tbuckley/cabot-search huggingface dataset:
Abstract A020: An early determination of patients eligibility for a bladder cancer immunotherapy using a data science approach
Authors: Kayode D. Olumoyin, Ahmet M. Aydin, Brittany L Bunch, Shari Pilon-Thomas, Michael Poch, Katarzyna A. Rejniak
Journal: Cancer Research (2024)

Abstract Advanced bladder cancer patients have limited treatment options and, in most cases, limited overall survival. The first clinical trial with adoptive T cell therapy (ACT) that uses bladder cancer patients’ autologous tumor-infiltrating T lymphocytes (TILs) has been recently opened at Moffitt. To help identify which patients could benefit from ACT-TIL we are developing a machine learning predictor that takes advantage of tumor evolution data, such as tumor stages and grades routinely collected in clinic. The first phase of ACT-TIL involves resection of the primary tumor and the growth of TILs followed by a rapid expansion protocol of the reactive T cells. It may take up to 4-5 weeks before bladder cancer patients can get their own expanded TILs. Our predictor aims to determine whether the tumor isolated from a patient will grow TILs. In this work, we present a machine learning predictor of expansion of tumor infiltrating lymphocytes (ML-PETIL) that uses retrospectively collected patient tumor data with either demographic, clinical, or biological sample-related features. The proposed method consists of a suite of algorithms to identify a robust set of predictive features (feature selection algorithms) that are used to discriminate the dataset into two classes-that with successful TIL expansion or with no TIL growth (classification algorithms). Our predictor includes metrics which are used to address data imbalance and handle small to medium-sized datasets that contain features of varying datatypes and magnitudes. The ML-PETIL protocol offers an early determination of patients who would be eligible for ACT-TIL therapy. Citation Format: Kayode D. Olumoyin, Ahmet M. Aydin, Brittany L Bunch, Shari Pilon-Thomas, Michael Poch, Katarzyna A. Rejniak. An early determination of patients eligibility for a bladder cancer immunotherapy using a data science approach [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 A020.
Abstract PR018: Adaptive local fitness landscapes for aneuploid karyotypes
Authors: Richard J. Beck, Noemi Andor
Journal: Cancer Research (2024)

Abstract Aneuploidy, which occurs in most solid tumors, dramatically alters the expression of many genes which in turn has broad phenotypic effects. Recent evidence suggests that specific aneuploid karyotypes confer a selective advantage. However, various questions remain unanswered regarding the magnitude of these selective advantages, as well as the repeatability and predictability of karyotype evolution. We developed ALFA-K (Adaptive Local Fitness landscapes for Aneuploid Karotypes) – the first approach to infer karyotype fitness landscapes in a context-dependent manner. Based on the dynamics of just a few subclones derived from longitudinal single cell sequencing data, our method can extrapolate the fitness of thousands of karyotypes. We validate the predictive power of our method on data from several P53 deficient cell lines which exhibited substantial subclonal evolution across multiple passages in vivo or in vitro. Based on topological analyses of the inferred fitness landscapes we investigated how mis-segregation rate itself shapes the stringency of karyotype evolution and whether karyotype evolution is subject to diminishing returns epistasis, sign epistasis or reciprocal sign epistasis. We adapted metrics from geo-statistics to study the fitness landscape of a P53 deficient 184-hTERT diploid breast epithelial cell line that underwent whole genome doubling (WGD) and found support for the hypothesis that WGD benefits tumor cells by enhancing their robustness against chromosomal instability. The stark phenotypic consequences of karyotype changes emphasize the value of predicting the evolutionary trajectory of karyotype composition when exposing cells to a new environment. Our work contributes to accumulating evidence that karyotype evolution is predictable. Citation Format: Richard J. Beck, Noemi Andor. Adaptive local fitness landscapes for aneuploid karyotypes [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 PR018.
Abstract A029: Merlin/NF2 inhibits pancreatic cancer metastasis by activation of AMPK-dependent autophagic degradation of p62
Authors: Yuxia Zhang, Virginia Ojeda, Xiaobo Wang, Ken Dunner, Filippo Giancotti, Hao Zhou, Jonathan Hernandez, Steven Q. Xu
Journal: Cancer Research (2024)

Abstract This abstract is being presented as a short talk in the scientific program. A full abstract is printed in the Proffered Abstracts section (PR004) of the Conference Program/Proceedings. Citation Format: Yuxia Zhang, Virginia Ojeda, Xiaobo Wang, Ken Dunner, Filippo Giancotti, Hao Zhou, Jonathan Hernandez, Steven Q. Xu. Merlin/NF2 inhibits pancreatic cancer metastasis by activation of AMPK-dependent autophagic degradation of p62 [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 A029.
Abstract B008: The polyclonal path towards malignant transformation in familial adenomatous polyposis
Authors: Ryan O. Schenck, Aziz Khan, Aaron M. Horning, Edward D. Esplin, William J. Greenleaf, James M. Ford, Michael P. Snyder, Christina Curtis
Journal: Cancer Research (2024)

Abstract The widely accepted paradigm that cancer initiation is a monoclonal event has not been definitively resolved. This is largely due to the limitations of experimental systems to study cancer initiation, a definitively transient step in tumorigenesis. Individuals with familial adenomatous polyposis (FAP) possess a germline APC mutation, leading to hundreds of colonic polyps in adolescence and an eventual 100% lifetime risk of colorectal cancer. As such, total colectomies are performed once polyp burden becomes unmanageable and provide the resected colons from FAP donors provide a source of histologically normal mucosa, benign and dysplastic polyps, and, on rare occasions, adenocarcinomas, and serve as an exemplary model disease to understand human tumorigenesis. Through whole genome sequencing of 123 independent samples from six patients, we reveal the polyclonal origins of tumorigenesis in FAP. We characterize and contrast the genomic landscape and relative timing of driver mutation acquisition across normal to dysplastic and invasive colon tissue and compare this with multi-region sequencing data from sporadic colorectal cancers (CRC). We find that only early sporadic CRC drivers are common across the more heterogeneous premalignant stages. Selection, as assessed based on the ratio of nonsynonymous to synonymous mutations (dN/dS), is higher in benign than dysplastic FAP lesions and in sporadic adenomas relative to carcinomas. Phylogenetic reconstruction and pairwise sample comparisons reveal extensive heterogeneity and subclonal sharing within a patient’s benign and dysplastic lesions. Using a mechanistic mathematical model, we infer polyclonality for 90-100% of benign and 53-83% of dysplastic samples. Based on these findings, we propose a conceptual framework for the polyclonal origin of intestinal malignancies where inconsequential mutations, arising during colon formation in utero, are present across the colon and are valuable markers to uncover polyclonal ancestry. Citation Format: Ryan O. Schenck, Aziz Khan, Aaron M. Horning, Edward D. Esplin, HTAN Consortia, William J. Greenleaf, James M. Ford, Michael P. Snyder, Christina Curtis. The polyclonal path towards malignant transformation in familial adenomatous polyposis [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 B008.
Abstract IA019: Multiscale systems approach to target tumor ecosystem responses for therapeutic benefit
Authors: Laura M. Heiser
Journal: Cancer Research (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 A032: <i>Cellos</i>: High-throughput deconvolution of 3D organoid dynamics at cellular resolution for cancer pharmacology
Authors: Patience Mukashyaka, Pooja Kumar, David J. Mellert, Shadae Nicholas, Javad Noorbakhsh, Mattia Brugiolo, Olga Anczukow, Edison T. Liu, Jeffrey H. Chuang
Journal: Cancer Research (2024)

Abstract Three-dimensional (3D) culture models, such as organoids, are flexible systems to interrogate cellular evolution, cellular growth and morphology, multicellular spatial architecture, and cell interactions in response to drug treatment. However, new computational methods to segment and analyze 3D models at cellular resolution with sufficiently high throughput are needed to realize these possibilities. Here we report Cellos (Cell and Organoid Segmentation), an accurate, high throughput image analysis pipeline for 3D organoid and nuclear segmentation analysis. Cellos segments organoids in 3D using classical algorithms and segments nuclei using a Stardist-3D convolutional neural network which we trained on manually annotated dataset of 3,862 cells. To evaluate the capabilities of Cellos we then analyzed 74,450 organoids with 1.65 million cells, from multiple experiments on triple negative breast cancer organoids containing clonal mixtures with complex cisplatin sensitivies. Cellos was able to accurately distinguish ratios of distinct fluorescently labeled cell populations in organoids, with &amp;lt; 3% deviation from seeding ratios in each well and was effective for both fluorescently labelled nuclei and independent Hoechst stained datasets. Cellos was able to recapitulate traditional luminescence-based drug response quantification by analyzing 3D images, including parallel analysis of multiple cancer clones in the same well. Moreover, Cellos was able to identify organoid and nuclei morphology features associated with treatment and unique to each of the clones. Finally, Cellos enables 3D analysis of cell spatial relationships, which we used to detect ecological affinity between cancer clones beyond what arises from local cell division and organoid composition. Cellos provides powerful tools to perform high throughput analysis for pharmacological testing and biological investigation of organoids based on 3D imaging. Citation Format: Patience Mukashyaka, Pooja Kumar, David J. Mellert, Shadae Nicholas, Javad Noorbakhsh, Mattia Brugiolo, Olga Anczukow, Edison T. Liu, Jeffrey H. Chuang. Cellos: High-throughput deconvolution of 3D organoid dynamics at cellular resolution for cancer pharmacology [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 A032.
Abstract IA012: Leveraging multiomics and machine learning towards a stepwise approach to multi-cancer screening
Authors: Jimmy Lin
Journal: Cancer Research (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.
Mn Single‐Atom Nanozyme Functionalized 3D‐Printed Bioceramic Scaffolds for Enhanced Antibacterial Activity and Bone Regeneration
Authors: Zongyan Gao, Zhenyu Song, Rong Guo, Meng Zhang, Jiamin Wu, Mingzhu Pan, Qiuzheng Du, Yaping He, Xuanzong Wang, Li Gao, Yi Jin, Ziwei Jing, Jia Zheng
Journal: Advanced Healthcare Materials (2024)

Abstract Infective bone defect is increasingly threatening human health. How to achieve the optimal antibacterial activity and regenerative repair of infective bone defect simultaneously is a huge challenge in clinic. Herein, this work reports a rational integration of Mn single‐atom nanozyme into the 3D‐printed bioceramic scaffolds (Mn/HSAE@BCP scaffolds). The integrated Mn/HSAE@BCP scaffolds can catalyze the conversion of H 2 O 2 to produce hydroxyl radical ( • OH) and superoxide anion (O 2 •− ) through cascade reaction. Besides, the prominent thermal conversion efficiency of Mn/HSAE@BCP scaffolds can be utilized for sonodynamic therapy (SDT). The synergetic strategy of chemodynamic therapy (CDT)/SDT enables the sufficient generation of reactive oxygen species (ROS) to kill Staphylococcus aureus ( S. aureus ) or Escherichia coli ( E. coli ). Furthermore, the enhanced antibacterial efficacy of Mn/HSAE@BCP scaffolds is beneficial to upregulate the expression of osteogenesis‐related markers (such as collagen 1(COL1), Runt‐related transcription factor 2 (Runx2), osteocalcin (OCN), and osteoprotegerin (OPG)) in vitro and further promote bone regeneration in vivo. The results demonstrate the good potential of Mn/HSAE@BCP scaffolds for the enhanced antibacterial activity and bone regeneration, which provide an effective method for the treatment of clinical infective bone defect.
Abstract A001: Genomic signatures of past and present chromosomal instability in Barrett’s esophagus and early esophageal adenocarcinoma
Authors: Chunyang Bao
Journal: Cancer Research (2024)

Abstract Abstract: The progression of precancerous lesions to malignancy is often accompanied by increasing complexity of chromosomal alterations but how these alterations arise is poorly understood. Here we perform haplotype-specific analysis of chromosomal copy-number evolution in the progression of Barrett’s esophagus (BE) to esophageal adenocarcinoma (EAC) on multiregional whole-genome sequencing data of BE with dysplasia and microscopic EAC foci. We identify distinct patterns of copy-number evolution indicating multigenerational chromosomal instability that is initiated by cell division errors but propagated only after p53 loss. While abnormal mitosis, including whole-genome duplication, underlies chromosomal copy-number changes, segmental alterations display signatures of successive breakage-fusion-bridge cycles and chromothripsis of unstable dicentric chromosomes. Our analysis elucidates how multigenerational chromosomal instability generates copy-number variation in BE cells, precipitates complex alterations including DNA amplifications, and promotes their independent clonal expansion and transformation. In particular, we suggest sloping copy-number variation as a signature of ongoing chromosomal instability that precedes copy-number complexity. These findings suggest copy-number heterogeneity in advanced cancers originates from chromosomal instability in precancerous cells and such instability may be identified from the presence of sloping copy-number variation in bulk sequencing data. Citation Format: Chunyang Bao. Genomic signatures of past and present chromosomal instability in Barrett’s esophagus and early esophageal adenocarcinoma [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 A001.
Abstract IA020: Microphysiological systems to decipher tumor-stromal interactions in colorectal cancer
Authors: Shannon Mumenthaler
Journal: Cancer Research (2024)

Abstract Cancer is a complex adaptive system driven by the intricate interplay between tumor cells and their microenvironment. Among these interactions, cancer-associated fibroblasts (CAFs), the predominant cellular component of the tumor stroma, are frequently linked with an unfavorable prognosis and play an important role in the progression of various cancers. Although extensive literature has underscored the impact of CAFs on cancer cell behaviors such as proliferation and invasion, the stromal influence on phenotypic plasticity in the context of treatment response and metastasis remains largely unexplored. Furthermore, preclinical treatment studies often concentrate on drug-induced changes in tumor cells with little investigation into the impact on surrounding stromal cells. To enhance our comprehension of the intricate and dynamic interactions between cancer cells and their microenvironment, we are utilizing quantitative high-content imaging in conjunction with more physiologically relevant patient-derived model systems, such as organoids and organ-on-chip technologies. Here we will describe our experimental workflows and demonstrate how the incorporation of mathematical modeling enhances our understanding of 3D organoid growth dynamics and facilitates a more detailed examination of drug resistance influenced by tumor plasticity. One illustrative example we will emphasize involves the treatment of CAFs with an anti-epidermal growth factor receptor (EGFR) targeted agent, cetuximab, resulting in an increased secretion of epidermal growth factor (EGF). This heightened EGF secretion was sufficient to render neighboring tumor cells resistant to cetuximab treatment. These efforts aim to increase our understanding of tumor-stromal interactions in cancer evolution, particularly emphasizing the functional and therapeutic potential of CAFs, through the integration of expertise across disciplines. Citation Format: Shannon Mumenthaler. Microphysiological systems to decipher tumor-stromal interactions in colorectal cancer [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 IA020.