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PURPOSECirculating tumor DNA (ctDNA) assays are promising tools for the prediction of cancer treatment response. Here, we build a framework for the design of ctDNA biomarkers of therapy response that incorporate variations in ctDNA dynamics driven by specific treatment mechanisms. These biomarkers are based on novel proposals for ctDNA sampling protocols, consisting of frequent sampling within a compact time window surrounding therapy initiation—which we hypothesize to hold valuable prognostic information on longer-term treatment response. METHODSWe develop mathematical models of ctDNA kinetics driven by tumor response to several therapy classes and use them to simulate randomized virtual patient cohorts to test candidate biomarkers. RESULTSUsing this approach, we propose specific biomarkers, on the basis of ctDNA longitudinal features, for targeted therapy and radiation therapy. We evaluate and demonstrate the efficacy of these biomarkers in predicting treatment response within a randomized virtual patient cohort data set. CONCLUSIONThis study highlights a need for tailoring ctDNA sampling protocols and interpretation methodology to specific biologic mechanisms of therapy response, and it provides a novel modeling and simulation framework for doing so. In addition, it highlights the potential of ctDNA assays for making early, rapid predictions of treatment response within the first days or weeks of treatment and generates hypotheses for further clinical testing. 

Tunneling nanotubes (TNTs) comprise a unique class of actin-rich nanoscale membranous protrusions. They enable long-distance intercellular communication and may play an integral role in tumor formation, progression, and drug resistance. TNTs are three-dimensional, but nearly all studies have investigated them using two-dimensional cell culture models. Here, we applied a unique 3D culture platform consisting of crosshatched and aligned fibers to fabricate synthetic suspended scaffolds that mimic the native fibrillar architecture of tumoral extracellular matrix (ECM) to characterize TNT formation and function in its native state. TNTs are upregulated in malignant mesothelioma; we used this model to analyze the biophysical properties of TNTs in this 3D setting, including cell migration in relation to TNT dynamics, rate of TNT-mediated intercellular transport of cargo, and conformation of TNT-forming cells. We found that highly migratory elongated cells on aligned fibers formed significantly longer but fewer TNTs than uniformly spread cells on crossing fibers. We developed new quantitative metrics for the classification of TNT morphologies based on shape and cytoskeletal content using confocal microscopy. In sum, our strategy for culturing cells in ECM-mimicking bioengineered scaffolds provides a new approach for accurate biophysical and biologic assessment of TNT formation and structure in native fibrous microenvironments. 

Ovarian cancer is routinely diagnosed long after the disease has metastasized through the fibrous submesothelium. Despite extensive research in the field linking ovarian cancer progression to increasingly poor prognosis, there are currently no validated cellular markers or hallmarks of ovarian cancer that can predict metastatic potential. To discern disease progression across a syngeneic mouse ovarian cancer progression model, here we fabricated extracellular matrix mimicking suspended fiber networks: cross-hatches of mismatch diameters for studying protrusion dynamics, aligned same diameter networks of varying interfiber spacing for studying migration, and aligned nanonets for measuring cell forces. We found that migration correlated with disease while a force-disease biphasic relationship exhibited F-actin stress fiber network dependence. However, unique to suspended fibers, coiling occurring at the tips of protrusions and not the length or breadth of protrusions displayed the strongest correlation with metastatic potential. To confirm that our findings were more broadly applicable beyond the mouse model, we repeated our studies in human ovarian cancer cell lines and found that the biophysical trends were consistent with our mouse model results. Altogether, we report complementary high throughput and high content biophysical metrics capable of identifying ovarian cancer metastatic potential on a timescale of hours.