Abemaciclib: The First FDA-Approved CDK4/6 Inhibitor for the Adjuvant Treatment of HR+ HER2- Early Breast Cancer

CDK Inhibitors and FDA: Approved and Orphan

The protein kinases are a large family of enzymes which catalyze protein phosphorylation at certain amino acids [...].

CIP/KIP and INK4 families as hostages of oncogenic signaling

CIP/KIP and INK4 families of Cyclin-dependent kinase inhibitors (CKIs) are well-established cell cycle regulatory proteins whose canonical function is binding to Cyclin-CDK complexes and altering their function. Initial experiments showed that these proteins negatively regulate cell cycle progression and thus are tumor suppressors in the context of molecular oncology. However, expanded research into the functions of these proteins showed that most of them have non-canonical functions, both cell cycle-dependent and independent, and can even act as tumor enhancers depending on their posttranslational modifications, subcellular localization, and cell state context. This review aims to provide an overview of canonical as well as non-canonical functions of CIP/KIP and INK4 families of CKIs, discuss the potential avenues to promote their tumor suppressor functions instead of tumor enhancing ones, and how they could be utilized to design improved treatment regimens for cancer patients.

Predictive, preventive, and personalized medicine in breast cancer: targeting the PI3K pathway

Breast cancer (BC) is a multifaceted disease characterized by distinct molecular subtypes and varying responses to treatment. In BC, the phosphatidylinositol 3-kinase (PI3K) pathway has emerged as a crucial contributor to the development, advancement, and resistance to treatment. This review article explores the implications of the PI3K pathway in predictive, preventive, and personalized medicine for BC. It emphasizes the identification of predictive biomarkers, such as PIK3CA mutations, and the utility of molecular profiling in guiding treatment decisions. The review also discusses the potential of targeting the PI3K pathway for preventive strategies and the customization of therapy based on tumor stage, molecular subtypes, and genetic alterations. Overcoming resistance to PI3K inhibitors and exploring combination therapies are addressed as important considerations. While this field holds promise in improving patient outcomes, further research and clinical trials are needed to validate these approaches and translate them into clinical practice.

The pathophysiology of the cell cycle in cancer and treatment strategies using various cell cycle checkpoint inhibitors

The cell cycle is the series of events that occur in a cell leading to its division and duplication. It can be divided into two main stages: interphase and mitosis. Interphase is the longest stage of the cell cycle and can be further divided into three phases: G1, S, and G2. During G1, the cell grows and prepares for DNA synthesis. In the S phase, DNA synthesis occurs, leading to the replication of the genetic material. In G2, the cell continues to grow and prepares for mitosis. After mitosis, the cell enters the final stage of the cell cycle, called cytokinesis, during which the cytoplasm is divided, resulting in two separate daughter cells. The cell cycle then begins again with interphase. Cell cycle dysregulation is a hallmark of cancer, and it can have several consequences that contribute to the development and progression of cancer. Cyclin inhibitors and checkpoint activators have shown promise in the treatment of cancer, particularly in combination with other therapies.

Diagnostic quality model (DQM): an integrated framework for the assessment of diagnostic quality when using AI/ML

BACKGROUND

Laboratory medicine has reached the era where promises of artificial intelligence and machine learning (AI/ML) seem palpable. Currently, the primary responsibility for risk-benefit assessment in clinical practice resides with the medical director. Unfortunately, there is no tool or concept that enables diagnostic quality assessment for the various potential AI/ML applications. Specifically, we noted that an operational definition of laboratory diagnostic quality - for the specific purpose of assessing AI/ML improvements - is currently missing.

METHODS

A session at the 3rd Strategic Conference of the European Federation of Laboratory Medicine in 2022 on "AI in the Laboratory of the Future" prompted an expert roundtable discussion. Here we present a conceptual diagnostic quality framework for the specific purpose of assessing AI/ML implementations.

RESULTS

The presented framework is termed diagnostic quality model (DQM) and distinguishes AI/ML improvements at the test, procedure, laboratory, or healthcare ecosystem level. The operational definition illustrates the nested relationship among these levels. The model can help to define relevant objectives for implementation and how levels come together to form coherent diagnostics. The affected levels are referred to as scope and we provide a rubric to quantify AI/ML improvements while complying with existing, mandated regulatory standards. We present 4 relevant clinical scenarios including multi-modal diagnostics and compare the model to existing quality management systems.

CONCLUSIONS

A diagnostic quality model is essential to navigate the complexities of clinical AI/ML implementations. The presented diagnostic quality framework can help to specify and communicate the key implications of AI/ML solutions in laboratory diagnostics.

Molecular and functional imaging in cancer-targeted therapy: current applications and future directions

Targeted anticancer drugs block cancer cell growth by interfering with specific signaling pathways vital to carcinogenesis and tumor growth rather than harming all rapidly dividing cells as in cytotoxic chemotherapy. The Response Evaluation Criteria in Solid Tumor (RECIST) system has been used to assess tumor response to therapy via changes in the size of target lesions as measured by calipers, conventional anatomically based imaging modalities such as computed tomography (CT), and magnetic resonance imaging (MRI), and other imaging methods. However, RECIST is sometimes inaccurate in assessing the efficacy of targeted therapy drugs because of the poor correlation between tumor size and treatment-induced tumor necrosis or shrinkage. This approach might also result in delayed identification of response when the therapy does confer a reduction in tumor size. Innovative molecular imaging techniques have rapidly gained importance in the dawning era of targeted therapy as they can visualize, characterize, and quantify biological processes at the cellular, subcellular, or even molecular level rather than at the anatomical level. This review summarizes different targeted cell signaling pathways, various molecular imaging techniques, and developed probes. Moreover, the application of molecular imaging for evaluating treatment response and related clinical outcome is also systematically outlined. In the future, more attention should be paid to promoting the clinical translation of molecular imaging in evaluating the sensitivity to targeted therapy with biocompatible probes. In particular, multimodal imaging technologies incorporating advanced artificial intelligence should be developed to comprehensively and accurately assess cancer-targeted therapy, in addition to RECIST-based methods.