Another TIME classification system that also applies to NSCLC has been proposed
[24]. In this system, the TIME is classified by the level of tumor PD-L1 expression and TILs: type I, PD-L1+ and TILs+; type II, PD-L1- and TILs-; type III, PD-L1+ and TILs-; type IV, PD-L1- and TILs+ (). Type I TIME is consistent with a state of adaptive immune resistance with T cell exhaustion mediated by the PD-1–PD-L1 inhibitory immune axis, which has been effectively targeted with anti-PD-(L)-1 blockade. Here, PD-(L)1 expression in the tumor-infiltrating CD8
+ T cells has been essential to PD-(L)1 ICI therapeutic efficacy
[19][25][26][19,25,26]. Type II TIME, which represents a state of immunological ignorance, has been associated with a lack of response to ICB
[19][24][19,24]. Type III TIME represents a state of constitutive PD-L1 expression on tumor cells resulting from oncogenic signaling pathway activation, which is more prevalent in oncogenic mutation-driven cancers, such as adenocarcinoma of the lung (LUAD). Increased PD-L1 expression has been observed on NSCLC cells with activating gene alterations in KRAS, EGFR, and ALK, which has been associated with upregulated MAPK, PI3K–AKT–mTOR signaling, and JAK–STAT3 activation
[27][28][29][30][31][32][33][27,28,29,30,31,32,33]. However, such expression is not due to the presence of functional TILs
[34]. Subsequently, response to anti-PD-(L)1 ICIs alone is poor, despite PD-L1 expression in tumor cells. This has been reported in NSCLC patients with EGFR mutations and ALK rearrangements, which are also associated with low tumor neoantigen load
[35][36][35,36]. Type IV TIME describes a state of ineffective IFN-γ signaling that fails to induce any PD-L1 expression
[37], or an environment of immune exhaustion through additional immune checkpoints. For NSCLC, alternative immune checkpoints, such as B7x and HHLA2, were found to be expressed in the majority of PD-L1-negative cases, which inhibited T cell receptor (TCR)-mediated CD4
+, CD8
+ T cell proliferation, and T cell cytokine production
[38].
M0 (<15%) |
Highest |
High |
Intermed. low |
Intermed. |
Lowest |
High |
M1 (<10%) |
Intermed. |
Highest |
Intermed. |
Intermed. low |
Lowest |
Intermed. |
M2 (>20%) |
Intermed. low |
Lowest |
Intermed. |
High |
Highest |
High |
Tumor proliferation rate |
Highest |
Highest |
Low |
High |
Lowest |
High |
Survival |
|
|
|
|
|
|
OS |
Intermediate |
Intermediate |
Best |
Worst |
Worse |
Worst |
PFI |
Intermediate |
Intermediate |
Best |
Worst |
Worse |
Worst |
NSCLC subtype |
Predom. in LUSC; third common in LUAD ** |
Second most common in LUAD and LUSC |
Predom. In LUAD *** |
LUSC ** |
|
|
Factors of immunogenecity |
|
|
|
|
|
|
DNA damage |
|
|
|
|
|
|
Tumor neoantigen load |
|
|
|
|
|
|
SNVs |
Highest |
Second highest |
|
|
Lowest |
|
Indels |
Highest |
Second highest |
|
|
Lowest |
|
ITH |
Elevated |
Elevated |
Lowest |
|
|
|
Enriched oncogenic driver mutations |
APC, JAK1, PIK3CA, FGFR3 |
PIK3CA, FGFR3 |
CDH1, PIK3CA, FGFR3 |
EGFR |
|
|
TCR diversity |
Intermediate |
Highest |
Intermediate |
Low |
Lowest |
Highest |
Immunomodulators |
|
|
|
|
|
|
Expression |
|
|
|
|
|
|
CXCL10 |
|
Highest |
|
|
Lowest |
Second Highest |
EDNRB |
Low |
Lowest |
|
|
Highest |
|
BTLA |
|
|
|
High |
High |
|
Networks modulating the immune response |
|
|
|
|
|
|
Predominant immune cells |
|
CD8 T cells |
CD8 T cells, CD4 T cells |
CD4 T cells |
|
CD4 T cells |
Intracellular regulatory networks |
|
|
|
|
|
|
TGF-β (somatic mut+) |
|
↓Leuk Fract. |
↑Leuk Fract. |
|
|
↓Leuk Fract. |
|
↑ | r | DC, M0, M1, M2, | r | NK, plasma cells |
↑E, | a | Mast, M0/2, | a | DC, | r | NK, TγΔ |
↑M1, M2, N, CD4, Treg |
↑M0,M1, | a | DC |
↑M0, Treg, | mr | CD4 |
↑ | r | DC |
|
↓ | a | NK, Treg, Tfh, CD8 |
↓CD8, Treg, Tfh, | a | NK |
↓DC, M0, Tfh, | m | B cells, plamsa cells |
↓monocytes |
↓ | n | CD4, CD8 |
|
Extracellular comm. networks |
|
|
|
|
|
|
|
|
IFN-γ (+) |
IFN-γ (+) |
|
|
|
|
|
TGF-β (+) |
TGF-β, TGF-βR(+) |
|
|
TGF-β, TGF-βR(+) |
T cell and macrophage-related signaling |
CD80-CTLA4 |
LAG-3, CD27/28 |
CD27, PD-1 |
TLR4, VEGFB |
TLR4 |
TLR4 |
|
CD70-CD27 |
TIGIT, ICOS, CTLA, PD-1 |
CCR4, 5; CXCR3 DARC |
|
EDN3-EDNRB, CX3CL1-CX3CR1 |
ITGB2 |
|
IL1A/1B-IL1R2 |
CXCR3, CCR1,4,5 |
|
|
|
CD276 |
|
Some are associated with increased leukocyte fraction (TP53, HLA-B, BRAF, PTEN, NF1, APC, and CASP8), while others are associated with decreased leukocyte fraction (IDH1 R132H, GATA3, KRAS, NRAS, CTNNB1, and NOTCH1). Their association with tumor neoantigen generation, IM expression, and ultimately leukocyte fraction provides further evidence for tumor intrinsic gene alterations’ role in the sculpting of the TIME, which warrants further exploration to guide the treatment of NSCLC and other solid tumors
[41].
The pattern of IM expression varies in different TIME subtypes. Stimulatory modulator CXCL10 is most highly expressed in the IFN-γ-dominant TIME, while inhibitory modulators, such as EDNRB and BTLA, are most highly expressed in the more immune-suppressive TIME subtypes. A balance between T cell activation and suppression is found in more immune-stimulatory TIME subtypes, which is evidenced by the expression of both stimulatory and inhibitory IM genes, such as SLAMF7, TNFSF4 (OX40L), IL10, CD40, and IDO1. On the contrary, modulators associated with immune infiltration are more frequently deleted in the immunologically quiet TIME (e.g., TGFB1, KIR2DL1, KIR2DL3), which is consistent with a lack of TILs in this TIME subtype. Overall, TIME subtypes with increased CD8
+ T cell infiltration have been associated with the expression of stimulatory IMs, while those with increased infiltration by CD4 T cells and macrophages were associated with increased TGF-β signaling (). This pattern of IM expression reflects the predominance of different extracellular signaling networks associated with the fraction of different immune cells in the TIME
[41].
Intrinsic tumor mutations interact with external signaling networks in a particular TIME with different driver mutations modulating IM expression in a TIME subtype-specific manner through common transcription factors (TFs). For example, ATM mutations and co-occurring STK11 and SMARCA4 mutations may drive wound healing TIME-specific gene expression through STAT5A in LUAD, while KEAP1 mutations, which often co-occur with STK11 and SMARCA4 mutations, drive the expression of genes specific to the immunologically quiet and TGF-β-dominant TIMEs through IRF8 in LUAD
[41][44][41,44]. In LUSC, NFE2L2 mutation may drive the expression of wound healing and IFN-γ-dominant TIME-specific genes through IRF4, as well as the TGF-β dominant TIME specific gene expression through NFKB2
[41]. TIME characterization may be further enhanced with identifying T cell associated receptors and ligands that are uniquely present or absent in particular TIME subtypes, such as the absence of CTLA, LAG-3, TIM-3, TIGIT, ICOS, and IL2A in the inflammatory TIME, or the presence of IL1B and VEGFB in the TGF-β dominant TIME
[41].