bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract B023: Paracrine enhancement of tumor cell proliferation provides indirect stroma-mediated chemoresistance via acceleration of tumor recovery between chemotherapy cycles
Authors: Miroshnychenko Daria , Miti Tatiana , Kumar Pragya , Miller Anna , Laurie Mark , Giraldo Nathalia , Bui Marilyn M. , Altrock Philipp M. , Basanta David , Marusyk Anriy 
Published: February 2024

Abstract
The ability of tumors to survive therapy reflects both cell-intrinsic and microenvironmental mechanisms. Across many cancers, including triple-negative breast cancer (TNBC), a high stroma/tumor ratio correlates with poor survival. In many contexts, this correlation can be explained by the direct reduction of therapy sensitivity by stroma-produced paracrine factors. We sought to explore whether this direct effect contributes to the link between stroma and poor responses to chemotherapies. Our in vitro studies with panels of TNBC cell line models and stromal isolates failed to detect a direct modulation of chemoresistance. At the same time, consistent with prior studies, we observed treatment-independent enhancement of tumor cell proliferation by fibroblast-produced secreted factors. Using spatial statistics analyses, we found that proximity to stroma is often associated with enhanced tumor cell proliferation in vivo. Based on these observations, we hypothesized an indirect link between stroma and chemoresistance, where stroma-augmented proliferation potentiates the recovery of residual tumors between chemotherapy cycles. To evaluate the feasibility of this hypothesis, we developed a spatial agent-based model of stroma impact on proliferation/death dynamics. The model was quantitatively parameterized using inferences from histological analyses and experimental studies. We found that the observed enhancement of tumor cell proliferation within stroma-proximal niches can enable tumors to avoid elimination over multiple chemotherapy cycles. Therefore, our study supports the existence of a novel, indirect mechanism of environment-mediated chemoresistance that might contribute to the negative correlation between stromal content and poor therapy outcomes.
Citation Format: Daria Miroshnychenko, Tatiana Miti, Pragya Kumar, Anna Miller, Mark Laurie, Nathalia Giraldo, Marilyn M. Bui, Philipp M. Altrock, David Basanta, Anriy Marusyk. Paracrine enhancement of tumor cell proliferation provides indirect stroma-mediated chemoresistance via acceleration of tumor recovery between chemotherapy cycles [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr B023.
Abstract IA004: Insights into lung cancer evolution and metastases in TRACERx and PEACE
Authors: Jamal-Hanjani Mariam 
Published: February 2024

Abstract
TRACERx is a prospective longitudinal cancer evolution programme which investigates the relationship between intratumour heterogeneity and clinical outcomes in non-small cell lung cancer (NSCLC). In an interim cohort of 421 patients with extended follow up, matched primary and metastatic tumour analysis has demonstrated the impact of cancer evolution on patient outcomes. Large dominant subclones in primary tumours were associated with positive subclonal selection, and mutations in specific genes were found to be under significant subclonal, and not clonal, selection. Subclonal genome doubling was identified in 20% of tumours and multiple genome doubling events were identified in 10% of tumours. Patients with tumours containing large recent subclonal expansions had significantly shorter disease-free survival (DFS), and as previously demonstrated, chromosomal instability was associated with poor outcomes, in particular, extrathoracic disease relapse. Metastatic relapse was found to occur late in tumour evolution in 75% of tumours, and early divergence (25% of tumours) was found to occur at predicted tumour volumes below the threshold for further investigation using radiological imaging, suggesting potential implications for clinical intervention. Metastasis-seeding subclones in primary tumours were subject to subclonal expansion, likely reflective of the acquisition of subclonal driver mutations and somatic copy number alterations. ctDNA analyses in this larger cohort demonstrated the value of ctDNA to predict the likelihood of future relapse, but also its use in tracking tumour growth dynamics and emergence of relapse. Patients who develop relapsed disease in TRACERx after surgery are co-recruited into PEACE, a national research autopsy programme. Early findings from PEACE have demonstrated the genomic complexity of cancer relapse and metastatic heterogeneity and the different patterns of cellular migration that aid metastatic dissemination with both primary-to-metastasis and metastasis-to-metastasis seeding.
Citation Format: Mariam Jamal-Hanjani. Insights into lung cancer evolution and metastases in TRACERx and PEACE [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr IA004.
Abstract IA005: Novel insights into pancreatic cancer evolution from carcinogenesis to metastasis
Authors: Iacobuzio-Donahue Christine 
Published: February 2024

Abstract
While the genetic features of pancreatic cancer are well described, the evolutionary features of this tumor type have not been systematically studied to date. We assembled a large cohort of pancreatic cancer research autopies to investigate evolutionary metrics across clinical contexts. The timing of driver mutations, including truncal and subtruncal events, do not differ significantly across stages at diagnosis or types of clinical management. However, high truncal mutation density is significantly associated with worse overall survival after adjusting for stage at diagnosis, age, smoking history and Type II diabetes status, representing a novel prognostic biomarker. Oligometastatic tumors have fewer drivers and allelic losses compared to aggressive metastases, suggesting a more genomically stable subset of tumors. These findings reveal novel insights into pancreatic cancer evolution beyond established paradigms.
Citation Format: Christine Iacobuzio-Donahue. Novel insights into pancreatic cancer evolution from carcinogenesis to metastasis [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr IA005.
Abstract IA007: Improving cancer treatment through data-driven game theory
Authors: Stankova Katerina 
Published: February 2024

Abstract
Standard of care in metastatic cancers typically applies the Maximum Tolerable Dose (MTD) of treatment or treatment combinations, either continuously or in repeated identical cycles, until unacceptable toxicity, cancer progression, or cure. Cure is rare due to fast-evolving therapy resistance. I will explain how game theory can help us not only understand why MTD fails but also design the first evolutionary anti-cancer therapies—therapies that anticipate and forestall the evolution of treatment-induced resistance in cancer cells. Such therapies can be designed through Stackelberg evolutionary games (SEGs), i.e., games between a rational leader (here, a physician) and evolutionary followers (here, evolving cancer cells). I will demonstrate how data science and machine learning can empower game theory to design evolutionary anti-cancer therapies and how evolutionary therapies lead to a better quality and quantity of patients’ lives.
Citation Format: Katerina Stankova. Improving cancer treatment through data-driven game theory [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr IA007.
Abstract IA009: Stem cells, clonal evolution and therapeutic resistance
Authors: Laplane Lucie 
Published: February 2024

Abstract
Cancer stem cells (CSCs) are the units of selection in cancer (Greaves and Maley 2012; Greaves 2013). If they exist. After a first stage of excitement and strong evidence for CSCs in blood cancers, the concept of CSC and their involvement in solid cancers have been subject to much debate. To understand the role of CSC in clonal evolution and treatment resistance, we need a clear understanding of what CSC are. Several attempts have been made by scientists and philosophers, leading to a dichotomous view of normal and cancer stem cells: either they refer to entities or to a plastic cell states. In the first case, CSCs are the units of selection, and they are involved in treatment resistance. The latter case comes instead with a rejection of the CSC model: in case of plasticity, CSCs are neither a privilege unit of selection, nor a target for cancer treatment. In this talk, I will show that this current dichotomous view is misleading and conflates parameters that need to be distinguished if we want to understand what CSCs are and what is their role in clonal evolution and treatment resistance. By deconvoluting these parameters, we can have a much more precise characterization of CSCs. From this characterization, we can make testable predictions about therapeutic resistance and open new avenues on how to think about cancer treatment.
Citation Format: Lucie Laplane. Stem cells, clonal evolution and therapeutic resistance [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr IA009.
Abstract IA011: The epigenetic-based liquid biopsy odyssey of bedside pancreatic cancer diagnostics
Authors: Ahuja Nita 
Published: February 2024

Abstract
Pancreatic cancer (PC), often diagnosed at an advanced stage, urgently requires innovative early detection methods to improve patient outcomes. Presently, there is no approved screening method for pancreas cancer and its high-risk individuals (HRIs), as existing techniques rely on costly and invasive imaging modalities, which face challenges in detecting interval cancers. This study presents an innovative approach that utilizes liquid biopsy technology combined with blood-based DNA methylation biomarkers—ADAMTS1, BNC1, and LRFN5. These biomarkers demonstrate a remarkable 94% diagnostic accuracy for early-stage PC detection, providing a transformative strategy in precision medicine. In line with precision medicine principles, the potential application of this liquid biopsy-based DNA methylation panel for monitoring HRIs offers a promising path toward personalized surveillance. The creation of an affordable bedside test with exceptional diagnostic accuracy has the potential to revolutionize early detection strategies for PCs. Identifying early curable stage PC through this liquid biopsy technique aligns perfectly with precision medicine, promising increased patient survival. If successfully integrated into routine healthcare, early detection could significantly advance the fight against pancreatic cancer, paving the way for more effective and patient-centric diagnostic approaches.
Citation Format: Nita Ahuja. The epigenetic-based liquid biopsy odyssey of bedside pancreatic cancer diagnostics [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr IA011.
Abstract IA012: Leveraging multiomics and machine learning towards a stepwise approach to multi-cancer screening
Authors: Lin Jimmy 
Published: February 2024

Abstract
Early cancer discovery is challenging due to heterogeneity of different cancers and even within cancers from the same organ. In order to tackle this problem, Freenome has built a multiomics discovery platform that looks for signals along the entire central dogma-DNA, methylation, RNA, protein, immunoprofiling, extracellular vesicles, circulating cells, among others. In order to featurize, train, and build the best models that are generalizable and robust, we have created our own machine learning platform incorporating different computational biological signals. All this is aligned with a step-wise investigation of cancers that can most benefit the population and make a difference to patients, starting with colorectal cancer and advanced adenoma. During this talk, we will discuss the clinical, scientific, and computational strategy that we think is important to create the best products to benefit the most patients.
Citation Format: Jimmy Lin. Leveraging multiomics and machine learning towards a stepwise approach to multi-cancer screening [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr IA012.
Abstract IA019: Multiscale systems approach to target tumor ecosystem responses for therapeutic benefit
Authors: Heiser Laura M. 
Published: February 2024

Abstract
Breast tumors arise and progress via processes that involve intrinsic deregulation of epithelial cells and that also alter the composition and function of associated stromal and immune cells. Together, these tumor-intrinsic and microenvironmental changes enable malignant epithelial cells in the tumor to acquire key cancer hallmarks, including proliferation, migration, immune evasion and further evolution. The resulting collection of cancer and stromal cells comprise a complex, adaptive tumor ecosystem. Long-term therapeutic success will require that drugs attack malignant cells, favorably activate the immune system, and create a tumor microenvironment that does not support tumor (re)growth. Creating a process for treatment selection will require an integrated systems biology approach to develop, test and refine a robust computational framework that takes into account the states, signaling relationships, and spatial organization that determine the emergent properties inherent in complex interacting systems. Toward this goal, we have developed a coordinated experimental-computational approach designed to enable identification of effective treatment strategies. To examine therapeutic responses of diverse aspects of the tumor ecosystem, we have deployed a novel drug delivery microdevice that enables rapid, high-throughput assessment of the effects of multiple therapies on tumor cells and the surrounding microenvironment. When coupled with multiplex tissue imaging, this platform provides a comprehensive assessment of the state and spatial organization of the tumor ecosystem as it adapts to therapy. Initial studies demonstrated that many drugs designed to target malignant epithelial cells strongly impact stromal and immune cells, providing new insights into the importance of considering multiple aspects of the tumor ecosystem when designing effective therapeutic strategies. The resultant data are being used to guide development of a multiscale computational model that considers the functional relationships between different tumor components and can be used to predict the effects of therapies on diverse aspects of tumor ecosystems. In total, our approach has the potential to transform cancer treatments through rational identification of effective therapeutic strategies that lead to long-term durable control of tumors.
Citation Format: Laura M. Heiser. Multiscale systems approach to target tumor ecosystem responses for therapeutic benefit [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr IA019.
Abstract PR001: Learning to adapt: Rational personalization of adaptive therapy using deep reinforcement learning
Authors: Gallagher Kit , Strobl Maximilian , Gatenby Robert , Maini Philip , Anderson Alexander 
Published: February 2024

Abstract
Standard-of-care treatment regimens have long been designed for maximal cell kill, yet these strategies often fail when applied to metastatic cancers due to the emergence of drug resistance. Adaptive treatment strategies have been developed as an alternative approach, dynamically adjusting treatment to suppress the growth of treatment-resistant populations, delaying, or even preventing, tumor progression. Promising clinical results in prostate cancer indicate the potential to optimize adaptive treatment protocols. We propose the application of deep reinforcement learning (DRL) to guide adaptive drug scheduling, and demonstrate that these treatment schedules can outperform the current adaptive protocols in a mathematical model calibrated to prostate cancer dynamics, more than doubling the time to progression. We show that the DRL strategies are robust to patient variability, including both tumor dynamics and clinical monitoring schedules. We demonstrate that the DRL framework can produce interpretable, adaptive strategies based on a single tumor burden threshold, replicating and informing optimal treatment strategies. Given the DRL framework has no knowledge of the underlying mathematical tumor model, this demonstrates the capability of DRL to help develop treatment strategies in novel or complex settings. Finally, we propose a five-step pathway combining mechanistic modeling with the DRL framework to translate this approach to the clinic with limited information for a given patient. Integrating conventional tools to ensure the interpretability of traditionally `black-box' DRL models, we generate personalized treatment schedules that consistently outperform clinical standard-of-care protocols.
Citation Format: Kit Gallagher, Maximilian Strobl, Robert Gatenby, Philip Maini, Alexander Anderson. Learning to adapt: Rational personalization of adaptive therapy using deep reinforcement learning [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr PR001.
Abstract PR002: Somatic mutation rates and strength of selection on driver genes change by age in melanoma
Authors: Liu Yihan , Rajaei Moein , Mandell Jeff , Townsend Jeffrey 
Published: February 2024

Abstract
The frequencies of driver mutations in melanoma vary in patients of different ages. However, it is unclear whether these differences in frequency are driven by variation in underlying mutation rates or by a changing selective regime in the aging body. Employing a bulk tumor sequence dataset from 5472 melanomas (459 whole-exome sequences and 5013 cancer gene-panel sequences) analyzed with the cancereffectsizeR package, we quantified somatic mutation rates and the strength of selection on somatic mutations, measuring the survival advantage conferred by a somatic mutation across the tumorigenic trajectory. Likelihood ratio tests were used to compare cancer effect sizes between age groups: &lt;40 y/o, 40-59 y/o and ≥ 60 y/o. The proportion of cancer effect attributable to each mutational source was calculated by averaging over bootstrapped resample of mutational signature weights. Mann-Whitney U statistics were used to test the difference in signature weights and cancer effect sources across the groups.The underlying mutation rate increased with age, with a mean of 4.49e-6, 4.88e-6, 6.45e-6 in patients &lt;40 y/o, 40-59 y/o and ≥ 60 y/o respectively. From the whole-exome and panel data, 23 variants differed significantly across age groups, among which BRAF V600E had the largest effect sizes, followed by NRAS Q61R, NRAS Q61K, NRAS Q61L. BRAF V600E were significantly larger in patients &lt;40 y/o, while NRAS Q61R, NRAS Q61K, NRAS Q61L were significantly larger in patients 40-59 y/o and ≥ 60 y/o. Although underlying mutation rates and strengths of selection varied across age groups, the origin of melanomas from patients in all three age groups was predominantly attributed to UV light. These observations illuminate numerous theoretical questions regarding the origins of cancer at different ages. They are helpful to widespread implementation of prevention strategies. Moreover, understanding the relative strength of selection on driver mutations within patients at distinct ages is crucial for improving precision therapeutic interventions.
Citation Format: Yihan Liu, Moein Rajaei, Jeff Mandell, Jeffrey Townsend. Somatic mutation rates and strength of selection on driver genes change by age in melanoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr PR002.
Abstract PR006: Evolution of evolvability
Authors: Gallaher Jill A. , Strobl Maximilian , West Jeffrey , Roberston-Tessi Mark , Anderson Alexander R. A. 
Published: February 2024

Abstract
The purpose of this work is to better understand how the rate of phenotypic adaptation of cells in a tumor affects its ability to survive treatment. Drug resistance is an ongoing problem for maintaining a sustained response in treating advanced cancers. Regardless of their lineage, heritable phenotypic adaptions may be beneficial for the tumor to survive stepwise changes in the environment during tumor growth. There may be further benefit for cells to develop the trait of being adaptable - to respond to large shifts in the microenvironment, survive metastasis to a new organ, and thrive when treatments are applied. Genomic instability and phenotypic redundancy may aid in adaptations, but there may be limits. With too much mutation and deviation from the parental phenotype, cells lose important regulatory and maintenance functions necessary to survive. So is there an optimal rate of evolvability? We use an off-lattice agent-based model to investigate how the rate of change through phenotypic trait space affects tumor growth and response to treatment. We assign cells with different combinations of proliferation rates and migration speeds and test how the variance from the parental cell on division affects the selection of trait combinations over time. During growth, heterogeneity is gained quickly and space matters less if evolvability is forced to continually increase, but there is more selection for proliferative phenotypes and slower evolvability if its rate is allowed to drift freely. We found that tradeoffs generally imposed a greater selection force leading to faster recurrence and the coexistence of proliferation or migration specialists. We compared responses to continuous, intermittent, and adaptive treatment schedules of an anti-proliferative drug. At some point with treatment, there is an optimal evolvability, as larger increases in diversity allows for cells to fall back into a sensitive state and be killed by treatment. We aim to design better treatment strategies that work for tumors with different levels of evolvability by understanding how the rate of tumor evolution affects growth and treatment response.
Citation Format: Jill A. Gallaher, Maximilian Strobl, Jeffrey West, Mark Roberston-Tessi, Alexander R. A. Anderson. Evolution of evolvability [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr PR006.