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

Liwen Zhu, Xinyu Li, Diandian Liu et al.

Lara Cavinato, Marco Rocchi, Luca Viganò et al.

Cancer subtyping plays a crucial role in informing prognosis and guiding personalized treatment strategies. However, conventional subtyping approaches often rely on static, biopsy-derived scores that hardly capture the biological heterogeneity and temporal evolution of the disease. In this study, we propose a novel trajectory-informed clustering method for cancer subtyping that integrates multi-modal clinical data and longitudinal patient trajectories. Our method constructs a patient similarity graph using time-varying imaging-derived features, clinical covariates, and transitions among key clinical states such as therapy, surveillance, relapse, and death. This graph structure enables the identification of patient subgroups that are not only phenotypically and genotypically distinct but also aligned with patterns of disease progression. We position our approach within the landscape of existing subtyping methods and highlight its advantages in terms of temporal modeling and graph-based interpretability. Through simulation studies and application to a real world dataset of liver metastases, we demonstrate the ability of our framework to uncover clinically relevant subtypes with distinct prognostic trajectories. Our results underscore the potential of trajectory-informed clustering to enhance personalized oncology by bridging cross-sectional biomarkers with dynamic disease evolution.

Haifeng Zhang, Yipu Qu, Wuyue Yang et al.

The interplay between tumor cells and macrophages plays a central regulatory role in cancer progression. In this study, we developed a mathematical model that incorporates tumor cells, M1 type macrophages, M2 type macrophages and an M3 type macrophage population characterized by dual phenotypic features. First, we analyzed the fundamental mathematical properties of the model and derived the conditions under which the system attains a tumor free stable state or a coexistence state of tumor and immune cells. Second, global sensitivity analysis revealed that key parameters governing macrophage polarization and intercellular communication vary dynamically during tumor development. Bifurcation analysis further identified the polarization rate of M1 type macrophages $κ$ and the baseline level of resting macrophages $M_0$ as critical determinants of the system's dynamical behavior. Notably, using approximate Bayesian computation for parameter inference and dynamic simulations, the model successfully recapitulated the evolutionary trajectories of eight tumor samples. The results demonstrate that lower tumor burden is significantly associated with higher M1 type macrophage infiltration and delayed peak time of M3 type macrophage activation. Moreover, survival analysis indicated that both enhanced M1 type macrophage infiltration and delayed peak time of M3 type macrophage activation are correlated with longer survival time. In summary, this study not only provides a theoretical framework for understanding the dynamic mechanisms underlying tumor macrophage interactions but also proposes two potential clinical prognostic markers: the level of M1 type macrophage infiltration and the peak time of M3 type macrophage activation.

Aniek Eijpe, Soufyan Lakbir, Melis Erdal Cesur et al.

While multimodal survival prediction models are increasingly more accurate, their complexity often reduces interpretability, limiting insight into how different data sources influence predictions. To address this, we introduce DIMAFx, an explainable multimodal framework for cancer survival prediction that produces disentangled, interpretable modality-specific and modality-shared representations from histopathology whole-slide images and transcriptomics data. Across multiple cancer cohorts, DIMAFx achieves state-of-the-art performance and improved representation disentanglement. Leveraging its interpretable design and SHapley Additive exPlanations, DIMAFx systematically reveals key multimodal interactions and the biological information encoded in the disentangled representations. In breast cancer survival prediction, the most predictive features contain modality-shared information, including one capturing solid tumor morphology contextualized primarily by late estrogen response, where higher-grade morphology aligned with pathway upregulation and increased risk, consistent with known breast cancer biology. Key modality-specific features capture microenvironmental signals from interacting adipose and stromal morphologies. These results show that multimodal models can overcome the traditional trade-off between performance and explainability, supporting their application in precision medicine.

Wei Liu, Yi Yu, Yi He et al.

Abstract Background Lidocaine is a traditional local anesthetic, which has been reported to trigger apoptosis through the mitochondrial pathway, independent of death receptor signaling. Cuproptosis is a copper triggered mitochondrial cell death mode. In this study, we explored the biological effects of lidocaine on cuproptosis in Hep-2 cells and studied the relevant mechanisms. Methods quantitative RT-PCR was used to measure the expression level of long noncoding RNA (IncRNA) DNMBP-AS1. DNMBP-AS1 siRNA (si-DNMBP-AS1) were transfected into Hep-2 cells to verify the roles of DNMBP-AS1 in cuproptosis. 24 h treatment with 20 nM elesclomol and 2 µM CuCl2 was performed to promote the occurrence of Cuproptosis. Cell proliferation, migration and apoptosis assays ware utilized to analyze biological effect of lidocaine and DNMBP-AS1 on Hep-2 cells. Active caspase-3 were also determined after treatment. Results DNMBP-AS1 was significantly upregulated during cuproptosis in Hep-2 cells. The si-DNMBP-AS1 significantly increased the cell viability with nonactivated caspase-3, promoted the cell migration and suppress the cuproptosis. Lidocaine was cytotoxic to the Hep-2 cells in a dose- and time-dependent manner. Exposure to 10 µM of lidocaine for 24 h did not reduce the viability or activated the caspase-3, but significantly increased the expression of DNMBP-AS1, and promote the cuproptosis. Anymore, si-DNMBP-AS1 reversed the pro-cuproptosis function of lidocaine. Conclusions lidocaine was cytotoxic to Hep-2 cells in a time- and dose-dependent manner, promoted the cuproptosis through up-regulating DNMBP-AS1. The results of this study offered initial optimism that lidocaine could be used in an adjuvant or neoadjuvant fashion in cancer treatment.

Jiwei Fu, Chunyu Yang

Metastasis is the leading cause of cancer-related mortality, yet most predictive models rely on shallow architectures and neglect patient-specific regulatory mechanisms. Here, we integrate classical machine learning and deep learning to predict metastatic potential across multiple cancer types. Gene expression profiles from the Cancer Cell Line Encyclopedia were combined with a transcription factor-target prior from DoRothEA, focusing on nine metastasis-associated regulators. After selecting differential genes using the Kruskal-Wallis test, ElasticNet, Random Forest, and XGBoost models were trained for benchmarking. Personalized gene regulatory networks were then constructed using PANDA and LIONESS and analyzed through a graph attention neural network (GATv2) to learn topological and expression-based representations. While XGBoost achieved the highest AUROC (0.7051), the GNN captured non-linear regulatory dependencies at the patient level. These results demonstrate that combining traditional machine learning with graph-based deep learning enables a scalable and interpretable framework for metastasis risk prediction in precision oncology.

Yang Luo, Shiru Wang, Jun Liu et al.

Breast cancer survival prediction in computational pathology presents a remarkable challenge due to tumor heterogeneity. For instance, different regions of the same tumor in the pathology image can show distinct morphological and molecular characteristics. This makes it difficult to extract representative features from whole slide images (WSIs) that truly reflect the tumor's aggressive potential and likely survival outcomes. In this paper, we present PathoHR, a novel pipeline for accurate breast cancer survival prediction that enhances any size of pathological images to enable more effective feature learning. Our approach entails (1) the incorporation of a plug-and-play high-resolution Vision Transformer (ViT) to enhance patch-wise WSI representation, enabling more detailed and comprehensive feature extraction, (2) the systematic evaluation of multiple advanced similarity metrics for comparing WSI-extracted features, optimizing the representation learning process to better capture tumor characteristics, (3) the demonstration that smaller image patches enhanced follow the proposed pipeline can achieve equivalent or superior prediction accuracy compared to raw larger patches, while significantly reducing computational overhead. Experimental findings valid that PathoHR provides the potential way of integrating enhanced image resolution with optimized feature learning to advance computational pathology, offering a promising direction for more accurate and efficient breast cancer survival prediction. Code will be available at https://github.com/AIGeeksGroup/PathoHR.

Guangshen Ma, Ravi Prakash, Beatrice Schleupner et al.

Surgical resection of malignant solid tumors is critically dependent on the surgeon's ability to accurately identify pathological tissue and remove the tumor while preserving surrounding healthy structures. However, building an intraoperative 3D tumor model for subsequent removal faces major challenges due to the lack of high-fidelity tumor reconstruction, difficulties in developing generalized tissue models to handle the inherent complexities of tumor diagnosis, and the natural physical limitations of bimanual operation, physiologic tremor, and fatigue creep during surgery. To overcome these challenges, we introduce "TumorMap", a surgical robotic platform to formulate intraoperative 3D tumor boundaries and achieve autonomous tissue resection using a set of multifunctional lasers. TumorMap integrates a three-laser mechanism (optical coherence tomography, laser-induced endogenous fluorescence, and cutting laser scalpel) combined with deep learning models to achieve fully-automated and noncontact tumor resection. We validated TumorMap in murine osteoscarcoma and soft-tissue sarcoma tumor models, and established a novel histopathological workflow to estimate sensor performance. With submillimeter laser resection accuracy, we demonstrated multimodal sensor-guided autonomous tumor surgery without any human intervention.

Anirban Das, Cynthia Hawkins, Uri Tabori et al.

Claudia De Intinis, Paolo Izzo, Massimo Codacci-Pisanelli et al.

Background and introduction: Lung cancer is a prevalent and deadly disease globally. Non-small cell lung cancer (NSCLC) is the most common subtype, comprising 85% of cases. Case report: A 65-year-old male ex-smoker presented to our facility with a nocturnal cough. Various investigations revealed that he had metastatic NSCLC, for which he underwent chemotherapy with cisplatin and gemcitabine, followed by immunotherapy with Nivolumab. He achieved a complete response to the therapy and has remained free from recurrence for over 7 years since the initial diagnosis. Discussion and Conclusions: The treatment of metastatic NSCLC remains a significant therapeutic challenge, but the implementation of new therapeutic techniques has expanded the possibilities of achieving complete and durable eradication of the disease.

George Alzeeb, Corinne Tortorelli, Jaqueline Taleb et al.

Colorectal cancer (CRC) remains a significant global health burden, emphasizing the need for innovative treatment strategies. 95% of the CRC population are microsatellite stable (MSS), insensitive to classical immunotherapies such as anti-PD-1; on the other hand, responders can become resistant and relapse. Recently, the use of cancer vaccines enhanced the immune response against tumor cells. In this context, we developed a therapeutic vaccine based on Stimulated Tumor Cells (STC) platform technology. This vaccine is composed of selected tumor cell lines stressed and haptenated in vitro to generate a factory of immunogenic cancer-related antigens validated by a proteomic cross analysis with patient’s biopsies. This technology allows a multi-specific education of the immune system to target tumor cells harboring resistant clones. Here, we report safety and antitumor efficacy of the murine version of the STC vaccine on CT26 BALB/c CRC syngeneic murine models. We showed that one cell line (1CL)-based STC vaccine suppressed tumor growth and extended survival. In addition, three cell lines (3CL)-based STC vaccine significantly improves these parameters by presenting additional tumor-related antigens inducing a multi-specific anti-tumor immune response. Furthermore, proteomic analyses validated that the 3CL-based STC vaccine represents a wider quality range of tumor-related proteins than the 1CL-based STC vaccine covering key categories of tumor antigens related to tumor plasticity and treatment resistance. We also evaluated the efficacy of STC vaccine in an MC38 anti-PD-1 resistant syngeneic murine model. Vaccination with the 3CL-based STC vaccine significantly improved survival and showed a confirmed complete response with an antitumor activity carried by the increase of CD8+ lymphocyte T cells and M1 macrophage infiltration. These results demonstrate the potential of this technology to produce human vaccines for the treatment of patients with CRC.

Farzana Tabassum, Sabrina Islam, Siana Rizwan et al.

Gene expression analysis is a critical method for cancer classification, enabling precise diagnoses through the identification of unique molecular signatures associated with various tumors. Identifying cancer-specific genes from gene expression values enables a more tailored and personalized treatment approach. However, the high dimensionality of mRNA gene expression data poses challenges for analysis and data extraction. This research presents a comprehensive pipeline designed to accurately identify 33 distinct cancer types and their corresponding gene sets. It incorporates a combination of normalization and feature selection techniques to reduce dataset dimensionality effectively while ensuring high performance. Notably, our pipeline successfully identifies a substantial number of cancer-specific genes using a reduced feature set of just 500, in contrast to using the full dataset comprising 19,238 features. By employing an ensemble approach that combines three top-performing classifiers, a classification accuracy of 96.61% was achieved. Furthermore, we leverage Explainable AI to elucidate the biological significance of the identified cancer-specific genes, employing Differential Gene Expression (DGE) analysis.

Shrinivas Rathod

Domenica Lorusso, Domenica Lorusso, Romano Danesi et al.

Jiani Wang, Lin Gui, Yuxin Mu et al.

Abstract Background The mammalian target of rapamycin (mTOR) kinase, a central component of the PI3K/AKT/mTOR pathway, plays a critical role in tumor biology as an attractive therapeutic target. We conducted this first-in-human study to investigate the safety, pharmacokinetics (PK), and pilot efficacy of LXI-15029, an mTORC1/2 dual inhibitor, in Chinese patients with advanced malignant solid tumors. Methods Eligible patients with advanced, unresectable malignant solid tumors after failure of routine therapy or with no standard treatment were enrolled to receive ascending doses (10, 20, 40, 60, 80, 110, and 150 mg) of oral LXI-15029 twice daily (BID) (3 + 3 dose-escalation pattern) until disease progression or intolerable adverse events (AEs). The primary endpoints were safety and tolerability. Results Between June 2017 and July 2021, a total of 24 patients were enrolled. LXI-15029 was well tolerated at all doses. Only one dose-limiting toxicity (grade 3 increased alanine aminotransferase) occurred in the 150 mg group, and the maximum tolerated dose was 110 mg BID. The most common treatment-related AEs were leukocytopenia (41.7%), increased alanine aminotransferase (20.8%), increased aspartate aminotransferase (20.8%), prolonged electrocardiogram QT interval (20.8%), and hypertriglyceridemia (20.8%). No other serious treatment-related AEs were reported. LXI-15029 was absorbed rapidly after oral administration. The increases in the peak concentration and the area under the curve were greater than dose proportionality over the dose range. Eight patients had stable disease. The disease control rate was 40.0% (8/20; 95% CI 21.7–60.6). In evaluable patients, the median progression-free survival was 29 days (range 29–141). Conclusions LXI-15029 demonstrated reasonable safety and tolerability profiles and encouraging preliminary antitumor activity in Chinese patients with advanced malignant solid tumors, which warranted further validation in phase II trials. Trial registration NCT03125746(24/04/2017), http://ClinicalTrials.gov/show/NCT03125746

Rolando Gonzales Martinez, Daan-Max van Dongen

We suggest that deep learning can be used for pre-screening cancer by analyzing demographic and anthropometric information of patients, as well as biological markers obtained from routine blood samples and relative risks obtained from meta-analysis and international databases. We applied feature selection algorithms to a database of 116 women, including 52 healthy women and 64 women diagnosed with breast cancer, to identify the best pre-screening predictors of cancer. We utilized the best predictors to perform k-fold Monte Carlo cross-validation experiments that compare deep learning against traditional machine learning algorithms. Our results indicate that a deep learning model with an input-layer architecture that is fine-tuned using feature selection can effectively distinguish between patients with and without cancer. Additionally, compared to machine learning, deep learning has the lowest uncertainty in its predictions. These findings suggest that deep learning algorithms applied to cancer pre-screening offer a radiation-free, non-invasive, and affordable complement to screening methods based on imagery. The implementation of deep learning algorithms in cancer pre-screening offer opportunities to identify individuals who may require imaging-based screening, can encourage self-examination, and decrease the psychological externalities associated with false positives in cancer screening. The integration of deep learning algorithms for both screening and pre-screening will ultimately lead to earlier detection of malignancy, reducing the healthcare and societal burden associated to cancer treatment.

Hazrat Ali, Farida Mohsen, Zubair Shah

Vision transformer-based methods are advancing the field of medical artificial intelligence and cancer imaging, including lung cancer applications. Recently, many researchers have developed vision transformer-based AI methods for lung cancer diagnosis and prognosis. This scoping review aims to identify the recent developments on vision transformer-based AI methods for lung cancer imaging applications. It provides key insights into how vision transformers complemented the performance of AI and deep learning methods for lung cancer. Furthermore, the review also identifies the datasets that contributed to advancing the field. Of the 314 retrieved studies, this review included 34 studies published from 2020 to 2022. The most commonly addressed task in these studies was the classification of lung cancer types, such as lung squamous cell carcinoma versus lung adenocarcinoma, and identifying benign versus malignant pulmonary nodules. Other applications included survival prediction of lung cancer patients and segmentation of lungs. The studies lacked clear strategies for clinical transformation. SWIN transformer was a popular choice of the researchers; however, many other architectures were also reported where vision transformer was combined with convolutional neural networks or UNet model. It can be concluded that vision transformer-based models are increasingly in popularity for developing AI methods for lung cancer applications. However, their computational complexity and clinical relevance are important factors to be considered for future research work. This review provides valuable insights for researchers in the field of AI and healthcare to advance the state-of-the-art in lung cancer diagnosis and prognosis. We provide an interactive dashboard on lung-cancer.onrender.com/.

Jin Niu, Charlotte Brown, Michael Law et al.

Background: The debate over daylight saving time has surged, with interests in the effects of sunlight exposure on health. \commentnj{Prior studies simulated daylight saving time and standard time conditions by analyzing different locations within time zones and neighboring areas across time zone borders. Methods: We analyzed cancer incidence rates from various longitudinal positions within time zones and at time zone borders in the contiguous United States. Using data from State Cancer Profiles (2016-2020), we analyzed total cancer of 19 types and specific rates for eight cancers, adjusted for age and includes all demographics. Log-linear regression is used to replicate a previous study, and spatial regression models are employed to explore discontinuities at borders. Results: Cancer rate differences lack statistical significance within time zones and near borders for total cancer and most individual cancers. Exceptions included breast, prostate, and liver \& bile duct cancers, which exhibited significant relationships with relative position at the 95\% significance level. Breast and liver and bile duct cancers saw decreases, while prostate cancer incidence increased from west to east within time zones. Conclusions: Relative position does not have a significant impact on cancer incidence, hence cancer development in general. Isolated exceptions may warrant further investigation as more data becomes available. Impact: Our findings challenge prior research, revealing numerous inconsistencies. These disparities urge a reconsideration of the potential disparities in human health associated with daylight saving time and standard time. They offer insights contribute to the ongoing discussion surrounding the retention or abandonment of DST.

Jinyuan Si MD, Xiuyong Ding MD, Zhuoxia Deng MD et al.

Background : Similar to that in other malignant tumors, distant metastasis is one of the most important causes of poor prognosis in nasopharyngeal carcinoma (NPC). However, the genetic hallmarks and networks that regulate the distant metastasis of NPC are not fully understood. Methods : In this study, we performed high-throughput screening of mRNA expression profiles in 92 NPC samples collected from 3hospitals and detected the mRNA expression levels of 31,503 genes in these samples. Gene functional enrichment analyses were performed using gene set enrichment analysis (GSEA). Least absolute shrinkage and selection operator (LASSO) was applied to select prognostic genes and a Cox proportional hazards regression model including these genes was constructed to predict prognosis. The Kaplan–Meier curve and time-dependent receiver operating characteristic (ROC) curve were plotted to assess the performance of this model. Univariate and multivariate analyses were performed using the Cox proportion hazard model to test the independence of prognostic effect of gene model and other clinical features. Results : A total of 1837 differentially expressed genes between patients with and without distant metastasis were identified in the training cohort, including 869 upregulated genes and 968 downregulated genes. Six gene sets, including the Wnt/β catenin signaling pathway, hedgehog (Hh) signaling pathway, Notch signaling pathway, mitotic spindle, apical surface, and estrogen response late, were enriched in patients with distant metastasis. A four-gene signature model was constructed in the training cohort, and according to the time-dependent ROC curve, this model had certain accuracy in predicting distant metastasis-free survival (DMFS) in both the training and validation cohorts. Conclusion : We developed a four-gene signature model that can evaluate the distant metastasis risk of NPC patients and may also provide novel therapeutic targets for NPC treatment in the near future.

Weiming Han, Wei Deng, Qifeng Wang et al.

BackgroundIt is still uncertain whether the newly released eighth American Joint Committee on Cancer (AJCC) post-neoadjuvant pathologic (yp) tumor-node-metastasis (TNM) stage for esophageal carcinoma can perform well regarding patient stratification. The current study aimed to assess the prognostication ability of the eighth AJCC ypTNM staging system and attempted to explore how to facilitate the staging system for more effective evaluation of prognosis.Materials and methodsA total of 486 patients treated with neoadjuvant radiotherapy/chemoradiotherapy (nRT/CRT) were enrolled. ypN stage was reclassified by recursive partitioning. Prognostic performance, monotonicity, homogeneity, and discriminatory of yp and modified yp (myp) staging systems were assessed by time-dependent receiver operating characteristic (ROC), linear trend log-rank test, likelihood ratio χ2 test, Harrell’s c statistic, and Akaike information criterion (AIC).ResultsThe ypT stage, ypN stage, and pathologic response were significant prognostic factors of overall survival. Survival was not discriminated well using the eighth AJCC ypN stage and ypTNM stage. Recursive partitioning reclassified mypN0-N2 as metastasis in 0, 1–2, and ≥3 regional lymph nodes. Applying the ypT stage, mypN stage, and pathologic response to construct the myp staging system, the myp stage performed better in time-dependent ROC, linear trend log-rank test, likelihood ratio χ2 test, Harrell’s c statistic, and AIC.ConclusionsThe eighth AJCC ypTNM staging system performed well in differentiating prognosis to some extent. By reclassifying the ypN stage and enrolling pathologic response as a staging element, the myp staging system holds significant potential for prognostic discrimination.