bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract A003: Genome evolution of a transmissible cancer in the soft-shell clam (Mya arenaria)
Authors: Hart Samuel F.M. , Giersch Rachael M. , Yonemitsu Marisa A. , Beal Brian , Goff Stephen P. , Metzger Michael J. 
Published: May 2022

Abstract
A small number of transmissible cancer lineages exist in which cancer cells metastasize repeatedly to new hosts and live past the death of their original hosts. Transmissible cancers have been observed in the dog, Tasmanian devil, and most recently in numerous bivalves species, including one lineage that has spread through the soft-shell clam (Mya arenaria) population along the east coast of North America. We assembled a highly contiguous 1.2 Gb soft-shell clam reference genome and characterized the genome evolution of this transmissible cancer lineage. Our analyses confirm that all cancer samples descended from a single founder clam, but cluster in two geographically separated sub-lineages. In both sub-lineages, we observe high somatic mutation density, widespread copy number gain, structural rearrangement and transposable element activity, all indicative an unstable cancer genome. We also discovered a novel mutational signature generating mutations at a stable rate, predicting the cancer to be 344 to 877 years old and indicating it spread undetected long before it was first observed in the 1970s.
Citation Format: Samuel F.M. Hart, Rachael M. Giersch, Marisa A. Yonemitsu, Brian Beal, Stephen P. Goff, Michael J. Metzger. Genome evolution of a transmissible cancer in the soft-shell clam (Mya arenaria) [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr A003.
Abstract A008: TP53 field defects in uterine fluid are associated with ovarian cancer risk
Authors: Risques Rosana , Smith Thomas H. , Norgaard Zachary K. , Katz Roniz , Lo Fang Yin , Schmidt Elizabeth K. , Higgins Jacob E. , Filipits Martin , Labidi-Galy Intidhar , Cibula David , Dostálek Lukáš , Jelenek Gabriel , Plch Magdalena , Bouda Jiří , Mustea Alexander , Condic Mateja , Grill Sabine , Gleeson Noreen , Oppelt Peter , Pristauz-Telsnigg Gunda , Vanderstichele Adriaan , Obrecht Siel , Rosenthal Adam , Speiser Paul , Salk Jesse 
Published: May 2022

Abstract
This abstract is being presented as a short talk in the scientific program. A full abstract is available in the Proffered Abstracts section (PR005) of the Conference Proceedings.
Citation Format: Rosana Risques, Thomas H. Smith, Zachary K. Norgaard, Roniz Katz, Fang Yin Lo, Elizabeth K. Schmidt, Jacob E. Higgins, Martin Filipits, Intidhar Labidi-Galy, David Cibula, Lukáš Dostálek, Gabriel Jelenek, Magdalena Plch, Jiří Bouda, Alexander Mustea, Mateja Condic, Sabine Grill, Noreen Gleeson, Peter Oppelt, Gunda Pristauz-Telsnigg, Adriaan Vanderstichele, Siel Obrecht, Adam Rosenthal, Paul Speiser, Jesse Salk. TP53 field defects in uterine fluid are associated with ovarian cancer risk [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr A008.
Abstract A011: Deciphering clonal evolution under chemotherapy in high-risk neuroblastoma using patient derived models
Authors: Andersson Natalie , Nuñez Adriana Mañas , Aaltonen Kristina , Hansson Karin , Seger Alexandra , Radke Katarzyna , Esfandyari Javanshir , Bexell Daniel , Gisselsson David 
Published: May 2022

Abstract
This abstract is being presented as a short talk in the scientific program. A full abstract is available in the Proffered Abstracts section (PR001) of the Conference Proceedings.
Citation Format: Natalie Andersson, Adriana Mañas Nuñez, Kristina Aaltonen, Karin Hansson, Alexandra Seger, Katarzyna Radke, Javanshir Esfandyari, Daniel Bexell, David Gisselsson. Deciphering clonal evolution under chemotherapy in high-risk neuroblastoma using patient derived models [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr A011.
Abstract A015: Inferring phylogenetic trees based on copy number aberrations from methylation array
Authors: Ding Chuling , Herrero Javier 
Published: May 2022

Abstract
Background: The development of methylation array platforms has allowed large scale studies on cancer epigenomes. Apart from intended analyses on methylation patterns, it is also possible to infer copy number profiles by leveraging the signal from methylation array data. To disentangle complex mixtures of phylogenetically related clones, it is best to jointly analyze different samples (different regions, timepoints, etc.) from the same tumor. However, current methods for studying copy number aberrations (CNAs) from methylation arrays consider tumor samples independently. In this project, we introduce Multi-Sample EPIC Phylogenetic Reconstruction (MultiSEPhyR), aiming to investigate the copy number profiles and clonal compositions from multi-sample methylation array data. Material and methods: As part of the GCGR (Glioma Cellular Genetic Resource; gcgr.org.uk) project, we analyze 53 samples from 26 cases with multiple methylation array data of primary glioblastomas, tumor-derived cell lines and xenograft re-derived cell lines. Copy Number Aberrations (CNAs) called from the methylation array data are validated using WGS data from a subset of the cohort. By jointly clustering the segmented LogR ratios from related samples, we can define tumor clones based on clustered CNA events. We use a two-step approach, where we firstly identify clonal events to obtain sample purity and tumor ploidy and use these values to subsequently infer the copy number state and the clonality of the remaining events. Results and Discussions: On a simulated dataset, MultiSEPhyR could accurately estimate sample purity and identify both clonal and sub-clonal CNA events. We then apply our method on GCGR dataset and show similar extent of intra-tumor heterogeneity in primary glioblastomas and derived cell lines. We also investigate clonal dynamics in each case. In the cell lines, while the heterogeneity is well captured, some sub-clonal CNA events may exhibit significantly different clonality compared to the primary glioblastomas. For example, we observe a clonal expansion of the clones harboring a deletion in chromosome 6 in xenograft re-derived cell lines. Conclusion We present a systematic way to call CNAs and resolve clonal compositions from methylation array data, leading to a cost-efficient way to study tumor evolution.
Citation Format: Chuling Ding, Javier Herrero. Inferring phylogenetic trees based on copy number aberrations from methylation array [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr A015.
Abstract A018: Lung cancer associated DNA Damageome reveals a genome destabilizing role of Aquaporin 3
Authors: Xia Jun , Lin Mu , Song Zhuoyi , Fernandes Gail , Rosenberg Susan , Amos Chris 
Published: May 2022

Abstract
Lung cancer is a multifactorial disease caused by both environmental exposures and genetic factors. Genome and transcriptome-wide association studies (GWAS and TWAS) successfully identified many genetic variants and candidate causal genes associated with lung cancer. However, the lack of functional studies of these genes remains a major bottleneck to clinical translation. Recently, we discovered that large networks of evolutionarily conserved DNA damageome proteins (DDPs) promote DNA damage and genome instability. Here, we discovered a lung cancer-associated DNA damageome network. We further discovered that one of the lung cancer DDPs, Aquaporin 3 (AQP3), can promote ROS dependent DNA damage upon overproduction. Furthermore, it potentiates DNA damage by a low dose of arsenic exposure, creating unique double strand break hotspots which help predict mutational signatures. We screened for high endogenous DNA damage levels for more than 40 TWAS and GWAS-nominated candidates using high-throughput single cell-based flow cytometric assays. We optimized and performed Mre11-Cut&Tag (Cleavage Under Targets and Tagmentation) to map double strand breaks, which are the most deleterious form of DNA damage. In addition, we used END-Seq to provide a much higher resolution of the DSB map and uncovered hotspots in arsenic-detoxifying genes. We are implementing duplex sequencing to map the mutational signatures caused by arsenic and the arsenic-Aquaporin 3 interaction. Our study brings function to endogenous DNA damage and DNA damageome proteins that interact with environmental toxicants. Mechanistic insights into how low-dose arsenic interacts with risk genes yield critical knowledge for the prevention, diagnosis, and treatment of arsenic-associated diseases. We also uncovered several early biomarkers to potentially predict the long-term health impacts of arsenic.
Citation Format: Jun Xia, Mu Lin, Zhuoyi Song, Gail Fernandes, Susan Rosenberg, Chris Amos. Lung cancer associated DNA Damageome reveals a genome destabilizing role of Aquaporin 3 [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr A018.
Abstract A019: Mutation agnostic diagnosis of clonal hematopoiesis of indeterminate potential (CHIP) using fluctuating methylation clocks
Authors: Schenck Ryan O. , Jakobsen Niels Asger , Turati Virginia , Shibata Darryl , Vyas Paresh , Leedham Simon , Anderson Alexander R.A. 
Published: May 2022

Abstract
Clonal hematopoiesis (CH), such as clonal hematopoiesis of indeterminant potential (CHIP), is diagnosed based on somatic genomic alterations in the absence of hematologic malignancy. At present, CHIP is diagnosed using peripheral blood, where putative driver point mutations and small insertions/deletions whose variant allele frequency is greater or equal to two percent. Generally, the prevalence of CH increases as an individual ages and conveys a risk for progression to a malignancy. CH is thought to be driven by the underlying hematopoietic stem cells of an unknown quantity, with estimates in the literature for stem cell numbers differing by orders of magnitude. Previously, we developed a method using fluctuating CpG (fCpG) sites to serve as a fluctuating methylation clock to uncover stem cell dynamics in glandular tissues and orthogonally validated our method using publicly available datasets of human blood from normal cohorts and malignant cohorts. Here we expand on this work by presenting 38 new patients with distinct VAF groups from 1-2% VAF up to greater than 10% VAF for putative drivers with corresponding DNA methylation profiles using the Illumina EPIC array platform. We identify fCpG from our normal and CHIP cohorts to train and validate a machine learning approach that allows us to diagnose CHIP without DNA sequencing. Importantly, our approach allows for the identification of patients who may have CH driven by structural variants such as copy number alterations. We use this method to evaluate two publicly available methylation datasets of reportedly normal patients (n=656 and n=732) showing that evidence of CHIP can be found in 19% and 29% of these datasets, respectively. We then evaluate copy number differences in burden within our CHIP cohort and these newly identified CHIP cohorts. Using a mechanistic model of hematopoietic stem cells containing fCpGs we examine the temporal dynamics of competing founder CHIP drivers and the number of stem cells in the hematopoietic stem cell compartment.
Citation Format: Ryan O. Schenck, Niels Asger Jakobsen, Virginia Turati, Darryl Shibata, Paresh Vyas, Simon Leedham, Alexander R.A. Anderson. Mutation agnostic diagnosis of clonal hematopoiesis of indeterminate potential (CHIP) using fluctuating methylation clocks [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr A019.
Abstract A020: Multi-species regulatory analysis of osteosarcoma to reduce clinical trial failure rates
Authors: Torabi Tina Salehi , Gottipati Soumya , Kahveci Tamer , Graim Kiley 
Published: May 2022

Abstract
Osteosarcoma is a rare pediatric cancer characterized by poor prognosis and a high likelihood of metastasis, for which no systemic treatments have been successfully developed in the past 30 years [1]. The lack of effective biologically relevant osteosarcoma tumor models is a significant obstacle to the development of effective therapeutics [2]. Therefore, developing a framework that addresses this issue is essential in further advancing therapeutics studies. We postulate that a comparative genomics approach, evaluating whole-transcriptome osteosarcoma response in species closely related to humans, would highlight immune and other differences in each species that will better guide the design of human therapeutics. To investigate this, we compile RNA-seq data from studies of mice, human, and canine osteosarcoma [3], and perform an integrated analysis of disease in these three species. We build a tri-species multi-layer network of regulatory interactions in osteosarcomas to identify evolutionary links between osteosarcomas. Each layer of our network represents the interactions in one species, and the integrated network captures interactions preserved across species. We perform enrichment analysis on these networks and statistically evaluate differences between the topologies of the regulatory networks of these three species, investigating the functional impact of species-specific interactions and how each affects drug response. Additionally, we survey the literature to identify mutations in osteosarcomas from each species, and calculate diffusion scores for mutated genes in each layer of the network compared to genes mutated in osteosarcoma. As future work, we aim to utilize our comparative genomics approach and leverage the differences between interaction networks of related species to compute the likelihood of identifying early-stage therapeutic trials that will fail in later stages, thus avoiding costly experiments and guiding drug development.
Citation Format: Tina Salehi Torabi, Soumya Gottipati, Tamer Kahveci, Kiley Graim. Multi-species regulatory analysis of osteosarcoma to reduce clinical trial failure rates [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr A020.
Abstract A028: Selective pressures on bone cancer evolution and treatment resistance emerging from the interactions with the bone ecosystem
Authors: Basanta David , Lynch Conor , Miller Anna , Bishop Ryan 
Published: May 2022

Abstract
The bone is the organ of origin of several types of cancer including multiple myeloma, as well as a host to many metastatic lesions such as those characterizing prostate and breast cancer. The evolutionary dynamics that drive primary and metastatic progression in the bone, as well as those that explain the emergence of resistance to treatments such as proteosome inhibitors and hormonal deprivation therapies, result from the interactions between the tumor and the bone ecosystem. Models that can capture the complex ecological interactions in the bone are key to define the selective pressures underpinning somatic evolution in the bone. Our approach combines computational agent-based models with both in vitro and pre-clinical models in order to shed lights on some open questions in regard to tumor-stromal interactions. As well as the integration of biological and computational models, our approach is hypotheses-driven and captures the homeostasis of the bone that emerges from the interactions of key cellular species such as osteoblasts and osteoclasts. We have explored our models to investigate explore how prostate cancer cells can successfully initiate metastases in the bone, novel therapeutic approaches to treat bone metastatic prostate cancer, explore the emergence of treatment resistance in multiple myeloma and tease apart the contribution of environmentally-mediated drug resistance from cell-intrinsic resistance to proteosome inhibitors.
Citation Format: David Basanta, Conor Lynch, Anna Miller, Ryan Bishop. Selective pressures on bone cancer evolution and treatment resistance emerging from the interactions with the bone ecosystem [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr A028.
Abstract A034: Bayesian adaptive design for phase I drug combination trials
Authors: Yuan Ying , Liu Suyu 
Published: May 2022

Abstract
Drug combination therapy is the most commonly used approach to overcome acquired resistance to cancer therapy. Designing phase I drug combination trials faces several challenges beyond the conventional single-agent dose escalation studies, e.g., two-dimensional dose space and partial order in toxicity. In this presentation, we will overview novel Bayesian adaptive designs for drug combination trials. In particularly, we will focus on model-assisted designs that are simply to implement, while yielding superior operating characteristics. Trial example and software will be provided to illustrate the application of the novel designs.
Citation Format: Ying Yuan, Suyu Liu. Bayesian adaptive design for phase I drug combination trials [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr A034.
Abstract B007: Design and construction of evolutionary-guided "selection gene drive" therapy
Authors: Leighow Scott M. , Inam Haider , Pritchard Justin R. 
Published: May 2022

Abstract
This abstract is being presented as a short talk in the scientific program. A full abstract is available in the Proffered Abstracts section (PR014) of the Conference Proceedings.
Citation Format: Scott M. Leighow, Haider Inam, Justin R. Pritchard. Design and construction of evolutionary-guided "selection gene drive" therapy [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr B007.
Abstract B009: Integrating mathematical modelling and wet-lab experiments to examine the scope for adaptive treatment scheduling of PARP inhibitors in ovarian cancer
Authors: Strobl Maximilian , Damaghi Mehdi , Martin Alexandra , Byrne Samantha , Robertson-Tessi Mark , Gatenby Robert , Wenham Robert , Maini Philip , Anderson Alexander R.A. 
Published: May 2022

Abstract
PARP inhibitors (PARPis) represent a great advancement in the treatment of ovarian cancer, yet these drugs often fail after a few months due to emerging drug resistance. A recent clinical trial in prostate cancer showed that evolutionary-inspired, adaptive drug scheduling significantly delayed time to progression. This approach adaptively skipped treatment to maintain a pool of drug-sensitive cells that suppressed resistant cells through competition. Here, we present results from a combined modelling and experimental study in which we investigated whether adaptive therapy could delay resistance to the PARPi olaparib in ovarian cancer.We performed a series of in vitro experiments in which we used Incucyte Zoom time-lapse microscopy to characterize the cell population dynamics under different PARPi schedules. Leveraging these data we developed an ordinary differential equation mathematical model of treatment response, and used this model to test different plausible adaptive treatment schedules. Our model can accurately predict the in vitro treatment dynamics, even to new schedules, and suggests that treatment modifications need to be carefully timed, or one risks losing control over tumor growth, even in the absence of any resistance. This is because multiple rounds of cell division are required for cells to acquire sufficient DNA damage to induce apoptosis. As a result, adaptive therapy algorithms that modulate treatment but never completely withdraw it are predicted to perform better in this setting than strategies based on treatment interruptions. Subsequent experiments confirm this prediction in vivo. To conclude, we present preliminary work aiming at investigating the scope for clinical translation of our results by confronting our model with longitudinal CA-125 data from 53 ovarian cancer patients receiving olaparib at Moffitt. Overall, this study contributes to a better understanding of the impact of scheduling on treatment outcome for PARPis, and showcases some of challenges involved in developing adaptive therapies for new treatment settings.
Citation Format: Maximilian Strobl, Mehdi Damaghi, Alexandra Martin, Samantha Byrne, Mark Robertson-Tessi, Robert Gatenby, Robert Wenham, Philip Maini, Alexander R.A. Anderson. Integrating mathematical modelling and wet-lab experiments to examine the scope for adaptive treatment scheduling of PARP inhibitors in ovarian cancer [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr B009.
Abstract B015: Eco-evolutionary dynamics of poly-aneuploid cancer cells: A life history model
Authors: Bukkuri Anuraag , Pienta Kenneth J. , Austin Robert H. , Hammarlund Emma U. , Amend Sarah R. , Brown Joel S. 
Published: May 2022

Abstract
Despite major advances in cancer research over the last several decades, advanced cancers largely remain incurable due to the evolution of resistance. Recent experimental evidence suggests that a poly-aneuploid cancer cell (PACC) state may contribute to the emergence of therapeutic resistance by allowing cancer cells to avoid the effects of and adapt to extreme environmental stressors. The PACC state is enabled when aneuploid cells undergo whole genome doubling but subsequently bypass mitosis, cytokinesis, or both. We create a novel mathematical framework for modeling the eco-evolutionary dynamics of state-structured populations and use this framework to construct a model of cancer populations with an aneuploid and a PACC state. Using in silico simulations, we explore how the PACC state allows cancer cells to 1) survive extreme environmental conditions by exiting the cell cycle after S phase and protecting genomic material and 2) aid in adaptation to environmental stressors by increasing the cancer cell's evolvability (ability to generate heritable variation) through the increase in genomic content that accompanies polyploidization. In doing so, we demonstrate the ability of the PACC state to contribute to therapeutic resistance and stress the importance of targeting PACC populations for improved therapeutic outcomes.
Citation Format: Anuraag Bukkuri, Kenneth J. Pienta, Robert H. Austin, Emma U. Hammarlund, Sarah R. Amend, Joel S. Brown. Eco-evolutionary dynamics of poly-aneuploid cancer cells: A life history model [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr B015.