Explore the vast range of titles available.

HPV-DNA positive (HPVDNA+) oropharyngeal squamous cell carcinoma (OSCC) has better clinical outcome than HPV-DNA negative (HPVDNA-) OSCC. Current treatment may be unnecessarily extensive for most HPV+ OSCC, but before de-escalation, additional markers are needed together with HPV status to better predict treatment response. Here the influence of HLA class I/HLA class II expression was explored. Pre-treatment biopsies, from 439/484 OSCC patients diagnosed 2000-2009 and treated curatively, were analyzed for HLA I and II expression, p16(INK4a) and HPV DNA. Absent/weak as compared to high HLA class I intensity correlated to a very favorable disease-free survival (DFS), disease-specific survival (DSS) and overall survival (OS) in HPVDNA+ OSCC, both in univariate and multivariate analysis, while HLA class II had no impact. Notably, HPVDNA+ OSCC with absent/weak HLA class I responded equally well when treated with induction-chemo-radiotherapy (CRT) or radiotherapy (RT) alone. In patients with HPVDNA- OSCC, high HLA class I/class II expression correlated in general to a better clinical outcome. p16(INK4a) overexpression correlated to a better clinical outcome in HPVDNA+ OSCC. Absence of HLA class I intensity in HPVDNA+ OSCC suggests a very high survival independent of treatment and could possibly be used clinically to select patients for randomized trials de-escalating therapy.

Prediction of clinical outcome in cancer is usually achieved by histopathological evaluation of tissue samples obtained during surgical resection of the primary tumor. Traditional tumor staging (AJCC/UICC-TNM classification) summarizes data on tumor burden (T), presence of cancer cells in draining and regional lymph nodes (N) and evidence for metastases (M). However, it is now recognized that clinical outcome can significantly vary among patients within the same stage. The current classification provides limited prognostic information, and does not predict response to therapy. Recent literature has alluded to the importance of the host immune system in controlling tumor progression. Thus, evidence supports the notion to include immunological biomarkers, implemented as a tool for the prediction of prognosis and response to therapy. Accumulating data, collected from large cohorts of human cancers, has demonstrated the impact of immune-classification, which has a prognostic value that may add to the significance of the AJCC/UICC TNM-classification. It is therefore imperative to begin to incorporate the ‘Immunoscore’ into traditional classification, thus providing an essential prognostic and potentially predictive tool. Introduction of this parameter as a biomarker to classify cancers, as part of routine diagnostic and prognostic assessment of tumors, will facilitate clinical decision-making including rational stratification of patient treatment. Equally, the inherent complexity of quantitative immunohistochemistry, in conjunction with protocol variation across laboratories, analysis of different immune cell types, inconsistent region selection criteria, and variable ways to quantify immune infiltration, all underline the urgent requirement to reach assay harmonization. In an effort to promote the Immunoscore in routine clinical settings, an international task force was initiated. This review represents a follow-up of the announcement of this initiative, and of the J Transl Med. editorial from January 2012. Immunophenotyping of tumors may provide crucial novel prognostic information. The results of this international validation may result in the implementation of the Immunoscore as a new component for the classification of cancer, designated TNM-I (TNM-Immune).

Strategies to help improve the efficacy of the immune system against cancer represent an important innovation, with recent attention having focused on anti-programmed death (PD)-1/PD-ligand 1 (L1) monoclonal antibodies. Clinical trials have shown objective clinical activity of these agents (e.g., nivolumab, pembrolizumab) in several malignancies, including melanoma, non-small-cell lung cancer, bladder cancer, squamous head and neck cancer, renal cell cancer, ovarian cancer, microsatellite-unstable colorectal cancer, and Hodgkin’s lymphoma. Expression of PD-L1 in the tumor microenvironment appears to be crucial for therapeutic activity, and initial trials suggested positive PD-L1 tumor expression was associated with higher response rates. However, subsequent observations have questioned the prospect of using PD-L1 expression as a biomarker for selecting patients for therapy, especially since many patients considered PD-L1-negative experience a benefit from treatment. Importantly, there is not yet a definitive test for determination of PD-L1 and a cut-off reference for PD-L1-positive status has not been established. Immunohistochemistry with different antibodies and different thresholds has been used to define PD-L1 positivity (1–50 %), with no clear superiority of one threshold over another for identifying which patients respond. Moreover, the type of cells on which PD-L1 expression is most relevant is not yet clear, with immune infiltrate cells and tumor cells both being used. In conclusion, while PD-L1 expression is often a predictive factor for treatment response, it must be complemented by other biomarkers or histopathologic features, such as the composition and amount of inflammatory cells in the tumor microenvironment and their functional status. Multi-parameter quantitative or semi-quantitative algorithms may become useful and reliable tools to guide patient selection.

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.

[...]the use of PD-L1 as a biomarker is problematic and other biomarkers are emerging as potentially more important predictive factors of response.

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.

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.

Merkel cell carcinoma (MCC) is a rare and aggressive skin cancer where Merkel cell Polyomavirus (MCPyV) contributes to the pathogenesis. In an adjuvant setting, radiotherapy (RT) is believed to give a survival benefit. The prognostic impact of sex related to MCPyV-status and adjuvant RT were analyzed in patients referred to Karolinska University Hospital. Data were collected from 113 patients’ hospital records and MCPyV analyses were made in 54 patients (48%). We found a significantly better overall survival (OS) for women compared to men and a significant difference in OS in patients receiving adjuvant RT. Furthermore, we found that men with virus negative MCC have an increased risk for earlier death (HR 3.6). This indicates that MCPyV positive and negative MCC act as two different diseases, and it might be due to different mechanism in the immune response between male and female patients. This could have significance in tailoring treatment and follow-up in MCC patients in the future.

The real-life application of immune checkpoint inhibitors (ICIs) may yield different outcomes compared to the benefit presented in clinical trials. For this reason, there is a need to define the group of patients that may benefit from treatment. We retrospectively investigated 578 metastatic melanoma patients treated with ICIs at the Istituto Nazionale Tumori IRCCS Fondazione “G. Pascale” of Napoli, Italy (INT-NA). To compare patients’ clinical variables (i.e., age, lactate dehydrogenase (LDH), neutrophil–lymphocyte ratio (NLR), eosinophil, BRAF status, previous treatment) and their predictive and prognostic power in a comprehensive, non-hierarchical manner, a clinical categorization algorithm (CLICAL) was defined and validated by the application of a machine learning algorithm—survival random forest (SRF-CLICAL). The comprehensive analysis of the clinical parameters by log risk-based algorithms resulted in predictive signatures that could identify groups of patients with great benefit or not, regardless of the ICI received. From a real-life retrospective analysis of metastatic melanoma patients, we generated and validated an algorithm based on machine learning that could assist with the clinical decision of whether or not to apply ICI therapy by defining five signatures of predictability with 95% accuracy.

HLA abnormalities on tumour cells for immune escape have been widely described. In addition, cellular components of the tumour microenvironment, in particular myeloid derived suppressor cells (MDSC) and alternatively activated M2 tumour-associated macrophages (TAMs), are involved in tumour promotion, progression, angiogenesis and suppression of anti-tumour immunity. However, the role of HLA in these activities is poorly understood. This review details MHC class I characteristics and describes MHC class I receptors functions. This analysis established the basis for a reflection about the crosstalk among the tumour cells, the TAMs and the cells mediating an immune response. The tumour cells and TAMs exploit MHC class I molecules to modulate the surrounding immune cells. HLA A, B, C and G molecules down-regulate the macrophage myeloid activation through the interaction with the inhibitory LILRB receptors. HLA A, B, C are able to engage inhibitory KIR receptors negatively regulating the Natural Killer and cytotoxic T lymphocytes function while HLA-G induces the secretion of pro-angiogenic cytokines and chemokine thanks to an activator KIR receptor expressed by a minority of peripheral NK cells. The open conformer of classical MHC-I is able to interact with LILRA receptors described as being associated to the Th2-type cytokine response, triggering a condition for the M2 like TAM polarization. In addition, HLA-E antigens on the surface of the TAMs bind the inhibitory receptor CD94/NKG2A expressed by a subset of NK cells and activated cytotoxic T lymphocytes protecting from the cytolysis. Furthermore MHC class II expression by antigen presenting cells is finely regulated by factors provided with immunological capacities. Tumour-associated macrophages show an epigenetically controlled down-regulation of the MHC class II expression induced by the decoy receptor DcR3, a member of the TNFR, which further enhances the M2-like polarization. BAT3, a positive regulator of MHC class II expression in normal macrophages, seems to be secreted by TAMs, consequently lacking its intracellular function, it looks like acting as an immunosuppressive factor. In conclusion HLA could cover a considerable role in tumour-development orchestrated by tumour-associated macrophages.