Pub Date : 2024-07-26DOI: 10.1038/s43018-024-00798-x
Marcel Arias-Badia, Ryan Chang, Lawrence Fong
While the effector cells that mediate anti-tumor immunity have historically been attributed to αβ T cells and natural killer cells, γδ T cells are now being recognized as a complementary mechanism mediating tumor rejection. γδ T cells possess a host of functions ranging from antigen presentation to regulatory function and, importantly, have critical roles in eliciting anti-tumor responses where other immune effectors may be rendered ineffective. Recent discoveries have elucidated how these differing functions are mediated by γδ T cells with specific T cell receptors and spatial distribution. Their relative resistance to mechanisms of dysfunction like T cell exhaustion has spurred the development of therapeutic approaches exploiting γδ T cells, and an improved understanding of these cells should enable more effective immunotherapies. Fong and colleagues provide a Review on γδ T cells as mediators of anti-tumor immunity, discuss their role in the tumor microenvironment and reflect on therapeutic approaches to exploit γδ T cells.
介导抗肿瘤免疫的效应细胞历来被认为是 αβ T 细胞和自然杀伤细胞，而 γδ T 细胞现在被认为是介导肿瘤排斥反应的补充机制。γδT细胞具有从抗原递呈到调节功能的一系列功能，重要的是，在其他免疫效应因子可能失效的情况下，γδT细胞在激发抗肿瘤反应方面发挥着关键作用。最新发现阐明了具有特定 T 细胞受体和空间分布的 γδ T 细胞是如何介导这些不同功能的。γδT细胞对T细胞衰竭等功能障碍机制具有相对抵抗力，这推动了利用γδT细胞的治疗方法的发展。
{"title":"γδ T cells as critical anti-tumor immune effectors","authors":"Marcel Arias-Badia, Ryan Chang, Lawrence Fong","doi":"10.1038/s43018-024-00798-x","DOIUrl":"10.1038/s43018-024-00798-x","url":null,"abstract":"While the effector cells that mediate anti-tumor immunity have historically been attributed to αβ T cells and natural killer cells, γδ T cells are now being recognized as a complementary mechanism mediating tumor rejection. γδ T cells possess a host of functions ranging from antigen presentation to regulatory function and, importantly, have critical roles in eliciting anti-tumor responses where other immune effectors may be rendered ineffective. Recent discoveries have elucidated how these differing functions are mediated by γδ T cells with specific T cell receptors and spatial distribution. Their relative resistance to mechanisms of dysfunction like T cell exhaustion has spurred the development of therapeutic approaches exploiting γδ T cells, and an improved understanding of these cells should enable more effective immunotherapies. Fong and colleagues provide a Review on γδ T cells as mediators of anti-tumor immunity, discuss their role in the tumor microenvironment and reflect on therapeutic approaches to exploit γδ T cells.","PeriodicalId":18885,"journal":{"name":"Nature cancer","volume":null,"pages":null},"PeriodicalIF":23.5,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141766741","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}
Pub Date : 2024-07-24DOI: 10.1038/s43018-024-00791-4
Aaron J. Stonestrom, Ross L. Levine
The mainly hematologic expression profile of phosphatidylinositol-3-kinase-γ (PI3Kγ) makes it an attractive therapeutic target. Recent work from three independent groups shows that inhibiting PI3Kγ impairs the metabolism and growth of acute myeloid leukemia cells — a finding that justifies further mechanistic and clinical exploration.
{"title":"Inhibiting PI3Kγ in acute myeloid leukemia","authors":"Aaron J. Stonestrom, Ross L. Levine","doi":"10.1038/s43018-024-00791-4","DOIUrl":"10.1038/s43018-024-00791-4","url":null,"abstract":"The mainly hematologic expression profile of phosphatidylinositol-3-kinase-γ (PI3Kγ) makes it an attractive therapeutic target. Recent work from three independent groups shows that inhibiting PI3Kγ impairs the metabolism and growth of acute myeloid leukemia cells — a finding that justifies further mechanistic and clinical exploration.","PeriodicalId":18885,"journal":{"name":"Nature cancer","volume":null,"pages":null},"PeriodicalIF":23.5,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141759878","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}
Pub Date : 2024-07-24DOI: 10.1038/s43018-024-00795-0
Our non-randomized single-center clinical trial demonstrates the safety, cost-saving and time-saving potential of artificial intelligence (AI) assistance in the detection of breast cancer metastases in sentinel lymph nodes. AI assistance shows important benefits for pathologists and the laboratory workflow, which are needed as cancer incidence and diagnostics continue to rise.
{"title":"AI-assisted detection of lymph node metastases safely reduces costs and time","authors":"","doi":"10.1038/s43018-024-00795-0","DOIUrl":"10.1038/s43018-024-00795-0","url":null,"abstract":"Our non-randomized single-center clinical trial demonstrates the safety, cost-saving and time-saving potential of artificial intelligence (AI) assistance in the detection of breast cancer metastases in sentinel lymph nodes. AI assistance shows important benefits for pathologists and the laboratory workflow, which are needed as cancer incidence and diagnostics continue to rise.","PeriodicalId":18885,"journal":{"name":"Nature cancer","volume":null,"pages":null},"PeriodicalIF":23.5,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141759877","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}
Pub Date : 2024-07-23DOI: 10.1038/s43018-024-00790-5
Cancer dependency maps have accelerated the discovery of essential genes and potential drug targets. Here we used machine learning to build translational dependency maps of patients’ tumors and normal tissue biopsies, which identified oncogenes and synthetic lethalities that are predictive of drug responses and patients’ outcomes.
{"title":"Using machine learning to translate tumor dependencies","authors":"","doi":"10.1038/s43018-024-00790-5","DOIUrl":"10.1038/s43018-024-00790-5","url":null,"abstract":"Cancer dependency maps have accelerated the discovery of essential genes and potential drug targets. Here we used machine learning to build translational dependency maps of patients’ tumors and normal tissue biopsies, which identified oncogenes and synthetic lethalities that are predictive of drug responses and patients’ outcomes.","PeriodicalId":18885,"journal":{"name":"Nature cancer","volume":null,"pages":null},"PeriodicalIF":23.5,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141752145","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}
Pub Date : 2024-07-23DOI: 10.1038/s43018-024-00792-3
Current prostate cancer risk predictors are not able to fully capture a patient’s risk of recurrence at the time of diagnosis. Evolutionary metrics of tumor diversity, based on low-cost sequencing and digital pathology, might provide a new dimension of information to close the gap between prediction and outcome.
{"title":"Predicting the risk of prostate cancer recurrence through the lens of evolution","authors":"","doi":"10.1038/s43018-024-00792-3","DOIUrl":"10.1038/s43018-024-00792-3","url":null,"abstract":"Current prostate cancer risk predictors are not able to fully capture a patient’s risk of recurrence at the time of diagnosis. Evolutionary metrics of tumor diversity, based on low-cost sequencing and digital pathology, might provide a new dimension of information to close the gap between prediction and outcome.","PeriodicalId":18885,"journal":{"name":"Nature cancer","volume":null,"pages":null},"PeriodicalIF":23.5,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141752144","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}
Pub Date : 2024-07-17DOI: 10.1038/s43018-024-00794-1
Bailey M. Robertson, Mitchell E. Fane, Ashani T. Weeraratna, Vito W. Rebecca
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.
{"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":null,"pages":null},"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}
Pub Date : 2024-07-15DOI: 10.1038/s43018-024-00789-y
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
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.
{"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":null,"pages":null},"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}
Pub Date : 2024-07-15DOI: 10.1038/s43018-024-00796-z
Gizem Efe, Anil K. Rustgi, Carol Prives
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.
{"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":null,"pages":null},"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}
Pub Date : 2024-07-12DOI: 10.1038/s43018-024-00787-0
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
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.
{"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":null,"pages":null},"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}
Pub Date : 2024-07-11DOI: 10.1038/s43018-024-00781-6
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
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.
{"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":null,"pages":null},"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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：