Abstract

Precision medicine continues to be the benchmark toward which we strive in cancer research. Conventionally, it is the term applied to the use of genomic information to guide molecularly targeted therapy. However, the advent of clinically effective cancer immunotherapies has posed a challenge for this concept of precision medicine, as robust biomarkers that can differentiate responders from nonresponders have not been described. Here, we review the current scientific efforts using novel technologies to develop biomarkers for immunotherapeutics, to ultimately achieve “personalized immunotherapy.” We first examine the role of programmed death ligand 1 expression and tumor mutational burden, the two most-studied tumoral response biomarkers; and subsequently discuss innovative candidate biomarkers including integrated “omics” approaches utilizing serial tumor, blood, and microbiome sampling. We also detail the challenges in unifying these approaches into a patient-focused immunogram to truly personalize immunotherapy.

Introduction

The recognition of specific molecular aberrations that conferred sensitivity of cancer cells to targeted therapies led to the concept of “personalized medicine.” This concept of a tailored approach to patient treatment based on the molecular analysis of tumor genes and proteins has been validated with highly efficacious kinase inhibitors that target specific subgroups of patients.[1–3] The advent of cancer immunotherapy has created new challenges to this approach. Numerous immunotherapeutics have shown efficacy across tumor types recently, [4–8] with durable long-lasting responses mainly seen in subgroups of patients. This clinically apparent asymmetry to the benefits of immunotherapy combined with the high cost of these agents has fostered a search for response biomarkers. Certainly, to date, currently available predictive biomarkers for response to immunotherapy have lacked precision. In this review article, we summarize currently used cancer immunotherapy biomarkers and focus on how recent developments in “omics” technologies can be leveraged to better clarify the biological and clinical nuances of immunotherapy responses.

Background

Although the immune system’s importance in cancer control has been long established, [9] the paucity of nontoxic immunotherapeutics had previously limited their role in the oncological armamentarium. This has changed since the discovery of the importance of regulatory immune checkpoints and the recognition of their potential for therapeutic manipulation.[10] The two best-described checkpoints are the CTLA4-B7 checkpoint, for which ipilimumab is a licensed antagonist and the programmed cell death 1 (PD-1) – programmed death ligand 1 (PD-L1) checkpoint. The landmark study demonstrating the efficacy of ipilimumab in metastatic melanoma first validated immune checkpoint inhibition as a viable therapeutic strategy.[8] Therapeutic manipulation of the PD-1/PD-L1 checkpoint has been even more successful, and there are now multiple Food and Drug Administration (FDA) licensed therapeutics targeting both receptor and ligand. Although immune checkpoint inhibitors are often grouped together, the differences in their mechanisms of action result in meaningful differences in activity profile, toxicity, and synergy in combinations. CTLA4-B7 inhibitors target priming and antigen-presentation aspects of the cytotoxic T-cell response, whereas PD-1/PD-L1 inhibitors target the effector mechanism.

Given the enormous pipeline of immunotherapy drugs undergoing clinical testing currently, clinicians are faced with many challenges regarding their optimal use. Clinical challenges include deciding when to cease treatment given an observed lack of correlation between progression-free survival and overall survival.[ 11] Furthermore, pseudoprogression, or appearances suggestive of radiological progression due to immune infiltration, further complicates clinical decision-making.[12] There is also confusion regarding the optimal duration of treatment. Much of the initial excitement of immunotherapy was in the apparent durability of responses, especially seen with ipilimumab in melanoma.[13] However, recent evidence has suggested that ongoing treatment is necessary for disease control, arguing against the durability of immune responses created by PD-1 inhibition.[14] These commonly encountered clinical difficulties are accompanied by concerns regarding the sustainability of funding these therapies. As the indications for the use of immunotherapeutics continue to expand, there is a resultant spiraling, unsustainable cost to health-care systems globally.[15]

The combination of the clinical challenges of assessing immune response and the emphasis on cost containment systemically contextualizes the importance of developing robust response biomarkers. Biomarker development is complex, as their importance and utility are context-dependent. There are two general types of predictive biomarkers. The first group of biomarkers is characterized by binary outcomes (present/absent) and is highly predictive of response to therapy. Examples of such biomarkers include oncogenic driver mutations for which highly effective targeted kinase inhibitor therapy is available (e.g., gefitinib for epidermal growth factor receptor’s-mutant nonsmall cell lung cancer [NSCLC][1]). The second group of biomarkers is more nuanced in their application and is not binary in nature. Most currently available biomarkers for immune checkpoint therapy fall into the latter category.

The Present

Programmed death ligand 1 immunohistochemistry

The rapid emergence of multiple therapies targeting the PD-1/PD-L1 axis achieving FDA approval across tumor types[4, 5, 7] has been accompanied by a focus on developing biomarkers to enrich patients for response. Tumoral PD-L1 expression by immunohistochemistry was first identified in the initial Phase I study of nivolumab as a candidate biomarker and is the best-studied biomarker to date.[16]Table 1 notes reported results of various PD-L1 assays across tumor types. Taken together, data from these trials suggest that PD-L1 positive tumors, particularly NSCLC and melanoma, are enriched for response to PD-1/PD-L1 inhibitors compared with PD-L1 negative tumors. Theoretical advantages of PD-L1 immunohistochemistry as a biomarker include its mechanistic validity, the widespread availability of immunohistochemistry in clinical laboratories and its relatively low cost.

Table 1:

Selected studies of programmed death ligand 1 immunohistochemistry

Selected studies of programmed death ligand 1 immunohistochemistry
Selected studies of programmed death ligand 1 immunohistochemistry

Out of all tumor types, clinical trials in NSCLC have demonstrated the best and most consistent correlation between the degree of PDL1 expression and magnitude of benefit to immune checkpoint therapy.[11, 25, 26] The recent KEYNOTE 042 presented as ASCO 2018 is consistent with this theme, showing the superiority of pembrolizumab over chemotherapy in all patients with PDL1 expression of >1%, although higher PDL expression clearly equated to greater degree of benefit in terms of progression-free survival (PFS) and response rate.[30]

However, there are several limitations with PD-L1 immunohistochemistry as a biomarker when used in isolation. Most significantly, the absence of PD-L1 expression does not necessitate lack to benefit to immune checkpoint inhibition. Indeed, there is a growing body of literature demonstrating robust responses in patients who are PD-L1 negative in various tumor types.[21, 23] Even in NSCLC, the lack of PDL 1 expression can no longer be used as a “Go versus no Go” decision-making tool for choosing immunotherapy in the first line. A number of recent Phase III trials presented at the ASCO and AACR in 2018, including KEYNOTE 189, [31] KEYNOTE 407,[32] CHECKMATE 227,[33] and ImPOWER150[34] have all shown that even in the low and absent PDL1 expressing NSCLC population, the addition of an immune checkpoint inhibitor to cytotoxic chemotherapy shows a significant survival advantage compared to chemotherapy alone. These studies also give impetus to the strategy that the addition of chemotherapy to immune checkpoint inhibitors may make a PDL1 negative, otherwise checkpoint inhibitor monotherapy unresponsive patient actually respond. Although to truly verify this hypothesis, Phase III studies of immunotherapy plus chemotherapy verses immunotherapy alone in the PDL1 negative or low expression NSCLC population need to be performed.

Denying patients potentially highly efficacious therapy without absolute evidence of lack of benefit is a consequent clinical dilemma. This fundamental inadequacy is compounded by several interrelated issues.

First, there is contention in the literature as to whether tumoral PD-L1 expression is more important than its expression in the microenvironment or in immune cells. While the initial trials suggested positive PD-L1 tumor expression was associated with higher response rates, it now appears that the expression of PD-L1 in the tumor microenvironment also appears to be crucial for therapeutic activity. PD-L1 is upregulated by activated effector T-cells but is also very highly expressed by tolerant and exhausted T-cells.[35] In addition, there is heterogeneity within the exhausted T-cell populations with differing potential for reinvigoration by PD-1 pathway blockade.[36]

This confusion over the primacy of tumoral versus immune PD-L1 expression has been further corroborated in clinical trials. Clinical trials of atezolizumab have been paired with companion diagnostics evaluating PD-L1 expression in immune cells rather than tumor alone, [7, 37] with some suggestion that PD-L1 expression in immune cells may better correlate with the clinical outcomes. In contrast, the POPLAR study suggests that PD-L1 expression on tumor cells and immune cells may have nonredundant roles in the regulation of antitumor immunity and predicting response to therapy. However, it must be noted that PD-L1 expression on immune cells in this study correlated with a previously validated interferon-gamma-associated gene signature which correlated with survival in the patients treated with atezolizumab. Although many studies have primarily reported tumoral PD-L1 expression [Table 1], the debate over the role of immune PD-L1 expression casts some doubt on this as a biomarker.

In addition, the second major issue with PD-L1 expression as a biomarker is the dynamic nature of the tumor/tumor microenvironment interaction. This spatial and temporal heterogeneity argues against the validity of static immunohistochemistry performed on mostly archival tumor biopsies. Unsurprisingly, significantly discordant results (>20%) have been noted between matched nodal and primary specimens of tumors, for example, in renal cell carcinoma, raising concerns that this approach may result in clinically significant false-negative results.[38] Furthermore, the majority of clinical trials have utilized archival noncontemporaneous tissue samples that may not reflect the tumor/microenvironment at the time of immunotherapy commencement.[39] Exploratory efforts to overcome this issue of heterogeneity are ongoing, such as the use of liquid biopsies to obtain circulating tumor cells in real time and the refinement of assays to detect PD-L1 on circulating tumor cells.[40]

The final issue limiting the widespread applicability of PD-L1 immunohistochemistry is the technical limitations of the assay. To date, studies have used different antibodies with significant interantibody variability, membranous versus cytoplasmic staining patterns, and varying cutoff points for identifying positive from negative samples.[41, 42] Efforts are, however, underway to harmonize assessment of PD-L1 status, including standardized pathological training to assess PD-L1 status.[43] The blueprint industrial-academic collaborative partnership in NSCLC has already published Phase I results looking at four different PD-L1 assays (22C3, 28–8, SP142, and SP263), demonstrating reproducible assay performance bar the SP142 assay, which exhibited lower staining overall.[44] Significantly, there was more interantibody variability with staining of immune cells compared to tumor cells, which has been observed previously.[42] Thus, for individual patients, despite broadly similar results between assays, interchanging assays may lead to different results with therapeutic implications. This Gordian knot has driven the immense efforts to develop more precise biomarkers for immunotherapy as will be discussed in the following sections.

Mismatch repair testing

Pivotal work by Alexandrov et al.[45] demonstrated a strong correlation between immunotherapy responsive tumor types and typical somatic TMB. This tied in neatly with work from other laboratories showing increased numbers of effector CD8+ tumor-infiltrating lymphocytes, expressing high levels of PD-1, in tumors with a high mutational load.[46] Deficiencies in mismatch repair (MMR) result in microsatellite instability (MSI). Tumors with deficient MMR may give rise to substantial nonsynonymous single-nucleotide variants, resulting in more neoepitopes and a theoretical enhanced responsiveness to PD-1 inhibition. This hypothesis was tested in a proof-of-principle early phase trial by Le et al. with striking findings.[47] First, the correlation between MMR status and total nonsynonymous TMB was observed. More significantly, the study demonstrated marked clinical divergence in outcomes: Patients with MMR proficient tumors did not respond to PD-1 inhibition, in stark contrast to patients with MMR deficient (MMRd) tumors where a high response rate (40%) was observed.[47] This finding has been corroborated in other settings. Two siblings with recurrent, highly mutated constitutional MMRd glioblastoma multiforme treated with nivolumab had profound and durable responses, compared to poor survival outcomes normally expected in this clinical scenario.[48]

Pooled data from 5 uncontrolled, multicenter, single-arm clinical trials involving 149 patients with microsatellite instability or MMRd tumors led to the accelerated approval of pembrolizumab for the treatment of adult and pediatric patients with metastatic MMRd tumors.[49–52] Most patients on these studies had advanced metastatic disease and taken together, the pooled patients on the study had an overall response rate of 39.6% with pembrolizumab, including 11 (7.4%) complete responses and 48 (32.2%) partial responses. For those who responded, 78% had responses that lasted for at least 6 months. Given the FDA approval, rapid and accurate identification of patients with MMRd tumors who could benefit from immunotherapy is of paramount importance.

Currently, two types of MMR testing are in clinical use: immunohistochemical staining, which assesses the expression levels of MMR proteins (MLH1, MSH2, MSH6, and PMS2) within the tumor and polymerase chain reaction (PCR), which detects mutations in microsatellite regions. For PCR tests, revised Bethesda criteria recommend using a “pentaplex” of five mononucleotide microsatellites (BAT25, BAT26, NR21, NR24, and NR27).[53] Tumors with three or more unstable markers are called MSI-high and tumors with one unstable marker are called MSI-low. Although PCR and immunohistochemical techniques are sensitive, specific, and have high concordance (>95%), [54, 55] these studies have largely been carried out in colorectal cancer and their utility across cancer types is unknown. Moving forward, concerted efforts to catalog large series of microsatellite loci that are frequently altered in numerous cancers[56] would serve to further refine the identification of patients who are likely to benefit from immunotherapy. In addition, non-MSI DNA repair defects resulting in hypermutation, such as those in polymerase ε (POLE), [57] which may confer susceptibility to checkpoint inhibition[58] will not be routinely identified with current testing protocols.

The Future: Key Technologies in Precision Medicine for Cancer Immunotherapy

Rapid advances in technology with decreasing costs and improved throughput are now enabling the collection of large amounts of information on different cancer “omic” landscapes. The criteria for the use of omics-based predictors in clinical trials have recently been published by the US National Cancer Institute [59] and will be key in ushering in the era of personalized immunotherapy. Table 2 lists some of the key technologies that will be leveraged in an era of personalized immunotherapy.

Table 2:

Key technologies and their applications for personalized immunotherapy

Key technologies and their applications for personalized immunotherapy
Key technologies and their applications for personalized immunotherapy

Next-generation sequencing of DNA

Numerous clinical studies have utilized next-generation sequencing technology to build on the work of Alexandrov et al., [48] showing that total nonsynonymous TMB, which correlates with neoantigen load, is predictive of response to immunotherapy.[60, 61] The has led to the addition of TMB – a quantitative measure of the total number of nonsynonymous mutations per coding area of a tumor genome to commonly performed commercially available next-generation sequencing assays, including both Foundation Medicine©[61] and Caris©. [62]

While some overlap exists between high PDL1 IHC expression and high TMB, clearly both also function independently to predict efficacy of immune checkpoint inhibitors. Using the Foundation Medicine CDx assay, with a cutoff in TMB of 10 mutations per megabase, the recent Phase III CHECKMATE 227 trial clearly demonstrated superiority of combination ipilimumab and nivolumab over chemotherapy as the first-line therapy in patients with high TMB, in terms of response rate (45.3% vs. 26.9%) and PFS (1 year PFS 42.6% vs. 13.2%), independent of PDL1 expression.[63]

While tumors harboring more mutations are more likely to respond to immunotherapy; there remains significant variability between panels and bioinformatics algorithms used, combined with lack of validated cutoffs which limits clinical use currently. For instance, a study by Qiu et al. has demonstrated that although there is high concordance in the raw data achieved from whole-exome sequencing, the final outputs can demonstrate substantial variability.[64] There are, however, some advantages in next-generation sequencing approaches to TMB. Novel bioinformatic algorithms such as mSINGS[65] and MANTIS[66, 67] have been used to manipulate sequencing data to derive MSI status. Moreover, TMB may also enable identification of non-MSI hypermutated tumors which respond to immunotherapy such as POLE mutations and be utilized in tumor types that currently do not undergo routine MMR testing.

Of greater importance, are the emerging studies suggesting that T-cell reactivity may rely on a small proportion of neoantigens that are particularly immunogenic, rather than an overall quantitative neoantigen load.[68, 69] This is corroborated by other studies showing that only a fraction of predicted neoepitope peptides are actually expressed on major histocompatibility complex I molecules on tumors, [70] indicating that there are many other factors contributing to the antigen presentation apparatus that are yet to be fully delineated. One such example is renal cell carcinoma which is typically immunogenic but does not have high TMB as measured by nonsynonymous single-nucleotide variant counts; recent work has identified novel immunogenic mutational classes such as insertions and deletions which create novel open reading frames and a large quantity of mutagenic peptides highly distinct from self that contribute to the immunogenic phenotype.[71]

Increasing evidence suggests that the immune system interacts actively with tumor antigens in a process now termed “cancer immunoediting.” [72] Neoantigen evolution occurs with ongoing DNA alterations that accumulate with tumor growth over time and immune silencing of these neoantigens occurs through T-cell modulation.[73] This leads to intratumor neoantigen landscape heterogeneity. An integrated analysis of intratumoral heterogeneity and neoantigen burden on a series of lung adenocarcinomas showed that decreased neoantigen intratumoral heterogeneity was associated with improved clinical benefit.[ 46] Immunoediting, therefore, poses challenges with interpretation of neoantigen data as a biomarker. Accurate capture of neoantigens that are responsible for tumor immunogenicity going forward may require serial monitoring of tumors to accurately reflect neoantigen heterogeneity, as well as developing algorithms to filter data obtained from whole exome sequencing.

The widespread use of next-generation sequencing technology has also generated a wealth of insights into key genes/pathways involved in mediating immune resistance. This is likely to increase in importance with the broader use of immune checkpoint therapies in multiple indications. As the denominator of treated patients increases, acquired resistance will become an increasing problem. For example, loss of PTEN, which enhances PI3K signaling and is a common genomic defect across multiple cancers, was found to be associated with resistance to immune checkpoint therapy.[74] Loss of PTEN in tumor cells in preclinical models of melanoma inhibits T-cell-mediated tumor killing and decreases T-cell trafficking into tumors. PTEN loss in tumor cells was associated with increased expression of immunosuppressive cytokines, which results in decreased autophagy and reduced T-cell-mediated tumor apoptosis. In melanoma patients, PTEN loss correlated with decreased T-cell infiltration at tumor sites, reduced likelihood of successful T-cell expansion from resected tumors, and inferior outcomes with PD-1 inhibitor therapy.[57]

Constitutive activation of the beta-catenin/WNT signaling pathway has also been shown to induce resistance to T-cell checkpoint blockade.[75] In a comparison of the genomic profiles of T-cell inflamed versus noninflamed melanoma tumor samples, Spranger et al. found that 94% of tumors lacking infiltration of T-cells exhibited high levels of beta-catenin signaling, while only 4% of T-cell infiltrated tumors expressed high beta-catenin activation.[75] Tumors with elevated B-catenin expression lacked a subset of dendritic cells (DCs) known as CD103+ DCs, due to decreased expression of the chemokine ligands 4. Murine tumors lacking B-catenin responded effectively to immune checkpoint therapy, whereas b-catenin-positive tumors did not due to defective recruitment of CD103+ DCs and impaired priming and infiltration of T-cells into the tumor microenvironment.[58]

Another mechanism by which cancer cells could escape cytotoxic T-cell killing is by downregulating or mutating molecules involved in the interferon-gamma signaling pathway, which goes through the interferon-gamma receptor chains janus kinase 1(JAK1) and/or JAK2 and the signal transducer and activators of transcription (STATs). Zaretsky et al. demonstrated copy-number-neutral loss-of-function mutations in JAK1 or JAK2, concurrent with loss of heterozygosity due to deletion of the wild-type allele, in two out of four cases of late acquired resistance to pembrolizumab, which were absent in the baseline biopsies.[ 76] More recently, the relevance of the JAK-STAT pathway has been corroborated by Shin et al. in preclinical models of tumors with high TMB which would theoretically predispose to innate immunogenicity.[77] Functional abrogation of the JAK-STAT pathway due to inactivating mutations resulted in impairment of PD-L1 expression upon interferon-gamma exposure, thereby rendering PD-1 inhibitors ineffective.

Acquired resistance can also occur through loss of the shared component of human leukocyte antigen (HLA) Class I molecules, beta 2 microglobulin (B2M), which leads to the absence of surface expression of HLA Class I.[60] B2M is crucial to the antigen presenting machinery, playing an essential role in HLA Class I folding and transport to the cell surface. Mutations in B2M thus can disrupt major histocompatibility complex-restricted antigen presentation, which is crucial to generating a cytotoxic T-cell response to cancer.

Gene expression profiling

Nongenomic signatures are also being studied as predictive biomarkers for treatment response. Gene expression profiling using transcriptomics in a series of patients with melanoma treated with immune checkpoint inhibitors led to the identification of a unique innate PD-1 resistance signature featuring a distinct set of genes, particularly associated with the epithelial–mesenchymal transition, which typically conferred resistance to PD-1 inhibition.[78] Importantly, genes contributing to this signature such as AXL have also been identified as contributing to T-cell exclusion in preclinical models of immune resistance.[79] Other small studies have suggested immune activation (T-cell/interferon-gamma) signatures may be able to predict response and benefit from therapy.[26, 80, 81]

A complementary approach using targeted gene expression profiling with NanoString panels composed of immune-related genes identified a distinct adaptive immune signature of differentially expressed genes between responders and nonresponders.[82] This study in patients with melanoma treated with immune checkpoint inhibitors showed a profound and highly statistically significant difference in the expression of markers for T-cell subsets and immunomodulatory molecules in responders versus nonresponders to therapy in early on-treatment tumor samples. Intriguingly, the investigators suggest that early on-treatment biopsies may better predict response compared to pretreatment biopsies (although rather than be a bona fide predictive biomarker, this signature may be a result of the treatment itself).

Immune cell profiling

Flow cytometry-based techniques have been used to examine immune cell populations in peripheral blood. Huang et al. used immune profiling of peripheral blood from patients with advanced melanoma before and after treatment with the PD-1-targeting antibody pembrolizumab and identified pharmacodynamic changes in circulating exhausted-phenotype CD8 T-cells (Tex cells).[83] He demonstrated that response to PD-1 inhibition was functionally correlated to reinvigoration of exhausted T-cells, rather than expansion of the effector T-cell pool. Other groups including Ghoneim et al. used a similar approach to analyze T-cell subsets to evaluate mechanisms of resistance to immunotherapeutics.[84] By obtaining serial samples of peripheral blood in a preclinical model, they demonstrate progressive T-cell exhaustion and show that de novo DNA methylation occurring during and after the peak effector, T-cell response is critical for establishing exhaustion. These approaches have been hypothesis generating and may be exploited clinically in the future.

Interrogating the complex host of factors in the tumor microenvironment, which collectively influence the immune response to cancer has been more challenging. The density and distribution of CD8+ lymphocytic infiltration within the tumor microenvironment has been shown to be associated with improved patient outcomes in other tumor types, including melanoma, lung, and bladder cancers.[42, 85–88] Efforts are underway to further refine the mapping of immune cell infiltrates in tumor, for example, using multicolor immunohistochemistry, [82] multiplex immune fluorescence, [89] or mass cytometry (CyTOF) technology.[90]

A combination of these techniques, termed “imaging CyTOF” offers an unparalleled method for evaluating the immune microenvironment.[ 91] First, antibody labeling using traditional immunohistochemical methods occurs. CyTOF using probes that are labeled with heavy metal ions through covalently coupled chelation polymers, rather than fluorescent probes is then applied. The sample is then positioned in a high-resolution laser ablation system before the subsequent readout by the mass cytometer. This allows simultaneous detection of many more unique probes (32 compared to traditional flow cytometry techniques which are limited by spectral overlap), with little or no spillover between detector channels. Together with advancing bioinformatics software, this enables the automated generation of maps of the tumor microenvironment. With spatial resolution of protein expression in samples at the single-cell level, this approach promises the study of immune cell subpopulations in the tissue microenvironment allowing delineation of cell subpopulations, cell–cell interactions, as well as highlighting tumor heterogeneity.[91, 92] The adoption of this technology may help further elucidate the conceptual framework through which the tumoral immune microenvironment is viewed. Currently, the immune microenvironment can be segregated into three separate phenotypes: “inflamed,” “noninflamed,” or immune-excluded phenotype and an “immune desert.”[93] Accurately delineating the lymphocyte subsets correlating with these phenotypes could guide novel clinical trial designs. For instance, this could enable the rational design of combinatorial strategies to overcome the hurdles of immune-excluded versus immune desert phenotypes, which are resistant to currently available immunotherapies.

Proteomics

Measuring cytokine production is an integral part of measuring immune response during immunotherapy and has been explored as potential blood-based biomarker. Sanmamed et al. longitudinally monitored serum interleukin (IL)-8 levels using a sandwich ELISA and found that in responding patients, serum IL-8 levels significantly decreased between baseline and best response and subsequently significantly increased upon progression.[94] In nonresponders, IL-8 levels significantly increased between baseline and progression suggesting that peripheral changes in serum IL-8 levels could be used to monitor and predict clinical benefit from immune checkpoint blockade.

Several other immunoproteomics approaches, such as SerologicProteomeAnalysis (SERPA) which screens an antibody reactivity profile in sera from patients are currently being explored as surrogate marker for measuring the adaptive immune response to cancer.[95]

Commensal microbiota

Perhaps, the most interesting research on personalized medicine in the immunotherapeutic era has been conducted regarding the human microbiome, which consists of over 100 trillion microbes, the majority of which resides in the gastrointestinal tract. The modification of commensal microbiota in the gut has been shown to correlate with immune checkpoint inhibitor efficacy in multiple murine models.[96, 97] Three separate studies recently simultaneously published in Science have suggested the importance of the commensal gut microbiota in predicting response to immunotherapy.[98–100] In a cohort of 112 patients with melanoma, Gopalakrishnan et al.[99] assessed the oral and gut microbiome by 16S RNA sequencing and found that PD-1 responsive patients had a significantly higher degree of biodiversity of gut microbiome compared with nonresponders. Specifically, responders had an increased abundance of Clostridiales bacteria, while nonresponders had higher proportions of Bacteroidales bacteria. Importantly, no correlations were observed in the oral cavity, suggesting the primacy of gut commensal microbiota for any potential immune synergy between microbiota and therapy. The groups of Matson et al.,[100] as well as Routy et al.[98] made similar observations, finding relative abundance of some species in responders compared to nonresponders. Taken together, the authors hypothesized that microbial diversity increased responsiveness to PD-1 inhibition. Antibiotic therapy taken during immunotherapy negatively correlated with response while a role for fecal microbiota transplantation was hinted at by the accompanying preclinical work. This arena remains an active area of study, and how this translates clinically will remain to be seen.

Challenges for the future

We have discussed the challenges of each individual high-throughput assays above, but it will be the combination of these technologies that will truly personalize immunotherapy. The concept of a personalized immunogram as a framework for describing the different interactions between cancer and the immune system for individual patients has been suggested, reflecting relative ascendancy of various domains of the cancer immunity cycle.[101, 102] Karasaki et al.[101] used this approach integrating genomic, transcriptomic, and immunohistochemical data to categorize the immunogenicity of NSCLC. The authors recognized eight domains which created axes for the immunogram for the cancer immunity cycle. These included T-cell immunity, tumor antigenicity, priming and activation, trafficking and infiltration, recognition of tumor cells, absence of inhibitory cells, absence of checkpoint expression, and absence of inhibitory molecules. Utilizing gene set enrichment analyses of RNA sequencing data and immunohistochemical evaluation, scores were added from each domain to derive an output category of either T-cell rich, T-cell intermediate, or T-cell poor tumors. Interestingly, some of the findings were broadly counterintuitive, such as overrepresentation of neoantigens in T-cell poor tumors and increased inhibitory molecules and checkpoints seen in T-cell rich tumors. This highlights the dynamic nature of this concept. Although the immunogram is still experimental, its logical categorization enables simpler conceptualization of the underlying interactions between the immune microenvironment and tumors and provides a springboard from which personalized immunotherapy can become a reality.

In the future, patients with advanced cancer could conceivably have their cancer immunogram interrogated utilizing multiple validated biomarker assays and technologies in “real-time,” and have this reassessed longitudinally by the evaluation of fresh serial human specimens (tumor, blood, serum, and microbiome) during treatment (at baseline pretreatment, early-on-treatment, and progression time points). This could allow deep analysis to unveil potential mechanisms of therapeutic resistance [Figure 1]. Furthermore, novel biological metrics may be developed with the potential to monitor immune-related adverse events on treatment to minimize the risk to patients.

Figure 1:

Pathway for personalized immunotherapy. Baseline assessment of a cancer patient includes profiling of the tumor for driver mutations, mutational load, and gene expression profiles; the tumor microenvironment including programmed death ligand 1 expression and analysis of infiltrating immune populations; and host immune responses and the microbiome. Longitudinal evaluation of fresh serial samples – tumor (brown), blood (red), and microbiome (purple) during treatment (pretreatment, early-on-treatment, response, and progression time points) may unveil potential predictive response biomarkers, resistance mechanisms, and further therapeutic strategies.

Figure 1:

Pathway for personalized immunotherapy. Baseline assessment of a cancer patient includes profiling of the tumor for driver mutations, mutational load, and gene expression profiles; the tumor microenvironment including programmed death ligand 1 expression and analysis of infiltrating immune populations; and host immune responses and the microbiome. Longitudinal evaluation of fresh serial samples – tumor (brown), blood (red), and microbiome (purple) during treatment (pretreatment, early-on-treatment, response, and progression time points) may unveil potential predictive response biomarkers, resistance mechanisms, and further therapeutic strategies.

To make personalization of cancer immunotherapies a reality, a continuous effort is required translate the insights from translational clinical studies to validated, high-throughput immune assessments. Collaborative efforts such as the Immune Biomarkers Task Force convened by the Society for Immunotherapy of Cancer and further nimble academic-industry partnerships will be required to fully harness the prodigious potential of personalized immune oncology.

Financial support and sponsorship

The authors declared no funding related to the study.

Conflicts of interest

The authors declared no conflicts of interest.

References

References
1.
Lynch
TJ,
Bell
DW,
Sordella
R,
Gurubhagavatula
S,
Okimoto
RA,
Brannigan
BW,
et al
.
Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib
.
N Engl J Med
2004
;
350
:
2129
39
.
2.
Shaw
AT,
Kim
DW,
Nakagawa
K,
Seto
T,
Crinó
L,
Ahn
MJ,
et al
.
Crizotinib versus chemotherapy in advanced ALK-positive lung cancer
.
N Engl J Med
2013
;
368
:
2385
94
.
3.
Chapman
PB,
Hauschild
A,
Robert
C,
Haanen
JB,
Ascierto
P,
Larkin
J,
et al
.
Improved survival with vemurafenib in melanoma with BRAF V600E mutation
.
N Engl J Med
2011
;
364
:
2507
16
.
4.
Wolchok
JD,
Kluger
H,
Callahan
MK,
Postow
MA,
Rizvi
NA,
Lesokhin
AM,
et al
.
Nivolumab plus ipilimumab in advanced melanoma
.
N Engl J Med
2013
;
369
:
122
33
.
5.
Garon
EB,
Rizvi
NA,
Hui
R,
Leighl
N,
Balmanoukian
AS,
Eder
JP,
et al
.
Pembrolizumab for the treatment of non-small-cell lung cancer
.
N Engl J Med
2015
;
372
:
2018
28
.
6.
Seiwert
TY,
Haddad
RI,
Gupta
S,
Mehra
R,
Tahara
M,
Berger
R,
et al
.
Antitumor activity and safety of pembrolizumab in patients (pts) with advanced squamous cell carcinoma of the head and neck (SCCHN): Preliminary results from KEYNOTE-012 expansion cohort
.
J Clin Oncol
2015
;
33
:
18_suppl
,
LBA6008-LBA6008
.
7.
Rosenberg
JE,
Hoffman-Censits
J,
Powles
T,
van der Heijden
MS,
Balar
AV,
Necchi
A,
et al
.
Atezolizumab in patients with locally advanced and metastatic urothelial carcinoma who have progressed following treatment with platinum-based chemotherapy: A single-arm, multicentre, phase 2 trial
.
Lancet
2016
;
387
:
1909
20
.
8.
Hodi
FS,
O’Day
SJ,
McDermott
DF,
Weber
RW,
Sosman
JA,
Haanen
JB,
et al
.
Improved survival with ipilimumab in patients with metastatic melanoma
.
N Engl J Med
2010
;
363
:
711
23
.
9.
Nauts
HC,
Swift
WE,
Coley
BL.
The treatment of malignant tumors by bacterial toxins as developed by the late William B. Coley, M.D. reviewed in the light of modern research
.
Cancer Res
1946
;
6
:
205
16
.
10.
Leach
DR,
Krummel
MF,
Allison
JP.
Enhancement of antitumor immunity by CTLA-4 blockade
.
Science
1996
;
271
:
1734
6
.
11.
Herbst
RS,
Baas
P,
Kim
DW,
Felip
E,
Pérez-Gracia
JL,
Han
JY,
et al
.
Pembrolizumab versus docetaxel for previously treated, PD-L1-positive, advanced non-small-cell lung cancer (KEYNOTE-010): A randomised controlled trial
.
Lancet
2016
;
387
:
1540
50
.
12.
Wolchok
JD,
Hoos
A,
O’Day
S,
Weber
JS,
Hamid
O,
Lebbé
C,
et al
.
Guidelines for the evaluation of immune therapy activity in solid tumors: Immune-related response criteria
.
Clin Cancer Res
2009
;
15
:
7412
20
.
13.
Schadendorf
D,
Hodi
FS,
Robert
C,
Weber
JS,
Margolin
K,
Hamid
O,
et al
.
Pooled analysis of long-term survival data from phase II and phase III trials of ipilimumab in unresectable or metastatic melanoma
.
J Clin Oncol
2015
;
33
:
1889
94
.
14.
Spigel
DR,
McLeod
M,
Hussein
MA,
Waterhouse
DM,
Einhorn
L,
Horn
L,
et al
.
Randomized results of fixed-duration (1-yr) vs continuous nivolumab in patients (pts) with advanced non-small cell lung cancer (NSCLC)
.
Annals of Oncology
2017
;
28
(
suppl 5
).
15.
Saltz
LB.
The value of considering cost, and the cost of not considering value
.
J Clin Oncol
2016
;
34
:
659
60
.
16.
Brahmer
JR,
Drake
CG,
Wollner
I,
Powderly
JD,
Picus
J,
Sharfman
WH,
et al
.
Phase I study of single-agent anti-programmed death-1 (MDX-1106) in refractory solid tumors: Safety, clinical activity, pharmacodynamics, and immunologic correlates
.
J Clin Oncol
2010
;
28
:
3167
75
.
17.
Grosso
J,
Horak
CE,
Inzunza
D,
Cardona
DM,
Simon
JS,
Gupta
AK,
et al
.
Association of tumor PD-L1 expression and immune biomarkers with clinical activity in patients (pts) with advanced solid tumors treated with nivolumab (anti-PD-1; BMS-936558; ONO-4538)
.
J Clin Oncol
2013
;
31
15 Suppl
:
3016
.
18.
Kefford
R,
Ribas
A,
Hamid
O,
Robert
C,
Daud
A,
Wolchok
JD,
et al
.
Clinical efficacy and correlation with tumor PD-L1 expression in patients (pts) with melanoma (MEL) treated with the anti-PD-1 monoclonal antibody MK-3475
.
J Clin Oncol
2014
;
32
15 Suppl
:
3005
.
19.
Weber
JS,
D’Angelo
SP,
Minor
D,
Hodi
FS,
Gutzmer
R,
Neyns
B,
et al
.
Nivolumab versus chemotherapy in patients with advanced melanoma who progressed after anti-CTLA-4 treatment (CheckMate 037): A randomised, controlled, open-label, phase 3 trial
.
Lancet Oncol
2015
;
16
:
375
84
.
20.
Taube
JM,
Klein
A,
Brahmer
JR,
Xu
H,
Pan
X,
Kim
JH,
et al
.
Association of PD-1, PD-1 ligands, and other features of the tumor immune microenvironment with response to anti-PD-1 therapy
.
Clin Cancer Res
2014
;
20
:
5064
74
.
21.
Robert
C,
Long
GV,
Brady
B,
Dutriaux
C,
Maio
M,
Mortier
L,
et al
.
Nivolumab in previously untreated melanoma without BRAF mutation
.
N Engl J Med
2015
;
372
:
320
30
.
22.
Weber
JS,
Kudchadkar
RR,
Yu
B,
Gallenstein
D,
Horak
CE,
Inzunza
HD,
et al
.
Safety, efficacy, and biomarkers of nivolumab with vaccine in ipilimumab-refractory or -naive melanoma
.
J Clin Oncol
2013
;
31
:
4311
8
.
23.
Brahmer
J,
Reckamp
KL,
Baas
P,
Crinò
L,
Eberhardt
WE,
Poddubskaya
E,
et al
.
Nivolumab versus docetaxel in advanced squamous-cell non-small-cell lung cancer
.
N Engl J Med
2015
;
373
:
123
35
.
24.
Borghaei
H,
Paz-Ares
L,
Horn
L,
Spigel
DR,
Steins
M,
Ready
NE,
et al
.
Nivolumab versus docetaxel in advanced nonsquamous non-small-cell lung cancer
.
N Engl J Med
2015
;
373
:
1627
39
.
25.
Reck
M,
Rodríguez-Abreu
D,
Robinson
AG,
Hui
R,
Csőszi
T,
Fülöp
A,
et al
.
Pembrolizumab versus chemotherapy for PD-L1-positive non-small-cell lung cancer
.
N Engl J Med
2016
;
375
:
1823
33
.
26.
Fehrenbacher
L,
Spira
A,
Ballinger
M,
Kowanetz
M,
Vansteenkiste
J,
Mazieres
J,
et al
.
Atezolizumab versus docetaxel for patients with previously treated non-small-cell lung cancer (POPLAR): A multicentre, open-label, phase 2 randomised controlled trial
.
Lancet
2016
;
387
:
1837
46
.
27.
Rittmeyer
A,
Barlesi
F,
Waterkamp
D,
Park
K,
Ciardiello
F,
von Pawel
J,
et al
.
Atezolizumab versus docetaxel in patients with previously treated non-small-cell lung cancer (OAK): A phase 3, open-label, multicentre randomised controlled trial
.
Lancet
2017
;
389
:
255
65
.
28.
Motzer
RJ,
Escudier
B,
McDermott
DF,
George
S,
Hammers
HJ,
Srinivas
S,
et al
.
Nivolumab versus everolimus in advanced renal-cell carcinoma
.
N Engl J Med
2015
;
373
:
1803
13
.
29.
Motzer
RJ,
Rini
BI,
McDermott
DF,
Redman
BG,
Kuzel
TM,
Harrison
MR,
et al
.
Nivolumab for metastatic renal cell carcinoma: Results of a randomized phase II trial
.
J Clin Oncol
2015
;
33
:
1430
7
.
30.
Lopes
G,
Wu
YL,
Kudaba
I,
Kowalski
D,
Cho
BC,
Castro
G,
et al
.
Pembrolizumab (pembro) versus platinum-based chemotherapy (chemo) as first-line therapy for advanced/metastatic NSCLC with a PD-L1 tumor proportion score (TPS)≥ 1%: Open-label, phase 3 KEYNOTE-042 study
.
J Clin Oncol
2018
;
36
Suppl
:
abstr LBA4
.
31.
Gandhi
L,
Rodríguez-Abreu
D,
Gadgeel
S,
Esteban
E,
Felip
E,
De Angelis
F,
et al
.
Pembrolizumab plus chemotherapy in metastatic non-small-cell lung cancer
.
N Engl J Med
2018
;
378
:
2078
92
.
32.
Paz-Ares
LG,
Luft
A,
Tafreshi
A,
Gumus
M,
Mazieres
J,
Hermes
B,
Senler
FC,
et al
.
Phase 3 study of carboplatin-paclitaxel/nab-paclitaxel (Chemo) with or without pembrolizumab (Pembro) for patients (Pts) with metastatic squamous (Sq) non-small cell lung cancer (NSCLC)
.
J Clin Oncol
2018
;
36
Suppl
:
abstr105
.
33.
Hossein
B,
Hellmann
MD,
Paz-Ares
LG,
Ramalingam
SS,
Reck
M,
O'Byrne
KJ,
et al
.
Nivolumab (Nivo) + platinum-doublet chemotherapy (Chemo) vs. chemo as first-line (1L) treatment (Tx) for advanced non-small cell lung cancer (NSCLC) with <1% tumor PD-L1 expression: Results from CheckMate 227
.
J Clin Oncol
2018
;
36
Suppl
:
abstr 9001
.
34.
Socinski
MA,
Jotte
RM,
Cappuzzo
F,
Orlandi
FJ,
Stroyakovskiy
D,
Nogami
N,
et al
.
Overall survival (OS) analysis of IMpower150, a randomized Ph 3 study of atezolizumab (atezo) + chemotherapy (chemo) ± bevacizumab (bev) vs. chemo + bev in 1L nonsquamous (NSQ) NSCLC
.
J Clin Oncol
2018
;
36
Suppl
:
abstr9002
.
35.
Barber
DL,
Wherry
EJ,
Masopust
D,
Zhu
B,
Allison
JP,
Sharpe
AH,
et al
.
Restoring function in exhausted CD8 T cells during chronic viral infection
.
Nature
2006
;
439
:
682
7
.
36.
Blackburn
SD,
Shin
H,
Freeman
GJ,
Wherry
EJ.
Selective expansion of a subset of exhausted CD8 T cells by alphaPD-L1 blockade
.
Proc Natl Acad Sci U S A
2008
;
105
:
15016
21
.
37.
McDermott
DF,
Sosman
JA,
Sznol
M,
Massard
C,
Gordon
MS,
Hamid
O,
et al
.
Atezolizumab, an anti-programmed death-ligand 1 antibody, in metastatic renal cell carcinoma: Long-term safety, clinical activity, and immune correlates from a phase ia study
.
J Clin Oncol
2016
;
34
:
833
42
.
38.
Callea
M,
Albiges
L,
Gupta
M,
Cheng
SC,
Genega
EM,
Fay
AP,
et al
.
Differential expression of PD-L1 between primary and metastatic sites in clear-cell renal cell carcinoma
.
Cancer Immunol Res
2015
;
3
:
1158
64
.
39.
Gerlinger
M,
Rowan
AJ,
Horswell
S,
Math
M,
Larkin
J,
Endesfelder
D,
et al
.
Intratumor heterogeneity and branched evolution revealed by multiregion sequencing
.
N Engl J Med
2012
;
366
:
883
92
.
40.
Ahmad
Z,
Fraser-Fish
J,
Kumar
R,
Ebbs
B,
Fowler
G,
Flohr
P,
et al
.
Abstract 2243: Characterization of PD-L1 expression on circulating tumor cells (CTCs) isolated with a label-free inertial microfluidic system from advanced non-small cell lung cancer patients (NSCLC pts
).
Cancer Res
2016
;
76
14 Suppl
:
2243
.
41.
Rivalland
G,
Ameratunga
M,
Asadi
K,
Walkiewicz
M,
Knight
S,
John
T,
et al
.
Programmed death–ligand 1 (PD-L1) immumohistochemistry in NSCLC: Comparison and correlation between two antibodies
.
J Clin Oncol
2016
;
34
15 Suppl
:
e20036
.
42.
Ameratunga
M,
Asadi
K,
Lin
X,
Walkiewicz
M,
Murone
C,
Knight
S,
et al
.
PD-L1 and tumor infiltrating lymphocytes as prognostic markers in resected NSCLC
.
PLoS One
2016
;
11
:
e0153954
.
43.
Tsao
MS,
Kerr
KM,
Dacic
S,
Yatabe
Y,
Hirsch
FR.
IASLC atlas of PD-L1 Immunohistochemistry Testing in Lung Cancer
.
Aurora, CO
:
International Association for the Study of Lung Cancer
;
2017
.
44.
Hirsch
FR,
McElhinny
A,
Stanforth
D,
Ranger-Moore
J,
Jansson
M,
Kulangara
K,
et al
.
PD-L1 immunohistochemistry assays for lung cancer: Results from phase 1 of the blueprint PD-L1 IHC assay comparison project
.
J Thorac Oncol
2017
;
12
:
208
22
.
45.
Alexandrov
LB,
Nik-Zainal
S,
Wedge
DC,
Aparicio
SA,
Behjati
S,
Biankin
AV,
et al
.
Signatures of mutational processes in human cancer
.
Nature
2013
;
500
:
415
21
.
46.
McGranahan
N,
Furness
AJ,
Rosenthal
R,
Ramskov
S,
Lyngaa
R,
Saini
SK,
et al
.
Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade
.
Science
2016
;
351
:
1463
9
.
47.
Le
DT,
Uram
JN,
Wang
H,
Bartlett
BR,
Kemberling
H,
Eyring
AD,
et al
.
PD-1 blockade in tumors with mismatch-repair deficiency
.
N Engl J Med
2015
;
372
:
2509
20
.
48.
Bouffet
E,
Larouche
V,
Campbell
BB,
Merico
D,
de Borja
R,
Aronson
M,
et al
.
Immune checkpoint inhibition for hypermutant glioblastoma multiforme resulting from germline biallelic mismatch repair deficiency
.
J Clin Oncol
2016
;
34
:
2206
11
.
49.
Le
DT,
Durham
JN,
Smith
KN,
Wang
H,
Bartlett
BR,
Aulakh
LK,
et al
.
Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade
.
Science
2017
;
357
:
409
13
.
50.
Le
DT,
Yoshino
T,
Jäger
D,
Andre
T,
Bendell
JC,
Wang
R,
et al
.
KEYNOTE-164: Phase II study of pembrolizumab (MK-3475) for patients with previously treated, microsatellite instability-high advanced colorectal carcinoma
.
J Clin Oncol
2016
34
:
4_suppl
,
TPS787-TPS787
.
51.
Seiwert
TY,
Burtness
B,
Mehra
R,
Weiss
J,
Berger
R,
Eder
JP,
et al
.
Safety and clinical activity of pembrolizumab for treatment of recurrent or metastatic squamous cell carcinoma of the head and neck (KEYNOTE-012): An open-label, multicentre, phase 1b trial
.
Lancet Oncol
2016
;
17
:
956
65
.
52.
Diaz
LA,
Marabelle
A,
Delord
JP,
Shapira-Frommer
R,
Geva
R,
Peled
N,
et al
.
Pembrolizumab therapy for microsatellite instability high (MSI-H) colorectal cancer (CRC) and non-CRC
.
J Clin Oncol
2017
;
35
15 Suppl
:
3071
.
53.
Vasen
HF,
Blanco
I,
Aktan-Collan
K,
Gopie
JP,
Alonso
A,
Aretz
S,
et al
.
Revised guidelines for the clinical management of Lynch syndrome (HNPCC): Recommendations by a group of European experts
.
Gut
2013
;
62
:
812
23
.
54.
McConechy
MK,
Talhouk
A,
Li-Chang
HH,
Leung
S,
Huntsman
DG,
Gilks
CB,
et al
.
Detection of DNA mismatch repair (MMR) deficiencies by immunohistochemistry can effectively diagnose the microsatellite instability (MSI) phenotype in endometrial carcinomas
.
Gynecol Oncol
2015
;
137
:
306
10
.
55.
Bartley
AN,
Luthra
R,
Saraiya
DS,
Urbauer
DL,
Broaddus
RR.
Identification of cancer patients with lynch syndrome: Clinically significant discordances and problems in tissue-based mismatch repair testing
.
Cancer Prev Res (Phila)
2012
;
5
:
320
7
.
56.
Cortes-Ciriano
I,
Lee
S,
Park
WY,
Kim
TM,
Park
PJ.
A molecular portrait of microsatellite instability across multiple cancers
.
Nat Commun
2017
;
8
:
15180
.
57.
Palles
C,
Cazier
JB,
Howarth
KM,
Domingo
E,
Jones
AM,
Broderick
P,
et al
.
Germline mutations affecting the proofreading domains of POLE and POLD1 predispose to colorectal adenomas and carcinomas
.
Nat Genet
2013
;
45
:
136
44
.
58.
Johanns
TM,
Miller
CA,
Dorward
IG,
Tsien
C,
Chang
E,
Perry
A,
et al
.
Immunogenomics of hypermutated glioblastoma: A patient with germline POLE deficiency treated with checkpoint blockade immunotherapy
.
Cancer Discov
2016
;
6
:
1230
6
.
59.
McShane
LM,
Cavenagh
MM,
Lively
TG,
Eberhard
DA,
Bigbee
WL,
Williams
PM,
et al
.
Criteria for the use of omics-based predictors in clinical trials
.
Nature
2013
;
502
:
317
20
.
60.
Rizvi
NA,
Hellmann
MD,
Snyder
A,
Kvistborg
P,
Makarov
V,
Havel
JJ,
et al
.
Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer
.
Science
2015
;
348
:
124
8
.
61.
Kowanetz
M,
Zou
W,
Shames
DS,
Cummings
C,
Rizvi
N,
Spira
AI,
et al
.
Tumor mutation load assessed by FoundationOne (FM1) is associated with improved efficacy of atezolizumab (atezo) in patients with advanced NSCLC
.
Ann Oncol
2016
;
27
Suppl 6
:
77P
.
62.
Salem
ME,
Xiu
J,
Lenz
HJ,
Atkins
MB,
Philip
PA,
Hwang
JJ,
et al
.
Characterization of tumor mutation load (TML) in solid tumors
.
J Clin Oncol
2017
;
35
15 Suppl
:
11517
.
63.
Hellmann
MD,
Ciuleanu
TE,
Pluzanski
A,
Lee
JS,
Otterson
GA,
Audigier-Valette
C,
et al
.
Nivolumab plus ipilimumab in lung cancer with a high tumor mutational burden
.
N Engl J Med
2018
;
378
:
2093
104
.
64.
Qiu
P,
Pang
L,
Arreaza
G,
Maguire
M,
Chang
KC,
Marton
MJ,
et al
.
Data interoperability of whole exome sequencing (WES) based mutational burden estimates from different laboratories
.
Int J Mol Sci
2016
;
17
.
[PubMed]
.
65.
Salipante
SJ,
Scroggins
SM,
Hampel
HL,
Turner
EH,
Pritchard
CC.
Microsatellite instability detection by next generation sequencing
.
Clin Chem
2014
;
60
:
1192
9
.
66.
Bonneville
R,
Krook
MA,
Kautto
EA,
Miya
J,
Wing
MR,
Chen
HZ,
et al
.
Landscape of microsatellite instability across 39 cancer types
.
JCO Precis Oncol
2017
;
2017
:
1
15
.
67.
Kautto
EA,
Bonneville
R,
Miya
J,
Yu
L,
Krook
MA,
Reeser
JW,
et al
.
Performance evaluation for rapid detection of pan-cancer microsatellite instability with MANTIS
.
Oncotarget
2017
;
8
:
7452
63
.
68.
Tran
E,
Ahmadzadeh
M,
Lu
YC,
Gros
A,
Turcotte
S,
Robbins
PF,
et al
.
Immunogenicity of somatic mutations in human gastrointestinal cancers
.
Science
2015
;
350
:
1387
90
.
69.
Kvistborg
P,
Philips
D,
Kelderman
S,
Hageman
L,
Ottensmeier
C,
Joseph-Pietras
D,
et al
.
Anti-CTLA-4 therapy broadens the melanoma-reactive CD8+ T cell response
.
Sci Transl Med
2014
;
6
:
254ra128
.
70.
Topalian
SL,
Taube
JM,
Anders
RA,
Pardoll
DM.
Mechanism-driven biomarkers to guide immune checkpoint blockade in cancer therapy
.
Nat Rev Cancer
2016
;
16
:
275
87
.
71.
Turajlic
S,
Litchfield
K,
Xu
H,
Rosenthal
R,
McGranahan
N,
Reading
JL,
et al
.
Insertion-and-deletion-derived tumour-specific neoantigens and the immunogenic phenotype: A pan-cancer analysis
.
Lancet Oncol
2017
;
18
:
1009
21
.
72.
Dunn
GP,
Bruce
AT,
Ikeda
H,
Old
LJ,
Schreiber
RD.
Cancer immunoediting: From immunosurveillance to tumor escape
.
Nat Immunol
2002
;
3
:
991
8
.
73.
Matsushita
H,
Vesely
MD,
Koboldt
DC,
Rickert
CG,
Uppaluri
R,
Magrini
VJ,
et al
.
Cancer exome analysis reveals a T-cell-dependent mechanism of cancer immunoediting
.
Nature
2012
;
482
:
400
4
.
74.
Peng
W,
Chen
JQ,
Liu
C,
Malu
S,
Creasy
C,
Tetzlaff
MT,
et al
.
Loss of PTEN promotes resistance to T cell-mediated immunotherapy
.
Cancer Discov
2016
;
6
:
202
16
.
75.
Spranger
S,
Bao
R,
Gajewski
TF.
Melanoma-intrinsic β-catenin signalling prevents anti-tumour immunity
.
Nature
2015
;
523
:
231
5
.
76.
Zaretsky
JM,
Garcia-Diaz
A,
Shin
DS,
Escuin-Ordinas
H,
Hugo
W,
Hu-Lieskovan
S,
et al
.
Mutations associated with acquired resistance to PD-1 blockade in melanoma
.
N Engl J Med
2016
;
375
:
819
29
.
77.
Shin
DS,
Zaretsky
JM,
Escuin-Ordinas
H,
Garcia-Diaz
A,
Hu-Lieskovan
S,
Kalbasi
A,
et al
.
Primary resistance to PD-1 blockade mediated by JAK1/2 mutations
.
Cancer Discov
2017
;
7
:
188
201
.
78.
Hugo
W,
Zaretsky
JM,
Sun
L,
Song
C,
Moreno
BH,
Hu-Lieskovan
S,
et al
.
Genomic and transcriptomic features of response to anti-PD-1 therapy in metastatic melanoma
.
Cell
2016
;
165
:
35
44
.
79.
Aguilera
TA,
Giaccia
AJ.
Molecular pathways: Oncologic pathways and their role in T-cell exclusion and immune evasion-A new role for the AXL receptor tyrosine kinase
.
Clin Cancer Res
2017
;
23
:
2928
33
.
80.
Ayers
M,
Lunceford
J,
Nebozhyn
M,
Murphy
E,
Loboda
A,
Kaufman
DR,
et al
.
IFN-γ-related mRNA profile predicts clinical response to PD-1 blockade
.
J Clin Invest
2017
;
127
:
2930
40
.
81.
Prat
A,
Navarro
A,
Paré
L,
Reguart
N,
Galván
P,
Pascual
T,
et al
.
Immune-related gene expression profiling after PD-1 blockade in non-small cell lung carcinoma, head and neck squamous cell carcinoma, and melanoma
.
Cancer Res
2017
;
77
:
3540
50
.
82.
Chen
PL,
Roh
W,
Reuben
A,
Cooper
ZA,
Spencer
CN,
Prieto
PA,
et al
.
Analysis of immune signatures in longitudinal tumor samples yields insight into biomarkers of response and mechanisms of resistance to immune checkpoint blockade
.
Cancer Discov
2016
;
6
:
827
37
.
83.
Huang
AC,
Postow
MA,
Orlowski
RJ,
Mick
R,
Bengsch
B,
Manne
S,
et al
.
T-cell invigoration to tumour burden ratio associated with anti-PD-1 response
.
Nature
2017
;
545
:
60
5
.
84.
Ghoneim
HE,
Fan
Y,
Moustaki
A,
Abdelsamed
HA,
Dash
P,
Dogra
P,
et al
.
De novo epigenetic programs inhibit PD-1 blockade-mediated T cell rejuvenation
.
Cell
2017
;
170
:
142
57.e19
.
85.
Tumeh
PC,
Harview
CL,
Yearley
JH,
Shintaku
IP,
Taylor
EJ,
Robert
L,
et al
.
PD-1 blockade induces responses by inhibiting adaptive immune resistance
.
Nature
2014
;
515
:
568
71
.
86.
Schalper
KA,
Brown
J,
Carvajal-Hausdorf
D,
McLaughlin
J,
Velcheti
V,
Syrigos
KN,
et al
.
Objective measurement and clinical significance of TILs in non-small cell lung cancer
.
J Natl Cancer Inst
2015
;
107
.
[PubMed]
.
87.
Al-Shibli
KI,
Donnem
T,
Al-Saad
S,
Persson
M,
Bremnes
RM,
Busund
LT,
et al
.
Prognostic effect of epithelial and stromal lymphocyte infiltration in non-small cell lung cancer
.
Clin Cancer Res
2008
;
14
:
5220
7
.
88.
Donnem
T,
Hald
SM,
Paulsen
EE,
Richardsen
E,
Al-Saad
S,
Kilvaer
TK,
et al
.
Stromal CD8+T-cell density – A promising supplement to TNM staging in non-small cell lung cancer
.
Clin Cancer Res
2015
;
21
:
2635
43
.
89.
Altan
M,
Pelekanou
V,
Schalper
KA,
Toki
M,
Gaule
P,
Syrigos
K,
et al
.
B7-H3 expression in NSCLC and its association with B7-H4, PD-L1 and tumor-infiltrating lymphocytes
.
Clin Cancer Res
2017
;
23
:
5202
9
.
90.
Villarroel-Espindola
F,
Carvajal-Hausdorf
D,
Datar
I,
Esch
A,
Rashidi
N,
Nassar
A,
et al
.
Abstract 1635: Multiplexed analysis of fixed tumor tissues using imaging mass cytometry
.
Cancer Res
2017
;
77
13 Suppl
:
1635
.
91.
Giesen
C,
Wang
HA,
Schapiro
D,
Zivanovic
N,
Jacobs
A,
Hattendorf
B,
et al
.
Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry
.
Nat Methods
2014
;
11
:
417
22
.
92.
Di Palma
S,
Bodenmiller
B.
Unraveling cell populations in tumors by single-cell mass cytometry
.
Curr Opin Biotechnol
2015
;
31
:
122
9
.
93.
Chen
DS,
Mellman
I.
Elements of cancer immunity and the cancer-immune set point
.
Nature
2017
;
541
:
321
30
.
94.
Sanmamed
MF,
Perez-Gracia
JL,
Schalper
KA,
Fusco
JP,
Gonzalez
A,
Rodriguez-Ruiz
ME,
et al
.
Changes in serum interleukin-8 (IL-8) levels reflect and predict response to anti-PD-1 treatment in melanoma and non-small-cell lung cancer patients
.
Ann Oncol
2017
;
28
:
1988
95
.
95.
Fulton
KM,
Twine
SM.
Immunoproteomics: Current technology and applications
.
Methods Mol Biol
2013
;
1061
:
21
57
.
96.
Vétizou
M,
Pitt
JM,
Daillère
R,
Lepage
P,
Waldschmitt
N,
Flament
C,
et al
.
Anticancer immunotherapy by CTLA-4 blockade relies on the gut microbiota
.
Science
2015
;
350
:
1079
84
.
97.
Sivan
A,
Corrales
L,
Hubert
N,
Williams
JB,
Aquino-Michaels
K,
Earley
ZM,
et al
.
Commensal bifidobacterium promotes antitumor immunity and facilitates anti-PD-L1 efficacy
.
Science
2015
;
350
:
1084
9
.
98.
Routy
B,
Le Chatelier
E,
Derosa
L,
Duong
CPM,
Alou
MT,
Daillère
R,
et al
.
Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors
.
Science
2018
;
359
:
91
7
.
99.
Gopalakrishnan
V,
Spencer
CN,
Nezi
L,
Reuben
A,
Andrews
MC,
Karpinets
TV,
et al
.
Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients
.
Science
2018
;
359
:
97
103
.
100.
Matson
V,
Fessler
J,
Bao
R,
Chongsuwat
T,
Zha
Y,
Alegre
ML,
et al
.
The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients
.
Science
2018
;
359
:
104
8
.
101.
Karasaki
T,
Nagayama
K,
Kuwano
H,
Nitadori
JI,
Sato
M,
Anraku
M,
et al
.
An immunogram for the cancer-immunity cycle: Towards personalized immunotherapy of lung cancer
.
J Thorac Oncol
2017
;
12
:
791
803
.
102.
Chen
DS,
Mellman
I.
Oncology meets immunology: The cancer-immunity cycle
.
Immunity
2013
;
39
:
1
10
.

Author notes

For reprints contact:reprints@medknow.com