JAK2 regulates paclitaxel resistance in triple negative breast cancers

Han, Jong-Min; Yun, Jihui; Quan, Mingji; Kang, Wonyoung; Jung, Jessica; Heo, Woohang; Li, Songbin; Lee, Kyu Jin; Son, Hye Youn; Kim, Ju Hee; Choi, Jaeyong; Noh, Dong Young; Na, Deukchae; Ryu, Han Suk; Lee, Charles; Kim, Jong‐Il; Moon, Hyeong‐Gon

doi:10.1007/s00109-021-02138-3

Cited by 21 publications

(16 citation statements)

References 54 publications

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“…This was proved by a single cell sequencing analysis obtained from TNBC patients showing adaptive selection by neo-adjuvant chemotherapy treatment [ 66 ] and leading to high treatment failure in TNBC patients. Furthermore, numerous pathways are known to regulate TNBC CSCs survival [ 66 , 67 ], including Hedgehog [ 68 ], Wnt/β-catenin [ 65 , 69 ], JAK/STAT [ 70 , 71 ], and HIPPO pathways [ 72 ]. Despite significant progress being made in understanding the mechanism behind persistence, there is still an urgent need for successful clinical targeting of these specific cancer cells.…”

Section: Tnbc and Drug-resistant Cellsmentioning

confidence: 99%

Challenges for Triple Negative Breast Cancer Treatment: Defeating Heterogeneity and Cancer Stemness

Mahmoud

Ordóñez‐Morán

Allegrucci

2022

Cancers

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The Triple Negative Breast Cancer (TNBC) subtype is known to have a more aggressive clinical course compared to other breast cancer subtypes. Targeted therapies for this type of breast cancer are limited and patients are mostly treated with conventional chemo- and radio-therapies which are not specific and do not target resistant cells. Therefore, one of the major clinical challenges is to find compounds that target the drug-resistant cell populations which are responsible for reforming secondary tumours. The molecular profiling of the different TNBC subtypes holds a promise for better defining these resistant cells specific to each tumour. To this end, a better understanding of TNBC heterogeneity and cancer stemness is required, and extensive genomic analysis can help to understand the disease complexity and distinguish new molecular drivers that can be targeted in the clinics. The use of persister cancer cell-targeting therapies combined with other therapies may provide a big advance to improve TNBC patients’ survival.

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Section: Tnbc and Drug-resistant Cellsmentioning

confidence: 99%

Challenges for Triple Negative Breast Cancer Treatment: Defeating Heterogeneity and Cancer Stemness

Mahmoud

Ordóñez‐Morán

Allegrucci

2022

Cancers

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“…Ruxolitinib has been shown to enhance the efficacy of paclitaxel in a synergistic manner in ovarian cancer cells (Han, et al, 2018). Recent studies of triple negative breast cancer (TNBC) samples has shown that inhibition of JAK1/2 by ruxolitinib sensitizes cancer cells to paclitaxel, both in-vitro and in-vivo (Han, et al, 2021; Lian, et al, 2020). A phase I clinical trial of the combination of these two drugs in HER2-negative metastatic breast cancer patients was recently completed and showed these two drugs to be well tolerated by the participants (Lynce, et al, 2021).…”

Section: Resultsmentioning

confidence: 99%

MARSY: A multitask deep learning framework for prediction of drug combination synergy scores

Khili

Memon

Emad

2022

Preprint

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Motivation: Combination therapies have emerged as a treatment strategy for cancers to reduce the probability of drug resistance and to improve outcome. Large databases curating the results of many drug screening studies on preclinical cancer cell lines have been developed, capturing the synergistic and antagonistic effects of combination of drugs in different cell lines. However, due to the high cost of drug screening experiments and the sheer size of possible drug combinations, these databases are quite sparse. This necessitates the development of transductive computational models to accurately impute these missing values. Results: Here, we developed MARSY, a deep learning multi-task model that incorporates information on gene expression profile of cancer cell lines, as well as the differential expression signature induced by each drug to predict drug-pair synergy scores. By utilizing two encoders to capture the interplay between the drug-pairs, as well as the drug-pairs and cell lines, and by adding auxiliary tasks in the predictor, MARSY learns latent embeddings that improve the prediction performance compared to state-of-the-art and traditional machine learning models. Using MARSY, we then predicted the synergy scores of 133,722 new drug-pair cell line combinations, which we have made available to the community as part of this study. Moreover, we validated various insights obtained from these novel predictions using independent studies, confirming the ability of MARSY in making accurate novel predictions. Availability and Implementation: An implementation of the algorithms in Python and cleaned input datasets are provided in https://github.com/Emad-COMBINE-lab/MARSY.

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“…Although paclitaxel confers anti-tumor activity against a variety of solid cancers, the development of clinical drug resistance severely restricts this effect ( Han et al, 2021 , Alharbi et al, 2018 ). Our data show that hypoxia mediates the induction of miR-100 expression and that miR-100 directly downregulates USP15 expression.…”

Section: Discussionmentioning

confidence: 99%

Hypoxia-induced paclitaxel resistance in cervical cancer modulated by miR-100 targeting of USP15

Nishi¹,

Ono²,

Ohno³

et al. 2023

Gynecologic Oncology Reports

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JAK2 regulates paclitaxel resistance in triple negative breast cancers

Cited by 21 publications

References 54 publications

Challenges for Triple Negative Breast Cancer Treatment: Defeating Heterogeneity and Cancer Stemness

Challenges for Triple Negative Breast Cancer Treatment: Defeating Heterogeneity and Cancer Stemness

MARSY: A multitask deep learning framework for prediction of drug combination synergy scores

Hypoxia-induced paclitaxel resistance in cervical cancer modulated by miR-100 targeting of USP15

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