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Biology subjects

Carter, B. Z.

Publications and source records attributed to Carter, B. Z..

2 recordsLinked to original sources

Dual MYC and GSPT1 Protein Degrader for MYC-Driven Cancers

Direct targeting of the oncoprotein MYC has long been attempted in cancer therapy, with limited success. We here identify a novel co-regulatory feedback loop of MYC and G1 to S phase transition protein 1 (GSPT1), where MYC promotes transcription of GSPT1, and GSPT1 senses stop codon of MYC to promote its translation. We report on the first-in-class dual MYC/GSPT1 protein degrader, GT19630. GT19630 significantly induced integrated stress response, abrogated oxidative phosphorylation through inhibition of the TCA cycle and induced cell death. Protein degradation of MYC was critical for efficacy of GT19630. GT19630 induced profound anti-proliferative effects and apoptosis agnostic to TP53 in a broad range of cancer cells, and is highly active in vivo in multiple, therapy-resistant hematologic and solid tumor models. Dual MYC/GSPT1 degradation was well tolerated in humanized CrbnI391V mice. In conclusion, we propose a novel treatment approach by directly targeting the MYC-GSPT1 axis in MYC-driven cancers. Statement of significanceMYC has been considered an undruggable protein. We found a targetable, novel positive co-regulatory feedback of MYC and GSPT1, a key translation terminator. The dual MYC/GSPT1 degrader GT19630 is highly active in MYC-driven tumors, with moderate effects on humanized Crbn mice, providing opportunities to improve treatment outcome of MYC-driven cancers.

cancer biology↗

Application of machine learning methods in clinical trials for precision medicine

ObjectiveA key component for precision medicine is a good prediction algorithm for patients response to treatments. We aim to implement machine learning (ML) algorithms into the response-adaptive randomization (RAR) design and improve the treatment outcomes. Materials and MethodsWe incorporated nine ML algorithms to model the relationship of patient responses and biomarkers in clinical trial design. Such a model predicted the response rate of each treatment for each new patient and provide guidance for treatment assignment. Realizing that no single method may fit all trials well, we also built an ensemble of these nine methods. We evaluated their performance through quantifying the benefits for trial participants, such as the overall response rate and the percentage of patients who receive their optimal treatments. ResultsSimulation studies showed that the adoption of ML methods resulted in more personalized optimal treatment assignments and higher overall response rates among trial participants. Compared with each individual ML method, the ensemble approach achieved the highest response rate and assigned the largest percentage of patients to their optimal treatments. For the real-world study, we successfully showed the potential improvements if the proposed design had been implemented in the study. ConclusionIn summary, the ML-based RAR design is a promising approach for assigning more patients to their personalized effective treatments, which makes the clinical trial more ethical and appealing. These features are especially desirable for late-stage cancer patients who have failed all the FDA-approved treatment options and only can get new treatments through clinical trials.

bioinformatics↗