{"title":"Uncovering gene and cellular signatures of immune checkpoint response via machine learning and single-cell RNA-seq.","authors":"Asaf Pinhasi, Keren Yizhak","doi":"10.1038/s41698-025-00883-z","DOIUrl":"https://doi.org/10.1038/s41698-025-00883-z","url":null,"abstract":"<p><p>Immune checkpoint inhibitors have transformed cancer therapy. However, only a fraction of patients benefit from these treatments. The variability in patient responses remains a significant challenge due to the intricate nature of the tumor microenvironment. Here, we harness single-cell RNA-sequencing data and employ machine learning to predict patient responses while preserving interpretability and single-cell resolution. Using a dataset of melanoma-infiltrated immune cells, we applied XGBoost, achieving an initial AUC score of 0.84, which improved to 0.89 following Boruta feature selection. This analysis revealed an 11-gene signature predictive across various cancer types. SHAP value analysis of these genes uncovered diverse gene-pair interactions with non-linear and context-dependent effects. Finally, we developed a reinforcement learning model to identify the most informative single cells for predictivity. This approach highlights the power of advanced computational methods to deepen our understanding of cancer immunity and enhance the prediction of treatment outcomes.</p>","PeriodicalId":19433,"journal":{"name":"NPJ Precision Oncology","volume":"9 1","pages":"95"},"PeriodicalIF":6.8,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143764545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Raquel V Mendes, Joana M Ribeiro, Helena Gouveia, Cátia Rebelo de Almeida, Mireia Castillo-Martin, Maria José Brito, Rita Canas-Marques, Eva Batista, Celeste Alves, Berta Sousa, Pedro Gouveia, Miguel Godinho Ferreira, Maria João Cardoso, Fatima Cardoso, Rita Fior
{"title":"Zebrafish Avatar testing preclinical study predicts chemotherapy response in breast cancer.","authors":"Raquel V Mendes, Joana M Ribeiro, Helena Gouveia, Cátia Rebelo de Almeida, Mireia Castillo-Martin, Maria José Brito, Rita Canas-Marques, Eva Batista, Celeste Alves, Berta Sousa, Pedro Gouveia, Miguel Godinho Ferreira, Maria João Cardoso, Fatima Cardoso, Rita Fior","doi":"10.1038/s41698-025-00882-0","DOIUrl":"https://doi.org/10.1038/s41698-025-00882-0","url":null,"abstract":"<p><p>Chemotherapy remains the mainstay in most high-risk breast cancer (BC) settings, with several equivalent options of treatment. However, the efficacy of each treatment varies between patients and there is currently no test to determine which option will be the most effective for each individual patient. Here, we developed a fast in-vivo test for BC therapy screening: the zebrafish patient-derived-xenograft model (zAvatars), where in-vivo results can be obtained in just 10 days. To determine the predictive value of the BC zAvatars we performed a preclinical study, where zAvatars were treated with the same therapy as the donor-patient and their response to therapy was compared. Our data show a 100% concordance (18 out of 18) between the zAvatar-test and the corresponding patient's clinical response to treatment. Altogether, our results suggest that the zAvatar model constitutes a promising in-vivo assay to optimize cancer treatments in a truly personalized manner.</p>","PeriodicalId":19433,"journal":{"name":"NPJ Precision Oncology","volume":"9 1","pages":"94"},"PeriodicalIF":6.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143764546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Multi-omics in immunotherapy research for HNSCC: present situation and future perspectives.","authors":"Xuan-Hao Liu, Guang-Rui Wang, Nian-Nian Zhong, Wei-Yu Wang, Bing Liu, Zheng Li, Lin-Lin Bu","doi":"10.1038/s41698-025-00886-w","DOIUrl":"10.1038/s41698-025-00886-w","url":null,"abstract":"<p><p>Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer worldwide, significantly impacting patient survival and quality of life. The recent emergence of immunotherapy has provided new hope for HNSCC patients, improving survival rates; however, only 15%-20% of patients benefit, and side effects are inevitable. With advancements in omics technologies and the growing prevalence of bioinformatics research, the immune microenvironment of HNSCC has become increasingly well understood, and the molecular mechanisms underlying immunotherapy responses continue to be elucidated. In this review, we summarize commonly used omics techniques and their applications in the research of HNSCC immunotherapy, including predicting and enhancing efficacy, formulating personalized treatment plans, establishing robust preclinical research models, and identifying new immunotherapy targets. Finally, we explore future perspective in terms of sequencing samples, data integration analysis, emerging technologies, clinicopathological features, and interdisciplinary approaches.</p>","PeriodicalId":19433,"journal":{"name":"NPJ Precision Oncology","volume":"9 1","pages":"93"},"PeriodicalIF":6.8,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143743301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Element-specific estimation of background mutation rates in whole cancer genomes through transfer learning.","authors":"Farideh Bahari, Reza Ahangari Cohan, Hesam Montazeri","doi":"10.1038/s41698-025-00871-3","DOIUrl":"10.1038/s41698-025-00871-3","url":null,"abstract":"<p><p>Mutational burden tests are essential for detecting signals of positive selection in cancer driver discovery by comparing observed mutation rates with background mutation rates (BMRs). However, accurate BMR estimation is challenging due to the diversity of mutational processes across genomes, complicating driver discovery efforts. Existing methods rely on various genomic regions and features for BMR estimation but lack a model that integrates both intergenic intervals and functional genomic elements on a comprehensive set of genomic features. Here, we introduce eMET (element-specific Mutation Estimator with boosted Trees), which employs 1372 (epi)genomic features from intergenic data and fine-tunes it with element-specific data through transfer learning. Applied to PCAWG somatic mutations, eMET significantly improves BMR accuracy and has potential to enhance driver discovery. Additionally, we provide an extensive analysis of BMR estimation, examining different machine learning models, genomic interval strategies, feature categories, and dimensionality reduction techniques.</p>","PeriodicalId":19433,"journal":{"name":"NPJ Precision Oncology","volume":"9 1","pages":"92"},"PeriodicalIF":6.8,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11953285/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143743299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Taurine and proline promote lung tumour growth by co-regulating Azgp1/mTOR signalling pathway.","authors":"Tu-Liang Liang, Ying Chen, Nan-Jie Zhou, Xiao Shu, Jia-Ning Mi, Gang-Yuan Ma, Yao Xiao, Xi Yang, Chen Huang, Jia-Xin Li, Ying Xie, Pei-Yu Yan, Xiao-Jun Yao, Liang Liu, Hu-Dan Pan, Elaine Lai-Han Leung, Run-Ze Li","doi":"10.1038/s41698-025-00872-2","DOIUrl":"10.1038/s41698-025-00872-2","url":null,"abstract":"<p><p>Accurate metabolic biomarkers for lung cancer prognosis remain scarce but crucial. Taurine and proline, two metabolites, are consistently elevated across various cancer stages in previous studies, hinting at their potential role in disease progression. This study is the first to reveal how these metabolites contribute to poor prognosis. Transcriptomic analysis uncovered that taurine and proline downregulated Zinc-α2-glycoprotein (Azgp1), a gene linked to key metabolic pathways. Additionally, Azgp1 could also significantly affect downstream lipid metabolic pathways in lung cancer. Both taurine and proline influenced lipid metabolism via mammalian target of rapamycin (mTOR). When Azgp1 was overexpressed, lung cancer progression slowed significantly, alongside reduced mTOR activity. These findings underscore the pro-cancer role of taurine and proline, highlighting the Azgp1/mTOR axis as a vital, yet overlooked, pathway in lung cancer. This study not only advances our understanding but also identifies new therapeutic avenues.</p>","PeriodicalId":19433,"journal":{"name":"NPJ Precision Oncology","volume":"9 1","pages":"90"},"PeriodicalIF":6.8,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11953302/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143743303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jones T Nauseef, Timothy R Chu, William F Hooper, Alicia Alonso, Ali Oku, Heather Geiger, Zoe R Goldstein, Minita Shah, Michael Sigouros, Jyothi Manohar, Zoe Steinsnyder, Lara Winterkorn, Brian D Robinson, Andrea Sboner, Himisha Beltran, Olivier Elemento, Iman Hajirasouliha, Marcin Imielinski, David M Nanus, Scott T Tagawa, Nicolas Robine, Juan Miguel Mosquera
{"title":"A complex phylogeny of lineage plasticity in metastatic castration resistant prostate cancer.","authors":"Jones T Nauseef, Timothy R Chu, William F Hooper, Alicia Alonso, Ali Oku, Heather Geiger, Zoe R Goldstein, Minita Shah, Michael Sigouros, Jyothi Manohar, Zoe Steinsnyder, Lara Winterkorn, Brian D Robinson, Andrea Sboner, Himisha Beltran, Olivier Elemento, Iman Hajirasouliha, Marcin Imielinski, David M Nanus, Scott T Tagawa, Nicolas Robine, Juan Miguel Mosquera","doi":"10.1038/s41698-025-00854-4","DOIUrl":"10.1038/s41698-025-00854-4","url":null,"abstract":"<p><p>Aggressive variant and androgen receptor (AR)-independent castration resistant prostate cancers (CRPC) represent the most significant diagnostic and therapeutic challenges in prostate cancer. This study examined a case of simultaneous progression of both adenocarcinoma and squamous tumors from the same common origin. Using whole-genome and transcriptome sequencing from 17 samples collected over >6 years, we established the clonal relationship of all samples, defined shared complex structural variants, and demonstrated both divergent and convergent evolution at AR. Squamous CRPC-associated circulating tumor DNA was identified at clinical progression prior to biopsy detection of any squamous differentiation. Dynamic changes in the detection rate of histology-specific clones in circulation reflected histology-specific sensitivity to treatment. This dataset serves as an illustration of non-neuroendocrine transdifferentiation and highlights the importance of serial sampling at progression in CRPC for the detection of emergent non-adenocarcinoma histologies with implications for the treatment of lineage plasticity and transdifferentiation in metastatic CRPC.</p>","PeriodicalId":19433,"journal":{"name":"NPJ Precision Oncology","volume":"9 1","pages":"91"},"PeriodicalIF":6.8,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11953479/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143743298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wenju Niu, Junyu Yan, Min Hao, Yibo Zhang, Tianshi Li, Chen Liu, Qijian Li, Zihao Liu, Yincheng Su, Bo Peng, Yan Tan, Xiaochun Wang, Lei Wang, Hui Zhang, Guoqiang Yang
{"title":"MRI transformer deep learning and radiomics for predicting IDH wild type TERT promoter mutant gliomas.","authors":"Wenju Niu, Junyu Yan, Min Hao, Yibo Zhang, Tianshi Li, Chen Liu, Qijian Li, Zihao Liu, Yincheng Su, Bo Peng, Yan Tan, Xiaochun Wang, Lei Wang, Hui Zhang, Guoqiang Yang","doi":"10.1038/s41698-025-00884-y","DOIUrl":"10.1038/s41698-025-00884-y","url":null,"abstract":"<p><p>This study aims to predict IDH wt with TERTp-mut gliomas using multiparametric MRI sequences through a novel fusion model, while matching model classification metrics with patient risk stratification aids in crafting personalized diagnostic and prognosis evaluations.Preoperative T1CE and T2FLAIR sequences from 1185 glioma patients were analyzed. A MultiChannel_2.5D_DL model and a 2D DL model, both based on the cross-scale attention vision transformer (CrossFormer) neural network, along with a Radiomics model, were developed. These were integrated via ensemble learning into a stacking model. The MultiChannel_2.5D_DL model outperformed the 2D_DL and Radiomics models, with AUCs of 0.806-0.870. The stacking model achieved the highest AUC (0.855-0.904) across validation sets. Patients were stratified into high-risk and low-risk groups based on stacking model scores, with significant survival differences observed via Kaplan-Meier analysis and log-rank tests. The stacking model effectively identifies IDH wt TERTp-mutant gliomas and stratifies patient risk, aiding personalized prognosis.</p>","PeriodicalId":19433,"journal":{"name":"NPJ Precision Oncology","volume":"9 1","pages":"89"},"PeriodicalIF":6.8,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11950645/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143730550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Olayode O Babatunde, Sara Coca Membribes, Cristina Anthonescu, Martina Bradic, Bernard O'Malley, Irina Linkov, Edmund Bartlett, Parisa Momtaz, Kaled Alektiar, Mrinal M Gounder, Evan Rosenbaum, William D Tap, Sandra P D'Angelo, Ciara M Kelly
{"title":"Immunologic correlates in a CIC::DUX4 fusion-positive sarcoma responsive to dual immune checkpoint blockade.","authors":"Olayode O Babatunde, Sara Coca Membribes, Cristina Anthonescu, Martina Bradic, Bernard O'Malley, Irina Linkov, Edmund Bartlett, Parisa Momtaz, Kaled Alektiar, Mrinal M Gounder, Evan Rosenbaum, William D Tap, Sandra P D'Angelo, Ciara M Kelly","doi":"10.1038/s41698-025-00878-w","DOIUrl":"10.1038/s41698-025-00878-w","url":null,"abstract":"<p><p>CIC::DUX4 sarcoma (CDS) is a rare and aggressive subtype of soft tissue sarcoma with poor prognosis and limited treatment options. Immunotherapy has not been studied in this disease. To our knowledge, response to immune checkpoint blockade (ICB) has not been previously reported. Here, we present the first case of a patient with CDS responding to dual ICB with nivolumab and relatlimab. Immunohistochemical (IHC) analysis of pre-treatment samples revealed minimal immune cell infiltration, with scarce CD3+, CD8+, and FOXP3+ T-cells and negligible expression of PD-L1 and PD-1 markers. Post-treatment tumor samples revealed a significant shift in the immune microenvironment, with increased CD8 + T-cell infiltration and co-expression of exhaustion markers PD-1 and LAG-3 following treatment. These findings suggest that doublet ICB can activate an antitumor immune response in CDS, overcoming the immune cold phenotype typically associated with this sarcoma. This case provides the first evidence of dual PD-1/LAG-3 blockade inducing an immune response in CDS. The favorable response and tolerability observed in this patient highlight the potential of dual ICB as a therapeutic option in CDS that merits further investigation.</p>","PeriodicalId":19433,"journal":{"name":"NPJ Precision Oncology","volume":"9 1","pages":"85"},"PeriodicalIF":6.8,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11933392/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143701142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Julio Herrero Colomina, Eleanor Johnston, Kate Duffus, Zoulikha M Zaïr, Fiona Thistlethwaite, Matthew Krebs, Louise Carter, Donna Graham, Natalie Cook
{"title":"Real-world experience of Molecular Tumour Boards for clinical decision-making for cancer patients.","authors":"Julio Herrero Colomina, Eleanor Johnston, Kate Duffus, Zoulikha M Zaïr, Fiona Thistlethwaite, Matthew Krebs, Louise Carter, Donna Graham, Natalie Cook","doi":"10.1038/s41698-025-00863-3","DOIUrl":"10.1038/s41698-025-00863-3","url":null,"abstract":"<p><p>Molecular Tumour Boards (MTBs) play a crucial role in interpreting genomic results and providing treatment recommendations. We investigated the real-world impact of MTBs on clinical decision-making by surveying health care professionals (HCPs) across the UK; 44 participants from 11 MTBs took part in the study. 97.7% of respondents felt that MTBs increased awareness of available clinical trials matched to genomic alterations, 84% reported more confidence in interpreting genomic data, and 95.4% valued MTBs as educational. Hurdles to the discussion at MTBs included frequency and capacity of MTBs (ctDNA), sample collection and laboratory turnaround time (Tissue samples). One-third of respondents encountered challenges attending MTBs regularly due to workload. The survey highlighted areas for optimisation, such as meeting efficiency, rapid molecular analysis turnaround time, reliable trial matching tools, and ensuring MTBs are included in HCP's job plans.</p>","PeriodicalId":19433,"journal":{"name":"NPJ Precision Oncology","volume":"9 1","pages":"87"},"PeriodicalIF":6.8,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11937402/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143710701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Thomas J Herzog, Thomas C Krivak, Stephen Bush, John P Diaz, Scott Lentz, Navya Nair, Nadim Bou Zgheib, Camille Gunderson-Jackson, Abhijit Barve, Krista L Denning, Seth T Lirette, Candace M Howard, Jagan Valluri, Pier Paolo Claudio
{"title":"ChemoID-guided therapy improves objective response rate in recurrent platinum-resistant ovarian cancer randomized clinical trial.","authors":"Thomas J Herzog, Thomas C Krivak, Stephen Bush, John P Diaz, Scott Lentz, Navya Nair, Nadim Bou Zgheib, Camille Gunderson-Jackson, Abhijit Barve, Krista L Denning, Seth T Lirette, Candace M Howard, Jagan Valluri, Pier Paolo Claudio","doi":"10.1038/s41698-025-00874-0","DOIUrl":"10.1038/s41698-025-00874-0","url":null,"abstract":"<p><p>Patients with recurrent platinum-resistant ovarian cancer (PROC) have poor clinical outcomes, owing mainly to the presence of therapy-resistant cancer stem cells (CSCs). The NCT03949283 randomized clinical trial enrolled patients with recurrent PROC to receive ChemoID-guided chemotherapy or the best physician-choice regimen selected from the same list of thirteen mono or combination chemotherapies. The primary outcome was objective response rate (ORR) assessed on CT scans using the RECIST 1.1 criteria at 6 months follow-up. Subjects treated with the ChemoID assay had an ORR of 55% (CI<sub>95</sub> 39% - 73%), compared to 5% (CI<sub>95</sub> 0% - 11%) for those treated with physician's choice chemotherapy (p <0.0001). Secondary endpoints of duration of response (DOR) and progression-free survival (PFS) of subjects treated with chemotherapies guided by the ChemoID assay versus physician's choice chemotherapy were a median of 8 months vs. 5.5 months (p <0.0001), and 11.0 months (CI<sub>95</sub> 8.0- NA) vs 3.0 months (CI<sub>95</sub> 2.0- 3.5) with 27% of hazard ratio (CI95, 0.15-0.49; p <0.001), respectively.</p>","PeriodicalId":19433,"journal":{"name":"NPJ Precision Oncology","volume":"9 1","pages":"86"},"PeriodicalIF":6.8,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11937309/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143710684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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