1/ 🚨New research alert! 🚨Our study in @BloodCancerJnl shows that high WEE1 expression is an independent predictor of poor survival in multiple myeloma (MM), with @RossFirestone, @MalinHultcrantz, @LarryNortonMD, @szusmani, and the rest of the @MSKCancerCenter team! 🧵👇.

RT @MSKCancerCenter: #Lungcancer can co-opt genes that normally help a fetus develop and evade the mother’s immune system, according to res….

RT @ItaiYanai: 90% of doing science is being open to new ideas.

9/ Want to try ORCO? If you're working with omics data & want to see how network curvature can enhance your analysis, I’d love to help! Please reach out. #Bioinformatics #NetworkBiology #SystemsBiology.

8/ ORCO is open-source & [relatively] easy to use! 🎉 Install via pip and start exploring network robustness in your data. Please reach out if you find any bugs or issues! . 📌 Paper:

7/ Past successes of ORC:. 🔹 Identified novel gene signatures for high-risk multiple myeloma (Simhal et al. 2023). 🔹 Revealed therapeutic targets in sarcoma (Elkin et al. 2024). 🔹 Improved graph neural networks for cancer survival prediction (Zhu et al. 2023).

6/ ORC has already provided insights into multiple cancers 🦠 and neurodevelopmental disorders 🧠 by highlighting network vulnerabilities and functional cooperation in gene signaling.

5/ How does ORCO work?. ✅ Input: a biological network (e.g., gene or protein interactions). ✅ Input: omics data (RNA-seq, proteomics). ✅ Output: Edge-based values that describes the robustness between nodes (e.g., genes).

4/ Why does this matter? ORCO focuses on interactions—revealing how biological systems maintain function under stress (or break down when fragile). This may uncover new disease mechanisms & drug targets!.

3/ How does ORC work?. 🔹 At a very high level, if two genes have many connections, their interaction has positive curvature = robust. 🔹 If a connection is a single weak link, easily disrupted, it has negative curvature = fragile.

2/ ORCO is a network analysis tool that applies Ollivier-Ricci curvature (ORC) to omics data. It identifies robust and fragile network interactions, helping uncover key patterns of dysregulation. Let's break it down! 🧵.

Link to manuscript: Thanks to @jianlincheng for the comments and editing!.

🚀 Just published in Bioinformatics! Introducing ORCO: Ollivier-Ricci Curvature-Omics, a python package for analyzing robustness in biological systems. A 🧵 on what it is, why it matters, and how you can use it. With the great team from @MSKCancerCenter.

RT @oncodaily: Myeloma Paper of the Day, February 22nd, suggested by @Myeloma_Doc .@RossFirestone . #Myeloma #Canc….

RT @Myeloma_Doc: #Myeloma Paper of the Day: WEE1 expression is prognostic independent of known biomarkers, differentiates outcomes associa….

13/ Thanks to @VincentRK & the @BloodCancerJnl team for bringing this research out!.

12/ Bottom line:.🔹 High WEE1 = worse survival in MM.🔹 WEE1 is independent of known risk factors.🔹 Targeting WEE1 might be a new therapeutic avenue.🔗 Read more:

11/ Machine learning insights 🤖.Random survival forest showed that WEE1 expression alone has as much prognostic power as ISS staging. Random forest modeling of the local WEE1 genomic network showed overexpression of WEE1is independent of any 1-hop network genes.

10/ More work to do to figure out the TP53 WEE1 connection, but differential gene expression analysis implicated the hallmark P53 pathway. Faulty P53 function may lead to a larger reliance on WEE1.

9/ The P53 connection 🧬.Differences in PFS among patients with TP53 deletions when stratifying by WEE1 expression were massive! Our results seemed to show that patients with TP53 deletions but without high WEE1 expression may not be at high risk after all.

8/ So why is this important?.✅ WEE1 expression could be used as a new prognostic biomarker in MM. ✅ WEE1 inhibitors, already in trials for other cancers (including those by @ZentalisP & @DebiopharmNews) might be a therapeutic option for MM patients.