Lung Cancer and Personalized Medicine
Lung cancer is by far the deadliest form of cancer and in many cases, there are no available therapeutic options for patients with this disease. Targeted therapies that inhibit oncogenic signaling and immunotherapy has improved lung cancer patient survival, but monotherapy with these agents will only delay disease progression and in addition only in limited subsets of patients. It is becoming increasingly clear that the reason for this lack of efficacy is that lung cancer is a heterogeneous disease. This is displayed in two ways.
First, the molecular paths to lung cancer are not the same in different patients. This means that different patients will have different molecular cancer drivers and cancer phenotypes and consequently they should be treated in different ways for optimal therapy response. Importantly, the cancer phenotype cannot be predicted based solely on knowledge of individual mutations since the phenotype depends on complex interactions between all functional mutations and the environment. For this reason, mutation analysis by genomics needs to be complemented by molecular phenotype analysis to fully understand the biology of individual tumors.
Second, the tumor cells that together build up a tumor in a patient are also heterogeneous and each tumor is composed of several different clones of cells that can differ in mutation spectrum and cell types. These different cells within a single tumor will also respond differently to cancer therapy. A drug that efficiently kills one type of cells in the tumor can still leave other cancer cells unaffected. To efficiently kill all cancer cells within a tumor and ultimately cure the patient we need to combine several types of therapy. To personalize cancer therapy and identify efficient therapy combinations we need biomarkers that can be used to predict if a specific type of therapy will be efficient in a specific patient. Once identified, the presence of these biomarkers can be measured in the tumor of a patient, and this information can then be used for rational selection of therapy.
Functional Proteomics and Systems Biology
The expression, and functions of proteins in a cell is what determines the phenotype, in other words the characteristics of the cell. For example, the expression and functional activity of proteins in a cell will determine if a cell is cancerous or not, and whether a cancer cell is sensitive for a specific type of treatment. Proteomics is the large-scale study of proteins in biological systems with the aim to comprehensively describe the protein landscape of a biological sample. In our lab we use high-resolution mass spectrometry (MS) to identify and quantify thousands of proteins in biological samples. This analytical depth gives us the possibility to describe the molecular phenotype of cancer
samples and the response of cancer cells to cancer therapy in detail, which is essential to understand the biology of individual cancers and the effects of treatment.
The proteomics concept can be further developed into functional proteomics where the ambition is to also describe the functional state of proteins in large-scale experiments. The activity of a protein is not only determined by the abundance of the protein. Since it is important for a cell to tightly regulate all cellular processes, the proteins are equipped with “molecular switches” that turn on or off certain functions of the proteins. This regulation is performed by adding or removing functional groups to the proteins in processes referred to as protein post-translational modification (PTM). The most well characterized type of PTM is protein phosphorylation and the phosphorylation pattern of proteins in cancer cells are commonly disturbed, which can result in for example increased cell proliferation or decreased apoptosis (cell death). Many targeted cancer therapies are aiming to restore the phosphorylation pattern in cancer cells to stop the uncontrolled cell growth and induce cell death. To fully understand the consequences of targeted therapies, it is therefore important to study changes in protein phosphorylation in response to treatment. In our lab we are applying methods to specifically study protein phosphorylation to analyze cancer signaling and the effects of treatment on the phospho-proteome.
The functions and activities of proteins are also determined by their specific localization in the cell. A protein can have one type of function when localized in the nucleus, and a completely different function when localized in the cytoplasm. More common maybe is that a protein is active when it is residing in one subcellular compartment and inactive when sequestered in another. Treatment of cells with different types of drugs will alter the subcellular localization of proteins, i.e. induce protein relocalization, which will alter the function of affected proteins. We have developed methods to comprehensively study the localization of proteins in cells, and we use these methods to understand how proteins shuttle between different subcellular compartments in response to cancer therapy, and ultimately how this affects the drug sensitivity.
Systems biology is the combination of different levels of biological information to see the full picture. In practice this means that we merge the information generated by several different types of experiments used to study the same biological question. The data included in our systems biology analysis is generated by the proteomics methods described above, but we also include data describing the transcriptional activity of cancer cells (mRNA-level analysis) as well as the genomic background of the cells (DNA-level analysis). All this data is then used in concert to understand why the cancer cells from different tumors respond differently to cancer drugs. Ultimately this knowledge will help us tailor the best cancer therapy to each lung cancer patient with the ambition to cure him or her from the disease.
Clinical proteomics in cancer diagnostics
Ultimately our goal is to provide new knowledge and methods that can improve clinical decision making in lung cancer. To enable such clinical implementation of our findings we are developing methods for analysis of cancer samples in a clinical setting. This work includes defining biomarkers and biomarker-panels that have a potential clinical value in lung cancer precision medicine as described above. In addition, we develop MS-based methods that will allow the assessment of these biomarkers first in clinical trials, and if successful in clinical practice.