Teriflunomide/leflunomide synergize with chemotherapeutics by decreasing mitochondrial fragmentation via DRP1 in SCLC.

Although up to 80% small cell lung cancer (SCLC) patients' response is good for first-line chemotherapy regimen, most patients develop recurrence of the disease within weeks to months. Here, we report cytostatic effect of leflunomide (Leflu) and teriflunomide (Teri) on SCLC cell proliferation through inhibition of DRP1 phosphorylation at Ser616 and decreased mitochondrial fragmentation. When administered together, Teri and carboplatin (Carbo) act synergistically to significantly inhibit cell proliferation and DRP1 phosphorylation, reduce abundance of intermediates in pyrimidine de novo pathway, and increase apoptosis and DNA damage. Combination of Leflu&Carbo has anti-tumorigenic effect in vivo. Additionally, lurbinectedin (Lur) and Teri potently and synergistically inhibited spheroid growth and depleted uridine and DRP1 phosphorylation in mouse tumors. Our results suggest combinations of Carbo and Lur with Teri or Leflu are efficacious and underscore how the relationship between DRP1/DHODH and mitochondrial plasticity serves as a potential therapeutic target to validate these treatment strategies in SCLC clinical trials.

Leveraging Cancer Phenotypic Plasticity for Novel Treatment Strategies.

Cancer cells, like all other organisms, are adept at switching their phenotype to adjust to the changes in their environment. Thus, phenotypic plasticity is a quantitative trait that confers a fitness advantage to the cancer cell by altering its phenotype to suit environmental circumstances. Until recently, new traits, especially in cancer, were thought to arise due to genetic factors; however, it is now amply evident that such traits could also emerge non-genetically due to phenotypic plasticity. Furthermore, phenotypic plasticity of cancer cells contributes to phenotypic heterogeneity in the population, which is a major impediment in treating the disease. Finally, plasticity also impacts the group behavior of cancer cells, since competition and cooperation among multiple clonal groups within the population and the interactions they have with the tumor microenvironment also contribute to the evolution of drug resistance. Thus, understanding the mechanisms that cancer cells exploit to tailor their phenotypes at a systems level can aid the development of novel cancer therapeutics and treatment strategies. Here, we present our perspective on a team medicine-based approach to gain a deeper understanding of the phenomenon to develop new therapeutic strategies.

Emerging biomarkers and molecular targets for precision medicine in cervical cancer.

Cervical cancer remains a significant global health burden, necessitating innovative approaches for improved diagnostics and personalized treatment strategies. Precision medicine has emerged as a promising paradigm, leveraging biomarkers and molecular targets to tailor therapy to individual patients. This review explores the landscape of emerging biomarkers and molecular targets in cervical cancer, highlighting their potential implications for precision medicine. By integrating these biomarkers into comprehensive diagnostic algorithms, clinicians can identify high-risk patients at an earlier stage, enabling timely intervention and improved patient outcomes. Furthermore, the identification of specific molecular targets has paved the way for the development of targeted therapies aimed at disrupting key pathways implicated in cervical carcinogenesis. In conclusion, the evolving landscape of biomarkers and molecular targets presents exciting opportunities for advancing precision medicine in cervical cancer. By harnessing these insights, clinicians can optimize treatment selection, enhance patient outcomes, and ultimately transform the management of this devastating disease.

A Nexus between Genetic and Non-Genetic Mechanisms Guides KRAS Inhibitor Resistance in Lung Cancer.

Several studies in the last few years have determined that, in contrast to the prevailing dogma that drug resistance is simply due to Darwinian evolution-the selection of mutant clones in response to drug treatment-non-genetic changes can also lead to drug resistance whereby tolerant, reversible phenotypes are eventually relinquished by resistant, irreversible phenotypes. Here, using KRAS as a paradigm, we illustrate how this nexus between genetic and non-genetic mechanisms enables cancer cells to evade the harmful effects of drug treatment. We discuss how the conformational dynamics of the KRAS molecule, that includes intrinsically disordered regions, is influenced by the binding of the targeted therapies contributing to conformational noise and how this noise impacts the interaction of KRAS with partner proteins to rewire the protein interaction network. Thus, in response to drug treatment, reversible drug-tolerant phenotypes emerge via non-genetic mechanisms that eventually enable the emergence of irreversible resistant clones via genetic mutations. Furthermore, we also discuss the recent data demonstrating how combination therapy can help alleviate KRAS drug resistance in lung cancer, and how new treatment strategies based on evolutionary principles may help minimize or even preclude the emergence of drug resistance.

Artificial intelligence and allied subsets in early detection and preclusion of gynecological cancers.

Gynecological cancers including breast, cervical, ovarian, uterine, and vaginal, pose the greatest threat to world health, with early identification being crucial to patient outcomes and survival rates. The application of machine learning (ML) and artificial intelligence (AI) approaches to the study of gynecological cancer has shown potential to revolutionize cancer detection and diagnosis. The current review outlines the significant advancements, obstacles, and prospects brought about by AI and ML technologies in the timely identification and accurate diagnosis of different types of gynecological cancers. The AI-powered technologies can use genomic data to discover genetic alterations and biomarkers linked to a particular form of gynecologic cancer, assisting in the creation of targeted treatments. Furthermore, it has been shown that the potential benefits of AI and ML technologies in gynecologic tumors can greatly increase the accuracy and efficacy of cancer diagnosis, reduce diagnostic delays, and possibly eliminate the need for needless invasive operations. In conclusion, the review focused on the integrative part of AI and ML based tools and techniques in the early detection and exclusion of various cancer types; together with a collaborative coordination between research clinicians, data scientists, and regulatory authorities, which is suggested to realize the full potential of AI and ML in gynecologic cancer care.

Regression of ovarian cancer xenografts by depleting or inhibiting RLIP.

Ovarian cancer (OC) is the most prevalent and deadly cancer of the female reproductive system. Women will continue to be impacted by OC-related morbidity and mortality. Despite the fact that chemotherapy with cisplatin is the main component as the first-line anticancer treatment for OC, chemoresistance and unfavorable side effects are important obstacles to effective treatment. Targets for effective cancer therapy are required for cancer cells but not for non-malignant cells because they are expressed differently in cancer cells compared to normal cells. Targets for cancer therapy should preferably be components that already exist in biochemical and signalling frameworks and that significantly contribute to the development of cancer or regulate the response to therapy. RLIP is an important mercapturic acid pathway transporter that is crucial for survival and therapy resistance in cancers, therefore, we examined the role of RLIP in regulating essential signalling proteins involved in relaying the inputs from upstream survival pathways and mechanisms contributing to chemo-radiotherapy resistance in OC. The findings of our research offer insight into a novel anticancer effect of RLIP depletion/inhibition on OC and might open up new therapeutic avenues for OC therapy.

Targeting ITGB4/SOX2-driven lung cancer stem cells using proteasome inhibitors.

This study investigates the role of integrin ß4 (ITGB4) and stemness-associated factor SOX2 in platinum resistance in lung squamous cell carcinoma (LUSC). The expression of SOX2 and ITGB4 is found to be high in all LUSC subtypes, but the impact of ITGB4 expression on overall patient survival varies by subtype. Cancer stem cells (CSCs) isolated from LUSC patients were found to be resistant to cisplatin, but knocking down ITGB4 or SOX2 sensitized them to cisplatin. Carfilzomib (CFZ) synergized with cisplatin and suppressed CSC growth by inhibiting ITGB4 and SOX2 expression. Additionally, CFZ was found to inhibit SOX2 expression epigenetically by inhibiting histone acetylation at the SOX2 promoter site. CFZ also suppressed the growth of SOX2-dependent small cell lung cancer cells in vitro and in vivo. The study highlights the unique function of CFZ as a transcriptional suppressor of SOX2, independent of its proteasome inhibitory function.

Recent Advancement in Breast Cancer Research: Insights from Model Organisms-Mouse Models to Zebrafish.

Animal models have been utilized for decades to investigate the causes of human diseases and provide platforms for testing novel therapies. Indeed, breakthrough advances in genetically engineered mouse (GEM) models and xenograft transplantation technologies have dramatically benefited in elucidating the mechanisms underlying the pathogenesis of multiple diseases, including cancer. The currently available GEM models have been employed to assess specific genetic changes that underlay many features of carcinogenesis, including variations in tumor cell proliferation, apoptosis, invasion, metastasis, angiogenesis, and drug resistance. In addition, mice models render it easier to locate tumor biomarkers for the recognition, prognosis, and surveillance of cancer progression and recurrence. Furthermore, the patient-derived xenograft (PDX) model, which involves the direct surgical transfer of fresh human tumor samples to immunodeficient mice, has contributed significantly to advancing the field of drug discovery and therapeutics. Here, we provide a synopsis of mouse and zebrafish models used in cancer research as well as an interdisciplinary 'Team Medicine' approach that has not only accelerated our understanding of varied aspects of carcinogenesis but has also been instrumental in developing novel therapeutic strategies.

Radiation Sensitivity: The Rise of Predictive Patient-Derived Cancer Models.

Patient-derived cancer models have been used for decades to improve our understanding of cancer and test anticancer treatments. Advances in radiation delivery have made these models more attractive for studying radiation sensitizers and understanding an individual patient's radiation sensitivity. Advances in the use of patient-derived cancer models lead to a more clinically relevant outcome, although many questions remain regarding the optimal use of patient-derived xenografts and patient-derived spheroid cultures. The use of patient-derived cancer models as personalized predictive avatars through mouse and zebrafish models is discussed, and the advantages and disadvantages of patient-derived spheroids are reviewed. In addition, the use of large repositories of patient-derived models to develop predictive algorithms to guide treatment selection is discussed. Finally, we review methods for establishing patient-derived models and identify key factors that influence their use as both avatars and models of cancer biology.

A Systems Biology Approach for Addressing Cisplatin Resistance in Non-Small Cell Lung Cancer.

Translational research in medicine, defined as the transfer of knowledge and discovery from the basic sciences to the clinic, is typically achieved through interactions between members across scientific disciplines to overcome the traditional silos within the community. Thus, translational medicine underscores 'Team Medicine', the partnership between basic science researchers and clinicians focused on addressing a specific goal in medicine. Here, we highlight this concept from a City of Hope perspective. Using cisplatin resistance in non-small cell lung cancer (NSCLC) as a paradigm, we describe how basic research scientists, clinical research scientists, and medical oncologists, in true 'Team Science' spirit, addressed cisplatin resistance in NSCLC and identified a previously approved compound that is able to alleviate cisplatin resistance in NSCLC. Furthermore, we discuss how a 'Team Medicine' approach can help to elucidate the mechanisms of innate and acquired resistance in NSCLC and develop alternative strategies to overcome drug resistance.

Addressing Drug Resistance in Cancer: A Team Medicine Approach.

Drug resistance remains one of the major impediments to treating cancer. Although many patients respond well initially, resistance to therapy typically ensues. Several confounding factors appear to contribute to this challenge. Here, we first discuss some of the challenges associated with drug resistance. We then discuss how a 'Team Medicine' approach, involving an interdisciplinary team of basic scientists working together with clinicians, has uncovered new therapeutic strategies. These strategies, referred to as intermittent or 'adaptive' therapy, which are based on eco-evolutionary principles, have met with remarkable success in potentially precluding or delaying the emergence of drug resistance in several cancers. Incorporating such treatment strategies into clinical protocols could potentially enhance the precision of delivering personalized medicine to patients. Furthermore, reaching out to patients in the network of hospitals affiliated with leading academic centers could help them benefit from such innovative treatment options. Finally, lowering the dose of the drug and its frequency (because of intermittent rather than continuous therapy) can also have a significant impact on lowering the toxicity and undesirable side effects of the drugs while lowering the financial burden carried by the patient and insurance providers.