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
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.
Fast 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
dPCR
High sensitivity Cost efficient Limited targets detection
Mass spectrometry
Enabling to detect low frequency mutations High multiplex capability Low input DNA requirement
NGS
High sensitivity and specificity High throughout Expensive and time-consuming
scRNA-seq
Droplet-based
High 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-based
Low throughput Low dropout rates High sequencing cost per cell Broad spectrum of gene expression
Spatial transcriptomics
In situ hybridization- or image-based
High 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-based
Matched downstream analysis tools High throughput Robust and high-sensitivity Multi-scale field of views
Digital pathology
WSI and AI
High 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
Application
Tumor
Input parameters
No. of slides
No. of patients
Ext. validation
AUC (95% CI)
PMID
Assessing tumor-infiltrating CD8 cells and response to immunotherapy
Prediction of response to PD-(L)1 blockade in NSCLC
NSCLC
10 genomic features, 72 radiomic features, 2 pathology features
Not reported
247
Yes
0.80 (0.74–0.86)
36038778
Tumor-infiltrating lymphocytes classification in NSCLC
NSCLC
Tiles from slides
3166
561
Yes
0.9539 for CE; 0.9871 for CS; 0.9591 for TIL
35271299
Classification of HCC from histopathology images
HCC
Tiles from slides
491
377
Yes
0.961 (0.939–0.981)
32550270
Classification of NSCLC from histopathology images
NSCLC
Tiles from slides
1634
Not reported
Yes
0.97
30224757
Prediction of MSI in gastrointestinal cancer
Gastrointestinal cancer
Tiles from slides
Not reported
360
Yes
0.84
31160815
Prediction of 5-year disease-specific survival in patients with CRC
CRC
Tiles from slides
Not reported
420
Yes
0.69
29467373
Prediction of overall survival of patients with HCC
HCC
Tiles from slides
732
522
Yes
0.7
32108950
Classification of patients to high risk or low risk in order to predict overall survival in BC
BC
Tiles from slides
1299
1299
No
0.58
31119567
Pan-cancer prediction of gene expression
Solid tumors
Tiles from slides
10514
8725
No
0.81
32747659
Tab.2
Fig.3
Strategies
Therapy
Agent
Cancer type
Trial number
Trial phase
Modulating genetic processes of tumor cells
PARP inhibitors
Olaparib
HER2-negative BC
NCT05340413
Phase II
Olaparib + pembrolizumab
Metastatic CRC
NCT05201612
Phase II
Niraparib + pembrolizumab
NSCLC
NCT04475939
Phase III
TKIs
BPI-7711
NSCLC
NCT03866499
Phase III
Osimertinib
NSCLC
NCT05120349
Phase III
EGF816 + gefitinib
NSCLC
NCT03292133
Phase II
WX-0593 + chemoradiotherapy
NSCLC
NCT05351320
Phase II
Promoting activation of antigen presenting cells
STING agonists
E7766
Solid tumors
NCT04144140
Phase I
MK1454 + pembrolizumab
HNSCC
NCT04220866
Phase II
MK1454 + pembrolizumab
Solid tumors, lymphomas
NCT03010176
Phase I
MIW815
Solid tumors, lymphomas
NCT03172936
Phase I
SNX281 + pembrolizumab
Solid tumor
NCT04609579
Phase I
TLR agonists
SD-101 + ICBs
Primary liver tumors
NCT05220722
Phase I/II
SD-101 + pembrolizumab
Prostatic neoplasms
NCT03007732
Phase II
VTX-2337 + MEDI4736
Ovarian cancer
NCT02431559
Phase I/II
SD-101 + pebrolizumab
Pancreatic adenocarcinoma
NCT05607953
Phase I
CD40 agonists
SEA-CD40 + other drugs
Melanoma, NSCLC
NCT04993677
Phase II
Selicrelumab
Solid tumors
NCT02304393
Phase I
APX005M
Ovarian cancer
NCT05201001
Phase II
APX005M + nivolumab + ipilimumab
Melanoma, RCC
NCT04495257
Phase I
ADC-1013
Ametastatic pancreatic cancer
NCT05650918
Phase I
T cell priming
Oncolytic viruses
T-VEC
Melanoma
NCT04330430
Phase II
T-VEC
Melanoma
NCT04427306
Phase II
H101 + camrelizumab
Bladder cancer
NCT05564897
Phase II
Gebasaxturev + pembrolizumab
Melanoma
NCT04303169
Phase I/II
Gebasaxturev + pembrolizumab
Melanoma
NCT04152863
Phase II
Tumor vaccines
ISA101b
SCC of oropharynx
NCT04398524
Phase II
ISA101b + pembrolizumab + cisplatin
HPV-related HNSCC
NCT04369937
Phase II
MAGE-A3
NSCLC
NCT04908111
Phase I/II
MAGE-A3 + pembrolizumab
Metastatic melanoma
NCT03773744
Phase I
Galinpepimut
AML
NCT04229979
Phase III
pTVG-HP + nivolumab
Prostate cancer
NCT03600350
Phase II
RO7198457 + atezolizumab
NSCLC
NCT04267237
Phase II
Peptides + poly-ICLC
Melanoma
NCT01970358
Phase I
RO7198457 + pembrolizumab
Melanoma
NCT03815058
Phase II
NEO-PV-01 + nivolumab
Melanoma, lung cancer; bladder cancer
NCT02897765
Phase I
Trafficking of T cells into tumors
Antiangiogenic monoclonal antibodies
Bevacizumab + atezolizumab
HCC
NCT04732286
Phase III
Bevacizumab + osimertinib
Lung cancer
NCT04988607
Phase II
Bevacizumab + paclitaxel + carboplatin
NSCLC
NCT05654454
Phase III
Ramucirumab + pembrolizumab
Gastric cancer
NCT04632459
Phase II
Ramucirumab + atezolimuab + N-803
NSCLC
NCT05007769
Phase II
Antiangiogenic TKIs
Sorafenib + vemurafenib
Pancreas cancer
NCT05068752
Phase II
Sorafenib
HCC
NCT05117957
Phase II
Regorafenib
HCC
NCT04718909
Phase II
Regorafenib + fulvestrant
Ovarian cancer
NCT05113368
Phase II
Fruquintinib
CRC
NCT02314819
Phase III
Anlotinib
NSCLC
NCT02388919
Phase III
T cell reprogramming
CAR-T cells
CEA CAR-T cells
Solid tumors
NCT05538195
Phase I/II
CEA CAR-T cells
Solid tumors
NCT05415475
Phase I
U87 CAR-T cells
Pancreatic cancer
NCT05605197
Phase I
CD70 CAR-T cells
Advanced/metastatic solid tumors
NCT05518253
Phase I
B7H3 CAR-T cells
Liver cancer
NCT05323201
Phase I/II
IM92 CAR-T cells
Gastric or pancreatic cancer
NCT05275062
Phase I
Anti-PD-L1 armored anti-CD22 CAR-T cells
Solid tumors
NCT04556669
Phase I
NKG2D CAR-T cells
Metastatic CRC
NCT05248048
Phase I
TCR-T cells
TCR-T cells
Pancreatic cancer
NCT05438667
Phase I
E7 TCR-T cells
HPV-related cancers
NCT05686226
Phase II
MAGE-C2 TCR-T cells
Melanoma, HNSCC
NCT04729543
Phase I/II
SCG101
HBV-related HCC
NCT05417932
Phase I/II
AFP TCR-T
HCC
NCT03132792
Phase I
TILs
TILs + CAR-TILs
Solid tumors
NCT04842812
Phase I
TILs
Solid tumors
NCT04967833
Phase I
TILs
Melanoma
NCT05098184
Phase I
C-TIL051
Metastatic NSCLC
NCT05676749
Phase I
ScTIL
Solid tumors
NCT04571892
Phase I/II
Inhibition of tumor immune evasion
A2aR blockades
Ciforadenant + daratumumab
Multiple myeloma
NCT04280328
Phase I
AZD4635 + durvalumab + cabazitaxel
Metastatic CRPC
NCT04495179
Phase II
Inupadenant
Solid tumors
NCT05117177
Phase I
Anti-CD73 antibodies
Oleclumab + durvalumab
Metastatic sarcoma
NCT04668300
Phase II
Sym024
Solid tumors
NCT04672434
Phase I
AK119
Solid tumors
NCT05173792
Phase I
Anti-CD39 antibodies
TTX-030 + immunotherapy/chemotherapy
Solid tumors
NCT04306900
Phase I
IPH5201
Solid tumors
NCT04261075
Phase I
SRF617
Solid tumors
NCT04336098
Phase I
TGF-β inhibitors
M7824
HNSCC
NCT04247282
Phase I/II
Vactosertib + pembrolizumab
NSCLC
NCT04515979
Phase II
Galunisertib + capecitabine
Advanced CRC
NCT05700656
Phase I/II
SRK-181 + anti-PD-(L)1 antibody
Solid tumors
NCT04291079
Phase I
IDO1 inhibitors
Epacadostat
Urothelial carcinoma
NCT04586244
Phase II
Epacadostat
Rectal cancer
NCT03516708
Phase I
BMS-986205
Solid tumors
NCT03459222
Phase I/II
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