In the era of precision medicine, the selection of cancer treatment relies increasingly on the identification of genomic alterations; however, it is not yet clear how frequently genomic profiling should be performed over the disease course of a patient with metastatic cancer. A large-scale study investigates the genomic evolution of cancer metastases under therapeutic pressure and finds that the actionable genome has remained relatively stable. The findings support the sufficiency of a single biopsy of cancer metastasis for therapeutic decision-making.
{"title":"Genomic evolution of cancer metastasis under therapeutic pressure","authors":"Qiu-Luo Liu, Xi Li, Hai-Ning Chen","doi":"10.1002/mog2.5","DOIUrl":"10.1002/mog2.5","url":null,"abstract":"<p>In the era of precision medicine, the selection of cancer treatment relies increasingly on the identification of genomic alterations; however, it is not yet clear how frequently genomic profiling should be performed over the disease course of a patient with metastatic cancer. A large-scale study investigates the genomic evolution of cancer metastases under therapeutic pressure and finds that the actionable genome has remained relatively stable. The findings support the sufficiency of a single biopsy of cancer metastasis for therapeutic decision-making.</p>","PeriodicalId":100902,"journal":{"name":"MedComm – Oncology","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mog2.5","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80944216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
About 400 B.C., the ancient Greek physician Hippocrates used karkinos, Greek for cancer, to denote certain types of malignant tumors that thrust out the invasive “talons” into the surrounding tissues. Cancer has been present throughout the ages. According to the latest World Health Statistics of the World Health Organization, cancer has become the second leading cause of human death, which is responsible for about 16% of deaths globally, affecting people of all ages and from all regions of the world.
It is the best time for oncology. With the advances in molecular biology and modern medicine, especially the launch of the Human Genome Project, cancer research has entered a new era since the end of the last century. By profiling the genetic fingerprint and molecular makeup, similar hallmarks including sustaining proliferative signaling, activating metastasis, and avoiding immune destruction are defined among distinct cancer subtypes. Moreover, the concepts of cancer heterogeneity, epigenetic regulation, posttranslational modification, and tumor microenvironment, have expanded the scientific scope of oncology. Driven by translational medicine and bioengineering, diverse precise diagnostics and therapeutics have been developed to improve cancer management, such as the artificial intelligence-assisted computed tomography for lung cancer diagnosis, the liquid biopsy for the detection of circulating tumor cell and circulating tumor DNA, human papillomavirus vaccines against cervical cancer, and the immune checkpoint inhibitor PD-1/PD-L1 monoclonal antibodies for the treatment of multiple cancers.
It is the worst time for oncology. The emergence of the many cutting-edge techniques including multi-omics, high-resolution imaging, single-cell methodologies, and so forth, has characterized many new biomolecules (e.g., noncoding RNAs and small-molecule metabolites) as tumor drivers, making the molecular regulatory network of cancer cells unprecedentedly sophisticated. Cancer is also appreciated as a dynamic disease. The molecular signature, phenotype, and lethality of cancers are variable with disease typing, clinical stage, and therapy. Thus, it is challenging for researchers to clarify the pivotal oncogenic mechanism from massive bioinformatic and experimental evidence, or to develop effective methodologies for diagnosis and treatment. It requires collaboration across multi-disciplines, integration of theory and techniques, and the ambition of patients, clinicians, and scientists to defeat cancer.
Governments and foundations worldwide are providing increasing financial supports to oncology research. Unquestionably, critical discoveries in this field will be achieved in the near future. Nevertheless, the limited influential journals in oncology cannot guarantee the timely publication of these achievements. To this end, MedComm - Oncology is launched to meet the demand.
{"title":"MedComm - Oncology announcement","authors":"Guido Kroemer, Yong Peng","doi":"10.1002/mog2.13","DOIUrl":"10.1002/mog2.13","url":null,"abstract":"<p>About 400 B.C., the ancient Greek physician Hippocrates used <i>karkinos</i>, Greek for cancer, to denote certain types of malignant tumors that thrust out the invasive “talons” into the surrounding tissues. Cancer has been present throughout the ages. According to the latest World Health Statistics of the World Health Organization, cancer has become the second leading cause of human death, which is responsible for about 16% of deaths globally, affecting people of all ages and from all regions of the world.</p><p>It is the best time for oncology. With the advances in molecular biology and modern medicine, especially the launch of the Human Genome Project, cancer research has entered a new era since the end of the last century. By profiling the genetic fingerprint and molecular makeup, similar hallmarks including sustaining proliferative signaling, activating metastasis, and avoiding immune destruction are defined among distinct cancer subtypes. Moreover, the concepts of cancer heterogeneity, epigenetic regulation, posttranslational modification, and tumor microenvironment, have expanded the scientific scope of oncology. Driven by translational medicine and bioengineering, diverse precise diagnostics and therapeutics have been developed to improve cancer management, such as the artificial intelligence-assisted computed tomography for lung cancer diagnosis, the liquid biopsy for the detection of circulating tumor cell and circulating tumor DNA, human papillomavirus vaccines against cervical cancer, and the immune checkpoint inhibitor PD-1/PD-L1 monoclonal antibodies for the treatment of multiple cancers.</p><p>It is the worst time for oncology. The emergence of the many cutting-edge techniques including multi-omics, high-resolution imaging, single-cell methodologies, and so forth, has characterized many new biomolecules (e.g., noncoding RNAs and small-molecule metabolites) as tumor drivers, making the molecular regulatory network of cancer cells unprecedentedly sophisticated. Cancer is also appreciated as a dynamic disease. The molecular signature, phenotype, and lethality of cancers are variable with disease typing, clinical stage, and therapy. Thus, it is challenging for researchers to clarify the pivotal oncogenic mechanism from massive bioinformatic and experimental evidence, or to develop effective methodologies for diagnosis and treatment. It requires collaboration across multi-disciplines, integration of theory and techniques, and the ambition of patients, clinicians, and scientists to defeat cancer.</p><p>Governments and foundations worldwide are providing increasing financial supports to oncology research. Unquestionably, critical discoveries in this field will be achieved in the near future. Nevertheless, the limited influential journals in oncology cannot guarantee the timely publication of these achievements. To this end, <i>MedComm - Oncology</i> is launched to meet the demand.</p><p><i>MedComm - Oncology</i> is a peer-reviewed and ","PeriodicalId":100902,"journal":{"name":"MedComm – Oncology","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mog2.13","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81980991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-30DOI: 10.21203/rs.3.rs-885745/v1
Zhong-lin Fan, Jingjing Duan, Lei Zhang, Qiong Chen, Wen-hui Xiao, P. Luo, L. Shao, J. Meng, Jianghong An, Gengwei Zhang, Xiaohua Tan
Background: Uveal melanoma (UM) is an aggressive primary intraocular tumor in adults, with high metastatic capacity and high morbidity. However, the mechanisms of UM metastasis have not been clearly elucidated.Methods: The PTK2 expression and activation were performed in the Cancer Genome Atlas (TCGA) database and 25 patients of UM. The role of PTK2 in promoting metastasis was explored in vitro and in vivo. Subsequently, we revealed the correlation between PTK2 expression and epithelial-to-mesenchymal transition (EMT). Finally, we explored the reason for the high expression of PTK2 in UM.Results: Our study found that protein tyrosine kinase 2 (PTK2) was overexpressed in UM specimens, and as a novel independent risk factor, its overexpression predicted the poor survival of UM patients. For the molecular mechanism, PTK2 promoted EMT phenotype, thus leading to tumor metastasis in UM cells. Subsequently, we have demonstrated that PTK2 was a functional gene of chromosome 8q gain accounting for UM metastasis, providing a novel molecular mechanism for the aberrantly expression and activation of PTK2 in UM.Conclusion: Our data reveal the important role and mechanism of PTK2 in the metastatic process of UM, which may clue to a new predictive biomarker for UM metastasis and a new therapeutic target for UM treatment.
{"title":"PTK2 Promotes Uveal Melanoma Metastasis by Activating Epithelial-to-Mesenchymal Transition","authors":"Zhong-lin Fan, Jingjing Duan, Lei Zhang, Qiong Chen, Wen-hui Xiao, P. Luo, L. Shao, J. Meng, Jianghong An, Gengwei Zhang, Xiaohua Tan","doi":"10.21203/rs.3.rs-885745/v1","DOIUrl":"https://doi.org/10.21203/rs.3.rs-885745/v1","url":null,"abstract":"\u0000 Background: Uveal melanoma (UM) is an aggressive primary intraocular tumor in adults, with high metastatic capacity and high morbidity. However, the mechanisms of UM metastasis have not been clearly elucidated.Methods: The PTK2 expression and activation were performed in the Cancer Genome Atlas (TCGA) database and 25 patients of UM. The role of PTK2 in promoting metastasis was explored in vitro and in vivo. Subsequently, we revealed the correlation between PTK2 expression and epithelial-to-mesenchymal transition (EMT). Finally, we explored the reason for the high expression of PTK2 in UM.Results: Our study found that protein tyrosine kinase 2 (PTK2) was overexpressed in UM specimens, and as a novel independent risk factor, its overexpression predicted the poor survival of UM patients. For the molecular mechanism, PTK2 promoted EMT phenotype, thus leading to tumor metastasis in UM cells. Subsequently, we have demonstrated that PTK2 was a functional gene of chromosome 8q gain accounting for UM metastasis, providing a novel molecular mechanism for the aberrantly expression and activation of PTK2 in UM.Conclusion: Our data reveal the important role and mechanism of PTK2 in the metastatic process of UM, which may clue to a new predictive biomarker for UM metastasis and a new therapeutic target for UM treatment.","PeriodicalId":100902,"journal":{"name":"MedComm – Oncology","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78638251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-03-20DOI: 10.21203/rs.3.rs-18127/v1
L. Du, Shasha Li, Xue Xiao, Jin Li, Yuanfang Sun, Shuai Ji, Huizi Jin, Z. Hua, Juming Ma, Xi Wang, Shikai Yan
Background: Gastric cancer (GC) remains one of the most common cancers all over the world. The greatest challenge for GC is that it is often detected at advanced stages, leading to the loss of optimum time for treatment and giving rise to poor prognosis. Thus, there is a critical need to develop effective and noninvasive strategies for early diagnosis of the disease process. Methods: In total, 82 participants were enrolled in the study, including 50 chronic superficial gastritis (CSG) patients, 7 early gastric cancer (EGC) and 25 advanced gastric cancer (AGC) ones. Metabolites profiling on patient plasma was performed using ultra-high performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry ( UPLC-Q-TOF/MS ). Principal components analysis as well as orthogonal partial least squares-discriminant analysis was utilized to evaluate the variation on endogenous metabolites for GC patients and to screen potential biomarkers. Furthermore, the biomarker panels detected above were used to create logistic regression models, which discrimination efficiency and accuracy was ascertained by receiver operating characteristic curve (ROC) analysis. Metabolic pathways were carried out on MetaboAnalyst. Results: Totally 50 metabolites were detected differentially expressed among CSG, EGC and AGC patients. L-carnitine, L-proline, pyruvaldehyde, phosphatidylcholines (PC) (14:0/18:0), lysophosphatidylcholine (14:0) (LysoPC 14:0), lysinoalanine were defined as the potential biomarker panel for the diagnosis among CSG and EGC patients. Compared with EGC patients, 6 significantly changed metabolites, PC(O-18:0/0:0) and LysoPC(20:4(5Z,8Z,11Z,14Z)) were found to be up-regulated, whereas L-proline, L-valine, adrenic acid and pyruvaldehyde to be down-regulated in AGC patients. ROC analysis demonstrated a high diagnostic performance for metabolite panels with area under the curve (AUC) of 0.931 to 1. Moreover, the metabolomic pathway analysis revealed several metabolism pathway disruptions, including amino acid and lipid metabolisms, in GC patients. Conclusions: In this study, a total of six differential metabolites that contributed to GC and precancerous stages were identified, respectively. The biomarker panels further improve diagnostic performance for detecting GC, with AUC values of more than 93.1%. It indicated that the biomarker panels may be sensitive to the early diagnosis of GC disease, which can be used as a promising diagnostic and prognostic tool for disease stratification studies.
{"title":"Metabolomic profiling on plasma reveals potential biomarkers for screening and early diagnosis of gastric cancer and precancerous stages","authors":"L. Du, Shasha Li, Xue Xiao, Jin Li, Yuanfang Sun, Shuai Ji, Huizi Jin, Z. Hua, Juming Ma, Xi Wang, Shikai Yan","doi":"10.21203/rs.3.rs-18127/v1","DOIUrl":"https://doi.org/10.21203/rs.3.rs-18127/v1","url":null,"abstract":"\u0000 Background: Gastric cancer (GC) remains one of the most common cancers all over the world. The greatest challenge for GC is that it is often detected at advanced stages, leading to the loss of optimum time for treatment and giving rise to poor prognosis. Thus, there is a critical need to develop effective and noninvasive strategies for early diagnosis of the disease process. Methods: In total, 82 participants were enrolled in the study, including 50 chronic superficial gastritis (CSG) patients, 7 early gastric cancer (EGC) and 25 advanced gastric cancer (AGC) ones. Metabolites profiling on patient plasma was performed using ultra-high performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry ( UPLC-Q-TOF/MS ). Principal components analysis as well as orthogonal partial least squares-discriminant analysis was utilized to evaluate the variation on endogenous metabolites for GC patients and to screen potential biomarkers. Furthermore, the biomarker panels detected above were used to create logistic regression models, which discrimination efficiency and accuracy was ascertained by receiver operating characteristic curve (ROC) analysis. Metabolic pathways were carried out on MetaboAnalyst. Results: Totally 50 metabolites were detected differentially expressed among CSG, EGC and AGC patients. L-carnitine, L-proline, pyruvaldehyde, phosphatidylcholines (PC) (14:0/18:0), lysophosphatidylcholine (14:0) (LysoPC 14:0), lysinoalanine were defined as the potential biomarker panel for the diagnosis among CSG and EGC patients. Compared with EGC patients, 6 significantly changed metabolites, PC(O-18:0/0:0) and LysoPC(20:4(5Z,8Z,11Z,14Z)) were found to be up-regulated, whereas L-proline, L-valine, adrenic acid and pyruvaldehyde to be down-regulated in AGC patients. ROC analysis demonstrated a high diagnostic performance for metabolite panels with area under the curve (AUC) of 0.931 to 1. Moreover, the metabolomic pathway analysis revealed several metabolism pathway disruptions, including amino acid and lipid metabolisms, in GC patients. Conclusions: In this study, a total of six differential metabolites that contributed to GC and precancerous stages were identified, respectively. The biomarker panels further improve diagnostic performance for detecting GC, with AUC values of more than 93.1%. It indicated that the biomarker panels may be sensitive to the early diagnosis of GC disease, which can be used as a promising diagnostic and prognostic tool for disease stratification studies.","PeriodicalId":100902,"journal":{"name":"MedComm – Oncology","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90268484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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