Biology subjects

Li, A. K.

Publications and source records attributed to Li, A. K..

4 recordsLinked to original sources

bioRxiv2026

Explaining and predicting pseudoprogression in immunotherapythrough joint modeling of immune infiltration and ctDNA dynamics

Pseudoprogression in response to immune checkpoint inhibitor (ICI) therapy can be a complex challenge during treatment response evaluation. Erroneous classification of progression may result in stopping effective treatment for patients, while delayed confirmation of progression can prolong an ineffective treatment course. We develop a mechanistic mathematical model of tumor-immune dynamics under ICI treatment in which observed tumor volume includes contributions from both tumor burden and immune infiltration. We show analytically how pseudoprogression can arise from increased immune infiltration and proliferation as well as continued tumor growth during the ``ramp-up" phase. The model also incorporates circulating tumor DNA (ctDNA) dynamics, which we demonstrate are able to closely fit existing longitudinal tumor and ctDNA data and extract realistic parameter ranges to generate representative in silico data. Using model-generated cohorts, we train a random forest (RF) classifier using longitudinal tumor and ctDNA measurements to distinguish pseudoprogression and true progression, and demonstrate that it improves response classification relative to the Response Evaluation Criteria in Solid Tumors (RECIST 1.1) and provides classification earlier than the immunotherapy-specific iRECIST, even in the presence of tumor measurement noise. This work predicts that paired longitudinal tumor-ctDNA measurements may enable earlier discrimination of true progression from pseudoprogression, and motivates collection of imaging and ctDNA data synchronously during ICI therapy, especially in the setting of suspected disease progression.

cancer biology↗

bioRxiv2025

Using a Pharmacokinetic Model to Design and Evaluate an Early ctDNA Biomarker for Response to Targeted Therapy

Early prediction of response to therapy or lack thereof can help physicians plan treatment more efficiently. Biomarkers based on circulating tumor DNA (ctDNA) are promising. However, biomarkers beyond direct comparison to baseline have not been thoroughly explored. We develop a model for ctDNA shedding under targeted therapy that incorporates pharmacokinetics. Using a simulated cohort of virtual patients with varied parameters, we define and analyze a biomarker based on ctDNA samples at baseline, 12 hours, and 24 hours after initiation of treatment. The biomarker identified patients who would achieve partial or complete response with high sensitivity and specificity and was able to match the performance of a neural network classifier. Our result highlights the potential of ctDNA as a biomarker and underlines the importance of early ctDNA data collection.

cancer biology↗

bioRxiv2024

Early ctDNA kinetics as a dynamic biomarker of cancer treatment response

Circulating tumor DNA 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. We develop mathematical models of ctDNA kinetics driven by tumor response to several therapy classes, and utilize them to simulate randomized virtual patient cohorts to test candidate biomarkers. Using this approach, we propose specific biomarkers, based on ctDNA longitudinal features, for targeted therapy, chemotherapy and radiation therapy. We evaluate and demonstrate the efficacy of these biomarkers in predicting treatment response within a randomized virtual patient cohort dataset. 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. This study highlights a need for tailoring ctDNA sampling protocols and interpretation methodology to specific biological 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.

cancer biology↗

bioRxiv2023

Multiple beta cell-independent mechanisms drive hypoglycemia in Timothy syndrome

The canonical G406R gain of function mutation that reduces inactivation and increases Ca2+ influx through the CACNA1C-encoded CaV1.2 voltage gated Ca2+ channel underlies the multisystem disorder Timothy syndrome (TS), characterized by invariant Long QT syndrome and consequent life-threatening arrhythmias. Severe episodic hypoglycemia, which exacerbates arrhythmia risk, is among the myriad non-cardiac TS pathologies that are poorly characterized. While hypoglycemia is thought to result from increased Ca2+ influx through CaV1.2 channels in pancreatic beta cells and consequent hyperinsulinism, this mechanism has never been demonstrated due to a lack of informative animal models, thus hampering development of preventive strategies. We generated a CaV1.2 G406R knockin mouse model that recapitulates key TS features including hypoglycemia. Unexpectedly, these mice did not show hyperactive beta cells or hyperinsulinism in the setting of normal intrinsic beta cell function, suggesting dysregulated glucose homeostasis. We discovered multiple alternative contributors to hypoglycemia, including perturbed counterregulatory hormone responses with defects in glucagon secretion and abnormal hypothalamic glucose sensing. Together, these data provide new insights into physiological contributions of the broadly expressed CaV1.2 channel and reveal integrated consequences of the mutant channel that underlie the life-threatening events in TS. Brief SummaryGain of function mutant CaV1.2 channels drive hypoglycemia through adverse effects on counterregulatory hormones and central nervous system glucose sensing

physiology↗