Principal investigator of the group and director of the Systems Oncology research program. For more information see personal homepage.
I study cancer evolution before and after treatments. My goal is to understand how cancer is affected by chemotherapy and how resistance evolves. This goal is largely affected by tumor heterogeneity, which causes variability in responses and makes HGSC an interesting disease to study. I focus on DNA level changes and track them through therapies by using tissue, ascites and ctDNA samples.
My passion in science is DNA methylation. I am currently focusing on epigenetic mechanisms underlying tumor spread and chemoresistance in high-grade serous ovarian cancer.
I am a geneticist working on translational genomics, to identify actionable aberrations and develop improved treatment strategies for HGSC patients. I am studying genetic etiology of HGSC and mechanisms of resistance in chemo-refractory patients.
I am currently focusing on tumor heterogeneity and especially visualization of the clonal evolution in cancer. We are developing a software for visualizing the sub-clonal evolutionary process both in longitudinal and spatial dimensions from a large number of patient samples collected in DECIDER project. In addition, I am involved in developing AI-assisted clinical decision support system that suggest possible personalized treatment options for patients based on findings from multimodal health data analytics. I am also responsible for the maintenance and development of our lab's computing infrastructure.
My main tasks are focused around clinical data and its management. These include for example developing and maintaining databases, automating ETL processes, data scrubbing, and creating different data products for other lab members. Besides data engineering, my research activity focuses on clinical data analysis, either with machine learning tools or more traditional and robust methods, trying to find signals from often patchy and incomplete data. I also consult other lab members on interpreting clinical data and other medicine-related questions.
My research focuses on identifying different histopathological and molecular characteristics of high grade serous ovarian cancer in hope to discover novel predictive and prognostic tools. I also assist my fellow researchers with matters concerning histopathology since I am also a medical doctor specializing in pathology.
As a doctoral researcher, my primary focus lies in identifying resistance mechanisms in patients with ovarian high-grade serous carcinoma (HGSC) using a combination of transcriptomics, genomics, and tissue microarray data. I am particularly interested in exploring how whole transcriptome gene expression, along with other omics such as whole genome copy-number alterations, could be used to identify novel potential cancer biomarkers. In addition, I have developed an RNA in situ hybridization image analysis pipeline to introduce new cancer biomarkers based on spatial gene expression and expression heterogeneity. Overall, my research efforts to advance our understanding of HGSC and contribute to the development of more effective cancer therapies.
My main interest is on the cancer transcriptomics, at both single-cell and bulk level, to understand the mechanisms leading to chemoresistance in ovarian cancer. I am also working on the development of computational pipeline to analyze circulating tumor DNA for somatic and germline mutations.
My research focuses on the analysis of histopathological images, mainly of HGSC and colorectal cancer, using AI-based approaches; specifically, I mostly work on pre-processing and feature extraction methods for discovering new information from histological samples that can be useful for revealing chemotherapy resistance causes.
My research project focuses on the study of circulating tumor DNA (ctDNA) and its potential use in monitoring cancer treatment for patients with high-grade serous carcinoma (HGSC), a common and aggressive form of ovarian cancer. Specifically, I am investigating the use of ctDNA to detect copy-number variations, which are common genetic alterations in cancer cells, in order to improve treatment monitoring and patient outcomes. By combining advanced sequencing techniques with clinical data, my goal is to develop a more accurate and personalized approach to cancer treatment.
I study ovarian cancer working on both genomic and DNA methylation patient data. I'm involved in genomic analyses working on whole genome sequencing and circulating tumor DNA data and I'm studying changes in the methylome during treatment and in different tissues.
While my research focuses primarily on genomic data visualization, I'm also involved in occasional copy-number analysis, research on tumor evolution, and general data wrangling. I'm developing GenomeSpy (https://genomespy.app), a grammar-based, GPU-accelerated visualization toolkit for genomic visualization and interactive cohort analysis.
I investigate RNA sequencing data and its application in clinical diagnostics, trying to find transcriptome subtypes of HGSOC and their relation to survival metrics. I also perform the deconvolution analysis of bulk RNA-seq samples and study the impact of different tissues and treatment phases to the bulk expressions. In future, I plan to expend the approach to multi-omics analysis.
My main interests revolves around AI and deep learning applied to histopathological images. Specifically, I'm developing methods for segmentation of whole slide images (WSI) along with tools for explainable feature extraction. The goal of these methods is to find quantifiable and explainable patterns related to the chemo-resistance mechanisms and heterogeneity in HGSC patients. In addition to working with HGSC samples, I also work on cervix cancer samples.
As a dictoral student, the main focus of my research is the discovery and study of the resistance mechanisms working in ovarian High-Grade Serous Carcinoma (HGSC). My project is focused in the study of Copy Number alterations in cancer genomes. The aim is finding recurrent patterns of this type of variation and to attribute them to biological processes. The project involves the use of bulk DNA sequencing and circulating tumor DNA samples from different tissues and time points in order to reconstruct the evolution of the tumor during treatment and eventual relapse. In addition, I have developed a pipeline for somatic copy number alteration extraction that exploits structural variants breakpoints.
My work is focused on data analysis of whole-genome sequenced samples from HGSC patients, including developing and maintaining computational workflows to process raw read data to annotated somatic and germline short variants. I am also involved in the identification of variants underlying DNA repair deficiencies as well as subsequent genetic changes that partially restore DNA repair. Furthermore, I am analysing mutational signatures to investigate their use in clinical outcome prediction as well as to study mechanisms of platinum resistance through the genomic footprints of platinum-based chemotherapy.
I am finishing my master's studies in Life Science Technologies in Aalto University. I am currently working on unsupervised segmentation of the genome using DNA methylation data. The aim is to find functional segments looking at patterns of CpG methylation, and to eventually gain more understanding of the role of DNA-methylation in ovarian cancer. I have previously worked with clustering and batch correction of RNA-seq data (POIBM).
I am a third year medical student. I work with H&E images and I prepare them for AI and other applications.
I am currently working on a multiomics study to assess the role of ABC-transporters in chemoresistance in high-grade serous ovarian cancer.
I am working with RNA-seq and whole genome sequencing data. I will use eQTL(expression trait loci) analysis with the aim to find links between genetic variants and drug response in high-grade serous carcinoma patients.
I am a first year medical student focusing in actionable genetic aberrations and HGSC treatment improvements.
Student of medicine and mathematics. I study how histological features affect patient outcomes in cervical cancer.
Consulting the lab on infra related issues.
I am the Academic coordinator of the group and mainly reponsible for the administration of the EU-project DECIDER.
I am the project coordinator for the Hautaniemi lab. I take care of all the administration tasks and issues of the lab.
I'm a part time coordinator of the Systems Oncology research program directed by Sampsa Hautaniemi. I also work as a midwife at the Womens hospital.
I am a bioinformatician and computational biologist associated with the research groups of Systems biology of drug resistance in cancer and Medical systems biology (taipalelabs.org). I am interested in the cancer epigenomics, especially DNA methylation landscape in HGSC tumor samples from diagnosis through treatment to relapse and between the tissues. My research interest include also non-coding regulatory elements and transcription factor binding sites at them.