Cancer Biology Commons^™

Chilling Insights: A Comparative Study Of Cancer Tissue Preservation, Rebecca Bascom, Paige Nelson, Jacob Harris, Jack Davis, Mackenzie Burr, Jeffrey Okojie, David Booker, Ken Dixon, Jared Barrott

Library/Life Sciences Undergraduate Poster Competition 2025

It is a fact that personalized cancer treatment leads to better patient outcomes. But what if the current treatments aren't as personalized as they could be?

Clinical cancer tissue samples are typically sent to labs as formalin-fixed and paraffin-embedded (FFPE) tissues. A recent study4 indicates that cryo-preserved (CP) tissues offer significantly better DNA quality and quantity than FFPE samples, suggesting a need to reevaluate preservation methods. We aim to expand this research by comparing protein expression in FFPE and CP samples.

Specifically, we will focus on PD-L1, a protein that inhibits T-cells and is overexpressed in all cancer cells. The …

Integrating Clinical Cancer And Ptm Proteomics Data Identifies A Mechanism Of Ack1 Kinase Activation, Eranga R. Balasooriya, Deshan Madhusanka, Tania P. López-Palacios, Riley J. Eastmond, Dasun Jayatunge, Jake J. Owen, Jack S. Gashler, Christina M. Egbert, Chanaka Bulathsinghalage, Lu Liu, Stephen R. Piccolo, Joshua L. Andersen

Faculty Publications

Beyond the most common oncogenes activated by mutation (mut-drivers), there likely exists a variety of low-frequency mut-drivers, each of which is a possible frontier for targeted therapy. To identify new and understudied mut-drivers, we developed a machine learning (ML) model that integrates curated clinical cancer data and posttranslational modification (PTM) proteomics databases. We applied the approach to 62,746 patient cancers spanning 84 cancer types and predicted 3,964 oncogenic mutations across 1,148 genes, many of which disrupt PTMs of known and unknown function. The list of putative mut-drivers includes established drivers and others with poorly understood roles in cancer. This ML …

Toward A Methodology For Evaluating Dna Variants In Nuclear Families, Dustin B. Miller, Reid Robison, Stephen Piccolo

Faculty Publications

The genetic underpinnings of most pediatric-cancer cases are unknown. Population-based studies use large sample sizes but have accounted for only a small proportion of the estimated heritability of pediatric cancers. Pedigree-based studies are infeasible for most human populations. One alternative is to collect genetic data from a single nuclear family and use inheritance patterns within the family to filter candidate variants. This approach can be applied to common and rare variants, including those that are private to a given family or to an affected individual. We evaluated this approach using genetic data from three nuclear families with 5, 4, and …

Predicting Drug Sensitivity Of Cancer Cells Based On Dna Methylation Levels, Sofia P. Miranda, Fernanda A. Baião, Julia L. Fleck, Stephen R. Piccolo

Faculty Publications

Cancer cell lines, which are cell cultures derived from tumor samples, represent one of the least expensive and most studied preclinical models for drug development. Accurately predicting drug responses for a given cell line based on molecular features may help to optimize drug-development pipelines and explain mechanisms behind treatment responses. In this study, we focus on DNA methylation profiles as one type of molecular feature that is known to drive tumorigenesis and modulate treatment responses. Using genome-wide, DNA methylation profiles from 987 cell lines in the Genomics of Drug Sensitivity in Cancer database, we used machine-learning algorithms to evaluate the …

A Systematic Review Of Datasets That Can Help Elucidate Relationships Among Gene Expression, Race, And Immunohistochemistry-Defined Subtypes In Breast Cancer., Ifeanyichukwu O. Nwosu, Stephen Piccolo

Faculty Publications

Scholarly requirements have led to a massive increase of transcriptomic data in the public domain, with millions of samples available for secondary research. We identified gene-expression datasets representing 10,214 breast-cancer patients in public databases. We focused on datasets that included patient metadata on race and/or immunohistochemistry (IHC) profiling of the ER, PR, and HER-2 proteins. This review provides a summary of these datasets and describes findings from 32 research articles associated with the datasets. These studies have helped to elucidate relationships between IHC, race, and/or treatment options, as well as relationships between IHC status and the breast-cancer intrinsic subtypes. We …

Functional And Transcriptomic Characterization Of Cisplatin-Resistant Ags And Mkn-28 Gastric Cancer Cell Lines, Barbara Mora-Lagos, Irene Cartas-Espinel, Ismael Riquelme, Alyssa C. Parker, Stephen R. Piccolo, Tamara Viscarra, Maria Elena Reyes, Louise Zanella, Kurt Buchegger, Carmen Ili, Priscilla Brebi

Faculty Publications

Gastric cancer (GC) is a significant cancer-related cause of death worldwide. The most used chemotherapeutic regimen in GC is based on platinum drugs such as cisplatin (CDDP). However, CDDP resistance reduces advanced GC survival. In vitro drug-resistant cell model would help in the understanding of molecular mechanisms underlying this drug-resistance phenomenon. The aim of this study was to characterize new models of CDDP-resistant GC cell lines (AGS R-CDDP and MKN-28 R-CDDP) obtained through a stepwise increasing drug doses method, in order to understand the molecular mechanisms underlying chemoresistance as well as identify new therapeutic targets for the treatment of GC. …

Functional And Transcriptomic Characterization Of Carboplatin-Resistant A2780 Ovarian Cancer Cell Line, Tamara Viscarra, Kurt Buchegger, Ignacio Jofre, Ismael Riquelme, Louise Zanella, Michel Abanto, Alyssa C. Parker, Stephen R. Piccolo, Juan Carlos Roa, Carmen Ili, Priscilla Brebi

Faculty Publications

Background

Ovarian cancer is a significant cancer-related cause of death in women worldwide. The most used chemotherapeutic regimen is based on carboplatin (CBDCA). However, CBDCA resistance is the main obstacle to a better prognosis. An in vitro drug-resistant cell model would help in the understanding of molecular mechanisms underlying this drug-resistance phenomenon. The aim of this study was to characterize cellular and molecular changes of induced CBDCA-resistant ovarian cancer cell line A2780.

Methods

The cell selection strategy used in this study was a dose-per-pulse method using a concentration of 100 μM for 2 h. Once 20 cycles of exposure to …

Does Family Communication Matter? Exploring Knowledge Of Breast Cancer Genetics In Cancer Families, Deborah Himes, Sarah H. Davis, Jane Lassetter Phd, Rn, Neil E. Peterson, Margaret F. Clayton, Wendy C. Birmingham, Anita Y. Kinney

Faculty Publications

Purpose: Knowledge of breast cancer genetics is critical for those at increased hereditary risk who must make decisions about breast cancer screening options. This descriptive study explored theory-based relationships among cognitive and emotional variables related to knowledge of breast cancer genetics in cancer families. Methods: Participants included first-degree relatives of women with breast cancer who had received genetic counseling and testing. Study participants themselves did not have breast cancer and had not received genetic counseling or testing. Data were collected by telephone interviews and surveys. Variables analyzed included numeracy, health literacy, cancer-related distress, age, education, and the reported amount of …

Frost450, A Novel Microtubule Targeting Agent: Resistance And Cross-Resistance In An Ovarian Cancer Cell Line, Jonathan Chancellor Fox

Undergraduate Honors Theses

Drug resistance remains problematic for chemotherapy treatments. As primary treatment strategies tend to lose potency against cancer, there is a need for treatment options that overcome drug resistance mechanisms. FROST450, a novel microtubule destabilizing agent, has proven anticancer effects that overcome taxane and multidrug resistance. However, to better understand the clinical potential of FROST450, in this study we examine the mechanisms and implications of acquired FROST450 resistance in an A2780 ovarian cancer cell line. Results indicate FROST450 resistance is associated with increased mitochondrial respiration and with changes in gene sequence or expression level of proteins associated with microtubules, and that …

Using Non Stem-Cells To Understand Early Tumor Growth, Jake Hogan, Heiko Enderling, Joel Brown, Robert A. Gatenby

Library/Life Sciences Undergraduate Poster Competition 2018

Tumors of similar size and shape can exhibit different responses to the same treatment. Targeted therapy aims to better treat these tumors by classifying them according to genotypic traits. A better understanding of how tumor traits such as non-stem cells influence tumor growth could improve targeted therapy. We hypothesize that the production of non-stem cells may aid tumor growth in avascular tumors (tumors lacking blood vessels).

Membrane Expression Of Thymidine Kinase 1 And Potential Clinical Relevance In Lung, Breast, And Colorectal Malignancies, Evita G. Weagel, Weston Burrup, Roman Kovtun, Edwin J. Valazquez, Abigail M. Felsted, Michelle H. Townsend, Zachary E. Ence, Erica Suh, Stephen Piccolo, K. Scott Weber, Richard A. Robison, Kim Oneill

Faculty Publications

Background: Lung, breast, and colorectal malignancies are the leading cause of cancer-related deaths in the world causing over 2.8 million cancer-related deaths yearly. Despite efforts to improve prevention methods, early detection, and treatments, survival rates for advanced stage lung, breast, and colon cancer remain low, indicating a critical need to identify cancer-specific biomarkers for early detection and treatment. Thymidine kinase 1 (TK1) is a nucleotide salvage pathway enzyme involved in cellular proliferation and considered an important tumor proliferation biomarker in the serum. In this study, we further characterized TK1’s potential as a tumor biomarker and immunotherapeutic target and clinical relevance. …

Combating Subclonal Evolution Of Resistant Cancer Phenotypes, Samuel W. Brady, Jasmine A. Mcquerry, Yi Qiao, Stephen Piccolo, Gajendra Shrestha, David F. Jenkins, Ryan M. Layer, Brent S. Pedersen, Ryan H. Miller, Amanda Esch, Sara R. Selitsky, Joel S. Parker, Layla A. Anderson, Brian K. Dalley, Rachel E. Factor, Chakravarthy B. Reddy, Jonathan P. Boltax, Dean Li, Philip J. Moos, Joe W. Gray, Laura M. Heiser, Saundra S. Buys, Adam L. Cohen, W. Evan Johnson, Aaron R. Quinlan, Gabor Marth, Theresa L. Werner, Andrea H. Bild

Faculty Publications

Metastatic breast cancer remains challenging to treat, and most patients ultimately progress on therapy. This acquired drug resistance is largely due to drug-refractory sub-populations (subclones) within heterogeneous tumors. Here, we track the genetic and phenotypic subclonal evolution of four breast cancers through years of treatment to better understand how breast cancers become drug-resistant. Recurrently appearing post-chemotherapy mutations are rare. However, bulk and single-cell RNA sequencing reveal acquisition of malignant phenotypes after treatment, including enhanced mesenchymal and growth factor signaling, which may promote drug resistance, and decreased antigen presentation and TNF-α signaling, which may enable immune system avoidance. Some of these …

Engineering Fret Biosensors For Microrna Presence/Absence Analysis, Nicholas E. Larkey, Sean M. Burrows

Biomedical Engineering Western Regional Conference

No abstract provided.

Assign: Context-Specific Genomic Profiling Of Multiple Heterogeneous Biological Pathways, Ying Shen, Mumtahena Rahman, Stephen R. Piccolo, Daniel Gusenleitner, Nader N. El-Chaar, Luis Cheng, Stefano Monti, Andrea H. Bild, W. Evan Johnson

Faculty Publications

Motivation: Although gene-expression signature-based biomarkers are often developed for clinical diagnosis, many promising signatures fail to replicate during validation. One major challenge is that biological samples used to generate and validate the signature are often from heterogeneous biological contexts—controlled or in vitro samples may be used to generate the signature, but patient samples may be used for validation. In addition, systematic technical biases from multiple genome-profiling platforms often mask true biological variation. Addressing such challenges will enable us to better elucidate disease mechanisms and provide improved guidance for personalized therapeutics.

Results: Here, we present a pathway profiling toolkit, Adaptive Signature …

Robust Meta-Analysis Shows That Glioma Transcriptional Subtyping Complements Traditional Approaches, Sanghoon Lee, Stephen Piccolo, Kristina Allen-Brady

Faculty Publications

Background Gliomas traditionally have been sub-classified
based on histopathological observations. However, this ap-
proach is subject to inter-observer variability, and histopatho-
logical features may not reflect the biological mechanisms that
drive tumor growth. High-throughput transcriptional profiling
has shown promise in objectively and reproducibly identify-
ing glioma subtypes. Most prior studies have typically used
only modest sample sizes and have sometimes overlooked
important data-processing steps to ensure sample quality and
to evaluate the robustness of quantitative findings. The pur-
pose of our study was to define robust glioma subtypes by
applying rigorous preprocessing and validation steps to 1,952
microarray samples aggregated …

Predicting Phenotypic Severity Of Uncertain Gene Variants In The Ret Proto-Oncogene, David K. Crockett, Stephen R. Piccolo, Perry G. Ridge, Rebecca L. Margraf, Elaine Lyon, Marc S. Williams, Joyce A. Mitchell

Faculty Publications

Although reported gene variants in the RET oncogene have been directly associated with multiple endocrine neoplasia type 2 and hereditary medullary thyroid carcinoma, other mutations are classified as variants of uncertain significance (VUS) until the associated clinical phenotype is made clear. Currently, some 46 non-synonymous VUS entries exist in curated archives. In the absence of a gold standard method for predicting phenotype outcomes, this follow up study applies feature selected amino acid physical and chemical properties feeding a Bayes classifier to predict disease association of uncertain gene variants into categories of benign and pathogenic. Algorithm performance and VUS predictions were …