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Abstract Enabling the examination of cell-cell relationships in tissue, spatially resolved omics technologies have revolutionised our perspectives on cancer biology. Clinically, the development of immune checkpoint inhibitors (ICI) has advanced cancer therapeutics. However, a major challenge of effective implementation is the identification of predictive biomarkers of response. In this review we examine the potential added predictive value of spatial biomarkers of response to ICI beyond current clinical benchmarks.

Analyses of inequalities related to prevention and cancer therapeutics/care show disparities between countries with different economic standing, and within countries with high Gross Domestic Product. The development of basic technological and biological research provides clinical and prevention opportunities that make their implementation into healthcare systems more complex, mainly due to the growth of Personalized/Precision Cancer Medicine (PCM). Initiatives like the USA‐Cancer Moonshot and the EU‐Mission on Cancer and Europe's Beating Cancer Plan are initiated to boost cancer prevention and therapeutics/care innovation and to mitigate present inequalities. The conference organized by the Pontifical Academy of Sciences in collaboration with the European Academy of Cancer Sciences discussed the inequality problem, dependent on the economic status of a country, the increasing demands for infrastructure supportive of innovative research and its implementation in healthcare and prevention programs. Establishing translational research defined as a coherent cancer research continuum is still a challenge. Research has to cover the entire continuum from basic to outcomes research for clinical and prevention modalities. Comprehensive Cancer Centres (CCCs) are of critical importance for integrating research innovations to preclinical and clinical research, as for ensuring state‐of‐the‐art patient care within healthcare systems. International collaborative networks between CCCs are necessary to reach the critical mass of infrastructures and patients for PCM research, and for introducing prevention modalities and new treatments effectively. Outcomes and health economics research are required to assess the cost‐effectiveness of new interventions, currently a missing element in the research portfolio. Data sharing and critical mass are essential for innovative research to develop PCM. Despite advances in cancer research, cancer incidence and prevalence is growing. Making cancer research infrastructures accessible for all patients, considering the increasing inequalities, requires science policy actions incentivizing research aimed at prevention and cancer therapeutics/care with an increased focus on patients' needs and cost‐effective healthcare.

Abstract The development of immune checkpoint inhibitors (ICIs) has revolutionized cancer therapy but only a fraction of patients benefits from this therapy. Model‐informed drug development can be used to assess prognostic and predictive clinical factors or biomarkers associated with treatment response. Most pharmacometric models have thus far been developed using data from randomized clinical trials, and further studies are needed to translate their findings into the real‐world setting. We developed a tumor growth inhibition model based on real‐world clinical and imaging data in a population of 91 advanced melanoma patients receiving ICIs (i.e., ipilimumab, nivolumab, and pembrolizumab). Drug effect was modeled as an ON/OFF treatment effect, with a tumor killing rate constant identical for the three drugs. Significant and clinically relevant covariate effects of albumin, neutrophil to lymphocyte ratio, and Eastern Cooperative Oncology Group (ECOG) performance status were identified on the baseline tumor volume parameter, as well as NRAS mutation on tumor growth rate constant using standard pharmacometric approaches. In a population subgroup (n = 38), we had the opportunity to conduct an exploratory analysis of image‐based covariates (i.e., radiomics features), by combining machine learning and conventional pharmacometric covariate selection approaches. Overall, we demonstrated an innovative pipeline for longitudinal analyses of clinical and imaging RWD with a high‐dimensional covariate selection method that enabled the identification of factors associated with tumor dynamics. This study also provides a proof of concept for using radiomics features as model covariates.

Abstract Outcomes for patients with melanoma have improved over the past decade with the clinical development and approval of immunotherapies targeting immune checkpoint receptors such as programmed death-1 (PD-1), programmed death ligand 1 (PD-L1) or cytotoxic T lymphocyte antigen-4 (CTLA-4). Combinations of these checkpoint therapies with other agents are now being explored to improve outcomes and enhance benefit-risk profiles of treatment. Alternative inhibitory receptors have been identified that may be targeted for anti-tumor immune therapy, such as lymphocyte-activation gene-3 (LAG-3), as have several potential target oncogenes for molecularly targeted therapy, such as tyrosine kinase inhibitors. Unfortunately, many patients still progress and acquire resistance to immunotherapy and molecularly targeted therapies. To bypass resistance, combination treatment with immunotherapies and single or multiple TKIs have been shown to improve prognosis compared to monotherapy. The number of new combinations treatment under development for melanoma provides options for the number of patients to achieve a therapeutic benefit. Many diagnostic and prognostic assays have begun to show clinical applicability providing additional tools to optimize and individualize treatments. However, the question on the optimal algorithm of first- and later-line therapies and the search for biomarkers to guide these decisions are still under investigation. This year, the Melanoma Bridge Congress (Dec 1st–3rd, 2022, Naples, Italy) addressed the latest advances in melanoma research, focusing on themes of paramount importance for melanoma prevention, diagnosis and treatment. This included sessions dedicated to systems biology on immunotherapy, immunogenicity and gene expression profiling, biomarkers, and combination treatment strategies.

Abstract The success of cancer immunotherapy depends in part on the strength of antigen recognition by T cells. Here, we characterize the T cell receptor (TCR) functional (antigen sensitivity) and structural (monomeric pMHC-TCR off-rates) avidities of 371 CD8 T cell clones specific for neoantigens, tumor-associated antigens (TAAs) or viral antigens isolated from tumors or blood of patients and healthy donors. T cells from tumors exhibit stronger functional and structural avidity than their blood counterparts. Relative to TAA, neoantigen-specific T cells are of higher structural avidity and, consistently, are preferentially detected in tumors. Effective tumor infiltration in mice models is associated with high structural avidity and CXCR3 expression. Based on TCR biophysicochemical properties, we derive and apply an in silico model predicting TCR structural avidity and validate the enrichment in high avidity T cells in patients’ tumors. These observations indicate a direct relationship between neoantigen recognition, T cell functionality and tumor infiltration. These results delineate a rational approach to identify potent T cells for personalized cancer immunotherapy.

The treatment of BRAFV600E mutant melanoma has been revolutionized by BRAF inhibitors. Furthermore, the BRAF/MEK combination has shown further improvement in clinical outcomes in advanced and in adjuvant melanoma patients. In low-grade ovarian tumors, BRAF inhibitor use has been also proposed. Here we present a patient with an excellent, lasting response to BRAF therapy alone. At first progression, after more than two years on BRAF monotherapy, we could not identify any molecular mechanisms explaining resistance. After a switch to dual BRAF/MEK therapy, the patient responded. However, despite the initial response clinical the patient again progressed, this time with the appearance of a KRAS G12C mutation, which could not be overcome by BRAF/MEK therapy. We provide evidence that BRAF inhibitor alone can be highly beneficial in BRAF mutant low-grade ovarian tumors and the resistance mechanisms are similar to that of other BRAF mutant tumors, including in melanoma.

BackgroundManagement of severe symptomatic immune-related adverse events (IrAEs) related to immune checkpoint inhibitors (ICIs) can be facilitated by timely detection. As patients face a heterogeneous set of symptoms outside the clinical setting, remotely monitoring and assessing symptoms by using patient-reported outcomes (PROs) may result in shorter delays between symptom onset and clinician detection. ObjectiveWe assess the effect of a model of care for remote patient monitoring and symptom management based on PRO data on the time to detection of symptomatic IrAEs from symptom onset. The secondary objectives are to assess its effects on the time between symptomatic IrAE detection and intervention, IrAE grade (severity), health-related quality of life, self-efficacy, and overall survival at 6 months. MethodsFor this study, 198 patients with cancer receiving systemic treatment comprising ICIs exclusively will be recruited from 2 Swiss university hospitals. Patients are randomized (1:1) to a digital model of care (intervention) or usual care (control group). Patients are enrolled for 6 months, and they use an electronic app to complete weekly Functional Assessment of Cancer Therapy-General questionnaire and PROMIS (PROs Measurement Information System) Self-Efficacy to Manage Symptoms questionnaires. The intervention patient group completes a standard set of 37 items in a weekly PROs version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE) questionnaire, and active symptoms are reassessed daily for the first 3 months by using a modified 24-hour recall period. Patients can add items from the full PRO-CTCAE item library to their questionnaire. Nurses call patients in the event of new or worsening symptoms and manage them by using a standardized triage algorithm based on the United Kingdom Oncology Nursing Society 24-hour triage tool. This algorithm provides guidance on deciding if patients should receive in-person care, if monitoring should be increased, or if self-management education should be reinforced. ResultsThe Institut Suisse de Recherche Expérimentale sur le Cancer Foundation and Kaiku Health Ltd funded this study. Active recruitment began since November 2021 and is projected to conclude in November 2023. Trial results are expected to be published in the first quarter of 2024 and will be disseminated through publications submitted at international scientific conferences. ConclusionsThis trial is among the first trials to use PRO data to directly influence routine care of patients treated with ICIs and addresses some limitations in previous studies. This trial collects a wider spectrum of self-reported symptom data daily. There are some methodological limitations brought by changes in evolving treatment standards for patients with cancer. This trial's results could entail further academic discussions on the challenges of diagnosing and managing symptoms associated with treatment remotely by providing further insights into the burden symptoms represent to patients and highlight the complexity of care procedures involved in managing symptomatic IrAEs. Trial RegistrationClinicalTrials.gov NCT05530187; https://www.clinicaltrials.gov/study/NCT05530187 International Registered Report Identifier (IRRID)DERR1-10.2196/48386

The haem enzyme indoleamine 2,3-dioxygenase 1 (IDO1) catalyses the rate-limiting step in the kynurenine pathway of tryptophan metabolism and plays an essential role in immunity, neuronal function, and ageing. Expression of IDO1 in cancer cells results in the suppression of an immune response, and therefore IDO1 inhibitors have been developed for use in anti-cancer immunotherapy. Here, we report an extension of our previously described highly efficient haem-binding 1,2,3-triazole and 1,2,4-triazole inhibitor series, the best compound having both enzymatic and cellular IC50 values of 34 nM. We provide enzymatic inhibition data for almost 100 new compounds and X-ray diffraction data for one compound in complex with IDO1. Structural and computational studies explain the dramatic drop in activity upon extension to pocket B, which has been observed in diverse haem-binding inhibitor scaffolds. Our data provides important insights for future IDO1 inhibitor design.

Abstract Advances in immune checkpoint and combination therapy have led to improvement in overall survival for patients with advanced melanoma. Improved understanding of the tumor, tumor microenvironment and tumor immune-evasion mechanisms has resulted in new approaches to targeting and harnessing the host immune response. Combination modalities with other immunotherapy agents, chemotherapy, radiotherapy, electrochemotherapy are also being explored to overcome resistance and to potentiate the immune response. In addition, novel approaches such as adoptive cell therapy, oncogenic viruses, vaccines and different strategies of drug administration including sequential, or combination treatment are being tested. Despite the progress in diagnosis of melanocytic lesions, correct classification of patients, selection of appropriate adjuvant and systemic theràapies, and prediction of response to therapy remain real challenges in melanoma. Improved understanding of the tumor microenvironment, tumor immunity and response to therapy has prompted extensive translational and clinical research in melanoma. There is a growing evidence that genomic and immune features of pre-treatment tumor biopsies may correlate with response in patients with melanoma and other cancers, but they have yet to be fully characterized and implemented clinically. Development of novel biomarker platforms may help to improve diagnostics and predictive accuracy for selection of patients for specific treatment. Overall, the future research efforts in melanoma therapeutics and translational research should focus on several aspects including: (a) developing robust biomarkers to predict efficacy of therapeutic modalities to guide clinical decision-making and optimize treatment regimens, (b) identifying mechanisms of therapeutic resistance to immune checkpoint inhibitors that are potentially actionable, (c) identifying biomarkers to predict therapy-induced adverse events, and (d) studying mechanism of actions of therapeutic agents and developing algorithms to optimize combination treatments. During the Melanoma Bridge meeting (December 2nd-4th, 2021, Naples, Italy) discussions focused on the currently approved systemic and local therapies for advanced melanoma and discussed novel biomarker strategies and advances in precision medicine as well as the impact of COVID-19 pandemic on management of melanoma patients.

ObjectivesThe objective of this study is the exploration of Artificial Intelligence and Natural Language Processing techniques to support the automatic assignment of the four Response Evaluation Criteria in Solid Tumors (RECIST) scales based on radiology reports. We also aim at evaluating how languages and institutional specificities of Swiss teaching hospitals are likely to affect the quality of the classification in French and German languages.MethodsIn our approach, 7 machine learning methods were evaluated to establish a strong baseline. Then, robust models were built, fine-tuned according to the language (French and German), and compared with the expert annotation.ResultsThe best strategies yield average F1-scores of 90% and 86% respectively for the 2-classes (Progressive/Non-progressive) and the 4-classes (Progressive Disease, Stable Disease, Partial Response, Complete Response) RECIST classification tasks.ConclusionsThese results are competitive with the manual labeling as measured by Matthew's correlation coefficient and Cohen's Kappa (79% and 76%). On this basis, we confirm the capacity of specific models to generalize on new unseen data and we assess the impact of using Pre-trained Language Models (PLMs) on the accuracy of the classifiers.

Abstract The BRAF kinase is attracting a lot of attention in oncology as alterations of its amino acid sequence can constitutively activate the MAP kinase signaling pathway, potentially contributing to the malignant transformation of the cell but at the same time rendering it sensitive to targeted therapy. Several pathologic BRAF variants were grouped in three different classes (I, II and III) based on their effects on the protein activity and pathway. Discerning the class of a BRAF mutation permits to adapt the treatment proposed to the patient. However, this information is lacking new and experimentally uncharacterized BRAF mutations detected in a patient biopsy. To overcome this issue, we developed a new in silico tool based on machine learning approaches to predict the potential class of a BRAF missense variant. As class I only involves missense mutations of Val600, we focused on the mutations of classes II and III, which are more diverse and challenging to predict. Using a logistic regression model and features including structural information, we were able to predict the classes of known mutations with an accuracy of 90%. This new and fast predictive tool will help oncologists to tackle potential pathogenic BRAF mutations and to propose the most appropriate treatment for their patients.

The growing availability of clinical real-world data (RWD) represents a formidable opportunity to complement evidence from randomized clinical trials and observe how oncological treatments perform in real-life conditions. In particular, RWD can provide insights on questions for which no clinical trials exist, such as comparing outcomes from different sequences of treatments. To this end, process mining is a particularly suitable methodology for analyzing different treatment paths and their associated outcomes. Here, we describe an implementation of process mining algorithms directly within our hospital information system with an interactive application that allows oncologists to compare sequences of treatments in terms of overall survival, progression-free survival and best overall response. As an application example, we first performed a RWD descriptive analysis of 303 patients with advanced melanoma and reproduced findings observed in two notorious clinical trials: CheckMate-067 and DREAMseq. Then, we explored the outcomes of an immune-checkpoint inhibitor rechallenge after a first progression on immunotherapy versus switching to a BRAF targeted treatment. By using interactive process-oriented RWD analysis, we observed that patients still derive long-term survival benefits from immune-checkpoint inhibitors rechallenge, which could have direct implications on treatment guidelines for patients able to carry on immune-checkpoint therapy, if confirmed by external RWD and randomized clinical trials. Overall, our results highlight how an interactive implementation of process mining can lead to clinically relevant insights from RWD with a framework that can be ported to other centers or networks of centers.

During the acute phase of the COVID-19 pandemic, hospitals faced a challenge to manage patients, especially those with other comorbidities and medical needs, such as cancer patients. Here, we use Process Mining to analyze real-world therapeutic pathways in a cohort of 1182 cancer patients of the Lausanne University Hospital following COVID-19 infection. The algorithm builds trees representing sequences of coarse-grained events such as Home, Hospitalization, Intensive Care and Death. The same trees can also show probability of death or time-to-event statistics in each node. We introduce a new tool, called Differential Process Mining, which enables comparison of two patient strata in each node of the tree, in terms of hits and death rate, together with a statistical significance test. We thus compare management of COVID-19 patients with an active cancer in the first vs. second COVID-19 waves to quantify hospital adaptation to the pandemic. We also compare patients having undergone systemic therapy within 1 year to the rest of the cohort to understand the impact of an active cancer and/or its treatment on COVID-19 outcome. This study demonstrates the value of Process Mining to analyze complex event-based real-world data and generate hypotheses on hospital resource management or on clinical patient care.

Background As management and prevention strategies against COVID-19 evolve, it is still uncertain whether prior exposure to immune checkpoint inhibitors (ICIs) affects COVID-19 severity in patients with cancer.Methods In a joint analysis of ICI recipients from OnCovid (NCT04393974) and European Society for Medical Oncology (ESMO) CoCARE registries, we assessed severity and mortality from SARS-CoV-2 in vaccinated and unvaccinated patients with cancer and explored whether prior immune-related adverse events (irAEs) influenced outcome from COVID-19.Findings The study population consisted of 240 patients diagnosed with COVID-19 between January 2020 and February 2022 exposed to ICI within 3 months prior to COVID-19 diagnosis, with a 30-day case fatality rate (CFR30) of 23.6% (95% CI 17.8 to 30.7%). Overall, 42 (17.5%) were fully vaccinated prior to COVID-19 and experienced decreased CFR30 (4.8% vs 28.1%, p=0.0009), hospitalization rate (27.5% vs 63.2%, p

Background Immune checkpoint inhibitors (ICIs) have become a mainstay of cancer treatment. Their immune-boosting quality has one major drawback, their proclivity to induce a broad array of immune-related adverse events (irAEs) affecting, among others, the liver and sharing some similarities with classic autoimmune liver diseases (AILD).We aimed to compare clinical, laboratory and histological features of patients with liver-related irAEs and AILD.Methods We systematically compared liver irAEs with AILD, namely autoimmune hepatitis (AIH) and primary biliary cholangitis, regarding their clinical, laboratory, and histological features.Results Twenty-seven patients with liver irAEs (ICI group) and 14 patients with AILD were identified. We observed three distinct ICI-induced histological liver injury patterns: hepatitic (52%), cholangitic (19%), and mixed (29%). When comparing the ICI and AILD groups, centrilobular injury as well as granuloma formation were more prevalent in the former (p=0.067 and 0.002, respectively). CD4+/CD8+ T cell ratios were heterogeneous between the two groups, without statistically significant difference but with a trend toward increased CD8+ T cells among hepatitic irAEs as compared with AIH. Pattern of liver function test alteration was predictive for the type of irAEs but did not correlate with histological severity.Conclusions Liver irAEs have broad clinical, laboratory and histological presentations. Histological features of irAEs and AILD are distinct, likely underpinning their different immunological mechanisms.

Neoadjuvant immunotherapy has emerged as a promising approach in the treatment of various malignancies, with preclinical studies showing improved immune responses in the preoperative setting. FDA-approved neoadjuvant-immunotherapy-based approaches include triple-negative breast cancer and early non-small cell lung cancer on the basis of improvement in pathological response and event free survival. Nevertheless, current trials have only shown benefits in a fraction of patients. It is therefore crucial to identify predictive biomarkers to improve patient selection for such approaches. This review aims to provide an overview of potential biomarkers of neoadjuvant immunotherapy in early triple-negative breast cancer, bladder cancer, melanoma, non-small cell lung cancer, colorectal cancer and gastric cancer. By the extrapolation of the metastatic setting, we explore known predictive biomarkers, i.e., PD-L1, mismatch repair deficiency and tumour mutational burden, as well as potential early-disease-specific biomarkers. We also discuss the challenges of identifying reliable biomarkers and the need for standardized protocols and guidelines for their validation and clinical implementation.

Systemic light chain amyloidosis (AL) is caused by the production of toxic light chains and can be fatal, yet effective treatments are often not possible due to delayed diagnosis. Here the authors show that a machine learning platform analyzing light chain somatic mutations allows the prediction of light chain toxicity to serve as a possible tool for early diagnosis of AL.

Background Acral melanoma is a rare melanoma subtype with poor prognosis. Importantly, these patients were not identified as a specific subgroup in the landmark melanoma trials involving ipilimumab and the anti-programmed cell death protein-1 (PD-1) agents nivolumab and pembrolizumab. There is therefore an absence of prospective clinical trial evidence regarding the efficacy of checkpoint inhibitors (CPIs) in this population. Acral melanoma has lower tumor mutation burden (TMB) than other cutaneous sites, and primary site is associated with differences in TMB. However the impact of this on the effectiveness of immune CPIs is unknown. We examined the efficacy of CPIs in acral melanoma, including by primary site.Methods Patients with unresectable stage III/IV acral melanoma treated with CPI (anti-PD-1 and/or ipilimumab) were studied. Multivariable logistic and Cox regression analyses were conducted. Primary outcome was objective response rate (ORR); secondary outcomes were progression-free survival (PFS) and overall survival (OS).Results In total, 325 patients were included: 234 (72%) plantar, 69 (21%) subungual and 22 (7%) palmar primary sites. First CPI included: 184 (57%) anti-PD-1, 59 (18%) anti-PD-1/ipilimumab combination and 82 (25%) ipilimumab. ORR was significantly higher with initial anti-PD-1/ipilimumab compared with anti-PD-1 (43% vs 26%, HR 2.14, p=0.0004) and significantly lower with ipilimumab (15% vs 26%, HR 0.49, p=0.0016). Landmark PFS at 1 year was highest for anti-PD-1/ipilimumab at 34% (95% CI 24% to 49%), compared with 26% (95% CI 20% to 33%) with anti-PD-1 and 10% (95% CI 5% to 19%) with ipilimumab. Despite a trend for increased PFS, anti-PD-1/ipilimumab combination did not significantly improve PFS (HR 0.85, p=0.35) or OS over anti-PD-1 (HR 1.30, p=0.16), potentially due to subsequent therapies and high rates of acquired resistance. No outcome differences were found between primary sites.Conclusion While the ORR to anti-PD-1/ipilimumab was significantly higher than anti-PD-1 and PFS numerically higher, in this retrospective cohort this benefit did not translate to improved OS. Future trials should specifically include patients with acral melanoma, to help determine the optimal management of this important melanoma subtype.

Background Patients with V600BRAF mutant metastatic melanoma have higher rates of progression-free survival (PFS) and overall survival (OS) with first-line anti-PD1 (PD1]+anti-CTLA-4 (IPI) versus PD1. Whether this is also true after BRAF/MEKi therapy is unknown. We aimed to determine the efficacy and safety of PD1 versus IPI +PD1 after BRAF/MEK inhibitors (BRAF/MEKi).Methods Patients with V600BRAF mutant metastatic melanoma treated with BRAF/MEKi who had subsequent PD1 versus IPI+PD1 at eight centers were included. The endpoints were objective response rate (ORR), PFS, OS and safety in each group.Results Of 200 patients with V600E (75%) or non-V600E (25%) mutant metastatic melanoma treated with BRAF/MEKi (median time of treatment 7.6 months; treatment cessation due to progressive disease in 77%), 115 (57.5%) had subsequent PD1 and 85 (42.5%) had IPI+PD1. Differences in patient characteristics between PD1 and IPI+PD1 groups included, age (med. 63 vs 54 years), time between BRAF/MEKi and PD1±IPI (16 vs 4 days), Eastern Cooperative Oncology Group Performance Status (ECOG PS) of ≥1 (62% vs 44%), AJCC M1C/M1D stage (72% vs 94%) and progressing brain metastases at the start of PD1±IPI (34% vs 57%). Median follow-up from PD1±IPI start was 37.8 months (95% CI, 33.9 to 52.9). ORR was 36%; 34% with PD1 vs 39% with IPI+PD1 (p=0.5713). Median PFS was 3.4 months; 3.4 with PD1 vs 3.6 months with IPI+PD1 (p=0.6951). Median OS was 15.4 months; 14.4 for PD1 vs 20.5 months with IPI+PD1 (p=0.2603). The rate of grade 3 or 4 toxicities was higher with IPI+PD1 (31%) vs PD1 (7%). ORR, PFS and OS were numerically higher with IPI+PD1 vs PD1 across most subgroups except for females, those with 3 years OS (area under the curve, AUC=0.74), while ECOG PS ≥1, progressing brain metastases and presence of bone metastases predicted primary progression (AUC=0.67).Conclusions IPI+PD1 and PD1 after BRAF/MEKi have similar outcomes despite worse baseline prognostic features in the IPI+PD1 group, however, IPI+PD1 is more toxic. A combination of clinical factors can identify long-term survivors, but less accurately those with primary resistance to immunotherapy after targeted therapy.

Swiss-PO is a new web tool to map gene mutations on the 3D structure of corresponding proteins and to intuitively assess the structural implications of protein variants for precision oncology. Swiss-PO is constructed around a manually curated database of 3D structures, variant annotations, and sequence alignments, for a list of 50 genes taken from the Ion AmpliSeqTM Custom Cancer Hotspot Panel. The website was designed to guide users in the choice of the most appropriate structure to analyze regarding the mutated residue, the role of the protein domain it belongs to, or the drug that could be selected to treat the patient. The importance of the mutated residue for the structure and activity of the protein can be assessed based on the molecular interactions exchanged with neighbor residues in 3D within the same protein or between different biomacromolecules, its conservation in orthologs, or the known effect of reported mutations in its 3D or sequence-based vicinity. Swiss-PO is available free of charge or login at https://www.swiss-po.ch .

Background Survival heterogeneity and limited trial follow-up present challenges for estimating lifetime benefits of oncology therapies. This study used CheckMate 067 (NCT01844505) extended follow-up data to assess the predictive accuracy of standard parametric and flexible models in estimating the long-term overall survival benefit of nivolumab plus ipilimumab (an immune checkpoint inhibitor combination) in advanced melanoma. Methods Six sets of survival models (standard parametric, piecewise, cubic spline, mixture cure, parametric mixture, and landmark response models) were independently fitted to overall survival data for treatments in CheckMate 067 (nivolumab plus ipilimumab, nivolumab, and ipilimumab) using successive data cuts (28, 40, 52, and 60 mo). Standard parametric models allow survival extrapolation in the absence of a complex hazard. Piecewise and cubic spline models allow additional flexibility in fitting the hazard function. Mixture cure, parametric mixture, and landmark response models provide flexibility by explicitly incorporating survival heterogeneity. Sixty-month follow-up data, external ipilimumab data, and clinical expert opinion were used to evaluate model estimation accuracy. Lifetime survival projections were compared using a 5% discount rate. Results Standard parametric, piecewise, and cubic spline models underestimated overall survival at 60 mo for the 28-mo data cut. Compared with other models, mixture cure, parametric mixture, and landmark response models provided more accurate long-term overall survival estimates versus external data, higher mean survival benefit over 20 y for the 28-mo data cut, and more consistent 20-y mean overall survival estimates across data cuts. Conclusion This case study demonstrates that survival models explicitly incorporating survival heterogeneity showed greater accuracy for early data cuts than standard parametric models did, consistent with similar immune checkpoint inhibitor survival validation studies in advanced melanoma. Research is required to assess generalizability to other tumors and disease stages. Highlights Given that short clinical trial follow-up periods and survival heterogeneity introduce uncertainty in the health technology assessment of oncology therapies, this study evaluated the suitability of conventional parametric survival modeling approaches as compared with more flexible models in the context of immune checkpoint inhibitors that have the potential to provide lasting survival benefits. This study used extended follow-up data from the phase III CheckMate 067 trial (NCT01844505) to assess the predictive accuracy of standard parametric models in comparison with more flexible methods for estimating the long-term survival benefit of the immune checkpoint inhibitor combination of nivolumab plus ipilimumab in advanced melanoma. Mixture cure, parametric mixture, and landmark response models provided more accurate estimates of long-term overall survival versus external data than other models tested. In this case study with immune checkpoint inhibitor therapies in advanced melanoma, extrapolation models that explicitly incorporate differences in cancer survival between observed or latent subgroups showed greater accuracy with both early and later data cuts than other approaches did.

Abstract The Great Debate session at the 2019 Melanoma Bridge congress (December 5-7, Naples, Italy) featured counterpoint views from experts on five topical issues in melanoma. These were whether to choose local intratumoral treatment or systemic treatment, whether patients with stage IIIA melanoma require adjuvant therapy or not, whether treatment is better changed at disease progression or during stable disease, whether adoptive cell transfer (ACT) therapy is more appropriate used before or in combination with checkpoint inhibition therapy, and whether treatment can be stopped while the patient is still on response. As was the case for previous meetings, the debates were assigned by meeting Chairs. As such, positions taken by each of the melanoma experts during the debates may not have reflected their respective personal approach.

Abstract Radiomics, artificial intelligence, and deep learning figure amongst recent buzzwords in current medical imaging research and technological development. Analysis of medical big data in assessment and follow-up of personalised treatments has also become a major research topic in the area of precision medicine. In this review, current research trends in radiomics are analysed, from handcrafted radiomics feature extraction and statistical analysis to deep learning. Radiomics algorithms now include genomics and immunomics data to improve patient stratification and prediction of treatment response. Several applications have already shown conclusive results demonstrating the potential of including other “omics” data to existing imaging features. We also discuss further challenges of data harmonisation and management infrastructure to shed a light on the much-needed integration of radiomics and all other “omics” into clinical workflows. In particular, we point to the emerging paradigm shift in the implementation of big data infrastructures to facilitate databanks growth, data extraction and the development of expert software tools. Secured access, sharing, and integration of all health data, called “holomics”, will accelerate the revolution of personalised medicine and oncology as well as expand the role of imaging specialists.

The development of targeted therapies for non-BRAF p.Val600-mutant melanomas remains a challenge. Triple wildtype (TWT) melanomas that lack mutations in BRAF, NRAS, or NF1 form 10% of human melanomas and are heterogeneous in their genomic drivers. MAP2K1 mutations are enriched in BRAF-mutant melanoma and function as an innate or adaptive resistance mechanism to BRAF inhibition. Here we report the case of a patient with TWT melanoma with a bona fide MAP2K1 mutation without any BRAF mutations. We performed a structural analysis to validate that the MEK inhibitor trametinib could block this mutation. Although the patient initially responded to trametinib, he eventually progressed. The presence of a CDKN2A deletion prompted us to combine a CDK4/6 inhibitor, palbociclib, with trametinib but without clinical benefit. Genomic analysis at progression showed multiple novel copy number alterations. Our case illustrates the challenges of combining MEK1 and CDK4/6 inhibitors in case of resistance to MEK inhibitor monotherapy.

Abstract Background Immune checkpoint inhibitors (ICPis) have revolutionised the treatment of melanoma by significantly increasing survival rates and disease control. However, ICPis can have specific immune-related adverse events, including rare but severe neurological toxicity. Case presentation We report a 44-year-old man diagnosed with stage IIIB melanoma who developed metastatic disease (pulmonary and brain metastases) and was treated with stereotactic radiosurgery and nivolumab immunotherapy. He developed asymptomatic multifocal diffuse white matter lesions consistent with active central nervous system demyelination seen on brain MRI. One month after cessation of the immunotherapy, spontaneous regression of the demyelinating lesions was observed, suggesting a nivolumab-related toxicity. Conclusion We report the first case of a melanoma patient with an asymptomatic and spontaneously reversible central nervous system demyelination following nivolumab immunotherapy. This case highlights the need for better recognition of such atypical and rare neurological toxicities which could be mistaken for progressive brain metastases. Early recognition and appropriate management are crucial to reduce severity and duration of these toxicities, especially for patients with less favourable evolution.

Epithelial ovarian cancer (EOC) is usually diagnosed at an advanced stage and significantly contributes to cancer mortality in women. Despite multimodal treatment associating chemotherapy and surgery, most patients ultimately progress and require palliative systemic therapy.In EOC, the efficacy of anti-HER2 agents is minimal even after selecting patients for HER2 expression. ERBB2 gene amplification is observed in 3–10% of patients, depending on the specific method of detection and cutoffs. We report the case of a young woman with a FIGO stage IV high-grade serous ovarian cancer with an amplification of ERBB2. She was treated with the association of trastuzumab – pertuzumab after two lines of standard treatment and presented an excellent long-lasting partial response after 36 months of treatment.The association of trastuzumab and pertuzumab, without chemotherapy, has not been previously tested in this context and could be more efficacious than monotherapy with either agent. In addition, the significant benefit observed in this case could be attributed to the presence of a high-level focal amplification that is relatively rare and probably more specific than an increase in HER2 expression. In conclusion, prospective trials of the trastuzumab and pertuzumab combination should be considered in an appropriately selected EOC patient population.

Hit finding, scaffold hopping, and structure–activity relationship studies are important tasks in rational drug discovery. Implementation of these tasks strongly depends on the availability of compounds similar to a known bioactive molecule. SwissSimilarity is a web tool for low-to-high-throughput virtual screening of multiple chemical libraries to find molecules similar to a compound of interest. According to the similarity principle, the output list of molecules generated by SwissSimilarity is expected to be enriched in compounds that are likely to share common protein targets with the query molecule and that can, therefore, be acquired and tested experimentally in priority. Compound libraries available for screening using SwissSimilarity include approved drugs, clinical candidates, known bioactive molecules, commercially available and synthetically accessible compounds. The first version of SwissSimilarity launched in 2015 made use of various 2D and 3D molecular descriptors, including path-based FP2 fingerprints and ElectroShape vectors. However, during the last few years, new fingerprinting methods for molecular description have been developed or have become popular. Here we would like to announce the launch of the new version of the SwissSimilarity web tool, which features additional 2D and 3D methods for estimation of molecular similarity: extended-connectivity, MinHash, 2D pharmacophore, extended reduced graph, and extended 3D fingerprints. Moreover, it is now possible to screen for molecular structures having the same scaffold as the query compound. Additionally, all compound libraries available for screening in SwissSimilarity have been updated, and several new ones have been added to the list. Finally, the interface of the website has been comprehensively rebuilt to provide a better user experience. The new version of SwissSimilarity is freely available starting from December 2021.

Abstract Background The prevalence of connective tissue disease (CTD) induced by immune checkpoint inhibitors (CPIs) in the absence of pre-existing autoimmunity is unknown. Case presentation We report the case of a melanoma patient treated for 8 months with pembrolizumab who developed a subacute ataxic sensory neuronopathy (SNN), including a right trigeminal neuropathy. Salivary gland biopsy showed inflammatory changes suggestive of Sjögren’s syndrome, while brain MRI revealed enhancement of the right trigeminal ganglia. A high level of protein and pleocytosis was found in the cerebrospinal fluid, with negative cultures. Nerve conduction studies revealed the absence of sensory nerve action potentials in the upper and lower limbs and reduced motor responses in the upper limbs, fulfilling criteria for SNN. Blood tests revealed an important inflammatory syndrome, hemolytic anemia, elevation of total IgG levels and the presence of ANA autoantibodies specific to anti-SSA (52 and 60 kd). All these elements were absent before the initiation of the treatment with pembrolizumab. Initially, there was a clinical response following intravenous frontline methylprednisone, but the subacute relapse required the introduction of second-line treatment with intravenous immunoglobulins and then rituximab, which led to a quick clinical improvement. Conclusions Herein, we describe the first case of a patient who developed a typical SNN as a complication of severe neuro-Sjögren’s syndrome induced by pembrolizumab treatment.

We report here two cases of Herpes simplex virus encephalitis (HSE) in adult patients with very rare, previously uncharacterized, non synonymous heterozygous G634R and R203W substitution in mannan-binding lectin serine protease 2 (MASP2), a gene encoding a key protease of the lectin pathway of the complement system. None of the 2 patients had variants in genes involved in the TLR3-interferon signaling pathway. Both MASP2 variants induced functional defects in vitro, including a reduced (R203W) or abolished (G634R) protein secretion, a lost capability to cleave MASP-2 precursor into its active form (G634R) and an in vivo reduced antiviral activity (G634R). In a murine model of HSE, animals deficient in mannose binding lectins (MBL, the main pattern recognition molecule associated with MASP-2) had a decreased survival rate and an increased brain burden of HSV-1 compared to WT C57BL/6J mice. Altogether, these data suggest that MASP-2 deficiency can increase susceptibility to adult HSE.

Epithelial ovarian cancer (EOC) has low mutational load. Here the authors analyze circulating and tumor-infiltrating lymphocytes (TILs) from 19 EOC patients and report frequent recovery of neo-antigen-reactive T cells from both compartments but with distinct TCR repertoires that have higher affinity in TILs.

Abstract Hepatoblastoma is the most common malignant pediatric liver cancer. Histological evaluation of tumor biopsies is used to distinguish among the different subtypes of hepatoblastoma, with fetal and embryonal representing the two main epithelial components. With frequent CTNNB1 mutations, hepatoblastoma is a Wnt/β‐catenin‐driven malignancy. Considering that Wnt activation has been associated with tumor metabolic reprogramming, we characterized the metabolic profile of cells from hepatoblastoma and compared it to cells from hepatocellular carcinoma. First, we demonstrated that glucose transporter GLUT3 is a direct TCF4/β‐catenin target gene. RNA sequencing enabled to identify molecular and metabolic features specific to hepatoblastoma and revealed that several glycolytic enzymes are overexpressed in embryonal‐like compared to fetal‐like tumor cells. This led us to implement successfully three biomarkers to distinguish embryonal from fetal components by immunohistochemistry from a large panel of human hepatoblastoma samples. Functional analyses demonstrated that embryonal‐like hepatoblastoma cells are highly glycolytic and sensitive to hexokinase‐1 silencing. Altogether, our findings reveal a new, metabolic classification of human hepatoblastoma, with potential future implications for patients’ diagnosis and treatment.

We report a case of an uveal melanoma patient with GNAQ p.Gly48Leu who responded to MEK inhibition. At the time of the molecular analysis, the pathogenicity of the mutation was unknown. A tridimensional structural analysis showed that Gαq can adopt active and inactive conformations that lead to substantial changes, involving three important switch regions. Our molecular modelling study predicted that GNAQ p.Gly48Leu introduces new favorable interactions in its active conformation, whereas little or no impact is expected in its inactive form. This strongly suggests that GNAQ p.Gly48Leu is a possible tumor-activating driver mutation, consequently triggering the MEK pathway. In addition, we also found an FGFR4 p.Cys172Gly mutation, which was predicted by molecular modelling analysis to lead to a gain of function by impacting the Ig-like domain 2 folding, which is involved in FGF binding and increases the stability of the homodimer. Based on these analyses, the patient received the MEK inhibitor trametinib with a lasting clinical benefit. This work highlights the importance of molecular modelling for personalized oncology.

Immune checkpoints are crucial in the maintenance of antitumor immune responses. The activation or blockade of immune checkpoints is dependent on the interactions between receptors and ligands; such interactions can provide inhibitory or stimulatory signals, including the enhancement or suppression of T-cell proliferation, differentiation, and/or cytokine secretion. B-and T-lymphocyte attenuator (BTLA) is a lymphoid-specific cell surface receptor which is present on T-cells and interacts with herpes virus entry mediator (HVEM), which is present on tumor cells. The binding of HVEM to BTLA triggers an inhibitory signal which attenuates the immune response. This feature is interesting for studying the molecular interactions between HVEM and BTLA, as they may be targeted for novel immunotherapies. This work was based on the crystal structure of the BTLA/HVEM complex showing that BTLA binds the N-terminal cysteine-rich domain of HVEM. We investigated the amino acid sequence of HVEM and used molecular modeling methods to develop inhibitors of the BTLA/HVEM interaction. We synthesized novel compounds and determined their ability to interact with the BTLA protein and inhibit the formation of the BTLA/HVEM complex. Our results suggest that the HVEM (14−39) peptide is a potent inhibitor of the formation of the BTLA/HVEM protein complex.

Increased density of tumor-associated lymphatic vessels correlates with poor patient survival in melanoma and other cancers, yet lymphatic drainage is essential for initiating an immune response. Here we asked whether and how lymphatic vessel density (LVD) correlates with immune cell infiltration in primary tumors and lymph nodes (LNs) from patients with cutaneous melanoma. Using immunohistochemistry and quantitative image analysis, we found significant positive correlations between LVD and CD8+ T cell infiltration as well as expression of the immunosuppressive molecules inducible nitric oxide synthase (iNOS) and 2,3-dioxygénase (IDO). Interestingly, similar associations were seen in tumor-free LNs adjacent to metastatic ones, indicating loco-regional effects of tumors. Our data suggest that lymphatic vessels play multiple roles at tumor sites and LNs, promoting both T cell infiltration and adaptive immunosuppressive mechanisms. Lymph vessel associated T cell infiltration may increase immunotherapy success rates provided that the treatment overcomes adaptive immune resistance.

Antibody based immune-checkpoint blockade therapy is a major breakthrough in oncology, leading to clinical benefit for cancer patients. Among the growing family of inhibitory receptors, the B and T lymphocyte attenuator (BTLA), which interacts with herpes virus entry mediator (HVEM), is a promising target for immunotherapy. Indeed, BTLA inhibits T-cell proliferation and cytokine production. The crystal structure of the BTLA/HVEM complex has shown that the HVEM(26-38) fragment is directly involved in protein binding. We designed and analyzed the capacity of several analogs of this fragment to block the ligation between BTLA and HVEM, using competitive ELISA and cellular assay. We found that the HVEM(23-39) peptide can block BTLA/HVEM ligation. However, the blocking ability was due to the Cys encompassed in this peptide and that even free cysteine targeted the BTLA protein and blocked its interaction with HVEM. These data highlight a Cys-related artefact in vitro, which should be taken in consideration for future development of BTLA/HVEM blocking compounds.

Proteins belonging to the CAP superfamily are present in all kingdoms of life and have been implicated in different physiological processes. Their molecular mode of action, however, is poorly understood. Saccharomyces cerevisiae expresses three members of this superfamily, pathogen-related yeast (Pry)1, -2, and -3. We have recently shown that Pry function is required for the secretion of cholesteryl acetate and that Pry proteins bind cholesterol and cholesteryl acetate, suggesting that CAP superfamily members may generally act to bind sterols or related small hydrophobic compounds. Here, we analyzed the mode of sterol binding by Pry1. Computational modeling indicates that ligand binding could occur through displacement of a relatively poorly conserved flexible loop, which in some CAP family members displays homology to the caveolin-binding motif. Point mutations within this motif abrogated export of cholesteryl acetate but did not affect binding of cholesterol. Mutations of residues located outside the caveolin-binding motif, or mutations in highly conserved putative catalytic residues had no effect on export of cholesteryl acetate or on lipid binding. These results indicate that the caveolin-binding motif of Pry1, and possibly of other CAP family members, is crucial for selective lipid binding and that lipid binding may occur through displacement of the loop containing this motif.

Specific metabolic pathways are activated by different nutrients to adapt the organism to available resources. Although essential, these mechanisms are incompletely defined. Here, we report that medium-chain fatty acids contained in coconut oil, a major source of dietary fat, induce the liver ω-oxidation genes Cyp4a10 and Cyp4a14 to increase the production of dicarboxylic fatty acids. Furthermore, these activate all ω- and β-oxidation pathways through peroxisome proliferator activated receptor (PPAR) α and PPARγ, an activation loop normally kept under control by dicarboxylic fatty acid degradation by the peroxisomal enzyme L-PBE. Indeed, L-pbe−/− mice fed coconut oil overaccumulate dicarboxylic fatty acids, which activate all fatty acid oxidation pathways and lead to liver inflammation, fibrosis, and death. Thus, the correct homeostasis of dicarboxylic fatty acids is a means to regulate the efficient utilization of ingested medium-chain fatty acids, and its deregulation exemplifies the intricate relationship between impaired metabolism and inflammation.

The European Society for Medical Oncology (ESMO) and the American Society of Clinical Oncology (ASCO) are publishing a new edition of the ESMO/ASCO Global Curriculum (GC) thanks to contribution of 64 ESMO-appointed and 32 ASCO-appointed authors. First published in 2004 and updated in 2010, the GC edition 2016 answers to the need for updated recommendations for the training of physicians in medical oncology by defining the standard to be fulfilled to qualify as medical oncologists. At times of internationalisation of healthcare and increased mobility of patients and physicians, the GC aims to provide state-of-the-art cancer care to all patients wherever they live. Recent progress in the field of cancer research has indeed resulted in diagnostic and therapeutic innovations such as targeted therapies as a standard therapeutic approach or personalised cancer medicine apart from the revival of immunotherapy, requiring specialised training for medical oncology trainees. Thus, several new chapters on technical contents such as molecular pathology, translational research or molecular imaging and on conceptual attitudes towards human principles like genetic counselling or survivorship have been integrated in the GC. The GC edition 2016 consists of 12 sections with 17 subsections, 44 chapters and 35 subchapters, respectively. Besides renewal in its contents, the GC underwent a principal formal change taking into consideration modern didactic principles. It is presented in a template-based format that subcategorises the detailed outcome requirements into learning objectives, awareness, knowledge and skills. Consecutive steps will be those of harmonising and implementing teaching and assessment strategies.

19F-MRI and 19F-MRS can identify specific cell types after in-vitro or in-vivo 19F-labeling. Knowledge on the potential to track in-vitro 19F-labeled immune cells in tumor models by 19F-MRI/MRS is scarce.To study 19F-based MR techniques for in-vivo tracking of adoptively transferred immune cells after in-vitro 19F-labeling, i.e. to detect and monitor their migration non-invasively in melanoma-bearing mice.Splenocytes (SP) were labeled in-vitro with a perfluorocarbon (PFC) and IV-injected into non-tumor bearing mice. In-vitro PFC-labeled ovalbumin (OVA)-specific T cells from the T cell receptor-transgenic line OT-1, activated with anti-CD3 and anti-CD28 antibodies (Tact) or OVA-peptide pulsed antigen presenting cells (TOVA-act), were injected into B16 OVA melanoma-bearing mice. The distribution of the 19F-labelled donor cells was determined in-vivo by 19F-MRI/MRS. In-vivo 19F-MRI/MRS results were confirmed by ex-vivo 19F-NMR and flow cytometry.SP, Tact, and TOVA-act were successfully PFC-labeled in-vitro yielding 3x1011-1.4x1012 19F-atoms/cell in the 3 groups. Adoptively transferred 19F-labeled SP, TOVA-act, and Tact were detected by coil-localized 19F-MRS in the chest, abdomen, and left flank in most animals (corresponding to lungs, livers, and spleens, respectively, with highest signal-to-noise for SP vs TOVA-act and Tact, p

BACKGROUND: Cancer/testis (CT) genes are expressed only in the germ line and certain tumors and are most frequently located on the X-chromosome (the CT-X genes). Amongst the best studied CT-X genes are those encoding several MAGE protein families. The function of MAGE proteins is not well understood, but several have been shown to potentially influence the tumorigenic phenotype. METHODOLOGY/PRINCIPAL FINDINGS: We undertook a mutational analysis of coding regions of four CT-X MAGE genes, MAGEA1, MAGEA4, MAGEC1, MAGEC2 and the ubiquitously expressed MAGEE1 in human melanoma samples. We first examined cell lines established from tumors and matching blood samples from 27 melanoma patients. We found that melanoma cell lines from 37% of patients contained at least one mutated MAGE gene. The frequency of mutations in the coding regions of individual MAGE genes varied from 3.7% for MAGEA1 and MAGEA4 to 14.8% for MAGEC2. We also examined 111 fresh melanoma samples collected from 86 patients. In this case, samples from 32% of the patients exhibited mutations in one or more MAGE genes with the frequency of mutations in individual MAGE genes ranging from 6% in MAGEA1 to 16% in MAGEC1. SIGNIFICANCE: These results demonstrate for the first time that the MAGE gene family is frequently mutated in melanoma.

Ipilimumab and tremelimumab are human monoclonal antibodies (Abs) against cytotoxic T-lymphocyte antigen-4 (CTLA-4). Ipilimumab was the first agent to show a statistically significant benefit in overall survival in advanced melanoma patients. Currently, there is no proven association between the BRAFV600 mutation and the disease control rate in response to ipilimumab. This analysis was carried out to assess if BRAFV600 and NRAS mutation status affects the clinical outcome of anti-CTLA-4-treated melanoma patients. This is a retrospective multi-center analysis of 101 patients, with confirmed BRAF and NRAS mutation status, treated with anti-CTLA-4 antibodies from December 2006 until August 2012. The median overall survival, defined from the treatment start date with the anti-CTLA-4. Abs-treatment to death or till last follow up, of BRAFV600 or NRAS mutant patients (n = 62) was 10.12 months (95% CI 6.78-13.2) compared to 8.26 months (95% CI 6.02-19.9) in BRAFV600/NRASwt subpopulation (n = 39) (p = 0.67). The median OS of NRAS mutated patients (n = 24) was 12.1 months and although was prolonged compared to the median OS of BRAF mutated patients (n = 38, mOS = 8.03 months) or BRAFV600/NRASwt patients (n = 39, mOS = 8.26 months) the difference didn't reach statistical significance (p = 0.56). 69 patients were able to complete 4 cycles of anti-CTLA-4 treatment. Of the 24 patients treated with selected BRAF- or MEK-inhibitors, 16 patients received anti-CTLA 4 Abs following either a BRAF or MEK inhibitor with only 8 of them being able to finish 4 cycles of treatment. Based on our results, there is no difference in the median OS in patients treated with anti-CTLA-4 Abs implying that the BRAF/NRAS mutation status alone is not sufficient to predict the outcome of patients treated with anti-CTLA-4 Abs.

The cross-recognition of peptides by cytotoxic T lymphocytes is a key element in immunology and in particular in peptide based immunotherapy. Here we develop three-dimensional (3D) quantitative structure-activity relationships (QSARs) to predict cross-recognition by Melan-A-specific cytotoxic T lymphocytes of peptides bound to HLA A*0201 (hereafter referred to as HLA A2). First, we predict the structure of a set of self- and pathogen-derived peptides bound to HLA A2 using a previously developed ab initio structure prediction approach [Fagerberg et al., J. Mol. Biol., 521-46 (2006)]. Second, shape and electrostatic energy calculations are performed on a 3D grid to produce similarity matrices which are combined with a genetic neural network method [So et al., J. Med. Chem., 4347-59 (1997)] to generate 3D-QSAR models. The models are extensively validated using several different approaches. During the model generation, the leave-one-out cross-validated correlation coefficient (q (2)) is used as the fitness criterion and all obtained models are evaluated based on their q (2) values. Moreover, the best model obtained for a partitioned data set is evaluated by its correlation coefficient (r = 0.92 for the external test set). The physical relevance of all models is tested using a functional dependence analysis and the robustness of the models obtained for the entire data set is confirmed using y-randomization. Finally, the validated models are tested for their utility in the setting of rational peptide design: their ability to discriminate between peptides that only contain side chain substitutions in a single secondary anchor position is evaluated. In addition, the predicted cross-recognition of the mono-substituted peptides is confirmed experimentally in chromium-release assays. These results underline the utility of 3D-QSARs in peptide mimetic design and suggest that the properties of the unbound epitope are sufficient to capture most of the information to determine the cross-recognition.

Persistent viruses are kept in check by specific lymphocytes. The clonal T cell receptor (TCR) repertoire against Epstein-Barr virus (EBV), once established following primary infection, exhibits a robust stability over time. However, the determinants contributing to this long-term persistence are still poorly characterized. Taking advantage of an in vivo clinical setting where lymphocyte homeostasis was transiently perturbed, we studied EBV antigen-specific CD8 T cells before and after non-myeloablative lympho-depleting chemotherapy of melanoma patients. Despite more advanced T cell differentiation, patients T cells showed clonal composition comparable to healthy individuals, sharing a preference for TRBV20 and TRBV29 gene segment usage and several co-dominant public TCR clonotypes. Moreover, our data revealed the presence of relatively few dominant EBV antigen-specific T cell clonotypes, which mostly persisted following transient lympho-depletion (TLD) and lymphocyte recovery, likely related to absence of EBV reactivation and de novo T cell priming in these patients. Interestingly, persisting clonotypes frequently co-expressed memory/homing-associated genes (CD27, IL7R, EOMES, CD62L/SELL and CCR5) supporting the notion that they are particularly important for long-lasting CD8 T cell responses. Nevertheless, the clonal composition of EBV-specific CD8 T cells was preserved over time with the presence of the same dominant clonotypes after non-myeloablative chemotherapy. The observed clonotype persistence demonstrates high robustness of CD8 T cell homeostasis and reconstitution.

Inhibitory receptors mediate CD8 T-cell hyporesponsiveness against cancer and infectious diseases. PD-1 and CTLA-4 have been extensively studied, and blocking antibodies have already shown clinical benefit for cancer patients. Only little is known on extended co-expression of inhibitory receptors and their ligands. Here we analyzed the expression of eight inhibitory receptors by tumor-antigen specific CD8 T-cells. We found that the majority of effector T-cells simultaneously expressed four or more of the inhibitory receptors BTLA, TIM-3, LAG-3, KRLG-1, 2B4, CD160, PD-1 and CTLA-4. There were major differences depending on antigen-specificity, differentiation and anatomical localization of T-cells. On the other hand, naive T-cells were only single or double positive for BTLA and TIM-3. Extended co-expression is likely relevant for effector T-cells, as we found expression of multiple ligands in metastatic lesions of melanoma patients. Together, our data suggest that naive T-cells are primarily regulated by BTLA and TIM-3, whereas effector cells interact via larger numbers of inhibitory receptors. Blocking multiple inhibitory receptors simultaneously or sequentially may improve T-cell based therapies, but further studies are necessary to clarify the role of each receptor-ligand pair.

Crystallographic data about T-Cell Receptor - peptide - major histocompatibility complex class I (TCRpMHC) interaction have revealed extremely diverse TCR binding modes triggering antigen recognition. Understanding the molecular basis that governs TCR orientation over pMHC is still a considerable challenge. We present a simplified rigid approach applied on all non-redundant TCRpMHC crystal structures available. The CHARMM force field in combination with the FACTS implicit solvation model is used to study the role of long-distance interactions between the TCR and pMHC. We demonstrate that the sum of the coulomb interactions and the electrostatic solvation energies is sufficient to identify two orientations corresponding to energetic minima at 0° and 180° from the native orientation. Interestingly, these results are shown to be robust upon small structural variations of the TCR such as changes induced by Molecular Dynamics simulations, suggesting that shape complementarity is not required to obtain a reliable signal. Accurate energy minima are also identified by confronting unbound TCR crystal structures to pMHC. Furthermore, we decompose the electrostatic energy into residue contributions to estimate their role in the overall orientation. Results show that most of the driving force leading to the formation of the complex is defined by CDR1,2/MHC interactions. This long-distance contribution appears to be independent from the binding process itself, since it is reliably identified without considering neither short-range energy terms nor CDR induced fit upon binding. Ultimately, we present an attempt to predict the TCR/pMHC binding mode for a TCR structure obtained by homology modeling. The simplicity of the approach and the absence of any fitted parameters make it also easily applicable to other types of macromolecular protein complexes.