Plasma Proteome-Based Test for First-Line Treatment Selection in Metastatic Non-Small Cell Lung Cancer.

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 (R2 = 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.

Biological insights from plasma proteomics of non-small cell lung cancer patients treated with immunotherapy.

Introduction: Immune checkpoint inhibitors have made a paradigm shift in the treatment of non-small cell lung cancer (NSCLC). However, clinical response varies widely and robust predictive biomarkers for patient stratification are lacking. Here, we characterize early on-treatment proteomic changes in blood plasma to gain a better understanding of treatment response and resistance. Methods: Pre-treatment (T0) and on-treatment (T1) plasma samples were collected from 225 NSCLC patients receiving PD-1/PD-L1 inhibitor-based regimens. Plasma was profiled using aptamer-based technology to quantify approximately 7000 plasma proteins per sample. Proteins displaying significant fold changes (T1:T0) were analyzed further to identify associations with clinical outcomes using clinical benefit and overall survival as endpoints. Bioinformatic analyses of upregulated proteins were performed to determine potential cell origins and enriched biological processes. Results: The levels of 142 proteins were significantly increased in the plasma of NSCLC patients following ICI-based treatments. Soluble PD-1 exhibited the highest increase, with a positive correlation to tumor PD-L1 status, and, in the ICI monotherapy dataset, an association with improved overall survival. Bioinformatic analysis of the ICI monotherapy dataset revealed a set of 30 upregulated proteins that formed a single, highly interconnected network, including CD8A connected to ten other proteins, suggestive of T cell activation during ICI treatment. Notably, the T cell-related network was detected regardless of clinical benefit. Lastly, circulating proteins of alveolar origin were identified as potential biomarkers of limited clinical benefit, possibly due to a link with cellular stress and lung damage. Conclusions: Our study provides insights into the biological processes activated during ICI-based therapy, highlighting the potential of plasma proteomics to identify mechanisms of therapy resistance and biomarkers for outcome.

Prolonged Medical Cannabis Treatment is Associated With Quality of Life Improvement and Reduction of Analgesic Medication Consumption in Chronic Pain Patients.

Introduction: Chronic non-cancer pain (CNCP) is one of the most prevalent indications for medical cannabis (MC) treatment globally. In this study, we investigated CNCP parameters in patients during prolonged MC treatment, and assessed the interrelation between CNCP parameters and the chemical composition of MC chemovar used. Methods: A cross-sectional questionnaire-based study was performed in one-month intervals for the duration of six months. Subjects were adult patients licensed for MC treatment who also reported a diagnosis of CNCP by a physician. Data included self-reported questionnaires. MC treatment features included administration route, cultivator, cultivar name and monthly dose. Comparison statistics were used to evaluate differences between the abovementioned parameters and the monthly MC chemovar doses at each time point. Results: 429, 150, 98, 71, 77 and 82 patients reported fully on their MC treatment regimens at six one-month intervals, respectively. Although pain intensities did not change during the study period, analgesic medication consumption rates decreased from 46 to 28% (p < 0.005) and good Quality of Life (QoL) rates increased from 49 to 62% (p < 0.05). These changes overlapped with increase in rates of (-)-Δ9-trans-tetrahydrocannabinol (THC) and α-pinene high dose consumption. Conclusion: Even though we observed that pain intensities did not improve during the study, QoL did improve and the rate of analgesic medication consumption decreased alongside with increasing rates of high dose THC and α-pinene consumption. Understanding MC treatment composition may shed light on its long-term effects.

Specific phytocannabinoid compositions are associated with analgesic response and adverse effects in chronic pain patients treated with medical cannabis.

Medical cannabis (MC) treatment for chronic pain is increasing, but evidence regarding short- and long-term efficacy and associated adverse effects (AEs) of the different cannabis plant components is limited. Most reports focus on two phytocannabinoids, (-)-Δ9-trans-tetrahydrocannabinol (Δ9-THC) and cannabidiol (CBD). This study, aimed to identify patterns of phytocannabinoid compositions associated with MC treatment response and with related AEs. Participants in this multicenter prospective cohort were patients with chronic non-cancer pain that were prescribed MC by physicians. Data was collected before MC treatment, at one month (short-term) and at 12 months (long-term). Simultaneously, liquid chromatography mass spectrometry identification and quantification of phytocannabinoids from the cultivars were performed. The monthly dose of each phytocannabinoid for each patient was z-scaled and clustered into ten groups to assess the difference in analgesic treatment response (≥30%/50% pain intensity reduction) and AEs rates. We identified ten clusters that had similar analgesic treatment response rates. However, there were significant differences in AEs rates both at short- and long-term. We identified specific phytocannabinoid compositions that were associated with overall AEs rates (5% compared to 53% at short-term and 44% at long-term) and with specific AEs rates such as MC related central nervous system, gastrointestinal and psychological AEs. To conclude, Evaluating only Δ9-THC or CBD is insufficient to find associations with MC related AEs. Therefore, comprehensive profiling of phytocannabinoids is needed to discover associations to related AEs and help physicians prescribe safer cannabis with less AEs while still relieving pain.

Medical cannabis treatment for chronic pain: Outcomes and prediction of response.

BACKGROUND: Although studied in a few randomized controlled trials, the efficacy of medical cannabis (MC) for chronic pain remains controversial. Using an alternative approach, this multicentre, questionnaire-based prospective cohort was aimed to assess the long-term effects of MC on chronic pain of various aetiologies and to identify predictors for MC treatment success. METHODS: Patients with chronic pain, licensed to use MC in Israel, reported weekly average pain intensity (primary outcome) and related symptoms before and at 1, 3, 6, 9 and 12 months following MC treatment initiation. A general linear model was used to assess outcomes and identify predictors for treatment success (≥30% reduction in pain intensity). RESULTS: A total of 1,045 patients completed the baseline questionnaires and initiated MC treatment, and 551 completed the 12-month follow-up. At 1 year, average pain intensity declined from baseline by 20% [-1.97 points (95%CI = -2.13 to -1.81; p < 0.001)]. All other parameters improved by 10%-30% (p < 0.001). A significant decrease of 42% [reduction of 27 mg; (95%CI = -34.89 to 18.56, p < 0.001)] from baseline in morphine equivalent daily dosage of opioids was also observed. Reported adverse effects were common but mostly non-serious. Presence of normal to long sleep duration, lower body mass index and lower depression score predicted relatively higher treatment success, whereas presence of neuropathic pain predicted the opposite. CONCLUSIONS: This prospective study provides further evidence for the effects of MC on chronic pain and related symptoms, demonstrating an overall mild-to-modest long-term improvement of the tested measures and identifying possible predictors for treatment success.

The heterogeneity and complexity of Cannabis extracts as antitumor agents.

The Cannabis plant contains over 100 phytocannabinoids and hundreds of other components. The biological effects and interplay of these Cannabis compounds are not fully understood and yet influence the plant's therapeutic effects. Here we assessed the antitumor effects of whole Cannabis extracts, which contained significant amounts of differing phytocannabinoids, on different cancer lines from various tumor origins. We first utilized our novel electrospray ionization liquid chromatography mass spectrometry method to analyze the phytocannabinoid contents of 124 Cannabis extracts. We then monitored the effects of 12 chosen different Cannabis extracts on 12 cancer cell lines. Our results show that specific Cannabis extracts impaired the survival and proliferation of cancer cell lines as well as induced apoptosis. Our findings showed that pure (-)-Δ9-trans-tetrahydrocannabinol (Δ9-THC) did not produce the same effects on these cell lines as the whole Cannabis extracts. Furthermore, Cannabis extracts with similar amounts of Δ9-THC produced significantly different effects on the survival of specific cancer cells. In addition, we demonstrated that specific Cannabis extracts may selectively and differentially affect cancer cells and differing cancer cell lines from the same organ origin. We also found that cannabimimetic receptors were differentially expressed among various cancer cell lines and suggest that this receptor diversity may contribute to the heterogeneous effects produced by the differing Cannabis extracts on each cell line. Our overall findings indicate that the effect of a Cannabis extract on a specific cancer cell line relies on the extract's composition as well as on certain characteristics of the targeted cells.

The tilted helix model of dynamin oligomers.

Dynamin proteins assemble into characteristic helical structures around necks of clathrin-coated membrane buds. Hydrolysis of dynamin-bound GTP results in both fission of the membrane neck and partial disruption of the dynamin oligomer. Imaging by atomic force microscopy reveals that, on GTP hydrolysis, dynamin oligomers undergo a dynamic remodeling and lose their distinctive helical shape. While breakup of the dynamin helix is a critical stage in clathrin-mediated endocytosis, the mechanism for this remodeling of the oligomer has not been resolved. In this paper, we formulate an analytical, elasticity-based model for the reshaping and disassembly of the dynamin scaffold. We predict that the shape of the oligomer is modulated by the orientation of dynamin's pleckstrin homology (PH) domain relative to the underlying membrane. Our results indicate that tilt of the PH domain drives deformation and fragmentation of the oligomer, in agreement with experimental observations. This model motivated the introduction of the tilted helix: a curve that maintains a fixed angle between its normal and the normal of the embedding surface. Our findings highlight the importance of tilt as a key regulator of size and morphology of membrane-bound oligomers.