The Role of mTOR Inhibitors in Hematologic Disease: From Bench to Bedside

Feng, Yimei; Chen, Xiaoli; Cassady, Kaniel; Zou, Zhongmin; Yang, Shijie; Wang, Zheng; Zhang, Xi

doi:10.3389/fonc.2020.611690

Cited by 37 publications

(29 citation statements)

References 145 publications

(126 reference statements)

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“…The pharmacological inhibition of mTOR through rapamycin and its derivatives (rapalogs) has been approved for various treatments, including cancers, and studied in the context of lymphomas. Feng et al, discussed in detail the development of these compounds against hematological malignancies (37). To mention a few, several phase I-II trials have been conducted to test the efficacy of these compounds (e.g., everolimus and temsirolimus) and have shown some efficacy as monotherapy or in combination with other standard chemotherapy regimens such as R-CHOP or lenalidomide (38)(39)(40)(41).…”

Section: Discussionmentioning

confidence: 99%

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Combined Inhibition of Akt and mTOR Is Effective Against Non-Hodgkin Lymphomas

Rivera-Soto

Dittmer

et al. 2021

Front. Oncol.

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Non-Hodgkin lymphoma (NHL) are a diverse group of hematological malignancies comprised of over 60 subtypes. These subtypes range from indolent to aggressive. The PI3K/Akt/mTOR pathway has been shown to contribute to cell survival and proliferation and is constitutively active in most NHL. MK-7075 (miransertib) and MK-4440 are small molecules that effectively inhibit Akt and have entered clinical development. Using in vitro and in vivo models of NHL, we explored targeting the kinase Akt with miransertib and MK-4440 alone or in combination with the mTORC1 inhibitor, rapamycin (sirolimus). Both Akt inhibitors inhibited the pathway and NHL proliferation in a subtype-dependent manner. However, these compounds had a minimal effect on the viability of primary B-cells. Importantly, the combination of miransertib and sirolimus synergistically reduced cell proliferation in NHL, including in one indolent subtype, e.g., follicular lymphoma (FL), and two aggressive subtypes, e.g., diffuse large B-cell lymphoma (DLBCL) and primary effusion lymphoma (PEL). To establish in vivo efficacy, we used several xenograft models of FL, DLBCL, and PEL. The results obtained in vivo were consistent with the in vitro studies. The FL xenograft was highly sensitive to the inhibition of Akt alone; however, the tumor burden of PEL xenografts was only significantly reduced when both Akt and mTORC1 were targeted. These data suggest that targeting the PI3K/Akt/mTOR pathway with Akt inhibitors such as miransertib in combination with mTOR inhibitors serves as a broadly applicable therapeutic in NHL.

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Section: Discussionmentioning

confidence: 99%

“…For the highly aggressive primary effusion lymphoma model, only mice treated with the combination of Akt and mTOR inhibitors suppressed tumor growth (created with BioRender.com). some clinical value, the development of these compounds for such indications has slowed down as demonstrated by the lack of active phase III trials (37).…”

Section: Discussionmentioning

confidence: 99%

Combined Inhibition of Akt and mTOR Is Effective Against Non-Hodgkin Lymphomas

Rivera-Soto

Dittmer

et al. 2021

Front. Oncol.

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“…The mTOR inhibitors are often used to target aberrant mTOR activation and signaling. 59 , 60 The STAT1 transcription factor is constitutively activated in human AML cell lines and might contribute to the autonomous proliferation of AML blasts. The inhibition of this pathway can be of great interest for AML treatments.…”

Section: Resultsmentioning

confidence: 99%

Application of machine learning methods in clinical trials for precision medicine

Wang

Carter

et al. 2022

JAMIA Open

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Objective A 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 Methods We incorporated 9 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 9 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. Results Simulation 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. Conclusion In 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 Food and Drug Administration (FDA)-approved treatment options and only can get new treatments through clinical trials.

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“…In AML, the mTOR signaling pathway is deregulated and activated as a consequence of genetic and cytogenetic abnormalities. The mTOR inhibitors are often used to target aberrant mTOR activation and signaling [14, 45]. The STAT1 transcription factor is constitutively activated in human AML cell lines and might contribute to the autonomous proliferation of AML blasts.…”

Section: Resultsmentioning

confidence: 99%

Application of machine learning methods in clinical trials for precision medicine

Wang

Carter

et al. 2021

Preprint

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A key component for precision medicine is a good prediction algorithm for patients' response to treatments. Machine learning methods have successfully demonstrated their superior prediction performance in many applications, but have not been applied to conduct response-adaptive randomization in clinical trials. In this article, we incorporate nine machine learning methods, such as gradient boosting machine, random forest, and artificial neural network, in clinical trial design. Realizing that no single method may fit all trials well, we also use an ensemble of these nine methods. We evaluate their performance through quantifying the benefits for trial participants, such as the percentage of patients who receive their optimal treatments and individual loss. To avoid the potential bias introduced by the adaptive scheme, we use the inverse probability of treatment weighted method to estimate the average treatment effect and the statistical power. Simulation studies show that the adoption of machine learning methods results in more personalized optimal treatment assignment and higher overall response rates among trial participants. Compared with the nine individual methods of machine learning, the ensemble approach achieves the highest response rate and assigns the largest percentage of patients to their optimal treatments. The proposed methods are applied to a real-world leukemia study.

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The Role of mTOR Inhibitors in Hematologic Disease: From Bench to Bedside

Cited by 37 publications

References 145 publications

Combined Inhibition of Akt and mTOR Is Effective Against Non-Hodgkin Lymphomas

Combined Inhibition of Akt and mTOR Is Effective Against Non-Hodgkin Lymphomas

Application of machine learning methods in clinical trials for precision medicine

Application of machine learning methods in clinical trials for precision medicine

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