Skip to content

Abstract B027: Programmable Raptamers enable a repeatable translational framework for pediatric cancer therapeutics

Sep 2026 · Cancer Research · 0 citations

Abstract

Despite being the leading disease-related cause of death in children, pediatric cancers comprise less than 1% of cancers, making drug development commercially unattractive. Multi-omics profiling has identified over 150 molecular targets, but drug development still lags substantially. Cell-surface receptors are especially attractive targets: internalizing receptors can deliver cytotoxic payloads, and surface-resident receptors can anchor immune-engagers. Our programmable Raptamer-drug conjugate (RapDC) platform creates a generalizable translational framework to translate validated pediatric receptor targets into therapeutics. Raptamers are synthetic peptidomimetic DNA ligands selected from combinatorial libraries. They combine antibody-like binding with fully synthetic modular manufacturing. Receptor selection, conjugate engineering, and translational evaluation, including target binding, receptor internalization, pharmacology, tumor payload delivery, efficacy, and tolerability, follow a common, reusable workflow independent of the target. CD127 (IL-7 receptor α), expressed in both hematologic malignancies (acute lymphoblastic leukemia; ALL) and solid tumors (osteosarcoma), together with IL1RAP (Ewing sarcoma; ES) and CD70 (diffuse intrinsic pontine glioma; DIPG), were selected to test if a common therapeutic development workflow could be generalized across pediatric cancer lineages. Our high-affinity CD127 Raptamer binds the target (4 nM Kd) and is efficiently internalized. Our lead RapDC demonstrates potent target-dependent cytotoxicity (IC50 ∼35 pM), maintains cytotoxicity in PgP -mediated drug-resistant cells and demonstrates durable tumor control with a 2-fold improved median survival in resistant CDX models (p=0.01). The RapDC showed 12-fold tumor-selective payload retention at 24 hours, and excellent tolerability at >12-fold the efficacious dose. A dual payload construct (MMAE+Exatecan) demonstrated improved IC50 and Emax over RapDCs with either payload alone. To demonstrate generalizability, the same development workflow is being applied to IL1RAP for ES and CD70 for DIPG, supported by Department of Defense and philanthropic funding, respectively. Tumor-associated cell-surface receptors in pediatric cancers can be systematically targeted using programmable synthetic RapDCs. Integrating rapid synthetic discovery, modular conjugate engineering, receptor-informed pharmacology, and a common translational workflow reduces the time, complexity, and resources required to translate validated pediatric cell-surface targets into precision therapeutics. Because the platform is receptor-centric, the same framework is adaptable to drug conjugates, targeted protein degraders, radiotherapeutics, and immune engagers. This establishes a new development pathway for pediatric oncology in which successive therapeutics are generated using shared scientific, manufacturing, and translational infrastructure, thereby substantially reducing the incremental effort required to develop successive pediatric therapeutics. Uksha Saini, Sougata Dey, Stephanie Vega, Dev Chatterjee, Philip Breitfeld, Atul Varadhachary. Programmable Raptamers enable a repeatable translational framework for pediatric cancer therapeutics [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Bridging Discovery and Clinical Impact in Pediatric Cancer; 2026 Sep 22-25; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2026;86(18_Suppl_1):Abstract nr B027.

View source

Similar papers

#computer vision Review Open access May 2015

A survey study on major technical barriers affecting the decision to adopt cloud services

The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.

Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al. · 111 citations · ⚡8
#computer vision Open access Feb 2018

Lean Internal Startups for Software Product Innovation in Large Companies: Enablers and Inhibitors

This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.

Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al. · 78 citations · ⚡6
#computer vision Book Open access Jul 2015

Understanding the affect of developers: theoretical background and guidelines for psychoempirical software engineering

This paper highlights the challenges to conduct proper affect-related studies with psychology, provides a comprehensive literature review in affect theory, and proposes guidelines for conducting psychoempirical software engineering.

D. Graziotin, Xiaofeng Wang, P. Abrahamsson · 56 citations · ⚡4
#machine learning Open access May 2017

What Influences the Speed of Prototyping? An Empirical Investigation of Twenty Software Startups

This study conducts a multiple case study on twenty European software startups and proposes a prototype-centric learning model in early stage software startups, and identifies factors that occur as barriers but also facilitators for prototyping in earlystage software startups.

Anh Nguyen-Duc, Xiaofeng Wang, P. Abrahamsson · 44 citations · ⚡5
#protein folding Open access Sep 2026

Programmable design of functional proteins from natural language

Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or seque...

Fengyuan Dai, Shiyang You, Yudian Zhu et al. · 31 citations · ⚡3

Related blog posts

Google DeepMind Blog Sep 30, 2026

Introducing SynthID Bio

Proof of concept for watermarking AI-generated proteins while preserving biological function.

MIT News · Artificial Intelligence Aug 27, 2026

Looking beyond natural sequences

A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.