tbuckley/cabot-search huggingface dataset:
Abstract IA016: Bayesian phylodynamics to quantify cancer growth across space and time
Authors: Maya Lewinsohn, Trevor Bedford, Nicola F. Müller, Alison Feder
Journal: Cancer Research (2024)

Abstract Phylogenetics has traditionally inferred shared genetic histories of species over macroevolutionary time. With the expansion of cancer multi-region and single-cell sequencing, phylogenetic approaches have found new uses mapping the evolutionary histories of tumors as well. Recently developed “phylodynamic” methods that integrate phylogenetic and population dynamic modeling offer further promise - uncovering cancer growth dynamics from the topologies and branch lengths of tumor trees. Here, I discuss two recent efforts in this area: 1) Heterogeneous growth rates across a tumor become encoded in the branching patterns of phylogenetic trees built from multi-region sequencing. We introduce a cancer-specific extension to a class of multi-state birth-death models to infer these growth rate differences across space. We apply our model (SDevo) to a sample of hepatocellular carcinomas and infer that these tumor peripheries grow 2-6 times faster than their interiors. 2) Tumors experience differential growth rates over time as they acquire new driver mutations and invade their surrounding tissues, and these growth dynamics similarly leave characteristic signatures in their phylogenies. We employ Bayesian skyline models to dissect tumor growth dynamics from a dataset of diverse cancers and reveal variation in cancer growth rates over time. As cancer sequencing continues to proliferate, phylodynamic approaches are poised to reveal new quantitative insights into cancer growth dynamics across time and space. Citation Format: Maya Lewinsohn, Trevor Bedford, Nicola F. Müller, Alison Feder. Bayesian phylodynamics to quantify cancer growth across space and time [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 IA016.
Abstract PR006: Evolution of evolvability
Authors: Jill A. Gallaher, Maximilian Strobl, Jeffrey West, Mark Roberston-Tessi, Alexander R. A. Anderson
Journal: Cancer Research (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.
Abstract A025: Adaptive therapy: Leveraging evolutionary dynamics for colorectal cancer management in vivo
Authors: Harley I. Richker, Gissel Marquez-Alcaraz, Carlo Maley
Journal: Cancer Research (2024)

Abstract Therapeutic resistance to chemotherapy is frequently the proximate cause of cancer treatment failure and, ultimately, patient mortality. Evolutionary therapies have been developed to address this problem and potentially reduce toxicities. The most successful strategy to date, adaptive therapy (AT), is designed to use chemosensitive cells to out-compete and suppress the chemoresistant cells. However, detailed observations to elucidate the mechanisms of AT's success are lacking. Typically, AT involves lowering the dose of the chemotherapy, thereby reducing toxicity and preserving the immune system. We are evaluating AT for the first time in colorectal cancer, using syngeneic orthotopic xenograft models. This project marks several firsts: 1) sensitive and resistant clones will be tracked longitudinally, 2) AT will be tested with a targeted therapy (MRTX1133), 3) AT will be tested in an immune competent model, 4) multiple drugs will be used for AT, and 5) extinction therapy will be tested in mice. We will employ genetic barcodes and differential luciferase tagging to track the tumor cell subpopulations longitudinally. Currently, we are conducting an in-vivo pilot experiment to validate our protocol, emphasizing tumor engraftment efficacy and tracking cell subpopulations. Additionally, we are investigating the potential of Lactobacillus acidophilus to inhibit tumor growth, augment chemotherapy, and bolster immune response in colorectal cancer. Citation Format: Harley I. Richker, Gissel Marquez-Alcaraz, Carlo Maley. Adaptive therapy: Leveraging evolutionary dynamics for colorectal cancer management in vivo [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 A025.
Abstract PR016: Topology of the tumor microenvironment: Integrating imaging, modeling, and topological data analysis
Authors: Rowan Barker-Clarke, Jason M. Gray, Jacob G. Scott
Journal: Cancer Research (2024)

Abstract Cancer evolution involves multiple stochastic processes across many scales. In addition to genomic and transcriptomic changes, hallmarks of cancer within solid tumors include the malignant co-evolution of the tumor microenvironment. Although the evaluation of tissue architecture plays a key role in pathologist staging, the automated extraction of additional structural features remains far from translation to the clinic. However, one particularly effective novel methodology for extracting patterns from these clinical images is topological data analysis(TDA). TDA is a field that studies the shape of data and its connectedness using concepts from geometric and algebraic topology. Recent work using topological data analysis has allowed the extraction of image features. Such features have been associated with clinical outcomes in both the analysis of H&amp;E images in prostate cancer and CT images in lung cancer. We set out to question how the topological features of tissues, including tumor and stromal composition, evolve during treatment and between original sites and metastasis. To analyze this we use a combination of tissue sections, imaging, and cell-automata models and persistent homology and cubical persistence techniques from TDA. We apply certain topological methods for the first time to tissue images (H&amp;E) and to the output of spatial simulations. We focus specifically on analyzing the evolution of topological features in ovarian and lung cancer and compare our features to 2D and 3D simulations (HAL) with varying game theoretical interactions as well as comparing features across tissues from matched primary and metastatic sites. We find that several topological features are conserved between primary and metastatic sites and some feature evolution is replicated in simulation. Citation Format: Rowan Barker-Clarke, Jason M. Gray, Jacob G. Scott. Topology of the tumor microenvironment: Integrating imaging, modeling, and topological data analysis [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 PR016.
Abstract IA009: Stem cells, clonal evolution and therapeutic resistance
Authors: Lucie Laplane
Journal: Cancer Research (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 IA007: Improving cancer treatment through data-driven game theory
Authors: Katerina Stankova
Journal: Cancer Research (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 B036: Mutational landscape and genetic feature of gastric adenocarcinoma in multiple Chinese ethnicities
Authors: Liang Zong
Journal: Cancer Research (2024)

Abstract Gastric cancer is a heterogeneous malignancy and the fifth most common cancer worldwide, with 44% of cases occurring in China. Elucidation of genetic subtypes may improve gastric cancer treatment planning. Matched tumour and peritumoural (non-malignant gastric mucosa) tissue samples were collected from 306 Chinese patients and subjected to whole-exome sequencing (WES). The results were used to produce a Chinese Gastric Cancer Genome Atlas (CGCGA). Patients from nine Chinese ethnic groups (Han, Zhuang, Hui, Uygur, Yi, Tibetan, Mongolian, Bai, and Kazakh), representing 95% of the Chinese population were included. Systematic comparison of CGCGA data with TCGA (The Cancer Genome Atlas) data, in which 437 gastric cancer cases are represented, showed that genomic alteration signatures in the present cohort differed markedly from those reported for Western cohorts. TP53 and FAT1 deletions were more frequent (35% vs. 0%) while ARID1A deletions (13% vs. 25%), MYC oncogene amplifications (53% vs. 70%), and SMAD4 deletions (34% vs. 46%) were less frequent in our CGCGA data than in the TCGA data. TRIM49B mutation was associated with worse survival. Thus, TRIM49B was identified as a novel oncogene. The Zhuang ethnic group had a much higher tumour mutation burden (TMB) than the other ethnic groups. Typically, tumours from Han group had microsatellite stability, whereas those from the other groups had microsatellite instability (MSI). We further propose a molecular classification of four gastric cancer subtypes that are associated with distinct survival outcomes: TP53 mutation positive (TP53+)/TTN_missense+; TP53+/TTN_missense–; TP53–/TTN_missense+, and TP53–/TTN_missense–. Our study established a comprehensive gastric cancer genome atlas in China and underscored the importance of considering population context in genomic mapping and rational treatment options for the disease. Citation Format: Liang Zong. Mutational landscape and genetic feature of gastric adenocarcinoma in multiple Chinese ethnicities [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 B036.
Abstract IA017: Extrachromosomal DNA (ecDNA): Cancer’s dynamic circular genome
Authors: Paul Mischel
Journal: Cancer Research (2024)

Abstract Human genes are arranged on 23 pairs of chromosomes, but in cancer, tumor-promoting genes and regulatory elements can free themselves from chromosomes and relocate to circular, extrachromosomal pieces of DNA (ecDNA). ecDNA poses one of the greatest challenges for cancer patients, affecting children and adults and women and men, with a wide variety of cancer types. Although ecDNA was first observed more than 50 years ago, its critical importance has only recently come to light. ecDNA, because of its non-chromosomal inheritance, drives high-copy number oncogene amplification and enables tumors to evolve their genomes rapidly, contributing to treatment resistance and shorter survival for patients. Furthermore, the circular ecDNA architecture fundamentally alters gene regulation and transcription. In this talk, I will discuss recent collaborative discoveries that highlight how the higher order, spatial organization of ecDNA, and its mechanism of inheritance, contributes to aggressive tumorigenesis, accelerated evolution, and treatment resistance. Citation Format: Paul Mischel. Extrachromosomal DNA (ecDNA): Cancer’s dynamic circular genome [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 IA017.
Selected Articles from This Issue
Journal: Clinical Cancer Research (2024)

Results from the phase II CAPTIVATE study demonstrated that first-line treatment with fixed-duration ibrutinib plus venetoclax provides deep, durable responses and sustained progression-free survival with a favorable safety profile in young, fit patients with chronic lymphocytic leukemia.In the current analysis, Jain and colleagues show that no resistanceassociated mutations in BTK, PLCG2, or BCL2 were identified in samples from patients experiencing progressive disease after completion of fixed-duration ibrutinib plus venetoclax.Clinical data for retreatment after progressive disease support the clinical relevance of these findings.These results suggest that fixed-duration combination treatment with ibrutinib plus venetoclax may mitigate the development of resistance mechanisms associated with continuous single-agent targeted therapies and allow for effective retreatment with ibrutinib or ibrutinib plus venetoclax, thereby extending clinical benefit with these agents.
Abstract PR002: Somatic mutation rates and strength of selection on driver genes change by age in melanoma
Authors: Yihan Liu, Moein Rajaei, Jeff Mandell, Jeffrey Townsend
Journal: Cancer Research (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: &amp;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 &amp;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 &amp;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 IA015: Phylomedicine of cancers and tumors: A molecular phylogenetic approach to oncology
Authors: Sudhir Kumar
Journal: Cancer Research (2024)

Abstract Genetic mutations arising from germline and somatic cell divisions cause cellular diversification and form the need for integrating an evolutionary perspective in oncology. Such mutations can lead to the emergence of aberrant cell types, driving the progression of tumorigenic and metastatic cancer subclones. Advances in single-cell and bulk sequencing technologies now routinely reveal a trove of genetic alterations in cancer cells, tissues, and genes. These data enable the reconstruction of detailed cellular phylogenies and the evolutionary trajectories of genes implicated in cancer. This presentation will introduce our approaches for reconstructing cellular phylogenies and their downstream applications in genomic medicine. They include novel approaches for inferring cellular phylogenies by correcting for inaccuracies in single-cell genomic data and separating mixed subclone signals in bulk sequencing data through molecular evolutionary principles. Incorporating accurate phylogenetic relationships, we have developed many computational methods, including diagnosing functional outcomes of nonsynonymous mutations, tracking metastatic pathways retrospectively, and predicting the risk of hematological malignancies. This presentation will briefly introduce these approaches and suggest that integrating molecular phylogenetics with genomic science—Phylomedicine—holds promise for enhancing the fundamental understanding of cancers and advancing approaches to predicting, diagnosing, and treating cancers. Citation Format: Sudhir Kumar. Phylomedicine of cancers and tumors: A molecular phylogenetic approach to oncology [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 IA015.