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Page 14 of 24 Tokuyasu et al. J Cancer Metastasis Treat 2018;4:2 I http://dx.doi.org/10.20517/2394-4722.2017.52

accumulation of high quality datasets should ideally go hand in hand with the ability to model the data, with
the ultimate goal of defining optimal interventions to reach a desired outcome (e.g. disease stabilization or
[77]
cure) .
An initial goal is the discovery of prognostic and predictive biomarkers. These can be used for treatment
selection [207] , e.g. high PD-L1 expression level for pembrolizumab treatment [208] . At a more rigorous level,
biomarkers indicate system state of the immune system, cancer, or both. The importance of prospective
studies for data collection and analysis has long been emphasized. REMARK guidelines (REporting
recommendations for tumour MARKer prognostic studies) attempt to capture the minimal information
needed to objectively assess the import of a given biomarker study [209] . This baseline however is still
commonly not met [210] .

The field of immune system-related prognostic and predictive biomarkers is complex and rapidly advancing.
Urgent efforts are now being made to translate current knowledge and capabilities to the understanding
of baseline immunity and response monitoring, and thence the choice of predictive biomarkers [68,211] .
Magnetic resonance imaging (MRI) based biomarkers of response to immunotherapy have been recently
proposed [212] . Systemic immune response coordinated across tissues has been observed to be essential to
tumor rejection [213] . The fraction of tumor-infiltrating partially exhausted cytotoxic T lymphocytes (peCTLs)
correlates with response to anti-PD-1 monotherapy, with a low fraction indicating the use of combination
checkpoint blockade therapy [214] . A possible implication is that checkpoint blockade therapy is most effective
when the immune system has already mounted a tumor-specific if suppressed response.

TCR repertoire profiling shows promise in immune monitoring and perhaps response prediction [215,216] .
Checkpoint blockade is seen to induce diversification of T cell receptor repertoire [217] , which has been
suggested as a biomarker for PD-1 inhibitor disease stabilization [218] . The assessment of TCR repertoire
diversity is becoming increasingly accessible [219] . Important choices such as library preparation method,
in-house versus service provider, output data type (raw and/or analyzed), and the use or not of unique
molecular identifiers must first be matched to project goals [220] . Basic features of the T cell receptor repertoire
are still being revealed, e.g. unexpectedly biased distributions of TCR receptors (CDR3 sequence similarity
networks) that change in stereotyped ways with aging, immunization, and antigen selection [221] . Progress has
been reported in developing statistical means of “reading” T cell memory, as relates e.g. to cytomegalovirus
status and HLA typing [222] .

As we dissect components and interactions in more detail, the research enterprise can begin to embrace
variation to learn better from animal models [223,224] and humans [225,226] , including with respect to age [227] . Data
sharing can help ensure technical advances are employed towards broad evidence-based progress [228] . In this
regard, standards for reporting neo-antigens, HLA alleles, and TCR repertoires may need to be developed.

Adoption of high throughput technologies such as massively parallel sequencing, immunosequencing,
microarrays, mass cytometry, and DNA-barcoded pMHC multimers has led to the advent of systems
[20]
immunology . Rather than dissect mechanistic relationships between a few actors, systems methods
attempt to capture the behavior of the immune system as a whole. The resulting descriptions tend to have
a multi-scale (hierarchical) character in both space and time [229,230] . The wide variety of available modeling
formalisms and applications has been surveyed by Narang et al. [231] .

Mathematical modeling has begun to impact the clinic through efforts to optimize dosage and timing
(“schedule optimization”), which have gained a foothold in chemotherapy [232,233] and radiotherapy [234,235] . There
is now a rich literature on the modeling of immunotherapy [236,237] . As an example, modeling the kinetics of
the immune response [238] reveals the possibility that a proper choice of schedule can summon a robust T cell