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With the invention of high-throughput screening technologies, innumerable drug sensitivity data for thousands of cancer cell lines and hundreds of compounds have been produced. Computational analysis of these data has opened a new horizon in the development of novel anti-cancer drugs. Previous deep-learning approaches to predict drug sensitivity showed drawbacks due to the casual integration of genomic features of cell lines and compound chemical features. The challenges addressed include the intricate interplay of diverse molecular features, interpretability of complex deep learning models, and the optimization of drug combinations for synergistic effects. Through the utilization of normalized discounted cumulative gain (NDCG) and root mean squared error (RMSE) as evaluation metrics, the models aim to concurrently assess the ranking quality of recommended drugs and the accuracy of predicted drug responses. The integration of the DRPO model into cancer drug response prediction not only tackles these challenges but also holds promise in facilitating more effective, personalized, and targeted cancer therapies. This paper proposes a new deep learning model, DRPO , for efficient integration of genomic and compound features in predicting the half maximal inhibitory concentrations (IC50). First, matrix factorization is used to map the drug and cell line into latent 'pharmacogenomic' space with cell line-specific predicted drug responses. Using these drug responses, we next obtained the essential drugs using a Normalized Discounted Cumulative Gain (NDCG) score. Finally, the essential drugs and genomic features are integrated to predict drug sensitivity using a deep model. Experimental results with RMSE 0.39 and NDCG 0.98 scores on Genomics of drug sensitivity in cancer (GDSC1) datasets show that our proposed approach has outperformed the previous approaches, including DeepDSC, CaDRRes, and KMBF. These good results show great potential to use our new model to discover novel anti-cancer drugs for precision medicine. [ABSTRACT FROM AUTHOR]

In recent decades, the prevalence of thyroid cancer has risen substantially, with papillary thyroid microcarcinoma (PTMC) constituting over 50% of cases. Although most PTMCs exhibit indolent growth and a favorable prognosis, some present an increased risk of recurrence and an unfavorable prognosis due to high-risk characteristics such as lymph node metastasis, extrathyroidal extension, and distant metastasis. The early identification of clinically progressing PTMC remains elusive. In this review, the authors summarize findings from PTMC progression-related literature, highlighting that factors such as larger tumor size, cervical lymph node metastasis, extrathyroidal extension, younger age, higher preoperative serum thyroid-stimulating hormone levels, family history, and obesity positively correlate with PTMC progression. The role of multifocality in promoting PTMC progression; however, remains contentious. Furthermore, recent studies have shed light on the impact of mutations, such as BRAF and TERT mutations, on PTMC progression. Researchers have identified several mRNAs, noncoding RNAs, and proteins associated with various features of PTMC progression. Some studies propose that peripheral and tumor tissue-infiltrating immune cells could serve as biomarkers for the clinical progression of PTMC. Collectively, these clinical and molecular features offer a rationale for the early detection and the development of precision theranostic strategies of clinically progressive PTMC. [ABSTRACT FROM AUTHOR]

Acute megakaryocytic leukemia (AMegL) is a biologically heterogenous subtype of acute myeloid leukemia (AML) that arises from megakaryocytes. Improvements in the accuracy of diagnosing AMegL as well as interest in the molecular analysis of leukemias have led to an increased amount of data available on this rare AML subtype. In this review, we will analyze the diverse molecular features unique to AMegL and how they have influenced the development of novel treatment strategies, including polyploidization. The review will also consider the data available on clinical outcomes in AMegL and how it is a poor individual prognostic factor for AML. Finally, the role of allogeneic hematopoietic stem cell transplant in AMegL will be explored. [ABSTRACT FROM AUTHOR]

Summary: Breast cancer is a heterogeneous disease with different morphologies, molecular profiles, clinical behaviour and response to therapy. The triple negative is a particular type of breast cancer defined by absence of oestrogen and progesterone receptor expression as well as absence of ERBB2 amplification. It is characterized by its biological aggressiveness, worse prognosis and lack of a therapeutic target in contrast with hormonal receptor positive and ERBB2+ breast cancers. Given these characteristics, triple-negative breast cancer is a challenge in today’s clinical practice. A new breast cancer classification emerged recently in the scientific scene based in gene expression profiles. The new subgroups (luminal, ERBB2, normal breast and basal-like) have distinct gene expression patterns and phenotypical characteristics. Triple-negative breast cancer shares phenotypical features with basal-like breast cancer, which is in turn the most aggressive and with worse outcome. Since microarray gene-expression assays are only used in the research setting, clinicians use the triple-negative definition as a surrogate of basal-like breast cancer. The aim of this review, that focuses on triple-negative breast cancer, is to summarize the most relevant knowledge on this particular type of cancer in terms of molecular features, pathogenesis, clinical characteristics, current treatments and the new therapeutic options that include the use of platinum compounds, EGFR antagonists, antiangiogenics and PARP inhibitors. Advances in research are promising and new types of active drugs will become a reality in the near future, making possible a better outcome for this subgroup of breast cancer patients. [Copyright &y& Elsevier]