Overcoming CEP85L-ROS1, MKRN1-BRAF and MET amplification as rare, acquired resistance mutations to Osimertinib

Lung cancer is the most common cancer-related cause of death worldwide, most of which are non-small cell lung cancers (NSCLC). Epidermal growth factor receptor (EGFR) mutations are common drivers of NSCLC. Treatment plans for NSCLC, specifically adenocarcinomas, rely heavily on the presence or absence of specific actionable driver mutations. Liquid biopsy can guide the treatment protocol to detect the presence of various mechanisms of resistance to treatment. We report three NSCLC EGFR mutated cases, each treated with Osimertinib in a combination therapy regimen to combat resistance mechanisms. The first patient presented with EGFR L858R/L833V compound mutation with MET amplification alongside CEP85L-ROS1 fusion gene, the second with EGFR exon 19del and MKRN1-BRAF fusion, and the last EGFR L858R/V834L compound mutation with MET amplification. Each regimen utilized a tyrosine kinase inhibitor or monoclonal antibody in addition to osimertinib and allowed for a prompt and relatively durable treatment response

Inquiring into the Differential Action of Interferons (IFNs): an IFN-α2 Mutant with Enhanced Affinity to IFNAR1 Is Functionally Similar to IFN-β

Alpha and beta interferons (IFN-α and IFN-β) are multifunctional cytokines that exhibit differential activities through a common receptor composed of the subunits IFNAR1 and IFNAR2. Here we combined biophysical and functional studies to explore the mechanism that allows the alpha and beta IFNs to act differentially. For this purpose, we have engineered an IFN-α2 triple mutant termed the HEQ mutant that mimics the biological properties of IFN-β. Compared to wild-type (wt) IFN-α2, the HEQ mutant confers a 30-fold higher binding affinity towards IFNAR1, comparable to that measured for IFN-β, resulting in a much higher stability of the ternary complex as measured on model membranes. The HEQ mutant, like IFN-β, promotes a differentially higher antiproliferative effect than antiviral activity. Both bring on a down-regulation of the IFNAR2 receptor upon induction, confirming an increased ternary complex stability of the plasma membrane. Oligonucleotide microarray experiments showed similar gene transcription profiles induced by the HEQ mutant and IFN-β and higher levels of gene induction or repression than those for wt IFN-α2. Thus, we show that the differential activities of IFN-β are directly related to the binding affinity for IFNAR1. Conservation of the residues mutated in the HEQ mutant within IFN-α subtypes suggests that IFN-α has evolved to bind IFNAR1 weakly, apparently to sustain differential levels of biological activities compared to those induced by IFN-β

Radiological artificial intelligence - predicting personalized immunotherapy outcomes in lung cancer

Abstract Personalized medicine has revolutionized approaches to treatment in the field of lung cancer by enabling therapies to be specific to each patient. However, physicians encounter an immense number of challenges in providing the optimal treatment regimen for the individual given the sheer complexity of clinical aspects such as tumor molecular profile, tumor microenvironment, expected adverse events, acquired or inherent resistance mechanisms, the development of brain metastases, the limited availability of biomarkers and the choice of combination therapy. The integration of innovative next-generation technologies such as deep learning—a subset of machine learning—and radiomics has the potential to transform the field by supporting clinical decision making in cancer treatment and the delivery of precision therapies while integrating numerous clinical considerations. In this review, we present a brief explanation of the available technologies, the benefits of using these technologies in predicting immunotherapy response in lung cancer, and the expected future challenges in the context of precision medicine

Rapid Response to the Combination of Lenvatinib and Pembrolizumab in Patients with Advanced Carcinomas (Lung Adenocarcinoma and Malignant Pleural Mesothelioma)

The new era of cancer treatments has made immune checkpoint inhibitors (ICIs) and emerging multikinase inhibitors (TKIs) the standards of care, thus drastically improving patient prognoses. Pembrolizumab is an anti-programmed cell death-1 antibody drug, and lenvatinib is a TKI with preferential antiangiogenic activity. We present, to our knowledge, the first reported series of cases consisting of patients with metastatic non–small cell lung cancer and malignant pleural mesothelioma who were treated with several types of chemotherapy combinations and ICIs followed by disease progression. They were subsequently treated with combined immunotherapy and TKI treatment, resulting in a near complete response within a very short time. Clinical responses were supported by in vitro testing of each patient’s lymphocytic response to pembrolizumab after pre-exposure of target cancer cells to lenvatinib

The EU-funded I3LUNG Project: Integrative Science, Intelligent Data Platform for Individualized LUNG Cancer Care With Immunotherapy

Although immunotherapy (IO) has changed the paradigm for the treatment of patients with advanced non-small cell lung cancers (aNSCLC), only around 30% to 50% of treated patients experience a long-term benefit from IO. Furthermore, the identification of the 30 to 50% of patients who respond remains a major challenge, as programmed Death-Ligand 1 (PD-L1) is currently the only biomarker used to predict the outcome of IO in NSCLC patients despite its limited efficacy. Considering the dynamic complexity of the immune system-tumor microenvironment (TME) and its interaction with the host's and patient's behavior, it is unlikely that a single biomarker will accurately predict a patient's outcomes. In this scenario, Artificial Intelligence (AI) and Machine Learning (ML) are becoming essential to the development of powerful decision-making tools that are able to deal with this high-complexity and provide individualized predictions to better match treatments to individual patients and thus improve patient outcomes and reduce the economic burden of aNSCLC on healthcare systems. I3LUNG is an international, multicenter, retrospective and prospective, observational study of patients with aNSCLC treated with IO, entirely funded by European Union (EU) under the Horizon 2020 (H2020) program. Using AI-based tools, the aim of this study is to promote individualized treatment in aNSCLC, with the goals of improving survival and quality of life, minimizing or preventing undue toxicity and promoting efficient resource allocation. The final objective of the project is the construction of a novel, integrated, AI-assisted data storage and elaboration platform to guide IO administration in aNSCLC, ensuring easy access and cost-effective use by healthcare providers and patients

Glass: Global lorlatinib for ALK(+) and ROS1(+) retrospective study: Real world data of 123 NSCLC patients

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Glass: Global lorlatinib for ALK(+) and ROS1(+) retrospective study: Real world data of 123 NSCLC patients

Lorlatinib is a third-generation tyrosine-kinases inhibitor (TKI) targeting ALK/ROS1 fusions. The FDA has approved lorlatinib for TKI-pretreated ALK(+) NSCLC, while its approval for ROS1( + ) is still pending. Here we present the largest real-world data of NSCLC patients harboring ALK/ROS1 rearrangements treated with lorlatinib.Methods: 123 patients were enrolled retrospectively (data cut-off 1/1/2019). Lorlatinib was administered through an early access program for patients with no other available therapy. Outcome and response were defined by each investigator upon RECIST 1.1 criteria.Results: 106 ALK(+) and 17 ROS1(+) patients recruited from 8 different countries. The ALK( + ) cohort included 50 % males, 73 % never-smokers and 68 % with brain metastases. Extracranial (EC) and intracranial (IC) response rates (RR) were 60 % and 62 %, with disease control rates (DCR) of 91 % and 88 % respectively. Mean duration of therapy (DoT) was 23.9 +/- 1.6 months and median overall survival (mOS) was 89.1 +/- 19.6 months. ROS1 cohort enrolled 53 % males, 65 % never-smokers and 65 % had brain metastases. EC and IC RR were 62 % and 67 % with DCR of 92 % and 78 % respectively. Median DoT was 18.1 +/- 2.5 months and mOS of 90.3 +/- 24.4 months. OS and DoT in both cohorts were not significantly correlated with line of therapy nor other parameters.The most common adverse events of any grade were peripheral edema (48 %), hyperlipidemia (47 %), weight gain (25 %) and fatigue (30 %). CNS adverse events such as cognitive effect of grade 1-2 were reported in 18 % of patients.Conclusion: Lorlatinib shows outstanding EC/IC efficacy in ALK/ROS1(+) NSCLC. The observed mOS of 89 +/- 19 months in ALK(+) NSCLC supports previous reports, while mOS from of 90 +/- 24 months is unprecedented for ROS1( + ) NSCLC.Pfize