Predictive and prognostic biomarkers for colorectal cancer patients

Colorectal cancer (CRC) is the second most frequent cause of cancer-related death worldwide. Detecting CRC in a premalignant or early stage can improve survival of patients. When the CRC is metastasized (mCRC) to other organs patients are offered systemic treatment to prolong survival. However, not all patients respond to the available treatment while they do suffer from treatment related toxicities. In this thesis we studied the effects of CRC screening programmes on incidence and mortality. In addition, we studied tumor genomics and clinicopathological factors for the prediction of response to systemic therapy in patients with mCRC. The first chapter of this thesis described the effects of CRC screening on incidence and survival of patients with CRC. It was expected that screening for CRC would result in the detection of 1600 additional stage I and II CRCs per year in the first few years after its introduction in The Netherlands. This increase in detected stage I and II resulted in a decrease in the proportion of CRCs diagnosed at stages III or IV from 47% to 20%. Studies described in the next chapters aimed to improve treatment of patients with mCRC and to reduce unnecessary treatment related toxicity by patient selection for systemic therapy. Patient selection can be done with predictive biomarkers based on tumor genomics. A large number of genomic tumor characteristics can be analysed. This high dimensional data makes statistics challenging, as corrections for multiple testing are crucial to avoid overoptimistic results. Moreover, stable estimation of parameters is crucial for determining reproducible biomarkers. In chapter 2 we described a method with multi-parameter shrinkage options to overcome these statistical challenges. In chapter 3 we demonstrated that the miRNA expression profiles of metastases closely resemble that of their corresponding primary CRCs (pCRC). Only 8 (0.5%) of the 1714 miRNAs were significantly different expressed between pCRC and their matched metastases. Based on these results, we expected that miRNA expression profiles of primary tumors and metastases may be of similar predictive value for predicting prognosis or treatment response for patients with mCRC. In chapter 4 we analysed the miRNA expression levels of metastasised colorectal tumors in addition to known predictive clinocopathological factors. This study demonstrated that expression levels of miR-17-5p, miR-20a-5p, miR-30a-5p, miR-92a-3p, miR-92b-3p and miR-98-5p in combination with age, tumor differentiation, adjuvant therapy and type of systemic treatment, were predictive for clinical benefit (response and stable disease (SD)) of first-line chemotherapy in the training cohort with an AUC of 0.78. In the validation cohort the addition of the six miRNA signature to the four clinicopathological factors demonstrated a significant increased AUC for predicting treatment response versus those with SD from 0.79 to 0.90. However, our six miRNA signature did not add predictive value to the four selected clinicopathological factors for separating patients with PD from those with SD or response. Besides miRNAs, other readouts of tumor genomics may be used to improve sensitivity and specificity. In chapter 5 we analysed tumor copy number aberrations, microsatellite instability and known cancer related mutations for their predictive value for treatment response. Based on baseline clinicopathological factors, response to first and second line treatment in patients with mCRC could be predicted with an AUC of 0.73 and 0.69 respectively. Unfortunately, these prediction characteristics could not be improved by the addition of mutation status and copy number aberrations. The addition of clinicopathological factors acquired during first line treatment significantly increased the performance to predict response to second line treatment (AUC of 0.75 (p = 0.04)). Again, this was not sufficient to guide treatment decisions.