Modeling tumor size dynamics based on real-world electronic health records and image data in advanced melanoma patients receiving immunotherapy.

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.

Semiautomated Pipeline to Quantify Tumor Evolution From Real-World Positron Emission Tomography/Computed Tomography Imaging.

PURPOSE: A semiautomated pipeline for the collection and curation of free-text and imaging real-world data (RWD) was developed to quantify cancer treatment outcomes in large-scale retrospective real-world studies. The objectives of this article are to illustrate the challenges of RWD extraction, to demonstrate approaches for quality assurance, and to showcase the potential of RWD for precision oncology. METHODS: We collected data from patients with advanced melanoma receiving immune checkpoint inhibitors at the Lausanne University Hospital. Cohort selection relied on semantically annotated electronic health records and was validated using process mining. The selected imaging examinations were segmented using an automatic commercial software prototype. A postprocessing algorithm enabled longitudinal lesion identification across imaging time points and consensus malignancy status prediction. Resulting data quality was evaluated against expert-annotated ground-truth and clinical outcomes obtained from radiology reports. RESULTS: The cohort included 108 patients with melanoma and 465 imaging examinations (median, 3; range, 1-15 per patient). Process mining was used to assess clinical data quality and revealed the diversity of care pathways encountered in a real-world setting. Longitudinal postprocessing greatly improved the consistency of image-derived data compared with single time point segmentation results (classification precision increased from 53% to 86%). Image-derived progression-free survival resulting from postprocessing was comparable with the manually curated clinical reference (median survival of 286 v 336 days, P = .89). CONCLUSION: We presented a general pipeline for the collection and curation of text- and image-based RWD, together with specific strategies to improve reliability. We showed that the resulting disease progression measures match reference clinical assessments at the cohort level, indicating that this strategy has the potential to unlock large amounts of actionable retrospective real-world evidence from clinical records.

Interactive process mining of cancer treatment sequences with melanoma real-world data.

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.

A differential process mining analysis of COVID-19 management for cancer patients.

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.

Safety and Efficacy of Ipilimumab plus Nivolumab and Sequential Selective Internal Radiation Therapy in Hepatic and Extrahepatic Metastatic Uveal Melanoma.

To assess the safety and efficacy of ipilimumab plus nivolumab around selective internal radiation therapy (SIRT) in patients with metastatic uveal melanoma (mUM). We present a retrospective, single center study of 32 patients with mUM divided into two groups based on the treatment received between April 2013 and April 2021. The SIRT_IpiNivo cohort was treated with Yttrium-90 microspheres and ipilimumab plus nivolumab before or after the SIRT (n = 18). The SIRT cohort underwent SIRT but did not receive combined immunotherapy with ipilimumab plus nivolumab (n = 14). Twelve patients (66.7%) of the SIRT_IpiNivo arm received SIRT as first-line treatment and six patients (33.3%) received ipilimumab plus nivolumab prior to SIRT. In the SIRT group, seven patients (50.0%) received single-agent immunotherapy. One patient treated with combined immunotherapy 68 months after the SIRT was included in this group. At the start of ipilimumab plus nivolumab, 94.4% (n = 17) presented hepatic metastases and 72.2% (n = 13) had extra liver disease. Eight patients (44.4%) of the SIRT_IpiNivo group experienced grade 3 or 4 immune related adverse events, mainly colitis and hepatitis. Median overall survival from the diagnosis of metastases was 49.6 months (95% confidence interval (CI); 24.1-not available (NA)) in the SIRT_IpiNivo group compared with 13.6 months (95% CI; 11.5-NA) in the SIRT group (log-rank p-value 0.027). The presence of extra liver metastases at the time of SIRT, largest liver lesion more than 8 cm (M1c) and liver tumor volume negatively impacted the survival. This real-world cohort suggests that a sequential treatment of ipilimumab plus nivolumab and SIRT is a well-tolerated therapeutic approach with promising survival rates.

Structural Prediction of Peptide-MHC Binding Modes.

The immune system is constantly protecting its host from the invasion of pathogens and the development of cancer cells. The specific CD8+ T-cell immune response against virus-infected cells and tumor cells is based on the T-cell receptor recognition of antigenic peptides bound to class I major histocompatibility complexes (MHC) at the surface of antigen presenting cells. Consequently, the peptide binding specificities of the highly polymorphic MHC have important implications for the design of vaccines, for the treatment of autoimmune diseases, and for personalized cancer immunotherapy. Evidence-based machine-learning approaches have been successfully used for the prediction of peptide binders and are currently being developed for the prediction of peptide immunogenicity. However, understanding and modeling the structural details of peptide/MHC binding is crucial for a better understanding of the molecular mechanisms triggering the immunological processes, estimating peptide/MHC affinity using universal physics-based approaches, and driving the design of novel peptide ligands. Unfortunately, due to the large diversity of MHC allotypes and possible peptides, the growing number of 3D structures of peptide/MHC (pMHC) complexes in the Protein Data Bank only covers a small fraction of the possibilities. Consequently, there is a growing need for rapid and efficient approaches to predict 3D structures of pMHC complexes. Here, we review the key characteristics of the 3D structure of pMHC complexes before listing databases and other sources of information on pMHC structures and MHC specificities. Finally, we discuss some of the most prominent pMHC docking software.

Estimated Costs of the Ipilimumab-Nivolumab Therapy and Related Adverse Events in Metastatic Melanoma.

Combined ipilimumab and nivolumab significantly improve outcomes in metastatic melanoma patients but bear an important financial impact on the healthcare system. Here, we analyze the treatment costs, focusing on irAE. We conducted a retrospective analysis of 62 melanoma patients treated with ipilimumab-nivolumab at the Lausanne University Hospital between 1 June 2016 and 31 August 2019. The frequency of irAEs and outcomes were evaluated. All melanoma-specific costs were analyzed from the first ipilimumab-nivolumab dose until the therapy given subsequently or death. A total of 54/62 (87%) patients presented at least one irAE, and 31/62 (50%) presented a grade 3-4 irAE. The majority of patients who had a complete response 12/14 (86%) and 21/28 (75%) of overall responders presented a grade 3-4 toxicity, and there were no responses in patients without toxicity. Toxicity costs represented only 3% of the total expenses per patient. The most significant contributions were medication costs (44%) and disease costs (39%), mainly disease-related hospitalization costs, not toxicity-related. Patients with a complete response had the lowest global median cost per week of follow up (EUR 2425) and patients who had progressive disease (PD), the highest one (EUR 8325). Except for one patient who had a Grade 5 toxicity (EUR 6043/week), we observe that less severe toxicity grades (EUR 9383/week for Grade 1), or even the absence of toxicity (EUR 9922/week), are associated with higher median costs per week (vs. EUR 3266/week for Grade 4 and EUR 2850/week for Grade 3). The cost of toxicities was unexpectedly low compared to the total costs, especially medication costs. Patients with higher toxicity grades had better outcomes and lower total costs due to treatment discontinuation.

Turning tumors from cold to inflamed to improve immunotherapy response.

Immune checkpoint inhibitors have revolutionized the treatment landscape for a number of cancers over the last few decades. Nevertheless, a majority of patients still do not benefit from these treatments. Such patient-specific lack of response can be predicted, in part, from the immune phenotypes present in the tumor microenvironment. We provide a perspective on options to reprogram the tumors and their microenvironment to increase the therapeutic efficacy of immunotherapies and expand their efficacy against cold tumors. Additionally, we review data from current preclinical and clinical trials aimed at testing the different therapeutic options in monotherapy or preferably in combination with checkpoint inhibitors.

Truly privacy-preserving federated analytics for precision medicine with multiparty homomorphic encryption.

Using real-world evidence in biomedical research, an indispensable complement to clinical trials, requires access to large quantities of patient data that are typically held separately by multiple healthcare institutions. We propose FAMHE, a novel federated analytics system that, based on multiparty homomorphic encryption (MHE), enables privacy-preserving analyses of distributed datasets by yielding highly accurate results without revealing any intermediate data. We demonstrate the applicability of FAMHE to essential biomedical analysis tasks, including Kaplan-Meier survival analysis in oncology and genome-wide association studies in medical genetics. Using our system, we accurately and efficiently reproduce two published centralized studies in a federated setting, enabling biomedical insights that are not possible from individual institutions alone. Our work represents a necessary key step towards overcoming the privacy hurdle in enabling multi-centric scientific collaborations.

Unusual mode of dimerization of retinitis pigmentosa-associated F220C rhodopsin.

Mutations in the G protein-coupled receptor (GPCR) rhodopsin are a common cause of autosomal dominant retinitis pigmentosa, a blinding disease. Rhodopsin self-associates in the membrane, and the purified monomeric apo-protein opsin dimerizes in vitro as it transitions from detergent micelles to reconstitute into a lipid bilayer. We previously reported that the retinitis pigmentosa-linked F220C opsin mutant fails to dimerize in vitro, reconstituting as a monomer. Using fluorescence-based assays and molecular dynamics simulations we now report that whereas wild-type and F220C opsin display distinct dimerization propensities in vitro as previously shown, they both dimerize in the plasma membrane of HEK293 cells. Unexpectedly, molecular dynamics simulations show that F220C opsin forms an energetically favored dimer in the membrane when compared with the wild-type protein. The conformation of the F220C dimer is unique, with transmembrane helices 5 and 6 splayed apart, promoting widening of the intracellular vestibule of each protomer and influx of water into the protein interior. FRET experiments with SNAP-tagged wild-type and F220C opsin expressed in HEK293 cells are consistent with this conformational difference. We speculate that the unusual mode of dimerization of F220C opsin in the membrane may have physiological consequences.

Probing the Conformational Dynamics of Affinity-Enhanced T Cell Receptor Variants upon Binding the Peptide-Bound Major Histocompatibility Complex by Hydrogen/Deuterium Exchange Mass Spectrometry.

Binding of the T cell receptor (TCR) to its cognate, peptide antigen-loaded major histocompatibility complex (pMHC) is a key interaction for triggering T cell activation and ultimately elimination of the target cell. Despite the importance of this interaction for cellular immunity, a comprehensive molecular understanding of TCR specificity and affinity is lacking. We conducted hydrogen/deuterium exchange mass spectrometry (HDX-MS) analyses of individual affinity-enhanced TCR variants and clinically relevant pMHC class I molecules (HLA-A*0201/NY-ESO-1157-165) to investigate the causality between increased binding affinity and conformational dynamics in TCR-pMHC complexes. Differential HDX-MS analyses of TCR variants revealed that mutations for affinity enhancement in TCR CDRs altered the conformational response of TCR to pMHC ligation. Improved pMHC binding affinity was in general observed to correlate with greater differences in HDX upon pMHC binding in modified TCR CDR loops, thereby providing new insights into the TCR-pMHC interaction. Furthermore, a specific point mutation in the ß-CDR3 loop of the NY-ESO-1 TCR associated with a substantial increase in binding affinity resulted in a substantial change in pMHC binding kinetics (i.e., very slow kon, revealed by the detection of EX1 HDX kinetics), thus providing experimental evidence for a slow induced-fit binding mode. We also examined the conformational impact of pMHC binding on an unrelated TRAV12-2 gene-encoded TCR directed against the immunodominant MART-126-35 cancer antigen restricted by HLA-A*0201. Our findings provide a molecular basis for the observed TRAV12-2 gene bias in natural CD8+ T cell-based immune responses against the MART-1 antigen, with potential implications for general ligand discrimination and TCR cross-reactivity processes.

A Graphic Encoding Method for Quantitative Classification of Protein Structure and Representation of Conformational Changes.

In order to successfully predict a proteins function throughout its trajectory, in addition to uncovering changes in its conformational state, it is necessary to employ techniques that maintain its 3D information while performing at scale. We extend a protein representation that encodes secondary and tertiary structure into fix-sized, color images, and a neural network architecture (called GEM-net) that leverages our encoded representation. We show the applicability of our method in two ways: (1) performing protein function prediction, hitting accuracy between 78 and 83 percent, and (2) visualizing and detecting conformational changes in protein trajectories during molecular dynamics simulations.

Acquired resistance to anti-MAPK targeted therapy confers an immune-evasive tumor microenvironment and cross-resistance to immunotherapy in melanoma.

How targeted therapies and immunotherapies shape tumors, and thereby influence subsequent therapeutic responses, is poorly understood. In the present study, we show, in melanoma patients and mouse models, that when tumors relapse after targeted therapy with MAPK pathway inhibitors, they are cross-resistant to immunotherapies, despite the different modes of action of these therapies. We find that cross-resistance is mediated by a cancer cell-instructed, immunosuppressive tumor microenvironment that lacks functional CD103+ dendritic cells, precluding an effective T cell response. Restoring the numbers and functionality of CD103+ dendritic cells can re-sensitize cross-resistant tumors to immunotherapy. Cross-resistance does not arise from selective pressure of an immune response during evolution of resistance, but from the MAPK pathway, which not only is reactivated, but also exhibits an increased transcriptional output that drives immune evasion. Our work provides mechanistic evidence for cross-resistance between two unrelated therapies, and a scientific rationale for treating patients with immunotherapy before they acquire resistance to targeted therapy.

What Role Can Process Mining Play in Recurrent Clinical Guidelines Issues? A Position Paper.

In the age of Evidence-Based Medicine, Clinical Guidelines (CGs) are recognized to be an indispensable tool to support physicians in their daily clinical practice. Medical Informatics is expected to play a relevant role in facilitating diffusion and adoption of CGs. However, the past pioneering approaches, often fragmented in many disciplines, did not lead to solutions that are actually exploited in hospitals. Process Mining for Healthcare (PM4HC) is an emerging discipline gaining the interest of healthcare experts, and seems able to deal with many important issues in representing CGs. In this position paper, we briefly describe the story and the state-of-the-art of CGs, and the efforts and results of the past approaches of medical informatics. Then, we describe PM4HC, and we answer questions like how can PM4HC cope with this challenge? Which role does PM4HC play and which rules should be employed for the PM4HC scientific community?

The combination of stereotactic radiosurgery with immune checkpoint inhibition or targeted therapy in melanoma patients with brain metastases: a retrospective study.

BACKGROUND: Evidence pointing to a synergistic effect of stereotactic radiosurgery (SRS) with concurrent immunotherapy or targeted therapy in patients with melanoma brain metastases (BM) is increasing. We aimed to analyze the effect on overall survival (OS) of immune checkpoint inhibitors (ICI) or BRAF/MEK inhibitors initiated during the 9 weeks before or after SRS. We also evaluated the prognostic value of patients' and disease characteristics as predictors of OS in patients treated with SRS. METHODS: We identified patients with BM from cutaneous or unknown primary origin melanoma treated with SRS between 2011 and 2018. RESULTS: We included 84 patients. The median OS was 12 months (95% CI 9-20 months). The median follow-up was 30 months (95% CI 28-49). Twenty-eight patients with newly diagnosed BM initiated anti-PD-1 +/-CTLA-4 therapy (n = 18), ipilimumab monotherapy (n = 10) or BRAF+/- MEK inhibitors (n = 11), during the 9 weeks before or after SRS. Patients who received anti-PD-1 +/-CTLA-4 mAb showed an improved survival in comparison to ipilimumab monotherapy (OS 24 vs. 7.5 months; HR 0.32, 95% 0.12-0.83, p = 0.02) and BRAF +/-MEK inhibitors (OS 24 vs. 7 months, respectively; HR 0.11, 95% 0.04-0.34, p = 0.0001). This benefit remained significant when compared to the subgroup of patients treated with dual BRAF/MEK inhibition (BMi) (n = 5). In a multivariate Cox regression analysis an age > 65, synchronous BM, > 2 metastatic sites, > 4 BM, and an ECOG > 1 were correlated with poorer prognosis. A treatment with anti-PD-1+/-CTLA-4 mAbs within 9 weeks of SRS was associated with better outcomes. The presence of serum lactate dehydrogenase (LDH) levels ≥ 2xULN at BM diagnosis was associated with lower OS (HR 1.60, 95% CI 1.03-2.50; p = 0.04). CONCLUSIONS: The concurrent administration of anti-PD-1+/-CTLA-4 mAbs with SRS was associated with improved survival in melanoma patients with newly diagnosed BM. In addition to CNS tumor burden, the extension of systemic disease retains its prognostic value in patients treated with SRS. Elevated serum LDH levels are predictors of poor outcome in these patients.

CD8 Binding of MHC-Peptide Complexes in cis or trans Regulates CD8+ T-cell Responses.

The coreceptor CD8αß can greatly promote activation of T cells by strengthening T-cell receptor (TCR) binding to cognate peptide-MHC complexes (pMHC) on antigen presenting cells and by bringing p56Lck to TCR/CD3. Here, we demonstrate that CD8 can also bind to pMHC on the T cell (in cis) and that this inhibits their activation. Using molecular modeling, fluorescence resonance energy transfer experiments on living cells, biochemical and mutational analysis, we show that CD8 binding to pMHC in cis involves a different docking mode and is regulated by posttranslational modifications including a membrane-distal interchain disulfide bond and negatively charged O-linked glycans near positively charged sequences on the CD8ß stalk. These modifications distort the stalk, thus favoring CD8 binding to pMHC in cis. Differential binding of CD8 to pMHC in cis or trans is a means to regulate CD8+ T-cell responses and provides new translational opportunities.

Endpoint-restricted adiabatic free energy dynamics approach for the exploration of biomolecular conformational equilibria.

A method for calculating the free energy difference between two structurally defined conformational states of a chemical system is developed. A path is defined using a previously reported collective variable that interpolates between two or more conformations, and a restraint is introduced in order to keep the system close to the path. The evolution of the system along the path, which typically presents a high free energy barrier, is generated using enhanced sampling schemes. Although the formulation of the method in terms of a path is quite general, an important advance in this work is the demonstration that prior knowledge of the path is, in fact, not needed and that the free energy difference can be obtained using a simplified definition of the path collective variable that only involves the endpoints. We first validate this method on cyclohexane isomerization. The method is then tested for an extensive conformational change in a realistic molecular system by calculating the free energy difference between the α-helix and ß-hairpin conformations of deca-alanine in solution. Finally, the method is applied to a biologically relevant system to calculate the free energy difference of an observed and a hypothetical conformation of an antigenic peptide bound to a major histocompatibility complex.

Substrate-modulated unwinding of transmembrane helices in the NSS transporter LeuT.

LeuT, a prokaryotic member of the neurotransmitter:sodium symporter (NSS) family, is an established structural model for mammalian NSS counterparts. We investigate the substrate translocation mechanism of LeuT by measuring the solution-phase structural dynamics of the transporter in distinct functional states by hydrogen/deuterium exchange mass spectrometry (HDX-MS). Our HDX-MS data pinpoint LeuT segments involved in substrate transport and reveal for the first time a comprehensive and detailed view of the dynamics associated with transition of the transporter between outward- and inward-facing configurations in a Na+- and K+-dependent manner. The results suggest that partial unwinding of transmembrane helices 1/5/6/7 drives LeuT from a substrate-bound, outward-facing occluded conformation toward an inward-facing open state. These hitherto unknown, large-scale conformational changes in functionally important transmembrane segments, observed for LeuT in detergent-solubilized form and when embedded in a native-like phospholipid bilayer, could be of physiological relevance for the translocation process.

Thermodynamic Coupling Function Analysis of Allosteric Mechanisms in the Human Dopamine Transporter.

Allostery plays a crucial role in the mechanism of neurotransmitter-sodium symporters, such as the human dopamine transporter. To investigate the molecular mechanism that couples the transport-associated inward release of the Na+ ion from the Na2 site to intracellular gating, we applied a combination of the thermodynamic coupling function (TCF) formalism and Markov state model analysis to a 50-µs data set of molecular dynamics trajectories of the human dopamine transporter, in which multiple spontaneous Na+ release events were observed. Our TCF approach reveals a complex landscape of thermodynamic coupling between Na+ release and inward-opening, and identifies diverse, yet well-defined roles for different Na+-coordinating residues. In particular, we identify a prominent role in the allosteric coupling for the Na+-coordinating residue D421, where mutation has previously been associated with neurological disorders. Our results highlight the power of the TCF analysis to elucidate the molecular mechanism of complex allosteric processes in large biomolecular systems.