Back

Thioredoxin reductase 3 inhibition sensitizes triple-negative breast cancer cells to EGFR inhibitors

Raninga, P. V.; Giner, G.; Sankarasubramanian, S.; Ambalathingal, G.; Kalimutho, M.; Herold, M. J.; Khanna, K. K.

2025-03-17 cancer biology
10.1101/2025.03.16.642742 bioRxiv
Show abstract

Triple-negative breast cancer (TNBC) is an aggressive subtype with poor prognosis and limited treatments. Although 40-70% of TNBC cases overexpress EGFR, anti-EGFR therapies show minimal clinical benefit. This may result from inherent resistance, inactive EGFR, or its absence on the plasma membrane. Here, we used genome wide CRISPR knock out library screening to identify factors that mediate resistance to EGFR inhibitor. We discovered that depletion of a redox protein, thioredoxin reductase 3 (TXNRD3), in MDA-MB-231 cells reduces cell survival after Erlotinib treatment suggesting that loss of TXNRD3 may sensitize TNBC cells to EGFR inhibitors. siRNA-induced knockdown or pharmacological inhibition of TXNRD3 using an FDA-approved drug Auranofin significantly sensitized EGFR-high TNBC cells to EGFR inhibitors. Mechanistically, TXNRD3 knockdown or inhibition using Auranofin increased oxidative stress-mediated accumulation of phosphorylated EGFR at Y1068 and increased surface accumulation. Interestingly, combination of Auranofin with EGFR inhibition markedly induced antibody-dependent cell mediated cytotoxicity (ADCC) and exerted a significant anti-cancer activity in vivo model. Overall, our findings indicate that targeting TXNRD3 with Auranofin can activate EGFR and enhance its surface localization, thereby sensitizing TNBC cells to anti-EGFR therapies. This approach offers a promising strategy for treating TNBC patients who are resistant to current EGFR-targeted treatments.

Matching journals

The top 10 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.