bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract LT008: Mapping the evolution of T cell states during response and resistance to adoptive cellular therapy
Authors: Bachireddy Pavan , Azizi Elham , Burdziak Cassandra , Nguyen Vinhkhang , Ennis Christina , Choo Zi- Ning , Li Shuqiang , Livak Kenneth , Neuberg Donna , Soiffer Robert , Ritz Jerome , Alyea Edwin , Pe'er Dana , Wu Catherine 
Published: March 2021

Abstract
Immune therapies have transformed the cancer therapeutic landscape but fail to benefit most patients. To elucidate the underlying mechanisms by which T cells mediate elimination of leukemia, we generated a high-resolution map of longitudinal T cell dynamics within the same tumor microenvironment (TME; bone marrow) during response or resistance to donor lymphocyte infusion (DLI), a widely used immunotherapy for relapsed leukemia. We analyzed 87,939 bone marrow-derived single T cell transcriptomes, along with chromatin accessibility and single T cell receptor clonality profiles, by developing novel machine learning tools for integrating longitudinal and multimodal data. We found that pre-treatment enrichment and post-treatment rapid, durable expansion of ‘terminal’ (TEX) and ‘precursor’ (TPEX) exhausted subsets, respectively, defined DLI response. In contrast to the common, shared pathways marking DLI response, a heterogeneous pattern of T cell dysfunction marked DLI resistance. Unexpectedly, TPEX cells that expanded in responders did not arise from the infusion product but instead from both pre-existing and novel clonotypes recruited to the TME. Further, we introduce a Bayesian method, Symphony, to define the T cell regulatory circuitry and master regulators underlying TEX and TPEX subsets that may be broadly relevant to other exhaustion antagonists across cancers. In conclusion, our data implicate the hierarchy of both TEX and TPEX subsets for immunotherapeutic responses in leukemia, extending the scope of their relevance beyond checkpoint blockade to adoptive cellular therapy. Moreover, our results provocatively suggest that immunologic ‘help’ from DLI, rather than direct transfer of anti-leukemic T cells, drove leukemic remission. Finally, we provide a general analysis paradigm for exploiting temporal single-cell genomic profiling for deep understanding of how immune therapies differentially shape the evolutionary trajectories of the TME in accordance with clinical outcome.
Citation Format: Pavan Bachireddy, Elham Azizi, Cassandra Burdziak, Vinhkhang Nguyen, Christina Ennis, Zi- Ning Choo, Shuqiang Li, Kenneth Livak, Donna Neuberg, Robert Soiffer, Jerome Ritz, Edwin Alyea, Dana Pe'er, Catherine Wu. Mapping the evolution of T cell states during response and resistance to adoptive cellular therapy [abstract]. In: Proceedings of the AACR Virtual Special Conference on the Evolving Tumor Microenvironment in Cancer Progression: Mechanisms and Emerging Therapeutic Opportunities; in association with the Tumor Microenvironment (TME) Working Group; 2021 Jan 11-12. Philadelphia (PA): AACR; Cancer Res 2021;81(5 Suppl):Abstract nr LT008.
Abstract LT021: Targeting AXL favors an anti-tumorigenic tumor microenvironment that enhances immunotherapy responses by decreasing HIF-1a levels in cancer cells
Authors: Goyette Marie-Anne , Gratton Jean-Philippe , Côté Jean-François 
Published: March 2021

Abstract
Breast cancer-related deaths are predominantly associated to complications arising from metastases. Hypoxia is an important biological process that fuels metastasis and contributes to deregulate the tumor microenvironment (TME) of solid tumors. Hypoxia also promotes resistance to therapies by a plethora of mechanisms including enhancing tumoral angiogenesis, metabolic reprogramming, epithelial-to-mesenchymal transition (EMT) and immune evasion. The expression of the receptor tyrosine kinase AXL, a hypoxia-induced gene, is associated with poor clinical prognosis and metastasis in various cancers, including breast cancer. Here, we hypothesize that genetic or pharmacological interference of AXL may alter hypoxic responses and associated modulation of the TME offering an ideal setting for immunotherapy. In a HER2 mouse model of breast cancer (MMTV-NIC), we found that the mammary gland epithelial cells specific deletion of AXL led to a reduction of lung metastases and improved the TME by reducing the hypoxic response in tumor cells. When breast cancer cells were subjected to hypoxic conditions, we found that AXL inhibition reduced the levels of the hypoxia transcription factor HIF-1a which resulted in an altered hypoxic response. Specifically, AXL deletion led to a reduction of hypoxia-induced EMT and invasion, and a reduction of the secretion of key cytokines for macrophages recruitment and polarization. We demonstrate in vivo that AXL pharmacological inhibition in the MMTV-NIC breast cancer model generated an anti-inflammatory TME that enhanced an anti-PD-1 immune checkpoint blockade response and decreased the metastatic burden. Collectively, these results suggest that targeting AXL could be a powerful approach to improve immunotherapy response by generating an anti-tumoral microenvironment therefore limiting the metastatic burden of breast cancer.
Citation Format: Marie-Anne Goyette, Jean-Philippe Gratton, Jean-François Côté. Targeting AXL favors an anti-tumorigenic tumor microenvironment that enhances immunotherapy responses by decreasing HIF-1a levels in cancer cells [abstract]. In: Proceedings of the AACR Virtual Special Conference on the Evolving Tumor Microenvironment in Cancer Progression: Mechanisms and Emerging Therapeutic Opportunities; in association with the Tumor Microenvironment (TME) Working Group; 2021 Jan 11-12. Philadelphia (PA): AACR; Cancer Res 2021;81(5 Suppl):Abstract nr LT021.
Abstract PR002: STAT3 in cancer-associated fibroblasts promotes an immunosuppressive tumor microenvironment in PDAC
Authors: Lefler Julia E. , MarElia Katie , Ostrowski Michael C. 
Published: March 2021

Abstract
One of the defining characteristics of pancreatic ductal adenocarcinoma (PDAC) is the formation of a dense stroma comprised of cancer associated fibroblasts (CAFs) and immune cell populations. This stroma is immunosuppressive and can act as a physical barrier against common therapeutic treatments. Attempts to therapeutically target the PDAC stroma have yielded contradictory results, suggesting both tumor promoting and tumor limiting roles for CAFs. These studies emphasize the need to understand important transsignaling pathways between CAFs, tumor cells, and the immune microenvironment. IL-6 is a pleiotropic cytokine involved in several physiological functions and its increased expression is strongly associated with poor survival rates in PDAC patients. STAT3 is a major downstream target of IL-6, and its aberrant activation has been implicated in PDAC tumor progression and immune evasion. IL-6 expression and the IL-6/STAT3 signaling axis in PDAC has been characterized in epithelial tumor cells, however its stromal-specific function on PDAC has yet to be elucidated. We hypothesized that the STAT3 signaling axis in pancreatic CAFs contributes to the immunosuppressive and fibrotic phenotype seen with disease progression. Employing CreLoxP technology, the fibroblast specific protein-1 (Fsp-Cre) transgene was used to conditionally delete STAT3 in fibroblasts in the PdxFlp; KrasG12D; p53frt/frt (KPF) PDAC mouse model developed by our lab. Deletion of STAT3 in fibroblasts significantly increased the survival in a cohort of KPF mice compared to those with intact STAT3. Loss of fibroblast STAT3 also resulted in a significant decrease in ECM deposition in the pancreas. In preliminary investigations into the immune microenvironment, we found an increase in overall T cell infiltration and decrease in regulatory T cell populations in the STAT3 deleted cohort. Further, STAT3 deletion resulted in an increase in the ratio of M1 to M2 macrophages within the PDAC stroma, suggesting CAF STAT3 signaling is important to maintaining an immune microenvironment conducive to tumor progression. Ultimately, these preliminary results suggest STAT3 signaling in PDAC CAFs contributes to an overall survival disadvantage, an immunosuppressive tumor microenvironment, and an increase in ECM deposition.
Citation Format: Julia E. Lefler, Katie MarElia, Michael C. Ostrowski. STAT3 in cancer-associated fibroblasts promotes an immunosuppressive tumor microenvironment in PDAC [abstract]. In: Proceedings of the AACR Virtual Special Conference on the Evolving Tumor Microenvironment in Cancer Progression: Mechanisms and Emerging Therapeutic Opportunities; in association with the Tumor Microenvironment (TME) Working Group; 2021 Jan 11-12. Philadelphia (PA): AACR; Cancer Res 2021;81(5 Suppl):Abstract nr PR002.
Abstract IA-03: Unsupervised resolution of intra- and inter-tumoral heterogeneity using deep learning
Authors: Diamandis Phedias 
Published: March 2021

Abstract
Applications of deep learning to histopathology has demonstrated expert-level performance, but approaches have been largely concentrated on supervised classification tasks requiring context-specific training and deployment. We demonstrate that deep neural networks, previously trained to recognize diverse histomorphologies in one environment can effectively extrapolate their learned representations to resolve clinically relevant intra- and inter-patient tissue pattern differences in other cancer types without explicit instruction or additional optimization. Moreover, this unsupervised approach highlighted numerous human misclassifications, suggesting that repositioning of existing histology-educated networks could provide scalable approaches for quality assurance and discovery of unappreciated subgroups of disease.
Citation Format: Phedias Diamandis. Unsupervised resolution of intra- and inter-tumoral heterogeneity using deep learning [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr IA-03.
Abstract IA-13: Machine learning for improved management of patients with pancreatic cysts
Authors: Karchin Rachel 
Published: March 2021

Abstract
Invasive pancreatic cancer is a deadly disease, with the lowest five year survival rate among all cancers. Cysts in the pancreas are relatively common, but while some have malignant potential, many are entirely benign, while surgical resection of the pancreas has relatively high morbidity and mortality. Clinical management of patients with pancreatic cysts must balance these risks. We developed a supervised machine learning approach to integrate clinical, imaging and molecular data, as part of a large retrospective international study of almost 900 patients with pancreatic cysts. Using the Multivariate Optimization of Combinatorial Alterations (MOCA) algorithm, patients were classified into those who required immediate surgery, those who required routine monitoring, and those who could be discharged without future surveillance, using a stepwise approach. The resulting diagnostic test, called CompCyst, was more accurate than current patient management which relies on imaging and clinical criteria. More than half of the patients sent to surgery were found to have unnecessary resection of their cysts, which could have been avoided by application of the CompCyst test. Our results demonstrate how machine learning and molecular features can be applied to improve pre-surgical diagnostic testing in the clinic.
Citation Format: Rachel Karchin. Machine learning for improved management of patients with pancreatic cysts [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr IA-13.
Abstract IA-17: Plumbing the depths of the non-coding cancer genome
Authors: Stein Lincoln D. 
Published: March 2021

Abstract
Cancer is driven by genomic mutations that alter common pathways that regulate cell growth and interaction with the microenvironment. More than 20,000 cancer genomes have been sequenced over the past decade, revealing multiple insights into the causes, evolution and consequences of cancer. However, almost everything we know about the cancer genome has been obtained using exome sequencing, which surveys the 2% of the genome that encodes protein coding genes. The 98% of the genome that is ignored by exome sequencing is the "dark matter" of the cancer genome and was until recently largely unexplored. In this talk, I will discuss highlights from the recently-published Pan-Cancer of Whole Genomes project (https://www.nature.com/articles/s41586-020-1969-6), in which an international team of researchers performed a comprehensive analysis of over 2,600 whole cancer genomes and matched normals, revealing new insights into origins, mechanisms and evolution of tumours. I'll also talk about how machine-learning techniques contributed to the Pan-Cancer project, including the development of an "electronic pathologist" that uses deep learning to accurately identify the tissue origins of tumours based on whole genome sequencing data.
Citation Format: Lincoln D. Stein. Plumbing the depths of the non-coding cancer genome [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr IA-17.
Abstract PO-006: Artificial intelligence aided interpretation of ALK fluorescent in situ hybridization for lung cancer: An algorithm development based on 10-year-annotated quality control files in central laboratory
Authors: Kim Tae-Jung , Kang Ho Chul , Lee Yonggeun , Seo Seokman , Kim Donghwan , Cho Sungjin 
Published: March 2021

Abstract
Molecular testing became the most important part of lung cancer diagnostics. Among them, an interpretation of fluorescent in situ hybridization (FISH) is challenging for the routine clinical samples with various amounts and quality. In this study, we trained a variant of DenseNet, a convolutional neural network model with the highest performance in image classification and suitable for quality assessment. Train dataset is composed of the arbitrarily classified dataset made and assessed by a professional pathologist during routine molecular test using whole-slide tissue images and molecular profiles including DNA density and the signal intensity of FISH obtained from 15,000 tests from lung cancer biopsies and resections, which were sent to the central laboratory for molecular studies from 45 hospitals nationwide. The performance of our platform is tested and compared to the quality assessment dataset made from each a general pathologist and a technician. The performance of our method for classifying DNA concentration and the signal intensity of FISH is comparable to that of a general pathologist and technicians. we are under improving its average area under the curve (AUC) over 0.85. This result suggests that a deep learning model based on a convolutional neural network can assist pathologists and pathology technician in rigorous assessing the quality of tissue morphology and signal of FISH for tissue samples with various molecular quality before or during the molecular test and can be applied to quality control of lung cancer molecular testing in a central laboratory.
Citation Format: Tae-Jung Kim, Ho Chul Kang, Yonggeun Lee, Seokman Seo, Donghwan Kim, Sungjin Cho. Artificial intelligence aided interpretation of ALK fluorescent in situ hybridization for lung cancer: An algorithm development based on 10-year-annotated quality control files in central laboratory [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-006.
Abstract PO-036: A sophisticated bioinformatics framework for integrative study of radiomics and genomics profiles of tumors
Authors: Sukhadia Shrey S. , Nagaraj Shivashankar H. , Gevaert Olivier , Arumugam Sivakumaran Theru , Tyagi Aayush , Mukherjee Pritam , Prathosh A.P. 
Published: March 2021

Abstract
The potential for radiomics to support oncology decision-making has grown substantially in recent years, as these scanning techniques have been found to offer unique information regarding the tumor phenotype and microenvironment that is distinct from that provided by genomic or proteomic assays. Radiomic and genomic (or proteomic) data can be correlated with one another, thereby facilitating radiogenomic efforts. Radiogenomically-informed biopsies have the potential to yield better pathological outcomes and can aid in the planning of more appropriate treatment strategies for cancer patients. However, the field lacks a unified software platform wherein radiomic and genomics/proteomic data could be brought together to conduct a variety of correlational analyses and build robust artificial intelligence models that would aid the prediction of genomic/proteomic profiles of tumors from their radiological images. We have built such a comprehensive platform that could be utilized by scientists and clinicians globally to conduct radiogenomic studies for a variety of cancer types, and further validate and deploy it in clinics to aid effective monitoring, diagnosis, and treatment of cancer patients.
Citation Format: Shrey S. Sukhadia, Shivashankar H. Nagaraj, Olivier Gevaert, Sivakumaran Theru Arumugam, Aayush Tyagi, Pritam Mukherjee, A.P. Prathosh. A sophisticated bioinformatics framework for integrative study of radiomics and genomics profiles of tumors [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-036.
Abstract PO-077: Image clustering of brain tumor patients using a deep neural network
Authors: Hosseini Hadi , Cheng Cheng , Glass John , Reddick Gene , Gajjar Amar , Lu Zhaohua 
Published: March 2021

Abstract
Purpose: The clustering of pediatric patients based on their unlabeled brain magnetic resonance (MR) images reveal the latent features and group structures. It is an unbiased approach to finding image-based markers and risk-stratification for neurocognitive functions. Analyzing large sets of MR images is time-intensive and challenging due to the MR images' high dimensionality and complexity. The extraction of low-dimension informative feature space instead of high-dimension MR image is an effective and fast solution to cluster images accurately. We aimed at developing a feature extraction method using deep learning. Our long-term research goal is to develop a feature extraction model using deep learning to automatically and accurately identify brain regions in association with neurocognitive outcomes. Methods: We used 124 registered fractional anisotropy (FA)-mapped of diffusion tensor (DT) images of pediatric medulloblastoma patients obtained after tumor surgery and before radiation from the SJMB03 clinical trial. First, we developed a 3D convolutional auto-encoder model based on a deep neural network for the voxel-wise analysis of 3D images to produce low-dimensional features, which can reconstruct input images accurately. Then, we applied the density-based clustering model, named OPTICS (Ordering Points To Identify the Clustering Structure), to the features. To train two models, we used a soft and dynamic algorithm to simultaneously combine two loss functions. Distance measure and CAHL (Cluster Assignment Loss Hardening) are used to measure feature learning and clustering performance. Results: Our experiment demonstrates that the proposed models yield results comparable in quality to manual clustering. The model trained regarding using a sufficient dataset and reducing dynamic loss function. Our model can cluster patients precisely and extract representative hidden features of the images, which is much more time- and labor-efficient compared to manual clustering by experts. Conclusions: In this study, we focused on a new feature extraction method and clustering of brain images. Because of the deep neural networks' unique advantages in image processing, we considered a 3D convolutional auto-encoder for the feature extraction part and OPTICS algorithm for the clustering part of the model. This method is highly useful in neuroimaging and neurocognitive research.
Citation Format: Hadi Hosseini, Cheng Cheng, John Glass, Gene Reddick, Amar Gajjar, Zhaohua Lu. Image clustering of brain tumor patients using a deep neural network [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-077.
Abstract PR-11: Developing an agnostic risk prediction model for acute kidney injury in cancer patients using a machine learning algorithm from blood results data
Authors: Scanlon Lauren A. , Barrusio Jorge , Garbett Alexander 
Published: March 2021

Abstract
Acute Kidney Injury (AKI) is a common complication in hospital inpatients with the annual number of excess inpatient deaths associated with AKI in England estimated to be above 40,000 and the annual cost of AKI-related inpatient care around £1.02 billion. Oncology patients are a known high risk population for developing AKI. The prevalence of AKI in oncology populations is 7.5 to 9.5%, with AKI incidence associated with lower remission rates and higher mortality. To reduce the impact of this condition in cancer patients we aimed to create a model to predict AKI events before occurrence allowing intervention and prevention strategies to be put in place. We trained a Random Forest Model on 597,403 blood test results from 48,865 patients undergoing cancer treatment at The Christie NHS Foundation Trust, Manchester, UK between January 2017 and 3rd May 2020, to identify AKI events upcoming in the next 30 days. From this model, risk levels of very low, low, medium, high and very high were assigned to the risk of an upcoming AKI event. We tested the model and risk assessment through a prospective analysis on patients’ blood tests results collected between 1st June 2020 to 31st August 2020. The trained model gave an area under the receiver operating characteristic curve (ROC AUC) of 88.1% (95% confidence interval 87.8% - 88.3%) when assessing predictions per blood test for AKI occurrences within 30 days. Assigning risk levels and testing the model through prospective validation across the period 1st June to the 31st August identified 61.2% of AKI occurrences with a risk alert of `Medium' or higher, identifying 73.8% of patients with AKI event before at least one AKI occurrence. Our results demonstrate the exciting potential for using routinely collected blood test data to assign AKI risk levels allowing early intervention and preventative strategies to be put in place.
Citation Format: Lauren A. Scanlon, Jorge Barrusio, Alexander Garbett. Developing an agnostic risk prediction model for acute kidney injury in cancer patients using a machine learning algorithm from blood results data [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PR-11.