Metastatic cancers resistant to immunotherapy require novel management strategies. DNA damage response (DDR) proteins, including ATR (ataxia telangiectasia and Rad3-related), ATM (ataxia telangiectasia mutated) and DNA-PK (DNA-dependent protein kinase), have been promising therapeutic targets for decades. Specific, potent DDR inhibitors (DDRi) recently entered clinical trials. Surprisingly, preclinical studies have now indicated that DDRi may stimulate anti-tumor immunity to augment immunotherapy. The mechanisms governing how DDRi could promote anti-tumor immunity are not well understood; however, early evidence suggests that they can potentiate immunogenic cell death to recruit and activate antigen-presenting cells to prime an adaptive immune response. Merkel cell carcinoma (MCC) is well suited to test these concepts. It is inherently immunogenic as ~50% of patients with advanced MCC persistently benefit from immunotherapy, making MCC one of the most responsive solid tumors. As is typical of neuroendocrine cancers, dysfunction of p53 and Rb with upregulation of Myc leads to the very rapid growth of MCC. This suggests high replication stress and susceptibility to DDRi and DNA-damaging agents. Indeed, MCC tumors are particularly radiosensitive. Given its inherent immunogenicity, cell cycle checkpoint deficiencies and sensitivity to DNA damage, MCC may be ideal for testing whether targeting the intersection of the DDR checkpoint and the immune checkpoint could help patients with immunotherapy-refractory cancers.

Approximately 50% of Merkel cell carcinoma (MCC) patients facing this highly aggressive skin cancer initially respond positively to PD-1-based immunotherapy. Nevertheless, the recurrence of MCC post-immunotherapy emphasizes the pressing need for more effective treatments. Recent research has highlighted Cyclin-dependent kinases 4 and 6 (CDK4/6) as pivotal cell cycle regulators gaining prominence in cancer studies. This study reveals that the CDK4/6 inhibitor, palbociclib can enhance PD-L1 gene transcription and surface expression in MCC cells by activating HIF2α. Inhibiting HIF2α with TC-S7009 effectively counteracts palbociclib-induced PD-L1 transcription and significantly intensifies cell death in MCC. Simultaneously, co-targeting CDK4/6 and HIF2α boosts ROS levels while suppressing SLC7A11, a key regulator of cellular redox balance, promoting ferroptosis- a form of immunogenic cell death linked to iron. Considering the rising importance of immunogenic cell death in immunotherapy, this strategy holds promise for improving future MCC treatments, markedly increasing immunogenic cell death various across various MCC cell lines, thus advancing cancer immunotherapy.

Adult stem cells and their niches communicate intricately for tissue maintenance and regeneration. However, effectively coordinating these complex interactions is challenging. Here, we demonstrate that transient dedifferentiation of a fraction of epithelial stem cell progenies orchestrates beneficial changes within the entire skin’s cellular networks to favor repair. We achieved this by inducing a mosaic and reversible expression of reprogramming factors ( Oct-4 , Sox2 , Klf4 , and c-Myc ) in the mouse epidermis. This in vivo partial epidermal reprogramming not only affected the partially reprogrammed cells, but also their microenvironment, including neighboring epithelial cells and T cells, conferring widespread healing characteristics even in the absence of injury. When a wound was introduced, these collective changes accelerated re-epithelialization in both wild-type and a hyperglycemic mouse disease model. Furthermore, the effects extended to dermal healing, leading to reduced scarring and altered angiogenesis. In conclusion, our work reveals that mosaic partial reprogramming of the epidermis influences various cell types within the skin during homeostasis and repair, leading to enhanced cutaneous wound healing.

This study advances bioelectronic platforms and cellular behavior analysis by enhancing the precision and scalability of nanopatterned membranes integrated with electrode arrays for real‐time, high‐throughput monitoring. By employing self‐assembled monolayers (SAMs) and optimizing imprinting parameters, uniform large‐area nanopatterns are successfully fabricated, overcoming challenges such as the “rabbit ears” effect and inconsistent pattern fidelity. The nanopatterned substrates, integrated within 96‐well plates with electrode arrays, enable real‐time impedance spectroscopy, providing a dynamic assessment of cellular behavior under chemotherapeutic drug exposure. The developed NanoIEA platform facilitates comprehensive investigations into cellular growth and drug interactions. RNA sequencing of MCF‐7 cells cultured on nanopatterned substrates reveals significant differential gene expression, suggesting that traditional flat‐surface cultures may induce artificial gene regulation, potentially biasing drug screening results. Patterned cell cultures that mimic physiological conditions yield more accurate and predictive outcomes for anticancer drug screening. This research underscores the critical role of nanopatterning in recapitulating in vivo‐like gene expression and highlights the profound impact of microenvironmental cues on cellular behavior. By integrating advanced nanofabrication with precise real‐time monitoring, this approach addresses technical limitations in bioelectronic sensing while providing deeper insights into dynamic cellular responses, reinforcing the importance of substrate design in tissue engineering and drug development.

Immune checkpoint inhibitors have revolutionized cancer therapy, yet their efficacy is frequently limited by primary and acquired resistance. This underscores the need for novel immune regulatory targets that can improve or broaden patient responsiveness to immunotherapy. Among these, immunoglobulin (Ig) domain-containing proteins play central roles in immune signaling and tumor immune evasion, but many remain underexplored as therapeutic targets. Although recent advances in RNA sequencing, single-cell technologies, and multi-omics have expanded our understanding of gene and protein expression, integrating these data into actionable therapeutic insights remains a major challenge. Recent developments in Artificial Intelligence (AI), particularly in natural language processing and large language models (LLMs), offer a promising solution. Models such as GPT-4o, Llama 3.1–8B, and Gemini 1.5 Flash have demonstrated exceptional reasoning capabilities in biomedical applications. However, their use in immunotherapy biomarker discovery has yet to be fully realized. In this study, we introduce a novel AI-based framework that employs a multi-LLM approach to systematically identify and prioritize candidate biomarkers from a curated set of Ig-domain-containing genes. By integrating multi-omics data with structured prompt engineering and comparative model reasoning, our platform enhances the robustness, reproducibility, and interpretability of gene selection while minimizing bias inherent to single-model predictions. To our knowledge, this is the first study to apply a multi-LLM strategy to immunotherapy biomarker discovery. Our findings support the broader utility of LLMs in precision oncology and highlight their potential to accelerate the identification of novel, clinically actionable targets.

Yu Jung Shin, Jung Hyun Lee. IJSC -0001;0:. https://doi.org/10.15283/ijsc23086