Globally, cervical cancer affects more than a half-million women each year, with disproportionate incidence and mortality for women in low- and middle-income countries. Early detection through cervical cancer screening saves lives but is hampered by poor coverage, suboptimal detection accuracy, and lack of access to and delays in effective treatment. Emerging evidence that indicates how the vaginal microbiome can modify progression of human papillomavirus (HPV) infection and cervical cancer pathogenesis is surveyed. This presentation features a discussion of how the vaginal microbiome may affect cervical cancer screening and how cervical cancer screening may incorporate vaginal microbiome health in low- and middle-income countries. Vaginal dysbiosis as a clinical syndrome may be called bacterial vaginosis (BV), a condition that represents a shift from a Lactobacillus-dominant vaginal microbiome to one that is polymicrobial and often associated with increased mucosal inflammation. Meta-analyses and prospective studies demonstrate an association between vaginal dysbiosis and increased risk of HPV incidence and persistence and high-grade lesions and cancer. Increasing vaginal microbiome diversity is associated with progression of cervical intraepithelial neoplasia. Vaginal microbiota that are associated with greater likelihood of HPV detection in molecular studies are also commonly associated with BV. There are numerous challenges to incorporating microbiome measurement in population-level cervical cancer screening and unanswered research questions on its immediate utility. BV may serve as a measure of vaginal microbiome health, although there are no guidelines or recommendations for regular BV screening and treatment. Ongoing and planned longitudinal studies should evaluate BV screening in association with high-risk HPV, results of cervical cancer screening, and progression of cervical intraepithelial neoplasia to assess the utility of BV screening and treatment as an adjunct to cervical cancer screening and potential intervention.
{"title":"The Vaginal Microbiome and Cervical Cancer Screening in Low- and Middle-Income Countries","authors":"S. Mehta","doi":"10.1200/jgo.19.15000","DOIUrl":"https://doi.org/10.1200/jgo.19.15000","url":null,"abstract":"Globally, cervical cancer affects more than a half-million women each year, with disproportionate incidence and mortality for women in low- and middle-income countries. Early detection through cervical cancer screening saves lives but is hampered by poor coverage, suboptimal detection accuracy, and lack of access to and delays in effective treatment. Emerging evidence that indicates how the vaginal microbiome can modify progression of human papillomavirus (HPV) infection and cervical cancer pathogenesis is surveyed. This presentation features a discussion of how the vaginal microbiome may affect cervical cancer screening and how cervical cancer screening may incorporate vaginal microbiome health in low- and middle-income countries. Vaginal dysbiosis as a clinical syndrome may be called bacterial vaginosis (BV), a condition that represents a shift from a Lactobacillus-dominant vaginal microbiome to one that is polymicrobial and often associated with increased mucosal inflammation. Meta-analyses and prospective studies demonstrate an association between vaginal dysbiosis and increased risk of HPV incidence and persistence and high-grade lesions and cancer. Increasing vaginal microbiome diversity is associated with progression of cervical intraepithelial neoplasia. Vaginal microbiota that are associated with greater likelihood of HPV detection in molecular studies are also commonly associated with BV. There are numerous challenges to incorporating microbiome measurement in population-level cervical cancer screening and unanswered research questions on its immediate utility. BV may serve as a measure of vaginal microbiome health, although there are no guidelines or recommendations for regular BV screening and treatment. Ongoing and planned longitudinal studies should evaluate BV screening in association with high-risk HPV, results of cervical cancer screening, and progression of cervical intraepithelial neoplasia to assess the utility of BV screening and treatment as an adjunct to cervical cancer screening and potential intervention.","PeriodicalId":15862,"journal":{"name":"Journal of global oncology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1200/jgo.19.15000","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48577665","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}
Sidrah Shah, B. Monare, Sandra Urusaro, R. Bhatia, Sherman Preet Singh, T. Ralefala, Givy Dhaliwal, S. Grover
Most cancer diagnoses are expected to be in low- and middle-income countries (LMICs) by 2025, and 65% of cancer deaths occur in LMICs. Treatment adherence and patient monitoring are essential to cancer care but are often not possible in LMICs. OP Care, a smartphone application developed to fill this gap, stores medical records virtually and texts appointment reminders to patients. This study assessed its usability and effectiveness. OP Care was piloted at Princess Marina Hospital in Gaborone, Botswana. The study was a cross-sectional study using surveys. All providers using the application were surveyed, along with all patients who were previously enrolled in the application and attended the gynecologic oncology clinic during the 3-week survey period. Staff demographics, reaction, opinions on usability, and patients’ reactions to appointment reminders were collected. Answers were recorded on a 1 (not at all) to 7 (extremely so) scale. Primary outcomes were the application’s usability and the effectiveness of the text reminders. The University of Pennsylvania Institutional Review Board and the Ministry of Health and Wellness in Botswana gave approval for the study. Patients provided written consent before enrollment. Nine staff and 15 patients were surveyed. Staff included three doctors and six nurses, all of whom own a smartphone and use a computer at home. Most staff (78%) did not feel OP Care would increase their work burden and were willing to use the application if implemented permanently (median response, 6; interquartile range [IQR], 1). Most usability questions (17 of 19), such as “I feel comfortable using this system,” scored a median of 6. Most patients believed that the reminder text messages were helpful (median, 6; IQR, 1) but wanted the text reminders to be in the Setswana language (median, 7; IQR, 1). High usability scores indicate the application is adaptable to other clinics. Although patients appreciate OP Care, the option for call and text reminders in Setswana is indicated. A potential limitation is that patients for whom the appointment reminders were not helpful were not necessarily included, because only patients in the clinic were surveyed. Strengths were inclusion of all involved staff, uniformity in survey administration, and inclusion of numerical analysis.
{"title":"Usability and Effectiveness of a Smartphone Application for Tracking Oncology Patients in Gaborone, Botswana","authors":"Sidrah Shah, B. Monare, Sandra Urusaro, R. Bhatia, Sherman Preet Singh, T. Ralefala, Givy Dhaliwal, S. Grover","doi":"10.1200/jgo.19.20000","DOIUrl":"https://doi.org/10.1200/jgo.19.20000","url":null,"abstract":"Most cancer diagnoses are expected to be in low- and middle-income countries (LMICs) by 2025, and 65% of cancer deaths occur in LMICs. Treatment adherence and patient monitoring are essential to cancer care but are often not possible in LMICs. OP Care, a smartphone application developed to fill this gap, stores medical records virtually and texts appointment reminders to patients. This study assessed its usability and effectiveness. OP Care was piloted at Princess Marina Hospital in Gaborone, Botswana. The study was a cross-sectional study using surveys. All providers using the application were surveyed, along with all patients who were previously enrolled in the application and attended the gynecologic oncology clinic during the 3-week survey period. Staff demographics, reaction, opinions on usability, and patients’ reactions to appointment reminders were collected. Answers were recorded on a 1 (not at all) to 7 (extremely so) scale. Primary outcomes were the application’s usability and the effectiveness of the text reminders. The University of Pennsylvania Institutional Review Board and the Ministry of Health and Wellness in Botswana gave approval for the study. Patients provided written consent before enrollment. Nine staff and 15 patients were surveyed. Staff included three doctors and six nurses, all of whom own a smartphone and use a computer at home. Most staff (78%) did not feel OP Care would increase their work burden and were willing to use the application if implemented permanently (median response, 6; interquartile range [IQR], 1). Most usability questions (17 of 19), such as “I feel comfortable using this system,” scored a median of 6. Most patients believed that the reminder text messages were helpful (median, 6; IQR, 1) but wanted the text reminders to be in the Setswana language (median, 7; IQR, 1). High usability scores indicate the application is adaptable to other clinics. Although patients appreciate OP Care, the option for call and text reminders in Setswana is indicated. A potential limitation is that patients for whom the appointment reminders were not helpful were not necessarily included, because only patients in the clinic were surveyed. Strengths were inclusion of all involved staff, uniformity in survey administration, and inclusion of numerical analysis.","PeriodicalId":15862,"journal":{"name":"Journal of global oncology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1200/jgo.19.20000","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43248607","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}
Prevalence of cervical cancer remains high in sub-Saharan Africa, including Nigeria. Literature has documented knowledge of cervical cancer as important in promoting the adoption of preventive strategies, including screening, but most research has focused on women and health organization settings. This study assessed knowledge and screening practices of cervical cancer among male and female adults in urban poor communities in Ibadan, Oyo State, Nigeria. A cross-sectional study was carried out in two urban poor community settings in Ibadan. Data were collected from 250 randomly selected consenting respondents in each of the two communities (N = 500). Data were collected with an electronic device using the electronic data capture tool (Open Data Kit) database. Descriptive statistics were summarized using frequencies and percentages for categorical variables, and mean and standard deviation were used for continuous variables. Knowledge was scored on a scale of 0 to 39 points (0 to 18, low knowledge; 19 to 23, fair knowledge; 24 to 39, high knowledge). Associations between variables were tested using χ2. Mean age of respondents was 35.36 years (± 12.24). The majority of respondents were female (70.6%), and more than half (52.6%) had completed secondary school education. The majority had never heard of the Papanicolaou test (93.6%) or cervical cancer screening (91.2%), and only 10% had ever heard of the human papillomavirus vaccine for the prevention of cervical cancer. The majority had low knowledge of cervical cancer (77.2%); knowledge included detection, symptoms, and risk factors for cervical cancer. Only 7.4% of females had ever heard of the Papanicolaou test, and few women (4%) had ever been screened for cervical cancer using the Papanicolaou test. Only one woman (0.2%) had been screened for cervical cancer using visual inspection with acetic acid, and four (0.8%) had ever taken human papillomavirus vaccine for protection against cervical cancer. There was a significant association between knowledge of cervical cancer and employment status of respondents (χ2 = 11.19; P < .05). Health promotion interventions and strategies for awareness creation about cervical cancer and screening practices should be used in alleviating low knowledge and screening practices in urban poor communities.
{"title":"Knowledge and Screening Practices for Cervical Cancer Among Urban Poor Communities in Ibadan, Nigeria","authors":"Y. John-Akinola, M. Oluwasanu, O. Oladepo","doi":"10.1200/jgo.19.10000","DOIUrl":"https://doi.org/10.1200/jgo.19.10000","url":null,"abstract":"Prevalence of cervical cancer remains high in sub-Saharan Africa, including Nigeria. Literature has documented knowledge of cervical cancer as important in promoting the adoption of preventive strategies, including screening, but most research has focused on women and health organization settings. This study assessed knowledge and screening practices of cervical cancer among male and female adults in urban poor communities in Ibadan, Oyo State, Nigeria. A cross-sectional study was carried out in two urban poor community settings in Ibadan. Data were collected from 250 randomly selected consenting respondents in each of the two communities (N = 500). Data were collected with an electronic device using the electronic data capture tool (Open Data Kit) database. Descriptive statistics were summarized using frequencies and percentages for categorical variables, and mean and standard deviation were used for continuous variables. Knowledge was scored on a scale of 0 to 39 points (0 to 18, low knowledge; 19 to 23, fair knowledge; 24 to 39, high knowledge). Associations between variables were tested using χ2. Mean age of respondents was 35.36 years (± 12.24). The majority of respondents were female (70.6%), and more than half (52.6%) had completed secondary school education. The majority had never heard of the Papanicolaou test (93.6%) or cervical cancer screening (91.2%), and only 10% had ever heard of the human papillomavirus vaccine for the prevention of cervical cancer. The majority had low knowledge of cervical cancer (77.2%); knowledge included detection, symptoms, and risk factors for cervical cancer. Only 7.4% of females had ever heard of the Papanicolaou test, and few women (4%) had ever been screened for cervical cancer using the Papanicolaou test. Only one woman (0.2%) had been screened for cervical cancer using visual inspection with acetic acid, and four (0.8%) had ever taken human papillomavirus vaccine for protection against cervical cancer. There was a significant association between knowledge of cervical cancer and employment status of respondents (χ2 = 11.19; P < .05). Health promotion interventions and strategies for awareness creation about cervical cancer and screening practices should be used in alleviating low knowledge and screening practices in urban poor communities.","PeriodicalId":15862,"journal":{"name":"Journal of global oncology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1200/jgo.19.10000","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45965544","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 : 2019-10-07DOI: 10.1200/jgo.2019.5.suppl.40
H. Ryu
40 Background: A complete response to chemotherapy for most cancer patients is, and there are many complications caused by this toxic therapy. Therefore, we sought to determine chemotherapy responses in breast cancer at the proteome level. Methods: Candidate proteins were filtered out by the proteomic-based multiple machine-learning algorithms. Results: The MS analysis of FFPE set yielded 6,069 protein groups. The filtered dataset resulted in 539 proteins with differential abundances. We searched for biological process in the Gene Ontology (GO) enrichment analysis in each proteomic cluster. Several immune responses process, apoptotic process, DNA replication process and aminoacylation for protein translation process primarily were represented in group with complete remission. On the other hand, cell adhesion process, cytoskeleton organization process, vesicle organization process and Golgi organization process represented in breast cancer which showed poor responses to the therapy. The machine learning approaches demonstrated the highest AUC value, 0.978 (sensitivity 1.0 and specificity 0.714) with a combination of 11 proteins. Among them the finally selected tyrosine aminoacyl-tRNA synthetase (YARS) showed AUC (AUC = 0.749) in the subsequent steps of verification using immunohistochemistry in 123 patient cohorts. We identified the predictive relevance of YARS. YARS induced tumor necroptosis was greatly enhanced when it was combined synergistically with a combination of SMAC mimetics and a BCL2 inhibitor. Conclusions: This suggested that YARS expression could serve as a new therapeutic target for improving the clinical benefits of chemotherapy.
{"title":"Tyrosine aminoacyl-tRNA synthetase sensitizes breast cancer to chemotherapy through a necroptosis-mediated mechanism.","authors":"H. Ryu","doi":"10.1200/jgo.2019.5.suppl.40","DOIUrl":"https://doi.org/10.1200/jgo.2019.5.suppl.40","url":null,"abstract":"40 Background: A complete response to chemotherapy for most cancer patients is, and there are many complications caused by this toxic therapy. Therefore, we sought to determine chemotherapy responses in breast cancer at the proteome level. Methods: Candidate proteins were filtered out by the proteomic-based multiple machine-learning algorithms. Results: The MS analysis of FFPE set yielded 6,069 protein groups. The filtered dataset resulted in 539 proteins with differential abundances. We searched for biological process in the Gene Ontology (GO) enrichment analysis in each proteomic cluster. Several immune responses process, apoptotic process, DNA replication process and aminoacylation for protein translation process primarily were represented in group with complete remission. On the other hand, cell adhesion process, cytoskeleton organization process, vesicle organization process and Golgi organization process represented in breast cancer which showed poor responses to the therapy. The machine learning approaches demonstrated the highest AUC value, 0.978 (sensitivity 1.0 and specificity 0.714) with a combination of 11 proteins. Among them the finally selected tyrosine aminoacyl-tRNA synthetase (YARS) showed AUC (AUC = 0.749) in the subsequent steps of verification using immunohistochemistry in 123 patient cohorts. We identified the predictive relevance of YARS. YARS induced tumor necroptosis was greatly enhanced when it was combined synergistically with a combination of SMAC mimetics and a BCL2 inhibitor. Conclusions: This suggested that YARS expression could serve as a new therapeutic target for improving the clinical benefits of chemotherapy.","PeriodicalId":15862,"journal":{"name":"Journal of global oncology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45602249","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 : 2019-10-07DOI: 10.1200/jgo.2019.5.suppl.47
Anusha Chimmiri, Haitao Wang, E. Yeo, K. Low, A. Tan, Wai Yee Woo, E. Ong, T. Tan, W. S. Looi, W. Nei, J. Tuan, Michael L C Wang, J. S. Tan, L. Lee, K. Tay, R. Kanesvaran, L. Khor, J. Yeong, Chien Sheng Tan, M. Chua
47 Background: Large-scale genome-wide association studies have established germline polygenic risk loci that underpin the susceptibility to prostate cancer (PCa). However, most trials conducted are in men of European ancestry with data missing for Asian male PCa. Here, we report on an in-house multidimensional bioinformatics pipeline that integrates OMICS and non-OMICS approaches in identifying true germline risk-variants for PCa in Asian men. Methods: We utilized a prospective cohort study of Asian men who were newly diagnosed with PCa. Whole exome sequencing (Illumina Hiseq, CA) of blood (100X) was performed. The OMICS-based approach entailed a stepwise screen for hallmarks of cancer-specific pathways. A genome-proteome network was then developed to filter for known pathogenic variants; this was followed by comparison against a large artificial database of aggregated germline variants (N = 95,000) with reported linkage to PCa susceptibility. Finally, mutations were filtered through a non-OMICS pipeline that entailed data synchronization with population-level statistics and clinical outcomes (recurrence and survival). Results: Preliminary analyses were based on 277 PCa cases; of which 50 were M1 cases. Screening using a non-combined unbiased approach yielded 36,157 germline variants. This contrast against our OMICS-based approach, which reduced the variant calls to 6,144 significantly associated mutations. Next, by focusing on pathway-specific genes related to hormonal regulation and known cancer hotspot mutations, we could further tighten our variant calls to 3,562 hormone-related variants (rs9269958 on HLA-DRB1) and 2,125 variants in known cancer genes, notably (rs8176320 on BRCA1/2, rs2555691 on LILRA2, rs8036934 on TP53BP1). Conclusions: Here, we show that application of an OMICS approach that combines pathway-driven analyses and an artificial dataset, along with population-level statistics and clinical relevance resulted in more robust annotation of germline variants that were associated with PCa.
{"title":"A novel computational OMICS and non-OMICS approach for identifying true pathogenic risk variants for Asian prostate cancer.","authors":"Anusha Chimmiri, Haitao Wang, E. Yeo, K. Low, A. Tan, Wai Yee Woo, E. Ong, T. Tan, W. S. Looi, W. Nei, J. Tuan, Michael L C Wang, J. S. Tan, L. Lee, K. Tay, R. Kanesvaran, L. Khor, J. Yeong, Chien Sheng Tan, M. Chua","doi":"10.1200/jgo.2019.5.suppl.47","DOIUrl":"https://doi.org/10.1200/jgo.2019.5.suppl.47","url":null,"abstract":"47 Background: Large-scale genome-wide association studies have established germline polygenic risk loci that underpin the susceptibility to prostate cancer (PCa). However, most trials conducted are in men of European ancestry with data missing for Asian male PCa. Here, we report on an in-house multidimensional bioinformatics pipeline that integrates OMICS and non-OMICS approaches in identifying true germline risk-variants for PCa in Asian men. Methods: We utilized a prospective cohort study of Asian men who were newly diagnosed with PCa. Whole exome sequencing (Illumina Hiseq, CA) of blood (100X) was performed. The OMICS-based approach entailed a stepwise screen for hallmarks of cancer-specific pathways. A genome-proteome network was then developed to filter for known pathogenic variants; this was followed by comparison against a large artificial database of aggregated germline variants (N = 95,000) with reported linkage to PCa susceptibility. Finally, mutations were filtered through a non-OMICS pipeline that entailed data synchronization with population-level statistics and clinical outcomes (recurrence and survival). Results: Preliminary analyses were based on 277 PCa cases; of which 50 were M1 cases. Screening using a non-combined unbiased approach yielded 36,157 germline variants. This contrast against our OMICS-based approach, which reduced the variant calls to 6,144 significantly associated mutations. Next, by focusing on pathway-specific genes related to hormonal regulation and known cancer hotspot mutations, we could further tighten our variant calls to 3,562 hormone-related variants (rs9269958 on HLA-DRB1) and 2,125 variants in known cancer genes, notably (rs8176320 on BRCA1/2, rs2555691 on LILRA2, rs8036934 on TP53BP1). Conclusions: Here, we show that application of an OMICS approach that combines pathway-driven analyses and an artificial dataset, along with population-level statistics and clinical relevance resulted in more robust annotation of germline variants that were associated with PCa.","PeriodicalId":15862,"journal":{"name":"Journal of global oncology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45614986","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 : 2019-10-07DOI: 10.1200/jgo.2019.5.suppl.37
P. Schöffski, Britt Van Renterghem, J. Cornillie, Yanni Wang, Y. Gebreyohannes, Che-Jui Lee, J. Wellens, U. Vanleeuw, Madita Nysen, D. Hompes, M. Stas, F. Sinnaeve, H. Wafa, B. Topal, T. Verbelen, M. Debiec-Rychter, R. Sciot, A. Wozniak
37 Background: STS is a family of rare, heterogeneous tumors with > 70 subtypes. There is an urgent need for reliable preclinical models, especially for orphan subtypes of STS, given the limited treatment options. Methods: A panel of PDX models was established by s.c. implantation of fresh tumor specimens in athymic NMRI mice. Growing pieces of tumor were re-transplanted to next generations of mice. At each passage fragments were collected for histological/molecular characterization. A model was considered “established” after observing stable features for at least 2 passages. Ex-mouse tissue samples were stored, characterized by immunohistochemistry/flow cytometry and used for in vitro drug testing. Results: Between 2011-2019, 329 samples from 301 consenting patients were transplanted; 56 models are established, 16 additional models are in early passaging. Clinical information about donor and tumor (including sensitivity to standard and experimental agents) is available. The platform includes models of dedifferentiated lipo- (10 models), myxofibro- (8), leiomyo- (7), synovial (2), intimal (2), CIC-positive round cell (1), mesenchymal chondro- (1), extraskeletal osteo- (1), myxoid lipo- (1), myxoinflammatory fibroblastic (1), rhabdomyo- (2) and high-grade undifferentiated pleomorphic sarcoma (7), as well as GIST (8), MPNST (4) and epithelioid hemangioendothelioma (1). Models are well-characterized, with molecular information on copy number changes (low-coverage whole genome sequencing) and gene expression profile (RNA-Seq) available. We also constructed tissue microarrays from the xenografts which are used for target identification and model selection for preclinical studies. Xenografts are available for in vivo testing of novel agents, and results already served as a rationale for a number of prospective clinical trials. Conclusions: XenoSarc offers opportunities for studying the biology of a variety of sarcoma subtypes including ultra-rare entities and is a valuable tool for early drug screening in preparation of clinical STS trials. The platform is well maintained and continuously expanded, and available to collaborators from academia and industry.
{"title":"XenoSarc: A comprehensive platform of patient-derived xenograft (PDX) models of soft tissue sarcoma (STS) for early drug testing.","authors":"P. Schöffski, Britt Van Renterghem, J. Cornillie, Yanni Wang, Y. Gebreyohannes, Che-Jui Lee, J. Wellens, U. Vanleeuw, Madita Nysen, D. Hompes, M. Stas, F. Sinnaeve, H. Wafa, B. Topal, T. Verbelen, M. Debiec-Rychter, R. Sciot, A. Wozniak","doi":"10.1200/jgo.2019.5.suppl.37","DOIUrl":"https://doi.org/10.1200/jgo.2019.5.suppl.37","url":null,"abstract":"37 Background: STS is a family of rare, heterogeneous tumors with > 70 subtypes. There is an urgent need for reliable preclinical models, especially for orphan subtypes of STS, given the limited treatment options. Methods: A panel of PDX models was established by s.c. implantation of fresh tumor specimens in athymic NMRI mice. Growing pieces of tumor were re-transplanted to next generations of mice. At each passage fragments were collected for histological/molecular characterization. A model was considered “established” after observing stable features for at least 2 passages. Ex-mouse tissue samples were stored, characterized by immunohistochemistry/flow cytometry and used for in vitro drug testing. Results: Between 2011-2019, 329 samples from 301 consenting patients were transplanted; 56 models are established, 16 additional models are in early passaging. Clinical information about donor and tumor (including sensitivity to standard and experimental agents) is available. The platform includes models of dedifferentiated lipo- (10 models), myxofibro- (8), leiomyo- (7), synovial (2), intimal (2), CIC-positive round cell (1), mesenchymal chondro- (1), extraskeletal osteo- (1), myxoid lipo- (1), myxoinflammatory fibroblastic (1), rhabdomyo- (2) and high-grade undifferentiated pleomorphic sarcoma (7), as well as GIST (8), MPNST (4) and epithelioid hemangioendothelioma (1). Models are well-characterized, with molecular information on copy number changes (low-coverage whole genome sequencing) and gene expression profile (RNA-Seq) available. We also constructed tissue microarrays from the xenografts which are used for target identification and model selection for preclinical studies. Xenografts are available for in vivo testing of novel agents, and results already served as a rationale for a number of prospective clinical trials. Conclusions: XenoSarc offers opportunities for studying the biology of a variety of sarcoma subtypes including ultra-rare entities and is a valuable tool for early drug screening in preparation of clinical STS trials. The platform is well maintained and continuously expanded, and available to collaborators from academia and industry.","PeriodicalId":15862,"journal":{"name":"Journal of global oncology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49090281","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 : 2019-10-07DOI: 10.1200/jgo.2019.5.suppl.52
Haitao Wang, E. Yeo, J. Hwang, G. S. Tan, E. Ong, K. Low, Anusha Chimmiri, Wai Yee Woo, W. Nei, K. Lim, M. Tan, J. H. Loh, Constance Teo, H. Heah, G. Tay, J. Wee, N. Iyer, Ying Sun, J. Bei, M. Chua
52 Background: Radiotherapy (RT) is a primary modality in the treatment of NPC. However, 30% of patients present with disease recurrence following RT of this radiosensitive tumor. Here, we investigated the molecular and immune profiles associated with radioresistant (RR) NPC. Additionally, we investigated for aberrant molecular pathways in paired recurrences of patients to uncover new drivers underpinning radioresistance. Methods: We prospectively recruited a cohort of 100 NPC patients who completed definitive RT/chemoRT; including 30 cases who were recruited at recurrence. Whole exome sequencing (WES) at 200x was performed to identify low frequencies ( < 1%) of true somatic nucleotide variants (SNVs) and copy number alterations (CNAs). Transcriptomic profiles from RNAseq were interrogated using supervised and unsupervised statistical approaches to determine aberrant pathways that were significantly associated with RR. Results: Genomic instabilityas characterized by percentage genome alteration (PGA) was comparable in our cohort. Additionally, we did not observe any common or exclusive CNAs between RR- and nr-NPC cases. Based on a constellation of immune-related signatures, we observed an “immune-cold” profile that is associated with RR-NPC compared to nr-NPC controls, which is characterized by low expression of CD8+ T cell infiltration and interferon-γ response. Expectedly, pathways relating to angiogenesis, hypoxia and NOTCH signaling were upregulated in the RR-NPC cohort. Interestingly, we observed a reversal of the immune phenotype from “cold” to an enrichment of effector T cell infiltration in the paired recurrences. Conclusions: Here, we present a comprehensive mutational landscape of RR-NPC, which revealed the potential role of the immune environment in modulating RR. The longitudinal immune dysregulation of the tumor microenvironment between the de novo tumors and recurrences could be a driver or passenger event during the onset of recurrence.
{"title":"Immune dysregulation underpins radioresistance in nasopharyngeal carcinoma (NPC).","authors":"Haitao Wang, E. Yeo, J. Hwang, G. S. Tan, E. Ong, K. Low, Anusha Chimmiri, Wai Yee Woo, W. Nei, K. Lim, M. Tan, J. H. Loh, Constance Teo, H. Heah, G. Tay, J. Wee, N. Iyer, Ying Sun, J. Bei, M. Chua","doi":"10.1200/jgo.2019.5.suppl.52","DOIUrl":"https://doi.org/10.1200/jgo.2019.5.suppl.52","url":null,"abstract":"52 Background: Radiotherapy (RT) is a primary modality in the treatment of NPC. However, 30% of patients present with disease recurrence following RT of this radiosensitive tumor. Here, we investigated the molecular and immune profiles associated with radioresistant (RR) NPC. Additionally, we investigated for aberrant molecular pathways in paired recurrences of patients to uncover new drivers underpinning radioresistance. Methods: We prospectively recruited a cohort of 100 NPC patients who completed definitive RT/chemoRT; including 30 cases who were recruited at recurrence. Whole exome sequencing (WES) at 200x was performed to identify low frequencies ( < 1%) of true somatic nucleotide variants (SNVs) and copy number alterations (CNAs). Transcriptomic profiles from RNAseq were interrogated using supervised and unsupervised statistical approaches to determine aberrant pathways that were significantly associated with RR. Results: Genomic instabilityas characterized by percentage genome alteration (PGA) was comparable in our cohort. Additionally, we did not observe any common or exclusive CNAs between RR- and nr-NPC cases. Based on a constellation of immune-related signatures, we observed an “immune-cold” profile that is associated with RR-NPC compared to nr-NPC controls, which is characterized by low expression of CD8+ T cell infiltration and interferon-γ response. Expectedly, pathways relating to angiogenesis, hypoxia and NOTCH signaling were upregulated in the RR-NPC cohort. Interestingly, we observed a reversal of the immune phenotype from “cold” to an enrichment of effector T cell infiltration in the paired recurrences. Conclusions: Here, we present a comprehensive mutational landscape of RR-NPC, which revealed the potential role of the immune environment in modulating RR. The longitudinal immune dysregulation of the tumor microenvironment between the de novo tumors and recurrences could be a driver or passenger event during the onset of recurrence.","PeriodicalId":15862,"journal":{"name":"Journal of global oncology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42116609","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 : 2019-10-07DOI: 10.1200/jgo.2019.5.suppl.35
I. Peták, C. Hegedűs, D. Tihanyi, R. Dóczi, P. Filotás, Attila Mate, M. Bacskai, R. Schwáb, I. Vályi-Nagy
35 Background: Most tumours harbor multiple driver genetic alterations and many driver alterations are linked to multiple targeted therapies with various level of evidence. In addition, a specific treatment can be linked to multiple genetic alterations in the same tumor. Several public and private databases and software solutions are available to link driver alterations to treatments options, but in clinical practice of precision oncology we need a solution to select the right treatment for our patients based on the highest level of evidence also in case of complex molecular profiles. Methods: We have developed an AI oncology algorithm and rule-engine to prioritise treatment options for every cancer patient based on the individual molecular of their tumor. This IT solution can now prioritise 1200 compounds in clinical use or clinical development based on the computing of 24,000 evidence-based associations (“rules”) between drivers, targets and compounds. The software calculates a numeric score, the “aggregated evidence level” for each driver alterations and compounds. We have linked this decision support software to a dynamic patient case management system, which records responds to therapy to create learning system to provide dynamic decision support through several lines of therapies of each patient and to use real-life evidence to further improve the algorithm. Results: Our first results indicate that system allows individualised decision of diagnostic option between single gene tests to comprehensive 600 gene NGS panels and identification of actionable alterations in 83% of cancer cases. Conclusions: This system can be a first working solution to standardise clinical decisions precision oncology, which also helps the real-life evaluation of novel multigene molecular diagnostic tests and therapies to find their best indications and accelerate their reimbursement by insurance companies and national health funds.
{"title":"AI oncology algorithm and dynamic real-world learning health care system for precision oncology.","authors":"I. Peták, C. Hegedűs, D. Tihanyi, R. Dóczi, P. Filotás, Attila Mate, M. Bacskai, R. Schwáb, I. Vályi-Nagy","doi":"10.1200/jgo.2019.5.suppl.35","DOIUrl":"https://doi.org/10.1200/jgo.2019.5.suppl.35","url":null,"abstract":"35 Background: Most tumours harbor multiple driver genetic alterations and many driver alterations are linked to multiple targeted therapies with various level of evidence. In addition, a specific treatment can be linked to multiple genetic alterations in the same tumor. Several public and private databases and software solutions are available to link driver alterations to treatments options, but in clinical practice of precision oncology we need a solution to select the right treatment for our patients based on the highest level of evidence also in case of complex molecular profiles. Methods: We have developed an AI oncology algorithm and rule-engine to prioritise treatment options for every cancer patient based on the individual molecular of their tumor. This IT solution can now prioritise 1200 compounds in clinical use or clinical development based on the computing of 24,000 evidence-based associations (“rules”) between drivers, targets and compounds. The software calculates a numeric score, the “aggregated evidence level” for each driver alterations and compounds. We have linked this decision support software to a dynamic patient case management system, which records responds to therapy to create learning system to provide dynamic decision support through several lines of therapies of each patient and to use real-life evidence to further improve the algorithm. Results: Our first results indicate that system allows individualised decision of diagnostic option between single gene tests to comprehensive 600 gene NGS panels and identification of actionable alterations in 83% of cancer cases. Conclusions: This system can be a first working solution to standardise clinical decisions precision oncology, which also helps the real-life evaluation of novel multigene molecular diagnostic tests and therapies to find their best indications and accelerate their reimbursement by insurance companies and national health funds.","PeriodicalId":15862,"journal":{"name":"Journal of global oncology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47466908","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 : 2019-10-07DOI: 10.1200/jgo.2019.5.suppl.27
Xiaohua Liu
27 Background: The prevalence of lung cancer has been increased markedly in worldwide range with growing clinical significance, the quantitative and qualitative analysis on lung nodules has proven to be important for the early-detection of lung cancer as well as its treatment in clinical practice. However, lung lesion screening performed by radiologists can be very time-consuming and its accuracy varies depending on doctor’s individual experiences. In this study, we aim to build up a robust CAD system that automatically detects the lesion locations and quantitatively characterizes the detected lesions on CT images. Methods: Specifically, we employed the deep learning analysis for lesion detection in patients and performed image processing techniques to generate quantitative morphology features for assisting lesion diagnosis . The data collected includes 3956 lung CT series (slice thickness≤3mm) with multiple lung nodules from 15 Class-A hospitals in China , 1155 lung CT scan from Luna16 dataset as well as CT scans from Kaggle dataset (Data Science Bowl 2017). Lung nodule annotation was then performed by two experienced radiologists and further assessed by four senior associate chief physicians. The obtained CT images were randomly selected and split to construct training, validation and test dataset. After preprocessing, a pre-trained ResNet18 framework is transferred to develop a robust detection system to detect the possible lung lesion locations with corresponding probabilities. Results: The resulting detection system yields FROC of 0.4663, recall of 82.46%, precision of 36.06% for 5~30mm nodules. Each detected lesion was labeled by its bounding box and was then analyzed through image processing algorithm to generate diagnostic assisting features, including longest diameter, shortest diameter, volume, largest cross section area as well as its density type (calcify, solid, partial solid, and ground-glass opacity). Conclusions: The proposed CAD system offers a fast and convenient approach for assisting the diagnosis of lung nodule pathologies, and it is beneficial to relate our research to the current framework of lung cancer diagnosis.
{"title":"Deep learning analysis for automatic lung nodule detection.","authors":"Xiaohua Liu","doi":"10.1200/jgo.2019.5.suppl.27","DOIUrl":"https://doi.org/10.1200/jgo.2019.5.suppl.27","url":null,"abstract":"27 Background: The prevalence of lung cancer has been increased markedly in worldwide range with growing clinical significance, the quantitative and qualitative analysis on lung nodules has proven to be important for the early-detection of lung cancer as well as its treatment in clinical practice. However, lung lesion screening performed by radiologists can be very time-consuming and its accuracy varies depending on doctor’s individual experiences. In this study, we aim to build up a robust CAD system that automatically detects the lesion locations and quantitatively characterizes the detected lesions on CT images. Methods: Specifically, we employed the deep learning analysis for lesion detection in patients and performed image processing techniques to generate quantitative morphology features for assisting lesion diagnosis . The data collected includes 3956 lung CT series (slice thickness≤3mm) with multiple lung nodules from 15 Class-A hospitals in China , 1155 lung CT scan from Luna16 dataset as well as CT scans from Kaggle dataset (Data Science Bowl 2017). Lung nodule annotation was then performed by two experienced radiologists and further assessed by four senior associate chief physicians. The obtained CT images were randomly selected and split to construct training, validation and test dataset. After preprocessing, a pre-trained ResNet18 framework is transferred to develop a robust detection system to detect the possible lung lesion locations with corresponding probabilities. Results: The resulting detection system yields FROC of 0.4663, recall of 82.46%, precision of 36.06% for 5~30mm nodules. Each detected lesion was labeled by its bounding box and was then analyzed through image processing algorithm to generate diagnostic assisting features, including longest diameter, shortest diameter, volume, largest cross section area as well as its density type (calcify, solid, partial solid, and ground-glass opacity). Conclusions: The proposed CAD system offers a fast and convenient approach for assisting the diagnosis of lung nodule pathologies, and it is beneficial to relate our research to the current framework of lung cancer diagnosis.","PeriodicalId":15862,"journal":{"name":"Journal of global oncology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44416615","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 : 2019-10-07DOI: 10.1200/jgo.2019.5.suppl.25
L. Pino, Eduardo Large, J. Mejía, I. Triana
25 Background: Cancer healthcare systems are an example of inequity and waste in low and middle income countries. Access to high quality cancer pathways focused in early diagnosis, molecular biology, proper staging and evidence based treatments are scarce and the patient`s care experience is dramatic and difficult in a majority of cases. There are no integrative healthcare models based on new technologies that improve outcomes and make more comfortable and expeditious all the patient and physician´s journey in cancer. Methods: Our team developed and trained a talkbot called MAIA (Medical Artificial Intelligence Assistant) using an algorithmic translation of medical language focused in the state or art for non small cell lung cancer. Our clinical team developed decision trees in diagnosis, staging, medical and surgical treatment and molecular biology that were incorporated in a virtual platform and then integrated onto a narrow artificial intelligence bot brain using neural networks with the proposal of generate clinical support to the physician and create a standard text using the verbal information captured in the oncological consultation and integrated images (reports) through a image edition software and then create a unique medical record without using computers by the physician. MAIA also can create medical treatment choices in first line of treatment and create alerts and alarms through an own app (MAIA Hip). Results: Our proof of concept was released in video at this link https://drive.google.com/file/d/12YtiOkhfEmIsL2bFp9T3QyfHHWxBvvKU/view?ts=5ceec096 Due to our decision trees size we can´t upload them, but are available for presentation. Conclusions: A talkbot trained as a narrow artificial intelligence interface for an integrative cancer healthcare platform (HIP) is possible through the clinical and engineer integration of languages using a neural network method and other software tools. MAIA is for now a patient and physician experience improvement, but the real impact will be in the data standarization and acquisition for advanced analytics. The final scope of MAIA HIP will be a blockchain for cancer in low and middle income countries.
{"title":"MAIA (Medical Artificial Intelligence Assistant) as interface for a new cancer healthcare integrative platform.","authors":"L. Pino, Eduardo Large, J. Mejía, I. Triana","doi":"10.1200/jgo.2019.5.suppl.25","DOIUrl":"https://doi.org/10.1200/jgo.2019.5.suppl.25","url":null,"abstract":"25 Background: Cancer healthcare systems are an example of inequity and waste in low and middle income countries. Access to high quality cancer pathways focused in early diagnosis, molecular biology, proper staging and evidence based treatments are scarce and the patient`s care experience is dramatic and difficult in a majority of cases. There are no integrative healthcare models based on new technologies that improve outcomes and make more comfortable and expeditious all the patient and physician´s journey in cancer. Methods: Our team developed and trained a talkbot called MAIA (Medical Artificial Intelligence Assistant) using an algorithmic translation of medical language focused in the state or art for non small cell lung cancer. Our clinical team developed decision trees in diagnosis, staging, medical and surgical treatment and molecular biology that were incorporated in a virtual platform and then integrated onto a narrow artificial intelligence bot brain using neural networks with the proposal of generate clinical support to the physician and create a standard text using the verbal information captured in the oncological consultation and integrated images (reports) through a image edition software and then create a unique medical record without using computers by the physician. MAIA also can create medical treatment choices in first line of treatment and create alerts and alarms through an own app (MAIA Hip). Results: Our proof of concept was released in video at this link https://drive.google.com/file/d/12YtiOkhfEmIsL2bFp9T3QyfHHWxBvvKU/view?ts=5ceec096 Due to our decision trees size we can´t upload them, but are available for presentation. Conclusions: A talkbot trained as a narrow artificial intelligence interface for an integrative cancer healthcare platform (HIP) is possible through the clinical and engineer integration of languages using a neural network method and other software tools. MAIA is for now a patient and physician experience improvement, but the real impact will be in the data standarization and acquisition for advanced analytics. The final scope of MAIA HIP will be a blockchain for cancer in low and middle income countries.","PeriodicalId":15862,"journal":{"name":"Journal of global oncology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43750016","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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