Computational and systems oncology最新文献

{"title":"National Trends and Disparities in Cancer Mortality Among Individuals With Alzheimer's Disease and Related Dementias in the United States, 1999–2020","authors":"Aasim Ali,&nbsp;Javed Iqbal,&nbsp;Aimen Anwaar Pannun,&nbsp;Muhammad Talha,&nbsp;Fiza Nisar,&nbsp;Ufaq Reyaz,&nbsp;Muhammad Saad Ansari,&nbsp;Sameen Sarfaraz,&nbsp;Ahmad Butt,&nbsp;Hammad Azam,&nbsp;Muhammad Shamoon,&nbsp;Labeeba Abdul Ghafoor,&nbsp;Armaghana Abdullah,&nbsp;Muhammad Asad Shabbir,&nbsp;Mukesh Kumar Sharma","doi":"10.1002/cso2.70019","DOIUrl":"https://doi.org/10.1002/cso2.70019","url":null,"abstract":"<p>As population aging accelerates, the coexistence of cancer and Alzheimer's disease and related dementias (ADRD) is becoming increasingly common. While each condition independently contributes to mortality, national trends in cancer-related deaths occurring alongside ADRD—and their demographic and geographic disparities—remain poorly defined. We conducted a retrospective population-based analysis using mortality data from the CDC WONDER (1999–2020). Deaths listing both malignant neoplasms (ICD-10: C00–C97) and ADRD (F01, F03, G30) were identified. Age-adjusted mortality rates (AAMRs) per 100,000 population were calculated using the 2000 U.S. standard population. Temporal trends were assessed using joinpoint regression to estimate annual percent change (APC) with 95% confidence intervals. Stratified analyses by sex, race, region, and state were performed to evaluate disparities. A total of 89,514 deaths with concurrent cancer and ADRD were identified. The AAMR more than doubled from 2.0 per 100,000 in 1999 to 4.6 in 2020, peaking in 2012. Overall mortality increased significantly from 1999 to 2009 (APC 9.11%, <i>p</i> &lt; 0.05), followed by stabilization. Sex-specific analyses showed continued increases among females, whereas males demonstrated a significant decline after 2010. By race, Black individuals experienced a steeper early rise followed by a significant decline, while trends among White individuals plateaued after 2009. Regionally, the South exhibited a significant post-2010 decline, whereas other regions stabilized. Marked state-level variation was observed, with the highest rates in the Midwest and the lowest in the Southwest. Cancer mortality with concurrent ADRD increased substantially in the early 2000s, followed by divergent trends across demographic and geographic subgroups. Persistent disparities highlight the need for integrated oncologic and cognitive care strategies and targeted public health interventions addressing inequities in aging populations.</p>","PeriodicalId":72658,"journal":{"name":"Computational and systems oncology","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cso2.70019","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148282417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sex-Modulated TACSTD2 Methylation and Hormone-Receptor Axis Interactions in Glioblastoma: Insights Toward Mechanistic Understanding","authors":"Tahreem Fatima","doi":"10.1002/cso2.70015","DOIUrl":"https://doi.org/10.1002/cso2.70015","url":null,"abstract":"&lt;p&gt;Sexual dimorphism in glioblastoma (GBM) outcomes is well-established but mechanistically poorly understood. The DNA methylation at promoter-proximal CpGs can modulate transcription-factor binding, and it may interact with sex-specific hormone-receptor signaling. Utilizing TCGA-GBM (&lt;i&gt;n&lt;/i&gt; = 403) clinical, methylation, and expression data, we conducted sex-stratified survival analysis of tumor-associated calcium signal transducer 2 (&lt;i&gt;TACSTD2&lt;/i&gt;) CpG methylation via cSurvival, complemented by MethSurv and SMART App analyses and independent validation in Chinese Glioma Genome Atlas (CGGA) (&lt;i&gt;n&lt;/i&gt; = 123). We conducted regulatory motif analysis (FIMO), chromatin context mapping (UCSC/ENCODE), network analysis (STRING), transcription factor enrichment (EnrichR), and expression–methylation correlations. We validated protein expression using CPTAC proteomics, tissue localization using Human Protein Atlas immunohistochemistry, expression heterogeneity using GEPIA and TISCH2, and cross-cancer patterns. Findings were validated in the CGGA (&lt;i&gt;n&lt;/i&gt; = 123). Systematic screening of 16 &lt;i&gt;TACSTD2&lt;/i&gt; CpG probes identified two sites with profound sex-methylation interactions: cg16080552 (chr1:58,577,527, 246 bp upstream of TSS, &lt;i&gt;χ&lt;/i&gt;&lt;sup&gt;2&lt;/sup&gt; = 38.1, &lt;i&gt;p&lt;/i&gt; = 2.6 × 10&lt;sup&gt;−8&lt;/sup&gt;) and cg27398499 (chr1:58,578,200, 674 bp upstream of TSS, &lt;i&gt;χ&lt;/i&gt;&lt;sup&gt;2&lt;/sup&gt; = 20.3, &lt;i&gt;p&lt;/i&gt; = 1 × 10&lt;sup&gt;−&lt;/sup&gt;&lt;sup&gt;4&lt;/sup&gt;). Hypomethylated males showed worst outcomes (cg16080552: observed = 135 events, expected = 89.4, &lt;i&gt;χ&lt;/i&gt;&lt;sup&gt;2&lt;/sup&gt; contribution = 23.23), whereas hypermethylated females showed best survival. Methylation–expression correlation analysis revealed 14/16 probes significantly correlated with &lt;i&gt;TACSTD2&lt;/i&gt; expression (&lt;i&gt;r&lt;/i&gt; = −0.25 to &lt;i&gt;r&lt;/i&gt; = −0.57), with cg16080552 showing strong correlation (&lt;i&gt;r&lt;/i&gt; = −0.5, &lt;i&gt;p&lt;/i&gt; = 3.7 × 10&lt;sup&gt;−5&lt;/sup&gt;) and cg27398499 weaker correlation (&lt;i&gt;r&lt;/i&gt; = −0.19, &lt;i&gt;p&lt;/i&gt; = 0.14). Methylation was independent of copy number variation (&lt;i&gt;p&lt;/i&gt; = 0.57). FIMO analysis within ±200 bp of key CpGs identified 350 significant transcription factor binding sites (&lt;i&gt;p &lt;/i&gt;&lt; 1 × 10&lt;sup&gt;−&lt;/sup&gt;&lt;sup&gt;4&lt;/sup&gt;), including &lt;i&gt;NR1H4&lt;/i&gt;::&lt;i&gt;RXRA&lt;/i&gt; (&lt;i&gt;p&lt;/i&gt; = 5.29 × 10&lt;sup&gt;−6&lt;/sup&gt;), &lt;i&gt;NR2F1&lt;/i&gt; (&lt;i&gt;p&lt;/i&gt; = 5.39 × 10&lt;sup&gt;−6&lt;/sup&gt;), &lt;i&gt;ESRRB&lt;/i&gt; (&lt;i&gt;p&lt;/i&gt; = 9 × 10&lt;sup&gt;−6&lt;/sup&gt;), &lt;i&gt;ESRRA&lt;/i&gt; (&lt;i&gt;p&lt;/i&gt; = 1.14 × 10&lt;sup&gt;−5&lt;/sup&gt;), &lt;i&gt;Spi1&lt;/i&gt; (&lt;i&gt;p&lt;/i&gt; = 2.72 × 10&lt;sup&gt;−7&lt;/sup&gt;), and &lt;i&gt;SPIB&lt;/i&gt; (&lt;i&gt;p&lt;/i&gt; = 6.55 × 10&lt;sup&gt;−&lt;/sup&gt;&lt;sup&gt;7&lt;/sup&gt;). EnrichR multi-database validation confirmed hormone-receptor enrichment across ChEA 2022, ENCODE Consensus, JASPAR PWM, ARCHS4, and TRANSFAC. ChIP-Atlas analysis revealed &gt;40 independent ChIP-seq experiments showing &lt;i&gt;ESR1&lt;/i&gt;, &lt;i&gt;AR&lt;/i&gt;, &lt;i&gt;FOXA1&lt;/i&gt;, and &lt;i&gt;GATA3&lt;/i&gt; binding at the &lt;i&gt;TACSTD2&lt;/i&gt; locus in hormone-responsive epithelial models, establishing regulatory precedence. STRING network analysis positioned &lt;i&gt;TACSTD2&lt;/i&gt; within hormone receptor regulatory networks (&lt;i&gt;A","PeriodicalId":72658,"journal":{"name":"Computational and systems oncology","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cso2.70015","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148166932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sex-Modulated TACSTD2 Methylation and Hormone-Receptor Axis Interactions in Glioblastoma: Insights Toward Mechanistic Understanding","authors":"Tahreem Fatima","doi":"10.1002/cso2.70015","DOIUrl":"https://doi.org/10.1002/cso2.70015","url":null,"abstract":"&lt;p&gt;Sexual dimorphism in glioblastoma (GBM) outcomes is well-established but mechanistically poorly understood. The DNA methylation at promoter-proximal CpGs can modulate transcription-factor binding, and it may interact with sex-specific hormone-receptor signaling. Utilizing TCGA-GBM (&lt;i&gt;n&lt;/i&gt; = 403) clinical, methylation, and expression data, we conducted sex-stratified survival analysis of tumor-associated calcium signal transducer 2 (&lt;i&gt;TACSTD2&lt;/i&gt;) CpG methylation via cSurvival, complemented by MethSurv and SMART App analyses and independent validation in Chinese Glioma Genome Atlas (CGGA) (&lt;i&gt;n&lt;/i&gt; = 123). We conducted regulatory motif analysis (FIMO), chromatin context mapping (UCSC/ENCODE), network analysis (STRING), transcription factor enrichment (EnrichR), and expression–methylation correlations. We validated protein expression using CPTAC proteomics, tissue localization using Human Protein Atlas immunohistochemistry, expression heterogeneity using GEPIA and TISCH2, and cross-cancer patterns. Findings were validated in the CGGA (&lt;i&gt;n&lt;/i&gt; = 123). Systematic screening of 16 &lt;i&gt;TACSTD2&lt;/i&gt; CpG probes identified two sites with profound sex-methylation interactions: cg16080552 (chr1:58,577,527, 246 bp upstream of TSS, &lt;i&gt;χ&lt;/i&gt;&lt;sup&gt;2&lt;/sup&gt; = 38.1, &lt;i&gt;p&lt;/i&gt; = 2.6 × 10&lt;sup&gt;−8&lt;/sup&gt;) and cg27398499 (chr1:58,578,200, 674 bp upstream of TSS, &lt;i&gt;χ&lt;/i&gt;&lt;sup&gt;2&lt;/sup&gt; = 20.3, &lt;i&gt;p&lt;/i&gt; = 1 × 10&lt;sup&gt;−&lt;/sup&gt;&lt;sup&gt;4&lt;/sup&gt;). Hypomethylated males showed worst outcomes (cg16080552: observed = 135 events, expected = 89.4, &lt;i&gt;χ&lt;/i&gt;&lt;sup&gt;2&lt;/sup&gt; contribution = 23.23), whereas hypermethylated females showed best survival. Methylation–expression correlation analysis revealed 14/16 probes significantly correlated with &lt;i&gt;TACSTD2&lt;/i&gt; expression (&lt;i&gt;r&lt;/i&gt; = −0.25 to &lt;i&gt;r&lt;/i&gt; = −0.57), with cg16080552 showing strong correlation (&lt;i&gt;r&lt;/i&gt; = −0.5, &lt;i&gt;p&lt;/i&gt; = 3.7 × 10&lt;sup&gt;−5&lt;/sup&gt;) and cg27398499 weaker correlation (&lt;i&gt;r&lt;/i&gt; = −0.19, &lt;i&gt;p&lt;/i&gt; = 0.14). Methylation was independent of copy number variation (&lt;i&gt;p&lt;/i&gt; = 0.57). FIMO analysis within ±200 bp of key CpGs identified 350 significant transcription factor binding sites (&lt;i&gt;p &lt;/i&gt;&lt; 1 × 10&lt;sup&gt;−&lt;/sup&gt;&lt;sup&gt;4&lt;/sup&gt;), including &lt;i&gt;NR1H4&lt;/i&gt;::&lt;i&gt;RXRA&lt;/i&gt; (&lt;i&gt;p&lt;/i&gt; = 5.29 × 10&lt;sup&gt;−6&lt;/sup&gt;), &lt;i&gt;NR2F1&lt;/i&gt; (&lt;i&gt;p&lt;/i&gt; = 5.39 × 10&lt;sup&gt;−6&lt;/sup&gt;), &lt;i&gt;ESRRB&lt;/i&gt; (&lt;i&gt;p&lt;/i&gt; = 9 × 10&lt;sup&gt;−6&lt;/sup&gt;), &lt;i&gt;ESRRA&lt;/i&gt; (&lt;i&gt;p&lt;/i&gt; = 1.14 × 10&lt;sup&gt;−5&lt;/sup&gt;), &lt;i&gt;Spi1&lt;/i&gt; (&lt;i&gt;p&lt;/i&gt; = 2.72 × 10&lt;sup&gt;−7&lt;/sup&gt;), and &lt;i&gt;SPIB&lt;/i&gt; (&lt;i&gt;p&lt;/i&gt; = 6.55 × 10&lt;sup&gt;−&lt;/sup&gt;&lt;sup&gt;7&lt;/sup&gt;). EnrichR multi-database validation confirmed hormone-receptor enrichment across ChEA 2022, ENCODE Consensus, JASPAR PWM, ARCHS4, and TRANSFAC. ChIP-Atlas analysis revealed &gt;40 independent ChIP-seq experiments showing &lt;i&gt;ESR1&lt;/i&gt;, &lt;i&gt;AR&lt;/i&gt;, &lt;i&gt;FOXA1&lt;/i&gt;, and &lt;i&gt;GATA3&lt;/i&gt; binding at the &lt;i&gt;TACSTD2&lt;/i&gt; locus in hormone-responsive epithelial models, establishing regulatory precedence. STRING network analysis positioned &lt;i&gt;TACSTD2&lt;/i&gt; within hormone receptor regulatory networks (&lt;i&gt;A","PeriodicalId":72658,"journal":{"name":"Computational and systems oncology","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cso2.70015","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148166933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":72658,"journal":{"name":"Computational and systems oncology","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148062412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":72658,"journal":{"name":"Computational and systems oncology","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148062411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Update on Early-Onset Breast Cancer: Incidence, Risk Factors, Genetic Testing, and Treatment","authors":"Leila Jahangiri","doi":"10.1002/cso2.70012","DOIUrl":"10.1002/cso2.70012","url":null,"abstract":"<p>Early-onset breast cancer presents in patients typically under the age of 40, while very early-onset breast cancer is usually viewed as breast cancer occurring before the age of 35. Early-onset breast cancer demonstrates specific molecular properties and has worse outcomes compared to its late-onset breast cancer counterpart. Furthermore, the global burden of early-onset breast cancer, mortality rates, and incidence are on an upward trajectory on a global scale, highlighting the importance of gaining a better comprehension of this disease. This study aims to examine the global burden and incidence of early-onset breast cancer and a myriad of risk factors that contribute to the development of this cancer. Furthermore, the study will dissect the early-onset breast cancer patient knowledge, attitudes, and outcomes, in addition to aspects about genetic testing, disparities, diagnosis, and treatment. By advancing our understanding and knowledge of the molecular and clinical properties of early-onset breast cancer, the scientific community can lay the groundwork for improving patient experiences, outcomes, and therapy.</p>","PeriodicalId":72658,"journal":{"name":"Computational and systems oncology","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cso2.70012","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146139420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of Crucial Drug Targets and Pathways to Reprogram Drug Resistance Through Epigenetic Modulation in Advanced Lung Cancer Using Integrated Bioinformatics Approach","authors":"Okibur Rahman,&nbsp;Mossammat Rima Akter,&nbsp;Nur Alam","doi":"10.1002/cso2.70011","DOIUrl":"10.1002/cso2.70011","url":null,"abstract":"<p>Resistance to chemotherapy, which is demonstrated in almost every patient with advanced-stage lung cancer (ALC), underscores an urgent need to unravel the underlying molecular mechanisms and identify novel strategies to overcome drug resistance. In the present study, an attempt was made to identify epigenetic targets and modulators that can be exploited to reverse chemotherapeutic resistance in ALC. We performed an integrative analysis to identify epigenetically regulated key genes involved in drug resistance using clinical data from the “TCGA-LUAD” project. Transcriptomic and epigenetic analysis of 71 advanced-stage samples, compared with normal samples, revealed 8532 unique differentially expressed genes (DEGs) in ALC (5752 upregulated and 2779 downregulated genes) and 8313 differentially methylated genes (DMGs) (5816 hypermethylated and 2497 hypomethylated). A total of 143 methylation-driven drug resistance-related genes (mDRGs) were identified through the intersection of DEGs, DMGs, and drug-resistant genes in cancer. By correlating DMGs observed in ALC with crucial genes responsible for drug resistance, 10 hub genes, namely, FGFR2, BDNF, GFRA1, AGTR1, ENO1, GATA2, NTRK3, CXCL12, MSX1, and FGF2, were identified, which are supposed to be associated with the development of lung cancer and therapeutic resistance as well. Functional enrichment analysis revealed that mDRGs were mainly involved in the MAPK signaling pathway, Ras signaling pathway, chemokine signaling pathway, ErbB signaling pathway, and GPCR downstream signaling. Finally, the study identified three key genes, namely, AGTR1, NTRK3, and GFRA1, which can predict the survival of lung cancer patients as well as provide novel mechanisms of drug resistance in ALC. The findings were further validated using GEO datasets (GSE81089 and GSE66836) and were found to be consistent.</p>","PeriodicalId":72658,"journal":{"name":"Computational and systems oncology","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cso2.70011","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146136874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EGFR Signaling and Related Pathways: Potential Targets for CRISPR-Mediated Gene Editing System in the Colorectal Cancer","authors":"Mobina Tabibian,&nbsp;Solat Eslami,&nbsp;Soudeh Ghafouri-Fard","doi":"10.1002/cso2.70008","DOIUrl":"https://doi.org/10.1002/cso2.70008","url":null,"abstract":"<p>As the original member of the huge family of growth factor receptors, the epidermal growth factor receptor (EGFR) has shown inherent tyrosine kinase activity. In addition to its prototypic ligand, EGF, it is activated by TGF-α. EGFR and its ligand contribute to the pathogenesis of colorectal cancer and resistance to targeted therapy. The novel genome editing system, CRISPR/Cas9 has facilitated precise editing of oncogenic loci. This technique has been used in the context of colorectal cancer to either down-regulate EGFR signaling or amend/induce certain mutations affecting the response to tyrosine kinase inhibitors. This review summarizes the application of the mentioned technique in modulation of EGFR signaling and related pathways in the colorectal cancer. Moreover, we uniquely focused on compiling and interpreting results from CRISPR/Cas9 loss-of-function screens that directly investigate resistance to EGFR inhibition in colorectal cancer models. We also analyzed how these screens have identified key genes and pathways—within and beyond the canonical EGFR cascade—that drive resistance, providing a novel, gene-centric perspective on this critical clinical problem.</p>","PeriodicalId":72658,"journal":{"name":"Computational and systems oncology","volume":"5 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cso2.70008","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145824881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimization of Immune Checkpoint Blockade via a Multiscale Model System","authors":"Anne M. Talkington,&nbsp;Anthony J. Kearsley","doi":"10.1002/cso2.70007","DOIUrl":"https://doi.org/10.1002/cso2.70007","url":null,"abstract":"<p>Cancer progresses when cancer cells selectively bind to inhibitory receptors on a T cell surface, downregulating tumor immune response. One standard-of-care strategy to combat this process is immune checkpoint blockade. Immune checkpoint blockade occurs when a therapeutic agent binds to, and inhibits, inhibitory receptors on a T cell surface, such that immune stimulation is favored when T cells and cancer cells interact. However, many cancers fail to respond to immune checkpoint blockade treatments. Here we explore a whole-tumor and an individual cell-focused model system to test expected outcomes of blockade perturbations in tumor-immune interactions. We first observe a transition point at which patients become more likely to reach “remission” or “stable disease” as a terminal state, and a “progressive disease” state is less likely. We propose a physical, agent-based framework for testing blockade strategies at the cellular level. This offers valuable guidance for blockade efficacy optimization in future development and design of therapeutic antibodies.</p>","PeriodicalId":72658,"journal":{"name":"Computational and systems oncology","volume":"5 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cso2.70007","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145626712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mathematical Modeling and the Spreading of the Cholera Epidemic Through Numerical Methods","authors":"Abeer Aljohani,&nbsp;Amjid Hussain,&nbsp;Ali Shokri","doi":"10.1002/cso2.70006","DOIUrl":"https://doi.org/10.1002/cso2.70006","url":null,"abstract":"<p>This research examines a cholera outbreak, a serious intestinal illness caused by a significant presence of harmful bacteria in the body. We developed a mathematical model to investigate how diseases spread following exposure to pathogens, emphasizing the emergence of symptoms. Initially, the model's predictions were consistent, but it later shifted to different mathematical equations, enhancing our understanding of the disease's molecular mechanisms. Our results indicate that the fixed-pattern model can both provide a biological explanation for the disorder's unpredictable patterns and reach a stable equilibrium. We backed up our conclusions with mathematical ideas that show how the system behaves over time, which will be essential for cholera research in the future. To gain a better understanding of the fundamental causes of the disease, we developed a particular technique called the RK-4 and Non-Standard Finite Difference scheme (NSFD) for the continuous model. This approach, which employs a variety of criteria to assess the stability of intervals with and without the presence of the disease under various conditions, facilitates a comprehensive analysis of the disease's dynamics. Researchers can learn crucial information about the disease's behavior and community effects because of this approach. The results of this study can be used to forecast the spread of various infectious diseases through theoretical and numerical analyses. By using this method, researchers can gain important insight into how diseases behave and how they might affect the affected communities. This study's theoretical and numerical analyses may help forecast how different infectious diseases will spread.</p>","PeriodicalId":72658,"journal":{"name":"Computational and systems oncology","volume":"5 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cso2.70006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145470163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}