Janne Lehtiö

Every malignant tumor that is detected has managed to evade the body's immune system. The immune system is responsible for detecting and eliminating cancer cells. It is done via foreign proteins, called cancer antigens, which are created by the cancer cell's altered DNA. We know very little about the types of cancer antigens that can be recognized by our immune system and new research suggests that there are significantly more antigen types than we know today, e.g. ones made from old viral genes in the genome. To understand this better, we need to study the proteins expressed by cancer cells that represent their "molecular fingerprint". There are large knowledge gaps regarding protein level regulation in cancer immunology. We have developed a new method for detecting cancer antigens, where proteins from cancer cells are identified and compared with the patient's DNA. It gives us a more comprehensive picture of the cancer antigens recognized by our immune system. The aim is to use this knowledge to understand how cancer cells evade the immune system, and to develop biomarkers that can predict how cancer cells make themselves invisible. We will start by studying patient samples, then use model systems to test our findings, and finally validate results again on patient samples. Our goal is that this research should lead to new knowledge about the mechanisms by which cancer cells hide from the immune system. This new knowledge should enable new ways to treat cancer by helping our immune system to recognize cancer cells with precision, for example via the development of cancer vaccines, and find better biomarkers to choose effective treatment combinations.

The rapid development of technologies for molecular profiling paves the way for precision medicine approaches to match patients with effective treatment. Through the development of a coordinating support unit, we aim to provide access to SciLifeLab’s technology platforms to support academic and industry-initiated clinical trials. We will establish customized diagnostic packages jointly with clinical trials units across the country to strengthen our attractiveness as a clinical trial site and to ensure early access of patients to both innovative technologies and emerging treatments.Our goal is to identify mature technologies to be evaluated in observational studies and adopted in interventional biomarker-driven clinical trials. To build this capacity, connecting SciLifeLab national infrastructure to clinical practice, key developments supporting cross-platform data, sample and analysis flows with timelines and quality complying with the needs of clinical trials are needed. Our unit will unite services from current and emerging sequencing technologies, to proteome, metabolome, spatial biology techniques, and drug efficacy testing. Our competitive edge is the repertoire of technologies at SciLifeLab, which once combined provide a unique opportunity for clinical trials. With the ‘service packages’ we aim to make Sweden competitive as a trial site, catalyze academy-industry ecosystems and provide opportunities for early access to the latest diagnostic technologies and treatments.

This nursing science precision health project, focus on complex interactions among biological, social, and behavioural factors, and their effects on outcomes. By researching patients’ experience of symptoms and linking these to biological data, we aim to increase the sensitivity and specificity of LC diagnosis and to aid in investigate molecular determinants of common symptoms, e.g. fatigue and cachexia. Furthermore, we aim to develop our measures for use to monitor treatment response in patients receiving immunotherapy. With previous VR funding (2016-1712, 2019-1222), we developed an interactive questionnaire, Peklung, to generate detailed descriptions about early health changes in LC and used it to collect data from ∼700 patients at diagnostic LC work-up

in parallel we also collected plasma samples from  these patients. We apply here for continued VR funding to link patient-reported data from Peklung with biomolecular and imaging profiles, to determine biomarkers of LC and other lung diseases, and to investigate new molecular determinants of symptoms with unclear aetiology and mechanism, e.g. fatigue, cachexia. Establishing symptomatology related to early LC can: decrease diagnostic delay, with increased chance of curative treatment options and shorter time spans to curative or palliative treatment, thus reducing distress for patients/families

connect detailed symptom with omics data to increase understanding of mechanisms of poorly understood symptoms.

Genomics has contributed greatly to tailored treatment of cancer patients, but the impact on overall cancer survival has been lower than expected. Predicting drug response based on genetic profile alone has proven difficult and even more difficult when aiming to understand synergistic effects of multiple genetic changes. Molecular information on other levels about cell function is required to gain more knowledge about which drug(s) the patient will respond best to. Proteomics, where all the proteins of the cell are studied, offers unique and important information about the actual functional level of the cancer cell. We have developed a proteogenomics method that has been used for the analysis of lung cancer, AML and CLL to gain knowledge of how genetic changes affect the proteome. We have found distinct groups/clusters of tumors that are related to clinical outcome, highlighting the importance of including proteome data for treatment selection. We aim to build on this analysis with large clinical cohorts with proteogenomics and try to connect discovered phenotype clusters to different cancer drugs including immunotherapy drugs. Within the project, panels of biomarkers will be used as a starting point for proteogenomics-based clinical trials. The purpose of the project is to create better conditions for the individualization of cancer treatment by combining genome and proteome analysis. With this, the right cancer treatment can be given to the right patient for the best effect and with the least possible side effects. Furthermore, we generate unique proteomics data that will contribute with new fundamental knowledge about how genome deviation can synergistically affect the growth ability of tumor cells and thus contribute with important pieces of the puzzle for the development of new treatments. This project will also form a workflow towards clinical implementation of proteogenomics in Sweden.

Proteogenomics is a new field of research in which mass spectrometry-based proteomics data, DNA and RNA data are combined for new knowledge of how changes in effect affect the proteome. These methods allow for the study of protein variants and tumor-specific proteins. Cancer is caused by an accumulation of alterations that give rise to foreign proteins found in tumors. These foreign proteins can activate the immune response and with immunotherapy, the immune system can be strengthened to attack cancer cells. For effective immunotherapy, tumor-specific proteins need to be identified. One limitation with today's methods of identifying tumor-specific proteins is that it is based only on data from the DNA and RNA level and not on the presence of the protein itself. This leads to the loss of information about certain types of tumor-specific proteins and it is difficult to know which modifications ultimately produce proteins. We have developed a proteogenomic method, where we have shown that we can identify and quantify these tumor-specific proteins. The method generates a more complete and specific identification of these proteins, which increases the potential for improved and more effective immunotherapy to combat cancer. The project will primarily focus on the identification and quantification of tumor-specific proteins at protein level in three different tumor forms

malignant melanoma, lung cancer and leukemia. We intend to optimize the workflow for MS-based proteogenomics in order to obtain a robust method flow to identify tumor-specific proteins at the protein level. We intend to evaluate these mutated proteins as possible predictive markers for treatment with immunotherapy drugs. We also expect to gain valuable knowledge of tumor-specific protease at protein level, which was previously lacking, which can improve and enhance today's cancer immunotherapy.

Proteogenomics is a new field of research in which mass spectrometry-based proteomics data, DNA and RNA data are combined for new knowledge of how changes in effect affect the proteome. These methods allow for the study of protein variants and tumor-specific proteins. Cancer is caused by an accumulation of alterations that give rise to foreign proteins found in tumors. These foreign proteins can activate the immune response and with immunotherapy, the immune system can be strengthened to attack cancer cells. For effective immunotherapy, tumor-specific proteins need to be identified. One limitation with today's methods of identifying tumor-specific proteins is that it is based only on data from the DNA and RNA level and not on the presence of the protein itself. This leads to the loss of information about certain types of tumor-specific proteins and it is difficult to know which modifications ultimately produce proteins. We have developed a proteogenomic method, where we have shown that we can identify and quantify these tumor-specific proteins. The method generates a more complete and specific identification of these proteins, which increases the potential for improved and more effective immunotherapy to combat cancer. The project will primarily focus on the identification and quantification of tumor-specific proteins at protein level in three different tumor forms

malignant melanoma, lung cancer and leukemia. We intend to optimize the workflow for MS-based proteogenomics in order to obtain a robust method flow to identify tumor-specific proteins at the protein level. We intend to evaluate these mutated proteins as possible predictive markers for treatment with immunotherapy drugs. We also expect to gain valuable knowledge of tumor-specific protease at protein level, which was previously lacking, which can improve and enhance today's cancer immunotherapy.

Cancer is a disease caused by gene mutations and chromosomal changes. The development in DNA and RNA sequencing has led to an explosion of available data on these changes. Despite this enormous increase in knowledge, the knowledge of the influence of changes on the function of the cell is lacking. A new field called proteogenomics is a strategy that integrates data from genomic and proteomic studies. We have further developed a method in proteogenomics, which makes it possible to describe protein coding genes with protein level analysis, and can be used both for interpretation of gene sequence data and for finding new disease-related genes. With our method, we have discovered almost 100 new human protein coding genes, several of which were pseudogens. Initially, we will study these pseudogenic proteins and their role in cancer. We will also analyze the protein variants that arise from one and the same gene, with particular focus on how different protein variants are formed and how this affects the properties of the tumor. Finally, we will combine data on DNA, RNA and protein levels from analyzes of lung and breast cancer, where we will study in detail how different genetic changes affect the signaling pathways that drive tumor growth and survival. In this project we will generate new information and knowledge about how gene and chromosomal changes affect the functional level in tumor biology. The project will add a new knowledge base at protein level, especially about pseudogenic proteins and the function of protein variants, which is relevant for cancer and its development. With this new information we intend to discover new biomarkers and new "targets" for cancer therapy, with a focus on lung cancer, breast cancer and endocrine tumors. It can create new treatment options for patients, especially for those patients who today show resistance to today's standardized therapy.

Cancer is a disease caused by gene mutations and chromosomal changes. The development in DNA and RNA sequencing has led to an explosion of available data on these changes. Despite this enormous increase in knowledge, the knowledge of the influence of changes on the function of the cell is lacking. A new field called proteogenomics is a strategy that integrates data from genomic and proteomic studies. We have further developed a method in proteogenomics, which makes it possible to describe protein coding genes with protein level analysis, and can be used both for interpretation of gene sequence data and for finding new disease-related genes. With our method, we have discovered almost 100 new human protein coding genes, several of which were pseudogens. Initially, we will study these pseudogenic proteins and their role in cancer. We will also analyze the protein variants that arise from one and the same gene, with particular focus on how different protein variants are formed and how this affects the properties of the tumor. Finally, we will combine data on DNA, RNA and protein levels from analyzes of lung and breast cancer, where we will study in detail how different genetic changes affect the signaling pathways that drive tumor growth and survival. In this project we will generate new information and knowledge about how gene and chromosomal changes affect the functional level in tumor biology. The project will add a new knowledge base at protein level, especially about pseudogenic proteins and the function of protein variants, which is relevant for cancer and its development. With this new information we intend to discover new biomarkers and new "targets" for cancer therapy, with a focus on lung cancer, breast cancer and endocrine tumors. It can create new treatment options for patients, especially for those patients who today show resistance to today's standardized therapy.

Cancer is a disease caused by gene mutations and chromosomal changes. The development in DNA and RNA sequencing has led to an explosion of available data on these changes. Despite this enormous increase in knowledge, the knowledge of the influence of changes on the function of the cell is lacking. A new field called proteogenomics is a strategy that integrates data from genomic and proteomic studies. We have further developed a method in proteogenomics, which makes it possible to describe protein coding genes with protein level analysis, and can be used both for interpretation of gene sequence data and for finding new disease-related genes. With our method, we have discovered almost 100 new human protein coding genes, several of which were pseudogens. Initially, we will study these pseudogenic proteins and their role in cancer. We will also analyze the protein variants that arise from one and the same gene, with particular focus on how different protein variants are formed and how this affects the properties of the tumor. Finally, we will combine data on DNA, RNA and protein levels from analyzes of lung and breast cancer, where we will study in detail how different genetic changes affect the signaling pathways that drive tumor growth and survival. In this project we will generate new information and knowledge about how gene and chromosomal changes affect the functional level in tumor biology. The project will add a new knowledge base at protein level, especially about pseudogenic proteins and the function of protein variants, which is relevant for cancer and its development. With this new information we intend to discover new biomarkers and new "targets" for cancer therapy, with a focus on lung cancer, breast cancer and endocrine tumors. It can create new treatment options for patients, especially for those patients who today show resistance to today's standardized therapy.