Mobilizing and Evaluating Anticancer T Cells

Table 1. Pitfalls and solutions for mobilizing anticancer T cells.
Molecules/cells/environment	Subject	Pitfall^†	Solution^†
Tumor antigen-specific T cells	T-cell numbers	Insufficient numbers and frequencies of anticancer T cells (despite immunotherapy)	Active immunotherapy: vaccines capable of inducing strong proliferation of antigen-specific T cells.Passive immunotherapy/ACT: Transfer of large T-cell numbers and/or cells with strong proliferative potential
	T-cell differentiation	Too high degree of terminal differentiation of anticancer T cells (despite immunotherapy)	Active immunotherapy: vaccines promoting memory T cells, rather than only effector T cells. Passive immunotherapy/ACT: Use of naïve or memory T cells
	TCR specificity	Paucity of anticancer T cells (despite immunotherapy)	Active immunotherapy: selective activation of (highly) tumor-specific T cells.Passive immunotherapy/ACT: use of TILs
	TCR affinity	Low TCR affinity, too low for e.g., recognition of naturally presented antigen	Active immunotherapy: Vaccines with preferential induction of high-affinity T cells.Passive immunotherapy/ACT: transduction of TCRs with high affinity
Tumor antigens (tumor targeting), APCs (antigen-presenting cells) and DCs (dendritic cells)	Tumor peptides	Postulated 'tumor peptides' are not processed and presented	Verification of each tumor antigen, as to whether it mediates tumor recognition by specific T cells
	Antigen presentation	Presentation of peptide antigens by non-professional APCs, potentially tolerizing	Antigen targeting to DCs, e.g. via specific surface receptors, or use of DCs for vaccination
	Presentation of tumor peptides by MHC class I of DCs	Lack or inefficiency of cross-presentation	Use of DNA, RNA or recombinant viruses, or targeting and activation of DCs capable of cross-presentation or use of DCs for vaccination
	DC activation	Insufficiently activated DC may tolerize T cells	Use of innate immune stimulator targeting, for example, TLRs, RLRs or NLRs, or use of DCs for vaccination
	DC migration	Inefficient DC migration to draining LNs	Use of innate immune stimulator targeting or intranodal injection of DC vaccines
Tumor recognition by T cells	Efficiency of interactions between T cells and tumor cells or APCs	Low pMHC density on tumor cells	Therapies that increase cytokines in tumors
		Low-affinity TCR	See above
		Microenvironmental factors	See below
	Tissue specificity	T cells are (cross-) reactive to normal tissues	Targeting of highly tumor-specific antigens
Tumor microenvironment, immune suppression	Immunological properties of tumor cells	Tumor cells with enhanced proliferation and resistance to apoptosis, antigen loss, production of immune-suppressive factors	Multitargeted therapy avoiding selective growth of novel tumor cell mutants
	Regulatory T cells (Tregs)	Blockade of Th1 and CTL responses, interference with T-cell metabolism or apoptosis	Blocking of TGFb, IL-10, cAMP, depletion of Tregs
	DCs	Paucity of DCs, tolerogenic DCs	Targeting innate immune receptors, for example, TLRs, RLRs or NLRs
	Cancer-promoting and immune suppressive myeloid cells (M2 cells, MDSCs)	Blockade of Th1 and CTL responses, production of immunosuppressive molecules (Arg-1, iNOS, ROS, reactive nitrogen oxide species)	Therapeutic blockade of specific growth factors and of immunosuppressive molecules
	Cancer-associated fibroblasts	Factors supporting tumor progression (EGFs, VEGFs, chemokines, etc. attracting tumor-promoting cells)	Therapeutic blockade of EGFs, VEGFs, CXCL14, etc.
	Lymphoid-like reticular cells	Lymphoid-tissue inducer cells recruited by tumors, for example, via expression of CCL21, induction of immune tolerance

Some 'solutions' have been implemented and are being improved for clinical application. Other 'solutions' remain theoretical, since attempts to resolve the corresponding pitfalls were not yet successful in vivo.
^† The term 'anti-cancer T cells' is used for tumor antigen-specific CD8⁺ T cells, and CD4⁺ T cells mainly with Th1 polarization.
ACT: Adoptive cell transfer.

Table 2. Pitfalls and solutions for evaluating anti-cancer T cells.
Experimental approach/technique	Discrepancies	Possible reasons	Conclusions
Analysis of 'total cell counts,' for example, total CD8⁺ T cells or total CD4⁺ T cells, in search of correlatives of protection	Lymphocyte numbers or their activation states may correlate with disease regression in some studies, whereas other studies may provide discrepant results	'Total' CD8⁺ or CD4⁺ T cells are very heterogeneous, often precluding conclusions. In contrast, the analysis of antigen-specific cells may distinguish pro- from antitumoral T cells	T cells with different antigen specificity, and/or polarization may play opposing roles
Use of high doses (~1 μM) of synthetic peptide for the analyses of CD8⁺ T cells	Efficient T cell functions in vitro, but lack of immune protection from disease	High antigen dose reveal T cells with (very) low TCR affinity, unable to recognize naturally presented antigen and unable to protect from cancer progression	Experiments reflect reality when working with naturally processed and presented antigen, rather than (high doses of) synthetic peptide
Use of pMHC multimers (tetramers), solely for determining T-cell frequencies	No correlation of 'tetramer' data with disease outcome	Some T cells identified with multimers may be nonfunctional. And, multimers often bind high- and low-affinity TCR-expressing cells	Full characterization of T cell functions and TCR affinity/avidity
Phenotyping of antigen-specific (multimer +) T cells	Some studies show correlations of clinical outcome with long-lived memory T cells, and others with short-lived effector cells (SLECs)	Despite their short half-lives, SLECs may represent a powerful arm of the immune response, provided that they are maintained at high levels by progeny from long lived memory cells	Analysis of absolute numbers, not only percentages (the reason for low percentages of memory cells may simply be high percentages of SLECs)
	Expression of inhibitory receptors (e.g., PD-1) as evidence of inhibition or dysfunction (so-called T-cell exhaustion)	Inhibitory receptors (PD-1, CTLA-4, Tim-3, Lag-3, 2B4, CD160, KLRG-1) are widely expressed by activated T cells, they are not 'exhaustion markers'	Enhanced expression of inhibitory receptors cannot be used as a surrogate of dysfunctional T cells
Functional assessment of inhibitory lymphocyte receptors	CTLA-4 may mediate T-cell inhibition in vivo, whereas no role for CTLA-4 in CD8⁺ T cell function has been shown (e.g., no inhibition in in vitro T-cell assays)	The precise mechanisms of CTLA-4-mediated T-cell inhibition remain to be elucidated	CTLA-4 may (also) act via T-cell extrinsic mechanisms. Possibly, similar mechanisms are involved in inhibition by several further receptors
Histology, immunohistochemistry	Disagreements on correlations between immune infiltrates and clinical outcome	Insufficient definitions of micro-anatomical regions. Lack of systematic quantification of infiltrating immune cells	It is necessary to distinguish tumor cell nests from stroma, (peri-) vascular regions, etc., and to quantify cell infiltrates (using image analyzer software)
Histology, immunohistochemistry	CD4⁺ T cells are associated with tumor progression versus regression	Lack of distinction between conventional CD4⁺ T cells, Tregs and cells of other polarization	Importance of precise characterization of cell subsets, for example, among Tregs
Analysis of T cells after in vitro proliferation	Lack of correlations with clinical outcome	After proliferation, T cells differ in quantity and quality, precluding direct conclusions	In vivo or direct ex vivo analysis is preferable. In case T-cell frequencies are too low for this, quantification can be done with the so-called 'limiting dilution' technique

Experimental assessment of molecular properties and functions of T cells may provide results that are not (directly) associated with immunity (i.e., immune responses protecting from disease). Examples for such situations are shown in this table. Obviously, a prerequisite is that all analyses are designed and performed by state-of-the-art experimentation including all necessary positive and negative controls.

Sidebar

Key Issues

T cells are the main players in antitumor immune responses; however, the ability of these cells to effect antitumor activity is curbed by multiple mechanisms in the tumor-bearing host.
Immunotherapy aims at strengthening tumor-specific T-cell responses. Three main strategies exist: passive immunization, active immunization and immune modulation.
T-cell intrinsic characteristics that should be considered in immunotherapeutic protocols include proliferation to sufficient numbers, differentiation to a memory status and development of sufficient specificity and affinity to tumor antigens.
T-cell extrinsic characteristics that should be considered in immunotherapeutic protocols include selection or generation of immunogenic antigens, proper maturation and differentiation of antigen-presenting cells and inhibition of suppressive mechanisms within the tumor microenvironment.
Tumor-specific T-cell responses can be evaluated and characterized by a multitude of methods including IHC, multiparameter flow cytometry combining fluorescent peptide/MHC multimers, staining of surface and intracellular marker targets, as well as ex vivo functionality assays, gene arrays and proteomic strategies.
Discrepancies between the tumor-specific T-cell responses measured in vitro and those relevant in vivo can be minimized by performing direct ex vivo experiments whenever possible, utilization of physiologically relevant amounts of antigen in vitro and careful manipulation of tissue specimens and cells.
Future research should focus on the identification of intrinsic and/or extrinsic biological properties (biomarkers) of tumor-specific T cells that correlate with therapeutic success in clinical trials.
The immunotherapy research community should attempt to adhere to published recommendations concerning the standardization of both methodologies utilized to evaluate anticancer immune responses, and the system to report data resulting from clinical trials.