bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract 5363: MicroArray-based MethylationToActivity: Advancing biological knowledges from DNA methylation profiles of patient tumors
Authors: Jones Karissa Dieseldorff , Quarni Waise , Putnam Daniel , Singh Shivendra , Wu Qiong , Yang Jun , Chen Xiang 
Published: June 2023

Abstract
DNA methylation (DNAm) is a relatively stable regulatory epigenetic mechanism. Although accurate DNAm classifiers have been built for early detection of cancer and subtype classifications, its transcriptional regulatory roles remain unclear. To address this challenge, we developed a deep-learning framework MethylationToActivity (M2A) that accurately predicts individual promoter activity from WGBS methylomes. As array-based methylome is the most common epigenetic data from patients’ tumor samples, we redesigned M2A’s raw features and model topology for array methylomes, which shows an accurate prediction of the promoter activity (R2 = 0.74), approaching its WGBS counterpart (R2 = 0.79). Transfer learning improves the prediction accuracy to 0.77. In a primary rhabdomyosarcoma cohort, M2A prioritized candidate genes with strong subtype-specific alternative promoter usage (APU) and identified distinct isoforms of a subgroup of APU genes including NAV2 expressed in the two major subtypes (aRMS and eRMS). Molecular experiments revealed that PAX3-FOXO1 directly binds to the aRMS-specific promoter and is required for its active transcription. Genetic deletion of PAX3-FOXO1 leads to NAV2 APU. Overexpression of aRMS-specific NAV2 isoform significantly promoted cell proliferation, supporting its oncogenic function. These data indicate that PAX3-FOX1 is critical for APU in aRMS. We conclude that the new M2A provides vast insight into downstream functional interpretation of differential DNAm patterns.
Citation Format: Karissa Dieseldorff Jones, Waise Quarni, Daniel Putnam, Shivendra Singh, Qiong Wu, Jun Yang, Xiang Chen. MicroArray-based MethylationToActivity: Advancing biological knowledges from DNA methylation profiles of patient tumors. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5363.
Abstract 537: Validating USP27X as a drug target in lung cancer
Authors: Alam Shamshad , Zunic Amanda , Venkat Swati , Feigin Michael E , Atanassov Boyko 
Published: June 2023

Abstract
Over the past two decades, tremendous efforts have been dedicated to developing and improving receptor tyrosine kinase (RTK) targeted therapies to combat the development and progression of different cancers. Despite the significant progress made, these therapies are not durable: not all patients respond to treatment, and primary or acquired resistances are common. Interestingly, however, while RTK-targeted therapies have no effect on resistant lung cancer cell lines, si/shRNA-mediated RTK depletion or combined treatments that affect RTK protein levels profoundly impact the growth and survival of these cells, indicating that RTKs are still critical for their proliferation. Thus, strategies to target RTK protein levels hold promise as an option for treating RTK-driven cancers. In our recent studies, we discovered that ubiquitin-specific protease 27X (USP27X) functions are crucial for properly recycling and maintaining EGFR steady-state levels in lung cancer cells. Ablation of USP27X leads to a severe decrease of EGFR steady levels, inhibition of xenograft tumor growth, and sensitization of lung cancer cell lines to targeted therapy. To fully understand the in vivo functions of USP27X in EGFR regulation and lung carcinogenesis, we created a new mouse line carrying a conditional Usp27X allele and introduced this allele into the well-established and clinically relevant EGFRL858R mouse lung cancer model. Ongoing studies will determine whether USP27X can be a critical therapeutic in EGFR-driven lung cancer.
Citation Format: Shamshad Alam, Amanda Zunic, Swati Venkat, Michael E Feigin, Boyko Atanassov. Validating USP27X as a drug target in lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 537.
Abstract 5372: Supervised manifold learning and classification; application to expression data visualization and cancer prediction
Authors: Webber James , Elias Kevin 
Published: June 2023

Abstract
Background: Manifold Learning (ML) has become essential in recent years for expression data visualization and dimension reduction. ML operates under the assumption that the data points lie on a manifold embedded in Euclidean space. The goal of ML is to learn an inverse embedding function to decipher the manifold structure. Cancer classification is a widely investigated application of expression profile data (e.g., miRNA) currently in the literature. The non-linear ML techniques commonly applied to expression data, such as t-distributed Stochastic Neighborhood Embedding (TSNE), Isomap, and Locally Linear Embedding (LLE), are intended for visualization, and cannot easily be applied to classification or unseen data, more generally. Such methods are also unsupervised, and do not factor in class information. Conventional linear ML methods, such as Principal Component Analysis (PCA), while applicable to unseen data and easy to use, are limited and can learn only linear manifolds.
Results: We introduce a novel, non-linear ML technique, which incorporates class memberships, and is readily applicable to unseen data and classification. Specifically, we construct a series of neighborhood graphs which describe the manifold structure locally within each cluster and among the cluster centroids. Our objective function uses the neighborhood graph information to preserve the class separations and manifold structure in reduced dimension space, and we train a neural network to learn an explicit, inverse embedding function. This allows for fast visualization and classification of unseen data. The technique is compared against similar methods from the literature which use neural networks for ML, and is shown to offer improved cancer classification performance on expression data from The Cancer Genome Atlas (TCGA) and The Cancer Proteome Atlas (TCPA).
Conclusion: We present a new, supervised ML technique designed specifically for classification, which can be efficiently applied to unseen data. The results show promise on multiple, large-scale expression data sets, and thus warrant further research into supervised ML methods for cancer classification and expression data visualization.
Citation Format: James Webber, Kevin Elias. Supervised manifold learning and classification; application to expression data visualization and cancer prediction. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5372.
Abstract 5379: Deep learning enables label-free tracking of heterogeneous subpopulations
Authors: Jost Tyler , Gardner Andrea , Brock Amy 
Published: June 2023

Abstract
Observing and quantifying the proliferation of subpopulations in cancer is key to understanding how heterogeneous groups of cells interact and respond to therapy, their environment, and each other. Prior research has demonstrated that cell-state properties such as metastatic potential and genotype perturbation are encoded in cellular morphology and can be identified with various machine-learning approaches. This encoding spans multiple imaging modalities such as brightfield, phase contrast, and stained whole-slide images, and phenotype prediction can be accomplished using both classical and deep machine learning methods. Here we show that not only do these prediction capabilities extend to transcriptomic subpopulations, but that they can be used to track these populations in high throughput longitudinal live-cell imaging experiments. Using single-cell RNA sequencing, we observed that, among untreated MDA-MB-231 triple negative breast cancer cells, there exist two transcriptomically distinct populations of cells. Using fluorescence-activated cell sorting based on the differentially expressed surface marker ESAM, we isolated these subpopulations and fluorescently labeled them with mCherry or GFP depending on their transcriptomic cluster. Cells were then grown in monoculture or coculture and imaged every 4 hours at 20x resolution. To identify the associated transcriptomic cluster, we trained an instance segmentation algorithm, Mask R-CNN, to both segment and classify cells. We find that, despite being derived from the same cell line, these phenotypes can be predicted using phase contrast images alone. These results demonstrate that cellular phenotypes manifested by distinctive RNA expression signatures can now be surveilled in a high-throughput manner across multiple samples and conditions without further RNA sequencing or biomarker labeling. We anticipate that this methodology of high throughput tracking will be applicable to other heterogeneous subpopulations, such as isolated therapy-resistant or sensitive cells. Tracking transcriptomically distinct populations using only high-throughput imaging will increase the granularity of population analysis and enable more rapid assessment of cancer cell evolutionary dynamics.
Citation Format: Tyler Jost, Andrea Gardner, Amy Brock. Deep learning enables label-free tracking of heterogeneous subpopulations. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5379.
Abstract 5395: Improving survival prediction using flexible late fusion machine learning framework for multi-omics data integration
Authors: Nikolaou Nikos , Salazar Domingo , RaviPrakash Harish , Goncalves Miguel , Argoty Gustavo Alonso Arango , Burlutsky Nikolay , Markuzon Natasha , Jacob Etai 
Published: June 2023

Abstract
Improving cancer patients Overall Survival (OS) prognosis is critical for personalization of treatment using model-identified drivers of cancer progression. Current cancer prognosis models largely rely on clinical and demographic patient characteristics. Adding ‘omics’-based modalities can help improve patient OS prediction and lead to better disease categorization and understanding. We introduce a data driven methodology for combining multi-omics and clinical data, including clinical/demographics, mutations, gene expression, long non-coding and micro-RNA expression, DNA methylation, and proteomics for improving prediction of OS in cancer patients. High dimensionality of ‘omics’ modalities present challenges to combining them into one model. We propose a late stage modalities fusion where we construct a separate data driven model for OS prediction for each modality, later combining individual predictions in a final linear OS prediction model. With a limited number of patients to develop the model, such an approach helps to better protect against overfitting, and allows to account for different degrees of informativeness of modalities by weighting them according to individual success. We introduce a robust machine learning pipeline with rigorous training, testing and evaluation capabilities, and demonstrate its effectiveness on a suit of TCGA data. When comparing early vs. late fusion of omics and clinical modalities for survival prediction using NSCLC TCGA data, we observe the C-index improvement from 0.57±.04 to 0.61±.01. Best individual modality performance was at 0.59±.02 using clinical modality. Dominant modalities in unimodal survival analysis varied between cancers, with clinical, RNA, and miRNA for LUAD, and clinical, RNA, and RPPA for LUSC. Using pan-cancer TCGA data for survival prediction, the best C-index = 0.77 was achieved using multi-omics model, followed by 0.76±.01 for clinical, 0.75±.01 for RNA seq, and 0.73±.01 for RPPA unimodal models.
Citation Format: Nikos Nikolaou, Domingo Salazar, Harish RaviPrakash, Miguel Goncalves, Gustavo Alonso Arango Argoty, Nikolay Burlutsky, Natasha Markuzon, Etai Jacob. Improving survival prediction using flexible late fusion machine learning framework for multi-omics data integration. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5395.
Abstract 5396: Early detection of ovarian cancer using mRNA sample data to investigate the correlation of specific gene mutations with ovarian cancer
Authors: Saghaie Zahra , Richardson Christine , Mostafavi M. Taghi 
Published: June 2023

Abstract
Introduction: Ovarian cancer (OC) is the second most common gynecologic cancer in the U.S, and is the deadliest cancer of the female reproductive system. Because of the complex nature of OC, diagnoses are often by display of symptoms at late stages of disease. Identifying OC at early stages can increase the 5-year survival rate from 40% in stage IV to 94% in stage I. Therefore, effective screening and detection of OC at early stages is imperative. The main objective of this study is to develop a diagnostic panel of markers that correlate with early-stage OC, and ultimately, improve the survival rate of patients.
Background: Approaches in the past several years to detect malignant OC have focused on processing relevant images or screening genomic data. These studies use liquid-based biopsies, sonographic images, cyst images, CT images, MR imaging and genomics data. Some studies have focused on distinguishing malignant tumors versus benign tumor cells. In addition to statistical methods to examine available genomic datasets, Machine Learning models including K-Nearest Neighbor, K-means, Convolutional Neural Networks, Deep Learning, Support Vector Machine, Random Forest, and Fuzzy algorithms have been used.
Methods: In this study we proposed a method to determine a possible correlation between some actively expressed genes and OC, using publicly available mRNA data sets. The samples were retrieved from the GSE106817 dataset via the NCBI GEO site, with 333 cancerous and 2759 Non-cancerous samples. The objective is to define a set of genes which is able to illustrate prediction of correlation through the use of PCA, NMF and SVD. We further verified our findings by testing on one additional different mRNA datasets. For this, we compared several machine learning algorithms to potentially correlate active genes and OC stages, from the available data.
Results and Discussions: Through our study we have developed Machine Learning models valuable to identify patterns of disease at different stages, thus, potentially be useful in developing predictive models. Our initial experimental results have been demonstrated with a set of mRNA signatures, which may be used to create a panel of biomarkers valuable for detecting early-stage OC. Our results confirm the correlation between OC and PAX8, PEG3, and BIRC5, and MYB and RAC2. Continuation of this study highlights methods towards the early detection of OC and to improve disease identification outcome.
Citation Format: Zahra Saghaie, Christine Richardson, M. Taghi Mostafavi. Early detection of ovarian cancer using mRNA sample data to investigate the correlation of specific gene mutations with ovarian cancer. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5396.
Abstract 5397: Prediction of conversion surgery completion following neoadjuvant modified FOLFIRINOX in borderline resectable and locally advanced pancreatic adenocarcinoma: machine learning algorithm analysis
Authors: Yoon Hyunseok , Kim Kyu-Pyo , Park Inkeun , Jeong Jae Ho , Chang Heung-Moon , Ryoo Baek-Yeol , Yoo Changhoon 
Published: June 2023

Abstract
Background: Neoadjuvant modified (m) FOLFIRINOX is a standard therapy for medically fit patients with borderline resectable pancreatic cancer (BRPC) and locally advanced unresectable pancreatic cancer (LAPC). Completion of conversion surgery following mFOLFIRINOX is an important prognostic factor. In this study, we evaluated multiple machine learning models to predict completion of conversion surgery following neoadjuvant mFOLFIRINOX in BRPC and LAPC.
Methods: Between January 2017 and December 2020, a total of 647 patients with BRPC and LAPC treated with neoadjuvant mFOLFIRINOX at Asan Medical Center, Seoul, South Korea were enrolled. All demographic and clinicopathological data were retrospectively collected; age, ECOG performance status, BMI, resectability as per the NCCN guidelines, tumor location, tumor size, maximum standardized uptake value assessed by 18F-FDG PET/CT, number of cycles of mFOLFIRINOX, tumor response to mFOLFIRINOX, and serum CA 19-9. Several machine learning models, such as logistic regression and random forest classifier, were trained to predict curative-intent resection and were then internally validated. The relative importance of each variable was analyzed using both Gini importance and permutation importance.
Results: Among 647 patients, 173 (26.7%) underwent curative-intent conversion surgery (R0 or R1) following mFOLFIRINOX. The patients who underwent surgery showed significantly better overall survival (median 42.1 vs 17.2 months, P&lt;0.0001) than those who did not. In multivariable analysis using logistic regression, age, resectability as per the NCCN guidelines, tumor size, number of cycles of mFOLFIRINOX, tumor response to mFOLFIRINOX, and CA 19-9 change rate (percentage change in CA 19-9 after mFOLFIRINOX) were significantly correlated to tumor removal with curative-intent conversion surgery. Random forest classifier showed the best predictive capability with AUC at 0.832. The most important feature was the CA 19-9 change rate.
Conclusions: In this large cohort-based analysis, completion of conversion surgery could be predicted successfully in BRPC and LAPC patients treated with neoadjuvant mFOLFIRINOX using machine learning models. Further external validation is necessary for generalization.
Citation Format: Hyunseok Yoon, Kyu-Pyo Kim, Inkeun Park, Jae Ho Jeong, Heung-Moon Chang, Baek-Yeol Ryoo, Changhoon Yoo. Prediction of conversion surgery completion following neoadjuvant modified FOLFIRINOX in borderline resectable and locally advanced pancreatic adenocarcinoma: machine learning algorithm analysis. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5397.
Abstract 5464: Predictive biomarkers for PD-1 and PD-L1 checkpoint inhibitor response in NSCLC: An analysis of clinical trial and real-world data
Authors: So WeiQing Venus , Dejardin David , Rossmann Eva , Charo Jehad 
Published: June 2023

Abstract
Importance: Largest analysis combining clinical trial and real-world data (RWD) to assess the clinical utility of biomarkers for anti-PD-1/PD-L1 checkpoint inhibitors (CPI) in NSCLC patients.
Objective: To assess the association between tumor PD-L1, TMB and blood analytes with clinical outcomes in patients with advanced NSCLC following CPI therapy.
Design: Retrospective cohort study with patients stratified into high and low biomarker groups. Correlation with treatment outcome in the different biomarker groups was investigated and compared between patients treated with CPI versus chemotherapy.
Setting: Data derived from electronic health records mostly from community oncology settings were combined with clinical trial data.
Participants: Starting with 71850 patients with advanced NSCLC who received CPI or chemotherapy, we selected 24152 patients from an electronic health record-derived de-identified NSCLC clinicogenomic database (CGDB) from a real-world cohort and nine Roche atezolizumab trials. Patients were diagnosed between January 2011 and February 2020 (data collection cut-off date).
Main Outcomes and Measures: Drug response was assessed by RECIST criteria for clinical trials and real-world response for RWD. Durable response was defined as having CR/PR without progression during the study period of 270 days. Associations of biomarker levels with treatment outcomes were analyzed using Fisher’s exact test.
Results: High expression of PD-L1 on tumors (≥50%) were associated with lower risk of resistance to CPI (odds ratio [OR] 0.21; 95% CI = 0.14, 0.32; P&lt; .001). The association was stronger in patients who had prior chemotherapy than first line CPI, with non-squamous than squamous histology, and smokers than non-smokers. Higher TMB (≥9.57 mut/Mb) was also associated with durable response (OR = 0.41; CI = 0.30, 0.55; P&lt; .001). The combination of high TMB and PD-L1 expression was the strongest predictor of durable response (OR = 0.04; CI = 0.00, 0.20; P&lt; .001). There was no evidence of an association between PD-L1/TMB and response to chemotherapy, suggesting a CPI-specific predictive effect. In contrast, blood analytes were prognostic, having correlation with clinical outcome irrespective of treatment type.
Conclusion: This study supports the predictive utility of PD-L1 expression and TMB for NSCLC patients receiving CPI therapy and further elucidates their utility in patient sub-populations.
Citation Format: WeiQing Venus So, David Dejardin, Eva Rossmann, Jehad Charo. Predictive biomarkers for PD-1 and PD-L1 checkpoint inhibitor response in NSCLC: An analysis of clinical trial and real-world data. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5464.