Biology subjects

Ferrall-Fairbanks, M. C.

Publications and source records attributed to Ferrall-Fairbanks, M. C..

6 recordsLinked to original sources

bioRxiv2026

Computational alternative to cardiac biopsy in multiple myeloma: utilizing the shape of routine laboratory data for the classification of AL cardiac amyloidosis

Background: Patients with multiple myeloma (MM) are at increased risk for light chain cardiac amyloidosis (CA) due to high circulating levels of light chains. ALCA is a clinical diagnosis based on biopsy, cardiac imaging findings of restrictive cardiomyopathy, and serum monoclonal proteins. To overcome these challenges, we have developed machine learning models that accurately predict ALCA status while identifying key diagnostic features based on routine laboratory measurements and using limited data. Methods: Utilizing electronic medical data (EMR) data, we built support vector machine models (SVMs) using the MISTIC framework. Data including diagnosis history, treatment, and laboratory values for patients with MM or monoclonal gammopathy of unknown significance (MGUS) were collected as a retrospective cohort study at Moffit Cancer Center and Research Institute between 2014 and 2019. To utilize the wealth of information in longitudinal EMR data, we fit regression models to the EMR data and used the parameters and characteristics of the fits as features for our SVMs. We tested three different sets of features: descriptive statistics alone, parameters and characteristics of fit only, and both the descriptive statistics and parameters of fit (DescFits). A classifier using raw data at a timepoint proximal to the date of ALCA diagnostic imaging (EchoAdj), as well as analogous random forest and XGBoost models were used for comparison. Findings: SVM classifiers trained on the DescFits feature set (AUC= 1.0) outperformed blind model predictions of the EchoAdj model (AUC = 0.36) and other conventional machine learning approaches in identifying which patients were diagnosed with ALCA. The DescFits model selected features describing the temporal relationship of physiologic parameters including hematologic state, and cardiac and renal function. Interpretation: We demonstrated that the MISTIC SVM framework and the shape of EMR data can aid in identifying patients with cardiac amyloidosis without cardiac biopsy or imaging.

bioengineering↗

bioRxiv2025

Metabolic plasticity drives mechanisms of PARP inhibitor persistence in ovarian cancer

PARP inhibitors (PARPi) are the standard maintenance therapy for ovarian cancer, but the frequent emergence of resistance remains a major clinical challenge. Emerging evidence suggests that tumor cell plasticity is an important mechanism enabling this adaptive resistance. In this study, we combined both experimental and computational techniques to model this evolutionary process by long term treatment of the OVCAR3 ovarian cancer cell line with Olaparib (a PARPi). We then performed single-cell RNA sequencing (scRNAseq) on both naive and persistent cells to capture cancer cells heterogenous response. scRNAseq profiling suggested an adaptive metabolic reprogramming in response to long term Olaparib treatment. We then scored single cells for metabolic function, and persister cells were found to have reprogrammed their glutamine metabolism. To test these findings, both persister and naive cells were cultured in glutamine-deprived media and persister cells were found to have higher viability than the naive cells particularly when exposed to Olaparib. To test the cells capacity to switch their metabolic state toward glycolysis, we performed Seahorse mitochondrial and glycolytic stress tests. These analyses revealed that persister cells were less dependent on glutamine metabolism and instead exhibited increased glycolytic capacity compared to naive cells. This metabolic shift resulted in an acquired dependency on glucose, representing a potential therapeutic vulnerability. Together, these results indicate that prolonged Olaparib exposure promotes metabolic plasticity in ovarian cancer cells. To explore how this plasticity might be exploited therapeutically, we developed a proof-of-concept mathematical model that leverages metabolic state transitions to optimize treatment scheduling. ONE SENTENCE SUMMARYOvarian cancer cells exploit metabolic plasticity to increase their evolvability to persist PARP inhibitor treatment.

cancer biology↗

bioRxiv2020

The Harsh Microenvironment in Early Breast Cancer Select for a Warburg Phenotype

The harsh microenvironment of ductal carcinoma in situ (DCIS) exerts strong evolutionary selection pressures on cancer cells. We hypothesize that the poor metabolic conditions near the ductal center foment the emergence of a Warburg Effect (WE) phenotype, wherein cells rapidly ferment glucose to lactic acid, even in normoxia. To test this hypothesis, we subjected pre-malignant breast cancer cells to different microenvironmental selection pressures using combinations of hypoxia, acidosis, low glucose, and starvation for many months, and isolated single clones for metabolic and transcriptomic profiling. The two harshest conditions selected for constitutively expressed WE phenotypes. RNA-seq analysis of WE clones identified the transcription factors NF[kcy]B and KLF4 as potential inducers of the WE phenotype. NF[kcy]B was highly phosphorylated in the glycolytic clones. In stained DCIS samples, KLF4 expression was enriched in the area with the harshest microenvironmental conditions. We simulated in vivo DCIS phenotypic evolution using a mathematical model calibrated from the in vitro results. The WE phenotype emerged in the poor metabolic conditions near the necrotic core. We propose that harsh microenvironments within DCIS select for a Warburg phenotype through constitutive transcriptional reprogramming, thus conferring a survival advantage and facilitating further growth and invasion.

cancer biology↗

bioRxiv2019

Modeling adaptive therapy in non-muscle invasive bladder cancer

Bladder cancer is the 9th most commonly diagnosed cancer. Nearly half of patients with early stage bladder cancer treated with the immune-stimulating agent BCG have disease recurrence, while 13% progress to invasive bladder cancer. Here we explored the potential of tumor mutational heterogeneity and the role of pro- and anti-inflammatory cytokines to identify different subtypes of bladder cancer that may predict therapeutic response to BCG. Further, we used mathematical modeling of dosing strategies to infer tumor response to varying doses and time schedules f BCG administration. As a proof-of-concept, present adaptive therapy scheduling of BCG as a viable strategy to control tumor size and minimize recurrence.

cancer biology↗

bioRxiv2019

Predictive modeling of co-evolving growing populations

How can we best explore and fit density and frequency dependence in the evolutionary and ecological dynamics of growing tumors? Here, we present an introduction to recent developments in our lab, and give two examples of complex interaction-driven tumor growth combined with statistical outcomes of treatment.

cancer biology↗

bioRxiv2018

Leveraging single cell RNA sequencing experiments to model intra-tumor heterogeneity

PURPOSEMany cancers can be treated with targeted therapy. Almost inevitably, tumors develop resistance to targeted therapy, either from preexistence or by evolving new genotypes and traits. Intra-tumor heterogeneity serves as a reservoir for resistance, which often occurs due to selection of minor cellular sub-clones. On the level of gene expression, the clonal heterogeneity can only be revealed by high-dimensional single cell methods. We propose to use a general diversity index (GDI) to quantify heterogeneity on multiple scales and relate it to disease evolution.\n\nMETHODSWe focused on individual patient samples probed with single cell RNA sequencing to describe heterogeneity. We developed a pipeline to analyze single cell data, via sample normalization, clustering and mathematical interpretation using a generalized diversity measure, and exemplify the utility of this platform using single cell data.\n\nRESULTSWe focused on three sources of RNA sequencing data: two healthy bone marrow (BM) samples, two acute myeloid leukemia (AML) patients, each sampled before and after BM transplant (BMT), four samples of pre-sorted lineages, and six lung carcinoma patients with multi-region sampling. While healthy/normal samples scored low in diversity overall, GDI further quantified in which respect these samples differed. While a widely used Shannon diversity index sometimes reveals less differences, GDI exhibits differences in the number of potential key drivers or clonal richness. Comparing pre and post BMT AML samples did not reveal differences in heterogeneity, although they can be very different biologically.\n\nCONCLUSIONGDI can quantify cellular heterogeneity changes across a wide spectrum, even when standard measures, such as the Shannon index, do not. Our approach offers wide applications to quantify heterogeneity across samples and conditions.

cancer biology↗