bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract 1856: Adaptation to dopamine impairs the anti-cancer effect of ONC201 and ONC206
Authors: Zhang Yiqun , El-Deiry Wafik S. 
Published: June 2022

Abstract
ONC201 (originally discovered as TRAIL-Inducing Compound #10 or TIC10) and analogue ONC206 have been found to induce an integrated stress response with suggested primary targets and mechanisms involving agonist activity against mitochondrial ClpP and antagonism of dopamine receptors D2/3 (DRD2/3). We hypothesized that dopamine, the agonist of DRD2, may compete with ONC201 or ONC206 for DRD2/3 and impair the anti-cancer effect of ONC201 or ONC206. We therefore pre-treated cancer cells from different tissue origins including breast cancer, pancreatic cancer, colorectal cancer, lung cancer, GBM, and DIPG with dopamine for 12 hours, 24 hours or 48 hours, respectively, followed by treatment of ONC201 or ONC206 for 72hours. Cell viability was determined by the CellTiter Glo assay. Single treatment with dopamine did not increase cell viability. We observed that 48 hours of pre-treatment with dopamine at 50 μM, 100 μM or 200 μM impaired the cell viability suppression effect of ONC201 at 4 μM and 8 μM, while ONC206 at 0.5 μM, 1 μM and 2 μM led to similar protection for loss of cell viability in pancreatic cancer cells. The impairment of the anti-cancer effect by dopamine was also observed in colorectal cancer cells with 48 hours pre-treatment with dopamine at 50 μM, 100 μM, 200 μM or 400 μM and treatment with ONC201 at 4 μM and 8 μM, or ONC206 at 0.5 μM, 1 μM, or 2 μM. In addition, we treated pancreatic cancer and colorectal cancer cells with ONC212 after 48 hours pre-treatment with dopamine and observed a similar impairment of the anti-cancer effect, even though ONC212 is not a DRD2 antagonist. We pre-treated pancreatic cancer and colorectal cancer cells with specific DRD2 agonist, sumanirole, for 48 hours followed by treatment of ONC201, ONC206, or ONC212 for 72 hours, and no impairment was seen. We pre-treated multiple cancer cell lines with 2 μM or 10 μM dopamine for one week followed by ONC201, ONC206, or ONC212 treatment and performed colony assays. Pre-treatment with dopamine at 10 μM impaired the anti-cancer effect of ONC201 or ONC206 in pancreatic cancer. Pre-treatment with dopamine at 2 μM or 10 μM impaired the anti-tumor effect of ONC201 in GBM cells. By contrast to the cell viability result, no impairment of ONC212 effect from dopamine was seen in pancreatic cancer cells. Western blotting was conducted to investigate whether dopamine pre-treatment impacted signaling pathways reported to be affected by ONC201. Dopamine pre-treatment did not impact changes induced by ONC201 in ATF4, CHOP, DR5, ClpX, clpP, pERK, pAkt and pS6. We are pursuing whether the protective effect of dopamine is due to the agonism of DRD2 or alternative pro-survival mechanisms.
Citation Format: Yiqun Zhang, Wafik S. El-Deiry. Adaptation to dopamine impairs the anti-cancer effect of ONC201 and ONC206 [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1856.
Abstract 1861: Trophoblast cell surface antigen 2 (TROP2) expression in human tumors: A tissue microarray study on 18,563 tumors
Authors: Dum David , Hube-Magg Claudia , Simon Ronald , Bady Elena , Mandelkow Tim , Blessin Niclas C. , Lennartz Maximilian , Sauter Guido , Minner Sarah , Luebke Andreas M. 
Published: June 2022

Abstract
Trophoblast cell surface antigen 2 (TROP2) is the target of sacituzumab govitecan (SG), an antibody-drug conjugate that was recently approved for previously treated triple negative breast cancer and urothelial carcinomas. To comprehensively determine TROP2 expression in normal and neoplastic tissues, a tissue microarray containing 18,563 samples from 150 different tumor types and subtypes as well as 608 samples of 76 different normal tissue types was analyzed by immunohistochemistry. TROP2 positivity was found in most normal epithelial cell types and in 109 of 150 tumor categories, including 92 of 95 epithelial tumor categories. Particularly high rates of TROP2 positivity and highest expression levels were seen in squamous cell carcinomas of various origins and various categories of urothelial, breast, prostate, pancreatic, and ovarian cancers (&gt;95% positive). High TROP2 expression was linked to advanced stage (p=0.0069) and nodal metastasis (p&lt;0.0001) in colorectal cancer as well as to nodal metastasis in gastric adenocarcinoma (p=0.0246) and papillary thyroid cancer (p=0.0013). Low TROP2 expression was linked to advanced stage in urothelial carcinoma (p&lt;0.0001), high pT (p=0.0024) and high grade (p&lt;0.0001) in breast cancer, as well as with high grade (p=0.0005) and pT stage (p=0.0009) in papillary renal cell carcinomas. Associations between TROP2 expression and clinicopathological features were not found in clear cell renal cell carcinomas, high grade serous ovarian carcinomas, pancreatic adenocarcinomas and in endometroid endometrium carcinomas. In summary, TROP2 is abundantly expressed in a broad range of epithelial neoplasms. Both TROP2 upregulation and downregulation can be associated with cancer progression in a tumor type dependent manner. As anti-TROP2 cancer drugs have demonstrated efficiency and induce tolerable side effects they may be applicable to a broad range of tumor entities in the future.
Citation Format: David Dum, Claudia Hube-Magg, Ronald Simon, Elena Bady, Tim Mandelkow, Niclas C. Blessin, Maximilian Lennartz, Guido Sauter, Sarah Minner, Andreas M. Luebke. Trophoblast cell surface antigen 2 (TROP2) expression in human tumors: A tissue microarray study on 18,563 tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1861.
Abstract 1898: SNAF: Accurate and compatible computational framework for identifying splicing derived neoantigens
Authors: Li Guangyuan , Salomonis Nathan 
Published: June 2022

Abstract
Patient outcome with immune checkpoint blockade (ICB) therapy relies largely on the presence and detection of tumor neoantigens. Neoantigens can be produced from a broad spectrum of genetic (SNV, fusion) and non-genetic (splicing, alternative promoter) sources. While alternative splicing is a broad source of neoantigens, concerns regarding their tumor specificity have hindered their evaluation as therapeutic targets relative to SNVs. To systematically define tumor-specific and likely immunogenic neoantigens, we developed SNAF (Splicing Neo Antigen Finder), an easy-to-use and open-source computational workflow. SNAF automates the prediction of high-confidence patient-specific and shared splicing derived neoantigens from RNA-Seq data. This workflow leveraged deep learning and hierarchical Bayesian models to prioritize potential splicing-neoantigen candidates by their tumor specificity (inferred off-target effects) and immunogenicity (T cell reactivity). SNAF is compatible with all major splicing identification algorithms and can be easily integrated into any existing splicing analysis workflows. SNAF predicted splicing neoantigens were validated with an improved accuracy over alternative approaches in ovarian cancer HLA targeted MS profiles. Applied to 825 breast cancer patients in TCGA, we find that splicing neoantigens are frequently shared among patients and overall burden is highly associated with overall survival and specific clinical subtypes. These data suggest alternative genomic monitoring of splice-derived neoantigen burden can be used to prioritize selection of patients for ICB.
Citation Format: Guangyuan Li, Nathan Salomonis. SNAF: Accurate and compatible computational framework for identifying splicing derived neoantigens [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1898.
Abstract 191: Establishment and characterization of patient-derived cancer cell models for pharmaceutical drug screening
Authors: Griffiths Gareth J. , Dodd Abbie , Nee Olivia , Thuringer Tilly , Singh Subir , Howard-Jones Rachel , Fan Yuen Ngan , Bezman Albert , Bell Rachel , Sloma Ido , Noakes Chris , James Dominic I. 
Published: June 2022

Abstract
Patient-derived cell (PDC) models are a tool that bridge the gap between conventional cancer lines that do not faithfully replicate clinical responses and costly and time-consuming patient derived xenograft (PDX) models. Imagen Therapeutics is establishing the world’s largest biobank of PDC models for both patient diagnostic (PredictRx) and biopharma drug development (PredictTx) studies that show a high degree of correlation between PDC and patient drug response. Through our research, we have established unique culture conditions that specifically select for cancer stem cells, and these have been characterized using both 2-D and 3-D short-term high content image (HCI)-based analysis. To better understand the genomic and phenotypic profiles of these models we analyzed Next Generation Sequencing data and phenotypic cell killing using a panel of FDA-approved agents. Our results showed that mRNA expression of several known cancer stem cell markers were upregulated in our PDCs in addition to multidrug resistance (MDR) genes. Drug resistance in the phenotypic assay was then correlated with stemness and MDR expression profiles. Finally, sequential PDCs (models developed from same patient at different stages in disease progression), in addition to PDCs developed from the same patient at multiple lesion sites, were compared in terms of genomic sequencing and drug responses. Together our data supports the premise that PDCs recapitulate the cancer stem cell population and are a useful platform with which to interrogate drug responses in a variety of different cancer types.
Citation Format: Gareth J. Griffiths, Abbie Dodd, Olivia Nee, Tilly Thuringer, Subir Singh, Rachel Howard-Jones, Yuen Ngan Fan, Albert Bezman, Rachel Bell, Ido Sloma, Chris Noakes, Dominic I. James. Establishment and characterization of patient-derived cancer cell models for pharmaceutical drug screening [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 191.
Abstract 1925: Ovarian tumor diagnosis using deep convolutional neural networks and denoising convolutional autoencoder
Authors: Choi Youn Jin , Jung Yuyeon 
Published: June 2022

Abstract
Objective: Discrimination of ovarian tumors is a necessary procedure for proper treatment. In this study, we developed a convolutional neural network model with a convolutional autoencoder (CNN-CAE) to classify ovarian tumors.
Methods: A total of 1,613 ultrasound images of ovarian tumors with the known pathologic diagnosis were pre-processed and augmented for deep learning analysis. We designed the CNN-CAE model that removes the unnecessary information on ultrasound images (e.g. calipers and annotations) and classifies ovarian tumors into five classes. We assessed the CNN-CAE model performance using a validation set of 327 images with the following metrics: accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC). Gradient-weighted class activation mapping (Grad-CAM) was applied to visualize and verify the CNN-CAE model results qualitatively.
Results: In classifying normal versus ovarian tumors, the CNN-CAE model showed 96.0% accuracy, 96.5% sensitivity, and 0.9837 AUC with DenseNet161 CNN architecture. In determining malignancy of ovarian tumors, the CNN-CAE model showed 86.85% accuracy, 75.68% sensitivity, and 0.9225 AUC with DenseNet121 CNN architecture. The Grad-CAM results show that the CNN-CAE model finds valid texture and morphology features from the ultrasound images and classifies ovarian tumors from these features.
Conclusion: CNN-CAE is a feasible diagnostic tool that is capable of robustly classifying ovarian tumors by eliminating marks on ultrasound images. CNN-CAE demonstrates a significant application value in clinical conditions.
Citation Format: Youn Jin Choi, Yuyeon Jung. Ovarian tumor diagnosis using deep convolutional neural networks and denoising convolutional autoencoder [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1925.
Abstract 1931: Multi-cancer classification: An analysis of neural network complexity
Authors: Webber James W. , Elias Kevin M. 
Published: June 2022

Abstract
Background: Often, in analysis of expression data, binary classification problems are considered, with the aim to separate a control group from a single cancer group (e.g., ovarian cancer). Such models lack any cancer specific information, as they are only trained on one cancer type. It is more useful and efficient to train a model on multiple cancer types, and controls, simultaneously, so that a physician can be directed to the correct area of the body for further testing.
Results: We introduce novel, data-driven neural network models to address the multi-cancer classification problem across a number of data types commonly applied in cancer prediction, including circulating miRNA expression (blood draw), protein (tissue sample), and mRNA (tissue sample). In particular, we present an analysis of neural network depth and complexity and investigate how this relates to classification performance. Comparisons of our models are also given to the state-of-the-art neural network models from the literature. The proposed models yield high accuracy (mean AUC greater than 0.95) across all data types considered and are shown to offer greater performance when compared to the models from the literature.
Conclusion: We have introduced a simple neural network framework which can be applied to multiple data types and which yields the best performance when compared to similar models from the literature. Upon analysis of the neural network complexity, we discover that shallow, feed-forward neural net architectures offer greater performance when compared to more complex deep feed-forward, Convolutional Neural Network (CNN), and Graph CNN (GCNN) architectures considered in the literature. The depth analysis indicates that shallow neural nets (e.g., 1 or 2 layers) are more favorable for this problem, when compared to deep architectures (e.g., more than 3 layers). The results show that multiple cancers and controls can be classified simultaneously, and accurately using the proposed models, across a range of expression technologies in cancer prediction.
Citation Format: James W. Webber, Kevin M. Elias. Multi-cancer classification: An analysis of neural network complexity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1931.
Abstract 1944: Prognostic biomarkers for gallbladder cancer: A machine learning approach
Authors: Jackson Linsey , Allotey Loretta , Kenneth Valles , Oliver Gavin , Nair Asha , O'Brien Daniel , Graham Rondell , Borad Mitesh , Athreya Arjun , Roberts Lewis 
Published: June 2022

Abstract
Gallbladder cancer (GBC) is one of the deadliest cancers, with a 5-year-survival-rate of less than 5 percent for late-stage disease. The response rate to chemotherapy among GBC patients is generally poor. Recent research has attempted to identify diagnostic, prognostic, and predictive biomarkers, however, currently, no biomarkers can accurately diagnose GBC and predict patients’ prognosis. Integrative analysis of molecular and clinical characterization has not been fully established, and minimal improvement has been made to the survival of these patients, in part due to the heterogeneity of GBC. Machine learning techniques have been proven to empower analysis of big data in oncology, allowing for improvement in the generation of biomarkers to predict patient outcomes. Using machine learning, we can utilize high-throughput RNA sequencing with clinicopathologic data to develop a predictive tool for GBC prognosis. Current predictive models for GBC outcomes often utilize clinical data only, with the highest C-statistic reported being 0.71. C-statistic values over 0.7 generally indicate good models, however 0.8 is the threshold for strong predictive models. We aim to build a superior algorithm to predict overall survival in GBC patients with advanced disease, using machine learning approaches to prioritize biomarkers for GBC prognosis. We have identified over 80 fresh frozen GBC tissue samples from Mayo Clinic Rochester, Dongsan Medical Center in Daegu, Korea, University of the Witwatersrand, in Johannesburg, South Africa, Lithuanian University of Health Science in Vilnius, Lithuania, and University of Calgary in Calgary, Canada, from patients enrolled between 2012 and 2021. We will perform next-generation RNA sequencing on these tissue samples. The patients’ clinical, pathologic and survival data will be abstracted from the medical record uniformly across sites. Feature engineering and dimensionality reduction will be performed. Then random forests, support vector machines, and gradient boosting machines will be applied to train the data. Variable importance will prioritize multi-omic markers. Standard 5-fold cross validation will be used to assess performance of each ML algorithm. If overall survival can be better predicted with the addition patients’ transcriptional sequencing data compared to using clinical profiles alone, we can gain a greater understanding of key biomarkers driving the tumor phenotype.
Citation Format: Linsey Jackson, Loretta Allotey, Valles Kenneth, Gavin Oliver, Asha Nair, Daniel O'Brien, Rondell Graham, Mitesh Borad, Arjun Athreya, Lewis Roberts. Prognostic biomarkers for gallbladder cancer: A machine learning approach [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1944.
Abstract 1956: Perioperative opioid analgesia affects the circulating tumor cells levels in colorectal cancer patients
Authors: Stejskal Pavel , Srovnal Josef , Berta Emil , Rehulkova Alona , Vecera Lubomir , Haiduk Filip , Michalek Pavel , Hajduch Marian 
Published: June 2022

Abstract
Purpose Colorectal cancer (CRC) is one of the most common cancers worldwide and metastases-related death is the main cause of its high mortality. Previous studies showed the poorer survival in colorectal cancer patients treated perioperatively with morphine in comparison to piritramide. Circulating tumor cells (CTCs) are considered as precursors of distant metastatic spread and can act as an independent prognostic and predictive biomarker. The hypothesis that different pain-killers used in perioperative period can affect the CTCs levels was tested.
Patients and methods In total, 100 CRC stage I-III patients undergoing radical surgery were enrolled in this prospective multicentric randomized pilot study (NCT03700411). Three peripheral blood samples were collected into Cell-Free DNA BCT® (Streck, Inc.) preoperatively, one day and one month after surgery respectively. Patients were randomized into three arms using different perioperative analgesia - morphine, piritramide and epidural analgesia. The CTCs were identified using the CytoTrack CT11࣪ (2/C, Denmark), a semi-automated immunofluorescence microscopy detecting the pan-cytokeratin and EpCAM signals. The method recovery rate was analyzed using SW-480 cancer cell line by flow cytometry.
Results The CTCs were found most frequent in the samples obtained at first day after surgery (44,9%) followed by the samples collected before surgery (36%). The lowest levels were observed in control samples one month after surgery (31%). The significant increase of CTCs levels was found in majority of patients treated with morphine (16,3%) compared to piritramide and epidural analgesia. However, the significant decrease of CTCs levels was observed in samples drawn one month after surgery in both, morphine and piritramide arm. There were no significant changes in CTCs levels of patients with epidural analgesia. The CTCs recovery rate of the method was 72%.
Conclusion The significant increase of CTCs levels was found in postoperative blood samples of CRC patients treated with morphine compared to the piritamide and epidural analgesia. The enrollment into the study is ongoing.
Acknowledgements This study was supported by Ministry of Health of the Czech Republic (NV18-03-00470), Ministry of Education, Youth and Sport of the Czech Republic (LM2018132), Palacky University Olomouc (LF 2021_019) and European Regional Development Fund (ENOCH CZ.02.1.01/0.0/0.0/16_019/0000868, ACGT CZ.02.1.01/0.0/0.0/16_026/0008448).
Citation Format: Pavel Stejskal, Josef Srovnal, Emil Berta, Alona Rehulkova, Lubomir Vecera, Filip Haiduk, Pavel Michalek, Marian Hajduch. Perioperative opioid analgesia affects the circulating tumor cells levels in colorectal cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1956.