Every cancer patient poses a distinctive challenge to physicians. Colorectalcancer is not one but rather a collection of similar diseases of abnormal tissue growth in the colon. Due to the variable underlying disease biology, tumours targeted by a drug may respond well to the treatment in one person but not in the other.
Certain strategies have emerged to match a person with their optimal drug, probing the unique disease biology of their cancer and tailoring the treatment to their individual needs – an effort called personalizedmedicine. Approaches involving genetic screening are invaluable in specific cases, yet not mature enough to help most patients. This project proposes a novel personalizedmedicine strategy.
In our approach, a sample is derived from the patient’s tumour and nurtured into clumps of cancer cells – spheroids. By selecting targeted drugs that affect their growth and survival, physicians are provided with valuable information on possible drug responses. However, the information collected from these spheroids is limited.
To elaborately characterize cells, we will adopt a promising technique from the field of phenotypicdrugscreening. Cell Painting can characterize cells down to the organelle level - by using an optimized mixture of dyes - and so provide an abundance of information about the single-cell responses. In combination with current machine learning technology, the vast amount of data can be categorized and boiled down to important drug responses.
We aim to implement the Cell Painting technique into an existing personalizedmedicine pipeline, hopefully allowing for the identification of potential resistance mechanisms, and variability between patients. This project aspires to reveal the effect of drug treatments on various cancer cell types while gaining invaluable insight into the biological processes underlying tumour variability. Ultimately, our contributions have the potential to improve future cancer treatment responses and survival.
Colon cancer treatment poses a challenge for physicians that lack sufficient decision criteria forpersonalized patient therapy. Current approaches in clinical research investigate how to adopt patient material to grow 3D spheroids and determine their viability against several candidate therapies based on simple and limited readouts. While these approaches provide rapid functional information to guide physicians towards choosing a personalized therapy for the patient, they lack in-depth information about the heterogeneity and cellular diversity within the patient’s tumor. Cell Painting is a state-of-the-art high-content microscopy technique, applied to characterize planar (2D) cell cultures, that by using an optimized mixture of dyes, allows for the phenotypic profiling of single cells down to the organelle level. In cells treated with a given drug, this technology provides a plethora of information about the single-cell responses, allowing for the identification of potential resistance mechanisms, and far exceeding traditional cell viability readouts. This project aims at applying Cell Painting to patient-derived samples, bridging the current clinical state-of-the-art with morphological profiling, providing a new level of functional personalizedmedicine by going from single data points and tumor-averaged measurements, to a full breadth of measurements from each individual cell. We envision that by using Cell Painting on patient-derived tumor samples, its composition and heterogeneity will be uncovered, providing highly valuable information about potential resistance, as well as sensitivity to a given treatment, within the cellular diversity of the tumor. After establishing this novel readout, it will be incorporated into the existing functional personalizedmedicine pipelines, aiding physicians to identify potential cancer therapies. Finally, the proposed project will lay the foundation for a long-term collaboration between the involved labs in Norway and Sweden.