Hyper progressive disease (HPD) is a paradoxical phenomenon characterized by accelerated tumor growth following treatment with immune checkpoint inhibitors. However, the pathogenic causality and its predictor remain unknown. We herein report a fatal case of HPD in a 50-year-old man with metastatic bladder cancer. He had achieved a complete response (CR) through chemoradiation therapy followed by twelve cycles of chemotherapy, maintaining CR for 24 months. Three weeks after initiating maintenance use of a PD-L1 inhibitor, avelumab, a massive amount of metastases developed, leading to the patient’s demise. Omics analysis, utilizing metastatic tissues obtained from an immediate autopsy, implied the contribution of M2 macrophages, TGF-β signaling, and interleukin-8 to HPD pathogenesis.
{"title":"Mechanistic insights into lethal hyper progressive disease induced by PD-L1 inhibitor in metastatic urothelial carcinoma","authors":"Kazuki Nishimura, Kiyoshi Takahara, Kazumasa Komura, Mitsuaki Ishida, Kensuke Hirosuna, Ryoichi Maenosono, Masahiko Ajiro, Moritoshi Sakamoto, Kengo Iwatsuki, Yuki Nakajima, Takuya Tsujino, Kohei Taniguchi, Tomohito Tanaka, Teruo Inamoto, Yoshinobu Hirose, Fumihito Ono, Yoichi Kondo, Akihide Yoshimi, Haruhito Azuma","doi":"10.1038/s41698-024-00707-6","DOIUrl":"10.1038/s41698-024-00707-6","url":null,"abstract":"Hyper progressive disease (HPD) is a paradoxical phenomenon characterized by accelerated tumor growth following treatment with immune checkpoint inhibitors. However, the pathogenic causality and its predictor remain unknown. We herein report a fatal case of HPD in a 50-year-old man with metastatic bladder cancer. He had achieved a complete response (CR) through chemoradiation therapy followed by twelve cycles of chemotherapy, maintaining CR for 24 months. Three weeks after initiating maintenance use of a PD-L1 inhibitor, avelumab, a massive amount of metastases developed, leading to the patient’s demise. Omics analysis, utilizing metastatic tissues obtained from an immediate autopsy, implied the contribution of M2 macrophages, TGF-β signaling, and interleukin-8 to HPD pathogenesis.","PeriodicalId":19433,"journal":{"name":"NPJ Precision Oncology","volume":null,"pages":null},"PeriodicalIF":6.8,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41698-024-00707-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142236095","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 : 2024-09-17DOI: 10.1038/s41698-024-00683-x
Leah Stein, Karthikeyan Murugesan, Julie W. Reeser, Zachary Risch, Michele R. Wing, Anoosha Paruchuri, Eric Samorodnitsky, Emily L. Hoskins, Thuy Dao, Amy Smith, Dat Le, Melissa A. Babcook, Yi Seok Chang, Matthew R. Avenarius, Muhammad Imam, Aharon G. Freud, Sameek Roychowdhury
Genomic alterations in fibroblast growth factor receptor (FGFR) genes are present in a small number of metastatic pancreatic ductal adenocarcinomas (PDAC) and may represent an emerging subgroup of patients likely to benefit from FGFR targeted therapies. Here we present four FGFR2 fusion-positive metastatic PDAC patients who exhibited durable responses or disease control to FGFR kinase inhibitors. Utilizing our custom FGFR focused cell-free DNA assay, FGFR-Dx, we serially monitored variant allele fractions of FGFR2 fusions during FGFR inhibitor treatment and observed dynamic changes correlating with clinical responses. Genomic analysis of 30,229 comprehensively profiled pancreatic cancers revealed FGFR1-3 fusions in 245 cases, an incidence of 0.81%. FGFR fusions were generally mutually exclusive from other known oncogenes. Our findings provide clinical evidence for identifying and treating FGFR2 fusion-positive PDAC patients with FGFR targeted therapy.
{"title":"FGFR2-fusions define a clinically actionable molecular subset of pancreatic cancer","authors":"Leah Stein, Karthikeyan Murugesan, Julie W. Reeser, Zachary Risch, Michele R. Wing, Anoosha Paruchuri, Eric Samorodnitsky, Emily L. Hoskins, Thuy Dao, Amy Smith, Dat Le, Melissa A. Babcook, Yi Seok Chang, Matthew R. Avenarius, Muhammad Imam, Aharon G. Freud, Sameek Roychowdhury","doi":"10.1038/s41698-024-00683-x","DOIUrl":"10.1038/s41698-024-00683-x","url":null,"abstract":"Genomic alterations in fibroblast growth factor receptor (FGFR) genes are present in a small number of metastatic pancreatic ductal adenocarcinomas (PDAC) and may represent an emerging subgroup of patients likely to benefit from FGFR targeted therapies. Here we present four FGFR2 fusion-positive metastatic PDAC patients who exhibited durable responses or disease control to FGFR kinase inhibitors. Utilizing our custom FGFR focused cell-free DNA assay, FGFR-Dx, we serially monitored variant allele fractions of FGFR2 fusions during FGFR inhibitor treatment and observed dynamic changes correlating with clinical responses. Genomic analysis of 30,229 comprehensively profiled pancreatic cancers revealed FGFR1-3 fusions in 245 cases, an incidence of 0.81%. FGFR fusions were generally mutually exclusive from other known oncogenes. Our findings provide clinical evidence for identifying and treating FGFR2 fusion-positive PDAC patients with FGFR targeted therapy.","PeriodicalId":19433,"journal":{"name":"NPJ Precision Oncology","volume":null,"pages":null},"PeriodicalIF":6.8,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41698-024-00683-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142236109","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}
Tumor ecosystem shapes cancer biology and potentially influence the response to immunotherapy, but there is a lack of direct clinical evidence. In this study, we utilized EcoTyper and publicly available scRNA-Seq cohorts from ICI-treated patients. We found a ecosystem subtype (ecotype) was linked to improved responses to immunotherapy. Then, a novel immunotherapy-responsive ecotype signature (IRE.Sig) was established and validated through the analysis of pan-cancer data. Utilizing IRE.Sig, machine learning models successfully predicted ICI responses in both validation and testing cohorts, achieving area under the curve (AUC) values of 0.72 and 0.71, respectively. Furthermore, using 5 CRISPR screening cohorts, we identified several potential drugs that may augment the efficacy of ICI. We also elucidated the candidate cellular biomarkers of response to the combined treatment of pembrolizumab plus eribulin in breast cancer. This signature has the potential to serve as a valuable tool for patients in selecting appropriate immunotherapy treatments.
{"title":"Integrative analysis of pan-cancer single-cell data reveals a tumor ecosystem subtype predicting immunotherapy response","authors":"Shengjie Zeng, Liuxun Chen, Jinyu Tian, Zhengxin Liu, Xudong Liu, Haibin Tang, Hao Wu, Chuan Liu","doi":"10.1038/s41698-024-00703-w","DOIUrl":"10.1038/s41698-024-00703-w","url":null,"abstract":"Tumor ecosystem shapes cancer biology and potentially influence the response to immunotherapy, but there is a lack of direct clinical evidence. In this study, we utilized EcoTyper and publicly available scRNA-Seq cohorts from ICI-treated patients. We found a ecosystem subtype (ecotype) was linked to improved responses to immunotherapy. Then, a novel immunotherapy-responsive ecotype signature (IRE.Sig) was established and validated through the analysis of pan-cancer data. Utilizing IRE.Sig, machine learning models successfully predicted ICI responses in both validation and testing cohorts, achieving area under the curve (AUC) values of 0.72 and 0.71, respectively. Furthermore, using 5 CRISPR screening cohorts, we identified several potential drugs that may augment the efficacy of ICI. We also elucidated the candidate cellular biomarkers of response to the combined treatment of pembrolizumab plus eribulin in breast cancer. This signature has the potential to serve as a valuable tool for patients in selecting appropriate immunotherapy treatments.","PeriodicalId":19433,"journal":{"name":"NPJ Precision Oncology","volume":null,"pages":null},"PeriodicalIF":6.8,"publicationDate":"2024-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41698-024-00703-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142233229","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 : 2024-09-14DOI: 10.1038/s41698-024-00696-6
Laura E. Johnston, Jamie Randall, Safae Chouraichi, Mary Luu, Allison L. Hunt, Lauren Mauro, Claudius Mueller, Justin B. Davis, Emanuel F. Petricoin, Thomas P. Conrads, Timothy L. Cannon, Jasmine Huynh
Recent trials have shown the efficacy of trastuzumab deruxtecan (T-DXd) in HER2-negative patients, but there is not yet a way to identify which patients will best respond, especially with the inability of current HER2 IHC and FISH assays to accurately determine HER2 expression in the unamplified setting. Here, we present a heavily pre-treated patient with triple-negative breast cancer (HER2 IHC 0 who had a complete response to T-DXd. In this case, we used a CLIA-certified reverse-phase protein array-based proteomic assay (RPPA) to determine that the patient had moderate HER2 protein expression (HER2Total 2+, 42%) and activation (HER2Y1248 1+, 23%). Using these results, we determined that the patient may benefit from T-Dxd despite being traditionally qualified as HER2 IHC 0. These findings highlight the potential for proteomics-based assays that may more accurately quantitate HER2 and (its activation) in the HER2 unamplified/IHC 0 setting to better select patients whose tumors are classically molecularly defined as HER2 IHC 0, but still could respond to HER2-directed therapy, and give patients access to therapies which for which they otherwise would not be eligible.
{"title":"Proteomics based selection achieves complete response to HER2 therapy in HER2 IHC 0 breast cancer","authors":"Laura E. Johnston, Jamie Randall, Safae Chouraichi, Mary Luu, Allison L. Hunt, Lauren Mauro, Claudius Mueller, Justin B. Davis, Emanuel F. Petricoin, Thomas P. Conrads, Timothy L. Cannon, Jasmine Huynh","doi":"10.1038/s41698-024-00696-6","DOIUrl":"10.1038/s41698-024-00696-6","url":null,"abstract":"Recent trials have shown the efficacy of trastuzumab deruxtecan (T-DXd) in HER2-negative patients, but there is not yet a way to identify which patients will best respond, especially with the inability of current HER2 IHC and FISH assays to accurately determine HER2 expression in the unamplified setting. Here, we present a heavily pre-treated patient with triple-negative breast cancer (HER2 IHC 0 who had a complete response to T-DXd. In this case, we used a CLIA-certified reverse-phase protein array-based proteomic assay (RPPA) to determine that the patient had moderate HER2 protein expression (HER2Total 2+, 42%) and activation (HER2Y1248 1+, 23%). Using these results, we determined that the patient may benefit from T-Dxd despite being traditionally qualified as HER2 IHC 0. These findings highlight the potential for proteomics-based assays that may more accurately quantitate HER2 and (its activation) in the HER2 unamplified/IHC 0 setting to better select patients whose tumors are classically molecularly defined as HER2 IHC 0, but still could respond to HER2-directed therapy, and give patients access to therapies which for which they otherwise would not be eligible.","PeriodicalId":19433,"journal":{"name":"NPJ Precision Oncology","volume":null,"pages":null},"PeriodicalIF":6.8,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41698-024-00696-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142231161","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 : 2024-09-14DOI: 10.1038/s41698-024-00679-7
Douglas I. Lin, Julia C. F. Quintanilha, Natalie Danziger, Lixin Lang, Diane Levitan, Cynthia Hayne, Matthew C. Hiemenz, David L. Smith, Lee A. Albacker, Jeffrey Leibowitz, Douglas A. Mata, Brennan Decker, Sotirios Lakis, Nimesh R. Patel, Ryon P. Graf, Julia A. Elvin, Jeffrey S. Ross, Varun Pattani, Richard S. P. Huang, Amy K. Wehn
Microsatellite instability high (MSI-H) and mismatch repair deficient (dMMR) tumor status have been demonstrated to predict patient response to immunotherapies. We developed and validated a next-generation sequencing (NGS)-based companion diagnostic (CDx) to detect MSI-H solid tumors via a comprehensive genomic profiling (CGP) assay, FoundationOne®CDx (F1CDx). To determine MSI status, F1CDx calculates the fraction of unstable microsatellite loci across >2000 loci using a fraction-based (FB) analysis. Across solid tumor types, F1CDx demonstrated a high analytical concordance with both PCR (n = 264) and IHC (n = 279) with an overall percent agreement (OPA) of 97.7% and 97.8%, respectively. As part of a retrospective bridging clinical study from KEYNOTE-158 Cohort K and KEYNOTE-164, patients with MSI-H tumors as determined by F1CDx demonstrated an objective response rate (ORR) of 43.0% to pembrolizumab. In real-world cancer patients from a deidentified clinicogenomic database, F1CDx was at least equivalent in assessing clinical outcome following immunotherapy compared with MMR IHC. Demonstrated analytical and clinical performance of F1CDx led to the pan-tumor FDA approval in 2022 of F1CDx to identify MSI-H solid tumor patients for treatment with pembrolizumab. F1CDx is an accurate, reliable, and FDA-approved method for the identification of MSI-H tumors for treatment with pembrolizumab.
{"title":"Pan-tumor validation of a NGS fraction-based MSI analysis as a predictor of response to Pembrolizumab","authors":"Douglas I. Lin, Julia C. F. Quintanilha, Natalie Danziger, Lixin Lang, Diane Levitan, Cynthia Hayne, Matthew C. Hiemenz, David L. Smith, Lee A. Albacker, Jeffrey Leibowitz, Douglas A. Mata, Brennan Decker, Sotirios Lakis, Nimesh R. Patel, Ryon P. Graf, Julia A. Elvin, Jeffrey S. Ross, Varun Pattani, Richard S. P. Huang, Amy K. Wehn","doi":"10.1038/s41698-024-00679-7","DOIUrl":"10.1038/s41698-024-00679-7","url":null,"abstract":"Microsatellite instability high (MSI-H) and mismatch repair deficient (dMMR) tumor status have been demonstrated to predict patient response to immunotherapies. We developed and validated a next-generation sequencing (NGS)-based companion diagnostic (CDx) to detect MSI-H solid tumors via a comprehensive genomic profiling (CGP) assay, FoundationOne®CDx (F1CDx). To determine MSI status, F1CDx calculates the fraction of unstable microsatellite loci across >2000 loci using a fraction-based (FB) analysis. Across solid tumor types, F1CDx demonstrated a high analytical concordance with both PCR (n = 264) and IHC (n = 279) with an overall percent agreement (OPA) of 97.7% and 97.8%, respectively. As part of a retrospective bridging clinical study from KEYNOTE-158 Cohort K and KEYNOTE-164, patients with MSI-H tumors as determined by F1CDx demonstrated an objective response rate (ORR) of 43.0% to pembrolizumab. In real-world cancer patients from a deidentified clinicogenomic database, F1CDx was at least equivalent in assessing clinical outcome following immunotherapy compared with MMR IHC. Demonstrated analytical and clinical performance of F1CDx led to the pan-tumor FDA approval in 2022 of F1CDx to identify MSI-H solid tumor patients for treatment with pembrolizumab. F1CDx is an accurate, reliable, and FDA-approved method for the identification of MSI-H tumors for treatment with pembrolizumab.","PeriodicalId":19433,"journal":{"name":"NPJ Precision Oncology","volume":null,"pages":null},"PeriodicalIF":6.8,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41698-024-00679-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142231160","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 : 2024-09-13DOI: 10.1038/s41698-024-00692-w
Quanwei Zhou, Xuejun Yan, Youwei Guo, Xingjun Jiang, Tuo Cao, Yiquan Ke
Tumor-associated macrophages (TAMs) play a vital role in glioma progression and are associated with poor outcomes in glioma patients. However, the specific roles of different subpopulations of TAMs remain poorly understood. Two distinct cell types, glioma and myeloid cells, were identified through single-cell sequencing analysis in gliomas. Within the TAMs-associated weighted gene co-expression network analysis (WGCNA) module, FPR3 emerged as a hub gene and was found to be expressed on CD163+ macrophages, while also being associated with clinical outcomes. Subsequently, a comprehensive assessment was undertaken to investigate the correlation between FPR3 expression and immune characteristics, revealing that FPR3 potentially plays a role in reshaping the glioma microenvironment. We identified a macrophage subset with the nonzero expression of CD163 and FPR3 (CD163+FPR3+). Using the expression profiles of CD163+FPR3+ macrophage-related signature, we employed ten machine learning algorithms to construct a prognostic model across six glioma cohorts. Subsequently, we employed an optimal algorithm to generate an artificial intelligence-driven prognostic signature specifically for CD163+FPR3+ macrophages. The development of this model was based on the average C-index observed in the aforementioned six cohorts. The risk score of this model consistently and effectively predicted overall survival, surpassing the accuracy of conventional clinical factors and 100 previously published signatures. Consequently, the CD163+FPR3+ macrophage-related score shows potential as a prognostic biomarker for glioma patients.
{"title":"Machine learning algorithms for predicting glioma patient prognosis based on CD163+FPR3+ macrophage signature","authors":"Quanwei Zhou, Xuejun Yan, Youwei Guo, Xingjun Jiang, Tuo Cao, Yiquan Ke","doi":"10.1038/s41698-024-00692-w","DOIUrl":"10.1038/s41698-024-00692-w","url":null,"abstract":"Tumor-associated macrophages (TAMs) play a vital role in glioma progression and are associated with poor outcomes in glioma patients. However, the specific roles of different subpopulations of TAMs remain poorly understood. Two distinct cell types, glioma and myeloid cells, were identified through single-cell sequencing analysis in gliomas. Within the TAMs-associated weighted gene co-expression network analysis (WGCNA) module, FPR3 emerged as a hub gene and was found to be expressed on CD163+ macrophages, while also being associated with clinical outcomes. Subsequently, a comprehensive assessment was undertaken to investigate the correlation between FPR3 expression and immune characteristics, revealing that FPR3 potentially plays a role in reshaping the glioma microenvironment. We identified a macrophage subset with the nonzero expression of CD163 and FPR3 (CD163+FPR3+). Using the expression profiles of CD163+FPR3+ macrophage-related signature, we employed ten machine learning algorithms to construct a prognostic model across six glioma cohorts. Subsequently, we employed an optimal algorithm to generate an artificial intelligence-driven prognostic signature specifically for CD163+FPR3+ macrophages. The development of this model was based on the average C-index observed in the aforementioned six cohorts. The risk score of this model consistently and effectively predicted overall survival, surpassing the accuracy of conventional clinical factors and 100 previously published signatures. Consequently, the CD163+FPR3+ macrophage-related score shows potential as a prognostic biomarker for glioma patients.","PeriodicalId":19433,"journal":{"name":"NPJ Precision Oncology","volume":null,"pages":null},"PeriodicalIF":6.8,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41698-024-00692-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142231143","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 : 2024-09-13DOI: 10.1038/s41698-024-00688-6
Xiaoyu Huang, Yong Huang, Kexin Liu, Fenglin Zhang, Zhou Zhu, Kai Xu, Ping Li
Epithelial ovarian cancer (EOC) presents considerable difficulties in prognostication and treatment strategy development. Bevacizumab, an anti-angiogenic medication, has demonstrated potential in enhancing progression-free survival (PFS) in EOC patients. Nevertheless, the identification of individuals at elevated risk of disease progression following treatment remains a challenging task. This study was to develop and validate a deep learning (DL) model using retrospectively collected computed tomography (CT) plain scans of inoperable and recurrent EOC patients receiving bevacizumab treatment diagnosed between January 2013 and January 2024. A total of 525 patients from three different institutions were retrospectively included in the study and divided into training set (N = 400), internal test set (N = 97) and external test set (N = 28). The model’s performance was evaluated using Harrell’s C-index. Patients were categorized into high-risk and low-risk group based on a predetermined cutoff in the training set. Additionally, a multimodal model was evaluated, incorporating the risk score generated by the DL model and the pretreatment level of carbohydrate antigen 125 as input variables. The Net Reclassification Improvement (NRI) metric quantified the reclassification performance of our optimal model in comparison to the International Federation of Gynecology and Obstetrics (FIGO) staging model. The results indicated that DL model achieved a PFS predictive C-index of 0.73 in the internal test set and a C-index of 0.61 in the external test set, along with hazard ratios of 34.24 in the training set (95% CI: 21.7, 54.1; P < 0.001) and 8.16 in the internal test set (95% CI: 2.5, 26.8; P < 0.001). The multimodal model demonstrated a C-index of 0.76 in the internal test set and a C-index of 0.64 in the external test set. Comparative analysis against FIGO staging revealed an NRI of 0.06 (P < 0.001) for the multimodal model. The model presents opportunities for prognostic assessment, treatment strategizing, and ongoing patient monitoring.
{"title":"Predicting prognosis for epithelial ovarian cancer patients receiving bevacizumab treatment with CT-based deep learning","authors":"Xiaoyu Huang, Yong Huang, Kexin Liu, Fenglin Zhang, Zhou Zhu, Kai Xu, Ping Li","doi":"10.1038/s41698-024-00688-6","DOIUrl":"10.1038/s41698-024-00688-6","url":null,"abstract":"Epithelial ovarian cancer (EOC) presents considerable difficulties in prognostication and treatment strategy development. Bevacizumab, an anti-angiogenic medication, has demonstrated potential in enhancing progression-free survival (PFS) in EOC patients. Nevertheless, the identification of individuals at elevated risk of disease progression following treatment remains a challenging task. This study was to develop and validate a deep learning (DL) model using retrospectively collected computed tomography (CT) plain scans of inoperable and recurrent EOC patients receiving bevacizumab treatment diagnosed between January 2013 and January 2024. A total of 525 patients from three different institutions were retrospectively included in the study and divided into training set (N = 400), internal test set (N = 97) and external test set (N = 28). The model’s performance was evaluated using Harrell’s C-index. Patients were categorized into high-risk and low-risk group based on a predetermined cutoff in the training set. Additionally, a multimodal model was evaluated, incorporating the risk score generated by the DL model and the pretreatment level of carbohydrate antigen 125 as input variables. The Net Reclassification Improvement (NRI) metric quantified the reclassification performance of our optimal model in comparison to the International Federation of Gynecology and Obstetrics (FIGO) staging model. The results indicated that DL model achieved a PFS predictive C-index of 0.73 in the internal test set and a C-index of 0.61 in the external test set, along with hazard ratios of 34.24 in the training set (95% CI: 21.7, 54.1; P < 0.001) and 8.16 in the internal test set (95% CI: 2.5, 26.8; P < 0.001). The multimodal model demonstrated a C-index of 0.76 in the internal test set and a C-index of 0.64 in the external test set. Comparative analysis against FIGO staging revealed an NRI of 0.06 (P < 0.001) for the multimodal model. The model presents opportunities for prognostic assessment, treatment strategizing, and ongoing patient monitoring.","PeriodicalId":19433,"journal":{"name":"NPJ Precision Oncology","volume":null,"pages":null},"PeriodicalIF":6.8,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41698-024-00688-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142231169","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}
DP303c is a HER2-targeted ADC with a cleavable linker-MMAE payload. Previous in vitro studies demonstrated that DP303c showed similar or better antitumor activity than T-DM1 in xenograft models. This was a multicenter, dose escalation and dose expansion phase 1 study in China. Eligible patients were 18-75 years old with HER2-positive advanced solid tumors who were unable to benefit from standard therapy. DP303c was administered intravenously every 3 weeks, with accelerated titration at lower dose of 0.5 mg/kg and 3 + 3 design with dose levels of 1.0, 2.0, 3.0 or 4.0 mg/kg at dose escalation part, followed by the selected dose level at dose expansion part. The primary endpoints were safety and tolerability, as well as identification of recommended phase 2 dose. As of Feb 28, 2023, 94 patients were enrolled and received DP303c (dose escalation: n = 22; dose expansion: n = 72), of whom 68 patients had breast cancer. One dose limiting toxicity (Grade 3 eye pain) was observed at 4.0 mg/kg dose, and the maximum tolerated dose was not reached. The most common treatment-related adverse events at grade 3 or higher were blurred vison (16.0%), dry eye (6.4%), and peripheral neuropathy (5.3%). No treatment-related death occurred. Overall, among 91 efficacy evaluable patients, 39 patients (42.9%) achieved an objective response. Disease control was observed in 62 patients (68.1%). In 66 efficacy evaluable patients with breast cancer, 34 patients achieved an objective response (51.5%). Disease control was achieved in 51 patients (77.3%). Median PFS was 6.4 months. On a molar basis, DP303c Cmax at 3.0 mg/kg doses was 132-folder higher than that for free MMAE. DP303c demonstrated promising anti-tumor activity with acceptable safety in patients with pre-treated advanced HER2 positive solid tumors, especially in breast cancer. Based on safety and efficacy results, 3.0 mg/kg Q3W was determined as recommended phase 2 dose for DP303c. (Trial registration: ClinicalTrials.gov Identifier: NCT04146610).
{"title":"First-in-human study of DP303c, a HER2-targeted antibody-drug conjugate in patients with HER2 positive solid tumors","authors":"Jian Zhang, Yiqun Du, Yanchun Meng, Xiaojun Liu, Yuxin Mu, Yunpeng Liu, Yehui Shi, Jufeng Wang, Aimin Zang, Shanzhi Gu, Tianshu Liu, Huan Zhou, Hongqian Guo, Silong Xiang, Xialu Zhang, Suqiong Wu, Huanhuan Qi, Mengke Li, Xichun Hu","doi":"10.1038/s41698-024-00687-7","DOIUrl":"10.1038/s41698-024-00687-7","url":null,"abstract":"DP303c is a HER2-targeted ADC with a cleavable linker-MMAE payload. Previous in vitro studies demonstrated that DP303c showed similar or better antitumor activity than T-DM1 in xenograft models. This was a multicenter, dose escalation and dose expansion phase 1 study in China. Eligible patients were 18-75 years old with HER2-positive advanced solid tumors who were unable to benefit from standard therapy. DP303c was administered intravenously every 3 weeks, with accelerated titration at lower dose of 0.5 mg/kg and 3 + 3 design with dose levels of 1.0, 2.0, 3.0 or 4.0 mg/kg at dose escalation part, followed by the selected dose level at dose expansion part. The primary endpoints were safety and tolerability, as well as identification of recommended phase 2 dose. As of Feb 28, 2023, 94 patients were enrolled and received DP303c (dose escalation: n = 22; dose expansion: n = 72), of whom 68 patients had breast cancer. One dose limiting toxicity (Grade 3 eye pain) was observed at 4.0 mg/kg dose, and the maximum tolerated dose was not reached. The most common treatment-related adverse events at grade 3 or higher were blurred vison (16.0%), dry eye (6.4%), and peripheral neuropathy (5.3%). No treatment-related death occurred. Overall, among 91 efficacy evaluable patients, 39 patients (42.9%) achieved an objective response. Disease control was observed in 62 patients (68.1%). In 66 efficacy evaluable patients with breast cancer, 34 patients achieved an objective response (51.5%). Disease control was achieved in 51 patients (77.3%). Median PFS was 6.4 months. On a molar basis, DP303c Cmax at 3.0 mg/kg doses was 132-folder higher than that for free MMAE. DP303c demonstrated promising anti-tumor activity with acceptable safety in patients with pre-treated advanced HER2 positive solid tumors, especially in breast cancer. Based on safety and efficacy results, 3.0 mg/kg Q3W was determined as recommended phase 2 dose for DP303c. (Trial registration: ClinicalTrials.gov Identifier: NCT04146610).","PeriodicalId":19433,"journal":{"name":"NPJ Precision Oncology","volume":null,"pages":null},"PeriodicalIF":6.8,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41698-024-00687-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142174411","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 : 2024-09-12DOI: 10.1038/s41698-024-00681-z
Yixin Ju, Dongzhi Xu, Miao-miao Liao, Yutong Sun, Wen-dai Bao, Fan Yao, Li Ma
Pancreatic ductal adenocarcinoma (PDAC) presents a fatal clinical challenge characterized by a dismal 5-year overall survival rate, primarily due to the lack of early diagnosis and limited therapeutic efficacy. Immunotherapy, a proven success in multiple cancers, has yet to demonstrate significant benefits in PDAC. Recent studies have revealed the immunosuppressive characteristics of the PDAC tumor microenvironment (TME), including immune cells with suppressive properties, desmoplastic stroma, microbiome influences, and PDAC-specific signaling pathways. In this article, we review recent advances in understanding the immunosuppressive TME of PDAC, TME differences among various mouse models of pancreatic cancer, and the mechanisms underlying resistance to immunotherapeutic interventions. Furthermore, we discuss the potential of targeting cancer cell-intrinsic pathways and TME components to sensitize PDAC to immune therapies, providing insights into strategies and future perspectives to break through the barriers in improving pancreatic cancer treatment.
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Pub Date : 2024-09-11DOI: 10.1038/s41698-024-00686-8
Jin Sheng, Hong Chen, Bang Fu, Hongming Pan, Jiabing Wang, Weidong Han
Aberrant activation of tropomyosin receptor kinases (TRKs) is a well-defined oncogenic driver for neurotrophic tropomyosin receptor kinase (NTRK)-fusion cancers, and acquired resistant mutations have emerged with clinical use of the first-generation TRK inhibitors. Here we present BPI-28592, a novel second-generation TRK inhibitor with efficacy against TRK fusion-positive cancers, including those with resistant mutations. Docking simulations indicated no steric hindrance between BPI-28592 and TRK mutants, suggesting its potential to overcome drug resistance. Biochemical assays showed strong inhibition and high selectivity against TRKA, TRKB, and TRKC. The inhibitor significantly reduced cell proliferation and blocked TRK signaling. In vivo studies demonstrated effective tumor suppression in xenograft models harboring TRK fusions with or without resistant mutations. Clinically, BPI-28592 achieved a complete response in a patient with malignant melanoma carrying an AP3S2-NTRK3 fusion (Clinicaltrials. gov identifier: NCT05302843).
{"title":"BPI-28592 as a novel second generation inhibitor for NTRK fusion tumors","authors":"Jin Sheng, Hong Chen, Bang Fu, Hongming Pan, Jiabing Wang, Weidong Han","doi":"10.1038/s41698-024-00686-8","DOIUrl":"10.1038/s41698-024-00686-8","url":null,"abstract":"Aberrant activation of tropomyosin receptor kinases (TRKs) is a well-defined oncogenic driver for neurotrophic tropomyosin receptor kinase (NTRK)-fusion cancers, and acquired resistant mutations have emerged with clinical use of the first-generation TRK inhibitors. Here we present BPI-28592, a novel second-generation TRK inhibitor with efficacy against TRK fusion-positive cancers, including those with resistant mutations. Docking simulations indicated no steric hindrance between BPI-28592 and TRK mutants, suggesting its potential to overcome drug resistance. Biochemical assays showed strong inhibition and high selectivity against TRKA, TRKB, and TRKC. The inhibitor significantly reduced cell proliferation and blocked TRK signaling. In vivo studies demonstrated effective tumor suppression in xenograft models harboring TRK fusions with or without resistant mutations. Clinically, BPI-28592 achieved a complete response in a patient with malignant melanoma carrying an AP3S2-NTRK3 fusion (Clinicaltrials. gov identifier: NCT05302843).","PeriodicalId":19433,"journal":{"name":"NPJ Precision Oncology","volume":null,"pages":null},"PeriodicalIF":6.8,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41698-024-00686-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142165817","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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