Nature cancer最新文献

{"title":"Determinants of resistance and response to melanoma therapy","authors":"Bailey M. Robertson, Mitchell E. Fane, Ashani T. Weeraratna, Vito W. Rebecca","doi":"10.1038/s43018-024-00794-1","DOIUrl":"10.1038/s43018-024-00794-1","url":null,"abstract":"Metastatic melanoma is among the most enigmatic advanced cancers to clinically manage despite immense progress in the way of available therapeutic options and historic decreases in the melanoma mortality rate. Most patients with metastatic melanoma treated with modern targeted therapies (for example, BRAFV600E/K inhibitors) and/or immune checkpoint blockade (for example, anti-programmed death 1 therapy) will progress, owing to profound tumor cell plasticity fueled by genetic and nongenetic mechanisms and dichotomous host microenvironmental influences. Here we discuss the determinants of tumor heterogeneity, mechanisms of therapy resistance and effective therapy regimens that hold curative promise. Rebecca and colleagues discuss the complex biology of metastatic melanoma, as well as determinants of resistance to therapy and existing and promising therapy strategies.","PeriodicalId":18885,"journal":{"name":"Nature cancer","volume":"5 7","pages":"964-982"},"PeriodicalIF":23.5,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141633979","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":"Building a translational cancer dependency map for The Cancer Genome Atlas","authors":"Xu Shi, Christos Gekas, Daniel Verduzco, Sakina Petiwala, Cynthia Jeffries, Charles Lu, Erin Murphy, Tifani Anton, Andy H. Vo, Zhiguang Xiao, Padmini Narayanan, Bee-Chun Sun, Aloma L. D’Souza, J. Matthew Barnes, Somdutta Roy, Cyril Ramathal, Michael J. Flister, Zoltan Dezso","doi":"10.1038/s43018-024-00789-y","DOIUrl":"10.1038/s43018-024-00789-y","url":null,"abstract":"Cancer dependency maps have accelerated the discovery of tumor vulnerabilities that can be exploited as drug targets when translatable to patients. The Cancer Genome Atlas (TCGA) is a compendium of ‘maps’ detailing the genetic, epigenetic and molecular changes that occur during the pathogenesis of cancer, yet it lacks a dependency map to translate gene essentiality in patient tumors. Here, we used machine learning to build translational dependency maps for patient tumors, which identified tumor vulnerabilities that predict drug responses and disease outcomes. A similar approach was used to map gene tolerability in healthy tissues to prioritize tumor vulnerabilities with the best therapeutic windows. A subset of patient-translatable synthetic lethalities were experimentally tested, including PAPSS1/PAPSS12 and CNOT7/CNOT78, which were validated in vitro and in vivo. Notably, PAPSS1 synthetic lethality was driven by collateral deletion of PAPSS2 with PTEN and was correlated with patient survival. Finally, the translational dependency map is provided as a web-based application for exploring tumor vulnerabilities. Shi et al. present a hybrid dependency map based on machine-learning analysis of gene essentiality data from the DEPMAP database, translated to data from TCGA. This application can be used to visualize other gene essentiality data.","PeriodicalId":18885,"journal":{"name":"Nature cancer","volume":"5 8","pages":"1176-1194"},"PeriodicalIF":23.5,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43018-024-00789-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141620409","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":"p53 at the crossroads of tumor immunity","authors":"Gizem Efe, Anil K. Rustgi, Carol Prives","doi":"10.1038/s43018-024-00796-z","DOIUrl":"10.1038/s43018-024-00796-z","url":null,"abstract":"The p53 tumor suppressor protein has a plethora of cell-intrinsic functions and consequences that impact diverse cell types and tissues. Recent studies are beginning to unravel how wild-type and mutant p53 work in distinct ways to modulate tumor immunity. This sets up a disequilibrium between tumor immunosurveillance and escape therefrom. The ability to exploit this emerging knowledge for translational approaches may shape immunotherapy and targeted therapeutics in the future, especially in combinatorial settings. Prives and colleagues comprehensively discuss the current knowledge on cancer-related mutations of p53 and the impact they have on anticancer immunity.","PeriodicalId":18885,"journal":{"name":"Nature cancer","volume":"5 7","pages":"983-995"},"PeriodicalIF":23.5,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141620410","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":"Tumor evolution metrics predict recurrence beyond 10 years in locally advanced prostate cancer","authors":"Javier Fernandez-Mateos, George D. Cresswell, Nicholas Trahearn, Katharine Webb, Chirine Sakr, Andrea Lampis, Christine Stuttle, Catherine M. Corbishley, Vasilis Stavrinides, Luis Zapata, Inmaculada Spiteri, Timon Heide, Lewis Gallagher, Chela James, Daniele Ramazzotti, Annie Gao, Zsofia Kote-Jarai, Ahmet Acar, Lesley Truelove, Paula Proszek, Julia Murray, Alison Reid, Anna Wilkins, Michael Hubank, Ros Eeles, David Dearnaley, Andrea Sottoriva","doi":"10.1038/s43018-024-00787-0","DOIUrl":"10.1038/s43018-024-00787-0","url":null,"abstract":"Cancer evolution lays the groundwork for predictive oncology. Testing evolutionary metrics requires quantitative measurements in controlled clinical trials. We mapped genomic intratumor heterogeneity in locally advanced prostate cancer using 642 samples from 114 individuals enrolled in clinical trials with a 12-year median follow-up. We concomitantly assessed morphological heterogeneity using deep learning in 1,923 histological sections from 250 individuals. Genetic and morphological (Gleason) diversity were independent predictors of recurrence (hazard ratio (HR) = 3.12 and 95% confidence interval (95% CI) = 1.34–7.3; HR = 2.24 and 95% CI = 1.28–3.92). Combined, they identified a group with half the median time to recurrence. Spatial segregation of clones was also an independent marker of recurrence (HR = 2.3 and 95% CI = 1.11–4.8). We identified copy number changes associated with Gleason grade and found that chromosome 6p loss correlated with reduced immune infiltration. Matched profiling of relapse, decades after diagnosis, confirmed that genomic instability is a driving force in prostate cancer progression. This study shows that combining genomics with artificial intelligence-aided histopathology leads to the identification of clinical biomarkers of evolution. Sottoriva and colleagues combine next-generation sequencing and AI-aided histopathology to assess tumor evolvability in patient samples with long-term follow-up and find that it can be a strong predictor of recurrence in high-risk prostate cancer.","PeriodicalId":18885,"journal":{"name":"Nature cancer","volume":"5 9","pages":"1334-1351"},"PeriodicalIF":23.5,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43018-024-00787-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141600622","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":"A first-in-class selective inhibitor of EGFR and PI3K offers a single-molecule approach to targeting adaptive resistance","authors":"Christopher E. Whitehead, Elizabeth K. Ziemke, Christy L. Frankowski-McGregor, Rachel A. Mumby, June Chung, Jinju Li, Nathaniel Osher, Oluwadara Coker, Veerabhadran Baladandayuthapani, Scott Kopetz, Judith S. Sebolt-Leopold","doi":"10.1038/s43018-024-00781-6","DOIUrl":"10.1038/s43018-024-00781-6","url":null,"abstract":"Despite tremendous progress in precision oncology, adaptive resistance mechanisms limit the long-term effectiveness of molecularly targeted agents. Here we evaluated the pharmacological profile of MTX-531 that was computationally designed to selectively target two key resistance drivers, epidermal growth factor receptor and phosphatidylinositol 3-OH kinase (PI3K). MTX-531 exhibits low-nanomolar potency against both targets with a high degree of specificity predicted by cocrystal structural analyses. MTX-531 monotherapy uniformly resulted in tumor regressions of squamous head and neck patient-derived xenograft (PDX) models. The combination of MTX-531 with mitogen-activated protein kinase kinase or KRAS-G12C inhibitors led to durable regressions of BRAF-mutant or KRAS-mutant colorectal cancer PDX models, resulting in striking increases in median survival. MTX-531 is exceptionally well tolerated in mice and uniquely does not lead to the hyperglycemia commonly seen with PI3K inhibitors. Here, we show that MTX-531 acts as a weak agonist of peroxisome proliferator-activated receptor-γ, an attribute that likely mitigates hyperglycemia induced by PI3K inhibition. This unique feature of MTX-531 confers a favorable therapeutic index not typically seen with PI3K inhibitors. Sebolt-Leopold and colleagues design and develop a small-molecule inhibitor that can target both epidermal growth factor receptor and phosphatidylinositol 3-OH kinase, which can be leveraged to overcome resistance to targeted therapies in vivo.","PeriodicalId":18885,"journal":{"name":"Nature cancer","volume":"5 8","pages":"1250-1266"},"PeriodicalIF":23.5,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43018-024-00781-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141586965","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":"Author Correction: Clinical implementation of artificial-intelligence-assisted detection of breast cancer metastases in sentinel lymph nodes: the CONFIDENT-B single-center, non-randomized clinical trial","authors":"C. van Dooijeweert, R. N. Flach, N. D. ter Hoeve, C. P. H. Vreuls, R. Goldschmeding, J. E. Freund, P. Pham, T. Q. Nguyen, E. van der Wall, G. W. J. Frederix, N. Stathonikos, P. J. van Diest","doi":"10.1038/s43018-024-00799-w","DOIUrl":"10.1038/s43018-024-00799-w","url":null,"abstract":"","PeriodicalId":18885,"journal":{"name":"Nature cancer","volume":"5 8","pages":"1285-1285"},"PeriodicalIF":23.5,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43018-024-00799-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141538197","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":"A deep-learning framework to predict cancer treatment response from histopathology images through imputed transcriptomics","authors":"Danh-Tai Hoang, Gal Dinstag, Eldad D. Shulman, Leandro C. Hermida, Doreen S. Ben-Zvi, Efrat Elis, Katherine Caley, Stephen-John Sammut, Sanju Sinha, Neelam Sinha, Christopher H. Dampier, Chani Stossel, Tejas Patil, Arun Rajan, Wiem Lassoued, Julius Strauss, Shania Bailey, Clint Allen, Jason Redman, Tuvik Beker, Peng Jiang, Talia Golan, Scott Wilkinson, Adam G. Sowalsky, Sharon R. Pine, Carlos Caldas, James L. Gulley, Kenneth Aldape, Ranit Aharonov, Eric A. Stone, Eytan Ruppin","doi":"10.1038/s43018-024-00793-2","DOIUrl":"10.1038/s43018-024-00793-2","url":null,"abstract":"Advances in artificial intelligence have paved the way for leveraging hematoxylin and eosin-stained tumor slides for precision oncology. We present ENLIGHT–DeepPT, an indirect two-step approach consisting of (1) DeepPT, a deep-learning framework that predicts genome-wide tumor mRNA expression from slides, and (2) ENLIGHT, which predicts response to targeted and immune therapies from the inferred expression values. We show that DeepPT successfully predicts transcriptomics in all 16 The Cancer Genome Atlas cohorts tested and generalizes well to two independent datasets. ENLIGHT–DeepPT successfully predicts true responders in five independent patient cohorts involving four different treatments spanning six cancer types, with an overall odds ratio of 2.28 and a 39.5% increased response rate among predicted responders versus the baseline rate. Notably, its prediction accuracy, obtained without any training on the treatment data, is comparable to that achieved by directly predicting the response from the images, which requires specific training on the treatment evaluation cohorts. Hoang et al. developed a deep-learning framework called ENLIGHT–DeepPT that predicts therapy response based on imputed transcriptomics and shows predictive power across patient cohorts and cancer types.","PeriodicalId":18885,"journal":{"name":"Nature cancer","volume":"5 9","pages":"1305-1317"},"PeriodicalIF":23.5,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141498463","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":"The proteogenomic landscape of multiple myeloma reveals insights into disease biology and therapeutic opportunities","authors":"Evelyn Ramberger, Valeriia Sapozhnikova, Yuen Lam Dora Ng, Anna Dolnik, Matthias Ziehm, Oliver Popp, Eric Sträng, Miriam Kull, Florian Grünschläger, Josefine Krüger, Manuela Benary, Sina Müller, Xiang Gao, Arunima Murgai, Mohamed Haji, Annika Schmidt, Raphael Lutz, Axel Nogai, Jan Braune, Dominik Laue, Christian Langer, Cyrus Khandanpour, Florian Bassermann, Hartmut Döhner, Monika Engelhardt, Christian Straka, Michael Hundemer, Dieter Beule, Simon Haas, Ulrich Keller, Hermann Einsele, Lars Bullinger, Stefan Knop, Philipp Mertins, Jan Krönke","doi":"10.1038/s43018-024-00784-3","DOIUrl":"10.1038/s43018-024-00784-3","url":null,"abstract":"Multiple myeloma (MM) is a plasma cell malignancy of the bone marrow. Despite therapeutic advances, MM remains incurable, and better risk stratification as well as new therapies are therefore highly needed. The proteome of MM has not been systematically assessed before and holds the potential to uncover insight into disease biology and improved prognostication in addition to genetic and transcriptomic studies. Here we provide a comprehensive multiomics analysis including deep tandem mass tag-based quantitative global (phospho)proteomics, RNA sequencing, and nanopore DNA sequencing of 138 primary patient-derived plasma cell malignancies encompassing treatment-naive MM, plasma cell leukemia and the premalignancy monoclonal gammopathy of undetermined significance, as well as healthy controls. We found that the (phospho)proteome of malignant plasma cells are highly deregulated as compared with healthy plasma cells and is both defined by chromosomal alterations as well as posttranscriptional regulation. A prognostic protein signature was identified that is associated with aggressive disease independent of established risk factors in MM. Integration with functional genetics and single-cell RNA sequencing revealed general and genetic subtype-specific deregulated proteins and pathways in plasma cell malignancies that include potential targets for (immuno)therapies. Our study demonstrates the potential of proteogenomics in cancer and provides an easily accessible resource for investigating protein regulation and new therapeutic approaches in MM. Krönke and colleagues present a multiomic resource of plasma cell malignancies, including multiple myeloma, that comprises phosphoproteomics, RNA and DNA sequencing and provides insights into cancer type biology and candidate therapeutic targets.","PeriodicalId":18885,"journal":{"name":"Nature cancer","volume":"5 8","pages":"1267-1284"},"PeriodicalIF":23.5,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43018-024-00784-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141469519","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":"Clinical implementation of artificial-intelligence-assisted detection of breast cancer metastases in sentinel lymph nodes: the CONFIDENT-B single-center, non-randomized clinical trial","authors":"C. van Dooijeweert, R. N. Flach, N. D. ter Hoeve, C. P. H. Vreuls, R. Goldschmeding, J. E. Freund, P. Pham, T. Q. Nguyen, E. van der Wall, G. W. J. Frederix, N. Stathonikos, P. J. van Diest","doi":"10.1038/s43018-024-00788-z","DOIUrl":"10.1038/s43018-024-00788-z","url":null,"abstract":"Pathologists’ assessment of sentinel lymph nodes (SNs) for breast cancer (BC) metastases is a treatment-guiding yet labor-intensive and costly task because of the performance of immunohistochemistry (IHC) in morphologically negative cases. This non-randomized, single-center clinical trial (International Standard Randomized Controlled Trial Number:14323711) assessed the efficacy of an artificial intelligence (AI)-assisted workflow for detecting BC metastases in SNs while maintaining diagnostic safety standards. From September 2022 to May 2023, 190 SN specimens were consecutively enrolled and allocated biweekly to the intervention arm (n = 100) or control arm (n = 90). In both arms, digital whole-slide images of hematoxylin–eosin sections of SN specimens were assessed by an expert pathologist, who was assisted by the ‘Metastasis Detection’ app (Visiopharm) in the intervention arm. Our primary endpoint showed a significantly reduced adjusted relative risk of IHC use (0.680, 95% confidence interval: 0.347–0.878) for AI-assisted pathologists, with subsequent cost savings of ~3,000 €. Secondary endpoints showed significant time reductions and up to 30% improved sensitivity for AI-assisted pathologists. This trial demonstrates the safety and potential for cost and time savings of AI assistance. Van Dooijeweert et al. conducted a prospective study on the clinical implementation of artificial-intelligence-assisted detection of sentinel lymph node metastasis in persons with breast cancer and report on its effects, including on time and cost.","PeriodicalId":18885,"journal":{"name":"Nature cancer","volume":"5 8","pages":"1195-1205"},"PeriodicalIF":23.5,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43018-024-00788-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141469518","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":"Pharmacological targeting of the cancer epigenome","authors":"Nathaniel W. Mabe, Jennifer A. Perry, Clare F. Malone, Kimberly Stegmaier","doi":"10.1038/s43018-024-00777-2","DOIUrl":"10.1038/s43018-024-00777-2","url":null,"abstract":"Epigenetic dysregulation is increasingly appreciated as a hallmark of cancer, including disease initiation, maintenance and therapy resistance. As a result, there have been advances in the development and evaluation of epigenetic therapies for cancer, revealing substantial promise but also challenges. Three epigenetic inhibitor classes are approved in the USA, and many more are currently undergoing clinical investigation. In this Review, we discuss recent developments for each epigenetic drug class and their implications for therapy, as well as highlight new insights into the role of epigenetics in cancer. Stegmaier and colleagues provide a review on the latest development in targeting the cancer epigenome, give an overview of distinct drug classes, and discuss therapeutic possibilities and challenges.","PeriodicalId":18885,"journal":{"name":"Nature cancer","volume":"5 6","pages":"844-865"},"PeriodicalIF":23.5,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141469520","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}