Biology subjects

Fojo, T.

Publications and source records attributed to Fojo, T..

3 recordsLinked to original sources

bioRxiv2024

Cancer Interception During Treatment: Using Growth Kinetics to Create a Continuous Variable for Assessing Disease Response

BackgroundWe applied 11 mathematical models of tumor growth to clinical trial data available from public and private sources. We have previously described the remarkable capacity for a simple biexponential model of tumor growth to fit thousands of datasets, and to correlate with overall survival. The goal of this study was to extend our analysis to additional tumor types and to evaluate whether alternate growth models could describe the time course of disease burden in the small subset of patients in whom the biexponential model failed. MethodsFor this analysis, we obtained data for tumor burden from 17,140 patients with six different tumor types. Imaging data and serum levels of tumor markers were available for 3,346 and 13,794 patients, respectively. Data from patients were first analyzed using the biexponential model to determine rates of tumor growth (g) and regression (d); for those whose data could not be described by this model, fit of their data was assessed using seven alternative models. The model that minimized the Akaike Information Criterion was selected as the best fit. Using the model that best fit an individual patients data, we estimated the rates of growth (g) and regression (d) of disease burden over time. The rates of tumor growth (g) were examined for association with a traditional endpoint (overall survival). FindingsFor each model, the number of patient datasets that fit the model were obtained. As we have previously reported, data from most patients fit a simple model of exponential growth and exponential regression (86%). Data from another 7% of patients fit an alternative model, including 3% fitting to a model of constant or linear regression and exponential growth of tumor on the surface and 3% fitting to model of exponential decay on tumor surface with asymmetric growth. As previously reported, we found that growth rate correlates well with overall survival, remarkably even when data from various histologies are considered together. For patients with multiple timepoints of tumor measurement, the growth rate could often be estimated even during the phase when only net regression of tumor quantity could be discerned. InterpretationThe validation of a simple mathematical model across different cancers and its application to existing clinical data allowed estimation of the rate of growth of a treatment resistant subpopulation of cancer cells. The quantification of available clinical data using the growth rate of tumors in individual patients and its strong correlation with overall survival makes the growth rate an excellent marker of the efficacy of therapy specific to the individual patient.

cancer biology↗

bioRxiv2024

Combined HDAC and eIF4A inhibition: A novel epigenetic therapy for pancreatic adenocarcinoma

Pancreatic ductal adenocarcinoma-(PDAC) needs innovative approaches due to its 12% 5-year survival despite current therapies. We show marked sensitivity of pancreatic cancer cells to the combination of a novel eIF4A inhibitor, des-methyl pateamine A (DMPatA), and a histone deacetylase inhibitor, romidepsin, inducing epigenetic reprogramming as an innovative therapeutic strategy. Exploring the mechanistic activity of this combination showed that with a short duration of romidepsin at low doses, robust acetylation persisted up to 48h with the combination, while histone acetylation rapidly faded with monotherapy. This represents an unexpected mechanism of action against PDAC cells that triggers transcriptional overload, metabolic stress, and augmented DNA damage. Structurally different class I HDAC inhibitors exhibit the same hyperacetylation patterns when co-administered with DMPatA, suggesting a class effect. We show efficacy of this combination regimen against tumor growth in a MIA PaCa-2 xenograft model of PDAC with persistent hyperacetylation confirmed in tumor samples. STATEMENT OF SIGNIFICANCEPancreatic ductal adenocarcinoma, a significant clinical challenge, could benefit from the latent potential of epigenetic therapies like HDAC inhibitors-(HDIs), typically limited to hematological malignancies. Our study shows that a synergistic low dose combination of HDIs with an eIF4A-inhibitor in pancreatic cancer models results in marked pre-clinical efficacy, offering a promising new treatment strategy.

cancer biology↗

bioRxiv2019

The Genomic Stability at the Coding Regions of the Multidrug Transporter Gene ABCB1: Insights into the Development of Alternative Drug Resistance Mechanisms in Human Leukemia Cells

Despite considerable efforts in reversing clinical multidrug resistance (MDR), targeting the predominant multidrug transporter ABCB1/P-glycoprotein (P-gp) based on small molecule inhibitors has been hindered. This may be due to the emergence of alternative drug resistance mechanisms. However, the non-specific P-gp inhibitor cyclosporine (CsA) showed significant clinical benefits in patients with acute myeloid leukemia (AML), which likely represents the only proof-of-principle clinical trial using several generations of MDR inhibitors. Nevertheless, the mechanisms that underlie this successful MDR modulation by CsA are not elucidated because of the absence of CsA-relevant cellular models. In this study, we report the development of two erythroleukemia variants, RVC and RDC, which were derived by step-wise co-selection of K562/R7 drug-resistant leukemia cells with the etoposide-CsA and doxorubicin-CsA drug combinations, respectively. Interestingly, both RVC and RDC, which retained P-gp expression, showed altered MDR phenotypes that were resistant to cyclosporine modulation. The ABCB1 coding regions were genetically stable even under long-term stringent drug selection. Genomically, ABCB1 is likely the most stable ABC transporter gene when comparing with several ABC superfamily members (such as ABCA1, ABCC1, CFTR, and ABCG2). Our findings suggested that non-P-gp mechanisms were likely responsible for the resistance to CsA modulation in both RVC and RDC cells. Moreover, we found that CsA played a role in undermining the selection of highly drug-resistant cells via induction of low level and unstable drug resistance, thus shedding some light on the benefits of CsA in treating certain types of AML patients.

cancer biology↗