Project background

In the DoMore! project, we will explore our unique combination of academic and industrial competence to radically improve prognostication and hence treatment of cancer by using digital tools for pathology.

Cancer is more prevalent than ever, the number of incidences is growing and expected to bring a further rise in cost, greater pressure on already strained healthcare systems, more suffering and premature deaths. Furthermore, demands will increase strongly due to an aging population in which diseases like cancer are predominant.

The successful treatment of cancer relies on a correct diagnosis, preferably at an early stage of the disease. Cancers have heterogeneous compositions with regions of different genetic or epigenetic aberrations and can follow multiple paths, not all of which progress to metastases and death. However, we have little, and no exact, information on what distinguishes a lethal cancer from an indolent one.

Before starting primary treatment, the patient’s disease is clinically staged, a process to determine the extent to which a cancer has progressed. The pathologist supplements this clinical staging by investigating the tumor material removed during surgery. Based on the degree to which the cancer cells resemble the tissue of origin, cancers are typically graded into three different categories: well, moderate and poorly differentiated cancers. Unfortunately, grading depends heavily on the pathologist’s expertise, and inter-observer and intra-observer agreements are moderate only, so the prognostic value of histological grading varies.

The heterogeneous composition of tumors, heterogeneity, is another great challenge for prognosis of cancer. The biomarkers we use to identify the aggressiveness of tumor progression are not distributed evenly throughout the tumor and unfortunately, sampling for markers other than histological grading is normally only performed on a small tissue sample or a small fraction of a single block. Even when the whole organ is kept and processed for analysis, as for prostate cancer, the sample examined is a mere 1:1000 of the tumor. By sampling such a minuscule amount of the tumor, there is a great risk of missing the cells that actually go on to kill the patient. Ultimately, patients are paying the price through under- or overtreatment.

Heterogeneity, the variability of grading systems’ abilities to prognosticate, and the subjectivity in using these systems, pose great challenges for accurate diagnosis and prognosis of cancer. These challenges are common knowledge in the field of pathology, but pathologists fail to address them. For this reason, we refer to these as the “elephant in the room,” and aim to address them through modeling and understanding heterogeneity in three common forms of cancer (prostate, colorectal, and lung, representing 43.7% of all cancer deaths).

The goal of the DoMore! project is to explore our unique combination of academic and industrial competence to radically improve prognostication and hence treatment of cancer by using digital tools for pathology.


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