Discrimination of 14 olive cultivars using morphological analysis and machine learning algorithms.

Traditional morphological analysis is a widely employed tool for the identification and discrimination of olive germplasm by using morphological markers which are monitored by subjective manual measurements that are labor intensive and time-consuming. Alternatively, an automated methodology can quantify the geometrical features of fruits, leaves and endocarps with high accuracy and efficiency in order to define their morphological characteristics. In this study, 24 characteristics for fruits, 16 for leaves and 25 for endocarps were determined and used in an automated way with basic classifiers combined with a meta-classsifier approach. This resulted to the discrimination of 14 olive cultivars utilizing data obtained from two consecutive olive growing periods. The cultivar classification algorithms were based on machine learning techniques. The 95% accuracy rate of the meta-classifier approach indicated that was an efficient tool to discriminate olive cultivars. The contribution of each morphological feature to cultivar discrimination was quantified, and the significance of each one was automatically detected in a quantitative way. The higher the contribution of each feature, the higher the significance for cultivar discrimination. The identification of most cultivars was guided by the features of both endocarps and fruits, while those of leaves were only efficient to identify the Kalamon cultivar. The combined use of morphological features of three olive organs might have an additive effect leading to higher capacity for discrimination of cultivars. The proposed methodology might be considered a phenomics tool for olive cultivar identification and discrimination in a wide range of applications including breeding.

Evolutionary dynamics of tipifarnib in HRAS mutated head and neck squamous cell carcinoma.

Head and neck squamous cell carcinoma (HNSCC) is a highly prevalent malignancy worldwide, with a significant proportion of patients developing recurrent and/or metastatic (R/M) disease. Despite recent advances in therapy, the prognosis for patients with advanced HNSCC remains poor. Here, we present the case of a patient with recurrent metastatic HNSCC harboring an HRAS G12S mutation who achieved a durable response to treatment with tipifarnib, a selective inhibitor of farnesyltransferase. The patient was a 48-year-old woman who had previously received multiple lines of therapy with no significant clinical response. However, treatment with tipifarnib resulted in a durable partial response that lasted 8 months. Serial genomic and transcriptomic analyses demonstrated upregulation of YAP1 and AXL in metastatic lesions compared with the primary tumor, the evolution of the tumor microenvironment from an immune-enriched to a fibrotic subtype with increased angiogenesis, and activation of the PI3K/AKT/mTOR pathway in tipifarnib treatment. Lastly, in HRAS-mutated PDXs and in the syngeneic HRAS model, we demonstrated that tipifarnib efficacy is limited by activation of the AKT pathway, and dual treatment with tipifarnib and the PI3K inhibitor, BYL719, resulted in enhanced anti-tumor efficacy. Our case study highlights the potential of targeting HRAS mutations with tipifarnib in R/M HNSCC and identifies potential mechanisms of acquired resistance to tipifarnib, along with immuno-, chemo-, and radiation therapy. Preclinical results provide a firm foundation for further investigation of drug combinations of HRAS-and PI3K -targeting therapeutics in R/M HRAS-driven HNSCC.

Diagnostic Utility of RNA-Seq for Evaluation of PD-L1 Expression in Clear Cell Renal Cell Carcinoma.

BACKGROUND: Although there are immune checkpoint inhibitors (ICIs) available for the treatment of renal cell carcinoma (RCC), the utility of PD-L1 detection by immunohistochemistry (IHC) as a predictive biomarker in clear cell RCC (ccRCC) remains controversial. Nevertheless, alternative methods for PD-L1 detection, such as RNA sequencing (RNA-Seq), may be clinically useful in ccRCC; therefore, we sought to determine the ability of RNA-Seq to accurately and sensitively detect PD-L1 expression across different ccRCC clinical samples in comparison with IHC. PATIENTS AND METHODS: Patients with ccRCC (n=127) who received treatment from Washington University in St. Louis between 2018 and 2020 were identified. Tumors from these patients were analyzed using RNA-Seq and IHC. RESULTS: PD-L1 detection by RNA-Seq strongly correlated with IHC (P < .001), which was further validated using two independent datasets. Furthermore, RNA-Seq analysis identified an immune-enriched (higher PD-L1 positivity) and an immune-desert (lower PD-L1 positivity) microenvironment of ccRCC, which also correlated with IHC (P < .00001). CONCLUSION: The results demonstrate the ability of RNA-Seq to detect PD-L1 in various ccRCC clinical samples compared to IHC. Ultimately, these findings suggest that PD-L1 detection by RNA-Seq can be further developed to determine the clinical utility of this methodology in ccRCC.