An incredible experience. 30 miles of hiking with new friends. The best part: the team raised $75K for MM research! So grateful for the opportunity @GD_251

Great seeing all of you!

Super cool study. A good reason not to fast.

Thrilled about this collaborative project evaluating venetoclax response in the myeloma subtype I’m so curious about-t(11;14). Grateful to all of the collaborators included here.

Standardizing FISH testing in myeloma is critical to reduce lab to lab variability and misinterpretation. We propose guidelines for FISH panel design and reporting to align with the new IMS/IMWG risk stratification. @CG_Consortium @MayoMyeloma

We will also be providing recommendations to other labs on how to perform and interpret to meet the IMS/IMWG definition with a publication led by @lbaughn in @BloodCancerJnl shortly. Stay tuned.

We’re hiring! Postdoc in Computational Genomics at @EdinUni_IGC Focus: myeloma genome, structural variants, heterochromatin, multiomics Deadline: 9 July 2025, 23:59 GMT Apply here: elxw.fa.em3.oraclecloud.com/… #myeloma #postdoc #genomics #bioinformatics #epigenetics #codeandkilts

Visualize genomic data with ease using gggenomes, an R package that extends ggplot2 to handle and display genomic information intuitively. Whether you’re comparing genomes, analyzing features, or showcasing synteny, gggenomes provides the tools you need to turn complex genomic data into clear, informative visualizations. Why use gggenomes? ✔️ Genomic-focused visualizations: Specifically designed for handling genomic data, including features, alignments, and comparative analysis. ✔️ Versatile and modular: Create detailed and layered plots for diverse genomic scenarios with flexibility for customization. ✔️ Built on ggplot2: Leverages ggplot2’s familiar framework, making it easy for users to adapt and enhance their visualizations. The example visualization shown here is taken directly from the gggenomes GitHub repository, demonstrating how it transforms genomic data into compelling plots: github.com/thackl/gggenomes Curious to learn more about creating data visualizations in R and using tools like ggplot2 and its extensions? Check out my online course, "Data Visualization in R Using ggplot2 & Friends!" Take a look here for more details: statisticsglobe.com/online-c… #datascienceeducation #Statistical #datastructure #RStats

The MMRF Immune Atlas team describes immune cells & clinical outcomes in myeloma. Emory, Beth Israel, WUSTL, MSSM, Mayo & MMRF profiled >1.1 million marrow cells from CoMMpass patients. Preprint at biorxiv.org/content/10.1101/… has details. #myeloma

We are eager to hear from the community clinicians!

Thank you to those who have completed the myeloma FISH survey! We are so grateful. If you haven’t yet completed the survey, we ask you to please consider making your voice heard. Your responses are so valuable. #MMSM @RahulBanerjeeMD

Grand challenge of moving cancer whole-genome sequencing into the clinic. Comment on the largest whole-genome sequencing study- a myriad of actionable alterations and logistical,technical and economic challenges #precisionmedicine @NatureMedicine bit.ly/3NY4yWo

Excited to see our new paper entitled "Genomic Classification and Individualized Prognosis in Multiple Myeloma" out on @JCO_ASCO! A fantastic collaboration between @SylvesterCancer, @univmiami, @MSKCancerCenter, @nyulangone, @MoffittNews, @HDMyeloma and many others!! #mmsm (1/x)

Our goal is to improve FISH reporting for myeloma. If you are a myeloma clinician, please consider this survey. Your responses will be helpful as we propose solutions for standardization and improve reporting clarity. Thank you! @MayoMyeloma

single-cell folks, what's the cell annotation tool do you recommend for immune cells and tumor cells? singleR? Seurat based label transfer? or sc-best-practices.org/cellul… I still find myself using marker genes manually quite often

A great opportunity at University of Minnesota