bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract 2091: DeepBTS: Prediction of recurrence-free survival of non-small cell lung cancer using time-binned deep neural network
Authors: An Ho Jung , Lee Bora , Chun Sang Hoon , Hong Ji Hyung , Woo In Sook , Kim Seoree , Jeong Joon Won , Kim Jae Jun , Lee Hyun Woo , Park Sungsoo , Jo Heejoon , Ko Yoon Ho 
Published: September 2020

Abstract
Introduction: Accurate prediction of non-small cell lung cancer (NSCLC) prognosis after surgery is still challenging. Cox proportional hazard model is widely used, but there are some limitations like proportional hazard assumption, computational complexity, and unspecified time component. In this study, we developed the novel neural network models using 30 clinicopathological features of surgically resected NSCLC patients.
Materials and Methods: We analyzed a dataset of training cohorts of 1,034 patients and external validation cohort of 303 patients, and compared the novel neural network model and Cox PH model to predict 3-year recurrence of the patient.
Results: We developed deep neural network for binned time survival analysis (DeepBTS) algorithm, which use root-mean-squared error (supervised learning model, s- DeepBTS) or negative log-likelihood (semi-unsupervised learning model, su- DeepBTS) as loss function. The su-DeepBTS algorithm showed the improved performance (C-index, 0.7166; AUC, 0.7606) compared to other models (Cox PH: 0.6861 and 0.7185; s-DeepBTS: 0.6926 and 0.7250, respectively). Top 10 features was selected from su-DeepBTS model and su-DeepBTS selector pair: the number of lymph node metastasis, tumor size, histologic subtype, vascular invasion, R0 resection, neoadjuvant /adjuvant treatment, and performance status of patients. These features distinguished low and high-risk group in the training cohort (p = 4.25×10-5) and the validation cohort (p = 1.07×10-11).
Conclusions: The su-DeepBTS model using 10 selected features can predict the prognosis of resected NSCLC patients. We plan to diversify the input features by radiologic, pathological imaging and genomic data to integrate multimodal medical information and enhance the performance of this model using deep neural network.
Citation Format: Ho Jung An, Bora Lee, Sang Hoon Chun, Ji Hyung Hong, In Sook Woo, Seoree Kim, Joon Won Jeong, Jae Jun Kim, Hyun Woo Lee, Sungsoo Park, Heejoon Jo, Yoon Ho Ko. DeepBTS: Prediction of recurrence-free survival of non-small cell lung cancer using time-binned deep neural network [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 2091.
Abstract 2102: Interpretable machine learning for perturbation biology
Authors: Shen Judy , Yuan Bo , Luna Augustin , Korkut Anil , Marks Debora , Ingraham John , Sander Chris 
Published: September 2020

Abstract
Systematic perturbation of cells followed by comprehensive measurements of molecular and phenotypic responses provides an informative data resource for constructing computational models of cell biology. Models that generalize well beyond training data can be used to identify combinatorial perturbations of potential therapeutic interest. Major challenges for machine learning on large biological datasets are to find global optima in an enormously complex multi-dimensional solution space and to mechanistically interpret the solutions. To address these challenges, we introduce a hybrid approach that combines explicit mathematical models of dynamic cell biological processes with a machine learning framework, implemented in Tensorflow. We tested the modeling framework on a perturbation-response dataset for a melanoma cell line after drug treatments. The models can be efficiently trained to accurately describe cellular behavior, as tested by cross-validation. Even though completely data-driven and independent of prior knowledge, the resulting de novo network models recapitulate known interactions. The main predictive application of our work is the identification of combinatorial candidates for cancer therapy. This approach is readily applicable to a wide range of kinetic models of cell biology.
Citation Format: Judy Shen, Bo Yuan, Augustin Luna, Anil Korkut, Debora Marks, John Ingraham, Chris Sander. Interpretable machine learning for perturbation biology [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 2102.
Abstract 2107: Seeing glycolysis on PDAC: Applying deep learning convolutional neural network model
Authors: Tsou Peiling , Wu Chang-Jiun 
Published: September 2020

Abstract
The reprogramming of cellular metabolism to support continuous proliferation is a hallmark of cancer. Accumulating evidence indicates that the reprogramming of tumor metabolism is under the control of various oncogenic signals. The Kras oncogene is known to induce aerobic glycolysis, and pancreatic adenocarcinoma (PDAC) cells have been shown previously to have metabolism consistent with elevated aerobic glycolysis. Several glycolytic inhibitors are currently in preclinical and clinical development. It had been reported that cancer metabolic status could play a role in the interaction with tumor microenvironment. We hypothesized that cancer glycolysis activity could affect tumor-stromal interaction in PDAC. As a proof-of-concept study, we trained a deep learning convolutional neural network (CNN) model (Google Inception v3) on histopathologic images obtained from The Cancer Genome Atlas (TCGA) PDAC cohort to classify tumors with high and low glycolysis pathway activity. We aimed to answer whether CNNs can predict PDAC glycolysis status using images as the only input. The accuracy on independent validation set is 77.4% with area under ROC curve as 0.698 (Pearson correlation=0.418, p=0.026). Survival analysis showed that higher glycolysis activity was associated with worse clinical outcome in TCGA PDAC. The current study demonstrates the potential of deep learning approaches for histopathologically classifying cancer based on metabolic status. This information could be of value in assisting clinical decisions on targeting cancer metabolism.
Citation Format: Peiling Tsou, Chang-Jiun Wu. Seeing glycolysis on PDAC: Applying deep learning convolutional neural network model [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 2107.
Abstract 2108: Application of random forest machine learning techniques on mixed data from breast cancer studies
Authors: Quist Jelmar , Taylor Lawson , Staaf Johan , Grigoriadis Anita 
Published: September 2020

Abstract
Integrative analysis of diverse high-dimensional molecular, histopathological and clinical data provides an effective way to identify biologically and clinically relevant subclasses across multi-level data. However, unbiased integrative methods to identify distinctive features and group structure in such data remains problematic. We propose a machine learning clustering technique based on random forest methods that enables unbiased integration. Using a permutation-based framework for the tree construction procedure and measuring of feature importance, robust and pure clusters can be produced. The performance of standard, regularised, and conditional inference random forest methods was evaluated using the adjusted Rand index, the Calinski-Harabasz index, and cluster and feature purity. In simulations studies, random forest clustering techniques were able to identify clusters of high purity. Using datasets from the UCI Machine Learning Repository as a proof of concept, all three techniques were able to identify clusters in mixed data, whereby the conditional inference method produced clusters with the highest feature purity. Next, we applied our clustering techniques to high-dimensional data obtained from two independent breast cancer studies: (i) International Cancer Genome Consortium (ICGC), consisting of 560 cases and 147 features, and (ii) Sweden Cancerome Analysis Network - Breast (SCAN-B), incorporating 241 cases and 53 features. Features included rearrangement and mutational signatures, somatic mutation in cancer drivers, germline mutations in BRCA1/2, genomic instability measures, intrinsic molecular breast cancer subtypes, and clinico-pathological characteristics. Despite dissimilarities in the breast cancer subtype composition between these two datasets, the conditional inference random forest method was able to identify concordant subgroups between the studies supported by molecular and histopathological characteristics. Moreover, novel relationships amongst molecular features with potential clinical relevance were revealed. For example, one cluster was enriched for BRCA2-deficient breast cancer cases with MYC amplifications, while another predominantly consisted of non-basal-like triple-negative breast cancers with PIK3CA mutations. Together, these results support the use of our machine learning clustering technique based on random forest methods to identify robust and biologically relevant group structures using complex high-dimensional mixed data.
Citation Format: Jelmar Quist, Lawson Taylor, Johan Staaf, Anita Grigoriadis. Application of random forest machine learning techniques on mixed data from breast cancer studies [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 2108.
Abstract 2129: Proteogenomic analysis points to relationship between LINE-1 retrotransposon expression and copy number variation
Authors: McKerrow Wilson H. , Wang Xuya , Grivainis Mark , Fenyö David 
Published: September 2020

Abstract
Changes in the DNA copy number of genes (copy number variation/CNV) are widespread in many cancers and affect the expression of tumor suppressors and oncogenes. Many of these CNVs arise from chromosomal aberrations that are the result of improperly repaired DNA damage. LINE-1 retrotransposons - genomic elements that cut DNA and then reverse transcribe themselves into the gap - are expressed in many cancers and likely contribute to DNA damage and CNV in cancer cells. Using multi-omic data from CPTAC, we developed cloud-based tools on the NCI funded Cancer Genomics Cloud to measure LINE-1 RNA, protein, phosphoprotein and insertion in five cancer types - breast, ovarian, endometrial, kidney and colon - and identified mutation, expression and phosphorylation correlates of LINE-1 expression. We found significant LINE-1 expression in all, but kidney cancer. LINE-1 expression was significantly correlated with global copy number variation in breast, endometrial and ovarian cancer, a trend that was reflected in the fact that we observed the highest LINE-1 expression in ovarian cancer, which is characterized by very high CNV, and the lowest LINE-1 expression in kidney cancer, which has less overall CNV. This correlation is mirrored in our finding that, in breast, ovarian and endometrial cancer, p53 mutants express more LINE-1, indicating that high LINE-1 expression occurs in a background that is tolerant of DNA damage and CNV. We also observed an association between LINE-1 expression and phosphorylation of 53BP1 in breast, colon and endometrial cancers. ATM phosphorylated 53BP1 localizes to double strand breaks and promotes repair by non-homologous end-joining (NHEJ). Taken together, this analysis supports the hypothesis that in many cancers LINE-1 contributes to DNA damage and copy number variation, perhaps involving mismatched repair by NHEJ.
Funded by NCI Contract No. HHSN261200800001E
Citation Format: Wilson H. McKerrow, Xuya Wang, Mark Grivainis, David Fenyö. Proteogenomic analysis points to relationship between LINE-1 retrotransposon expression and copy number variation [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 2129.
Abstract 2130: Human papillomavirus and hepatitis B virus in situ tumor oncoviral genotype and coinfection drive oncogenesis and patient clinical response in cross cancer analysis
Authors: Bubie Adrian P. , Testoni Barbara , Zoulim Fabien , Miles Brett , Posner Marshall , Villanueva Augusto , Losic Bojan 
Published: September 2020

Abstract
Genotype of hepatitis B virus (HBV) and human papillomavirus (HPV) infection is well known as a significant risk factor in developing liver (HCC), cervical, and head and neck cancers. However, the effect of tumor viral genotype and single-virus coinfection in driving oncogenesis and striating patient survival is generally unclear, as previous studies have not exhaustively genotyped viral-cofactors or profiled viral gene expression in tumors. In this work, we develop and deploy a new scalable, high throughput dynamic sequence alignment tool that facilitates extremely sensitive and precise oncoviral genotype deconvolution using tumor RNA sequencing data from 536 virally infected liver, cervical, and head and neck tumors across several cohorts. Pairing these results with tumor gene expression and survival analyses, we find HBV genotype C (HBV-C) associated HCC patients (n=79) are significantly upregulated in cancer proliferation and decreased cancer survival (FDR&lt;0.01) compared to HBV genotype B associated tumors (n=23). Across 3 liver cancer cohorts, oncogenes TERT, MLL4, and FN1 are found recurrently integrated in HBV-C but not HBV-B. HPV clade a9 infected cervical cancers (n=173) are associated with more aggressive tumor growth and worse survival than HPV clade a7 (n=39) infected tumors (FDR&lt;0.01), and are enriched in COSMIC mutational signatures linked with defective DNA repair and hypermutation. Furthermore, we demonstrate that HPV coinfection may drive patient response as neoantigens from cervical tumors with multiple HPV infections (n=18) are more immunogenic on average than single-infection tumors (n=23), controlling for HPV genotype (p&lt;0.001). Finally, we compare HPV gene expression in cervical and head and neck patients, demonstrating that HPV genotype drives viral gene expression over cancer type (p&lt;0.0001), and indicating viral protein expression may be similar across episomal and integration-based infections. Our results illustrate that HBV and HPV genotype, and coinfection of tumor with multiple genotypes, serve as significant predictors of patient survival and tumor immune responsiveness, and should be considered alongside clinical covariates to improve survival and treatment models.
Citation Format: Adrian P. Bubie, Barbara Testoni, Fabien Zoulim, Brett Miles, Marshall Posner, Augusto Villanueva, Bojan Losic. Human papillomavirus and hepatitis B virus in situ tumor oncoviral genotype and coinfection drive oncogenesis and patient clinical response in cross cancer analysis [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 2130.
Abstract 2136: RegTools: Integrative analysis of genomic and transcriptomic data to identify splice altering mutations across 35 cancer types
Authors: Cotto Kelsy C. , Feng Yang-Yang , Skidmore Zachary L. , Ramu Avinash , Kunisaki Jason , Conrad Donald F. , Lin Yiing , Chapman William , Uppaluri Ravindra , Govindan Ramaswamy , Griffith Obi L. , Griffith Malachi 
Published: September 2020

Abstract
The interpretation of variants in cancer is often focused on genomic alterations that have a known coding consequence. This analysis strategy excludes somatic mutations in non-coding regions of the genome and even exonic mutations may have unidentified non-coding consequences. To address this issue, we created RegTools, a free, open-source software package that integrates analysis of variant calls from genomic data with evidence of expressed splice junctions from transcriptomic data to efficiently identify variants that may cause aberrant splicing in tumors. To date, we have applied RegTools to over 9,000 samples from TCGA and the McDonnell Genome Institute in an effort to identify somatic variants that are associated with alternative splicing patterns within these tumors. For each mutation, we defined a set of control samples in the same cancer cohort that lacked the particular mutation. We then determined putative splicing-relevant mutations by identifying novel splice isoforms whose expression was significantly elevated in the presence of the associated mutation. To further characterize these somatic variants and their associated splice isoforms, we annotated them with the Variant Effect Predictor (VEP), SpliceAI, and Genotype-Tissue Expression (GTEx) junction counts. While some of these mutations would have been predicted by VEP or SpliceAI to be splicing relevant, our analysis provides additional detail by specifically associating a mutation with an altered junction or junctions, including ones not predicted by VEP/SpliceAI or observed within GTEx normal tissue data. RegTools is freely available and open source (www.regtools.org) and all analysis scripts are provided within the project's GitHub repo (https://github.com/griffithlab/regtools).
Citation Format: Kelsy C. Cotto, Yang-Yang Feng, Zachary L. Skidmore, Avinash Ramu, Jason Kunisaki, Donald F. Conrad, Yiing Lin, William Chapman, Ravindra Uppaluri, Ramaswamy Govindan, Obi L. Griffith, Malachi Griffith. RegTools: Integrative analysis of genomic and transcriptomic data to identify splice altering mutations across 35 cancer types [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 2136.
Abstract 2152: Characterisation of co-expression of CD39 with other IO-relevant markers on major T/NK cell TIL subsets
Authors: Hair Jim , Luheshi Nadia , Dovedi Simon J. , Wilkinson Robert W. 
Published: September 2020

Abstract
CD39 has recently been identified as critically differentiating tumour-reactive from bystander tumour-infiltrating leukocyte (TIL) CD8+ T cells. CD39 is an ectonucleotidase that degrades immunostimulatory ATP to ADP and AMP. CD73 then further metabolises AMP to generate immunosuppressive adenosine in the tumor microenvironment. To facilitate our understanding of CD39 as a therapeutic target we established a detailed receptor co-expression profile for CD39 with other immuno-oncology (IO)-relevant targets on TIL T/NK cells. TIL samples from three diseases (NSCLC, Renal cancer [RC] and Melanoma) were profiled, via the creation and use of a single large flow cytometry panel, combined with computational analysis.
Unbiased clustering identified the expected major T and NK cell populations, however one of the most striking findings was that CD39 was highly differentially expressed across the different cell subsets. Almost all TREG cells were CD39 positive, whereas CD8+ T cells and NK cells were highly variable in their degree of expression of CD39. CD4+CD8+ T cells predominantly did not express cell surface CD39 and only very few CD4+ TCONV cells expressed CD39.
The co-expression profile for TREG, TCONV and NK cells is presented, but we have also defined a detailed phenotype of the critical tumour-specific CD39+CD8+ T cell population. Our analyses show that CD39+CD8+ T cells express more markers associated with antigen-experienced T cells versus CD39-CD8+ T cells; a significantly greater proportion of CD39+CD8+ T cells expressed TIM3, CTLA-4, PD-1, TIGIT, CD137 and GITR versus CD39-CD8+ T cells. Significantly fewer CD39+CD8+ T cells than CD39-CD8+ T cells expressed CD28, a receptor known to be downregulated on antigen-experienced cytotoxic CD8+ T cells.
These data reveal the detailed co-expression patterns of key IO-relevant receptors across TIL T and NK cell subsets and are consistent with recent reports identifying CD39 as a key marker differentiating the critical tumor-specific, antigen-experienced tumor infiltrating CD8+ T cells.
Citation Format: Jim Hair, Nadia Luheshi, Simon J. Dovedi, Robert W. Wilkinson. Characterisation of co-expression of CD39 with other IO-relevant markers on major T/NK cell TIL subsets [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 2152.
Abstract 2168: A growth rate corrected metric for cytotoxic co-culture systems
Authors: Melms Johannes Christian , Yapp Clarence , Mills Caitlin Elizabeth , Sorger Peter , Izar Benjamin 
Published: September 2020

Abstract
Tumor/T-cell co-cultures in combination with large-scale genome-editing tools emerged as powerful tools for discovery of drivers of resistance to immunotherapies. Traditionally, chromium or lactate-dehydrogenase release and/or target cell viability counts before and after co-culture treatment are used as surrogate endpoint assays to estimate the selection pressure applied. These assays cannot dissect growth arrest, cell death, cell viability and target cell outgrowth, all of which occur as a consequence of released cytokines and direct tumor/T-cell interactions, hence, significantly impact the interpretation of small- and large-scale screens in co-culture models. These biases are particularly relevant when comparing cell lines or genotypes with different growth kinetics. Here, we use a dual reporter system and adopt pharmaco-mathematical principles to dissect these biases to create a growth-rate corrected co-culture metric (GRC), which normalizes T-cell mediated antiproliferative and cytotoxic effects in tumor target cells. We illustrate the value of this metric by comparing fast and slow-growing tumor cells in autologous patient-derived melanoma/T-cell co-culture pairs. At tumor-T-cell ratios resulting in a half maximal relative reduction of target cell counts compared to controls, the fast-proliferating cell line had not changed in absolute cell counts since baseline while the slow-growing cell line showed a 50% reduction. This indicates that in one instance (fast growing cell line) growth-retardation occurred due to a balance of immune editing and target cell proliferation, while in the other (slow-growing cell line) T-cell mediated killing resulted in net loss of tumor cells. Using GRC, these differences can be readily distinguished, and comparable selection conditions can be selected. This has important implications for large-scale screens with perturbations within cancer cells: drop-outs from a screen in the fast-growing cell line would not only contain perturbations that increase specific sensitivity to T-cell mediated cytotoxicity, but also perturbations causing a general loss in proliferation/survival fitness. In addition, some of the discrepancy in recently published screens may be explained by differences in models with varying proliferation rates and therefore selection conditions. In summary, the GRC metric enhances the interpretation of co-culture experiments and could contribute to improved selection of targets from large-scale screens investigating resistance to T-cell mediated toxicity for drug development.
Citation Format: Johannes Christian Melms, Clarence Yapp, Caitlin Elizabeth Mills, Peter Sorger, Benjamin Izar. A growth rate corrected metric for cytotoxic co-culture systems [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 2168.