Table 1 Summary of biomarkers currently under investigation for Immune Checkpoint Inhibition (ICI) therapies
Biomarker | Type | Pros | Cons | References |
|---|---|---|---|---|
PD-L1 | Predictive Therapeutic | • First FDA approved diagnostic for anti-PD1 therapy in NSCLC an melanoma • Direct target of anti-PD1/PD-L1 therapy | • Does not correlate well in all the cancer types • Quite a few technical and biological variabilities from cancer to cancer and patient to patient • Not 100% correlation between its expression and anti-PD1 treatment response | Garon, et al. 2015 [19] Borghaei, et al. 2015 [21] Brahmer, et al. 2015 [20] Larkin, et al. 2015a [22] McDermott, et al. 2016 [31] |
Molecules influencing the expression of PD-L1 | Predictive | • Very standard markers and therefore easy to access | • Not too many • Indirect • Controversial reports on their correlation to ICI therapies | Parsa, et al. 2007 [34] Song, et al. 2013 [35] Hellmann 2015 [36] Larkin, et al. 2015c [38] |
Cytokines | Predictive Surrogate | • Gives an idea about the activation status of other immune correlates • Could be used in conjunction with immune cell data to give a complete picture of the immune system • Uses less invasive method since could be assessed directly in the blood | • Different studies have reported changes in different types of cytokines • Larger studies are needed • Also need to check the tumors for the defects in cytokine signaling | Chang et al., 2013 [40] Selby et al., 2017 Yamazaki et al., 2017 [43] Zaretsky et al., 2016 [45] Gao et al., 2016 [46] |
NK cells | Predictive Surrogate | • Important as they offer the first line of defense • Involved in the production of important cytokines, brings about the activation/maturation of immune cells | • Controversial data from different studies on the changes in the number of NK subpopulations for anti-PD-1 treatment • Larger studies, and homogenization of the methods of detection are needed | Tietze et al., 2017 [63] Tallerico et al., 2015 [64] Tallerico et al., 2016 Liu et al., 2017 [66] |
CD8 + T cells | Predictive Surrogate Therapeutic | • High pre-treatment numbers of CD8 + T cells significantly correlate with better treatment outcomes for ICI therapies • Increased numbers are also predictive of irAE, allowing for close monitoring of the patient for early intervention • Tumor specific CD8 + T cells have a distinct profile which may allow for more accurate monitoring of treatment response • Uses less invasive method since could be assessed directly in the blood |  | Gros, et al. 2016 [74] Daud, et al. 2016 [72] Ngiow, et al. 2015 [73] Larkin, et al. 2015b [75] |
CD4 + T cells | Surrogate Therapeutic | • One of the very few markers for anti-CTLA-4 therapy. • CD4+ ICOS+ T-cells increases in a dose-dependent manner, highlighting their potential as a surrogate marker for pharmacodynamic monitoring of treatment response in anti-CTLA-4 therapy | • It’s role in combating cancer was recently unraveled and therefore it is relatively underexplored | Tran, et al. 2014 [77] Ng Tang, et al. 2013 [79] |
Regulatory T-cells Tregs | Predictive Surrogate Therapeutic | • High pre-treatment Tregsnumber in general is predictive of negative treatment outcome to ICI therapies • Being a direct target for anti-CTLA4 therapy, holds potential as a surrogate marker for monitoring treatment response in this specific type of ICI therapy | • A few controversial reports on the correlation between Treg number and treatment outcome for ICI therapies • Many studies have not considered all the different subtypes of Tregs | Hodi, et al. 2008 [93] Lowther, et al. 2016 [92] Romano, et al. 2015 [95] Li, et al. 2016 [96] |
Myeloid derived suppressor cells (MDSCs) | Predictive Surrogate Therapeutic | • High pretreatment MDSC numbers are predictive of negative ICI treatment outcome • Can be utilized as a marker for pharmacodynamic monitoring of treatment response • Targeting MDSCs restores sensitivity to ICI treatments, and therefore this approach is being considered for ICI combination therapies |  | Tarhini, et al. 2014 [100] Bjoern, et al. 2016 [101] Meyer, et al. 2014 [102] De Henau, et al. 2016 [103] |