Publications by authors named "Alessandra Cesano"

71 Publications

Next Generation Imaging Techniques to Define Immune Topographies in Solid Tumors.

Front Immunol 2020 27;11:604967. Epub 2021 Jan 27.

Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany.

In recent years, cancer immunotherapy experienced remarkable developments and it is nowadays considered a promising therapeutic frontier against many types of cancer, especially hematological malignancies. However, in most types of solid tumors, immunotherapy efficacy is modest, partly because of the limited accessibility of lymphocytes to the tumor core. This immune exclusion is mediated by a variety of physical, functional and dynamic barriers, which play a role in shaping the immune infiltrate in the tumor microenvironment. At present there is no unified and integrated understanding about the role played by different postulated models of immune exclusion in human solid tumors. Systematically mapping immune landscapes or "topographies" in cancers of different histology is of pivotal importance to characterize spatial and temporal distribution of lymphocytes in the tumor microenvironment, providing insights into mechanisms of immune exclusion. Spatially mapping immune cells also provides quantitative information, which could be informative in clinical settings, for example for the discovery of new biomarkers that could guide the design of patient-specific immunotherapies. In this review, we aim to summarize current standard and next generation approaches to define Cancer Immune Topographies based on published studies and propose future perspectives.
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http://dx.doi.org/10.3389/fimmu.2020.604967DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7873485PMC
January 2021

Diminished cytokine-induced Jak/STAT signaling is associated with rheumatoid arthritis and disease activity.

PLoS One 2021 14;16(1):e0244187. Epub 2021 Jan 14.

The Feinstein Institute for Medical Research and Northwell Health, Manhasset, New York, United States of America.

Rheumatoid arthritis (RA) is a systemic and incurable autoimmune disease characterized by chronic inflammation in synovial lining of joints. To identify the signaling pathways involved in RA, its disease activity, and treatment response, we adapted a systems immunology approach to simultaneously quantify 42 signaling nodes in 21 immune cell subsets (e.g., IFNα→p-STAT5 in B cells) in peripheral blood mononuclear cells (PBMC) from 194 patients with longstanding RA (including 98 patients before and after treatment), and 41 healthy controls (HC). We found multiple differences between patients with RA compared to HC, predominantly in cytokine-induced Jak/STAT signaling in many immune cell subsets, suggesting pathways that may be associated with susceptibility to RA. We also found that high RA disease activity, compared to low disease activity, was associated with decreased (e.g., IFNα→p-STAT5, IL-10→p-STAT1) or increased (e.g., IL-6→STAT3) response to stimuli in multiple cell subsets. Finally, we compared signaling in patients with established, refractory RA before and six months after initiation of methotrexate (MTX) or TNF inhibitors (TNFi). We noted significant changes from pre-treatment to post-treatment in IFNα→p-STAT5 signaling and IL-10→p-STAT1 signaling in multiple cell subsets; these changes brought the aberrant RA signaling profiles toward those of HC. This large, comprehensive functional signaling pathway study provides novel insights into the pathogenesis of RA and shows the potential of quantification of cytokine-induced signaling as a biomarker of disease activity or treatment response.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0244187PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7808603PMC
April 2021

CTLA-4 blockade and interferon-α induce proinflammatory transcriptional changes in the tumor immune landscape that correlate with pathologic response in melanoma.

PLoS One 2021 11;16(1):e0245287. Epub 2021 Jan 11.

Department of Cutaneous Oncology and Immunology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, United States of America.

Patients with locally/regionally advanced melanoma were treated with neoadjuvant combination immunotherapy with high-dose interferon α-2b (HDI) and ipilimumab in a phase I clinical trial. Tumor specimens were obtained prior to the initiation of neoadjuvant therapy, at the time of surgery and progression if available. In this study, gene expression profiles of tumor specimens (N = 27) were investigated using the NanoString nCounter® platform to evaluate associations with clinical outcomes (pathologic response, radiologic response, relapse-free survival (RFS), and overall survival (OS)) and define biomarkers associated with tumor response. The Tumor Inflammation Signature (TIS), an 18-gene signature that enriches for response to Programmed cell death protein 1 (PD-1) checkpoint blockade, was also evaluated for association with clinical response and survival. It was observed that neoadjuvant ipilimumab-HDI therapy demonstrated an upregulation of immune-related genes, chemokines, and transcription regulator genes involved in immune cell activation, function, or cell proliferation. Importantly, increased expression of baseline pro-inflammatory genes CCL19, CD3D, CD8A, CD22, LY9, IL12RB1, C1S, C7, AMICA1, TIAM1, TIGIT, THY1 was associated with longer OS (p < 0.05). In addition, multiple genes that encode a component or a regulator of the extracellular matrix such as MMP2 and COL1A2 were identified post-treatment as being associated with longer RFS and OS. In all baseline tissues, high TIS scores were associated with longer OS (p = 0.0166). Also, downregulated expression of cell proliferation-related genes such as CUL1, CCND1 and AAMP at baseline was associated with pathological and radiological response (unadjusted p < 0.01). In conclusion, we identified numerous genes that play roles in multiple biological pathways involved in immune activation, immune suppression and cell proliferation correlating with pathological/radiological responses following neoadjuvant immunotherapy highlighting the complexity of immune responses modulated by immunotherapy. Our observations suggest that TIS may be a useful biomarker for predicting survival outcomes with combination immunotherapy.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0245287PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7799833PMC
January 2021

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

J Immunother Cancer 2020 12;8(2)

John van Geest Cancer Research Centre, Nottingham Trent University, Nottingham, Nottinghamshire, UK

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.
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http://dx.doi.org/10.1136/jitc-2020-001472DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7745522PMC
December 2020

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

J Immunother Cancer 2020 10;8(2)

ESSA Pharma Inc, South San Francisco, California, USA

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.
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http://dx.doi.org/10.1136/jitc-2020-001389DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7604864PMC
October 2020

Improving the therapeutic index in adoptive cell therapy: key factors that impact efficacy.

J Immunother Cancer 2020 10;8(2)

Research & Development, Refuge Biotechnologies, Menlo Park, California, USA

The therapeutic index (TI) is a quantitative assessment of a drug safety proportional to its effectiveness. The estimation is intuitive when the engagement of the product with its target is dependent on stable chemistry and predictable pharmacokinetics as is the case for small molecules or antibodies. But for therapeutics with complex biodistribution and context-dependent potency such as adoptive cell therapy (ACT) products, TI estimations need to consider a broader array of factors. These include product-dependent variability such as functional fitness, unpredictable pharmacokinetics due to non-specific trapping, sequestration and extravasation into normal tissues and variable rates of in vivo expansion. In the case of solid malignancies, additional modifiers dependent on individual tumor immune biology may affect pharmacodynamics, including differential trafficking to benign compared with cancer tissue, hampered engagement with target cells, immune suppression and cellular dysfunction due to unfavorable metabolic conditions. Here, we propose a patient-specific assessment of factors affecting on-tumor from off-tumor activity in disparate immunologic environments that impact ACT's clinical efficacy and may favorably balance the TI. for ACT products.
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http://dx.doi.org/10.1136/jitc-2020-001619DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7539608PMC
October 2020

A 3-gene signature based on MYC, BCL-2 and NFKBIA improves risk stratification in diffuse large B-cell lymphoma.

Haematologica 2020 08 13. Epub 2020 Aug 13.

Division of Diagnostic Haematopathology, European Institute of Oncology IRCCS, Milan, Italy.

Recent randomized trials focused on gene expression-based determination of the cell of origin in diffuse large B-cell lymphoma could not show significant improvements by adding novel agents to standard chemoimmunotherapy. The aim of this study was the identification of a gene signature able to refine current prognostication algorithms and applicable to clinical practice. Here we used a targeted gene expression profiling panel combining the Lymph2Cx signature for cell of origin classification with additional targets including MYC, BCL-2 and NFKBIA, in 186 patients from 2 randomized trials (discovery cohort) (NCT00355199 and NCT00499018). Data were validated in 3 independent series (2 large public datasets and a real-life cohort). By integrating the cell of origin, MYC/BCL-2 double expressor status and NFKBIA expression, we defined a 3-gene signature combining MYC, BCL-2 and NFKBIA (MBN-signature), which outperformed the MYC/BCL-2 double expressor status in multivariate analysis, and allowed further risk stratification within the germinal center B-cell/unclassified subset. The high-risk (MBN Sig-high) subgroup identified the vast majority of double hit cases and a significant fraction of Activated B-Cell-derived diffuse large B-cell lymphomas. These results were validated in 3 independent series including a cohort from the REMoDL-B trial, where, in an exploratory ad hoc analysis, the addition of bortezomib in the MBN Sig-high subgroup provided a progression free survival advantage compared with standard chemoimmunotherapy. These data indicate that a simple 3-gene signature based on MYC, BCL-2 and NFKBIA could refine the prognostic stratification in diffuse large B-cell lymphoma, and might be the basis for future precision-therapy approaches.
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http://dx.doi.org/10.3324/haematol.2019.236455DOI Listing
August 2020

Immune landscapes predict chemotherapy resistance and immunotherapy response in acute myeloid leukemia.

Sci Transl Med 2020 06;12(546)

John van Geest Cancer Research Centre, Nottingham Trent University, Nottingham NG11 8NS, UK.

Acute myeloid leukemia (AML) is a molecularly and clinically heterogeneous hematological malignancy. Although immunotherapy may be an attractive modality to exploit in patients with AML, the ability to predict the groups of patients and the types of cancer that will respond to immune targeting remains limited. This study dissected the complexity of the immune architecture of AML at high resolution and assessed its influence on therapeutic response. Using 442 primary bone marrow samples from three independent cohorts of children and adults with AML, we defined immune-infiltrated and immune-depleted disease classes and revealed critical differences in immune gene expression across age groups and molecular disease subtypes. Interferon (IFN)-γ-related mRNA profiles were predictive for both chemotherapy resistance and response of primary refractory/relapsed AML to flotetuzumab immunotherapy. Our compendium of microenvironmental gene and protein profiles provides insights into the immuno-biology of AML and could inform the delivery of personalized immunotherapies to IFN-γ-dominant AML subtypes.
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http://dx.doi.org/10.1126/scitranslmed.aaz0463DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7427158PMC
June 2020

The Paradox of Cancer Immune Exclusion: Immune Oncology Next Frontier.

Cancer Treat Res 2020 ;180:173-195

Refuge Biotechnologies, Inc., Menlo Park, CA, USA.

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.
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http://dx.doi.org/10.1007/978-3-030-38862-1_6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7423459PMC
September 2020

The Biology of Immune-Active Cancers and Their Regulatory Mechanisms.

Cancer Treat Res 2020 ;180:149-172

Allogene, South San Francisco, CA, 94080, USA.

The development of cancer results from the evolutionary balance between the proliferating aptitude of cancer cells and the response of the host's tissues. Some cancers are characterized by genetic instability dependent upon impaired DNA repair mechanisms that lead to the chaotic disruption of multiple cellular functions often in excess of the cancer survival needs and may exert broad effects on surrounding tissues, some beneficial and some detrimental to cancer growth. Among them, inflammatory processes that accompany wound healing may initiate a reaction of the host against the neo-formation. This is possibly triggered by the release by dying cancer cells of molecules known as Damage-Associated Molecular Patterns (DAMPs) following a process termed Immunogenic Cell Death (ICD) that initiates an immune response through innate and adaptive mechanisms. Indeed, analysis of large cancer data sets has shown that ICD is strictly associated with the activation of other immune effector or immune-regulatory pathways. Here, we will describe how immune activation and compensatory immune-regulatory mechanisms balance anti-cancer immune surveillance and the roles that innate and adaptive immunity play including the weight that neo-epitopes may exert as initiators and sculptors of high-affinity memory and effector immune responses against cancer. We will discuss the evolutionary basis for the existence of immune checkpoints and how several theories raised to explain cancer resistance to immunotherapy represent a facet of a similar evolutionary phenomenon that we described in the Theory of Everything. We will show how the biology of immunogenicity and counterbalancing immune regulation is widespread across cancers independent of their ontogenesis while subtle idiosyncratic differences are discernible among them. Finally, we will suggest that overcoming immune resistance implies distinct approaches relevant to the immune context of individual cancers.
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http://dx.doi.org/10.1007/978-3-030-38862-1_5DOI Listing
September 2020

Consensus guidelines for the definition, detection and interpretation of immunogenic cell death.

J Immunother Cancer 2020 03;8(1)

Program of Immunology and Immunotherapy, Centro de Investigación Médica Aplicada (CIMA), University of Navarra, Pamplona, Spain.

Cells succumbing to stress via regulated cell death (RCD) can initiate an adaptive immune response associated with immunological memory, provided they display sufficient antigenicity and adjuvanticity. Moreover, multiple intracellular and microenvironmental features determine the propensity of RCD to drive adaptive immunity. Here, we provide an updated operational definition of immunogenic cell death (ICD), discuss the key factors that dictate the ability of dying cells to drive an adaptive immune response, summarize experimental assays that are currently available for the assessment of ICD in vitro and in vivo, and formulate guidelines for their interpretation.
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http://dx.doi.org/10.1136/jitc-2019-000337DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7064135PMC
March 2020

The tumor inflammation signature (TIS) is associated with anti-PD-1 treatment benefit in the CERTIM pan-cancer cohort.

J Transl Med 2019 11 4;17(1):357. Epub 2019 Nov 4.

University Paris Descartes, Paris, France.

Background: The 18-gene tumor inflammation signature (TIS) is a clinical research assay that enriches for clinical benefit to immune checkpoint blockade. We evaluated its ability to predict clinical benefit of immunotherapy in cancer patients treated with PD-1 checkpoint inhibitors in routine clinical care.

Methods: The CERTIM cohort is a prospective cohort which includes patients receiving immune checkpoint inhibitors in Cochin University hospital. RNA extracted from 58 archival formalin fixed paraffin embedded tumor blocks (including 38 lung cancers, 5 melanomas, 10 renal carcinomas, 4 urothelial carcinomas and 1 colon carcinoma) was hybridized to a beta version of the NanoString PanCancer IO360™ CodeSet using nCounter technology. Gene expression signatures were correlated with tumor responses (by RECIST criteria) and overall survival. PD-L1 immunostaining on tumor cells was assessed in 37 non-small cell lung cancer (NSCLC) samples and tumor mutational burden (TMB) measured by whole exome sequencing in 19 of these.

Results: TIS scores were significantly associated with complete or partial response to anti-PD-1 treatment in the whole cohort (odds ratio = 2.64, 95% CI [1.4; 6.0], p = 0.008), as well as in the NSCLC population (odds ratio = 3.27, 95% CI [1.2; 11.6], p = 0.03). Patients whose tumor had a high TIS score (upper tertile) showed prolonged overall survival compared to patients whose tumor had lower TIS scores, both in the whole cohort (hazard ratio = 0.37, 95% CI [0.18, 0.76], p = 0.005) and in the NSCLC population (hazard ratio = 0.36, 95% CI [0.14, 0.90], p = 0.02). In the latter, the TIS score was independent from either PD-L1 staining on tumor cells (spearman coefficient 0.2) and TMB (spearman coefficient - 0.2).

Conclusions: These results indicate that validated gene expression assay measuring the level of tumor microenvironment inflammation such as TIS, are accurate and independent predictive biomarkers and can be easily implemented in the clinical practice.
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http://dx.doi.org/10.1186/s12967-019-2100-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6829827PMC
November 2019

Development of Gene Expression-Based Biomarkers on the nCounter Platform for Immuno-Oncology Applications.

Methods Mol Biol 2020 ;2055:273-300

NanoString Technologies, Inc., Seattle, WA, USA.

Biomarkers based on transcriptional profiling can be useful in the measurement of complex and/or dynamic physiological states where other profiling strategies such as genomic or proteomic characterization are not able to adequately measure the biology. One particular advantage of transcriptional biomarkers is the ease with which they can be measured in the clinical setting using robust platforms such as the NanoString nCounter system. The nCounter platform enables digital quantitation of multiplexed RNA from small amounts of blood, formalin-fixed, paraffin-embedded tumors, or other such biological samples that are readily available from patients, and the chapter uses it as the primary example for diagnostic assay development. However, development of diagnostic assays based on RNA biomarkers on any platform requires careful consideration of all aspects of the final clinical assay a priori, as well as design and execution of the development program in a way that will maximize likelihood of future success. This chapter introduces transcriptional biomarkers and provides an overview of the design and development process that will lead to a locked diagnostic assay that is ready for validation of clinical utility.
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http://dx.doi.org/10.1007/978-1-4939-9773-2_13DOI Listing
November 2020

Status of Immune Oncology: Challenges and Opportunities.

Methods Mol Biol 2020 ;2055:3-21

Cancer Diagnosis Program, National Cancer Institute, NIH, Bethesda, MD, USA.

This volume is intended to review the methods used to identify biomarkers predictive of cancer responsiveness to immunotherapy. The successful development of clinically actionable biomarkers depends upon three features: (a) their biological role with respect to malignant transformation and tumor progression; (b) the ability to detect them with robust, reliable, and clinically applicable assays; and (c) their prognostic or predictive value, as validated in clinical trials.Identifying biomarkers that have predictive value for patient selection based on the likelihood of benefiting from anticancer immunotherapy is a lengthy and complex process. To date, few predictive biomarkers for anticancer immunotherapy have been robustly analytically and clinically validated (i.e., PD-L1 expression as measured by IHC assays and microsatellite instability (MSI)/dMMR as measured by PCR or IHC, respectively).This introductory chapter to this book focuses on scientific and technical aspects relevant to the identification and validation of predictive biomarkers for immunotherapy. We emphasize that methods should address both the biology of the tumor and the tumor microenvironment. Moreover, the identification of biomarkers requires highly sensitive, multiplexed, comprehensive techniques, especially for application in clinical care. Thus, in this chapter, we will define the outstanding questions related to the immune biology of cancer as a base for development of the biomarkers and assays using diverse methodologies. These biomarkers will likely be identified through research that integrates conventional immunological approaches along with high-throughput genomic and proteomic screening and the host immune response of individual patients that relates to individual tumor biology and immune drugs' mechanism of action.Checkpoint inhibitor therapy (CIT) is by now an accepted modality of cancer treatment. However, immune resistance is common, and most patients do not benefit from the treatment. The reasons for resistance are diverse, and approaches to circumvent it need to consider genetic, biologic, and environmental factors that affect anticancer immune response. Here, we propose to systematically address fundamental concepts based on the premise that malignant cells orchestrate their surroundings by interacting with innate and adaptive immune sensors. This principle applies to most cancers and governs their evolution in the immune-competent host. Understanding the basic requirement(s) for this evolutionary process will guide biomarker discovery and validation and ultimately guide to effective therapeutic choices. This volume will also discuss novel biomarker approaches aimed at informing an effective assay development from a mechanistic point of view, as well as the clinical implementation (i.e., patient enrichment) for immune therapies.
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http://dx.doi.org/10.1007/978-1-4939-9773-2_1DOI Listing
November 2020

Toward a comprehensive view of cancer immune responsiveness: a synopsis from the SITC workshop.

J Immunother Cancer 2019 05 22;7(1):131. Epub 2019 May 22.

Rutgers University, New Brunswick, NJ, USA.

Tumor immunology has changed the landscape of cancer treatment. Yet, not all patients benefit as cancer immune responsiveness (CIR) remains a limitation in a considerable proportion of cases. The multifactorial determinants of CIR include the genetic makeup of the patient, the genomic instability central to cancer development, the evolutionary emergence of cancer phenotypes under the influence of immune editing, and external modifiers such as demographics, environment, treatment potency, co-morbidities and cancer-independent alterations including immune homeostasis and polymorphisms in the major and minor histocompatibility molecules, cytokines, and chemokines. Based on the premise that cancer is fundamentally a disorder of the genes arising within a cell biologic process, whose deviations from normality determine the rules of engagement with the host's response, the Society for Immunotherapy of Cancer (SITC) convened a task force of experts from various disciplines including, immunology, oncology, biophysics, structural biology, molecular and cellular biology, genetics, and bioinformatics to address the complexity of CIR from a holistic view. The task force was launched by a workshop held in San Francisco on May 14-15, 2018 aimed at two preeminent goals: 1) to identify the fundamental questions related to CIR and 2) to create an interactive community of experts that could guide scientific and research priorities by forming a logical progression supported by multiple perspectives to uncover mechanisms of CIR. This workshop was a first step toward a second meeting where the focus would be to address the actionability of some of the questions identified by working groups. In this event, five working groups aimed at defining a path to test hypotheses according to their relevance to human cancer and identifying experimental models closest to human biology, which include: 1) Germline-Genetic, 2) Somatic-Genetic and 3) Genomic-Transcriptional contributions to CIR, 4) Determinant(s) of Immunogenic Cell Death that modulate CIR, and 5) Experimental Models that best represent CIR and its conversion to an immune responsive state. This manuscript summarizes the contributions from each group and should be considered as a first milestone in the path toward a more contemporary understanding of CIR. We appreciate that this effort is far from comprehensive and that other relevant aspects related to CIR such as the microbiome, the individual's recombined T cell and B cell receptors, and the metabolic status of cancer and immune cells were not fully included. These and other important factors will be included in future activities of the taskforce. The taskforce will focus on prioritization and specific actionable approach to answer the identified questions and implementing the collaborations in the follow-up workshop, which will be held in Houston on September 4-5, 2019.
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http://dx.doi.org/10.1186/s40425-019-0602-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6529999PMC
May 2019

Immune profiling of pre- and post-treatment breast cancer tissues from the SWOG S0800 neoadjuvant trial.

J Immunother Cancer 2019 04 10;7(1):88. Epub 2019 Apr 10.

Yale Cancer Center, Breast Medical Oncology, Yale School of Medicine, 300 George Street, Suite 120, Rm133, New Haven, CT, 06511, USA.

Background: How the immune microenvironment changes during neoadjuvant chemotherapy of primary breast cancer is not well understood.

Methods: We analyzed pre- and post-treatment samples from 60 patients using the NanoString PanCancer IO360™ assay to measure the expression of 750 immune-related genes corresponding to 14 immune cell types and various immune functions, and assessed TIL counts and PD-L1 protein expression by immunohistochemistry. Treatment associated changes in gene expression levels were compared using t-test with Bonferroni correction. TIL count, PD-L1 protein and immune metagenes were compared using Wilcoxon test. Baseline immune markers were correlated with pathologic complete response (pCR) using estrogen receptor and treatment arm adjusted logistic regression.

Results: At baseline, high TIL counts and high expression of chemoattractant cytokines (CCL21, CCL19) and cytotoxic T cell markers were associated with higher pCR rate. High expression of stromal genes (VEGFB, TGFB3, PDGFB, FGFR1, IGFR1), mast and myeloid inflammatory cell metagenes, stem cell related genes (CD90, WNT11, CTNNB1) and CX3CR1, and IL11RA were associated with residual disease (RD). After treatment, in cases with pCR, TIL counts and most immune genes decreased significantly. Among RD cases, TIL counts and PD-L1 expression did not change but cellular stress and hypoxia associated genes (DUSP1, EGR1), and IL6, CD36, CXCL2, CD69 and the IL8/VEGF metagene increased.

Conclusions: Activated T cells in the tumor microenvironment are associated with pCR whereas stromal functions are associated with residual disease. Most immune functions decrease during neoadjuvant chemotherapy but several immunotherapy targets (PD-L1, IL6, IL8) remain expressed in RD suggesting potential therapeutic strategies.
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http://dx.doi.org/10.1186/s40425-019-0563-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6457012PMC
April 2019

A gene expression assay for simultaneous measurement of microsatellite instability and anti-tumor immune activity.

J Immunother Cancer 2019 01 21;7(1):15. Epub 2019 Jan 21.

NanoString Technologies®, Inc, 530 Fairview Ave. N, Seattle, Washington, 98109, USA.

Background: Clinical benefit from checkpoint inhibitors has been associated in a tumor-agnostic manner with two main tumor traits. The first is tumor antigenicity, which is typically measured by tumor mutation burden, microsatellite instability (MSI), or Mismatch Repair Deficiency using gene sequence platforms and/or immunohistochemistry. The second is the presence of a pre-existing adaptive immune response, typically measured by immunohistochemistry (e.g. single analyte PD-L1 expression) and/or gene expression signatures (e.g. tumor "inflamed" phenotype). These two traits have been shown to provide independent predictive information. Here we investigated the potential of using gene expression to predict tumor MSI, thus enabling the measurement of both tumor antigenicity and the level of tumor inflammation in a single assay, possibly reducing sample requirement, turn-around time, and overall cost.

Methods: Using The Cancer Genome Atlas RNA-seq datasets with the greatest MSI-H incidence, i.e. those from colon (n = 208), stomach (n = 269), and endometrial (n = 241) cancers, we trained an algorithm to predict tumor MSI from under-expression of the mismatch repair genes MLH1, PMS2, MSH2, and MSH6 and from 10 additional genes with strong pan-cancer associations with tumor hypermutation. The algorithms were validated on the NanoString nCounter™ platform in independent cohorts of colorectal (n = 52), endometrial (n = 11), and neuroendocrine (n = 4) tumors pre-characterized using the MMR immunohistochemistry assay.

Results: In the validation cohorts, the algorithm showed high prediction accuracy of tumor MSI status, with sensitivity of at least 88% attained at thresholds chosen to achieve 100% specificity. Furthermore, MSI status was compared to the Tumor Inflammation Signature (TIS), an analytically validated diagnostic assay which measures a suppressed adaptive immune response in the tumor and enriches for response to immune checkpoint blockade. TIS score was largely independent of MSI status, suggesting that measuring both parameters may identify more patients that would respond to immune checkpoint blockade than either assay alone.

Conclusions: Development of a gene expression signature of MSI status raises the possibility of a combined diagnostic assay on a single platform which measures both tumor antigenicity and presence of a suppressed adaptive immune response. Such an assay would have significant advantages over multi-platform assays for both ease of use and turnaround time and could lead to a diagnostic test with improved clinical performance.
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http://dx.doi.org/10.1186/s40425-018-0472-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6341623PMC
January 2019

Pan-cancer adaptive immune resistance as defined by the Tumor Inflammation Signature (TIS): results from The Cancer Genome Atlas (TCGA).

J Immunother Cancer 2018 06 22;6(1):63. Epub 2018 Jun 22.

NanoString Technologies Inc Seattle WA USA

The Tumor Inflammation Signature (TIS) is an investigational use only (IUO) 18-gene signature that measures a pre-existing but suppressed adaptive immune response within tumors. The TIS has been shown to enrich for patients who respond to the anti-PD1 agent pembrolizumab. To explore this immune phenotype within and across tumor types, we applied the TIS algorithm to over 9000 tumor gene expression profiles downloaded from The Cancer Genome Atlas (TCGA). As expected based on prior evidence, tumors with known clinical sensitivity to anti-programmed cell death protein 1 (PD-1) blockade had higher average TIS scores. Furthermore, TIS scores were more variable within than between tumor types, and within each tumor type a subset of patients with elevated scores was identifiable although with different prevalence associated with each tumor type, the latter consistent with the observed clinical responsiveness to anti PD-1 blockade. Notably, TIS scores only minimally correlated with mutation load in most tumors and ranking tumors by median TIS score showed differing association to clinical sensitivity to PD-1/PD-1 ligand 1 (PD-L1) blockade than ranking of the same tumors by mutation load. The expression patterns of the TIS algorithm genes were conserved across tumor types yet appeared to be minimally prognostic in most cancers, consistent with the TIS score serving as a pan-cancer measurement of the inflamed tumor phenotype. Characterization of the prevalence and variability of TIS will lead to increased understanding of the immune status of untreated tumors and may lead to improved indication selection for testing immunotherapy agents.
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http://dx.doi.org/10.1186/s40425-018-0367-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6013904PMC
June 2018

Immune oncology, immune responsiveness and the theory of everything.

J Immunother Cancer 2018 06 5;6(1):50. Epub 2018 Jun 5.

Immune-Oncology Discovery, AbbVie, Redwood City, CA, USA.

Anti-cancer immunotherapy is encountering its own checkpoint. Responses are dramatic and long lasting but occur in a subset of tumors and are largely dependent upon the pre-existing immune contexture of individual cancers. Available data suggest that three landscapes best define the cancer microenvironment: immune-active, immune-deserted and immune-excluded. This trichotomy is observable across most solid tumors (although the frequency of each landscape varies depending on tumor tissue of origin) and is associated with cancer prognosis and response to checkpoint inhibitor therapy (CIT). Various gene signatures (e.g. Immunological Constant of Rejection - ICR and Tumor Inflammation Signature - TIS) that delineate these landscapes have been described by different groups. In an effort to explain the mechanisms of cancer immune responsiveness or resistance to CIT, several models have been proposed that are loosely associated with the three landscapes. Here, we propose a strategy to integrate compelling data from various paradigms into a "Theory of Everything". Founded upon this unified theory, we also propose the creation of a task force led by the Society for Immunotherapy of Cancer (SITC) aimed at systematically addressing salient questions relevant to cancer immune responsiveness and immune evasion. This multidisciplinary effort will encompass aspects of genetics, tumor cell biology, and immunology that are pertinent to the understanding of this multifaceted problem.
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http://dx.doi.org/10.1186/s40425-018-0355-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5989400PMC
June 2018

Bringing the next Generation of Immuno-Oncology Biomarkers to the Clinic.

Biomedicines 2018 Feb 2;6(1). Epub 2018 Feb 2.

NanoString, Inc., Seattle, WA 98109, USA.

The recent successes in the use of immunotherapy to treat cancer have led to a multiplicity of new compounds in development. Novel clinical-grade biomarkers are needed to guide the choice of these agents to obtain the maximal likelihood of patient benefit. Predictive biomarkers for immunotherapy differ from the traditional biomarkers used for targeted therapies: the complexity of the immune response and tumour biology requires a more holistic approach than the use of a single analyte biomarker. This paper reviews novel biomarker approaches for the effective development of immune-oncology therapies, highlighting the promise of the advances in next-generation gene expression profiling that allow biologic information to be efficiently organized and interpreted for a maximum predictive value at the individual patient level.
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http://dx.doi.org/10.3390/biomedicines6010014DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5874671PMC
February 2018

The need for a network to establish and validate predictive biomarkers in cancer immunotherapy.

J Transl Med 2017 11 3;15(1):223. Epub 2017 Nov 3.

Melanoma. Cancer Immunotherapy and Innovative Therapy Unit, Istituto Nazionale Tumori Fondazione "G. Pascale", Via Mariano Semmola, 80131, Naples, Italy.

Immunotherapies have emerged as one of the most promising approaches to treat patients with cancer. Recently, the entire medical oncology field has been revolutionized by the introduction of immune checkpoints inhibitors. Despite success in a variety of malignancies, responses typically only occur in a small percentage of patients for any given histology or treatment regimen. There are also concerns that immunotherapies are associated with immune-related toxicity as well as high costs. As such, identifying biomarkers to determine which patients are likely to derive clinical benefit from which immunotherapy and/or be susceptible to adverse side effects is a compelling clinical and social need. In addition, with several new immunotherapy agents in different phases of development, and approved therapeutics being tested in combination with a variety of different standard of care treatments, there is a requirement to stratify patients and select the most appropriate population in which to assess clinical efficacy. The opportunity to design parallel biomarkers studies that are integrated within key randomized clinical trials could be the ideal solution. Sample collection (fresh and/or archival tissue, PBMC, serum, plasma, stool, etc.) at specific points of treatment is important for evaluating possible biomarkers and studying the mechanisms of responsiveness, resistance, toxicity and relapse. This white paper proposes the creation of a network to facilitate the sharing and coordinating of samples from clinical trials to enable more in-depth analyses of correlative biomarkers than is currently possible and to assess the feasibilities, logistics, and collated interests. We propose a high standard of sample collection and storage as well as exchange of samples and knowledge through collaboration, and envisage how this could move forward using banked samples from completed studies together with prospective planning for ongoing and future clinical trials.
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http://dx.doi.org/10.1186/s12967-017-1325-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5670700PMC
November 2017

Immunotherapy biomarkers 2016: overcoming the barriers.

J Immunother Cancer 2017 03 21;5(1):29. Epub 2017 Mar 21.

Department of Medicine, Surgery and Immunology, University of Pittsburgh Cancer Institute, 5117 Centre Avenue, Pittsburgh, PA, 15213, USA.

This report summarizes the symposium, 'Immunotherapy Biomarkers 2016: Overcoming the Barriers', which was held on April 1, 2016 at the National Institutes of Health in Bethesda, Maryland. The symposium, cosponsored by the Society for Immunotherapy of Cancer (SITC) and the National Cancer Institute (NCI), focused on emerging immunotherapy biomarkers, new technologies, current hurdles to further progress, and recommendations for advancing the field of biomarker development.
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http://dx.doi.org/10.1186/s40425-017-0225-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5359902PMC
March 2017

Validation of biomarkers to predict response to immunotherapy in cancer: Volume I - pre-analytical and analytical validation.

J Immunother Cancer 2016 15;4:76. Epub 2016 Nov 15.

National Cancer Institute, Cancer Diagnosis Program, DCTD, National Institutes of Health, 9609 Medical Center Drive, Bethesda, 20892 MD USA ; Adaptive Biotechnologies, Inc, 1551 Eastlake Ave. E, Seattle, WA 98102 USA.

Immunotherapies have emerged as one of the most promising approaches to treat patients with cancer. Recently, there have been many clinical successes using checkpoint receptor blockade, including T cell inhibitory receptors such as cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) and programmed cell death-1 (PD-1). Despite demonstrated successes in a variety of malignancies, responses only typically occur in a minority of patients in any given histology. Additionally, treatment is associated with inflammatory toxicity and high cost. Therefore, determining which patients would derive clinical benefit from immunotherapy is a compelling clinical question. Although numerous candidate biomarkers have been described, there are currently three FDA-approved assays based on PD-1 ligand expression (PD-L1) that have been clinically validated to identify patients who are more likely to benefit from a single-agent anti-PD-1/PD-L1 therapy. Because of the complexity of the immune response and tumor biology, it is unlikely that a single biomarker will be sufficient to predict clinical outcomes in response to immune-targeted therapy. Rather, the integration of multiple tumor and immune response parameters, such as protein expression, genomics, and transcriptomics, may be necessary for accurate prediction of clinical benefit. Before a candidate biomarker and/or new technology can be used in a clinical setting, several steps are necessary to demonstrate its clinical validity. Although regulatory guidelines provide general roadmaps for the validation process, their applicability to biomarkers in the cancer immunotherapy field is somewhat limited. Thus, Working Group 1 (WG1) of the Society for Immunotherapy of Cancer (SITC) Immune Biomarkers Task Force convened to address this need. In this two volume series, we discuss pre-analytical and analytical (Volume I) as well as clinical and regulatory (Volume II) aspects of the validation process as applied to predictive biomarkers for cancer immunotherapy. To illustrate the requirements for validation, we discuss examples of biomarker assays that have shown preliminary evidence of an association with clinical benefit from immunotherapeutic interventions. The scope includes only those assays and technologies that have established a certain level of validation for clinical use (fit-for-purpose). Recommendations to meet challenges and strategies to guide the choice of analytical and clinical validation design for specific assays are also provided.
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http://dx.doi.org/10.1186/s40425-016-0178-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5109744PMC
February 2018

Validation of biomarkers to predict response to immunotherapy in cancer: Volume II - clinical validation and regulatory considerations.

J Immunother Cancer 2016 15;4:77. Epub 2016 Nov 15.

National Cancer Institute, Cancer Diagnosis Program, DCTD, National Institutes of Health, 9609 Medical Center Drive, Bethesda, 20892 MD USA.

There is growing recognition that immunotherapy is likely to significantly improve health outcomes for cancer patients in the coming years. Currently, while a subset of patients experience substantial clinical benefit in response to different immunotherapeutic approaches, the majority of patients do not but are still exposed to the significant drug toxicities. Therefore, a growing need for the development and clinical use of predictive biomarkers exists in the field of cancer immunotherapy. Predictive cancer biomarkers can be used to identify the patients who are or who are not likely to derive benefit from specific therapeutic approaches. In order to be applicable in a clinical setting, predictive biomarkers must be carefully shepherded through a step-wise, highly regulated developmental process. Volume I of this two-volume document focused on the pre-analytical and analytical phases of the biomarker development process, by providing background, examples and "good practice" recommendations. In the current Volume II, the focus is on the clinical validation, validation of clinical utility and regulatory considerations for biomarker development. Together, this two volume series is meant to provide guidance on the entire biomarker development process, with a particular focus on the unique aspects of developing immune-based biomarkers. Specifically, knowledge about the challenges to clinical validation of predictive biomarkers, which has been gained from numerous successes and failures in other contexts, will be reviewed together with statistical methodological issues related to bias and overfitting. The different trial designs used for the clinical validation of biomarkers will also be discussed, as the selection of clinical metrics and endpoints becomes critical to establish the clinical utility of the biomarker during the clinical validation phase of the biomarker development. Finally, the regulatory aspects of submission of biomarker assays to the U.S. Food and Drug Administration as well as regulatory considerations in the European Union will be covered.
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http://dx.doi.org/10.1186/s40425-016-0179-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5109653PMC
February 2018

Placental immune editing switch (PIES): learning about immunomodulatory pathways from a unique case report.

Oncotarget 2016 Dec;7(50):83817-83827

Nanostring Technologies, Immune Oncology, Seattle, WA, USA.

The hypothesis of this work is that, in order to escape the natural immune surveillance mechanisms, cancer cells and the surrounding microenvironment might express ectopically genes that are physiologically present in the placenta to mediate fetal immune-tolerance. These natural "placental immune-editing switch" mechanisms (PIES) may represent the result of millions of years of mammalian evolution developed to allow materno-fetal tolerance. Here, we introduce genes of the immune regulatory pathways that are either similarly over- or under-expressed in tumor vs normal tissue. Our analysis was carried out in primary breast cancer with metastatic homolateral axillary lymph nodes as well as placenta tissue (both uterine decidual tissue and term placenta tissue) from a pregnant woman. Gene expression profiling of paired non-self and self tissues (i.e. placenta/uterus; breast cancer/normal breast tissue; metastatic lymphnode/normal lymphnode tissue) was performed using the PanCancer Immune gene panel, a 770 Nanostring gene expression panel. Our findings reveal overlapping in specific immune gene expression in placenta and cancer tissue, suggesting that these genes might play an important role in maintaining immune tolerance both physiologically (in the placenta) and pathologically (in the cancer setting).
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http://dx.doi.org/10.18632/oncotarget.13306DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5347808PMC
December 2016

Future perspectives in melanoma research : Meeting report from the "Melanoma Bridge". Napoli, December 1st-4th 2015.

J Transl Med 2016 11 15;14(1):313. Epub 2016 Nov 15.

The University of Texas MD Anderson Cancer Center, Houston, TX, USA.

The sixth "Melanoma Bridge Meeting" took place in Naples, Italy, December 1st-4th, 2015. The four sessions at this meeting were focused on: (1) molecular and immune advances; (2) combination therapies; (3) news in immunotherapy; and 4) tumor microenvironment and biomarkers. Recent advances in tumor biology and immunology has led to the development of new targeted and immunotherapeutic agents that prolong progression-free survival (PFS) and overall survival (OS) of cancer patients. Immunotherapies in particular have emerged as highly successful approaches to treat patients with cancer including melanoma, non-small cell lung cancer (NSCLC), renal cell carcinoma (RCC), bladder cancer, and Hodgkin's disease. Specifically, many clinical successes have been using checkpoint receptor blockade, including T cell inhibitory receptors such as cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) and the programmed cell death-1 (PD-1) and its ligand PD-L1. Despite demonstrated successes, responses to immunotherapy interventions occur only in a minority of patients. Attempts are being made to improve responses to immunotherapy by developing biomarkers. Optimizing biomarkers for immunotherapy could help properly select patients for treatment and help to monitor response, progression and resistance that are critical challenges for the immuno-oncology (IO) field. Importantly, biomarkers could help to design rational combination therapies. In addition, biomarkers may help to define mechanism of action of different agents, dose selection and to sequence drug combinations. However, biomarkers and assays development to guide cancer immunotherapy is highly challenging for several reasons: (i) multiplicity of immunotherapy agents with different mechanisms of action including immunotherapies that target activating and inhibitory T cell receptors (e.g., CTLA-4, PD-1, etc.); adoptive T cell therapies that include tissue infiltrating lymphocytes (TILs), chimeric antigen receptors (CARs), and T cell receptor (TCR) modified T cells; (ii) tumor heterogeneity including changes in antigenic profiles over time and location in individual patient; and (iii) a variety of immune-suppressive mechanisms in the tumor microenvironment (TME) including T regulatory cells (Treg), myeloid derived suppressor cells (MDSC) and immunosuppressive cytokines. In addition, complex interaction of tumor-immune system further increases the level of difficulties in the process of biomarkers development and their validation for clinical use. Recent clinical trial results have highlighted the potential for combination therapies that include immunomodulating agents such as anti-PD-1 and anti-CTLA-4. Agents targeting other immune inhibitory (e.g., Tim-3) or immune stimulating (e.g., CD137) receptors on T cells and other approaches such as adoptive cell transfer are tested for clinical efficacy in melanoma as well. These agents are also being tested in combination with targeted therapies to improve upon shorter-term responses thus far seen with targeted therapy. Various locoregional interventions that demonstrate promising results in treatment of advanced melanoma are also integrated with immunotherapy agents and the combinations with cytotoxic chemotherapy and inhibitors of angiogenesis are changing the evolving landscape of therapeutic options and are being evaluated to prevent or delay resistance and to further improve survival rates for melanoma patients' population. This meeting's specific focus was on advances in immunotherapy and combination therapy for melanoma. The importance of understanding of melanoma genomic background for development of novel therapies and biomarkers for clinical application to predict the treatment response was an integral part of the meeting. The overall emphasis on biomarkers supports novel concepts toward integrating biomarkers into personalized-medicine approach for treatment of patients with melanoma across the entire spectrum of disease stage. Translation of the knowledge gained from the biology of tumor microenvironment across different tumors represents a bridge to impact on prognosis and response to therapy in melanoma. We also discussed the requirements for pre-analytical and analytical as well as clinical validation process as applied to biomarkers for cancer immunotherapy. The concept of the fit-for-purpose marker validation has been introduced to address the challenges and strategies for analytical and clinical validation design for specific assays.
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http://dx.doi.org/10.1186/s12967-016-1070-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5111349PMC
November 2016