Caspian: Os Results from a Randomised Phase 3 Study of First-Line Durvalumab ± Tremelimumab + Chemotherapy in ES-SCLC

Immune checkpoint blockade targeting the PD-1/PD-L1 pathway in combination with platinum-based chemotherapy (CT) has demonstrated improved clinical outcomes in patients (pts) with extensive-stage small-cell lung cancer (ES-SCLC). Durvalumab ± Tremelimumab in combination with etoposide and platinum-based CT (EP) as first-line treatment for pts with ES-SCLC. Results will be presented at WCLC 2019 including OS, key secondary endpoints, safety and tolerability

Patient-reported outcomes with first-line durvalumab plus platinum-etoposide versus platinum-etoposide in extensive-stage small-cell lung cancer (CASPIAN): a randomized, controlled, open-label, phase III study

Objectives In the phase III CASPIAN study, first-line durvalumab plus etoposide in combination with either cisplatin or carboplatin (EP) significantly improved overall survival (primary endpoint) versus EP alone in patients with extensive-stage small-cell lung cancer (ES-SCLC) at the interim analysis. Here we report patient-reported outcomes (PROs). Materials and methods Treatment-naïve patients with ES-SCLC received 4 cycles of durvalumab plus EP every 3 weeks followed by maintenance durvalumab every 4 weeks until progression, or up to 6 cycles of EP every 3 weeks. PROs, assessed with the European Organisation for Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire-Core 30 (QLQ-C30) version 3 and its lung cancer module, the Quality of Life Questionnaire-Lung Cancer 13 (QLQ-LC13), were prespecified secondary endpoints. Changes from baseline to disease progression or 12 months in prespecified key disease-related symptoms (cough, dyspnea, chest pain, fatigue, appetite loss) were analyzed with a mixed model for repeated measures. Time to deterioration (TTD) of symptoms, functioning, and global health status/quality of life (QoL) from randomization was analyzed. Results In the durvalumab plus EP and EP arms, 261 and 260 patients were PRO-evaluable. Patients in both arms experienced numerically reduced symptom burden over 12 months or until progression for key symptoms. For the improvements from baseline in appetite loss, the between-arm difference was statistically significant, favoring durvalumab plus EP (difference, −4.5; 99% CI: −9.04, −0.04; nominal p = 0.009). Patients experienced longer TTD with durvalumab plus EP versus EP for all symptoms (hazard ratio [95% CI] for key symptoms: cough 0.78 [0.600‒1.026]; dyspnea 0.79 [0.625‒1.006]; chest pain 0.76 [0.575‒0.996]; fatigue 0.82 [0.653‒1.027]; appetite loss 0.70 [0.542‒0.899]), functioning, and global health status/QoL. Conclusion Addition of durvalumab to first-line EP maintained QoL and delayed worsening of patient-reported symptoms, functioning, and global health status/QoL compared with EP

Proposed clinical phases for the improvement of personalized treatment of checkpoint inhibitor–related pneumonitis

BackgroundCheckpoint inhibitor–related pneumonitis (CIP) is a lethal immune-related adverse event. However, the development process of CIP, which may provide insight into more effective management, has not been extensively examined.MethodsWe conducted a multicenter retrospective analysis of 56 patients who developed CIP. Clinical characteristics, radiological features, histologic features, and laboratory tests were analyzed. After a comprehensive analysis, we proposed acute, subacute, and chronic phases of CIP and summarized each phase’s characteristics.ResultsThere were 51 patients in the acute phase, 22 in the subacute phase, and 11 in the chronic phase. The median interval time from the beginning of CIP to the different phases was calculated (acute phase: ≤4.9 weeks; subacute phase: 4.9~13.1 weeks; and chronic phase: ≥13.1 weeks). The symptoms relieved from the acute phase to the chronic phase, and the CIP grade and Performance Status score decreased (P<0.05). The main change in radiologic features was the absorption of the lesions, and 3 (3/11) patients in the chronic phase had persistent traction bronchiectasis. For histologic features, most patients had acute fibrinous pneumonitis in the acute phase (5/8), and most had organizing pneumonia in the subacute phase (5/6). Other histologic changes advanced over time, with the lesions entering a state of fibrosis. Moreover, the levels of interleukin-6, interleukin-10 and high-sensitivity C-reactive protein (hsCRP) increased in the acute phase and decreased as CIP progressed (IL-6: 17.9 vs. 9.8 vs. 5.7, P=0.018; IL-10: 4.6 vs 3.0 vs. 2.0, P=0.041; hsCRP: 88.2 vs. 19.4 vs. 14.4, P=0.005).ConclusionsThe general development process of CIP can be divided into acute, subacute, and chronic phases, upon which a better management strategy might be based devised

Deep Decision Tree Transfer Boosting

Instance transfer approaches consider source and target data together during the training process, and borrow examples from the source domain to augment the training data, when there is limited or no label in the target domain. Among them, boosting-based transfer learning methods (e.g., TrAdaBoost) are most widely used. When dealing with more complex data, we may consider the more complex hypotheses (e.g., a decision tree with deeper layers). However, with the fixed and high complexity of the hypotheses, TrAdaBoost and its variants may face the overfitting problems. Even worse, in the transfer learning scenario, a decision tree with deep layers may overfit different distribution data in the source domain. In this paper, we propose a new instance transfer learning method, i.e., Deep Decision Tree Transfer Boosting (DTrBoost), whose weights are learned and assigned to base learners by minimizing the data-dependent learning bounds across both source and target domains in terms of the Rademacher complexities. This guarantees that we can learn decision trees with deep layers without overfitting. The theorem proof and experimental results indicate the effectiveness of our proposed method

Mechanical metamaterials associated with stiffness, rigidity and compressibility: A brief review

Mechanical metamaterials are man-made structures with counterintuitive mechanical properties that originate in the geometry of their unit cell instead of the properties of each component. The typical mechanical metamaterials are generally associated with the four elastic constants, the Young\u27s modulus E, shear modulus G, bulk modulus K and Poisson\u27s ratio υ the former three of which correspond to the stiffness, rigidity, and compressibility of a material from an engineering point of view. Here we review the important advancements in structural topology optimisation of the underlying design principles, coupled with experimental fabrication, thereby to obtain various counterintuitive mechanical properties. Further, a clear classification of mechanical metamaterials have been established based on the fundamental material mechanics. Consequently, mechanical metamaterials can be divide into strong-lightweight (E/ρ), pattern transformation with tunable stiffness, negative compressibility (−4G/3 \u3c K \u3c 0), Pentamode metamaterials (G ≪ K) and auxetic metamaterials (G ≫ K), simultaneously using topology optimisation to share various fancy but feasible mechanical properties, ultralight, ultra-stiffness, well-controllable stiffness, vanishing shear modulus, negative compressibility and negative Poisson\u27s ratio. We provide here a broad overview of significant potential mechanical metamaterials together with the upcoming challenges in the intriguing and promising research field

Coexistence of a novel SRBD1-ALK, ALK-CACNA1D double-fusion in a lung adenocarcinoma patient and response to alectinib: A case report

A Chinese male patient with advanced lung adenocarcinoma experienced disease progression one and a half years after receiving first-line immunochemotherapy. The second biopsy was performed and tissue immunohistochemistry revealed Anaplastic lymphoma kinase (ALK) expression in the cytoplasm of tumor cells, so he began to receive Alectinib treatment. Then the next generation sequencing found double fusion variants of S1 RNA binding domain 1 (SRBD1)- ALK and ALK- Calcium voltage-gated channel subunit alpha1 D (CACNA1D). After continuous Alectinib treatment for 7 months, almost complete response (CR) was achieved. The patient is currently taking Alectinib for 13 months, the condition is stable, and is waiting for the next cycle of efficacy evaluation

Disease Control as a Predictor of Survival with Gefitinib and Docetaxel in a Phase III Study (V-15-32) in Advanced Non-small Cell Lung Cancer Patients

IntroductionThis post hoc analysis investigated the relationship between tumor response and overall survival (OS) in pretreated advanced non-small cell lung cancer (NSCLC).MethodsWe conducted landmark survival analyses of V-15-32, a phase III study comparing gefitinib with docetaxel in pretreated advanced NSCLC. Best response at weeks 8, 12, 16, and 20, and visit response at week 4, were evaluated.ResultsDisease control (DC; complete response [CR], partial response [PR], or stable disease) was a better predictor of OS than CR/PR at all time points. The strongest predictor of OS for both gefitinib and docetaxel was DC at week 8 (hazard ratio [HR] DC versus non-DC: 0.30, 95% confidence interval [CI] 0.20–0.45, p < 0.001 for both treatments). DC at week 4 was also associated with longer survival compared with non-DC for both treatments (HR 0.33, 95% CI 0.23–0.49, p < 0.001 for gefitinib; HR 0.30, 95% CI 0.19–0.47, p < 0.001 for docetaxel).DiscussionDC is a better predictor of OS with gefitinib and docetaxel than CR/PR in advanced pretreated NSCLC, with a best response of DC at week 8 the strongest predictor

Deciphering Linkages Between Microbial Communities and Priming Effects in Lake Sediments With Different Salinity

Priming effects (PEs) and their associated microbial drivers are not well studied in lake sediments. Here, we investigated PEs and underlying potential microbial drivers in the sediments of lakes on the Qinghai-Tibetan Plateau (QTP). Sediments were collected from three QTP lakes with different salinity, followed by microcosm construction and subsequent incubation at in situ temperature. The sediment microcosms were amended with C-13-labeled glucose, on which PE intensities were evaluated in the incubations on Days 7 and 42. Positive PEs were observed in all the studied lake sediment microcosms. PE intensities exhibited significantly (p < 0.05) linear correlations with most of the measured physicochemical factors (e.g., salinity, sediment total nitrogen/phosphorus, and ratios of carbon:nitrogen), and such linear correlations were inverse for the early (i.e., on Day 7) and late (i.e., on Day 42) PEs. Prokaryotic and fungal community compositions significantly changed owing to glucose addition in the studied lake microcosms, suggesting that both prokaryotes and fungi may contribute to the observed PEs. Network analysis showed that the numbers of positive correlations between fungal taxa and other microorganisms increased with the enhancement of the late PE intensity, suggesting that fungi and associated co-metabolisms may play key roles in late PEs in this study. Collectively, this study gives new insights into PE intensity and underlying microbial drivers of PE in lake sediments, and such knowledge is of great importance to understanding organic matter mineralization in lake ecosystems