Epidermal Growth Factor Receptor-Related Tumor Markers and Clinical Outcomes with Erlotinib in Non-small Cell Lung Cancer: An Analysis of Patients from German Centers in the TRUST Study

IntroductionRelationships between clinical outcomes and epidermal growth factor receptor (EGFR)-related tumor markers were investigated in patients with advanced non-small cell lung cancer.MethodsPatients with stage IIIB/IV non-small cell lung cancer (0–2 prior regimens) received erlotinib (150 mg PO per day). Response and survival were evaluated, and tumor samples were assessed by immunohistochemistry (EGFR, phosphorylated mitogen-activated protein kinase, and phosphorylated AKT protein expression), fluorescence in situ hybridization (FISH; EGFR gene copy number), and DNA sequencing (EGFR, KRAS gene mutations).ResultsAmong 311 patients, 8% had a complete/partial response; the disease control rate was 66%. Median Overall survival (OS) was 6.1 months; 1-year survival rate was 27.2%. Two of 4 patients with EGFR mutations had tumor responses, versus 2/68 with wild-type EGFR (p = 0.014). Progression-free survival (PFS) (HR = 0.31) and OS (HR = 0.33) were significantly prolonged in patients with EGFR mutations. Response rate was significantly higher in patients with EGFR FISH-positive (17%) than FISH-negative tumors (6%), and both PFS (HR = 0.58) and OS (HR = 0.63) significantly favored patients with EGFR FISH-positive tumors; median OS was 8.6 months in the EGFR FISH-positive group. None of 17 patients with a KRAS mutation had a tumor response, but the impact of KRAS mutation status on survival outcomes was of borderline statistical significance. Neither phosphorylated mitogen-activated protein kinase nor phosphorylated AKT immunohistochemistry status had a significant effect on PFS and OS with erlotinib.ConclusionsThe presence of EGFR mutations and EGFR FISH-positive tumors may predispose patients to achieving better outcomes on erlotinib, but may have a beneficial impact on prognosis (irrespective of treatment). Prospective, placebo-controlled studies are needed to determine the predictive value of the putative biomarkers

Covid-19 triage in the emergency department 2.0: how analytics and AI transform a human-made algorithm for the prediction of clinical pathways

The Covid-19 pandemic has pushed many hospitals to their capacity limits. Therefore, a triage of patients has been discussed controversially primarily through an ethical perspective. The term triage contains many aspects such as urgency of treatment, severity of the disease and pre-existing conditions, access to critical care, or the classification of patients regarding subsequent clinical pathways starting from the emergency department. The determination of the pathways is important not only for patient care, but also for capacity planning in hospitals. We examine the performance of a human-made triage algorithm for clinical pathways which is considered a guideline for emergency departments in Germany based on a large multicenter dataset with over 4,000 European Covid-19 patients from the LEOSS registry. We find an accuracy of 28 percent and approximately 15 percent sensitivity for the ward class. The results serve as a benchmark for our extensions including an additional category of palliative care as a new label, analytics, AI, XAI, and interactive techniques. We find significant potential of analytics and AI in Covid-19 triage regarding accuracy, sensitivity, and other performance metrics whilst our interactive human-AI algorithm shows superior performance with approximately 73 percent accuracy and up to 76 percent sensitivity. The results are independent of the data preparation process regarding the imputation of missing values or grouping of comorbidities. In addition, we find that the consideration of an additional label palliative care does not improve the results

Erlotinib in Advanced Non-small Cell Lung Cancer: Efficacy and Safety Findings of the Global Phase IV Tarceva Lung Cancer Survival Treatment Study

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Afatinib in Non-Small Cell Lung Cancer Harboring Uncommon EGFR Mutations Pretreated With Reversible EGFR Inhibitors

Background. Afatinib, an irreversible ErbB family blocker, is approved for treatment of patients with previously untreated non-small cell lung cancer (NSCLC) harboring activating epidermal growth factor receptor (EGFR) mutations. Efficacy of afatinib in EGFR tyrosine kinase inhibitor-naive (TKI-naive) patients with uncommon EGFR mutations (other than exon 19 deletions or exon 21 point mutations) has been reported; however, efficacy in TKI-pretreated patients with uncommon EGFR mutations is unknown. Materials and Methods. In the afatinib compassionate use program (CUP), patients with advanced or metastatic, histologically confirmed NSCLC progressing after at least one line of chemotherapy and one line of EGFR-TKI treatment were enrolled. Demographic data, mutation type, response rates, time to treatment failure (TTF), and safety in patients harboring uncommon EGFR mutations were reported. Results. In 60 patients (63% female, median age 63 years [range: 30-84 years]), a total of 66 uncommon EGFR mutations including 30T790M mutations were reported (18.4% and 11%, respectively, of known EGFR mutations within the CUP). Most patients (67%) received afatinib as third-or fourth-line treatment. Median TTF was 3.8 months (range: 0.2 to >24.6 months; p =.244) in patients with uncommon mutations compared with 5.1 months (range: 0.1 to >21.1 months) in patients with common mutations (n = 165). Pronounced activity was observed with E709X mutations (TTF>12months). No new safety signals were detected. Conclusion. Afatinib is clinically active and well tolerated in many TKI-pretreated NSCLC patients harboring uncommon EGFR mutations. Compared with results reported in TKI-naive patients, activity was also indicated in patients with T790M and exon 20 insertion mutations