Stand Up To Cancer - standup2cancer.orgThis is where the end of cancer begins
   Please leave this field empty

The SU2C-NSF Cancer Convergence Evolution Dream Team Progress Update

Share this:
Email

Like this page on Facebook

PI3K Dream Team | Pancreatic Dream Team | CTC-Chip Dream Team | Epigenetic Dream Team 2009 | Breast Cancer Dream Team | Prostate Cancer Dream Team 1 | Prostate Cancer Dream Team 2 | Melanoma Dream Team | Immunology Dream Team | Sta Op Tegen Kanker Translational Research Team | Pediatric Cancer Dream Team | Pancreatic Cancer Dream Team 2 | Tumor Organoids Dream Team | HPV Translational Research Team | Epigenetic Dream Team 2014 | Lung Cancer Dream Team | Colorectal Cancer Dream Team | Ovarian Cancer Dream Team | Convergence Breast Cancer Dream Team | Convergence Drug Combinations Dream Team | Convergence Cancer Evolution Dream Team | Convergence Pancreatic Cancer Dream Team


The SU2C-National Science Foundation Cancer Convergence Dream Team Progress Report

“Genetic, Epigenetic, and Immunological Underpinnings of Cancer Evolution through Treatment”

Grant Funded: January, 2016

Funding: $4,069,877

Convergence Team Leader:
• Ross Levine - Memorial Sloan Kettering Cancer Center

Principals (current):
• Jeffery A. Engelman, MD, PhD – Massachusetts General Hospital
• Chang S Chan, PhD – Rutgers Cancer Institute of New Jersey
• Harlan Robins, PhD – Fred Hutchinson Cancer Research Center
• Daniel S. Fisher, PhD – Stanford University
• Steven Altschuler, PhD – University of California at San Francisco

Through collaboration of research scholars in distinct disciplines, convergence grants offer a novel model research meant to spur innovation in new ways of combating cancer. By taking advantage of advances in information technology, nanotechnology, new material research, imaging, optics, quantum physics, and other physical sciences, often considered outside the realm of traditional biomedical research, the subsequent convergence grants may provide critical outcomes to advance the fight against cancer.

Project Background

The last two decades have seen the development of increasingly effective cancer therapies that target different vulnerabilities of cancer cells.  In some cancers, such as acute myeloid leukemia (AML) and non-small cell lung cancer (NSCLC) with activating EGFR mutations, these therapies can produce significant responses in patients. Unfortunately, most patients subsequently relapse with a cancer that is resistant to the original treatment. This Team is investigating the cause of the relapse and subsequent resistance in these two tumor types. 

The Team is combining genomic, computational, and laboratory studies to look at different timepoints in the course of treatment, remission and relapse.  Mathematical modeling approaches are used to understand the evolution of drug resistance and develop novel therapeutic strategies aimed to keep the cancers from adapting to treatments.  In order to accomplish this, the team has three specific aims:

1. Determine the evolution of resistance in patients. The Team is analyzing patient samples and conducting laboratory experiments to generate data that is used to create mathematical models of the evolution of resistance.

2. Immune system dynamics in response to cancer therapies.  The Team is examining immune T-cells present in the blood of cancer patients to understand how the reacts to cancer and how that reaction changes over time.

3. Functional interrogation of drug response/resistance.  It is not clear how a drug resistant tumor develops. Were the resistant cells present at the outset and do they only grow in large amounts if all other tumor cells are killed, do all cells try to adapt to evade a treatment and only the small number that are successful grow the new tumor, or are there stem cell like cells that are never hurt by the treatment so the tumor can regrow again and again.  The Team is looking at a wide range of factors both within the cancer cells and in their immediate environment to understand the evolutionary process in order to design treatments that thwart the development of drug resistance.

Status Updates

6 Months:

The Team has made progress toward all three specific aims:

1. To examine the evolution of resistance in NSCLC patients, multiple samples from different times and tissues have been assembled from 11 individuals.  Molecular analysis data from these samples will be used to computationally model the evolution of resistance in the patients.  To prepare for investigation of AML resistance, the Team has gotten approval and built infrastructure to collect samples from all patients newly diagnosed with AML at Memorial Sloan Kettering Cancer Center, along with follow-up sample collection at various time points during treatment.  Methods for isolating cancer cells from the blood have been optimized, and the first round of samples is undergoing molecular analysis.

2. To investigate changes in the immune system during treatment, the Team has initiated a study of 250 elderly patients with AML.  Blood samples have been collected before therapy and will be collected again at points during the course of treatment.  Immune T-cells within the blood are isolated and the DNA and RNA are being sequenced to understand how the immune system sees the cancer cells.

3. Work to date suggests that genetic mechanisms of resistance evolves during treatment and an understanding of that process in patients may point to innovative therapeutic strategies.  In NSCLC, the team is looking to understand the early stage mechanisms of resistance in order to target cells most likely to develop resistance. To that end, the Team has assembled a list of molecules that are outside the cell and commonly stimulate or support cancer growth.  Screens are underway to look for these molecules in NSCLC cell cultures.  In AML, the Team is focused on understanding the diversity of resistant cell populations that exist in a patient with this blood cancer. The team has devised a method for sorting cells into subpopulations and will soon begin testing each for its ability to develop resistance.