Tumor-infiltrating lymphocytes classification in NSCLCNSCLCTiles from slides3166561Yes0.9539 for CE; 0.9871 for CS; 0.9591 for TIL35271299
Classification of HCC from histopathology imagesHCCTiles from slides491377Yes0.961 (0.939–0.981)32550270
Classification of NSCLC from histopathology imagesNSCLCTiles from slides1634Not reportedYes0.9730224757
Prediction of MSI in gastrointestinal cancerGastrointestinal cancerTiles from slidesNot reported360Yes0.8431160815
Prediction of 5-year disease-specific survival in patients with CRCCRCTiles from slidesNot reported420Yes0.6929467373
Prediction of overall survival of patients with HCCHCCTiles from slides732522Yes0.732108950
Classification of patients to high risk or low risk in order to predict overall survival in BCBCTiles from slides12991299No0.5831119567
Pan-cancer prediction of gene expressionSolid tumorsTiles from slides105148725No0.8132747659
Tab.2  
Fig.3  
StrategiesTherapyAgentCancer typeTrial numberTrial phase
Modulating genetic processes of tumor cellsPARP inhibitorsOlaparibHER2-negative BCNCT05340413Phase II
Olaparib + pembrolizumabMetastatic CRCNCT05201612Phase II
Niraparib + pembrolizumabNSCLCNCT04475939Phase III
TKIsBPI-7711NSCLCNCT03866499Phase III
OsimertinibNSCLCNCT05120349Phase III
EGF816 + gefitinibNSCLCNCT03292133Phase II
WX-0593 + chemoradiotherapyNSCLCNCT05351320Phase II
Promoting activation of antigen presenting cellsSTING agonistsE7766Solid tumorsNCT04144140Phase I
MK1454 + pembrolizumabHNSCCNCT04220866Phase II
MK1454 + pembrolizumabSolid tumors, lymphomasNCT03010176Phase I
MIW815Solid tumors, lymphomasNCT03172936Phase I
SNX281 + pembrolizumabSolid tumorNCT04609579Phase I
TLR agonistsSD-101 + ICBsPrimary liver tumorsNCT05220722Phase I/II
SD-101 + pembrolizumabProstatic neoplasmsNCT03007732Phase II
VTX-2337 + MEDI4736Ovarian cancerNCT02431559Phase I/II
SD-101 + pebrolizumabPancreatic adenocarcinomaNCT05607953Phase I
CD40 agonistsSEA-CD40 + other drugsMelanoma, NSCLCNCT04993677Phase II
SelicrelumabSolid tumorsNCT02304393Phase I
APX005MOvarian cancerNCT05201001Phase II
APX005M + nivolumab + ipilimumabMelanoma, RCCNCT04495257Phase I
ADC-1013Ametastatic pancreatic cancerNCT05650918Phase I
T cell primingOncolytic virusesT-VECMelanomaNCT04330430Phase II
T-VECMelanomaNCT04427306Phase II
H101 + camrelizumabBladder cancerNCT05564897Phase II
Gebasaxturev + pembrolizumabMelanomaNCT04303169Phase I/II
Gebasaxturev + pembrolizumabMelanomaNCT04152863Phase II
Tumor vaccinesISA101bSCC of oropharynxNCT04398524Phase II
ISA101b + pembrolizumab + cisplatinHPV-related HNSCCNCT04369937Phase II
MAGE-A3NSCLCNCT04908111Phase I/II
MAGE-A3 + pembrolizumabMetastatic melanomaNCT03773744Phase I
GalinpepimutAMLNCT04229979Phase III
pTVG-HP + nivolumabProstate cancerNCT03600350Phase II
RO7198457 + atezolizumabNSCLCNCT04267237Phase II
Peptides + poly-ICLCMelanomaNCT01970358Phase I
RO7198457 + pembrolizumabMelanomaNCT03815058Phase II
NEO-PV-01 + nivolumabMelanoma, lung cancer; bladder cancerNCT02897765Phase I
Trafficking of T cells into tumorsAntiangiogenic monoclonal antibodiesBevacizumab + atezolizumabHCCNCT04732286Phase III
Bevacizumab + osimertinibLung cancerNCT04988607Phase II
Bevacizumab + paclitaxel + carboplatinNSCLCNCT05654454Phase III
Ramucirumab + pembrolizumabGastric cancerNCT04632459Phase II
Ramucirumab + atezolimuab + N-803NSCLCNCT05007769Phase II
Antiangiogenic TKIsSorafenib + vemurafenibPancreas cancerNCT05068752Phase II
SorafenibHCCNCT05117957Phase II
RegorafenibHCCNCT04718909Phase II
Regorafenib + fulvestrantOvarian cancerNCT05113368Phase II
FruquintinibCRCNCT02314819Phase III
AnlotinibNSCLCNCT02388919Phase III
T cell reprogrammingCAR-T cellsCEA CAR-T cellsSolid tumorsNCT05538195Phase I/II
CEA CAR-T cellsSolid tumorsNCT05415475Phase I
U87 CAR-T cellsPancreatic cancerNCT05605197Phase I
CD70 CAR-T cellsAdvanced/metastatic solid tumorsNCT05518253Phase I
B7H3 CAR-T cellsLiver cancerNCT05323201Phase I/II
IM92 CAR-T cellsGastric or pancreatic cancerNCT05275062Phase I
Anti-PD-L1 armored anti-CD22 CAR-T cellsSolid tumorsNCT04556669Phase I
NKG2D CAR-T cellsMetastatic CRCNCT05248048Phase I
TCR-T cellsTCR-T cellsPancreatic cancerNCT05438667Phase I
E7 TCR-T cellsHPV-related cancersNCT05686226Phase II
MAGE-C2 TCR-T cellsMelanoma, HNSCCNCT04729543Phase I/II
SCG101HBV-related HCCNCT05417932Phase I/II
AFP TCR-THCCNCT03132792Phase I
TILsTILs + CAR-TILsSolid tumorsNCT04842812Phase I
TILsSolid tumorsNCT04967833Phase I
TILsMelanomaNCT05098184Phase I
C-TIL051Metastatic NSCLCNCT05676749Phase I
ScTILSolid tumorsNCT04571892Phase I/II
Inhibition of tumor immune evasionA2aR blockadesCiforadenant + daratumumabMultiple myelomaNCT04280328Phase I
AZD4635 + durvalumab + cabazitaxelMetastatic CRPCNCT04495179Phase II
InupadenantSolid tumorsNCT05117177Phase I
Anti-CD73 antibodiesOleclumab + durvalumabMetastatic sarcomaNCT04668300Phase II
Sym024Solid tumorsNCT04672434Phase I
AK119Solid tumorsNCT05173792Phase I
Anti-CD39 antibodiesTTX-030 + immunotherapy/chemotherapySolid tumorsNCT04306900Phase I
IPH5201Solid tumorsNCT04261075Phase I
SRF617Solid tumorsNCT04336098Phase I
TGF-β inhibitorsM7824HNSCCNCT04247282Phase I/II
Vactosertib + pembrolizumabNSCLCNCT04515979Phase II
Galunisertib + capecitabineAdvanced CRCNCT05700656Phase I/II
SRK-181 + anti-PD-(L)1 antibodySolid tumorsNCT04291079Phase I
IDO1 inhibitorsEpacadostatUrothelial carcinomaNCT04586244Phase II
EpacadostatRectal cancerNCT03516708Phase I
BMS-986205Solid tumorsNCT03459222Phase I/II
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