bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract 1888: Identification and optimization of novel small molecule modulators of immune checkpoint resistance with a unified representation space for genomic and chemical perturbations
Authors: Bhandari Ashish , Cuccarese Michael F. , Fales Kevin , Nadella Kiran , Basso Rebecca Sarto , Beshnova Daria , Donnella Hayley , Shamloo Bahar , Cooper Jacob , Haque Imran , Alfa Ron , Rinaldi Jacob 
Published: June 2022

Abstract
Immune checkpoint inhibitors have revolutionized cancer treatment, producing a durable response consistent with immunologic memory in a subset of patients. However, the majority of patients demonstrate innate or acquired resistance that must be characterized and overcome to induce successful treatment. Advancements in human reverse translation and scaled in vivo CRISPR screening have uncovered novel molecular and genomic correlates of resistance, and promising druggable mechanisms - driven by highly complex interactions between tumor cells and the immune system. It is this core biology that must be disentangled to build the treatment paradigms of the future. Here we demonstrate a technique for massively parallel prioritization of new immuno-oncology hypotheses using industrial-scale experimentation and machine learning. Leveraging high-content imaging data from whole-genome CRISPR knockout and a library of &gt;250,000 compounds, a deep learning model was trained to construct a batch-invariant low dimensional representation of each perturbation. Millions of perturbations in multiple cell types were embedded in a unified representation space that was leveraged to increase the rate of discovery, accelerate reverse translation, yield novel biological insights, and guide the advancement of lead molecular series through SAR. Here we highlight multiple discovery programs driven by inferred relationships between small molecules and gene knockout with translation from inference to in vivo efficacy. Specifically, we prioritize molecules with activity in STK11-deficient tumors and additional immune checkpoint sensitizers.
Citation Format: Ashish Bhandari, Michael F. Cuccarese, Kevin Fales, Kiran Nadella, Rebecca Sarto Basso, Daria Beshnova, Hayley Donnella, Bahar Shamloo, Jacob Cooper, Imran Haque, Ron Alfa, Jacob Rinaldi. Identification and optimization of novel small molecule modulators of immune checkpoint resistance with a unified representation space for genomic and chemical perturbations [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 1888.
Abstract 1897: Systematic methods to identify cancer vulnerabilities from genome-wide loss-of-function screens: An interactive framework for target discovery
Authors: Krill-Burger John M. , Borah Ashir A. , Paolella Brenton R. , McFarland James M. , Vazquez Francisca 
Published: June 2022

Abstract
Over a thousand genome-scale loss-of-function screens have been performed, as part of efforts such as The Cancer Dependency Map (DepMap), to establish the landscape of genetic dependencies across a diverse set of cancer cell lines. A key challenge to using this resource for therapeutic target discovery is discerning common or tissue-related gene dependencies from those that represent true cancer-specific vulnerabilities. Although many successful cancer-specific targets have been identified by synthetic lethality with patient-prevalent driver mutations, the number of novel synthetic lethals identified as more cancer cell lines are screened has been limited compared to the total number of newly observed selective dependencies. Better understanding of how these selective dependencies are connected to molecular features of the sensitive cell lines could unlock a wealth of potential targets. As part of the DepMap project at the Broad Institute, we created a software pipeline and interactive web-tool for researchers to interrogate the compendium of CRISPR and RNAi screens and systematically rank potential targets by several key factors, most notably selectivity, disease indication, and predictability from multi-omics features (WES, RNAseq, methylation, proteomics). A strength of our framework is the ability to explore the relationships between a dependency and its top predictive features since this could provide insights into the mechanism underlying the cellular dependency and aid in generating therapeutic hypotheses. Additionally, users can perform disease-specific analyses and incorporate annotations for small molecule tractability or drug availability. We anticipate this tool will lower the barrier to systematic genome-wide target discovery using DepMap and provide insights into strategies and best practices for nominating promising targets.
Citation Format: John M. Krill-Burger, Ashir A. Borah, Brenton R. Paolella, James M. McFarland, Francisca Vazquez. Systematic methods to identify cancer vulnerabilities from genome-wide loss-of-function screens: An interactive framework for target discovery [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 1897.
Abstract 1934: An artificial intelligence-based framework for BLEACH&STAIN mfIHC facilitates automated prognosis marker assessment in breast cancer
Authors: Mandelkow Tim , Bady Elena , Lurati Magalie C. , Hube-Magg Claudia , Lennartz Maximilian , Sauter Guido , Blessin Niclas C. , Simon Ronald 
Published: June 2022

Abstract
The assessment of prognostic markers in routine clinical practice of breast cancer is currently performed using multi gene RNA panels. However, the unknown proportion of normal breast tissue in relation to malignant breast tissue can reduce the predictive value of such tests. Immunohistochemistry holds the potential for a better assessment of tumors because tumor cells can be separately analyzed. To enable automated prognosis marker detection (i.e. HER2, GATA3, progesterone receptor [PR], estrogen receptor [ER], androgen receptor [AR], TOP2A, Ki-67, TROP2, Mammaglobin), we have developed and validated a framework for automated breast cancer identification, which comprises three different artificial intelligence analysis steps and an algorithm for cell-distance analysis of 12+1 marker BLEACH&STAIN multiplex fluorescence immunohistochemistry staining, in 2004 breast cancers. The optimal distance between Myosin+ basal cells and benign panCK+ benign cells was identified as 31µm and used to exclude benign glands from the analysis combined with and deep learning-based algorithm for benign gland detection. Our deep learning-based framework discriminated normal glands from malignant glands with an area under receiver-operating characteristic curves (AUC) of 0.96 (95% confidence interval [CI], 0.92 to 0.99). The accuracy of the approach was also validated by several well-characterized biological findings, such as the identification of 13% HER2+, 73% PR+/ER+, and 14% triple negative cases in the study cohort as well as a significant higher Fraction of TOP2A in the HER2+ cases as compared to the HER2- cases (p&lt;0.001). Furthermore, the automated assessment of GATA3, PR, ER, TOP2A-LI, Ki-67-LI, TROP2, and Mammaglobin was significantly liked to the tumor grade (p&lt;0.001 each). Furthermore, a high expression level of HER2, GATA3, PR, and ER was associated with a prolonged overall survival (p≥0.002 each). In conclusion, a deep learning-based framework for automated breast cancer identification using BLEACH&STAIN multiplex fluorescence IHC facilitates automated prognosis marker quantification in breast cancer.
Citation Format: Tim Mandelkow, Elena Bady, Magalie C. Lurati, Claudia Hube-Magg, Maximilian Lennartz, Guido Sauter, Niclas C. Blessin, Ronald Simon. An artificial intelligence-based framework for BLEACH&STAIN mfIHC facilitates automated prognosis marker assessment in breast cancer [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 1934.
Abstract 1937: DIME-CT: Self-supervised learning for medical image analysis using patch-based embeddings
Authors: Fedden Leon , Zhang Zhenning , Baykaner Khan , Li Qin , Bordeaux Lucas 
Published: June 2022

Abstract
Whether tracking patient progress for clinical decision making, or investigating novel therapies, automated analysis of Computed Tomography (CT) imaging data is essential for the future of digital radiomics. In digital radiomics, as in all medical imaging, well annotated data is scarce, whereas unlabelled images are relatively plentiful. In other fields of image processing and medical imaging, the application of self-supervised learning (SSL) to large quantities of unlabelled data has resulted in great strides forward for fast, scalable, and interpretable image analysis. In this work we present a new approach applying SSL to CT imaging data which allows for: 1) Improved performance on image classification tasks, based on 2) dramatically reduced quantity of annotated CT imaging data, whilst also 3) enabling easy exploration and interpretation of the image regions.
We applied a selection of self-supervised approaches (BYOL, DINO, SimCLR, & inpainting) to CT imaging data. Because the high dimensionality of CT data prevents us from using them directly to out-of the box SSL models, we adopt a 3D patching approach to reduce the dimensionality of the neural net input, and process each patch independently.
We train our self-supervised models on public datasets (DeepLesion, NSCLC), and we specialize these models for tumor classification tasks that we evaluate on AstraZeneca sponsored clinical trials. The specialization is done in two different ways: 1) we use the pre-trained SSL model as an encoder that transforms the images of the clinical study into embeddings, on which we apply supervised classification models; 2) we use transfer learning to fine-tune supervised classification models that take the patches directly as inputs.
We find that self-supervised pre-training significantly improves the accuracy on tumor classification tasks compared against a supervised learning baseline. Additionally, using the SSL embeddings we build an interactive map of CT imaging data enabling quick and intuitive inspection of the relevant regions.
Our findings show that SSL constitutes an important tool for medical imaging analysis. SSL results in models that generalize better, and enable improved downstream interpretability and predictions. Furthermore, well trained SSL models can be re-applied to multiple indications because they are pre-trained on broad and diverse CT imaging data.
Citation Format: Leon Fedden, Zhenning Zhang, Khan Baykaner, Qin Li, Lucas Bordeaux. DIME-CT: Self-supervised learning for medical image analysis using patch-based embeddings [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 1937.
Abstract 1941: Accurate pan-cancer tumor purity estimation from gene expression data
Authors: Revkov Egor , Sung Ken W.-K. , Skanderup Anders J. 
Published: June 2022

Abstract
Tumors are complex masses composed of malignant and non-malignant cells. Variation in tumor purity (malignant cell fraction) can both confound integrative analysis and enable studies of tumor heterogeneity. Here we developed PUREE, which uses a weakly supervised learning approach to infer tumor purity from a tumor gene expression profile. PUREE was trained on gene expression data and genomic consensus purity estimates from approximately 8000 solid tumor samples. Using a linear model based on 170 input genes, PUREE predicted purity with high accuracy across distinct solid tumor types and generalized to tumor samples from unseen tumor types. PUREE input genes features were further validated using single-cell RNA-seq data from distinct tumor types. In a comprehensive benchmark, PUREE outperformed all existing transcriptome-based purity estimation approaches. We also show that the accuracy of a pan-cancer model is comparable to models optimized for individual tumor types, highlighting compositional properties of the tumor microenvironment conserved across tumor types. Overall, PUREE is a highly accurate and versatile method for estimating tumor purity and interrogating tumor heterogeneity from bulk tumor gene expression data.
Citation Format: Egor Revkov, Ken W.-K. Sung, Anders J. Skanderup. Accurate pan-cancer tumor purity estimation from gene expression data [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 1941.
Abstract 1942: Interpretation of machine learning methods for the prediction of breast and prostate cancer risk
Authors: Klie Adam , Talwar James , Pagadala Meghana , Carter Hannah 
Published: June 2022

Abstract
Neural networks are powerful tools for modeling genetic contributions to cancer risk that can theoretically capture nonlinear, epistatic interactions between disease-associated loci in heritable cancers. Recent studies applying neural networks and other machine learning methods to complex trait risk prediction from single nucleotide polymorphism (SNP) array data have shown promise in improving risk stratification. However, current performance gains for neural networks when compared to traditional polygenic risk scoring (PRS) approaches and other nonlinear and linear machine learning methods have been modest. Moreover, there remains substantial debate as to the effect of capturing epistatic interactions between SNPs in risk modeling. Central to the debate has been the difficulty in interpreting the complex, nonlinear mapping learned by neural networks and other nonlinear modeling approaches.
To decipher the importance of capturing nonlinear interactions in cancer risk modeling, we first applied several PRS approaches to the prediction of breast and prostate cancer status for individuals in the Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) and Elucidating Loci Involved in Prostate Cancer Susceptibility (ELLIPSE) datasets respectively. Consistent with previous studies of complex disease prediction using machine learning, we noted greater predictive capability upon inclusion of more loci in our modeling but only small performance gains when nonlinearity was captured in both cancer types. We then applied machine learning interpretation methods to derive a score for each variant per method, including several neural network interpretation methods which, to our knowledge, have not been applied in this context. We noted varying degrees of concordance between the scores assigned by each method. Finally, we performed pairwise in silico perturbations on salient features and techniques from network biology to identify epistatic interactions between loci captured by each model. Our work represents a comprehensive study of methods for inferring both variant level and epistatic interaction contributions to cancer risk.
Citation Format: Adam Klie, James Talwar, Meghana Pagadala, Hannah Carter. Interpretation of machine learning methods for the prediction of breast and prostate cancer risk [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 1942.
Abstract 196: Leveraging linked gut microbiome and breast cancer microfluidic chips to study the role of the estrobolome in cancer progression
Authors: Brasino Danielle S. , Speese Sean D. , Emerson Beverly M. 
Published: June 2022

Abstract
Gut microbiota are recognized as an integral part in maintaining health, with disturbances in composition linked to disease states ranging from cancer to depression. Breast cancer patient microbiome analysis has demonstrated a correlation between dysbiosis and disease with proposed mechanisms including immune cell regulation, enhanced inflammatory signaling, and modulation of estrogen metabolism through the estrobolome. Current models lack the ability to study communication in one direction or to modulate single variables such as bacterial species or xenobiotic additions without the use of mouse models which suffer from poor translation and assessment opportunities. We have developed two microfluidic chips, one to model the gut microbiome using oxygen impermeable plastic for maintenance of anaerobic bacteria and another to culture breast cancer spheroids under constant flow. Current work has demonstrated viability and polarity maintenance in chip-cultured spheroids and initial development of polarized villus-like gut epithelium. Using these two chips, we will study the impact of breast cancer associated bacteria capable of estrogen metabolism on breast cancer tumorigenic potential.
Citation Format: Danielle S. Brasino, Sean D. Speese, Beverly M. Emerson. Leveraging linked gut microbiome and breast cancer microfluidic chips to study the role of the estrobolome in cancer progression [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 196.
Abstract 1965: Complete remission in advanced epithelial ovarian cancer is predicted by metabolic signatures measured in the media of patient’s primary culture 3D tumor explants: A metabolomic analysis
Authors: Nagourney Adam J. , D'Amora Paulo , Dale Cotrim Guerreiro Silva Ismael , Evans Steven S. , Nagourney Robert A. 
Published: June 2022

Abstract
Ovarian cancer is the leading cause of gynecologic cancer death with over 21,000 new diagnosis and 13,000 deaths each year in the US. Treatment with platinum-based chemotherapy induces remission in up to 80% of patients yet the majority relapse and die from their disease within 5 years. We recently reported the analysis of patients with advanced ovarian cancers using plasma metabolomics to predict response and survival (D’Amora et al Gynecol Oncol, October 2021). As part of that study human tumor explants were submitted for the measurement of platinum resistance by ex vivo analysis of programmed cell death (EVA/PCD࣪). Media obtained from the non-drug exposed control cultures were examined by mass spectrometry for the quantification of 186 individual metabolites using the Biocrates’ p180 platform. Results obtained with the tissue culture media identify biochemical signatures that correlate with the achievement of complete pathologic remission. Among the most discrimination findings were the media concentrations of phosphatidyl choline (PC) aa c42:0 and the ratio of (PC) aa C40:2 divided by (PC) ae C42:1 suggesting either hyperactivity of lipid elongases or defective beta oxidation of very long chain fatty acids in drug refractory patients. When we compared the results of 8 patients who achieved pCR with 3 patients with residual disease the aa C42:0 concentration and the ratio aa C42:0/ae C42:1 provided an AUC= 0.958 (p. &lt; 0.01) The findings suggest that peroxisomal dysfunction leads to the accumulation of very long chain fatty acid lipids. These species are known to induce endoplasmic reticulum and oxidative stress and are associated with the neurodegenerative diseases like Zellweger syndrome. We have reported similar findings in drug refractory breast cancer patients (da Silva et al, Oncotarget, August Aug 2018) suggesting common lipid metabolic changes in drug resistant adenocarcinomas. The results of these analyses could provide prognostic and predictive information for patients diagnosed with advanced epithelial ovarian cancers. Further studies correlating these findings with plasma metabolic signatures are underway as will be reported.
Citation Format: Adam J. Nagourney, Paulo D'Amora, Ismael Dale Cotrim Guerreiro Silva, Steven S. Evans, Robert A. Nagourney. Complete remission in advanced epithelial ovarian cancer is predicted by metabolic signatures measured in the media of patient’s primary culture 3D tumor explants: A metabolomic analysis [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 1965.
Abstract 1969: Distinctive profiles on immunotherapy responders versus non responders in Chilean gastric cancer patients
Authors: Pinto Mauricio P. , Medel Matias Muñoz , Delgado Miguel Córdova , Retamal Ignacio N. , Manque Patricio , Berkovits Alejandro , Rodriguez Maria Paz , Rios Juvenal A. , Garcia-Bloj Benjamin , Owen Gareth , Garrido Marcelo 
Published: June 2022

Abstract
Background: Gastric cancer (GC) is the leading cause of cancer death in Chile, claiming &gt;3,000 lives per year. On April 2021, the US Food and Drug Administration approved the combined use of Nivolumab (immunotherapy) plus chemotherapy as frontline treatment for advanced GC. Unfortunately, as occurs with any therapy, a proportion of treated patients are either unresponsive or display poor responses. Our study sought to obtain a profile of GC patients categorized as responders or non-responders to immunotherapy in order to identify better predictors of response and novel targets for treatment refractory patients.
Methods: A total of 50 GC patients were included into our study. Demographic and basic information was obtained from medical records. Additionally, we retrieved information on response to treatment, histological type, PDL1 and HER2 status. Nucleic acid samples were collected from a subset of 45 patients (responders and non-responders) and analyzed by massive analysis of cDNA ends (MACE-seq) assessing 3’ mRNA sequencing.
Results: Patients were predominantly male (76%) and advanced stage (III/IV; 98%). Within this subset, 26 were responders and 24 were non-responders. Among clinical variables, body mass index was significantly higher on responders versus non-responders (p=0.026). Among responders, tumors were predominantly located on the gastro-esophagic junction. As expected, responders also displayed a higher prevalence of HER2- and PDL1+. Our 3’ MACE analysis confirmed distinct profiles on responders compared to non-responders.
Conclusions: Our findings confirm distinctive profiles among Chilean GC patients categorized as responders or non-responders to immunotherapy. These results could deliver novel markers that may serve as predictors of response to immunotherapy (as an alternative to PDL1) or to identify patients more likely to be refractory to treatment. Future studies should further confirm and validate these findings in a larger sample.
Citation Format: Mauricio P. Pinto, Matias Muñoz Medel, Miguel Córdova Delgado, Ignacio N. Retamal, Patricio Manque, Alejandro Berkovits, Maria Paz Rodriguez, Juvenal A. Rios, Benjamin Garcia-Bloj, Gareth Owen, Marcelo Garrido. Distinctive profiles on immunotherapy responders versus non responders in Chilean gastric 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 1969.