Predicting treatment outcome by DNA and organoids

Tailoring treatment to the unique cancer of each individual patient is important to improve patient outcomes, facilitate drug development and increase cost-effectiveness in health care. This thesis describes several research projects focused on personalized medicine in cancer. The first part of this thesis is about biomarker research. In chapter 2, we aimed to discover why a patient with a PEC-tumor had a remarkable response to treatment with the mTOR inhibitor everolimus. Whereas we expected there to be a genetic aberration somewhere in the mTOR pathway, that had resulted in its activation, no such aberration was found using either DNA sequencing or copy number analysis. Chapter 3 describes a biomarker identification study to predict benefit from treatment with everolimus. Patients with any type of advanced solid malignancy were included, underwent a pre-treatment tumor biopsy, and were treated with everolimus. Using DNA sequencing, copy number analysis and immunohistochemistry we found that mutations or deletions in PTEN could represent a tumor type agnostic biomarker for treatment benefit from everolimus (P=0.046). In Chapter 4, we describe the TTP ratio as a clinical trial endpoint to measure clinical benefit of everolimus. The TTP ratio uses an intra-patient control that corrects for natural tumor growth rate, and is especially useful to measure benefit of compounds that induce a reduction in tumor growth rate, as opposed to tumor regression. Response according to TTP ratio correlated to Overall Survival, and was also able to differentiate which patients had a longer Overall Survival within the RECIST stable disease cohort. The second part of this thesis focused on patient-derived tumor organoids. In chapter 5 we describe the characteristics of the tumor organoid culture platform and highlight all major publications in this field. In chapter 6, we describe the culture from single 18 Gauge metastatic biopsy specimens (71% success rate). More important though, is that the genetic landscape of these biopsies was preserved in culture. The few genetic aberrations that were discordant between these samples did not include any aberrations known to be of influence for drug sensitivity or tumor evolution. Chapter 7 describes an initial screen, in which treatment outcome is compared of paired in vitro and patient data for 5-FU/capecitabine + oxaliplatin chemotherapy. Whereas in vitro drug sensitivity did not correlate to clinical outcome (P=0.679), this does not imply that patient-derived tumor organoids are not able to predict treatment response of patients. It means that the assay in its current format is not yet sufficient for large scale testing of the expanded cohort. These results are also not representative for other tumor types and treatments. Chapter 8 describes the study design of the SENSOR, where treatment is selected for each individual patient with metastatic colorectal cancer on the basis of a comprehensive drug screen on their own tumor organoids. The first results demonstrate that the integration of patient-derived tumor organoids in the stratification of patients for an experimental treatment is feasible. The efficacy of this approach is currently under investigation