| Register
\(\newcommand{\Cat}{{\rm Cat}} \) \(\newcommand{\A}{\mathcal A} \) \(\newcommand{\freestar}{ \framebox[7pt]{$\star$} }\)

1. Top four problem areas

These are the top four problem areas as agreed upon by all workshop participants.


    1. Add remark

      Problem 1.1.

      Characterizing and modeling tumor heterogeneity
          Main questions:

      How do we characterize and account for heterogeneity? Are we targeting the right cell populations? Do cancer stem cells exist? If so, do they matter?

      Notes:

      (a) John Lowengrub (UC-Irvine) and Heiko Enderling (Tufts University) have studied math models about stem cells, and their results suggest that the presence of stem cells makes a big difference in outcomes.

      (b) Whether there is agreement about what the cells might be called; if there is agreement about their function, that is a starting point.

      (c) Stem cells might develop mutations and cause cancer, and non-stem cells may develop mutations and cause cancer.

      (d) There exist viruses that are too virulent for their own good, and thus die out while less virulent viruses remain. Is there a corresponding scenario for tumor cells? Can we characterize the prognostic threat from different cells in the tumor? For example, are some cells more cloaked from the immune system than others?

      (e) Research underway studying single cell heterogeneity in tumors, e.g., from Stephen Quake’s lab at Stanford (Dalerba et al. 2011).


        • Add remark

          Problem 1.2.

          Systems approaches to drug resistance
              What mechanisms are driving resistance? How do we predict better drug combinations to combat resistance? What kind of data/studies do we need to better understand resistance?


            • Add remark

              Problem 1.3.

              Linking signaling models to phenotype (e.g., tumor growth)
                  Consortium between pharma industry and academics? Multi-scale modeling from pathway to cell to whole tissue?


                • Add remark

                  Problem 1.4.

                  Translating pre-clinical models to human
                      How do we test and interpret predictability of pre-clinical animal models? How might an industrial consortium conduct blind pre-clinical studies on marketed and failed drugs?

                      Cite this as: AimPL: Systems approaches to drug discovery and development in oncology, available at http://aimpl.org/systemsoncology.