SITC 2018 workshop report: Immuno-Oncology Biomarkers: State of the Art

Whole-Blood Gene Expression Profiles Correlate with Response to Immune Checkpoint Inhibitors in Patients with Metastatic Renal Cell Carcinoma

In metastatic renal cell carcinoma (mRCC), the clinical response to immune checkpoint inhibitors (ICIs) is limited in a subset of patients and the need exists to identify non-invasive, blood-based, predictive biomarkers for responses. We performed RNA sequencing using whole-blood samples prospectively collected from 49 patients with mRCC prior to the administration of ipilimumab (IPI) and/or nivolumab (NIVO) to determine whether gene expression profiles were associated with responses. An analysis from 33 mRCC patients with complete responses (n = 5), partial responses (n = 14), and progressive disease (n = 14) showed 460 differentially expressed genes (DEGs) related to immune responses between the responder and non-responder groups with significant differences. A set of 14 genes generated from the initial 460 DEGs accurately classified responders (sensitivity 94.7% and specificity 50.0%) while consensus clustering defined clusters with significantly differing response rates (92.3% and 35.0%). These clustering results were replicated in a cohort featuring 16 additional SD patients (49 total patients): response rates were 95.8% and 48.0%. Collectively, whole-blood gene expression profiles derived from mRCC patients treated with ICIs clearly differed by response and hierarchical clustering using immune response DEGs accurately classified responder patients. These results suggest that such screening may serve as a predictor for ICI responses in mRCC patients.

A multi-omic single cell sequencing approach to develop a CD8 T cell specific gene signature for anti-PD1 response in solid tumors

Immune checkpoint blockade (ICB) has led to durable clinical responses in multiple cancer types. However, biomarkers that identify which patients are most likely to respond to ICB are not well defined. Many putative biomarkers developed from a small number of samples often fail to maintain their predictive status in larger validation cohorts. We show across multiple human malignancies and syngeneic murine tumor models that neither pretreatment T cell receptor (TCR) clonality nor changes in clonality after ICB correlate with response. Dissection of tumor infiltrating lymphocytes pre- and post-ICB by paired single-cell RNA sequencing and single-cell TCR sequencing reveals conserved and distinct transcriptomic features in expanded TCR clonotypes between anti-PD1 responder and nonresponder murine tumor models. Overall, our results indicate a productive anti-tumor response is agnostic of TCR clonal expansion. Further, we used single-cell transcriptomics to develop a CD8⁺ T cell specific gene signature for a productive anti-tumor response and show the response signature to be associated with overall survival (OS) on nivolumab monotherapy in CheckMate-067, a phase 3 clinical trial in metastatic melanoma. These results highlight the value of leveraging single-cell assays to dissect heterogeneous tumor and immune subsets and define cell-type specific transcriptomic biomarkers of ICB response.

Integrated transcriptional-phenotypic analysis captures systemic immunomodulation following antiangiogenic therapy in renal cell carcinoma patients

BACKGROUND

The combination of immune checkpoint blockade (ICB) with standard therapies is becoming a common approach for overcoming resistance to cancer immunotherapy in most human malignancies including metastatic renal cell carcinoma (mRCC). In this regard, insights into the immunomodulatory properties of antiangiogenic agents may help designing multidrug schedules based on specific immune synergisms.

METHODS

We used orthogonal transcriptomic and phenotyping platforms combined with functional analytic pipelines to elucidate the immunomodulatory effect of the antiangiogenic agent pazopanib in mRCC patients. Nine patients were studied longitudinally over a period of 6 months. We also analyzed transcriptional data from The Cancer Genome Atlas (TCGA) RCC cohort (N = 571) to assess the prognostic implications of our findings. The effect of pazopanib was assessed in vitro on NK cells and T cells. Additionally, myeloid-derived suppressor (MDSC)-like cells were generated from CD14+ monocytes transfected with mimics of miRNAs associated with MDSC function in the presence or absence of pazopanib.

RESULTS

Pazopanib administration caused a rapid and dramatic reshaping in terms of frequency and transcriptional activity of multiple blood immune cell subsets, with a downsizing of MDSC and regulatory T cells in favor of a strong enhancement in PD-1 expressing cytotoxic T and Natural Killer effectors. These changes were paired with an increase of the expression of transcripts reflecting activation of immune-effector functions. This immunomodulation was marked but transient, peaking at the third month of treatment. Moreover, the intratumoral expression level of a MDSC signature (MDSC INT) was strongly associated with poor prognosis in RCC patients. In vitro experiments indicate that the observed immunomodulation might be due to an inhibitory effect on MDSC-mediated suppression, rather than a direct effect on NK and T cells.

CONCLUSIONS

The marked but transient nature of this immunomodulation, peaking at the third month of treatment, provides the rationale for the use of antiangiogenics as a preconditioning strategy to improve the efficacy of ICB.

Implementation of a Novel Web-Based Lesion Selection Tool to Improve Acquisition of Tumor Biopsy Specimens

Introduction

For maximum utility of molecular characterization by next-generation sequencing (NGS) and better understanding of tumor microenvironment with immune correlates analysis, biopsy specimens must yield adequate tumor tissue, and sequential biopsy specimens should sample a consistent site. We developed a web-based lesion selection tool (LST) that enables management and tracking of the biopsy specimen collections.

Methods

Of 145 patients, the LST was used for 88 patients; the other 57 served as controls. We evaluated consistency of the lesion biopsied in longitudinal collections, number of cores obtained, and cores with adequate tumor cellularity for NGS. The Fisher exact test and Wilcoxon rank sum test were used to identify differences between the groups.

Results

The analysis included 30 of 88 (34%) patients in the LST group and 52 of 57 (91%) in the control group. The LST workflow ensured 100% consistency in the lesions biopsied compared with 75% in the control group in longitudinal collections and increased the proportion of patients in whom at least five cores were collected per biopsy.

Conclusions

The novel LST platform facilitates coordination, performance, and management of longitudinal biopsy specimens. Use of the LST enables sampling of the designated lesion consistently, which is likely to accurately inform us the effect of the treatment on tumor microenvironment and evolution of resistant pathways. Such studies are important translational component of any clinical trials and research as they guide the development of next line of therapy, which has significant effect on clinical utility. However, validation of this approach in a larger study is warranted.

Tumor immune infiltration estimated from gene expression profiles predicts colorectal cancer relapse

A substantial fraction of patients with stage I-III colorectal adenocarcinoma (CRC) experience disease relapse after surgery with curative intent. However, biomarkers for predicting the likelihood of CRC relapse have not been fully explored. Therefore, we assessed the association between tumor infiltration by a broad array of innate and adaptive immune cell types and CRC relapse risk. We implemented a discovery-validation design including a discovery dataset from Moffitt Cancer Center (MCC; Tampa, FL) and three independent validation datasets: (1) GSE41258 (2) the Molecular Epidemiology of Colorectal Cancer (MECC) study, and (3) GSE39582. Infiltration by 22 immune cell types was inferred from tumor gene expression data, and the association between immune infiltration by each cell type and relapse-free survival was assessed using Cox proportional hazards regression. Within each of the four independent cohorts, CD4+ memory activated T cell (HR: 0.93, 95% CI: 0.90-0.96; FDR = 0.0001) infiltration was associated with longer time to disease relapse, independent of stage, microsatellite instability, and adjuvant therapy. Based on our meta-analysis across the four datasets, 10 innate and adaptive immune cell types associated with disease relapse of which 2 were internally validated using multiplex immunofluorescence. Moreover, immune cell type infiltration was a better predictors of disease relapse than Consensus Molecular Subtype (CMS) and other expression-based biomarkers (Immune-AICMCC:238.1-238.9; CMS-AICMCC: 241.0). These data suggest that transcriptome-derived immune profiles are prognostic indicators of CRC relapse and quantification of both innate and adaptive immune cell types may serve as candidate biomarkers for predicting prognosis and guiding frequency and modality of disease surveillance.

Addressing resistance to immune checkpoint inhibitor therapy: an urgent unmet need

Immune checkpoint inhibitors (ICIs) have revolutionized the treatment of various cancers by reversing the immunosuppressive mechanisms employed by tumors to restore anticancer immunity. Although ICIs have demonstrated substantial clinical efficacy, patient response can vary in depth and duration, and many do not respond at all or eventually develop resistance. ICI resistance mechanisms can be tumor-intrinsic, related to the tumor microenvironment or patient-specific factors. Multiple resistance mechanisms may be present within one tumor subtype, or heterogeneity exists among patients with the same tumor type. Consequently, designing effective combination treatment strategies is challenging. This review will discuss ICI resistance mechanisms, and summarize findings from key preclinical and clinical trials of ICIs, to identify potential treatment strategies or pathways to overcome ICI resistance.

SITC cancer immunotherapy resource document: a compass in the land of biomarker discovery

Since the publication of the Society for Immunotherapy of Cancer's (SITC) original cancer immunotherapy biomarkers resource document, there have been remarkable breakthroughs in cancer immunotherapy, in particular the development and approval of immune checkpoint inhibitors, engineered cellular therapies, and tumor vaccines to unleash antitumor immune activity. The most notable feature of these breakthroughs is the achievement of durable clinical responses in some patients, enabling long-term survival. These durable responses have been noted in tumor types that were not previously considered immunotherapy-sensitive, suggesting that all patients with cancer may have the potential to benefit from immunotherapy. However, a persistent challenge in the field is the fact that only a minority of patients respond to immunotherapy, especially those therapies that rely on endogenous immune activation such as checkpoint inhibitors and vaccination due to the complex and heterogeneous immune escape mechanisms which can develop in each patient. Therefore, the development of robust biomarkers for each immunotherapy strategy, enabling rational patient selection and the design of precise combination therapies, is key for the continued success and improvement of immunotherapy. In this document, we summarize and update established biomarkers, guidelines, and regulatory considerations for clinical immune biomarker development, discuss well-known and novel technologies for biomarker discovery and validation, and provide tools and resources that can be used by the biomarker research community to facilitate the continued development of immuno-oncology and aid in the goal of durable responses in all patients.

Society for Immunotherapy of Cancer clinical and biomarkers data sharing resource document: Volume II-practical challenges

The development of strongly predictive validated biomarkers is essential for the field of immuno-oncology (IO) to advance. The highly complex, multifactorial data sets required to develop these biomarkers necessitate effective, responsible data-sharing efforts in order to maximize the scientific knowledge and utility gained from their collection. While the sharing of clinical- and safety-related trial data has already been streamlined to a large extent, the sharing of biomarker-aimed clinical trial derived data and data sets has been met with a number of hurdles that have impaired the progression of biomarkers from hypothesis to clinical use. These hurdles include technical challenges associated with the infrastructure, technology, workforce, and sustainability required for clinical biomarker data sharing. To provide guidance and assist in the navigation of these challenges, the Society for Immunotherapy of Cancer (SITC) Biomarkers Committee convened to outline the challenges that researchers currently face, both at the conceptual level (Volume I) and at the technical level (Volume II). The committee also suggests possible solutions to these problems in the form of professional standards and harmonized requirements for data sharing, assisting in continued progress toward effective, clinically relevant biomarkers in the IO setting.

Society for Immunotherapy of Cancer clinical and biomarkers data sharing resource document: Volume I-conceptual challenges

The sharing of clinical trial data and biomarker data sets among the scientific community, whether the data originates from pharmaceutical companies or academic institutions, is of critical importance to enable the development of new and improved cancer immunotherapy modalities. Through data sharing, a better understanding of current therapies in terms of their efficacy, safety and biomarker data profiles can be achieved. However, the sharing of these data sets involves a number of stakeholder groups including patients, researchers, private industry, scientific journals and professional societies. Each of these stakeholder groups has differing interests in the use and sharing of clinical trial and biomarker data, and the conflicts caused by these differing interests represent significant obstacles to effective, widespread sharing of data. Thus, the Society for Immunotherapy of Cancer (SITC) Biomarkers Committee convened to identify the current barriers to biomarker data sharing in immuno-oncology (IO) and to help in establishing professional standards for the responsible sharing of clinical trial data. The conclusions of the committee are described in two position papers: Volume I-conceptual challenges and Volume II-practical challenges, the first of which is presented in this manuscript. Additionally, the committee suggests actions by key stakeholders in the field (including organizations and professional societies) as the best path forward, encouraging the cultural shift needed to ensure responsible data sharing in the IO research setting.

Progress Toward Identifying Exact Proxies for Predicting Response to Immunotherapies

Clinical value and utility of checkpoint inhibitors, a drug class targeting adaptive immune suppression pathways (PD-1, PDL-1, and CTLA-4), is growing rapidly and maintains status of a landmark achievement in oncology. Their efficacy has transformed life expectancy in multiple deadly cancer types (melanoma, lung cancer, renal/urothelial carcinoma, certain colorectal cancers, lymphomas, etc.). Despite significant clinical development efforts, therapeutic indication of approved checkpoint inhibitors are not as wide as the oncology community and patients would like them to be, potentially bringing into question their universal efficacy across tumor histologies. With the main goal of expanding immunotherapy applications, identifying of biomarkers to accurately predict therapeutic response and treatment related side-effects are a paramount need in the field. Specificities surrounding checkpoint inhibitors in clinic, such as unexpected tumor response patterns (pseudo- and hyper-progression), late responders, as well as specific immune mediated toxicities, complicate the management of patients. They stem from the complexities and dynamics of the tumor/host immune interactions, as well as baseline tumor biology. Search for clinically effective biomarkers therefore calls for a holistic approach, rather than implementation of a single analyte. The goal is to achieve dynamic and comprehensive acquisition, analyses and interpretation of immunological and biologic information about the tumor and the immune system, and to compute these parameters into an actionable, maximally predictive value at the individual patient level. Limitation delaying swift incorporation of validated immuno-oncology biomarkers span from standardized biospecimens acquisition and processing, selection of proficient biomarker discovery and validation methods, to establishing multidisciplinary consortiums and data sharing platforms. Multi-disciplinary efforts have already yielded some approved (PDL-1 and MSI-status) and other advanced tests (TMB, neoantigen pattern, and TIL infiltration rate). Importantly, clinical trial taskforces now recognize the imperative of the biomarker-driven trial design and execution, to enable translating biomarker discoveries into the clinical setting. This will ensure we utilize the “conspiracy” between the peripheral and intra-tumoral dynamic markers in shaping responses to checkpoint blockade, for the ultimate patient benefit.

Bioinformatics Tools and Resources for Cancer Immunotherapy Study

In recent years, cancer immunotherapy has emerged as a highly promising approach to treat patients with cancer, as the patient's own immune system is harnessed to attack cancer cells. However, the application of these approaches is still limited to a minority of patients with cancer and it is difficult to predict which patients will derive the greatest clinical benefit.One of the challenges faced by the biomedical community in the search of more effective biomarkers is the fact that translational research efforts involve collecting and accessing data at many different levels: from the type of material examined (e.g., cell line, animal models, clinical samples) to multiple data type (e.g., pharmacodynamic markers, genetic sequencing data) to the scale of a study (e.g., small preclinical study, moderate retrospective study on stored specimen sets, clinical trials with large cohorts).This chapter reviews several publicly available bioinformatics tools and data resources for high throughput molecular analyses applied to a range of data types, including those generated from microarray, whole-exome sequencing (WES), RNA-seq, DNA copy number, and DNA methylation assays, that are extensively used for integrative multidimensional data analysis and visualization.

The Paradox of Cancer Immune Exclusion: Immune Oncology Next Frontier

Checkpoint inhibitor therapy (CIT) has revolutionized cancer treatment but it has also reached a standstill when an absent dialog between cancer and immune cells makes it irrelevant. This occurs with high prevalence in the context of "immune silent" and, even perhaps, "immune-excluded" tumors. The latter are characterized by T cells restricted to the periphery of cancer nests. Since in either case T cells do not come in direct contact with most cancer cells, CIT rests immaterial. Adoptive cell therapy (ACT), may also be affected by limited access to antigen-bearing cancer cells. While lack of immunogenicity intuitively explains the immune silent phenotype, immune exclusion is perplexing. The presence of T cells at the periphery suggests that chemo-attraction recruits them and an immunogenic stimulus promotes their persistence. However, what stops the T cells from infiltrating the tumors' nests and reaching the germinal center (GC)? Possibly, a concentric gradient of increased chemo-repulsion or decreased chemo-attraction demarcates an abrupt "do not trespass" warning. Various hypotheses suggest physical or functional barriers but no definitive consensus exists over the weight that each plays in human cancers. On one hand, it could be hypothesized that the intrinsic biology of cancer cells may degenerate from a "cancer stem cell" (CSC)-like phenotype in the GC toward a progressively more immunogenic phenotype prone to immunogenic cell death (ICD) at the periphery. On the other hand, the intrinsic biology of the cancer cells may not change but it is the disorderly architecture of the tumor microenvironment (TME) that alters in a centripetal direction cancer cell metabolism, both directly and indirectly, the function of surrounding stromal cells. In this chapter, we examine whether the paradoxical exclusion of T cells from tumors may serve as a model to understand the requirements for tumor immune infiltration and, correspondingly, we put forth strategies to restore the dialog between immune cells and cancer to enhance the effectiveness of immune oncology (IO) approaches.

The use of digital pathology and image analysis in clinical trials

Digital pathology and image analysis potentially provide greater accuracy, reproducibility and standardisation of pathology-based trial entry criteria and endpoints, alongside extracting new insights from both existing and novel features. Image analysis has great potential to identify, extract and quantify features in greater detail in comparison to pathologist assessment, which may produce improved prediction models or perform tasks beyond manual capability. In this article, we provide an overview of the utility of such technologies in clinical trials and provide a discussion of the potential applications, current challenges, limitations and remaining unanswered questions that require addressing prior to routine adoption in such studies. We reiterate the value of central review of pathology in clinical trials, and discuss inherent logistical, cost and performance advantages of using a digital approach. The current and emerging regulatory landscape is outlined. The role of digital platforms and remote learning to improve the training and performance of clinical trial pathologists is discussed. The impact of image analysis on quantitative tissue morphometrics in key areas such as standardisation of immunohistochemical stain interpretation, assessment of tumour cellularity prior to molecular analytical applications and the assessment of novel histological features is described. The standardisation of digital image production, establishment of criteria for digital pathology use in pre-clinical and clinical studies, establishment of performance criteria for image analysis algorithms and liaison with regulatory bodies to facilitate incorporation of image analysis applications into clinical practice are key issues to be addressed to improve digital pathology incorporation into clinical trials.