Circadian tumor infiltration and function of CD8+ T cells dictate immunotherapy efficacy.

The quality and quantity of tumor-infiltrating lymphocytes, particularly CD8+ T cells, are important parameters for the control of tumor growth and response to immunotherapy. Here, we show in murine and human cancers that these parameters exhibit circadian oscillations, driven by both the endogenous circadian clock of leukocytes and rhythmic leukocyte infiltration, which depends on the circadian clock of endothelial cells in the tumor microenvironment. To harness these rhythms therapeutically, we demonstrate that efficacy of chimeric antigen receptor T cell therapy and immune checkpoint blockade can be improved by adjusting the time of treatment during the day. Furthermore, time-of-day-dependent T cell signatures in murine tumor models predict overall survival in patients with melanoma and correlate with response to anti-PD-1 therapy. Our data demonstrate the functional significance of circadian dynamics in the tumor microenvironment and suggest the importance of leveraging these features for improving future clinical trial design and patient care.

Response to tumor-infiltrating lymphocyte adoptive therapy is associated with preexisting CD8+ T-myeloid cell networks in melanoma.

Adoptive cell therapy (ACT) using ex vivo-expanded tumor-infiltrating lymphocytes (TILs) can eliminate or shrink metastatic melanoma, but its long-term efficacy remains limited to a fraction of patients. Using longitudinal samples from 13 patients with metastatic melanoma treated with TIL-ACT in a phase 1 clinical study, we interrogated cellular states within the tumor microenvironment (TME) and their interactions. We performed bulk and single-cell RNA sequencing, whole-exome sequencing, and spatial proteomic analyses in pre- and post-ACT tumor tissues, finding that ACT responders exhibited higher basal tumor cell-intrinsic immunogenicity and mutational burden. Compared with nonresponders, CD8+ TILs exhibited increased cytotoxicity, exhaustion, and costimulation, whereas myeloid cells had increased type I interferon signaling in responders. Cell-cell interaction prediction analyses corroborated by spatial neighborhood analyses revealed that responders had rich baseline intratumoral and stromal tumor-reactive T cell networks with activated myeloid populations. Successful TIL-ACT therapy further reprogrammed the myeloid compartment and increased TIL-myeloid networks. Our systematic target discovery study identifies potential T-myeloid cell network-based biomarkers that could improve patient selection and guide the design of ACT clinical trials.

Effects of CTLA-4 Single Nucleotide Polymorphisms on Toxicity of Ipilimumab-Containing Regimens in Patients With Advanced Stage Melanoma.

Single nucleotide polymorphisms (SNPs) in the cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) gene, an inhibitor of T-cell priming, are associated with auto and alloimmunity. Studies implied a role for these SNPs as surrogate markers for immunotherapy-outcome in patients with melanoma. However, no predictive SNPs are defined to date. We analyzed different CTLA-4 SNPs in a large multicenter cohort of patients with ipilimumab-treated melanoma and investigated possible correlations with treatment-related outcomes. Archival blood and/or tumor tissue samples were collected from 361 patients with advanced-stage ipilimumab-treated (±nivolumab) in 6 Swiss and Dutch hospitals. Matrix-assisted laser desorption/ionization-time of flight mass spectrometry based DNA genotyping was performed for 10 different CTLA-4 SNPs: 49A>G, CT60G>A, Jo27T>C, Jo30G>A, Jo31G>T, -658C>T, -1722T>C, -1661A>G, 318C>T, and C>T rs1863800. Associations between different allele genotypes and occurrence of grade ≥3 adverse events (AEs) and survival were tested using univariable logistic regressions or Cox proportional hazard models. 262/361 (73%) patients could be analyzed; 65% of those were males, the median age was 58 years, 39% showed a partial or complete response, and 65% had ≥1 AEs. A TT-genotype of -1722T>C SNP was significantly associated with a lower incidence of grade ≥3 AEs ( P = 0.049), whereas the GG-genotype of CT60G>A correlated with a higher incidence of grade ≥3 AEs ( P = 0.026). The TT-genotype of Jo27T>C SNP ( P = 0.056) and GG-genotype of Jo31G>T ( P = 0.046) were associated with overall survival. CTLA-4 SNPs might predict treatment-related outcomes in patients with melanoma receiving ipilimumab. Confirmatory studies are needed to fully exploit those findings as predictive biomarkers for ipilimumab AEs.

Outcomes of patients with resected stage III/IV acral or mucosal melanoma, treated with adjuvant anti-PD-1 based therapy.

IMPORTANCE: Acral (AM) and mucosal melanomas (MM) are rare subtypes with a poor prognosis. In those with advanced disease, anti-PD-1 (PD1) therapy has reduced activity compared to that seen in non-acral cutaneous melanoma. OBJECTIVE: To determine the efficacy of adjuvant PD1 in resected AM or MM. DESIGN: An international, retrospective cohort study SETTING: Data up to November 2021 collected from 20 centres across 10 countries. PARTICIPANTS: One hundred and ninety four patients with resected stage III or IV1 AM or MM who received adjuvant PD1 were included and compared to matched patients from the Melanoma Institute Australia (MIA) database using a propensity score matching analysis. MAIN OUTCOMES AND MEASURES: Recurrence-free survival (RFS), distant metastasis-free survival (DMFS) and overall survival (OS) were investigated. RESULTS: Forty five of 139 (32%) AM and 9 of 55 (16%) MM patients completed adjuvant therapy. The main reason for early treatment cessation in both groups was disease recurrence: 51 (37%) and 30 (55%) in the AM and MM groups, respectively. In the AM group adjuvant PD1 was associated with a longer RFS [HR-0.69 (0.52-0.92, p = 0.0127)], DMFS [HR0.58 (0.38-0.89, p = 0.0134)] and OS [HR of 0.59 (0.38-0.92, p-value 0.0196)] when compared to the historical cohort. In the MM group there was no statistical difference in RFS [HR1.36 (0.69-2.68,p-value 0.3799], DMFS or OS. CONCLUSION AND RELEVANCE: After adjuvant PD1, both AM and MM have a high risk of recurrence. Our data suggests a benefit to using adjuvant PD1 therapy in resected AM but not in resected MM. Additional studies to investigate the efficacy of adjuvant PD1 for MM are needed.

Efficacy and safety of 'Second Adjuvant' therapy with BRAF/MEK inhibitors after local therapy for recurrent melanoma following adjuvant PD-1 based immunotherapy.

BACKGROUND: Anti-PD-1 antibodies and BRAK/MEK inhibitors (BRAF/MEKi) reduce the risk of recurrence for patients with resected stage III melanoma. BRAFV600-mutated (BRAFmut) melanoma patients who recur with isolated disease following adjuvant therapy may be suitable for 'second adjuvant' treatment after local therapy. We sought to examine the efficacy and safety of 'second adjuvant' BRAF/MEKi. PATIENTS AND METHODS: Patients with BRAFmut melanoma treated with adjuvant PD-1 based immunotherapy who recurred, underwent definitive local therapy and were then treated with adjuvant BRAF/MEKi were identified retrospectively from 13 centres (second adjuvant group). Demographics, disease and treatment characteristics and outcome data were examined. Outcomes were compared to BRAFmut patients who did not receive 'second adjuvant' therapy (no second adjuvant group). RESULTS: 73 patients were included; 61 who received 'second adjuvant' therapy and 12 who did not. Most initially recurred on PD-1 therapy (66%). There were no differences in characteristics between groups. 92% of second adjuvant group received dabrafenib and trametinib and median duration of therapy was 11.8 months (0.4, 34.5). 72% required dose adjustments, 23% had grade 3 + toxicity and 38% permanently discontinued drug due to toxicity. After median 26.1 months (1.9, 56.3) follow-up, recurrence-free survival (RFS) was improved in second adjuvant group versus no second adjuvant group (median 30.8 vs 4 months, HR 0.35; p = 0.014), largely driven by a delay in early recurrence, with no difference in overall survival (p = 0.59). CONCLUSION: This is the first study examining outcomes of 'second adjuvant' targeted therapy for melanoma, after failure of adjuvant PD-1 based immunotherapy. Data suggest a short-term improvement in RFS, but at the cost of toxicity. Alternative strategies and more data on sequencing adjuvant therapies are required to improve outcomes.

The features and management of acquired resistance to PD1-based therapy in metastatic melanoma.

BACKGROUND: Anti-PD-1 therapy (PD1) either alone or with anti-CTLA-4 (CTLA4), has high initial response rates, however 20% of patients (pts) with complete response (CR) and 30% with partial response (PR) within 12 months of treatment experience subsequent disease progression by 6 years. The nature and optimal management of this acquired resistance (AR) remains unknown. METHODS: Pts from 16 centres who responded to PD1-based therapy and who later progressed were examined. Demographics, disease characteristics and subsequent treatments were evaluated. RESULTS: 299 melanoma pts were identified, median age 64y, 44% BRAFV600m. 172 (58%) received PD1 alone, 114 (38%) PD1/CTLA4 and 13 (4%) PD1 and an investigational drug. 90 (30%) pts had CR, 209 (70%) PR. Median time to AR was 12.6 mo (95% CI, 11.3, 14.2). Most (N = 193, 65%) progressed in a single organ site, and in a solitary lesion (N = 151, 51%). The most frequent sites were lymph nodes (38%) and brain (25%). Management at AR included systemic therapy (ST, 45%), local therapy (LT) +ST (31%), LT alone (21%), or observation (3%). There was no statistical difference in PFS2 or OS based on management, however, PFS2 was numerically superior for pts treated with ST alone who progressed off PD1 therapy than those who progressed on PD1 (2-year PFS2 42% versus 25%, p = 0.249). mOS from AR was 38.0 months (95% CI, 29.5-NR); longer in single-site versus multi-site progression (2-year OS 70% vs 54%, p < 0·001). CONCLUSIONS: Acquired resistance to PD1 therapy in melanoma is largely oligometastatic, and pts may have a favorable survival outcome following salvage treatment.

Strategies to decrease inequalities in cancer therapeutics, care and prevention: Proceedings on a conference organized by the Pontifical Academy of Sciences and the European Academy of Cancer Sciences, Vatican City, February 23-24, 2023.

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.

Testing a Model of Care for Patients on Immune Checkpoint Inhibitors Based on Electronic Patient-Reported Outcomes: Protocol for a Randomized Phase II Controlled Trial.

BACKGROUND: Management 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. OBJECTIVE: We 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. METHODS: For 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. RESULTS: The 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. CONCLUSIONS: This 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 REGISTRATION: ClinicalTrials.gov NCT05530187; https://www.clinicaltrials.gov/study/NCT05530187. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/48386.

Novel targets for immune-checkpoint inhibition in cancer.

Immune-checkpoint inhibitors have revolutionized cancer therapy, yet many patients either do not derive any benefit from treatment or develop a resistance to checkpoint inhibitors. Intrinsic resistance can result from neoantigen depletion, defective antigen presentation, PD-L1 downregulation, immune-checkpoint ligand upregulation, immunosuppression, and tumor cell phenotypic changes. On the other hand, extrinsic resistance involves acquired upregulation of inhibitory immune-checkpoints, leading to T-cell exhaustion. Current data suggest that PD-1, CTLA-4, and LAG-3 upregulation limits the efficacy of single-agent immune-checkpoint inhibitors. Ongoing clinical trials are investigating novel immune-checkpoint targets to avoid or overcome resistance. This review provides an in-depth analysis of the evolving landscape of potentially targetable immune-checkpoints in cancer. We highlight their biology, emphasizing the current understanding of resistance mechanisms and focusing on promising strategies that are under investigation. We also summarize current results and ongoing clinical trials in this crucial field that could once again revolutionize outcomes for cancer patients.

Neoadjuvant Immunotherapy: A Promising New Standard of Care.

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.

Perspectives in Melanoma: meeting report from the Melanoma Bridge (December 1st-3rd, 2022-Naples, Italy).

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.

Development of an eHealth-enhanced model of care for the monitoring and management of immune-related adverse events in patients treated with immune checkpoint inhibitors.

PURPOSE: The use of electronic patient-reported outcome (ePRO) data in routine care has been tied to direct patient benefits such as improved quality of care and symptom control and even overall survival. The modes of action behind such benefits are seldom described in detail. Here, we describe the development of a model of care leveraging ePRO data to monitor and manage symptoms of patients treated with immune checkpoint inhibitors. METHODS: Development was split into four stages: (1) identification of an underlying theoretical framework, (2) the selection of an ePRO measure (ePROM), (3) the adaptation of an electronic application to collect ePRO data, and (4) the description of an ePRO-oriented workflow. The model of care is currently evaluated in a bicentric longitudinal randomized controlled phase II trial, the IePRO study. RESULTS: The IePRO model of care is grounded in the eHealth Enhanced Chronic Care Model. Patients are prompted to report symptoms using an electronic mobile application. Triage nurses are alerted, review the reported symptoms, and contact patients in case of a new or worsening symptom. Nurses use the UKONS 24-hour telephone triage tool to issue patient management recommendations to the oncology team. Adapted care coordinating procedures facilitate team collaboration and provide patients with timely feedback. CONCLUSION: This report clarifies how components of care are created and modified to leverage ePRO to enhance care. The model describes a workflow that enables care teams to be proactive and provide patients with timely, multidisciplinary support to manage symptoms.

Neoantigen-specific CD8 T cells with high structural avidity preferentially reside in and eliminate tumors.

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.

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.

Multilingual RECIST classification of radiology reports using supervised learning.

Objectives: The 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. Methods: In 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. Results: The 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. Conclusions: These 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.

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.

Response and Resistance to Trametinib in MAP2K1-Mutant Triple-Negative Melanoma.

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.