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Context.--Precision therapies for patients with driver mutations can offer deep and durable responses that correlate with diagnosis, metastasis prognosis, and improvement in survival. The use of such targeted therapies will continue to increase, pushing us to change our traditional approaches. We needed to search for new tools to effectively integrate technological advancements into our practices because of their capability to improve the efficiency and accuracy of our diagnostic and treatment approaches. Perhaps nothing is as relevant as identifying and implementing new workflows for processing pathologic specimens and for improving communication of critical laboratory information to and from clinicians for appropriate care of patients in an efficient and timely manner. Objectives.--To define the gold standard in delivering the best care for patients, to identify gaps in the process, and to identify potential solutions that would improve our process, including gaps related to knowledge, skills, attitudes, and practices. Design.--We assembled a multidisciplinary team to systematically perform a gap analysis study to clarify the discrepancy between the current reality in pathology specimen processing and the desired optimal situation to deliver the results intended for patient care. Results.--A practical collaborative workflow for specimen management that seeks the cooperation of stakeholders in each medical discipline to provide guidelines in specimen collection, delivery, processing, and reporting of results with the ultimate goal of improving patient outcomes is provided. Conclusions.--New tools are required to effectively integrate data-driven approaches in specimen processing to meet the new demands. (Arch Pathol Lab Med. 2022;146:603-610; doi: 10.5858/arpa.2021-0089-OA), As changes in the new standard of care are upon us, health care providers are faced with numerous complex challenges to establish systematic and data-driven approaches to meet the new [...]

Aim: Compare healthcare costs for patients with epidermal growth factor receptor mutated (EGFRm) metastatic non-small-cell lung cancer (mNSCLC) with and without progression and estimate costs of progression. Materials & methods: Retrospective claims analysis (2015–2020) from adults with EGFRm mNSCLC initiating EGFR tyrosine kinase inhibitors. Adjusted costs for 12 months were compared (with vs without progression) and cumulative costs for early versus late progression were predicted over 36 months. Results: A total of 228 patients with EGFRm mNSCLC were included. Patients with progression within 12 months incurred significantly higher total costs despite lower treatment costs (vs without progression). Medical costs were significantly higher among early versus late progressors. Conclusion: These data may aid providers aiming to administer quality care in a cost-efficient way. Plain Language Summary Lung cancer is the leading cause of cancer death among both men and women in the US. Among US patients with adenocarcinoma histology, approximately 17% have epidermal growth factor activating mutations (EGFRm) that include exon 19 deletions or L858R mutations. These common mutations make up approximately 85% of all EGFR mutations. The aim of this study was to compare healthcare resource utilization and costs for patients with EGFRm metastatic non-small-cell lung cancer with and without disease progression within the first 12 months following first-line treatment initiation using data from insurance claims. The results suggest that patients with EGFRm metastatic non-small-cell lung cancer with disease progression in the first 12 months (after treatment initiation) have significantly higher costs compared with patients without disease progression in the first 12 months (and highest in the first 6 months). These data may help inform oncology providers aiming to administer high quality cancer care in a cost-efficient way. Article highlights Patients with non-small-cell lung cancer face a high burden of healthcare costs, especially when the disease progresses. This was a retrospective study using administrative claims data of 228 adult patients with epidermal growth factor receptor mutated (EGFRm) metastatic non-small-cell lung cancer (mNSCLC) that initiated a first-line EGFR tyrosine kinase inhibitor (TKI) between 2015 and 2020. Healthcare costs were captured during the first 12 months after treatment initiation in patients with and without disease progression, and cumulative costs were predicted over 36 months for patients with early (post initiation months 0–6) versus late (post initiation months 7–12) disease progression. There were 152 (66.7%) patients whose disease progressed at any time during the 12 months after treatment initiation; 101 (66.4%) patients had early progression and 51 (34.2%) had late progression. Patients with any disease progression had greater healthcare resource utilization compared with patients whose disease did not progress during follow-up. Adjusted mean total healthcare costs were statistically significantly higher for patients with any progression compared with those without progression, mainly driven by inpatient costs. Adjusted monthly predicted cumulative costs were significantly higher among patients who progressed early compared with those that progressed late. Study limitations include lack of biomarker and histology data from claims data, misclassification of metastatic disease status and specific EGFR mutations based on coding errors and limited generalizability. This studies provides real-world costs of patients with EGFRm mNSCLC that initiate a TKI whose disease progresses in a 12-month time period; the cost burden was highest among patients with disease progression in the first 6 months after treatment initiation. This study highlights the importance of delaying EGFRm mNSCLC disease progression in a timely manner. [ABSTRACT FROM AUTHOR]

Background: Mutations in kinases are the most frequent genetic alterations in cancer; however, experimental evidence establishing their cancerous nature is available only for a small fraction of these mutants. Aims: Predicition analysis of kinome mutations is the primary aim of this study. Further objective is to compare the performance of various softwares in pathogenicity prediction of kinase mutations. Materials and methods: We employed a set of computational tools to predict the pathogenicity of over forty‐two thousand mutations and deposited the kinase‐wise data in Mendeley database (Estimated Pathogenicity of Kinase Mutants [EPKiMu]). Results: Mutations are more likely to be drivers when being present in the kinase domain (vs. non‐kinase domain) and belonging to hotspot residues (vs. non‐hotspot residues). We identified that, while predictive tools have low specificity in general, PolyPhen‐2 had the best accuracy. Further efforts to combine all four tools by consensus, voting, or other simple methods did not significantly improve accuracy. Discussion: The study provides a large dataset of kinase mutations along with their predicted pathogenicity that can be used as a training set for future studies. Furthermore, a comparative sensitivity and selectivity of commonly used computational tools is presented. Conclusion: Primary‐structure‐based in silico tools identified more cancerous/deleterious mutations in the kinase domains and at the hot spot residues while having higher sensitivity than specificity in detecting deleterious mutations. [ABSTRACT FROM AUTHOR]