<p><strong>Objective</strong>. To present the characteristics of the AKT1E117K gene variant and a description of the clinical application in a patient with metastatic breast cancer.</p><p><strong>Results</strong>. 63 y/o woman with Stage IV Invasive lobular carcinoma at diagnosis was treated with Palbociclib and aromatase inhibitors (AI). At progression, tissue was sent for comprehensive genomic profiling to Foundation Medicine (FM) which revealed AKT1E17K mutation. In lieu of available clinical data within the patient’s tumor type (HR+ HER2- breast cancer), extrapolated data from the Flatiron Health-FM (FH-FMI) Clinico-genomic Database (CGDB) was dis- cussed at our Molecular Tumor Board (MTB). After multidisciplinary discussion, the consensus recommendation was to start treatment with the combination of mTOR inhibitor everolimus, and AI, exemestane. Patient tolerated treatment without major side effects. By the second clinical visit the patient’s breast showed signs of improvement. PET/CT showed diminished left axillary uptake, decreased right paratracheal lymph node PET avidity, and stable bone disease consistent with a partial response. The most recent office visit in January 2021, breast exam revealed a normal-appearing skin with only faint erythema. All other skin lesions have resolved. Although, the role of AKT1 variant described here is not well defined and therapeutic significance of M-Tor inhibitors not established in metastatic breast cancers, comprehensive approach to this case unraveled new and successful therapeutic option in this patient. Conclusion. This demonstrates that applying available Precision Medicine tools like MTB and real world data sets from patient populations with similar clinical and genomic profiles may provide more options for treatment.</p>
Cytokines and other immune regulatory molecules are critical players in the immune response against cancer. There is growing interest in testing the potential utility of systemic immune biomarkers to track cancer progression and to use them as predictors of effective responses to cancer therapy. The central hypothesis guiding this project is that specific immune biomarkers will serve as predictors of effective vs. ineffective immunotherapy in patients with malignant diseases. The objective of this study was to establish baseline of immune markers in patients already started treatment with immunotherapy (n=10) (T), patients starting, but not yet treated (S) with immunotherapy (n=10) and subjects without diagnosed malignant disease (W) (n=10). Blood was collected and plasma was isolated and used in the biomarker (100 markers) analysis using a protein microarray method (RayBiotech). The biomarkers in the three groups were analyzed by Principal Component Analysis, heat map with clustering, and differential expression based on p value, and Significance Analysis of Microarrays (SAM). Although 15 biomarkers were significantly different between S vs. W groups, based on SAM, only seven were found differentially expressed. Similarly, although 10 biomarkers were significantly different between T vs. W groups, based on SAM, only one biomarker was found differentially expressed. Furthermore, SAM revealed that responders (n=4) vs. stable (n=5) subgroup of patients within the T group exhibited 22 differentially expressed biomarkers. Future larger studies will be needed to evaluate whether immune markers will be able to predict effective vs. ineffective responses to immunotherapy and whether they may have therapeutic potential.
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