Judges’ Queries and Presenter’s Replies

  • Icon for: Mary Albert

    Mary Albert

    Judge
    May 21, 2013 | 02:55 p.m.

    Great video – very clearly communicated! The design of the analysis is intriguing. I did not see a conclusion section – do you have preliminary results yet?

  • Icon for: Adam Labadorf

    Adam Labadorf

    Presenter
    May 21, 2013 | 04:46 p.m.

    Thanks Dr. Albert! The dataset we’re working with is still highly sensitive since it involves a new drug in clinical trials, so we unfortunately could only talk about our method and not specific results. We are working on analyzing other less sensitive datasets now in preparation for a manuscript, though. We wish we could have talked more about what we found too! Soon, hopefully. Thanks again for your comment.

  • May 21, 2013 | 05:47 p.m.

    Very nice video. You are definitely an effective communicator. So you envision being able to take your “snapshots” of gene expression and put them together to tell the whole story, so to speak. But you chose time points that were 4 – 12 hours apart. Would expect to be able to tell the story with such large intervals? For the guy throwing the potato, he would probably be in bed 12 hours later so we would never know if he was levitating the potato or not? what is the rationale for the time frame for your cell studies?

  • Icon for: Arjan van der Velde

    Arjan van der Velde

    Co-Presenter
    May 22, 2013 | 12:00 a.m.

    Hello Dr. Shin. Thank you very much for your question. First, the presented method can be used for experiments on any time scale. Second, in the specific data set analyzed here the time points 2h, 6h, 12h, 24h and 48h were determined based on prior experiments and chosen to capture both the early and late responses of the cell lines to the drug. Also, the drug under investigation interferes with the mechanisms involved in cell cycle (cell division), a process that generally occurs on average every 24 hours. The progression of time points is chosen such that short term processes are sampled on a more fine grained scale than long term responses. While technically it is possible to measure at many more time points, the number of time points is limited by the the cost of each microarray (on the order of hundreds of dollars) used to measure gene expression. In this data, 150 microarrays were used.

  • May 21, 2013 | 06:36 p.m.

    Yes, great video! I could be mistaken but my understanding is that time-series data are available but have been analyzed in a different way up until now. Is that true? If so, can you elaborate on how your analysis differs from previous time-series analyses?

  • Icon for: Heather Selby

    Heather Selby

    Co-Presenter
    May 22, 2013 | 12:34 a.m.

    Dr Buneo you are correct, there are already software packages available to analyze time series data (e.g. BETR, EDGE, SAMR, STEM, LIMMA). Each tool approaches the problem differently in how it formulates the time series statistical model and how it makes the results available to the user. Early in the project, we explored many of these tools and found them either to be not functional, difficult to use, or not exactly what we were looking for. We focused on making our tool as general purpose as possible without making it difficult to use, and tried to make exploring the results intuitive to non-technical users as well as those comfortable in computational environments. Our statistical model is not particularly novel, but we’re unaware of any other software that visualizes time series data in this way so as a whole package we feel the tool might be valuable to other investigators.

  • May 21, 2013 | 10:21 p.m.

    I enjoyed your video and poster, and particularly liked the way you illustrated the importance of understanding the entire time course and ability to be mislead by just looking at a “snapshot” with the levitating potato example. One question though is how do you know if “gene activity” is a direct result of a cellular response to the drug or perhaps a internal feedback control system response to the drug? Is the goal of your project to develop the software tool to help researchers visualize, understand and interpret all of the data generated from the microarrays or is your project looking at a specific question related to cancer or cancer treatment and if so what have you learned about large B cell lymphoma? Also since gene expression does not always correlate with protein/enzyme levels how do you know that a high gene activity or expression is actually having an impact on cellular metabolism/physiology?

  • Icon for: Adam Labadorf

    Adam Labadorf

    Presenter
    May 22, 2013 | 07:10 p.m.

    Thank you for your comments, you make very good points.

    Addressing your question about direct vs feedback responses, we can’t know for sure with our data, though we do know some specifics about the drug’s biochemical targets. It is true that this analysis method, and microarray data in general, cannot easily distinguish between direct and indirect responses to external stimuli. One might speculate that changes at the earliest time points after treatment are more likely to be direct responses, while later differential expression corresponds to reactive processes, but there would need to be experimental validation to confirm this. Indeed, one aim of the analysis is to identify testable hypotheses that can then be subject to targeted studies later. However, direct or indirect, the changes we observe in gene expression are all effects of the drug treatment in human cells by experimental design, which is what we seek to better understand at the genome-wide level. Simply understanding which genes change may have direct translational implications even without a detailed characterization of specific mechanisms.

    We agree that it is unclear how good expression is as a proxy for protein levels, though validation experiments often confirm protein activity predicted from gene expression analysis. Including other phenotypic data (e.g. viability), or high-throughput proteomic data in our analysis could lead to greater biological insight, but unfortunately those data are not available for this study.

    The purpose of our tool is to help biologists quickly generate specific hypotheses that can then be tested in the lab to directly investigate real biochemical relationships. The overall project has both of the goals you mention: develop a tool to better understand time course data and then apply it to our specific time series dataset. These two goals are synergistic in the sense that seeing how our tool captures (or misses) meaningful biology in our dataset will inform iterative development of the tool. Due to our data being highly sensitive, we can’t say much about what we’ve learned about DLBCL from this analysis yet, unfortunately. Watch for a publication within the next year. :)

  • Icon for: Peter Pfromm

    Peter Pfromm

    Judge
    May 21, 2013 | 11:34 p.m.

    While I understand that this is obviously work in progress, suppose you successfully analyze the data, what could be avenues to use your results? What conclusions could be drawn?

  • Icon for: Adam Labadorf

    Adam Labadorf

    Presenter
    May 22, 2013 | 12:37 p.m.

    Excellent question, Dr. Pfromm. One translational application of our findings might be to better understand the reasons underlying the heterogeneity of DLBCL treatment efficacy. For example, if we discover that the drug hits a particular pathway in one of our cell lines and not the others, we can look for differences between those cell lines to potentially develop tests that predict whether a patient will respond to the drug. The treatment targets aspects of cell cycle, which is involved in many diseases besides cancer, so better understanding what happens when cell cycle processes are perturbed could be valuable not only in other clinical applications but to basic biology as well.

  • Further posting is closed as the competition has ended.

Presentation Discussion

  • Small default profile

    cynthia evans

    Guest
    May 20, 2013 | 05:23 p.m.

    This explains a complicated process so even I can understand it.

  • Icon for: Stephanie Luff

    Stephanie Luff

    Trainee
    May 21, 2013 | 10:07 a.m.

    This was great! So do you guys have intentions that one day others will use your web tool as well?

  • Icon for: Adam Labadorf

    Adam Labadorf

    Presenter
    May 21, 2013 | 11:07 a.m.

    Thanks Stephanie! Yes, we are currently working on an R package that performs the analysis and produces the visualization. We hope to get it out sometime this summer.

  • Icon for: Stephanie Luff

    Stephanie Luff

    Trainee
    May 21, 2013 | 11:27 a.m.

    That would be great! My lab currently uses R for our microarray normalization. If you could remember, please let me know when it comes out, I’m very interested in it.

  • Icon for: Adam Labadorf

    Adam Labadorf

    Presenter
    May 22, 2013 | 07:15 p.m.

    I’ll put a reminder in the TODO file of our software repository. :)

  • Icon for: clare mahoney

    clare mahoney

    Trainee
    May 22, 2013 | 05:45 p.m.

    This is clever and well articulated! Well done!

  • Icon for: Adam Labadorf

    Adam Labadorf

    Presenter
    May 22, 2013 | 07:15 p.m.

    Thanks, glad you liked it.

  • Icon for: Samson Lai

    Samson Lai

    Trainee
    May 23, 2013 | 12:01 a.m.

    Good visuals. It sounds like you’re doing some important work here. Nicely done.

  • Further posting is closed as the competition has ended.

  1. Adam Labadorf
  2. http://www.igert.org/profiles/5394
  3. Graduate Student
  4. Presenter’s IGERT
  5. Boston University
  1. Heather Selby
  2. http://www.igert.org/profiles/5395
  3. Graduate Student
  4. Presenter’s IGERT
  5. Boston University
  1. Arjan van der Velde
  2. http://www.igert.org/profiles/5333
  3. Graduate Student
  4. Presenter’s IGERT
  5. Boston University

Novel Time Series Analysis of a Diffuse Large B-Cell Lymphoma Treatment

Diffuse large B-cell lymphoma (DLBCL) is the most common non-Hodgkin lymphoma in the United States. Forty percent of patients with DLBCL succumb to the disease, and new therapeutic approaches are needed. One such therapeutic is currently in clinical trials; however, the detailed biological mechanisms governing the response to this treatment in DLBCL are not well understood. Characterization of the transcriptional response to treatment is essential to understand the biological mechanisms of action of a drug. The focus of our project is the analysis of a large gene expression dataset consisting of a panel of DLBCL cell lines profiled at five time points before and after treatment. We developed a novel time series analysis approach to quantify the dynamic evolution of gene expression, and applied it to our dataset to carefully characterize the response to the pharmacological perturbation. The time series analysis identifies differential expression of genes, and enrichment of biologically relevant gene sets and pathways from publicly available repositories. We created a custom visualization tool to explore the various dimensions of our results at multiple levels of detail that is biologically intuitive. The combination of the time series analysis pipeline and the visualization tool identified both novel and previously known mechanisms of actions of the therapeutic treatment on DLBCL cell lines.