Expression quantitative trait loci of genes predicting outcome are associated with survival of multiple myeloma patients

Macauda, Angelica; Piredda, Chiara; Clay-Gilmour, Alyssa; Sáinz, Juan; Buda, Gabriele; Markiewicz, Mirosław; Barington, Torben; Ziv, Elad; Hildebrandt, Michelle A.T.; Belachew, Alem A.; Várkonyi, Judit; Prejzner, Witold; Druzd‐Sitek, Agnieszka; Spinelli, John J.; Andersen, Niels Frost; Hofmann, Jonathan N.; Dudziński, Marek; Martínez‐López, Joaquín; Iskierka‐Jażdżewska, Elżbieta; Milne, Roger L.; Mazur, Grzegorz; Giles, Graham G.; Ebbesen, Lene Hyldahl; Rymko, Marcin; Jamroziak, Krzysztof; Subocz, Edyta; Reis, Rui Manuel; Suska, Anna; Haastrup, Eva; Zawirska, Daria; Grząśko, Norbert; Vangsted, Annette Juul; Dumontet, Charles; Kruszewski, Marcin; Dutka, Magdalena; Camp, Nicola J.; Waller, Rosalie; Tomczak, Waldemar; Pelosini, Matteo; Raźny, Małgorzata; Marques, Herlander; Abildgaard, Niels; Wątek, Marzena; Jurczyszyn, Artur; Brown, Elizabeth E.; Berndt, Sonja I.; Butrym, Aleksandra; Vachon, Celine M.; Norman, Aaron D.; Slager, Susan L.; Gemignani, Federica; Canzian, Federico; Campa, Daniele

doi:10.1002/ijc.33547

Cited by 5 publications

(3 citation statements)

References 43 publications

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“…Thus, FKBP9, the most controlled gene in nodule "P", is known for promoting malignant behavior of glioblastoma cells [39] and poor prognosis of PCa patients [40]. TBRG4, the most stably expressed gene in "Q", was reported as being actively involved in myeloma [41], squamous carcinoma [42], osteosarcoma [43], glioblastoma [44], leukemia [45] and lung cancer [46]. The list of stably expressed genes also includes a long noncoding RNA, NDUFA6-AS1, identified recently as a biomarker for the prognostic of thyroid cancer [47].…”

Section: Overviewmentioning

confidence: 99%

Personalized 3-Gene Panel for Prostate Cancer Target Therapy

Iacobaş

2022

CIMB

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Many years and billions spent for research did not yet produce an effective answer to prostate cancer (PCa). Not only each human, but even each cancer nodule in the same tumor, has unique transcriptome topology. The differences go beyond the expression level to the expression control and networking of individual genes. The unrepeatable heterogeneous transcriptomic organization among men makes the quest for universal biomarkers and “fit-for-all” treatments unrealistic. We present a bioinformatics procedure to identify each patient’s unique triplet of PCa Gene Master Regulators (GMRs) and predict consequences of their experimental manipulation. The procedure is based on the Genomic Fabric Paradigm (GFP), which characterizes each individual gene by the independent expression level, expression variability and expression coordination with each other gene. GFP can identify the GMRs whose controlled alteration would selectively kill the cancer cells with little consequence on the normal tissue. The method was applied to microarray data on surgically removed prostates from two men with metastatic PCas (each with three distinct cancer nodules), and DU145 and LNCaP PCa cell lines. The applications verified that each PCa case is unique and predicted the consequences of the GMRs’ manipulation. The predictions are theoretical and need further experimental validation.

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Section: Overviewmentioning

confidence: 99%

Personalized 3-Gene Panel for Prostate Cancer Target Therapy

Iacobaş

2022

CIMB

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“…To date, the focus of MM research has been bioinformatics modelling of large genomics and transcriptomics datasets, as well as pharmacokinetic and pharmacodynamic (PKPD) simulations of drug interactions. These have led to significant findings, including identifying novel signalling pathways [ 38 , 39 ], genetic risk factors [ 40 , 41 ], biomarkers [ 42 , 43 ], RNA interactions [ 44 , 45 , 46 , 47 ], and oncogenic inflammation and microenvironments [ 48 , 49 ]. Similarly, these large datasets can be used to develop models of the effects and side effects of drug interventions [ 50 ] by modelling the signalling interactions of different cell types [ 48 ].…”

Section: Three-dimensional Models Of Multiple Myelomamentioning

confidence: 99%

Utilizing 3D Models to Unravel the Dynamics of Myeloma Plasma Cells’ Escape from the Bone Marrow Microenvironment

Verbruggen,

Freeman,

Freeman

2024

Cancers

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Recent therapeutic advancements have markedly increased the survival rates of individuals with multiple myeloma (MM), doubling survival compared to pre-2000 estimates. This progress, driven by highly effective novel agents, suggests a growing population of MM survivors exceeding the 10-year mark post-diagnosis. However, contemporary clinical observations indicate potential trends toward more aggressive relapse phenotypes, characterized by extramedullary disease and dominant proliferative clones, despite these highly effective treatments. To build upon these advances, it is crucial to develop models of MM evolution, particularly focusing on understanding the biological mechanisms behind its development outside the bone marrow. This comprehensive understanding is essential to devising innovative treatment strategies. This review emphasizes the role of 3D models, specifically addressing the bone marrow microenvironment and development of extramedullary sites. It explores the current state-of-the-art in MM modelling, highlighting challenges in replicating the disease’s complexity. Recognizing the unique demand for accurate models, the discussion underscores the potential impact of these advanced 3D models on understanding and combating this heterogeneous and still incurable disease.

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“…Thus, FKBP9, the most controlled gene in nodule "P", is known for promoting malignant behavior of glioblastoma cells [39] and poor prognosis of PCa patients [40]. TBRG4, the most stably expressed gene in "Q", was reported as actively involved in myeloma [41], squamous carcinoma [42], osteosarcoma [43], glioblastoma [44], leukemia [45] and lung cancer [46]. The list of stably expressed genes includes also a long noncoding RNA, NDUFA6-AS1, identified recently as a biomarker for the prognostic of thyroid cancer [47].…”

Section: Genementioning

confidence: 99%

Personalized 1-2-3-Gene(s) Tickets to Cancer-Free Prostate

Iacobaş¹,

Iacobaş²

2021

Preprint

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Many years and $$$ spent for research did not yet produced a universally effective gene therapy of prostate cancer (PCa). Our studies indicated that not only each human, but even each cancer nodule in the same tumor has unique and dynamic gene expression profile, control and coordination. The tumor heterogeneity of the transcriptome topology is a strong argument in favor of personalized gene therapies, tailored on patients’ primary tumor unique characteristics. Here, we propose a bioinformatics procedure by which to identify the Gene Master Regulators (GMR) of cancer cells from transcriptomic data and predict consequences of their experimental manipulation. The procedure, based on our Genomic Fabric Paradigm (GFP), can determine the most important gene in each cancer nodule whose controlled alteration would selectively kill the cancer cells. In this report, the method is applied to our microarray data on two men PCs (each with three distinct cancer nodules) and two standard human PCa cell lines (DU145 and LNCaP). We expect the industry to produce ready-to-use CRISPR constructs for all genes allowing the clinical oncologist to prescribe the adequate personalized CRISPR cocktail for his/her patient. Thus, the GMR approach may provide the most effective, yet affordable solution in fighting cancer.

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Expression quantitative trait loci of genes predicting outcome are associated with survival of multiple myeloma patients

Cited by 5 publications

References 43 publications

Personalized 3-Gene Panel for Prostate Cancer Target Therapy

Personalized 3-Gene Panel for Prostate Cancer Target Therapy

Utilizing 3D Models to Unravel the Dynamics of Myeloma Plasma Cells’ Escape from the Bone Marrow Microenvironment

Personalized 1-2-3-Gene(s) Tickets to Cancer-Free Prostate

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