Objective: 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.
Patients and methods: 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).
Results: 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.
Conclusions: 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.
{"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, Ghanashyam Biswas, Bala Reddy Bheemareddy, Sreenivasa Chary, Pankaj Thakur, Minish Jain, Tanveer Maksud, Suraj Pawar, Koushik Chatterjee, Murali Krishna Voonna, Anil Goel, Krishna Chaitanya Puligundla, Kuntegowdanahalli Chinnagiriyappa Lakshmaiah, Leela Talluri, Ramya Vattipalli, 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":null,"pages":null},"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}
Pub Date : 2023-01-01DOI: 10.1177/11769351231177269
Nagehan Pakasticali, Andrea Chobrutskiy, Dhruv N Patel, Monica Hsiang, Saif Zaman, Konrad J Cios, George Blanck, Boris I Chobrutskiy
Introduction: One of the most pressing goals for cancer immunotherapy at this time is the identification of actionable antigens.
Methods: 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.
Results: 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.
Conclusions: 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.
{"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, Andrea Chobrutskiy, Dhruv N Patel, Monica Hsiang, Saif Zaman, Konrad J Cios, George Blanck, 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":null,"pages":null},"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}
Pub Date : 2023-01-01DOI: 10.1177/11769351221150772
Reeshan Ul Quraish, Tetsuyuki Hirahata, Afraz Ul Quraish, Shahan Ul Quraish
Genomic instability is considered a fundamental factor involved in any neoplastic disease. Consequently, the genetically unstable cells contribute to intratumoral genetic heterogeneity and phenotypic diversity of cancer. These genetic alterations can be detected by several diagnostic techniques of molecular biology and the detection of alteration in genomic integrity may serve as reliable genetic molecular markers for the early detection of cancer or cancer-related abnormal changes in the body cells. These genetic molecular markers can detect cancer earlier than any other method of cancer diagnosis, once a tumor is diagnosed, then replacement or therapeutic manipulation of these cancer-related abnormal genetic changes can be possible, which leads toward effective and target-specific cancer treatment and in many cases, personalized treatment of cancer could be performed without the adverse effects of chemotherapy and radiotherapy. In this review, we describe how these genetic molecular markers can be detected and the possible ways for the application of this gene diagnosis for gene therapy that can attack cancerous cells, directly or indirectly, which lead to overall improved management and quality of life for a cancer patient.
{"title":"An Overview: Genetic Tumor Markers for Early Detection and Current Gene Therapy Strategies.","authors":"Reeshan Ul Quraish, Tetsuyuki Hirahata, Afraz Ul Quraish, Shahan Ul Quraish","doi":"10.1177/11769351221150772","DOIUrl":"https://doi.org/10.1177/11769351221150772","url":null,"abstract":"<p><p>Genomic instability is considered a fundamental factor involved in any neoplastic disease. Consequently, the genetically unstable cells contribute to intratumoral genetic heterogeneity and phenotypic diversity of cancer. These genetic alterations can be detected by several diagnostic techniques of molecular biology and the detection of alteration in genomic integrity may serve as reliable genetic molecular markers for the early detection of cancer or cancer-related abnormal changes in the body cells. These genetic molecular markers can detect cancer earlier than any other method of cancer diagnosis, once a tumor is diagnosed, then replacement or therapeutic manipulation of these cancer-related abnormal genetic changes can be possible, which leads toward effective and target-specific cancer treatment and in many cases, personalized treatment of cancer could be performed without the adverse effects of chemotherapy and radiotherapy. In this review, we describe how these genetic molecular markers can be detected and the possible ways for the application of this gene diagnosis for gene therapy that can attack cancerous cells, directly or indirectly, which lead to overall improved management and quality of life for a cancer patient.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":null,"pages":null},"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/7a/50/10.1177_11769351221150772.PMC9903029.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10688677","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}
Aim: In this study our aim was to elucidate whether advanced cancer patients benefit from antibiotic treatment in the last days of life in addition to reviewing the relevant costs and effects.
Materials and methods: We reviewed medical records from 100 end-stage cancer patients and their antibiotic use during the hospitalization in Imam Khomeini hospital. Patient's medical records were analyzed retrospectively for cause and periodicity of infections, fever, increase in acute phase proteins, cultures, type and cost of antibiotic.
Results: Microorganisms were found in only 29 patients (29%) and the most microorganism among the patients was E. coli (6%). About 78% of the patients had clinical symptoms. The highest dose of antibiotics was related to Ceftriaxone (40.2%) and in the second place was Metronidazole (34.7%) and the lowest dose was related to Levofloxacin, Gentamycin and Colistin (1.4%). Fifty-one patients (71%) did not have any side effects due to antibiotics. The most common side effect of antibiotics among patients was skin rash (12.5%). The average estimated cost for antibiotic use was 7 935 540 Rials (24.4 dollars).
Conclusion: Prescription of antibiotics was not effective in symptom control in advanced cancer patients. The cost of using antibiotics during hospitalization is very high and also the risk of developing resistant pathogens during admission should be considered. Antibiotic side effects also occur in patients, causing more harm to the patient at the end of life. Therefore, the benefits of antibiotic advice in this time is less than its negative effects.
{"title":"Antibiotic Treatment in End Stage Cancer Patients; Advantages and Disadvantages.","authors":"Tahmasebi Mamak, Hosamirudsari Hadiseh, Familrashtian Shirin, Parash Masoud, Salehi Mohammadreza, Abbaszadeh Mahsa","doi":"10.1177/11769351231161476","DOIUrl":"https://doi.org/10.1177/11769351231161476","url":null,"abstract":"<p><strong>Aim: </strong>In this study our aim was to elucidate whether advanced cancer patients benefit from antibiotic treatment in the last days of life in addition to reviewing the relevant costs and effects.</p><p><strong>Materials and methods: </strong>We reviewed medical records from 100 end-stage cancer patients and their antibiotic use during the hospitalization in Imam Khomeini hospital. Patient's medical records were analyzed retrospectively for cause and periodicity of infections, fever, increase in acute phase proteins, cultures, type and cost of antibiotic.</p><p><strong>Results: </strong>Microorganisms were found in only 29 patients (29%) and the most microorganism among the patients was E. coli (6%). About 78% of the patients had clinical symptoms. The highest dose of antibiotics was related to Ceftriaxone (40.2%) and in the second place was Metronidazole (34.7%) and the lowest dose was related to Levofloxacin, Gentamycin and Colistin (1.4%). Fifty-one patients (71%) did not have any side effects due to antibiotics. The most common side effect of antibiotics among patients was skin rash (12.5%). The average estimated cost for antibiotic use was 7 935 540 Rials (24.4 dollars).</p><p><strong>Conclusion: </strong>Prescription of antibiotics was not effective in symptom control in advanced cancer patients. The cost of using antibiotics during hospitalization is very high and also the risk of developing resistant pathogens during admission should be considered. Antibiotic side effects also occur in patients, causing more harm to the patient at the end of life. Therefore, the benefits of antibiotic advice in this time is less than its negative effects.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":null,"pages":null},"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/1f/9b/10.1177_11769351231161476.PMC10064464.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9234982","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 : 2023-01-01DOI: 10.1177/11769351231194273
Emad Fadhal
Background: Cancer development and progression involve a complex network of pathways among which certain pathways play a pivotal role in promoting tumor growth and survival. An important pathway in this context is the PI3K/AKT pathway, which regulates crucial cellular processes including proliferation, viability, and metabolic regulation. Dysregulation of this pathway has been strongly linked to the development of various types of cancers. Consequently, it is imperative to identify the key proteins within this pathway as potential targets for impeding cancer cell proliferation and survival.
Results: One of the key findings of this study was the identification of signaling proteins that dominate various forms of PI3K/Akt pathway. Furthermore, proteins play critical roles in cancer networks, acting as oncogenes that promote cancer development or as tumor suppressor genes that inhibit tumor growth. This study identified several genes, including KIT, ERBB2, PDGFRA, MET, FGFR2, and FGFR3, which are involved in various types of the PI3K/Akt pathways. Additionally, this study identified 55 proteins that are commonly found in various forms of PI3K/Akt, and these proteins play crucial roles in regulating various biological functions.
Conclusions: This study highlights the importance of identifying key proteins involved in the PI3K/AKT pathway. In this study, we identified several genes involved in different pathways that play essential roles in the activation, signaling, and regulation of the pathway. Understanding the proteins participating in the PI3K/AKT pathway is vital for the development of targeted therapies, not only for cancer but also for other related diseases. By elucidating their roles and functions, this study contributes to the advancement of knowledge in the field and paves the way for the development of effective treatments targeting this pathway.
{"title":"A Comprehensive Analysis of the PI3K/AKT Pathway: Unveiling Key Proteins and Therapeutic Targets for Cancer Treatment.","authors":"Emad Fadhal","doi":"10.1177/11769351231194273","DOIUrl":"https://doi.org/10.1177/11769351231194273","url":null,"abstract":"<p><strong>Background: </strong>Cancer development and progression involve a complex network of pathways among which certain pathways play a pivotal role in promoting tumor growth and survival. An important pathway in this context is the PI3K/AKT pathway, which regulates crucial cellular processes including proliferation, viability, and metabolic regulation. Dysregulation of this pathway has been strongly linked to the development of various types of cancers. Consequently, it is imperative to identify the key proteins within this pathway as potential targets for impeding cancer cell proliferation and survival.</p><p><strong>Results: </strong>One of the key findings of this study was the identification of signaling proteins that dominate various forms of PI3K/Akt pathway. Furthermore, proteins play critical roles in cancer networks, acting as oncogenes that promote cancer development or as tumor suppressor genes that inhibit tumor growth. This study identified several genes, including KIT, ERBB2, PDGFRA, MET, FGFR2, and FGFR3, which are involved in various types of the PI3K/Akt pathways. Additionally, this study identified 55 proteins that are commonly found in various forms of PI3K/Akt, and these proteins play crucial roles in regulating various biological functions.</p><p><strong>Conclusions: </strong>This study highlights the importance of identifying key proteins involved in the PI3K/AKT pathway. In this study, we identified several genes involved in different pathways that play essential roles in the activation, signaling, and regulation of the pathway. Understanding the proteins participating in the PI3K/AKT pathway is vital for the development of targeted therapies, not only for cancer but also for other related diseases. By elucidating their roles and functions, this study contributes to the advancement of knowledge in the field and paves the way for the development of effective treatments targeting this pathway.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":null,"pages":null},"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/ee/2d/10.1177_11769351231194273.PMC10462777.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10357349","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 : 2022-11-29eCollection Date: 2022-01-01DOI: 10.1177/11769351221139257
Jake T Murkin, Hope E Amos, Daniel W Brough, Karl D Turley
User measurement bias during subcutaneous tumor measurement is a source of variation in preclinical in vivo studies. We investigated whether this user variability could impact efficacy study outcomes, in the form of the false negative result rate when comparing treated and control groups. Two tumor measurement methods were compared; calipers which rely on manual measurement, and an automatic 3D and thermal imaging device. Tumor growth curve data were used to create an in silico efficacy study with control and treated groups. Before applying user variability, treatment group tumor volumes were statistically different to the control group. Utilizing data collected from 15 different users across 9 in vivo studies, user measurement variability was computed for both methods and simulation was used to investigate its impact on the in silico study outcome. User variability produced a false negative result in 0.7% to 18.5% of simulated studies when using calipers, depending on treatment efficacy. When using an imaging device with lower user variability this was reduced to 0.0% to 2.6%, demonstrating that user variability impacts study outcomes and the ability to detect treatment effect. Reducing variability in efficacy studies can increase confidence in efficacy study outcomes without altering group sizes. By using a measurement device with lower user variability, the chance of missing a therapeutic effect can be reduced and time and resources spent pursuing false results could be saved. This improvement in data quality is of particular interest in discovery and dosing studies, where being able to detect small differences between groups is crucial.
{"title":"In Silico Modeling Demonstrates that User Variability During Tumor Measurement Can Affect In Vivo Therapeutic Efficacy Outcomes.","authors":"Jake T Murkin, Hope E Amos, Daniel W Brough, Karl D Turley","doi":"10.1177/11769351221139257","DOIUrl":"https://doi.org/10.1177/11769351221139257","url":null,"abstract":"<p><p>User measurement bias during subcutaneous tumor measurement is a source of variation in preclinical in vivo studies. We investigated whether this user variability could impact efficacy study outcomes, in the form of the false negative result rate when comparing treated and control groups. Two tumor measurement methods were compared; calipers which rely on manual measurement, and an automatic 3D and thermal imaging device. Tumor growth curve data were used to create an in silico efficacy study with control and treated groups. Before applying user variability, treatment group tumor volumes were statistically different to the control group. Utilizing data collected from 15 different users across 9 in vivo studies, user measurement variability was computed for both methods and simulation was used to investigate its impact on the in silico study outcome. User variability produced a false negative result in 0.7% to 18.5% of simulated studies when using calipers, depending on treatment efficacy. When using an imaging device with lower user variability this was reduced to 0.0% to 2.6%, demonstrating that user variability impacts study outcomes and the ability to detect treatment effect. Reducing variability in efficacy studies can increase confidence in efficacy study outcomes without altering group sizes. By using a measurement device with lower user variability, the chance of missing a therapeutic effect can be reduced and time and resources spent pursuing false results could be saved. This improvement in data quality is of particular interest in discovery and dosing studies, where being able to detect small differences between groups is crucial.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/0a/81/10.1177_11769351221139257.PMC9716635.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35253512","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 : 2022-11-26eCollection Date: 2022-01-01DOI: 10.1177/11769351221140102
Elham Maserat
The CRISPR/Cas9 system offers a new approach to genome editing and cancer treatment. This approach is able to detect drug targets and genomic analysis of cancer. The use of artificial intelligence (AI) capacity to edit genomes through CRISPR/Cas9 enables modification of gene mutations, molecular simulation. AI approaches include knowledge discovery approaches, antigen and epitope prediction approaches, and agent based-model approaches. These methods in combination with CRISPR/Cas9 can be used in vaccine design.
{"title":"Integration of Artificial Intelligence and CRISPR/Cas9 System for Vaccine Design.","authors":"Elham Maserat","doi":"10.1177/11769351221140102","DOIUrl":"https://doi.org/10.1177/11769351221140102","url":null,"abstract":"<p><p>The CRISPR/Cas9 system offers a new approach to genome editing and cancer treatment. This approach is able to detect drug targets and genomic analysis of cancer. The use of artificial intelligence (AI) capacity to edit genomes through CRISPR/Cas9 enables modification of gene mutations, molecular simulation. AI approaches include knowledge discovery approaches, antigen and epitope prediction approaches, and agent based-model approaches. These methods in combination with CRISPR/Cas9 can be used in vaccine design.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/18/8c/10.1177_11769351221140102.PMC9703516.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40713568","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 : 2022-11-22eCollection Date: 2022-01-01DOI: 10.1177/11769351221136056
Jessica Weiss, Nhu-An Pham, Melania Pintilie, Ming Li, Geoffrey Liu, Frances A Shepherd, Ming-Sound Tsao, Wei Xu
Patient-derived tumor xenograft (PDX) models were used to evaluate the effectiveness of preclinical anticancer agents. A design using 1 mouse per patient per drug (1 × 1 × 1) was considered practical for large-scale drug efficacy studies. We evaluated modifiable parameters that could increase the statistical power of this design based on our consolidated PDX experiments. Real studies were used as a reference to investigate the relationship between statistical power with treatment effect size, inter-mouse variation, and tumor measurement frequencies. Our results showed that large effect sizes could be detected at a significance level of .2 or .05 under a 1 × 1 × 1 design. We found that the minimum number of mice required to achieve 80% power at an alpha level of .05 under all situations explored was 21 mice per group for a small effect size and 5 mice per group for a medium effect size.
{"title":"Optimizing Drug Response Study Design in Patient-Derived Tumor Xenografts.","authors":"Jessica Weiss, Nhu-An Pham, Melania Pintilie, Ming Li, Geoffrey Liu, Frances A Shepherd, Ming-Sound Tsao, Wei Xu","doi":"10.1177/11769351221136056","DOIUrl":"https://doi.org/10.1177/11769351221136056","url":null,"abstract":"<p><p>Patient-derived tumor xenograft (PDX) models were used to evaluate the effectiveness of preclinical anticancer agents. A design using 1 mouse per patient per drug (1 × 1 × 1) was considered practical for large-scale drug efficacy studies. We evaluated modifiable parameters that could increase the statistical power of this design based on our consolidated PDX experiments. Real studies were used as a reference to investigate the relationship between statistical power with treatment effect size, inter-mouse variation, and tumor measurement frequencies. Our results showed that large effect sizes could be detected at a significance level of .2 or .05 under a 1 × 1 × 1 design. We found that the minimum number of mice required to achieve 80% power at an alpha level of .05 under all situations explored was 21 mice per group for a small effect size and 5 mice per group for a medium effect size.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9685207/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40488601","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 : 2022-11-22eCollection Date: 2022-01-01DOI: 10.1177/11769351221136081
Emma Bigelow, Suchi Saria, Brian Piening, Brendan Curti, Alexa Dowdell, Roshanthi Weerasinghe, Carlo Bifulco, Walter Urba, Noam Finkelstein, Elana J Fertig, Alex Baras, Neeha Zaidi, Elizabeth Jaffee, Mark Yarchoan
Tumor mutational burden (TMB), a surrogate for tumor neoepitope burden, is used as a pan-tumor biomarker to identify patients who may benefit from anti-program cell death 1 (PD1) immunotherapy, but it is an imperfect biomarker. Multiple additional genomic characteristics are associated with anti-PD1 responses, but the combined predictive value of these features and the added informativeness of each respective feature remains unknown. We evaluated whether machine learning (ML) approaches using proposed determinants of anti-PD1 response derived from whole exome sequencing (WES) could improve prediction of anti-PD1 responders over TMB alone. Random forest classifiers were trained on publicly available anti-PD1 data (n = 104), and subsequently tested on an independent anti-PD1 cohort (n = 69). Both the training and test datasets included a range of cancer types such as non-small cell lung cancer (NSCLC), head and neck squamous cell carcinoma (HNSCC), melanoma, and smaller numbers of patients from other tumor types. Features used include summaries such as TMB and number of frameshift mutations, as well as more gene-level features such as counts of mutations associated with immune checkpoint response and resistance. Both ML algorithms demonstrated area under the receiver-operator curves (AUC) that exceeded TMB alone (AUC 0.63 "human-guided," 0.64 "cluster," and 0.58 TMB alone). Mutations within oncogenes disproportionately modulate anti-PD1 responses relative to their overall contribution to tumor neoepitope burden. The use of a ML algorithm evaluating multiple proposed genomic determinants of anti-PD1 responses modestly improves performance over TMB alone, highlighting the need to integrate other biomarkers to further improve model performance.
{"title":"A Random Forest Genomic Classifier for Tumor Agnostic Prediction of Response to Anti-PD1 Immunotherapy.","authors":"Emma Bigelow, Suchi Saria, Brian Piening, Brendan Curti, Alexa Dowdell, Roshanthi Weerasinghe, Carlo Bifulco, Walter Urba, Noam Finkelstein, Elana J Fertig, Alex Baras, Neeha Zaidi, Elizabeth Jaffee, Mark Yarchoan","doi":"10.1177/11769351221136081","DOIUrl":"10.1177/11769351221136081","url":null,"abstract":"<p><p>Tumor mutational burden (TMB), a surrogate for tumor neoepitope burden, is used as a pan-tumor biomarker to identify patients who may benefit from anti-program cell death 1 (PD1) immunotherapy, but it is an imperfect biomarker. Multiple additional genomic characteristics are associated with anti-PD1 responses, but the combined predictive value of these features and the added informativeness of each respective feature remains unknown. We evaluated whether machine learning (ML) approaches using proposed determinants of anti-PD1 response derived from whole exome sequencing (WES) could improve prediction of anti-PD1 responders over TMB alone. Random forest classifiers were trained on publicly available anti-PD1 data (n = 104), and subsequently tested on an independent anti-PD1 cohort (n = 69). Both the training and test datasets included a range of cancer types such as non-small cell lung cancer (NSCLC), head and neck squamous cell carcinoma (HNSCC), melanoma, and smaller numbers of patients from other tumor types. Features used include summaries such as TMB and number of frameshift mutations, as well as more gene-level features such as counts of mutations associated with immune checkpoint response and resistance. Both ML algorithms demonstrated area under the receiver-operator curves (AUC) that exceeded TMB alone (AUC 0.63 \"human-guided,\" 0.64 \"cluster,\" and 0.58 TMB alone). Mutations within oncogenes disproportionately modulate anti-PD1 responses relative to their overall contribution to tumor neoepitope burden. The use of a ML algorithm evaluating multiple proposed genomic determinants of anti-PD1 responses modestly improves performance over TMB alone, highlighting the need to integrate other biomarkers to further improve model performance.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/c7/0c/10.1177_11769351221136081.PMC9685115.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9390672","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 : 2022-11-15eCollection Date: 2022-01-01DOI: 10.1177/11769351221135141
Taejin Ahn, Kidong Kim, Hyojin Kim, Sarah Kim, Sangick Park, Kyoungbun Lee
Purpose: There is a lack of tools for identifying the site of origin in mucinous cancer. This study aimed to evaluate the performance of a transcriptome-based classifier for identifying the site of origin in mucinous cancer. Materials And Methods: Transcriptomic data of 1878 non-mucinous and 82 mucinous cancer specimens, with 7 sites of origin, namely, the uterine cervix (CESC), colon (COAD), pancreas (PAAD), stomach (STAD), uterine endometrium (UCEC), uterine carcinosarcoma (UCS), and ovary (OV), obtained from The Cancer Genome Atlas, were used as the training and validation sets, respectively. Transcriptomic data of 14 mucinous cancer specimens from a tissue archive were used as the test set. For identifying the site of origin, a set of 100 differentially expressed genes for each site of origin was selected. After removing multiple iterations of the same gene, 427 genes were chosen, and their RNA expression profiles, at each site of origin, were used to train the deep neural network classifier. The performance of the classifier was estimated using the training, validation, and test sets. Results: The accuracy of the model in the training set was 0.998, while that in the validation set was 0.939 (77/82). In the test set which is newly sequenced from a tissue archive, the model showed an accuracy of 0.857 (12/14). t-SNE analysis revealed that samples in the test set were part of the clusters obtained for the training set. Conclusion: Although limited by small sample size, we showed that a transcriptome-based classifier could correctly identify the site of origin of mucinous cancer.
目的:目前缺乏确定黏液癌起源部位的工具。本研究旨在评估基于转录组的分类器在鉴别黏液癌起源部位方面的性能。材料与方法:将来自the cancer Genome Atlas的1878例非黏液性癌和82例黏液性癌的转录组学数据分别作为训练集和验证集,这些样本分别来自宫颈(CESC)、结肠(COAD)、胰腺(PAAD)、胃(STAD)、子宫内膜(UCEC)、子宫癌肉瘤(UCS)和卵巢(OV)等7个起源部位。采用组织档案中14例黏液癌标本的转录组学数据作为测试集。为了确定起源位点,每个起源位点选择了一组100个差异表达基因。在去除同一基因的多次迭代后,选择了427个基因,并使用它们在每个起源位点的RNA表达谱来训练深度神经网络分类器。使用训练集、验证集和测试集来估计分类器的性能。结果:模型在训练集中的准确率为0.998,在验证集中的准确率为0.939(77/82)。在组织档案新测序的测试集中,该模型的准确率为0.857(12/14)。t-SNE分析显示,测试集中的样本是为训练集获得的聚类的一部分。结论:虽然样本量有限,但我们发现基于转录组的分类器可以正确识别黏液癌的起源部位。
{"title":"A transcriptome-Based Deep Neural Network Classifier for Identifying the Site of Origin in Mucinous Cancer.","authors":"Taejin Ahn, Kidong Kim, Hyojin Kim, Sarah Kim, Sangick Park, Kyoungbun Lee","doi":"10.1177/11769351221135141","DOIUrl":"https://doi.org/10.1177/11769351221135141","url":null,"abstract":"Purpose: There is a lack of tools for identifying the site of origin in mucinous cancer. This study aimed to evaluate the performance of a transcriptome-based classifier for identifying the site of origin in mucinous cancer. Materials And Methods: Transcriptomic data of 1878 non-mucinous and 82 mucinous cancer specimens, with 7 sites of origin, namely, the uterine cervix (CESC), colon (COAD), pancreas (PAAD), stomach (STAD), uterine endometrium (UCEC), uterine carcinosarcoma (UCS), and ovary (OV), obtained from The Cancer Genome Atlas, were used as the training and validation sets, respectively. Transcriptomic data of 14 mucinous cancer specimens from a tissue archive were used as the test set. For identifying the site of origin, a set of 100 differentially expressed genes for each site of origin was selected. After removing multiple iterations of the same gene, 427 genes were chosen, and their RNA expression profiles, at each site of origin, were used to train the deep neural network classifier. The performance of the classifier was estimated using the training, validation, and test sets. Results: The accuracy of the model in the training set was 0.998, while that in the validation set was 0.939 (77/82). In the test set which is newly sequenced from a tissue archive, the model showed an accuracy of 0.857 (12/14). t-SNE analysis revealed that samples in the test set were part of the clusters obtained for the training set. Conclusion: Although limited by small sample size, we showed that a transcriptome-based classifier could correctly identify the site of origin of mucinous cancer.","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9669684/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40477422","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}