Cancer Informatics最新文献

{"title":"Decision Support System and Web-Application Using Supervised Machine Learning Algorithms for Easy Cancer Classifications.","authors":"K Chandrashekar,&nbsp;Anagha S Setlur,&nbsp;Adithya Sabhapathi C,&nbsp;Satyam Suresh Raiker,&nbsp;Satyam Singh,&nbsp;Vidya Niranjan","doi":"10.1177/11769351221147244","DOIUrl":"https://doi.org/10.1177/11769351221147244","url":null,"abstract":"<p><p>Using a decision support system (DSS) that classifies various cancers provides support to the clinicians/researchers to make better decisions that can aid in early cancer diagnosis, thereby reducing chances of incorrect disease diagnosis. Thus, this work aimed at designing a classification model that can predict accurately for 5 different cancer types comprising of 20 cancer exomes, using the mutations identified from whole exome cancer analysis. Initially, a basic model was designed using supervised machine learning classification algorithms such as K-nearest neighbor (KNN), support vector machine (SVM), decision tree, naïve bayes and random forest (RF), among which decision tree and random forest performed better in terms of preliminary model accuracy. However, output predictions were incorrect due to less training scores. Thus, 16 essential features were then selected for model improvement using 2 approaches. All imbalanced datasets were balanced using SMOTE. In the first approach, all features from 20 cancer exome datasets were trained and models were designed using decision tree and random forest. Balanced datasets for decision tree model showed an accuracy of 77%, while with the RF model, the accuracy improved to 82% where all 5 cancer types were predicted correctly. Area under the curve for RF model was closer to 1, than decision tree model. In the second approach, all 15 datasets were trained, while 5 were tested. However, only 2 cancer types were predicted correctly. To cross validate RF model, Matthew's correlation co-efficient (MCC) test was performed. For method 1, the MCC test and MCC cross validation was found to be 0.7796 and 0.9356 respectively. Likewise, for second approach, MCC was observed to be 0.9365, corroborating the accuracy of the designed model. The model was successfully deployed using Streamlit as a web application for easy use. This study presents insights for allowing easy cancer classifications.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"22 ","pages":"11769351221147244"},"PeriodicalIF":2.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/c2/da/10.1177_11769351221147244.PMC9880585.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10591008","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}

{"title":"Erratum to \"Chemical Complementarity of Breast Cancer Resident, T-Cell Receptor CDR3 Domains and the Cancer Antigen, ARMC3, is Associated With Higher Levels of Survival and Granzyme Expression\".","authors":"","doi":"10.1177/11769351231189051","DOIUrl":"https://doi.org/10.1177/11769351231189051","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.1177/11769351231177269.].</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"22 ","pages":"11769351231189051"},"PeriodicalIF":2.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/24/b1/10.1177_11769351231189051.PMC10350781.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9827252","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}

{"title":"TP53 and its Regulatory Genes as Prognosis of Cutaneous Melanoma.","authors":"Safir Ullah Khan,&nbsp;Zahid Ullah,&nbsp;Hadia Shaukat,&nbsp;Sheeza Unab,&nbsp;Saba Jannat,&nbsp;Waqar Ali,&nbsp;Amir Ali,&nbsp;Muhammad Irfan,&nbsp;Muhammad Fiaz Khan,&nbsp;Rodolfo Daniel Cervantes-Villagrana","doi":"10.1177/11769351231177267","DOIUrl":"https://doi.org/10.1177/11769351231177267","url":null,"abstract":"<p><p>The present study was the first comprehensive investigation of genetic mutation and expression levels of the p53 signaling genes in cutaneous melanoma through various genetic databases providing large datasets. The mutational landscape of p53 and its signaling genes was higher than expected, with <i>TP53</i> followed by <i>CDKN2A</i> being the most mutated gene in cutaneous melanoma. Furthermore, the expression analysis showed <i>that TP53</i>, <i>MDM2</i>, <i>CDKN2A</i>, and <i>TP53BP1</i> were overexpressed, while <i>MDM4</i> and <i>CDKN2B</i> were under-expressed in cutaneous melanoma. Overall, TCGA data revealed that among all the other p53 signaling proteins, CDKN2A was significantly higher in both sun and non-sun-exposed healthy tissues than in melanoma. Likewise, MDM4 and TP53BP1 expressions were markedly greater in non-sun-exposed healthy tissues compared to other groups. However, CDKN2B expression was higher in the sun-exposed healthy tissues than in other tissues. In addition, various genes were expressed significantly differently among males and females. In addition, <i>CDKN2A</i> was highly expressed in the SK-MEL-30 skin cancer cell line, whereas, Immune cell type expression analysis revealed that the <i>MDM4</i> was highly expressed in naïve B-cells. Furthermore, all six genes were significantly overexpressed in extraordinarily overweight or obese tumor tissues compared to healthy tissues. <i>MDM2</i> expression and tumor stage were closely related. There were differences in gene expression across patient age groups and positive nodal status. <i>TP53</i> showed a positive correlation with B cells, <i>MDM2</i> with CD8+<i>T</i> cells, macrophages and neutrophils, and <i>MDM4</i> with neutrophils. <i>CDKN2A/B</i> had a non-significant correlation with all six types of immune cells. However, <i>TP53BP1</i> was positively correlated with all five types of immune cells except B cells. Only <i>TP53, MDM2</i>, and <i>CDKN2A</i> had a role in cutaneous melanoma-specific tumor immunity. All TP53 and its regulating genes may be predictive for prognosis. The results of the present study need to be validated through future screening, in vivo, and in vitro studies.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"22 ","pages":"11769351231177267"},"PeriodicalIF":2.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/c1/e4/10.1177_11769351231177267.PMC10475268.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10283819","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}

{"title":"Multi-omics Pathways Workflow (MOPAW): An Automated Multi-omics Workflow on the Cancer Genomics Cloud.","authors":"Trinh Nguyen,&nbsp;Xiaopeng Bian,&nbsp;David Roberson,&nbsp;Rakesh Khanna,&nbsp;Qingrong Chen,&nbsp;Chunhua Yan,&nbsp;Rowan Beck,&nbsp;Zelia Worman,&nbsp;Daoud Meerzaman","doi":"10.1177/11769351231180992","DOIUrl":"https://doi.org/10.1177/11769351231180992","url":null,"abstract":"<p><strong>Introduction: </strong>In the era of big data, gene-set pathway analyses derived from multi-omics are exceptionally powerful. When preparing and analyzing high-dimensional multi-omics data, the installation process and programing skills required to use existing tools can be challenging. This is especially the case for those who are not familiar with coding. In addition, implementation with high performance computing solutions is required to run these tools efficiently.</p><p><strong>Methods: </strong>We introduce an automatic multi-omics pathway workflow, a point and click graphical user interface to Multivariate Single Sample Gene Set Analysis (MOGSA), hosted on the Cancer Genomics Cloud by Seven Bridges Genomics. This workflow leverages the combination of different tools to perform data preparation for each given data types, dimensionality reduction, and MOGSA pathway analysis. The Omics data includes copy number alteration, transcriptomics data, proteomics and phosphoproteomics data. We have also provided an additional workflow to help with downloading data from The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium and preprocessing these data to be used for this multi-omics pathway workflow.</p><p><strong>Results: </strong>The main outputs of this workflow are the distinct pathways for subgroups of interest provided by users, which are displayed in heatmaps if identified. In addition to this, graphs and tables are provided to users for reviewing.</p><p><strong>Conclusion: </strong>Multi-omics Pathway Workflow requires no coding experience. Users can bring their own data or download and preprocess public datasets from The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium using our additional workflow based on the samples of interest. Distinct overactivated or deactivated pathways for groups of interest can be found. This useful information is important in effective therapeutic targeting.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"22 ","pages":"11769351231180992"},"PeriodicalIF":2.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/28/1c/10.1177_11769351231180992.PMC10278438.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9707715","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}

{"title":"Cell Adaptive Fitness and Cancer Evolutionary Dynamics.","authors":"Youcef Derbal","doi":"10.1177/11769351231154679","DOIUrl":"https://doi.org/10.1177/11769351231154679","url":null,"abstract":"Genome instability of cancer cells translates into increased entropy and lower information processing capacity, leading to metabolic reprograming toward higher energy states, presumed to be aligned with a cancer growth imperative. Dubbed as the cell adaptive fitness, the proposition postulates that the coupling between cell signaling and metabolism constrains cancer evolutionary dynamics along trajectories privileged by the maintenance of metabolic sufficiency for survival. In particular, the conjecture postulates that clonal expansion becomes restricted when genetic alterations induce a sufficiently high level of disorder, that is, high entropy, in the regulatory signaling network, abrogating as a result the ability of cancer cells to successfully replicate, leading to a stage of clonal stagnation. The proposition is analyzed in the context of an in-silico model of tumor evolutionary dynamics to illustrate how cell-inherent adaptive fitness may predictably constrain clonal evolution of tumors, which would have significant implications for the design of adaptive cancer therapies.","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"22 ","pages":"11769351231154679"},"PeriodicalIF":2.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/77/f7/10.1177_11769351231154679.PMC9969436.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9074198","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}

{"title":"In Silico Analysis of MicroRNA Expression Data in Liver Cancer.","authors":"Nourhan Abu-Shahba,&nbsp;Elsayed Hegazy,&nbsp;Faiz M Khan,&nbsp;Mahmoud Elhefnawi","doi":"10.1177/11769351231171743","DOIUrl":"https://doi.org/10.1177/11769351231171743","url":null,"abstract":"<p><p>Abnormal miRNA expression has been evidenced to be directly linked to HCC initiation and progression. This study was designed to detect possible prognostic, diagnostic, and/or therapeutic miRNAs for HCC using computational analysis of miRNAs expression. Methods: miRNA expression datasets meta-analysis was performed using the YM500v2 server to compare miRNA expression in normal and cancerous liver tissues. The most significant differentially regulated miRNAs in our study undergone target gene analysis using the mirWalk tool to obtain their validated and predicted targets. The combinatorial target prediction tool; miRror Suite was used to obtain the commonly regulated target genes. Functional enrichment analysis was performed on the resulting targets using the DAVID tool. A network was constructed based on interactions among microRNAs, their targets, and transcription factors. Hub nodes and gatekeepers were identified using network topological analysis. Further, we performed patient data survival analysis based on low and high expression of identified hubs and gatekeeper nodes, patients were stratified into low and high survival probability groups. Results: Using the meta-analysis option in the YM500v2 server, 34 miRNAs were found to be significantly differentially regulated (<i>P</i>-value ⩽ .05); 5 miRNAs were down-regulated while 29 were up-regulated. The validated and predicted target genes for each miRNA, as well as the combinatorially predicted targets, were obtained. DAVID enrichment analysis resulted in several important cellular functions that are directly related to the main cancer hallmarks. Among these functions are focal adhesion, cell cycle, PI3K-Akt signaling, insulin signaling, Ras and MAPK signaling pathways. Several hub genes and gatekeepers were found that could serve as potential drug targets for hepatocellular carcinoma. POU2F1 and PPARA showed a significant difference between low and high survival probabilities (<i>P</i>-value ⩽ .05) in HCC patients. Our study sheds light on important biomarker miRNAs for hepatocellular carcinoma along with their target genes and their regulated functions.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"22 ","pages":"11769351231171743"},"PeriodicalIF":2.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/64/09/10.1177_11769351231171743.PMC10185868.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9492897","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}

{"title":"RNA-seq and Single-Cell Transcriptome Analyses of TRAIL Receptors Gene Expression in Human Osteosarcoma Cells and Tissues.","authors":"Wenyu Feng,&nbsp;Haiyingjie Lin,&nbsp;Emel Rothzerg,&nbsp;Dezhi Song,&nbsp;Wenxiang Zhao,&nbsp;Tingting Ning,&nbsp;Qingjun Wei,&nbsp;Jinmin Zhao,&nbsp;David Wood,&nbsp;Yun Liu,&nbsp;Jiake Xu","doi":"10.1177/11769351231161478","DOIUrl":"https://doi.org/10.1177/11769351231161478","url":null,"abstract":"<p><p>Osteosarcoma (OS) is the most common primary cancer in the skeletal system, characterized by a high incidence of lung metastasis, local recurrence and death. Systemic treatment of this aggressive cancer has not improved significantly since the introduction of chemotherapy regimens, underscoring a critical need for new treatment strategies. TRAIL receptors have long been proposed to be therapeutic targets for cancer treatment, but their role in osteosarcoma remains unclear. In this study, we investigated the expression profile of four TRAIL receptors in human OS cells using total RNA-seq and single-cell RNA-seq (scRNA-seq). The results revealed that <i>TNFRSF10B</i> and <i>TNFRSF10D</i> but not <i>TNFRSF10A</i> and <i>TNFRSF10C</i> are differentially expressed in human OS cells as compared to normal cells. At the single cell level by scRNA-seq analyses, <i>TNFRSF10B, TNFRSF10D, TNFRSF10A</i> and <i>TNFRSF10C</i> are most abundantly expressed in endothelial cells of OS tissues among nine distinct cell clusters. Notably, in osteoblastic OS cells, <i>TNFRSF10B</i> is most abundantly expressed, followed by <i>TNFRSF10D, TNFRSF10A</i> and <i>TNFRSF10C.</i> Similarly, in an OS cell line U2-OS using RNA-seq, <i>TNFRSF10B</i> is most abundantly expressed, followed by <i>TNFRSF10D, TNFRSF10A</i> and <i>TNFRSF10C</i>. According to the TARGET online database, poor patient outcomes were associated with low expression of <i>TNFRSF10C</i>. These results could provide a new perspective to design novel therapeutic targets of TRAIL receptors for the diagnosis, prognosis and treatment of OS and other cancers.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"22 ","pages":"11769351231161478"},"PeriodicalIF":2.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/ab/49/10.1177_11769351231161478.PMC10123892.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9356658","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}

{"title":"Identification of Prognostic Biomarkers for Breast Cancer Metastasis Using Penalized Additive Hazards Regression Model.","authors":"Leili Tapak,&nbsp;Omid Hamidi,&nbsp;Payam Amini,&nbsp;Saeid Afshar,&nbsp;Siamak Salimy,&nbsp;Irina Dinu","doi":"10.1177/11769351231157942","DOIUrl":"https://doi.org/10.1177/11769351231157942","url":null,"abstract":"<p><strong>Background: </strong>Breast cancer (BC) has been reported as one of the most common cancers diagnosed in females throughout the world. Survival rate of BC patients is affected by metastasis. So, exploring its underlying mechanisms and identifying related biomarkers to monitor BC relapse/recurrence using new statistical methods is essential. This study investigated the high-dimensional gene-expression profiles of BC patients using penalized additive hazards regression models.</p><p><strong>Methods: </strong>A publicly available dataset related to the time to metastasis in BC patients (GSE2034) was used. There was information of 22 283 genes expression profiles related to 286 BC patients. Penalized additive hazards regression models with different penalties, including LASSO, SCAD, SICA, MCP and Elastic net were used to identify metastasis related genes.</p><p><strong>Results: </strong>Five regression models with penalties were applied in the additive hazards model and jointly found 9 genes including <i>SNU13</i>, <i>CLINT1</i>, <i>MAPK9</i>, <i>ABCC5</i>, <i>NKX3</i>-1, <i>NCOR2</i>, <i>COL2A1</i>, and <i>ZNF219</i>. According the median of the prognostic index calculated using the regression coefficients of the penalized additive hazards model, the patients were labeled as high/low risk groups. A significant difference was detected in the survival curves of the identified groups. The selected genes were examined using validation data and were significantly associated with the hazard of metastasis.</p><p><strong>Conclusion: </strong>This study showed that <i>MAPK9</i>, <i>NKX3</i>-1, <i>NCOR1</i>, <i>ABCC5</i>, and <i>CD44</i> are the potential recurrence and metastatic predictors in breast cancer and can be taken into account as candidates for further research in tumorigenesis, invasion, metastasis, and epithelial-mesenchymal transition of breast cancer.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"22 ","pages":"11769351231157942"},"PeriodicalIF":2.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/c1/12/10.1177_11769351231157942.PMC10034277.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9244892","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}

{"title":"Chemical Complementarity of Breast Cancer Resident, T-Cell Receptor CDR3 Domains and the Cancer Antigen, ARMC3, is Associated With Higher Levels of Survival and Granzyme Expression.","authors":"Nagehan Pakasticali,&nbsp;Andrea Chobrutskiy,&nbsp;Dhruv N Patel,&nbsp;Monica Hsiang,&nbsp;Saif Zaman,&nbsp;Konrad J Cios,&nbsp;George Blanck,&nbsp;Boris I Chobrutskiy","doi":"10.1177/11769351231177269","DOIUrl":"https://doi.org/10.1177/11769351231177269","url":null,"abstract":"<p><strong>Introduction: </strong>One of the most pressing goals for cancer immunotherapy at this time is the identification of actionable antigens.</p><p><strong>Methods: </strong>This study relies on the following considerations and approaches to identify potential breast cancer antigens: (i) the significant role of the adaptive immune receptor, complementarity determining region-3 (CDR3) in antigen binding, and the existence cancer testis antigens (CTAs); (ii) chemical attractiveness; and (iii) informing the relevance of the integration of items (i) and (ii) with patient outcome and tumor gene expression data.</p><p><strong>Results: </strong>We have assessed CTAs for associations with survival, based on their chemical complementarity with tumor resident T-cell receptor (TCR), CDR3s. Also, we have established gene expression correlations with the high TCR CDR3-CTA chemical complementarities, for Granzyme B, and other immune biomarkers.</p><p><strong>Conclusions: </strong>Overall, for several independent TCR CDR3 breast cancer datasets, the CTA, ARMC3, stood out as a completely novel, candidate antigen based on multiple algorithms with highly consistent approaches. This conclusion was facilitated by use of the recently constructed Adaptive Match web tool.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"22 ","pages":"11769351231177269"},"PeriodicalIF":2.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/43/52/10.1177_11769351231177269.PMC10259117.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10206790","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}

{"title":"A Real-World Study of Safety, Immunogenicity and Efficacy of Bevacizumab in Patients With Solid Malignancies: A Phase IV, Post-Marketing Study in India.","authors":"Shubhadeep D Sinha,&nbsp;Ghanashyam Biswas,&nbsp;Bala Reddy Bheemareddy,&nbsp;Sreenivasa Chary,&nbsp;Pankaj Thakur,&nbsp;Minish Jain,&nbsp;Tanveer Maksud,&nbsp;Suraj Pawar,&nbsp;Koushik Chatterjee,&nbsp;Murali Krishna Voonna,&nbsp;Anil Goel,&nbsp;Krishna Chaitanya Puligundla,&nbsp;Kuntegowdanahalli Chinnagiriyappa Lakshmaiah,&nbsp;Leela Talluri,&nbsp;Ramya Vattipalli,&nbsp;Sheejith Kakkunnath","doi":"10.1177/11769351231177277","DOIUrl":"https://doi.org/10.1177/11769351231177277","url":null,"abstract":"<p><strong>Objective: </strong>The aim of this study was to evaluate the post-marketing safety, tolerability, immunogenicity and efficacy of Bevacizumab (manufactured by Hetero Biopharma) in a broader population of patients with solid tumors.</p><p><strong>Patients and methods: </strong>This phase IV, prospective, multi-centric clinical study was carried out in Indian patients with solid malignancies (metastatic colorectal cancer, non-squamous non-small-cell lung cancer, metastatic renal cell carcinoma) treated with Bevacizumab between April 2018 and July 2019. This study included 203 patients from 16 tertiary care oncology centers across India for safety assessment, of which a subset of 115 patients who have consented were also evaluated for efficacy and immunogenicity. This study was prospectively registered in the Clinical Trial Registry of India (CTRI), and was commenced only after receiving approval from the competent authority (Central Drugs Standard Control Organization, CDSCO).</p><p><strong>Results: </strong>Out of the 203 enrolled patients, 121 (59.6%) patients reported 338 adverse events (AEs) during this study. Of 338 reported AEs, 14 serious adverse events (SAEs) were reported by 13 patients including 6 fatal SAEs, assessed as unrelated to the study medication and 7 non-fatal SAEs, 5 assessed as related, and 3 unrelated to Bevacizumab. Most AEs reported in this study (33.9%) were general disorders and administration site conditions, followed by gastrointestinal disorders (29.1%). The most frequently reported AEs were diarrhea (11.3%), asthenia (10.3%), headache (8.9%), pain (7.4%), vomiting (7.9%), and neutropenia (5.9%). At the end of the study, 2 (1.75%) of 69 patients reported antibodies to Bevacizumab without affecting safety and efficacy. However, at the end of 12 months, no patient had reported antibodies to Bevacizumab. Complete response (CR), partial response (PR), stable disease (SD), and progressive disease (PD) were reported in 18.3%, 22.6%, 9.6%, and 8.7% of patients, respectively. The overall response rate (CR + PR) was reported in 40.9% of patients at the end of the study. Disease control rate (DCR), also known as the clinical benefit rate (CBR) was reported in 50.4% of patients.</p><p><strong>Conclusions: </strong>Bevacizumab (Cizumab, Hetero Biopharma) was observed to be safe, well tolerated, lacking immunogenicity, and efficacious in the treatment of solid tumors. The findings of this phase IV study of Bevacizumab, primarily as a combination therapy regimen suggest its suitability and rationality for usage in multiple solid malignancies.</p><p><strong>Clinical trial registry number: </strong>CTRI/2018/4/13371 [Registered on CTRI http://ctri.nic.in/Clinicaltrials/advsearch.php : 19/04/2018]; Trial Registered Prospectively.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"22 ","pages":"11769351231177277"},"PeriodicalIF":2.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/23/14/10.1177_11769351231177277.PMC10259146.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9636114","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}