Top Tweets for #Sysbio4CancerResearch
@NCIsysbio @FertigLab @USCSysBio_Lab @christeeny513 @DrShelleyHwang @khanaziz84 @SohailTavazoie @MaxUmehGarcia @IamLinghua @mathoncbro Thank you all! I really learned a lot from you about exciting progress and opportunities in #CancerResearch using #SystemsBiology! #SysBio4CancerResearch
@NCIsysbio A9: I think it would be great to have more discussions/conversations involving patient advocates (like @christeeny513) and cancer researchers using #SystemsBiology approaches to help build collaborations with multiple perspectives. #SysBio4CancerResearch
@NCIsysbio A9: I would be interested in discussions related to getting the next generation of scientists (e.g., middle school, high school, and undergraduate students) interested in #CancerResearch using #SystemsBiology. #SysBio4CancerResearch
@FertigLab @NCIsysbio I totally agree! Everyone is talking about #AI! It’s everywhere and the technology is rapidly progressing. As you said, in #CancerResearch it’s also important to think about interpretable AI. https://t.co/GK3vo7dwtO #SysBio4CancerResearch #AI4CancerResearch @NCIDataSci
@NCIsysbio A9: I am just starting to understand how important it is to engage patient advocates in #CancerResearch. I would love to see more conversations with patients, where we learn from each other and get inspired about impactful #SystemsBiology work. #SysBio4CancerResearch
#SysBio4CancerResearch:
Another important future direction is the integration of AI/ML approaches with mechanistic modeling.
Combining highly predictive black-box approaches with parameterized math models to gain mechanistic insight into that black-box:
https://t.co/wktOKE5JKu
@NCIsysbio A8: I also think there may be opportunities for more translational #SystemsBiology initiatives like @ARPA_H ADAPT, which aims to harness advanced technologies and a deep understanding of tumor biology to build cancer #biomarkers. https://t.co/toOvfgyjkf #SysBio4CancerResearch
@NCIsysbio A8: I think, especially with the growing area of spatial biology, there are opportunities in #dataviz for cancer #SystemsBiology (which may be related to some conversations about #DataViz4Cancer). https://t.co/3lm33txhJd. #SysBio4CancerResearch
#SysBio4CancerResearch:
Mathematical modeling has made a significant recent impact in the design of treatment scheduling protocols that are evolution-based strategies to mitigate the evolution of resistance.
But there is still many unanswered questions, and #MathOnco or #SysBio approaches will be key to future work. We’ve outline open questions in the field here:
https://t.co/tJjjtDuPb0
@NCIsysbio A7: modeling of adaptive therapy from Bob Gatenby @MoffittNews has already led to clinical trials showing good outcomes (https://t.co/YqWUVw0cKW) #SysBio4CancerResearch 2/2
@NCIsysbio A7: tumors are constantly evolving and adapting to their environment. it is exciting to see #SystemsBiology models that exploit that evolution, leading to adaptive therapy. #SysBio4CancerResearch 1/2
@NCIsysbio A7: Dr. Forest White shares preclinical #SystemsBiology approaches that revealed potential new therapeutic strategies for incurable cancers in a @MIT seminar: https://t.co/2NJE0Lk7va
#SysBio4CancerResearch
#SysBio4CancerResearch: The best way to bring preclinical data to bear on clinical translation is the integration of mathematical models to interpolate or extrapolate these data, and to test & generate hypotheses about underlying biological mechanisms.
A great example of this model iteration process from Paras Jain & @mkjolly15:
https://t.co/XbETLmmwPI
@NCIsysbio A7: In a @CD_AACR study, Mundi et al. developed tumor-agnostic #SystemsBiology tools (i.e., OncoTarget and OncoTreat) to predict drug responses, which are scalable for the design of #ClinicalTrials.https://t.co/2WDAvHZkYR #SysBio4CancerResearch
#SysBio4CancerResearch: The best way to bring preclinical data to bear on clinical translation is the integration of mathematical models to interpolate or extrapolate these data, and to test & generate hypotheses about underlying biological mechanisms.
A recent great example of model iteration is @stroblmar’s paper on PARP inhibitors in ovarian cancer:
https://t.co/YnJE3VkdVu
@NCIsysbio A7: @xubin_li, @anilkorkut, et al. developed REFLECT (a predictive #CombinationTherapy #bioinformatics platform) & found that REFLECT-selected treatments showed improvements in efficacy & survival in preclinical & clinical settings. https://t.co/PkSK7y8fGy #SysBio4CancerResearch
On new technologies/approaches in #SysBio4CancerResearch:
Mistic: an open-source multiplexed image t-SNE viewer created by @sandhya212
t-SNE or UMAP is a nice way to visualize relationships in high dimensional data in a reduced dimension diagram, but you lose the visual, spatial original aspect of the data.
Why not use t-SNE / UMAP coordinates to spread out the images?
https://t.co/s92Y6cR3nh
Download: https://t.co/6DBom6cZIZ
On new technologies/approaches in #SysBio4CancerResearch:
A neat example of how @S_Marzban incorporated VALIS into our workflow for Lenia-based spatial models to determine the effect of ECM on immune infiltration (joint work w/ @AmelioLab)
Our preprint is here:
https://t.co/QtvVE2XyYt
On the question of new technologies #SysBio4CancerResearch, I’d like to point out a few software packages we’ve been using to analyze spatial data:
First, VALIS (Virtual Alignment of pathoLogy Image Series), published by @cgatenbee has been invaluable in our workflow, aligning serially stained slides, for subsequent use in agent-based spatial models:
https://t.co/5Q2k8GhZ24
Download: https://t.co/sTdnkuO8dh
@NCIsysbio A6: @PlevritisLab et al. developed a multi-scale model of the natural history of #LungCancer that predicts benefits associated with screening for #LungCancer by detecting #ctDNA. https://t.co/N0QANnmuNR #SysBio4CancerResearch #LCSM
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