tbuckley/cabot-search huggingface dataset:
Abstract B029: Centuries of genome instability and evolution in soft-shell clam (<i>Mya arenaria</i>) transmissible cancer
Authors: Samuel F.M. Hart, Michael J. Metzger
Journal: Cancer Research (2024)

Abstract Transmissible cancers are infectious parasitic clones that metastasize to new hosts, living past the death of the founder animal in which the cancer initiated. Using genomic and transcriptomic analyses, we investigated the evolutionary history of a recently identified transmissible cancer lineage that has spread though the soft-shell clam (Mya arenaria) population. We first assembled a chromosome-scale soft-shell clam reference genome and characterized somatic mutations in the cancer lineage, discovering a novel mutational signature that estimates the lineage to be &amp;gt;200 years old and observing a wide variety of mutation types indicative of an extremely unstable cancer genome. We next quantified gene expression, observing differential expression of stereotypical cancer hallmarks in addition to key pathways that may have facilitated the cancer’s ability to survive seawater transfer to new hosts and evade immune rejection. Finally, we developed genetic assays to track bivalve transmissible neoplasia in the soft-shell clam and identify a novel lineage in the Baltic clam (Macoma balthica). Taken together, this study reveals the long-term survival of an invertebrate cancer lineage and identifies adaptive mechanisms to evade physical and immune barriers to cancer transmission, which may have been facilitated in part by the adaptive potential of its unstable genome. Citation Format: Samuel F.M. Hart, Michael J. Metzger. Centuries of genome instability and evolution in soft-shell clam (Mya arenaria) transmissible 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 B029.
Abstract IA011: The epigenetic-based liquid biopsy odyssey of bedside pancreatic cancer diagnostics
Authors: Nita Ahuja
Journal: Cancer Research (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 A019: The role of tumor transcriptional variability in evading acute CD8+ T cell exposure in melanoma
Authors: Raymond W.S. Ng, Sydney M Shaffer
Journal: Cancer Research (2024)

Abstract Throughout melanoma progression and immunotherapy treatment, melanoma cells often develop an ability to evade the immune system. Thus, there is a pressing need to identify and target the melanoma cell-intrinsic mechanisms underlying immune evasion. Our laboratory has pioneered the identification of transcriptional pathways that underlie melanoma progression. We have found that within even clonal melanoma cell populations, subclones of cells demonstrate tremendous heterogeneity in their transcriptional state. We previously demonstrated that these varying transcriptional states correspond to varying sensitivities of melanoma cells to stressors such as targeted therapies, and that these states can be stable across multiple cell divisions. We hypothesize that varying transcriptional states also correspond to cells that have differing abilities to evade CD8+ T cells, which are major effector cells against melanoma. To study the interactions between melanoma cells and CD8+ T cells, we engineered an ovalbumin antigen and OT-I T cell receptor system using mouse melanoma cell lines. We combined this system with DNA barcoding, which enables us to track subclones of cells undergoing immune selection. When cocultured together in vitro, ovalbumin-expressing melanoma cells are effectively killed by OT-I cells. However, we find that subclones of melanoma cells consistently possess different sensitivity to OT-I cells, and these differences are not due to their genetic background nor their ability to express and present ovalbumin. We then leveraged our DNA barcoding system with single cell RNA sequencing (scRNA-seq) to link the lineage and transcriptional state of single melanoma cells that persist through OT-I coculture. By expanding on these preliminary results, we aim to identify novel melanoma cell targets to decrease immune evasion. Citation Format: Raymond W.S. Ng, Sydney M Shaffer. The role of tumor transcriptional variability in evading acute CD8+ T cell exposure 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 A019.
Abstract B038: A preliminary study on predicting pathological complete response to neoadjuvant chemotherapy in triple-negative breast cancer
Authors: Xi Chen, Zhiguo Zhou
Journal: Cancer Research (2024)

Abstract Purpose In this preliminary study, we are aiming to develop a deep learning model to predict pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) for triple-negative breast cancer (TNBC) patient based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Background Neoadjuvant chemotherapy (NAC) has become a grown treatment method for patients with triple-negative breast cancer (TNBC). However, not every patient can achieve pathological complete response (pCR) to NAC. NAC also has side effects such as nausea, hair loss, appetite loss, mouth sores, and nerve damage. Additionally, delaying surgery to remove the tumor can allow it to spread, making treatment more difficult. Therefore, it is necessary to predict pCR to NAC reliably in a non-invasive way. Deep learning methods, especially convolutional neural network (CNN), have been shown great success in image-based treatment response prediction. Methods In this preliminary study, totally 78 TNBC patients on DCE-MRI collected from public dataset (Duke University) were used, among which are 23 patients with pCR and 55 patients without pCR. For each patient, the median slice from the tumor area of the fat-saturated gradient echo T1-weighted pre-contrast sequence MRI was extracted. A convolutional neural network model was built to predict whether the patients receive pCR or not after NAC. In the constructed prediction model, three convolutional layers were used to capture the features from DCE-MRI. Each convolutional layer was followed by a max-pooling layer to reduce the dimensions of the feature maps. Two fully connected layers were added into the model so that the model has a binary outcome which provides the prediction of pCR. Additionally, “ReLU” was used as the activation function in all the layers except for the output layer which used “softmax” instead. By using augmentation technique, we solved the problems of small sample size and unbalance data. Results In this study, 75% dataset were used for training and the remaining 25% were used for testing. The model we developed has a 75% accuracy, with a sensitivity of 0.67 and a specificity of 0.79. These results indicate that the model performs well in predicting patients who do not achieve pathological complete response (pCR). Additionally, the area under the receiver operating characteristic (AUC) curve is calculated as 0.74, indicating the outstanding performance. Citation Format: Xi Chen, Zhiguo Zhou. A preliminary study on predicting pathological complete response to neoadjuvant chemotherapy in triple-negative breast 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 B038.
Abstract A031: Investigating the mechanism and impact of frequency-dependent interactions in non-small cell lung cancer
Authors: Eshan S. King, Dagim S. Tadele, Maximillian A. R. Strobl, Jacob G. Scott
Journal: Cancer Research (2024)

Abstract Introduction: Pre-treatment tumor heterogeneity in cancer is a major driver of drug resistance. Given that resistance mechanisms often impose a fitness cost, the presence of drug-resistant clones prior to treatment poses a paradox—less fit drug-resistant clones should be selected out of a growing population. Recent work has identified frequency-dependent interactions (FDIs) in cancer cell lines, where different proportions of drug-sensitive and resistant cells modulate their respective fitness. FDIs may help explain pre-treatment drug resistance in cancer. Here, we sought to characterize FDIs in a drug-resistant model of non-small cell lung cancer (NSCLC). Methods: We developed a drug-resistant cell line in NSCLC by transducing PC9 cells with BRAF V600E. We then quantified the frequency-dependent interaction between sensitive and resistant cell lines across a range of gefitinib concentrations using the previously published evolutionary game assay. We also implemented an agent-based spatial model of evolution to investigate the different potential molecular mechanisms of frequency-dependent interactions. Results: We found that, despite growing slower in monoculture, low proportions of resistant cells in co-culture with sensitive cells rescued their growth rate in the absence of drug. This phenomenon may help explain pre-treatment drug-resistance and may present a future targetable pathway. Using a computational model parameterized with this data, we investigated the impact of different molecular mechanisms of FDIs on tumor evolution. We found that a mechanism driven by compound diffusion, in contrast to a cell-cell contact mechanism, facilitates drug resistance to greater extent. Through a conditioned media transfer experiment, we found that the FDI in our experimental system is most likely governed by a compound released in the cell culture media. Conclusions: Here we have identified FDIs in an engineered model of drug resistance in NSCLC. Using a computational model, we demonstrated how different mechanisms of FDIs impact tumor evolution. Our ongoing work seeks to identify the specific molecular mechanism responsible for the observed FDIs. In the future, we may leverage this approach to modulate frequency-dependent interactions, allowing us to mitigate pre-treatment drug resistance in vivo. Citation Format: Eshan S. King, Dagim S. Tadele, Maximillian A. R. Strobl, Jacob G. Scott. Investigating the mechanism and impact of frequency-dependent interactions in non-small cell lung 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 A031.
Abstract B015: Tobacco-derived miRNAs and their crosstalk with human transcripts towards carcinogenesis: A cross kingdom approach
Authors: Saumya Patel, Manasi Bhavsar, Rakesh Rawal
Journal: Cancer Research (2024)

Abstract There are more than 70 different species of Nicotiana reported around the world; however, Nicotiana tabacum (common tobacco) and Nicotiana rustica (aztec tobacco) are widely used for snuffing, chewing, smoking, etc., worldwide. Early reports have described that tobacco causes cancer and is associated with many neurological and cardiac disorders. The current study was an effort to understand the paradoxical regulatory mechanism of tobacco-derived miRNAs on human transcripts at 3’ untranslated regions (UTRs) through a computational and experimental genomics approach. This is the first attempt at cross-kingdom analysis where tobacco-specific miRNAs were predicted from different available sources like express sequence tags (EST), genome-wide approaches, and deep sequencing. The miRNA-derived plant targets were identified using stringent bioinformatics methods. A total of 164 mature reported miRNAs, 38 miRNAs from EST datasets, and 21 miRNAs from the genome were studied in detail for cross-kingdom regulation. The potential predicted and reported tobacco-derived miRNAs were analysed against human transcripts. Interestingly, we found tobacco-derived miRNAs such as miR156, miR403, miR7997, miR8011, and miR8036 target tumour suppressor genes and have a putative role in pathways in cancer. Collectively, we have identified a comprehensive set of miRNAs from small RNA sequencing of N. rustica and revealed 842 new miRNAs that have not been reported yet and have putative targets in cancer pathways like AMPK and WNT signalling pathways. Conclusively, this study is the first report of intra- and interspecies relationships using N. tabacum miRNAs with Human and has created a clear road map to focus on specific cancer targets that are modulated by tobacco-derived miRNAs for future studies. Citation Format: Saumya Patel, Manasi Bhavsar, Rakesh Rawal. Tobacco-derived miRNAs and their crosstalk with human transcripts towards carcinogenesis: A cross kingdom 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 B015.
Abstract A005: Quantification of cancer effect sizes of somatic variants reveals stage-dependent evolution in primary and metastatic skin cutaneous melanoma
Authors: Rishi M Shah, Moein Rajaei, Jeff Mandell, Jeffrey P Townsend
Journal: Cancer Research (2024)

Abstract The identities of driver and passenger genes in skin cutaneous melanoma (SKCM) have been intensively studied. However, the relative enhancement of cancer cell lineage survival and proliferation by SKCM driver mutations, as well as how these cancer effects vary with cancer stage, remains limited. The cancer effect size (also known as the scaled selection coefficient) quantifies the relative cancer cell lineage survival and proliferation in the context of each driver mutation. Using a diverse set of 999 primary and 1149 metastatic SKCM samples from multiple datasets, including The Cancer Genome Atlas (TCGA) and the AACR Project GENIE, we computed the cancer effect size of all recurrent mutations in primary (Stage I–III) and metastatic (Stage IV) tumors. Our analysis revealed that both primary and metastatic tumors were alike in their trinucleotide mutation profiles. However, the underlying gene mutation rates typically increased from primary to metastatic tumors as the latter group typically features ineffective DNA repair machinery. Additionally, the two cohorts noticeably differed in both magnitudes of effect sizes and in which genes specific somatic variants were most highly selected. Effect sizes of several driver mutations, most notably BRAF V600E, were orders of magnitude higher in metastatic tumors than in primary tumors. Furthermore, certain variants, particularly NONO R184L, were highly selected along the trajectory from normal tissue to primary tumors, yet new mutations were no longer selected between primary tumor resection and the development of resectable metastases. Conversely, for several variants, including H3F3B R18L, TP53 R273L, and PIK3CA R115P, selection was very strong only along the trajectory from resectable primary tumors to metastases. These findings illustrate the shifting evolutionary trajectory of tumors across cancer stages, underscoring the importance of early detection of SKCM and informing the development of targeted therapeutics aimed at specific stages of tumorigenesis. Citation Format: Rishi M Shah, Moein Rajaei, Jeff Mandell, Jeffrey P Townsend. Quantification of cancer effect sizes of somatic variants reveals stage-dependent evolution in primary and metastatic skin cutaneous 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 A005.
Abstract A013: Cell-extrinsic mechanisms of accelerated therapy resistance mediated by altered pharmacokinetics
Authors: Pragya Kumar, Robert Vander Velde, Virginia Turati, Andriy Marusyk
Journal: Cancer Research (2024)

Abstract Background: Targeted therapies directed against oncogenic signaling addictions, including one resulting from EML4-ALK fusions, can induce strong and durable clinical responses. However, some tumors do not respond (intrinsic resistance), while the initially responsive advanced cancers eventually relapse (acquired resistance). Our earlier studies with experimental models of ALK+ lung cancer revealed that acquired resistance to ALK inhibitors represents a gradual, multifactorial adaptation that progresses through multiple intermediate stages. These evolutionary intermediates can display unique collateral sensitivities to pharmacological inhibitors, including high sensitivity to a KDM histone demethylase inhibitor JIB-04. Results: To explore the utility of using JIB04 to suppress the evolution of resistance in vivo, we examined its ability to enhance responses to clinical ALKi alectinib and lorlatinib in mouse xenograft models. Surprisingly, JIB04 blunted the in vivo tumor responses and accelerated their relapse. The discrepancy between the in vitro and in vivo effects of JIB04 implied environmental mediation. Therefore, we examined the potential involvement of several mechanisms of environment-mediated resistance. We found that rather than directly decreasing cell-intrinsic therapy sensitivity, JIB04 altered ALKi pharmacokinetics. Using mass spectrometry measurements, we found a strong reduction in systemic and intratumor levels of the ALKi. This reduction is attributable to an accelerated metabolic conversion and excretion of metabolites. Our ongoing and planned efforts focus on the elucidation of mechanistic details and the exploration of clinical correlation. Significance: Both intrinsic and acquired resistance are assumed to reflect reduced sensitivities of tumor cells due to mutational, plasticity-related, and microenvironmental mechanisms. Pharmacokinetics studies are the essential component of drug development and clinical trial pipelines. However, after clinical approval, drug concentrations are not routinely examined in clinics. Whereas JIB04 is not used in clinics, its effects are mediated by mechanisms that might be triggered by more relevant dietary or pharmacological factors. Our results highlight the potential danger of blind spots in consideration of causes of therapy resistance and warrant the inclusion of pharmacokinetics analyses in diagnosing/treating therapy resistance. Citation Format: Pragya Kumar, Robert Vander Velde, Virginia Turati, Andriy Marusyk. Cell-extrinsic mechanisms of accelerated therapy resistance mediated by altered pharmacokinetics [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 A013.
Abstract PR001: Learning to adapt: Rational personalization of adaptive therapy using deep reinforcement learning
Authors: Kit Gallagher, Maximilian Strobl, Robert Gatenby, Philip Maini, Alexander Anderson
Journal: Cancer Research (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.