Search bioRxiv⌕ Search

Biology subjects

Erickson, T. A.

Publications and source records attributed to Erickson, T. A..

2 recordsLinked to original sources

Drugging the Medullary Thyroid Cancer Surfaceome with T Cell Engagers Targeting CEA, GFRA4 and DLL3 as Monotherapy and In Combination with Tyrosine Kinase Inhibitors

Medullary thyroid cancer (MTC) is a rare form of thyroid cancer, and the definitive treatment is surgical resection. For patients who are not cured by surgery or for patients who present with distant metastatic disease, no curative therapy exists. T cell engagers (TCEs) are an emerging class of biologics that simultaneously bind to tumor cell surface antigens and to the CD3 domain of T cells, thereby redirecting endogenous T cells to lyse tumor cells. TCEs can mediate durable tumor regression, including complete responses, and are now approved to treat multiple cancer types outside of MTC. RNASeq analysis of 30 MTC tumors, cross-referencing with public databases and an extensive literature review were used to identify CEA, DLL3 and GFRA4 as promising tumor antigens to target with TCEs. Expression of these antigens was then validated on MTC cell lines. Herein, I describe the discovery, application and development of MTC-targeted TCEs (MTCEs) recognizing the MTC tumor antigens CEA, DLL3 and GFRA4. In vitro, MTCEs mediate potent target-dependent and T cell-dependent cytotoxicity against MTC cell lines at concentrations as low as 10 ng/mL. MTCEs also induce target-dependent IFN{gamma} secretion from T cells, with no measurable IFN{gamma} secretion observed in the presence of antigen-negative cell lines. Further, MTCEs are functionally compatible with the FDA-approved tyrosine kinase inhibitors selpercatinib and cabozantinib, and combination therapy numerically enhances cytotoxicity. These preclinical data provide strong rationale for continued development of MTCEs, which may one day revolutionize the treatment of metastatic MTC.

immunology↗

Improving Landsat predictions of rangeland fractional cover with multitask learning and uncertainty

O_LIOperational satellite remote sensing products are transforming rangeland management and science. Advancements in computation, data storage, and processing have removed barriers that previously blocked or hindered the development and use of remote sensing products. When combined with local data and knowledge, remote sensing products can inform decision making at multiple scales. C_LIO_LIWe used temporal convolutional networks to produce a fractional cover product that spans western United States rangelands. We trained the model with 52,012 on-the-ground vegetation plots to simultaneously predict fractional cover for annual forbs and grasses, perennial forbs and grasses, shrubs, trees, litter, and bare ground. To assist interpretation and to provide a measure of prediction confidence, we also produced spatiotemporal-explicit, pixel-level estimates of uncertainty. We evaluated the model with 5,780 on-the-ground vegetation plots removed from the training data. C_LIO_LIModel evaluation averaged 6.3% mean absolute error and 9.6% root mean squared error. Evaluation with additional datasets that were not part of the training dataset, and that varied in geographic range, method of collection, scope, and size, revealed similar metrics. Model performance increased across all functional groups compared to the previously produced fractional product. C_LIO_LIThe advancements achieved with the new rangeland fractional cover product expand the management toolbox with improved predictions of fractional cover and pixel-level uncertainty. The new product is available on the Rangeland Analysis Platform (https://rangelands.app/), an interactive web application that tracks rangeland vegetation through time. This product is intended to be used alongside local on-the-ground data, expert knowledge, land use history, scientific literature, and other sources of information when making interpretations. When being used to inform decision-making, remotely sensed products should be evaluated and utilized according to the context of the decision and not be used in isolation. C_LI

ecology↗