A phase I, dose-escalation study of volasertib combined with nintedanib in advanced solid tumors

Background: Volasertib is a potent and selective cell-cycle kinase inhibitor that induces mitotic arrest and apoptosis by targeting Polo-like kinases. This study determined the maximum tolerated dose (MTD) and pharmacokinetics of volasertib combined with nintedanib, a potent and orally bioavailable triple angiokinase inhibitor, in patients with advanced solid tumors. Patients and methods: This open-label, dose-escalation trial recruited patients with advanced metastatic solid tumors following failure of conventional treatment (NCT01022853; Study 1230.7). Volasertib was administered by intravenous infusion over 2 h, starting at 100 mg in the first dose cohort. Nintedanib was administered orally at a dose of 200 mg twice daily. The first treatment cycle comprised 28 days (days 1-7 and days 9-28: nintedanib; day 8: volasertib). From cycle 2 onwards, volasertib was administered on day 1 of a 21-day cycle and nintedanib was administered days 2-21. The primary objective was the MTD of volasertib in combination with nintedanib. Results: Thirty patients were treated. The MTD of volasertib plus fixed-dose nintedanib was 300 mg once every 3 weeks, the same as the recommended single-agent dose of volasertib in solid tumors. Two of 12 assessable patients treated with the MTD experienced dose-limiting toxicities [grade 3 increased alanine aminotransferase (ALT); grade 3 ALT increase and grade 3 increased aspartate aminotransferase]. Disease control [stable disease (SD)/partial response (PR)/complete response (CR)] was achieved in 18 patients (60%): 1 CR (breast cancer), 1 PR (nonsmall-cell lung cancer), and 16 patients with SD. Volasertib showed that multiexponential pharmacokinetic behavior and co-administration of nintedanib had no significant effects on its exposure. Conclusions: Volasertib could be combined with fixed-dose nintedanib at the recommended single-agent dose. At this dose, the combination had a manageable safety profile without unexpected or overlapping adverse events, and showed antitumor activity

Selpercatinib in Patients With RET Fusion–Positive Non–Small-Cell Lung Cancer: Updated Safety and Efficacy From the Registrational LIBRETTO-001 Phase I/II Trial

Selpercatinib; Lung cancer; SafetySelpercatinib; Cáncer de pulmón; SeguridadSelpercatinib; Càncer de pulmó; SeguretatPURPOSE Selpercatinib, a first-in-class, highly selective, and potent CNS-active RET kinase inhibitor, is currently approved for the treatment of patients with RET fusion–positive non–small-cell lung cancer (NSCLC). We provide a registrational data set update in more than double (n = 316) of the original reported population (n = 144) and better characterization of long-term efficacy and safety. METHODS Patients were enrolled to LIBRETTO-001, a phase I/II, single-arm, open-label study of selpercatinib in patients with RET-altered cancers. An analysis of patients with RET fusion–positive NSCLC, including 69 treatment-naive and 247 with prior platinum-based chemotherapy, was performed. The primary end point was objective response rate (ORR; RECIST v1.1, independent review committee). Secondary end points included duration of response (DoR), progression-free survival (PFS), overall survival, and safety. RESULTS In treatment-naive patients, the ORR was 84% (95% CI, 73 to 92); 6% achieved complete responses (CRs). The median DoR was 20.2 months (95% CI, 13.0 to could not be evaluated); 40% of responses were ongoing at the data cutoff (median follow-up of 20.3 months). The median PFS was 22.0 months; 35% of patients were alive and progression-free at the data cutoff (median follow-up of 21.9 months). In platinum-based chemotherapy pretreated patients, the ORR was 61% (95% CI, 55 to 67); 7% achieved CRs. The median DoR was 28.6 months (95% CI, 20.4 to could not be evaluated); 49% of responses were ongoing (median follow-up of 21.2 months). The median PFS was 24.9 months; 38% of patients were alive and progression-free (median follow-up of 24.7 months). Of 26 patients with measurable baseline CNS metastasis by the independent review committee, the intracranial ORR was 85% (95% CI, 65 to 96); 27% were CRs. In the full safety population (n = 796), the median treatment duration was 36.1 months. The safety profile of selpercatinib was consistent with previous reports. CONCLUSION In a large cohort with extended follow-up, selpercatinib continued to demonstrate durable and robust responses, including intracranial activity, in previously treated and treatment-naive patients with RET fusion–positive NSCLC.Supported by Loxo Oncology, a wholly owned subsidiary of Eli Lilly and Company. A.D. was supported in part by funding from the National Cancer Institute of the National Institutes of Health: 1R01CA251591- 01A1 and P30 CA008748. Partial support was likewise provided by LUNGevity

Metformin Enhances Cisplatin-Induced Apoptosis and Prevents Resistance to Cisplatin in Co-mutated KRAS/LKB1 NSCLC

Abstract Introduction We hypothesized that activating KRAS mutations and inactivation of the liver kinase B1 (LKB1) oncosuppressor can cooperate to sustain NSCLC aggressiveness. We also hypothesized that the growth advantage of KRAS/LKB1 co-mutated tumors could be balanced by higher sensitivity to metabolic stress conditions, such as metformin treatment, thus revealing new strategies to target this aggressive NSCLC subtype. Methods We retrospectively determined the frequency and prognostic value of KRAS/LKB1 co-mutations in tissue specimens from NSCLC patients enrolled in the TAILOR trial. We generated stable LKB1 knockdown and LKB1-overexpressing isogenic H1299 and A549 cell variants, respectively, to test the in vitro efficacy of metformin. We also investigated the effect of metformin on cisplatin-resistant CD133+ cells in NSCLC patient-derived xenografts. Results We found a trend towards worse overall survival in patients with KRAS/LKB1 co-mutated tumors as compared to KRAS-mutated ones (hazard ratio: 2.02, 95% confidence interval: 0.94–4.35, p = 0.072). In preclinical experiments, metformin produced pro-apoptotic effects and enhanced cisplatin anticancer activity specifically in KRAS/LKB1 co-mutated patient-derived xenografts. Moreover, metformin prevented the development of acquired tumor resistance to 5 consecutive cycles of cisplatin treatment (75% response rate with metformin-cisplatin as compared to 0% response rate with cisplatin), while reducing CD133+ cells. Conclusions LKB1 mutations, especially when combined with KRAS mutations, may define a specific and more aggressive NSCLC subtype. Metformin synergizes with cisplatin against KRAS/LKB1 co-mutated tumors, and may prevent or delay the onset of resistance to cisplatin by targeting CD133+ cancer stem cells. This study lays the foundations for combining metformin with standard platinum-based chemotherapy in the treatment of KRAS/LKB1 co-mutated NSCLC

ceritinib plus nivolumab in patients with advanced alk rearranged non small cell lung cancer results of an open label multicenter phase 1b study

Abstract Introduction Induction of programmed death ligand 1 (PD-L1) expression due to constitutive oncogenic signaling has been reported in NSCLC models harboring echinoderm microtubule associated protein like 4 gene (EML4)–ALK receptor tyrosine kinase gene (ALK) rearrangements. We assessed the safety and activity of ceritinib plus nivolumab in these patients. Methods In this open-label, phase 1B, multicenter, dose escalation and expansion study, previously treated (with ALK receptor tyrosine kinase [ALK] inhibitor [ALKI]/chemotherapy) or treatment-naive patients with stage IIIB or IV ALK-rearranged NSCLC received nivolumab, 3 mg/kg intravenously every 2 weeks, plus ceritinib, 450 mg/300 mg daily, with a low-fat meal. Results In total, 36 patients were treated (a 450-mg cohort [n=14] and a 300-mg cohort [n=22]). In the 450-mg cohort, four patients experienced dose-limiting toxicities. In the 300-mg cohort, two patients experienced dose-limiting toxicities. Among ALKI-naive patients, the overall response rate (ORR) was 83% (95% confidence interval [CI]: 35.9–99.6) in the 450-mg cohort and 60% (95% CI: 26.2–87.8) in the 300-mg cohort. Among ALKI-pretreated patients, the ORR was 50% (95% CI: 15.7–84.3) in the 450-mg cohort and 25% (95% CI: 5.5–57.2) in the 300-mg cohort. The ORR point estimate was observed to be greater in patients who were positive for PD-L1 than in those who were negative for PD-L1, with overlapping CIs (e.g., at a cutoff ≥1% PD-L1, 64% of patients [95% CI: 35.1–87.2] had confirmed responses as compared with those with negative PD-L1 staining (31% [95% CI: 11.0–58.7]). The most frequently reported grade 3 or 4 adverse events were increased alanine aminotransferase level (25%), increased gamma-glutamyl transferase level (22%), increased amylase level (14%), increased lipase level (11%), and maculopapular rash (11%). The incidence of all-grade rash (grouped term) was 64% in both cohorts; grade 3 rash was reported in 29% and 14% of patients in the 450-mg and 300-mg cohorts, respectively; no grade 4 rash was reported. Conclusion Ceritinib plus nivolumab has activity; ORR appears to correlate with PD-L1 at baseline. Toxicity, especially rash, is more common than with either single agent

Real-world data to build explainable trustworthy artificial intelligence models for prediction of immunotherapy efficacy in NSCLC patients

IntroductionArtificial Intelligence (AI) methods are being increasingly investigated as a means to generate predictive models applicable in the clinical practice. In this study, we developed a model to predict the efficacy of immunotherapy (IO) in patients with advanced non-small cell lung cancer (NSCLC) using eXplainable AI (XAI) Machine Learning (ML) methods. MethodsWe prospectively collected real-world data from patients with an advanced NSCLC condition receiving immune-checkpoint inhibitors (ICIs) either as a single agent or in combination with chemotherapy. With regards to six different outcomes - Disease Control Rate (DCR), Objective Response Rate (ORR), 6 and 24-month Overall Survival (OS6 and OS24), 3-months Progression-Free Survival (PFS3) and Time to Treatment Failure (TTF3) - we evaluated five different classification ML models: CatBoost (CB), Logistic Regression (LR), Neural Network (NN), Random Forest (RF) and Support Vector Machine (SVM). We used the Shapley Additive Explanation (SHAP) values to explain model predictions. ResultsOf 480 patients included in the study 407 received immunotherapy and 73 chemo- and immunotherapy. From all the ML models, CB performed the best for OS6 and TTF3, (accuracy 0.83 and 0.81, respectively). CB and LR reached accuracy of 0.75 and 0.73 for the outcome DCR. SHAP for CB demonstrated that the feature that strongly influences models' prediction for all three outcomes was Neutrophil to Lymphocyte Ratio (NLR). Performance Status (ECOG-PS) was an important feature for the outcomes OS6 and TTF3, while PD-L1, Line of IO and chemo-immunotherapy appeared to be more important in predicting DCR. ConclusionsIn this study we developed a ML algorithm based on real-world data, explained by SHAP techniques, and able to accurately predict the efficacy of immunotherapy in sets of NSCLC patients

Pimasertib Versus Dacarbazine in Patients With UnresectableNRAS-Mutated Cutaneous Melanoma: Phase II, Randomized, Controlled Trial with Crossover

This study investigated the efficacy and safety of pimasertib (MEK1/MEK2 inhibitor) versus dacarbazine (DTIC) in patients with untreated NRAS-mutated melanoma. Phase II, multicenter, open-label trial. Patients with unresectable, stage IIIc/IVM1 NRAS-mutated cutaneous melanoma were randomized 2:1 to pimasertib (60 mg; oral twice-daily) or DTIC (1000 mg/m2 ; intravenously) on Day 1 of each 21-day cycle. Patients progressing on DTIC could crossover to pimasertib. Primary endpoint: investigator-assessed progression-free survival (PFS); secondary endpoints: overall survival (OS), objective response rate (ORR), quality of life (QoL), and safety. Overall, 194 patients were randomized (pimasertib n = 130, DTIC n = 64), and 191 received treatment (pimasertib n = 130, DTIC n = 61). PFS was significantly improved with pimasertib versus DTIC (median 13 versus 7 weeks, respectively; hazard ratio (HR) 0.59, 95% confidence interval (CI) 0.42–0.83; p = 0.0022). ORR was improved with pimasertib (odds ratio 2.24, 95% CI 1.00–4.98; p = 0.0453). OS was similar between treatments (median 9 versus 11 months, respectively; HR 0.89, 95% CI 0.61–1.30); 64% of patients receiving DTIC crossed over to pimasertib. Serious adverse events (AEs) were more frequent for pimasertib (57%) than DTIC (20%). The most common treatment-emergent AEs were diarrhea (82%) and blood creatine phosphokinase (CPK) increase (68%) for pimasertib, and nausea (41%) and fatigue (38%) for DTIC. Most frequent grade ≥3 AEs were CPK increase (34%) for pimasertib and neutropenia (15%) for DTIC. Mean QoL scores (baseline and last assessment) were similar between treatments. Pimasertib has activity in NRAS-mutated cutaneous melanoma and a safety profile consistent with known toxicities of MEK inhibitors. Trial registration: ClinicalTrials.gov, NCT01693068

Real-world data to build explainable trustworthy artificial intelligence models for prediction of immunotherapy efficacy in NSCLC patients

IntroductionArtificial Intelligence (AI) methods are being increasingly investigated as a means to generate predictive models applicable in the clinical practice. In this study, we developed a model to predict the efficacy of immunotherapy (IO) in patients with advanced non-small cell lung cancer (NSCLC) using eXplainable AI (XAI) Machine Learning (ML) methods.MethodsWe prospectively collected real-world data from patients with an advanced NSCLC condition receiving immune-checkpoint inhibitors (ICIs) either as a single agent or in combination with chemotherapy. With regards to six different outcomes - Disease Control Rate (DCR), Objective Response Rate (ORR), 6 and 24-month Overall Survival (OS6 and OS24), 3-months Progression-Free Survival (PFS3) and Time to Treatment Failure (TTF3) - we evaluated five different classification ML models: CatBoost (CB), Logistic Regression (LR), Neural Network (NN), Random Forest (RF) and Support Vector Machine (SVM). We used the Shapley Additive Explanation (SHAP) values to explain model predictions.ResultsOf 480 patients included in the study 407 received immunotherapy and 73 chemo- and immunotherapy. From all the ML models, CB performed the best for OS6 and TTF3, (accuracy 0.83 and 0.81, respectively). CB and LR reached accuracy of 0.75 and 0.73 for the outcome DCR. SHAP for CB demonstrated that the feature that strongly influences models’ prediction for all three outcomes was Neutrophil to Lymphocyte Ratio (NLR). Performance Status (ECOG-PS) was an important feature for the outcomes OS6 and TTF3, while PD-L1, Line of IO and chemo-immunotherapy appeared to be more important in predicting DCR.ConclusionsIn this study we developed a ML algorithm based on real-world data, explained by SHAP techniques, and able to accurately predict the efficacy of immunotherapy in sets of NSCLC patients

Ceritinib plus Nivolumab in Patients with Advanced ALK-Rearranged Non–Small Cell Lung Cancer: Results of an Open-Label, Multicenter, Phase 1B Study

Ceritinib; NSCLC, NivolumabCeritinib; NSCLC; NivolumabCeritinib; NSCLC; NivolumabIntroduction Induction of programmed death ligand 1 (PD-L1) expression due to constitutive oncogenic signaling has been reported in NSCLC models harboring echinoderm microtubule associated protein like 4 gene (EML4)–ALK receptor tyrosine kinase gene (ALK) rearrangements. We assessed the safety and activity of ceritinib plus nivolumab in these patients. Methods In this open-label, phase 1B, multicenter, dose escalation and expansion study, previously treated (with ALK receptor tyrosine kinase [ALK] inhibitor [ALKI]/chemotherapy) or treatment-naive patients with stage IIIB or IV ALK-rearranged NSCLC received nivolumab, 3 mg/kg intravenously every 2 weeks, plus ceritinib, 450 mg/300 mg daily, with a low-fat meal. Results In total, 36 patients were treated (a 450-mg cohort [n=14] and a 300-mg cohort [n=22]). In the 450-mg cohort, four patients experienced dose-limiting toxicities. In the 300-mg cohort, two patients experienced dose-limiting toxicities. Among ALKI-naive patients, the overall response rate (ORR) was 83% (95% confidence interval [CI]: 35.9–99.6) in the 450-mg cohort and 60% (95% CI: 26.2–87.8) in the 300-mg cohort. Among ALKI-pretreated patients, the ORR was 50% (95% CI: 15.7–84.3) in the 450-mg cohort and 25% (95% CI: 5.5–57.2) in the 300-mg cohort. The ORR point estimate was observed to be greater in patients who were positive for PD-L1 than in those who were negative for PD-L1, with overlapping CIs (e.g., at a cutoff ≥1% PD-L1, 64% of patients [95% CI: 35.1–87.2] had confirmed responses as compared with those with negative PD-L1 staining (31% [95% CI: 11.0–58.7]). The most frequently reported grade 3 or 4 adverse events were increased alanine aminotransferase level (25%), increased gamma-glutamyl transferase level (22%), increased amylase level (14%), increased lipase level (11%), and maculopapular rash (11%). The incidence of all-grade rash (grouped term) was 64% in both cohorts; grade 3 rash was reported in 29% and 14% of patients in the 450-mg and 300-mg cohorts, respectively; no grade 4 rash was reported. Conclusion Ceritinib plus nivolumab has activity; ORR appears to correlate with PD-L1 at baseline. Toxicity, especially rash, is more common than with either single agent