Hasil untuk "Neoplasms. Tumors. Oncology. Including cancer and carcinogens"

Soham Chatterjee

As immunotherapies become standard cancer treatments, it is increasingly important to identify a patient's immune profile, which encompasses the activity of immune cells within the tumor microenvironment and the presence of specific biomarkers. However, we lack mechanistic explanations drivers of immune phenotypes. Despite advances in immune profiling with high-throughput sequencing, the mechanisms driving them remain unclear. This study aimed to identify novel, robust immune-related gene clusters (metagenes) and evaluate their prognostic significance and functional relevance across various pan-cancer types using a comprehensive computational pipeline. We acquired pan-cancer bulk RNA-seq and established immune subtypes from The Cancer Genome Atlas (TCGA). Using expression-based filtering and clustering of genes with ANOVA and Gaussian Mixture Model (GMM), we identified 48 unique metagenes. These metagenes achieved 87% accuracy in predicting the established subtypes. SHAP analysis revealed the most predictive metagenes per subtype, while functional enrichment analysis identified their associated pathways. Genes were ranked by differential expression between high- and low-expression groups. The metagenes revealed insights, including co-expression of immune activation and regulatory factors, links between cell cycle regulation and immune evasion, and dynamic microenvironment remodeling signatures. Kaplan-Meier survival analysis and multivariate Cox Regression revealed that many metagenes had prognostic value for overall survival. Overall, the metagenes represent coordinated biological programs across diverse cancer types, providing a foundation for developing robust, broadly applicable immuno-oncology biomarkers that extend beyond single-gene markers. They demonstrate prognostic value across cancer types and hold potential to guide immunotherapy treatment decisions.

A. Simonetti, Davide Tripaldella, Francesca Bardi et al.

Objective: Pain is among the most common and debilitating symptoms experienced by oncology patients and has been associated with adverse mental health outcomes, including depression and suicide. Nevertheless, the relationship between pain and suicide in oncology populations remains insufficiently characterized. A clearer understanding of this interplay is essential to guide personalized approaches aimed at reducing cancer-related burden and improving quality of life. Methods: We searched PubMed and PsycInfo without imposing limits regarding publication date using pain* AND (suicid* OR “self-harm” OR “self-injurious behavior” OR “self-inflicted injury” or “self-killing”) AND (cancer* OR oncolog* OR tumor* OR neoplasm* OR metasta*). A total of 832 articles were identified, and 15 of them were included in our review. Results: Inadequately managed pain in cancer patients is associated with a significantly elevated risk of suicidal ideation. This association is further exacerbated in individuals presenting with depressive symptoms, advanced-stage disease, or limited access to timely psychological support. These factors may interact synergistically, intensifying the emotional and cognitive burden of pain, thereby increasing vulnerability in cancer patients. Conclusions: Cancer-related pain should be conceptualized as a highly variable indicator of psychological vulnerability. Factors influencing this variability include cancer type and severity, as well as the presence of past psychopathology. These findings support the need for a personalized medicine approach, whereby pain management and psychosocial interventions are tailored to patient-specific factors such as disease stage, psychological comorbidity, and access to supportive care.

Alessia Ciringione, Federica Rizzi

Breast cancer (BC) is among the most common neoplasms globally and is the leading cause of cancer-related mortality in women. Despite significant advancements in prevention, early diagnosis, and treatment strategies made over the past two decades, breast cancer continues to pose a significant global health challenge. One of the major obstacles in the clinical management of breast cancer patients is the high intertumoral and intratumoral heterogeneity that influences disease progression and therapeutic outcomes. The inability of preclinical experimental models to replicate this diversity has hindered the comprehensive understanding of BC pathogenesis and the development of new therapeutic strategies. An ideal experimental model must recapitulate every aspect of human BC to maintain the highest predictive validity. Therefore, a thorough understanding of each model’s inherent characteristics and limitations is essential to bridging the gap between basic research and translational medicine. In this context, omics technologies serve as powerful tools for establishing comparisons between experimental models and human tumors, which may help address BC heterogeneity and vulnerabilities. This review examines the BC models currently used in preclinical research, including cell lines, patient-derived organoids (PDOs), organ-on-chip technologies, carcinogen-induced mouse models, genetically engineered mouse models (GEMMs), and xenograft mouse models. We emphasize the advantages and disadvantages of each model and outline the most important applications of omics techniques to aid researchers in selecting the most relevant model to address their specific research questions.

Dolores López-Terrada, Jens Stahlschmidt, Antonio R Perez-Atayde

Shitong Zhou, Shitong Zhou, Yu Jiang et al.

IntroductionHead and neck squamous cell carcinoma (HNSCC) is a highly heterogeneous malignancy with poor overall prognosis. Recent studies have suggested that propionate metabolism-related genes (PMRGs) may play key roles in tumor progression and immune regulation, yet their functions in HNSCC remain unclear.MethodsTranscriptomic data from 502 HNSCC tumor samples and 44 normal tissue samples were obtained from the UCSC Xena database as the training set. Two independent datasets (GSE41613 and GSE6631) from the GEO database were used for validation. Differentially expressed genes (DEGs), key module genes identified via weighted gene co-expression network analysis (WGCNA), and PMRGs were intersected to identify candidate genes. A prognostic model was constructed using Cox regression and LASSO analysis. Immune infiltration, somatic mutations, and drug sensitivity were compared between high- and low-risk groups. Gene expression was further validated by RT-qPCR using clinical samples.ResultsA total of 42 intersecting genes were identified, and four feature genes (PRKAA2, SLC7A5, GRIP2, CHGB) were selected to build the prognostic model. The model effectively stratified patients into high- and low-risk groups with significant survival differences in both the training and validation cohorts. The high-risk group exhibited marked differences in immune cell infiltration, immune checkpoint expression, and cancer immune cycle activity. Mutation burden and drug sensitivity also varied significantly between risk groups. A nomogram combining risk score and pathological N stage showed strong predictive performance.DiscussionThis study highlights the potential role of PMRGs in immune regulation and tumor progression in HNSCC. The proposed four-gene signature provides a novel tool for prognosis prediction and offers new insights for risk stratification and individualized therapy. Further multicenter validation and mechanistic studies are warranted.

Jun Wei, Jun Wei, Jun Wei et al.

Lactylation, a recently identified epigenetic modification derived from lactate metabolism, has emerged as a key regulator linking cellular metabolic states to chromatin remodeling and gene transcription. Acting through histone and non-histone protein lactylation (for example, Histone H3 Lysine 9 Lactylation [H3K9la], Histone H3 Lysine 18 Lactylation [H3K18la]), this modification reshapes chromatin accessibility and activates transcriptional programs, thereby driving tumor progression, metabolic reprogramming, immune evasion, and chemoresistance in digestive system malignancies. This review comprehensively summarizes the latest advances in lactylation across esophageal cancer (EC), gastric cancer (GC), colorectal cancer (CRC), hepatocellular carcinoma (HCC), pancreatic cancer (PC), and gallbladder cancer (GBC), emphasizing its role in epigenetic regulation of oncogenic signaling and metabolic–epigenetic crosstalk. Moreover, we discuss potential biomarkers, therapeutic targets, and pharmacologic strategies aimed at modulating lactylation. Despite promising translational potential, key challenges remain in standardizing detection methods and validating clinical efficacy. The intricate mechanisms of lactylation not only deepen our understanding of digestive tumor biology but also unveil a rich landscape of novel therapeutic targets. Future investigations should focus on deciphering lactylation-mediated epigenetic mechanisms in tumor immunotherapy and precision medicine, providing new directions for research and clinical insights for the early diagnosis and tailored treatment of digestive system tumors.

Pankaj Garg, Mostafa Ali, Mohammad Alomari et al.

Abstract Cardiac benign metastatic leiomyoma (BML) is a rare cardiac tumor that is usually asymptomatic, frequently misdiagnosed and may result in serious complications, including embolization, heart failure and death. This review highlights the importance of considering cardiac BML in the differential diagnosis of cardiac masses, especially in women with a history of uterine leiomyomas. This review summarizes the current knowledge about cardiac BML, including its demographics, clinical presentation, etio-pathogenesis, diagnosis, and management. The authors discuss the challenges associated with diagnosing cardiac BML and emphasize the importance of a thorough history, physical examination, and imaging studies. They also review the different treatment options for cardiac BML, including surgical resection and role of medical and surgical castration. Early diagnosis and management of cardiac BML is crucial to prevent complications. This review provides valuable insights for clinicians who may encounter this rare condition. By raising awareness of cardiac BML and its management strategies, this review can improve patient care and outcomes.

Huipeng Meng PhD, Yanlong Zhang PhD, Jinghao Duan PhD et al.

Objective To systematically investigate the impact of adjusting the relative weight of the built-in Stereotactic Radiosurgery Normal Tissue Objective (SRS-NTO) on dosimetric quality, plan complexity, and delivery efficiency in HyperArc™ stereotactic radiosurgery (SRS) for brain oligometastases. Methods In this retrospective planning study, a cohort of 20 patients with 1-3 brain oligometastases was analyzed. For each case, six distinct HyperArc plans were designed and optimized using the Varian Eclipse™ Treatment Planning System. To precisely isolate its impact, the relative weight of the SRS-NTO to the PTV objective was systematically varied across six levels—50%, 75%, 100% (default), 125%, 150%, and 200%—while all other planning parameters were held constant. A comprehensive comparative evaluation was then performed to assess the plans across four key domains: (i) dosimetric quality, evaluated by metrics including the Paddick Conformity Index (CI), Gradient Index (GI), and dose to Organs at Risk (OARs); (ii) plan complexity, characterized by various modulation and aperture-based indices; (iii) delivery efficiency, primarily quantified by the total Monitor Units (MUs); and (iv) physical deliverability, verified via Gamma analysis. Results Increasing NTO weight did not significantly alter dosimetric quality; key metrics for CI, GI, and OAR sparing remained statistically equivalent (p > .05). Conversely, higher NTO weights prompted a significant reduction in total MUs (p < .001) that reached an optimum at the 150% setting, and enhanced plan deliverability, evidenced by significantly higher Gamma passing rates under stricter verification criteria. An inflection point was observed beyond the 150% setting, with higher weights leading to degraded plan complexity and efficiency. Strategies within the 125% to 150% range demonstrated a superior balance, achieving optimal dosimetric trends while maximizing gains in efficiency and precision. Conclusion In HyperArc SRS for brain oligometastases, moderately increasing the SRS-NTO weight from the default 100% into the 125% to 150% range is a superior clinical strategy. This adjustment significantly enhances treatment efficiency and delivery precision by reducing plan complexity, without compromising dosimetric quality, thereby achieving a superior overall performance.

Feifan Zhang, Zhiwei Sun, Zhenyu Zhang et al.

Abstract E2F family genes are common transcription factors, abnormal in several malignant tumors. However, their complex involvement in colorectal cancer, particularly in prognosis, immune infiltration, and mutational landscape, remains unclear. We conducted a study using gene expression data from the TCGA and GEO datasets to examine the abnormal expression of E2Fs in colorectal cancer. And we performed consensus clustering and differential gene expression analyses to identify E2Fs-related genes. Then, we used Lasso regression and multivariate Cox regression to create a prognostic risk model for colorectal cancer. We analyzed the differences between the E2Fs-based gene risk and various clinical characteristics, gene mutations, immune cell infiltration, immunotherapy responses, and drug sensitivity using clinicopathological data, single-cell RNA sequences, multiple immune algorithms. Finally, we have developed a prognostic risk model that includes FMO5, NDUFA11, LIPG, FIGNL1, MOGAT2, and GZMB. We observed significant differences in clinical characteristics, immune cell infiltration, gene mutation landscapes, immunotherapy responses, and drug sensitivity between the high-risk and low-risk groups. The novel E2Fs-based gene risk model shows significant potential for contributing to the evaluation of prognosis and predicting immunotherapeutic outcomes for colorectal cancer patients.

Yue Du, Bianli Gu, Linlin Shi et al.

ABSTRACT Background Esophageal squamous cell carcinoma (ESCC) is a predominant and highly lethal form of esophageal cancer, with a five‐year survival rate below 20%. Despite advancements, most patients are diagnosed at advanced stages, limiting effective treatment options. Multi‐omics integration, encompassing somatic genomic alterations, inherited genetic mutations, transcriptomics, proteomics, metabolomics, and single‐cell sequencing, has enabled the identification of distinct molecular subtypes of ESCC. Method This article systematically reviewed the current status of molecular subtyping of ESCC based on big data, summarized unique subtypes with differing treatment responses and prognostic outcomes. Result Key findings included subtype‐specific genetic mutations, signaling pathway alterations, and metabolomic profiles, which offer novel biomarkers and therapeutic targets. Furthermore, this review discusses the link between molecular subtypes and immunotherapy efficacy, chemotherapy response, and drug development. Conclusion These insights highlight the potential of omics‐based molecular typing to transform ESCC management and facilitate personalized treatment strategies.

Lixue Qiao, Hao Li, Keyu Yin et al.

BackgroundThere is a potential bidirectional pathogenicity between thyroid and breast cancers. The association between sex hormones and two types of malignant tumors has emerged as a topic of intense academic debate in recent years. However, the role of sex hormone metabolism-related genes in thyroid cancer still needs to be further explored.MethodsWe obtained thyroid and breast cancer transcriptome data from the TCGA database and sex hormone metabolism-related gene sets from the MSigDB database, thus screening for sex hormone metabolism-related genes linked to the two malignant tumors. Univariate cox regression analysis was used for the screening of disease-free survival (DFS)-associated genes. The TCGA-THCA patients were classified as two categories via a consistent clustering algorithm, and the differential genes between the two categories were subsequently screened. A sex hormone metabolism-related prognostic model (TBSMRPM) of thyroid cancer versus breast cancer consisting of 10 genes was developed by Cox regression analyses and least absolute shrinkage with selection operator (LASSO) cox regression analysis. Finally, we performed clinicopathological subgroup analyses to analyze the correlation between TBSMRPM and clinical characteristics, immune infiltration, tumor mutation burden (TMB), and chemosensitivity, and verified the expression of TBSMRPM signature genes by qRT-PCR.ResultsWe identified 2 clusters correlated with sex hormone metabolism, and screened 10 prognostic differential genes related to thyroid cancer, breast cancer and sex hormone metabolism. After establishing the two risk groups for thyroid cancer originated from TBSMRPM, the results showed that the high-risk group exhibited the shorter DFS (P&lt;0.05). In further clinical stratification analysis, immune infiltration analysis, TMB and drug sensitivity analysis, the two TBSMRPM groups showed significant differences. The qRT-PCR results showed that C2CD4A, CERS1, MMP9, SLC5A1, HORMAD2 were highly expressed in the IHH4, KTC-1, and TPC-1 cell lines, while SLITRK2, ARHGEF37, PLP1, RNF223, and F3 were lowly expressed.ConclusionThe TBSMRPM established in this study has a certain value for the prognosis of thyroid cancer and contributes to refine clinicians’ treatment protocols.

Yi Luo, Yike Guo, Hamed Hooshangnejad et al.

Lung cancer remains the leading cause of cancerrelated deaths globally. Accurate delineation of internal gross tumor volume (IGTV) in PET/CT imaging is pivotal for optimal radiation therapy in mobile tumors such as lung cancer to account for tumor motion, yet is hindered by the limited availability of annotated IGTV datasets and attenuated PET signal intensity at tumor boundaries. In this study, we present a transfer learningbased methodology utilizing a multimodal interactive perception network with MAMBA, pre-trained on extensive gross tumor volume (GTV) datasets and subsequently fine-tuned on a private IGTV cohort. This cohort constitutes the PET/CT subset of the Lung-cancer Unified Cross-modal Imaging Dataset (LUCID). To further address the challenge of weak PET intensities in IGTV peripheral slices, we introduce a slice interaction module (SIM) within a 2.5D segmentation framework to effectively model inter-slice relationships. Our proposed module integrates channel and spatial attention branches with depthwise convolutions, enabling more robust learning of slice-to-slice dependencies and thereby improving overall segmentation performance. A comprehensive experimental evaluation demonstrates that our approach achieves a Dice of 0.609 on the private IGTV dataset, substantially surpassing the conventional baseline score of 0.385. This work highlights the potential of transfer learning, coupled with advanced multimodal techniques and a SIM to enhance the reliability and clinical relevance of IGTV segmentation for lung cancer radiation therapy planning.

Naman Dhariwal, Abeyankar Giridharan

Head and neck squamous cell carcinoma (HNSCC) presents significant challenges in clinical oncology due to its heterogeneity and high mortality rates. This study aims to leverage clinical data and machine learning (ML) principles to predict key outcomes for HNSCC patients: mortality, and relapse-free survival. Utilizing data sourced from the Cancer Imaging Archive, an extensive pipeline was implemented to ensure robust model training and evaluation. Ensemble and individual classifiers, including XGBoost, Random Forest, and Support Vectors, were employed to develop predictive models. The study identified key clinical features influencing HNSCC mortality outcomes and achieved predictive accuracy and ROC-AUC values exceeding 90\% across tasks. Support Vector Machine strongly excelled in relapse-free survival, with an recall value of 0.99 and precision of 0.97. Key clinical features including loco-regional control, smoking and treatment type, were identified as critical predictors of patient outcomes. This study underscores the medical impact of using ML-driven insights to refine prognostic accuracy and optimize personalized treatment strategies in HNSCC.

Wenqi Li, Yingli Chen, Keyang Zhou et al.

Pancreatic NEuroendocrine Tumors (pNETs) are very rare endocrine neoplasms that account for less than 5% of all pancreatic malignancies, with an incidence of only 1-1.5 cases per 100,000. Early detection of pNETs is critical for improving patient survival, but the rarity of pNETs makes segmenting them from CT a very challenging problem. So far, there has not been a dataset specifically for pNETs available to researchers. To address this issue, we propose a pNETs dataset, a well-annotated Contrast-Enhanced Computed Tomography (CECT) dataset focused exclusively on Pancreatic Neuroendocrine Tumors, containing data from 469 patients. This is the first dataset solely dedicated to pNETs, distinguishing it from previous collections. Additionally, we provide the baseline detection networks with a new slice-wise weight loss function designed for the UNet-based model, improving the overall pNET segmentation performance. We hope that our dataset can enhance the understanding and diagnosis of pNET Tumors within the medical community, facilitate the development of more accurate diagnostic tools, and ultimately improve patient outcomes and advance the field of oncology.

Morteza Rohanian, Tarun Mehra, Nicola Miglino et al.

Clinical oncology generates vast, unstructured data that often contain inconsistencies, missing information, and ambiguities, making it difficult to extract reliable insights for data-driven decision-making. General-purpose large language models (LLMs) struggle with these challenges due to their lack of domain-specific reasoning, including specialized clinical terminology, context-dependent interpretations, and multi-modal data integration. We address these issues with an oncology-specialized, efficient, and adaptable NLP framework that combines instruction tuning, retrieval-augmented generation (RAG), and graph-based knowledge integration. Our lightweight models prove effective at oncology-specific tasks, such as named entity recognition (e.g., identifying cancer diagnoses), entity linking (e.g., linking entities to standardized ontologies), TNM staging, document classification (e.g., cancer subtype classification from pathology reports), and treatment response prediction. Our framework emphasizes adaptability and resource efficiency. We include minimal German instructions, collected at the University Hospital Zurich (USZ), to test whether small amounts of non-English language data can effectively transfer knowledge across languages. This approach mirrors our motivation for lightweight models, which balance strong performance with reduced computational costs, making them suitable for resource-limited healthcare settings. We validated our models on oncology datasets, demonstrating strong results in named entity recognition, relation extraction, and document classification.

Paula Majewska, Alicja Staszek, Wiktoria Łoskot et al.

Introduction: Trousseau's phenomenon, also referred to as malignancy-associated thrombosis, represents a hypercoagulable state commonly encountered in oncology patients, contributing substantially to morbidity and mortality. This review examines the underlying mechanisms of Trousseau’s phenomenon, including the release of tumor-derived procoagulants and immune-inflammatory interactions, alongside contemporary diagnostic methodologies and emerging biomarker candidates. Additionally, therapeutic strategies, with a focus on anticoagulation management, are discussed, highlighting the clinical and prognostic significance of Trousseau’s phenomenon in evaluating cancer progression and risk stratification. The evolving understanding of this condition underscores the necessity of interdisciplinary collaboration in its clinical management and ongoing research efforts. Aim of these study: The aim of this study was to explore the issue of hypercoagulability in the cancer patient population and to investigate the underlying mechanisms contributing to its development. State of knowledge: It is well-established in scientific literature that oncology patients are at a significantly increased risk for thromboembolic events. Neoplasms promote a hypercoagulable state and its associated complications through diverse and complex pathophysiological mechanisms. Conclusions: Cancer-associated thrombosis is a significant clinical challenge in oncology patients. Understanding the underlying mechanisms, identifying specific risk factors, and ensuring early diagnosis are essential for improving prognosis and optimizing therapeutic outcomes.

I. V. Novikova, A. P. Polyakov, A. V. Mordovsky et al.

Surgery is the main method of treatment of patients with non-generalised resectable malignant neoplasm of the oral cavity. After the removal of the tumor, defects are formed, for the removal of which various types of plastic materials are used. Objective. Evaluation of the results of reconstruction of the tongue and floor of the mouth with a cheek mucomuscular flap after tumor removal. Material and methods. Seventeen patients (aged 59±9 years) underwent surgical treatment with defect reconstruction using mucomuscular cheek flaps based on the facial artery at the P.A. Herzen Moscow Oncology Research Institute. In four cases, the flaps were rotated in the oral cavity, while in 13 cases tunnel flaps were formed, including three patients who received contralateral flaps. Results. R0 surgical resection was achieved in 16 cases, while R1 resection was performed in one case. Nine patients underwent adjuvant radiotherapy with a dose of 61.6±3.8 Gy. The one-year disease-free survival was 92.6%. Flap necrosis occurred in one case on the second day after surgery. One patient (6.9%) had flap venous insufficiency with marginal necrosis. Limited mouth opening was observed in 5 cases (29.4%) due to cicatricial deformation at the donor site. Conclusion. The mucomuscular cheek flap demonstrates excellent anatomical and functional outcomes in the reconstruction of the tongue and floor of the mouth following minor resections, utilizing morphologically identical tissues, with minimal donor site damage and a low complications rate.

W. Ge, Vijay Govindarajan, Jing Yang et al.

Pancreatic cancer is among the most lethal malignancies, marked by aggressive progression, late diagnosis, and limited screening methods, resulting in a five-year survival rate of less than 10%. Early-stage tumors are especially challenging to detect with standard CT and MRI imaging, leading to treatment delays and poor outcomes. While deep learning offers promise, centralized training in healthcare raises serious privacy and data-sharing concerns. This study introduces a federated learning framework that integrates RegNetZ and the Swin-Transformer for automated detection, subtype classification, and prognosis prediction from multimodal inputs, including CT, MRI, histology, genomic, and clinical records. The Swin-Transformer models long-range dependencies, whereas the lightweight RegNetZ backbone ensures efficient local feature extraction. A Hybrid Aquila–Grey Wolf Optimizer (HA-GWO) is incorporated to balance exploration and exploitation during hyperparameter tuning, providing faster convergence and reduced computational cost compared to conventional search strategies. The proposed framework, evaluated across 5–7 simulated client institutions, achieves 99.2% accuracy, 98.9% sensitivity, 99.0% precision, and 99.4% AUC, outperforming both CNN-only and transformer-only baselines. It further minimizes false positives and false negatives, improving both subtype classification (adenocarcinoma, neuroendocrine, cystic neoplasms) and prognosis risk prediction (high vs. low risk). Hyperparameter sensitivity analysis identifies a learning rate of 0.003 with a batch size of 64 as optimal. By enabling decentralized model training without raw data exchange, the system enhances diagnostic accuracy while preserving privacy, offering a practical solution for real-time pancreatic cancer detection in federated healthcare environments. The framework is scalable across medical institutions and supports precision oncology by enabling early and reliable diagnosis at low computational cost.

Vural Tagal, Jackson Cole, Richard J Beck et al.

Cancer cells adapt to environmental and therapeutic pressures through karyotypic plasticity, including whole genome doubling, aneuploidy, and structural genome changes. Polyploid/polyaneuploid giant cancer cells (PGCCs/PACCs) represent an extreme state of this plasticity, arising through endoreplication as a common response to chemotherapy, irradiation, viral infection, hypoxia, or nutrient restriction. PGCCs act as drug-tolerant reservoirs that can later depolyploidize to repopulate tumors, underscoring their role in recurrence and therapy failure. Yet, how readily tumor cells access the PGCC state and how karyotype dynamics sculpt drug sensitivity remain poorly understood. To address this gap, we integrated (i) long-term evolution experiments (LTEEs), (ii) mathematical modeling, and (iii) computational analysis into a unified framework to define karyotype evolution as a predictive biomarker of therapy response. To study karyotype evolution under metabolic stress, we developed CLONEID, an LTEE platform that combines bright-field imaging, single-cell karyotyping and a neutral-drift framework distinguishing drift from selection. Its computer-vision module classifies PGCC versus proliferative states while PCA of other morphometrics revealed stress-specific phenotypes even without whole-genome doubling or overt ploidy change. LTEEs (>6 months) under glucose deprivation, phosphate restriction or hypoxia revealed stress- and ploidy-dependent trajectories. Glucose deprivation promoted whole-genome doubling whereas phosphate restriction and hypoxia drove chromosome loss toward near-diploid states. Strikingly, our in vivo studies confirmed hypoxia-induced ploidy reduction, which was predicted by LTEE trajectories within the 36 days and recapitulated by our model. Moreover, long-term adaptations also shifted therapy response. Glucose-deprived cultures gained resistance to gemcitabine and topotecan while phosphate-deprived cultures became more sensitive to taxanes and carboplatin. Furthermore, our functional assays showed that evolved cultures enhanced T-cell activation while control condition suppressed immunogenicity through tryptophan metabolism. To complement LTEEs, we built an ODE linear chain trick (LCT) model of the cell cycle, calibrated with isogenic diploid and tetraploid TNBC lines. Our model successfully captured ploidy-conditioned therapeutic responses. Specifically, gemcitabine, among 74 tested anticancer agents, eliminated near-diploid cells, but spared tetraploids that entered PGCC state and resumed proliferation after drug withdrawal. Overall, our findings identified that whole-genome doubling, polyploidy, and PGCC formation shape both long-term evolution and acute therapy responses. Integrating LTEEs with LCT modeling successfully links ploidy, karyotypic adaptation, and drug sensitivity. Our approach establishes the ground for “Drug–Karyotype” pairs as biomarkers and shows how metabolic niches sculpt KFLs, offering a path toward personalized, evolution-informed oncology. Vural Tagal, Jackson Cole, Richard J. Beck, Didem Ilter, Daria Myroshnychenko, Konstantin Martin, Andriy Marusyk, Ana P. Gomes, Noemi Andor. Modeling karyotype-driven adaptations to metabolic restrictions predicts therapeutic response and immunogenicity in cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Cancer Evolution: The Dynamics of Progression and Persistence; 2025 Dec 4-6; Albuquerque, NM. Philadelphia (PA): AACR; Cancer Res 2025;85(23_Suppl):Abstract nr B026.

E. Enaholo, G. Okoye, M. Musa et al.

BACKGROUND High-resolution optical coherence tomography (HR-OCT) has become an essential instrument in the screening and diagnosis of ocular surface neoplasms. Research demonstrates that HR-OCT possesses a diagnostic sensitivity ranging from 85% to 90% for ocular surface squamous neoplasia (OSSN). The connections between HR-OCT features and histological findings have consistently shown robustness, hence increasing the reliability of clinical diagnosis. AIM To examine the existing HR-OCT indicators employed in the identification of common non-benign ocular surface tumors, namely, basal cell carcinoma, OSSN, and melanocytic conjunctival lesions, and to assess their diagnostic efficacy, benefits, and prospective developments. METHODS A thorough literature review was performed to assess the published research on HR-OCT in the diagnosis of ocular surface cancers. Significant attention was given to research that compares HR-OCT characteristics with histopathologic validation, as well as on publications addressing the integration of emerging technologies and artificial intelligence in ocular oncology imaging. RESULTS HR-OCT exhibits elevated diagnostic sensitivity (85%-90%) for identifying OSSN and presents distinct imaging patterns that align closely with histology results. This approach has substantial clinical advantages due to its non-invasive characteristics, improved axial resolution, and real-time imaging capabilities. HR-OCT has demonstrated potential in assessing various lesions, including basal cell carcinoma and melanocytic conjunctival malignancies. CONCLUSION HR-OCT assumes an increasingly vital role in the early identification and clinical management of ocular surface malignancies. With advancements in imaging technology and the integration of artificial intelligence, HR-OCT is anticipated to enhance individualized diagnosis and treatment planning in ocular oncology, hence improving patient outcomes.