tbuckley/cabot-search huggingface dataset:
Abstract 2948: Anti-tumor effect of C. orbiculata is modulated by alternative splicing
Authors: Tshepiso Makhafola, David Bates, Zodwa Dlamini
Journal: Cancer Research (2020)

Abstract Recent research has revealed that alternative splice products can be linked to various genetic diseases and also play a role in carcinogenesis. It therefore appears that targeting splicing in cancer could offer novel effective cancer treatments. The aim of this study was to investigate the effects of a South African medicinal plant C. orbiculata on alternatively spliced genes in cancer cells. The effects of the crude extract on cell proliferation of colon (HCT116), esophageal (OE33 and KYSE70) and cervical (CASKI and C33A) cancer cell lines were evaluated using WST-1 assay. We also investigated the effects of this plant on splicing of hnRNPA2B1 and BCLx using RT-PCR and apoptosis induction via caspase-3-cleavage. Our data revealed that the crude C. orbiculata dose dependently decreased viability and also modulated splicing of hnRNPA2B1 and BCLx resulting in a decrease in hnRNPB1 splice variant and an increase in BCLxs splice variant in all 5 cancer cell lines. Additionally, the extract resulted in an increase in caspase-3-cleavage, an indication of apoptosis induction. We used siRNA knockdown of hnRNPB1 in HCT116 specifically to assess its functional role in cell proliferation and apoptosis. These cells had decreased viability and increased caspase-3-cleavage. We therefore concluded that in part, modulating hnRNPA2B1 splicing could be responsible for regulating the balance between pro/anti-apoptotic splice variants e.g. BCLxl/s resulting in apoptosis induction in colon cancer cells treated with C. orbiculata. Citation Format: Tshepiso Makhafola, David Bates, Zodwa Dlamini. Anti-tumor effect of C. orbiculata is modulated by alternative splicing [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2948.
Abstract 2189: Generating Merkel cell polyomavirus-specific T cells from healthy donors for adoptive immunotherapy
Authors: Sarah Davies, John Barrett, Pawel J. Muranski, Isaac Brownell
Journal: Cancer Research (2020)

Abstract Merkel Cell Carcinoma (MCC) is an aggressive neuroendocrine skin cancer that is highly immunogenic. Approximately 80% of MCC tumors are virus positive (VP-MCC) and express Merkel cell polyomavirus (MCPyV) T antigens that drive oncogenesis. As VP-MCC have a low mutation burden with few predicted neoantigens, viral oncogenes are thought to be primary targets for anti-cancer immunity. Immune checkpoint inhibitors improve MCC survival, yet not all patients have durable responses. VP-MCC patients who fail checkpoint inhibition or who have immune dysfunction may benefit from adoptive cellular therapy using virus-specific T cells from HLA-matched donors. As the risk of graft versus host disease is low with virus-specific T cells, this approach is being investigated for other viral diseases. We set out to generate MCPyV-specific T cells from healthy donors for potential adoptive immunotherapy. Naïve and memory T cells from donors were magnetically isolated prior to stimulation with autologous monocyte-derived dendritic cells pulsed with 15-mer overlapping peptide libraries of MCPyV T antigens. T cells were serially re-stimulated with peptide-pulsed irradiated, autologous PHA-blasts up to five times. Cultures were maintained in either standard growth cytokines (IL-2, IL-7, and IL-15) or a pro-inflammatory cytokine cocktail. T cell phenotype and reactivity were evaluated by flow cytometry. We found that standard cytokine conditions were unable to promote growth of MCPyV-specific mature T cells. Altering growth conditions to include a cocktail of pro-inflammatory cytokines promoted expansion of polyfunctional Th1 CD4+ T cells specific for MCPyV T antigen peptides. These cells produced TNFα and upregulated the activation markers CD154 and CD137 upon cognate antigen exposure. Cells were generated without bias for particular HLAs. These results suggest that peptide-based expansions may be a suitable platform to generate allogeneic adoptive T cell immunotherapies for patients with VP-MCC. Citation Format: Sarah Davies, John Barrett, Pawel J. Muranski, Isaac Brownell. Generating Merkel cell polyomavirus-specific T cells from healthy donors for adoptive immunotherapy [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2189.
Abstract 2097: AI powered platform to identify primary prostate cancer patients with high risk of recurrence
Authors: Wei Huang, Parag Jain, Raman Randhawa, Bijay Jaiswal, Samuel Hubbard, Hirak Basu, Rajat Roy
Journal: Cancer Research (2020)

Abstract Background: To-date, there is no approved adjuvant therapy for primary prostate cancer (PCa). Long time of recurrence and a lack of a strong biomarker predicting recurrence after the first line of therapy is prohibitory to short clinical trials within a reasonable time. Artificial intelligence (AI) offers a unique opportunity for extracting critical morphological features from PCa tissues as prognostic marker that have evaded human eyes. We have built a AI-based platform to analyze H&amp;E stained histological images of PCa. Our platform can accurately detect, grade and quantify PCa in patient tissue images. Here, we show how this platform can identify morphometric features that indicate biochemical recurrence within 3 years after radical prostatectomy (RP). Methods: Our dataset comprised of a cohort of 169 PCa patients who underwent RP. Whole slide H&amp;E images (WSI) of their tissue sections were scanned at 40x magnification. corresponding pathologic parameters such as Gleason grade, tumor volume, TNM staging, margin status and post-treatment follow-up data were also obtained. Among the 169 patients, 89 patients recurred within 3 years of RP. Our platform technology involved two different deep CNN (Convolutional Neural Network) architectures. The platform first divided each WSI into multiple tiles. Each tile was analyzed using a CNN that graded the tile and generated a high dimensional vector to provide a mathematical equivalent of the morphology. The combination of high dimensional vectors across the WSI was then fed into a second CNN that generated a morphological score. The clinical parameters were combined with the morphologic score using a logistic classifier to predict outcome. Results: Thus far, we have obtained 84% AUC (Area under the Curve) for the ROC (Receiver operating characteristics) curve, using 10-fold cross-validation. In contrast, the most predictive clinical covariate was the T stage with 71% AUC. Conclusion: Our AI based platform can identify high risk PCa patients early for tumor progression post RP and may be selected for more cost effective and much shorter Clinical Trials necessary to develop adjuvant therapy. Citation Format: Wei Huang, Parag Jain, Raman Randhawa, Bijay Jaiswal, Samuel Hubbard, Hirak Basu, Rajat Roy. AI powered platform to identify primary prostate cancer patients with high risk of recurrence [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2097.
Abstract LB-249: PiRADL: An advanced deep learning tool for piRNA detection and its application in cancer biomarker identification
Authors: Shaoqiu Chen, Youping Deng
Journal: Cancer Research (2020)

Abstract PIWI-interacting RNAs (piRNAs) are a large class of non-coding RNAs expressed in animal cells. Although many programs involved in identifying piRNAs from small RNA sequencing datasets, the boundaries are still hard to overcome. Here we developed a new tool called piRADL, by using deep learning to identify piRNA sequences. We transform the sequences into the 3D Numpy arrays(16X32X256), then the conventional neural network classifier was applied to train on the datasets from four different species (rat, human Caenorhabditis elegans, and Drosophila melanogaster). In the validation dataset, the piRADL shows better performance in comparison with other programs in accuracy, precision, sensitivity, and specificity. It is user-friendly, the user only needs to submit the sequences to the software. Based on this tool, we have found some interesting PiRNA prognosis biomarkers to predict colorectal, lung and breast cancer patient survival. Citation Format: Shaoqiu Chen, Youping Deng. PiRADL: An advanced deep learning tool for piRNA detection and its application in cancer biomarker identification [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr LB-249.
Abstract 2481: Concordance of PAM50 molecular subtyping between oligonucleotide microarray and nanoString nCounter assay for Taiwanese breast cancer
Authors: Chi-Cheng Huang, Chao-Chiang Tu, Mong-Hsun Tsai, Liang-chuan Lai, Ching-Shui Huang
Journal: Cancer Research (2020)

Abstract Concordance of PAM50 molecular subtyping between oligonucleotide microarray and nanoString nCounter assay for Taiwanese breast cancer Introduction Breast cancer is molecularly heterogeneous and at least five molecular subtypes have been defined according to intrinsic genes transcription. Currently microarrays, RT-PCR, and next generation RNA sequencing have been adopted for subtyping in addition to the original nanoString nCounter assay. This study aimed to evaluate the consistency in subtyping Taiwanese breast cancers. Methods A total of 109 Taiwanese breast cancers (24 with adjacent normal breast) were assayed with Affymetrix Human Genome U133 plus 2.0 arrays and 96 were assayed with the customized 145-gene nanoString Codeset BCeCSig, of which the academic PAM50 was investigated. Of note, 64 patients were assayed for both platforms. Subtyping with the nearest centroid (single sample prediction) was called for each patient; Spearman's rank correlation coefficients were used, and samples were unclassified if correlation coefficients to all 5 centroids were less than 0.1. Results Table 1 showed the distributions of molecular subtypes with clinical IHC results. For 24 matched normal breast tissues, 21 were classified as normal breast-like. For 64 breast cancers (one unclassified) tested for both platforms, 41 (65%) were classified with identical subtype, resulting in a fair Kappa statistic of 0.55. Taking nCounter as gold standard, classification accuracy was 50% (3/6), 72% (13/18), 53% (10/19), 87% (13/15), and 40% (2/5) for basal-like, HER2-enriched, luminal-A, luminal-B, and normal breast-like subtype using Affymetrix microarray. ER-/HER2- phenotype and basal-like subtype experienced worse relapse free survival at 8-year follow-up (55.6% and 63.6% censored). Table 1.Molecular subtypes and IHC phenotypes (two unclassified and one missing IHC result).Subtype IHCBasal-likeHER2-enrichedLuminal-ALuminal-BNormal breast-likeHR+/HER2+276174HR+/HER2-6442429HR-/HER2+625209HR-/HER2-144003 Conclusions: Using public domain microarrays as a handy shortcut for breast cancer molecular subtyping is luring in scientific communities. Our experiments showed that fundamental discrepancy existed between distinct approaches, and more cation should be taken without too much extrapolation. On the other hand, adjacent normal breast tissue did display normal breast-like character, and this subtype should be viewed as a continuum of surrounding stroma. Citation Format: Chi-Cheng Huang, Chao-Chiang Tu, Mong-Hsun Tsai, Liang-chuan Lai, Ching-Shui Huang. Concordance of PAM50 molecular subtyping between oligonucleotide microarray and nanoString nCounter assay for Taiwanese breast cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2481.
A nomogram for predicting mortality in patients with COVID-19 and solid tumors: a multicenter retrospective cohort study
Authors: Chao Liu, Li Li, Kehan Song, Zhi-Ying Zhan, Yi Yao, Hongyun Gong, Yuan Chen, Qun Wang, Xiaorong Dong, Zhibin Xie, Chun-Quan Ou, Qinyong Hu, Qibin Song
Journal: Journal for ImmunoTherapy of Cancer (2020)

Background Individualized prediction of mortality risk can inform the treatment strategy for patients with COVID-19 and solid tumors and potentially improve patient outcomes. We aimed to develop a nomogram for predicting in-hospital mortality of patients with COVID-19 with solid tumors. Methods We enrolled patients with COVID-19 with solid tumors admitted to 32 hospitals in China between December 17, 2020, and March 18, 2020. A multivariate logistic regression model was constructed via stepwise regression analysis, and a nomogram was subsequently developed based on the fitted multivariate logistic regression model. Discrimination and calibration of the nomogram were evaluated by estimating the area under the receiver operator characteristic curve (AUC) for the model and by bootstrap resampling, a Hosmer-Lemeshow test, and visual inspection of the calibration curve. Results There were 216 patients with COVID-19 with solid tumors included in the present study, of whom 37 (17%) died and the other 179 all recovered from COVID-19 and were discharged. The median age of the enrolled patients was 63.0 years and 113 (52.3%) were men. Multivariate logistic regression revealed that increasing age (OR=1.08, 95% CI 1.00 to 1.16), receipt of antitumor treatment within 3 months before COVID-19 (OR=28.65, 95% CI 3.54 to 231.97), peripheral white blood cell (WBC) count ≥6.93 ×10 9 /L (OR=14.52, 95% CI 2.45 to 86.14), derived neutrophil-to-lymphocyte ratio (dNLR; neutrophil count/(WBC count minus neutrophil count)) ≥4.19 (OR=18.99, 95% CI 3.58 to 100.65), and dyspnea on admission (OR=20.38, 95% CI 3.55 to 117.02) were associated with elevated mortality risk. The performance of the established nomogram was satisfactory, with an AUC of 0.953 (95% CI 0.908 to 0.997) for the model, non-significant findings on the Hosmer-Lemeshow test, and rough agreement between predicted and observed probabilities as suggested in calibration curves. The sensitivity and specificity of the model were 86.4% and 92.5%. Conclusion Increasing age, receipt of antitumor treatment within 3 months before COVID-19 diagnosis, elevated WBC count and dNLR, and having dyspnea on admission were independent risk factors for mortality among patients with COVID-19 and solid tumors. The nomogram based on these factors accurately predicted mortality risk for individual patients.
Abstract 4108: The use of conditionally reprogrammed cells for high throughput screening for novel drug combinations to treat drug resistant triple negative breast cancers
Authors: Adriana Aguilar-Mahecha, Catherine Chabot, Nancy Santos-Martinez, Cedric Darini, Midhet Hajira, Tim Kong, Genevieve Morin, Sidong Huang, Morag Park, Mark Basik
Journal: Cancer Research (2020)

Abstract Triple negative breast cancer (TNBC) is the most aggressive form of breast cancer, with prognosis tightly dependent on its responsiveness to cytotoxic chemotherapy. Indeed, overcoming chemoresistance is a major unmet need in this disease. Patient derived xenografts (PDXs) are now widely used in research since they faithfully represent the patient's tumor. However, testing novel drug combinations is a challenge in these models since it is time consuming and quite expensive to test each novel combination. The development of conditionally reprogrammed cells (CRCs) offers the opportunity to perform high throughput screens on clinically relevant models in a more timely and less expensive manner. To this end, we developed a 7 CRCs from PDXs of drug-resistant TNBC tumors. We characterized these CRCs at the molecular level and validated drug resistance to confirm they matched treatment response in both clinical and PDX tumors from which they were derived. These CRCs underwent high-throughput screens of both compound and shRNA libraries of genes targeted by FDA-approved drugs to identify novel drug combinations as well as genetic vulnerabilities that could re-sensitize these cells to carboplatin or paclitaxel chemotherapy. We found that drugs such as mitomycin C and oxfendazole were putative chemo-sensitizers while genes such as ATR and CDK2 were identified as novel genetic vulnerabilities. Treatment of these drug-resistant cells with combinations of carboplatin with mitomycin C or with an ATR inhibitor resulted in synergistic growth inhibition in proliferation and colony formation assays. We are further validating these combinations in organoid models derived from PDXs. These results have the potential to lead to novel therapeutic strategies against chemoresistant TNBCs. Citation Format: Adriana Aguilar-Mahecha, Catherine Chabot, Nancy Santos-Martinez, Cedric Darini, Midhet Hajira, Tim Kong, Genevieve Morin, Sidong Huang, Morag Park, Mark Basik. The use of conditionally reprogrammed cells for high throughput screening for novel drug combinations to treat drug resistant triple negative breast cancers [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4108.
Abstract CT152: First in human study with EOS100850, a novel potent A2A antagonist, shows excellent tolerance and clinical benefit in immune resistant advanced cancers
Authors: Laurence Buisseret, Sylvie Rottey, Johann de Bono, Manon Mossakowski, Brant Delafontaine, Thubeena Manickavasagar, Nuria Kotecki, Chiara Martinoli, Manfred Schneider, Olivier De Henau, Patricia Chevron, Joanne Lager, Jean-Pascal Machiels
Journal: Cancer Research (2020)

Abstract Adenosine is a strong immunosuppressive mediator often found at high extracellular levels in the microenvironment of most tumors. Blocking A2A receptors, predominantly expressed on tumor-infiltrating immune cells, is aimed at reversing the immunosuppressive effect of adenosine. EOS100850 is a potent and highly selective small molecule antagonist of the A2A receptor that remains active even at the high adenosine concentration found in tumors and that does not cross the blood-brain barrier. This multicenter FIH Phase I clinical trial (NCT02740985) assessed the safety, pharmacokinetic (PK), pharmacodynamic (PD), and preliminary clinical activity of EOS100850 monotherapy in subjects with refractory solid tumors. Pharmacodynamic assessments were performed in peripheral blood and tumor biopsies with simultaneous determination of plasma PK. Twenty-one adult participants with advanced malignancies were treated with EOS100850 monotherapy. Cohorts of 3 participants each were evaluated at 20 and 40 mg QD or 40 mg BID administration. Six participants were subsequently tested at doses of 80 mg BID and at the maximum administered dose of 160 mg BID. All subjects had at least 1 prior regimen (median 3). PK analysis indicated dose-proportional increase of EOS100850 plasma exposures up to 80 mg BID while PD evaluations revealed prolonged and maintained A2AR inhibition over the dosing interval. The most common adverse events (&amp;gt;15%) attributed to the investigational treatment included Grade 1-2 nausea, fatigue, vomiting, and liver enzyme abnormalities. No drug-related Grade 3-4 AEs and no dose-limiting toxicities were observed. As of the data cut off (15JAN20), stable disease was observed in 6 participants who remain on therapy, including head &amp; neck cancers, endometrial cancers, and checkpoint inhibitor-refractory melanoma. Further evaluation is ongoing as monotherapy and in combination with pembrolizumab or chemotherapy in both immune checkpoint-naïve and -refractory patients. Citation Format: Laurence Buisseret, Sylvie Rottey, Johann de Bono, Manon Mossakowski, Brant Delafontaine, Thubeena Manickavasagar, Nuria Kotecki, Chiara Martinoli, Manfred Schneider, Olivier De Henau, Patricia Chevron, Joanne Lager, Jean-Pascal Machiels. First in human study with EOS100850, a novel potent A2A antagonist, shows excellent tolerance and clinical benefit in immune resistant advanced cancers [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr CT152.
Abstract 6710: AVID200, a first-in-class selective TGF-beta 1 and -beta 3 inhibitor, sensitizes tumors to immune checkpoint blockade therapies
Authors: Gilles Tremblay, Tina Gruosso, Jean-François Denis, Rene Figueredo, Jim Koropatnick, Maureen O'Connor-McCourt
Journal: Cancer Research (2020)

Abstract AVID200 is a first-in-class rationally designed receptor ectodomain trap that selectively neutralizes TGF-beta 1 and -beta 3 with pM potency while sparing TGF-beta 2, thereby reducing the potential for toxicities associated with neutralization of this isoform. A Phase 1 dose-escalation clinical study of AVID200 monotherapy has recently been completed and established that the agent was well tolerated and exhibited target engagement at all dose levels tested without reaching a maximally tolerated dose. To explore the potential of AVID200 as a combination agent with immunotherapy, expression analysis of TGF-beta isoforms across various solid tumors has been carried out and the ability of AVID200 to increase anti-tumor immunity as a single agent and in combination has been explored in vivo. Analysis of &amp;gt;10,000 tumor samples from TCGA revealed that TGF-beta1 and -beta3 are the predominant isoforms expressed in solid tumors, with TGF-beta2 showing only minimal expression. This corroborates the attractiveness of TGF-beta1 and -beta3 as promising anti-cancer targets and emphasizes the need to target both isoforms simultaneously. Furthermore, AVID200 increased T-cell-mediated cytotoxicity as a single agent as well as potentiating the efficacy of immune checkpoint inhibitors (ICI) in syngeneic mouse tumor models. In line with these observations, we have found that AVID200 increases the infiltration of T-cells in the tumor microenvironment. In conclusion, AVID200, a first-in-class, potent and selective TGF-beta trap, enhances the activity of ICI agents in vivo and holds promise as a potential novel immunotherapy combination regimen which warrants exploration in patients with solid tumor malignancies. Phase 1 clinical trials are underway in various indications. Citation Format: Gilles Tremblay, Tina Gruosso, Jean-François Denis, Rene Figueredo, Jim Koropatnick, Maureen O'Connor-McCourt. AVID200, a first-in-class selective TGF-beta 1 and -beta 3 inhibitor, sensitizes tumors to immune checkpoint blockade therapies [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 6710.