Citations

Abstract Background: The prognosis of patients with MM with CNS mets is poor. E6201 is an intravenous (IV) MEK inhibitor with activity against BRAF V600-mutated MM cell lines, including MAPK resistance alteration. A previous phase I trial demonstrated activity of E6201 in BRAF V600E mutated MM with a Maximal Tolerated Dose of 320mg/m2 given IV once a week x 3 weeks q 28 days. 2/23 (8.7%) of patients had a PR, 8/23 (34.8%) had SD. Two patients with MM and brain mets achieved near-CR which led to preclinical studies that demonstrated E6201 is unaffected by P-gp and BCRP transport and achieved higher brain to blood concentration (266%).In this trial, patients with BRAF mutation + MM with brain mets, single agent E6201 given at 320mg/m2 twice weekly x 3 weeks q 28 days was determined to be safe. One patient had CNS SD and a PR in lung mets, and a second patient achieved CNS stable disease and CR in a leg melanotic lesion. The drug was well-tolerated with one reversible AE thrombocytopenia (Gr 2). The final review showed intracranial response of SD in 2/4 (50%) patients and systemic SD in 3/4 (75%) patients. Methods: We are currently accruing to the dose escalation cohort of E6201 with dabrafenib 150 mg twice daily orally (n=up to 22 evaluable patients). E6201 is administered IV at 320mg/m2 twice weekly for 3 weeks every 28 days. Patients must have stage IV melanoma with BRAF mutation with at least one active lesion in the brain. Prior local therapy is allowed if done at least 3 weeks prior to cycle 1. Participants must be on a stable dose of steroids for at least 7 days and have an ECOG <!–=2. The study allows one prior BRAF/MEK inhibitor therapy. The primary objectives are to determine the MTD of E6201 when combined with dabrafenib and the response rate of brain mets. The secondary objectives include duration of response, extracranial response, progression free survival, overall survival, and impact of BRAF mutation subtype and safety of the combination. Clinicaltrials.gov: NCT05388877 References:1.Gampa G, et al. Brain distribution of a novel MEK inhibitor E6201: implications in the treatment of melanoma brain metastases. 2018. Drug Metab Dispos.2.Tibes R et al. Safety, pharmacokinetics, and preliminary efficacy of E6201 in patients with advanced solid tumors, including melanoma: results of a phase I study. 2018. Brit J of Cancer. 3.Babiker Gm et al, E6201, an intravenous MEK1 inhibitor, achieves an exceptional response in BRAF V600E mutated metastatic malignant melanoma with brain metastases. 2018. Inv New Drugs4.Gampa G, Kim M, Cook-Rostie N, Laramy JK, Sarkaria JN, Paradiso L, DePalatis L, Elmquist WF. Brain distribution of a novel MEK inhibitor E6201: Implications in the treatment of melanoma brain metastases. 2018. Drug Met Disp.–> Citation Format: Mahesh Seetharam, Roxana Dronca, Arkadiusz Z. Dudek, Garth Nelson, Matthew Block, Skylar Starling, Amber Baskin, Renee Bradshaw, Courtney Nelson, Kylee Andrews, Alisha Birgin, Cristina Watson, Yiyi Yan, Daruka Mahadevan, Svetomir Markovic, Tom Myers, Linda Paradiso, Hani Babiker. Phase 1 trial of MEK1 inhibitor E6201 plus dabrafenib in patients (pts) with BRAF V600-mutated metastatic melanoma (MM) with central nervous system (CNS) metastases (mets) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr CT157.

PURPOSE Nodular lymphocyte–predominant Hodgkin lymphoma (NLPHL) is a rare cancer, and large international cooperative efforts are needed to evaluate the significance of clinical risk factors and immunoarchitectural patterns (IAPs) for all stages of pediatric and adult patients with NLPHL. METHODS Thirty-eight institutions participated in the Global nLPHL One Working Group retrospective study of NLPHL cases from 1992 to 2021. We measured progression-free survival (PFS), overall survival (OS), transformation rate, and lymphoma-specific death rate. We performed uni- and multivariable (MVA) Cox regression stratified by management to select factors for the lymphocyte-predominant international prognostic score (LP-IPS) validated by five-fold cross-validation. RESULTS We identified 2,243 patients with a median age of 37 years (IQR, 23-51). The median follow-up was 6.3 years (IQR, 3.4-10.8). Most had stage I to II (72.9%) and few B symptoms (9.9%) or splenic involvement (5.4%). IAP was scored for 916 (40.8%). Frontline management included chemotherapy alone (32.4%), combined modality therapy (30.5%), radiotherapy alone (24.0%), observation after excision (4.6%), rituximab alone (4.0%), active surveillance (3.4%), and rituximab and radiotherapy (1.1%). The PFS, OS, transformation, and lymphoma-specific death rates at 10 years were 70.8%, 91.6%, 4.8%, and 3.3%, respectively. On MVA, IAPs were not associated with PFS or OS, but IAP E had higher risk of transformation (hazard ratio [HR], 1.81; P < .05). We developed the LP-IPS with 1 point each for age ≥45 years, stage III-IV, hemoglobin <10.5 g/dL, and splenic involvement. Increasing LP-IPS was significantly associated with worse PFS (HR, 1.52) and OS (HR, 2.31) and increased risk of lymphoma-specific death (HR, 2.63) and transformation (HR, 1.41). CONCLUSION In this comprehensive study of all ages of patients with NLPHL, we develop the LP-IPS to identify high-risk patients and inform upcoming prospective clinical trials evaluating de-escalation of therapy for patients with low LP-IPS scores (<2).

Abstract Background: Immune checkpoint inhibitors (ICIs) have significantly improved outcomes for patients with metastatic non-small cell lung cancer (NSCLC). However, clinical management is challenging. The PD-L1 biomarker does not optimally guide the choice between ICI monotherapy and combination ICI-chemotherapy, and there are currently no biomarkers to predict immune-related adverse events (irAEs). Here, we describe a versatile platform based on plasma proteomics and machine learning for generating predictive tools to support treatment decisions. Methods: Pre-treatment plasma samples were collected from 616 NSCLC patients receiving ICI-based therapy (NCT04056247). Clinical benefit (CB) data and irAEs were recorded. Proteomic profiling of plasma samples was performed using the SomaScan® assay, covering ~7000 proteins per sample. Proteins associated with Clinical Benefit (progression-free survival at 12 months) were classified as resistance-associated proteins (RAPs). Proteins associated with grade ≥3 irAEs occurring within the first 100 days of treatment were classified as toxicity-associated proteins (TAPs). RAPs and TAPs were integrated into two separate machine learning-based models, yielding CB and irAE probabilities, respectively. The CB model used a CB probability threshold to assign patients a PROphet-positive or PROphet-negative result. Bioinformatic analysis was performed on RAPs and TAPs. Results: CB and irAE models displayed strong predictive performance with a high correlation between predicted probability and observed CB or irAE rate (CB model: R2=0.98; p-value&lt;0.0001; irAE model: R2= 0.92; p-value &lt;0.0001). In the CB model, patients classified as PROphet-positive achieved significantly longer overall survival (OS) than PROphet-negative patients, with a median OS of 25.9 vs. 10.8 months (hazard ratio, HR = 0.51; p-value&lt;0.001). Furthermore, patients with PD-L1 ≥50% tumors and a PROphet-negative result had significantly longer OS with ICI-chemotherapy in comparison to ICI monotherapy (HR=0.23, p-value = 0.0003), suggesting that combination therapy is preferable for such patients despite high PD-L1 levels. Bioinformatic analysis showed that RAPs related to immune regulation, angiogenesis, chemo-resistance, and other potential resistance mechanisms displayed higher expression levels in patients who did not benefit from treatment. Neutrophil- and inflammation-related proteins were significantly enriched in patients who experienced irAEs. Conclusions: We describe two machine-learning based predictive tools that may be used separately or in combination to inform treatment decisions based on the patient’s likelihood to benefit from ICI therapy and to develop serious irAEs. Our findings also provide biological insights related to treatment resistance and immune-related toxicity. Citation Format: Michal Harel, Petros Christopoulos, Igor Puzanov, Jair Bar, Iris Kamer, Niels Reinmuth, Ina Koch, Mor Moskovitz, Adva Levy-Barda, Alona Zer, Michal Lotem, David Farrugia, Rivka Katzenelson, Gillian Price, Abed Agbarya, Helen Cheley, Adam Hassani, Mahmoud Abu-Amna, Tom Geldart, Anirban Chatterjee, Andreas Polychronis, Maya Gottfried, Yanyan Lou, Tatiana Harkovsky, Alison Brewster, Ido Wolf, Ella Tepper, Ben Yellin, Itamar Sela, Coren Lahav, Yehonatan Elon, Raya Leibowitz, Adam P. Dicker, Young Kwang Chae, Ryan J. Sullivan, David Carbone, David Gandara, Jarushka Naidoo. Plasma proteomics-based models for predicting therapeutic benefit and immune-related adverse events in non-small cell lung cancer patients treated with immunotherapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1208.

PURPOSE Current guidelines for the management of metastatic non–small cell lung cancer (NSCLC) without driver mutations recommend checkpoint immunotherapy with PD-1/PD-L1 inhibitors, either alone or in combination with chemotherapy. This approach fails to account for individual patient variability and host immune factors and often results in less-than-ideal outcomes. To address the limitations of the current guidelines, we developed and subsequently blindly validated a machine learning algorithm using pretreatment plasma proteomic profiles for personalized treatment decisions. PATIENTS AND METHODS We conducted a multicenter observational trial (ClinicalTrials.gov identifier: NCT04056247 ) of patients undergoing PD-1/PD-L1 inhibitor–based therapy (n = 540) and an additional patient cohort receiving chemotherapy (n = 85) who consented to pretreatment plasma and clinical data collection. Plasma proteome profiling was performed using SomaScan Assay v4.1. RESULTS Our test demonstrates a strong association between model output and clinical benefit (CB) from PD-1/PD-L1 inhibitor–based treatments, evidenced by high concordance between predicted and observed CB ( R 2 = 0.98, P < .001). The test categorizes patients as either PROphet-positive or PROphet-negative and further stratifies patient outcomes beyond PD-L1 expression levels. The test successfully differentiates between PROphet-negative patients exhibiting high tumor PD-L1 levels (≥50%) who have enhanced overall survival when treated with a combination of immunotherapy and chemotherapy compared with immunotherapy alone (hazard ratio [HR], 0.23 [95% CI, 0.1 to 0.51], P = .0003). By contrast, PROphet-positive patients show comparable outcomes when treated with immunotherapy alone or in combination with chemotherapy (HR, 0.78 [95% CI, 0.42 to 1.44], P = .424). CONCLUSION Plasma proteome–based testing of individual patients, in combination with standard PD-L1 testing, distinguishes patient subsets with distinct differences in outcomes from PD-1/PD-L1 inhibitor–based therapies. These data suggest that this approach can improve the precision of first-line treatment for metastatic NSCLC.

Background Mental health characteristics such as negative mood, fear avoidance, unhelpful thoughts regarding pain, and low self-efficacy are associated with symptom intensity and capability among patients with hip and knee osteoarthritis (OA). Knowledge gaps remain regarding the conceptual and statistical overlap of these constructs and which of these are most strongly associated with capability in people with OA. Further study of these underlying factors can inform us which mental health assessments to prioritize and how to incorporate them into whole-person, psychologically informed care. Questions/purposes (1) What are the distinct underlying factors that can be identified using statistical grouping of responses to a multidimensional mental health survey administered to patients with OA? (2) What are the associations between these distinct underlying factors and capability in knee OA (measured using the Knee Injury and Osteoarthritis Outcome Score, Joint Replacement [KOOS JR]) and hip OA (measured using Hip Disability and Osteoarthritis Outcome Score, Joint Replacement [HOOS JR]), accounting for sociodemographic and clinical factors? Methods We performed a retrospective cross-sectional analysis of adult patients who were referred to our program with a primary complaint of hip or knee pain secondary to OA between October 2017 and December 2020. Of the 2006 patients in the database, 38% (760) were excluded because they did not have a diagnosis of primary osteoarthritis, and 23% (292 of 1246) were excluded owing to missing data, leaving 954 patients available for analysis. Seventy-three percent (697) were women, with a mean age of 61 ± 10 years; 65% (623) of patients were White, and 52% (498) were insured under a commercial plan or via their employer. We analyzed demographic data, patient-reported outcome measures, and a multidimensional mental health survey (the 10-item Optimal Screening for Prediction of Referral and Outcome-Yellow Flag [OSPRO-YF] assessment tool), which are routinely collected for all patients at their baseline new-patient visit. To answer our first question about identifying underlying mental health factors, we performed an exploratory factor analysis of the OSPRO-YF score estimates. This technique helped identify statistically distinct underlying factors for the entire cohort based on extracting the maximum common variance among the variables of the OSPRO-YF. The exploratory factor analysis established how strongly different mental health characteristics were intercorrelated. A scree plot technique was then applied to reduce these factor groupings (based on Eigenvalues above 1.0) into a set of distinct factors. Predicted factor scores of these latent variables were generated and were subsequently used as explanatory variables in the multivariable analysis that identified variables associated with HOOS JR and KOOS JR scores. Results Two underlying mental health factors were identified using exploratory factor analysis and the scree plot; we labeled them “pain coping” and “mood.” For patients with knee OA, after accounting for confounders, worse mood and worse pain coping were associated with greater levels of incapability (KOOS JR) in separate models but when analyzed in a combined model, pain coping (regression coefficient -4.3 [95% confidence interval -5.4 to -3.2], partial R2 0.076; p < 0.001) had the strongest relationship, and mood was no longer associated. Similarly, for hip OA, pain coping (regression coefficient -5.4 [95% CI -7.8 to -3.1], partial R2 0.10; p < 0.001) had the strongest relationship, and mood was no longer associated. Conclusion This study simplifies the multitude of mental health assessments into two underlying factors: cognition (pain coping) and feelings (mood). When considered together, the association between capability and pain coping was dominant, signaling the importance of a mental health assessment in orthopaedic care to go beyond focusing on unhelpful feelings and mood (assessment of depression and anxiety) alone to include measures of pain coping, such as the Pain Catastrophizing Scale or Tampa Scale for Kinesiophobia, both of which have been used extensively in patients with musculoskeletal conditions. Level of Evidence Level III, prognostic study.

Platelet activation and the complement system are mutually dependent. Here, we investigated the effects of storage time on complement activation and platelet function in routinely produced platelet concentrates. The platelet concentrates (n = 10) were stored at 22 °C for seven days and assessed daily for complement and platelet activation markers. Additionally, platelet function was analyzed in terms of their responsiveness to protease-activated receptor-1 (PAR-1) and thromboxane A2 receptor (TXA2R) activation and their capacity to adhere to collagen. Complement activation increased over the storage period for all analyzed markers, including the C1rs/C1-INH complex (fold change (FC) = 1.9; p < 0.001), MASP-1/C1-INH complex (FC = 2.0; p < 0.001), C4c (FC = 1.8, p < 0.001), C3bc (FC = 4.0; p < 0.01), and soluble C5b-9 (FC = 1.7, p < 0.001). Furthermore, the levels of soluble platelet activation markers increased in the concentrates over the seven-day period, including neutrophil-activating peptide-2 (FC = 2.5; p < 0.0001), transforming growth factor beta 1 (FC = 1.9; p < 0.001) and platelet factor 4 (FC = 2.1; p < 0.0001). The ability of platelets to respond to activation, as measured by surface expression of CD62P and CD63, decreased by 19% and 24% (p < 0.05) for PAR-1 and 69–72% (p < 0.05) for TXA2R activation, respectively, on Day 7 compared to Day 1. The extent of platelet binding to collagen was not significantly impaired during storage. In conclusion, we demonstrated that complement activation increased during the storage of platelets, and this correlated with increased platelet activation and a reduced ability of the platelets to respond to, primarily, TXA2R activation.

The Concise Guide to PHARMACOLOGY 2023/24 is the sixth in this series of biennial publications. The Concise Guide provides concise overviews, mostly in tabular format, of the key properties of approximately 1800 drug targets, and nearly 6000 interactions with about 3900 ligands. There is an emphasis on selective pharmacology (where available), plus links to the open access knowledgebase source of drug targets and their ligands (https://www.guidetopharmacology.org/), which provides more detailed views of target and ligand properties. Although the Concise Guide constitutes almost 500 pages, the material presented is substantially reduced compared to information and links presented on the website. It provides a permanent, citable, point‐in‐time record that will survive database updates. The full contents of this section can be found at http://onlinelibrary.wiley.com/doi/10.1111/bph.16180. Catalytic receptors are one of the six major pharmacological targets into which the Guide is divided, with the others being: G protein‐coupled receptors, ion channels, nuclear hormone receptors, enzymes and transporters. These are presented with nomenclature guidance and summary information on the best available pharmacological tools, alongside key references and suggestions for further reading. The landscape format of the Concise Guide is designed to facilitate comparison of related targets from material contemporary to mid‐2023, and supersedes data presented in the 2021/22, 2019/20, 2017/18, 2015/16 and 2013/14 Concise Guides and previous Guides to Receptors and Channels. It is produced in close conjunction with the Nomenclature and Standards Committee of the International Union of Basic and Clinical Pharmacology (NC‐IUPHAR), therefore, providing official IUPHAR classification and nomenclature for human drug targets, where appropriate.