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Table 1 Summary of biomarkers currently under investigation for Immune Checkpoint Inhibition (ICI) therapies

From: Biomarkers in immune checkpoint inhibition therapy for cancer patients: what is the role of lymphocyte subsets and PD1/PD-L1?

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]