One of the revolutions in cancer treatment over the past decade has been the introduction of molecularly targeted agents into the clinic. Especially the treatment of stage IV non-small cell lung cancer (NSCLC) has been dramatically changed by biological agents targeting oncogenic driver mutations, most prominently somatic activating mutations in the epidermal growth factor receptor (EGFR) and rearrangements involving the anaplastic lymphoma kinase (ALK).

Given the benefit derived from these targeted therapies as single agents in stage IV disease, there is increasing interest in integrating these agents with chemotherapy and radiotherapy (RT) in stage III disease. In this context our group investigates mechanistic bio-mathematical models to maximize the effectiveness of targeted agents together with radiation therapy, including dosimetric as well as spatiotemporal optimization:

  • Can targeted agents replace chemotherapy in concurrent chemo-radiation regimen? If yes, is the optimal sequencing and radiation regimen still the same is with the chemotherapy agent?
  • Which effect have different models of resistance development, pre-existing versus acquired (see Figure below), on the answer to those questions?
  • How does the resistance develop in the patient and can what can we infer directly from patient data?
  • How can we include imaging and biomarker information to drive model parameterizations and make patient-specific predictions?

Lead Team Member: David "Bo" McClatchy

Project Opportunities: If you’re interested in working on these or related questions, have a look at our list of open projects --> here


Model is fit to growth dynamics derived from patient imaging

Pre-existing resistance vs. persister evolution - which model explains the data better?



Model can determine optimal induction periods using EGFR-TKIs




[1] McClatchy, D. M., Paganetti, H., Hata, A., Piotrowska, Z., Sequist, L. V., Willers, H., & Grassberger, C. (2019). Optimizing the Administration of Tyrosine Kinase Inhibitors with Chemoradiotherapy to Improve Outcomes in Locally Advanced, EGFR+ NSCLC Using an Evolutionary Tumor Progression Model. International Journal of Radiation Oncology*Biology*Physics, 105(1), E795–E796.

[2] Grassberger C, McClatchy D 3rd, Geng C, et al. Patient-Specific Tumor Growth Trajectories Determine Persistent and Resistant Cancer Cell Populations during Treatment with Targeted Therapies. Cancer Res. 2019;79(14):3776-3788. doi:10.1158/0008-5472.CAN-18-3652

[3] Grassberger, C., Scott, J. G., & Paganetti, H. (2017). Biomathematical Optimization of Radiation Therapy in the Era of Targeted Agents. International Journal of Radiation Oncology• Biology• Physics, 97(1), 13-17.

[4] Grassberger, C., & Paganetti, H. (2016). Methodologies in the modeling of combined chemo-radiation treatments. Physics in medicine and biology, 61(21), R344.

[5] Grassberger, C., & Paganetti, H. (2016). WE‐H‐BRA‐03: Development of a Model to Include the Evolution of Resistant Tumor Subpopulations Into the Treatment Optimization Process for Schedules Involving Targeted Agents in Chemoradiation Therapy. Medical physics, 43(6), 3843-3843.