Kang, E. Y. | Search bioRxiv

bioRxiv2025

EGFR-targeted antisense oligonucleotides modified with boron clusters offer an innovative approach to cancer chemo-radiotherapy

Suppression of cancer-associated EGFR expression by antisense oligonucleotides is an attractive strategy to augment the efficacy of radiotherapy in cancer treatment. Boron clusters act as selective ligands for the epidermal growth factor receptor (EGFR) and provide an innovative platform for the delivery of naked boron cluster-conjugated therapeutic nucleic acids to cancer cells. Here, we demonstrate that the novel inhibitor B-ASOLNA-CHOL can also enhance efficient and cancer cell-selective uptake via the low-density lipoprotein receptor (LDLR). This conjugate exhibit strong cytotoxic effects on skin and liver cancer cells in combination with BNCT or XRT. We confirmed that intratumoral injections of B-ASOLNA-CHOL combined with local XRT significantly reduced tumor size and EGFR expression in human A431 xenografts implanted in immunodeficient mice. Our findings suggest that B-ASO-CHOL containing a CpG ODN motif exhibits immune adjuvant properties. The results underscore the potential of B-ASOLNA-CHOL technology for therapeutic applications in radiation immuno-oncology. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=119 SRC="FIGDIR/small/668562v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@a275edorg.highwire.dtl.DTLVardef@1ec2c5eorg.highwire.dtl.DTLVardef@54fc6aorg.highwire.dtl.DTLVardef@187bd60_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstractC_FLOATNO C_FIG

biochemistry↗

bioRxiv2017

No major flaws in "Identification of individuals by trait prediction using whole-genome sequencing data"

In a recently published PNAS article, we studied the identifiability of genomic samples using machine learning methods [Lippert et al., 2017]. In a response, Erlich [2017] argued that our work contained major flaws. The main technical critique of Erlich [2017] builds on a simulation experiment that shows that our proposed algorithm, which uses only a genomic sample for identification, performed no better than a strategy that uses demographic variables. Below, we show why this comparison is misleading and provide a detailed discussion of the key critical points in our analyses that have been brought up in Erlich [2017] and in the media. Further, not only faces may be derived from DNA, but a wide range of phenotypes and demographic variables. In this light, the main contribution of Lippert et al. [2017] is an algorithm that identifies genomes of individuals by combining multiple DNA-based predictive models for a myriad of traits.

genomics↗