Biology subjects

Häkkinen, A.

Publications and source records attributed to Häkkinen, A..

4 recordsLinked to original sources

bioRxiv2022

Evolutionary states and trajectories characterized by distinct pathways stratify ovarian high-grade serous carcinoma patients

Ovarian high-grade serous carcinoma (HGSC) is typically diagnosed at an advanced stage, with multiple genetically heterogeneous clones existing in the tumors long before therapeutic intervention. Herein we characterized HGSC evolutionary states using whole-genome sequencing data from 214 samples of 55 HGSC patients in the prospective, longitudinal, multiregion DECIDER study. Comparison of the tissues revealed that site-of-origin samples have 70% more unique clones than the metastatic tumors or ascites. By integrating clonal composition and topology of HGSC tumors we discovered three evolutionary states that represent a continuum from genomically highly variable to stable tumors with significant association to treatment response. The states and their evolutionary trajectories were characterized by unique, targetable pathways, which were validated with RNA-seq data. Our study reveals that genomic heterogeneity is unaffected by the current standard-of-care and pinpoints effective treatment targets for each group. All genomics data are available via an interactive visualization platform for rapid exploration.

cancer biology↗

bioRxiv2020

Agile workflow for interactive analysis of mass cytometry data

MotivationSingle-cell proteomics technologies, such as mass cytometry, have enabled characterization of cell-to-cell variation and cell populations at a single cell resolution. These large amounts of data, however, require dedicated, interactive tools for translating the data into knowledge. ResultsWe present a comprehensive, interactive method called Cyto to streamline analysis of large-scale cytometry data. Cyto is a workflow-based open-source solution that automatizes the use of of state-of-the-art single-cell analysis methods with interactive visualization. We show the utility of Cyto by applying it to mass cytometry data from peripheral blood and high-grade serous ovarian cancer (HGSOC) samples. Our results show that Cyto is able to reliably capture the immune cell sub-populations from peripheral blood as well as cellular compositions of unique immune- and cancer cell subpopulations in HGSOC tumor and ascites samples. AvailabilityThe method is available as a Docker container at https://hub.docker.com/r/anduril/cyto and the user guide and source code are available at https://bitbucket.org/anduril-dev/cyto Contactsampsa.hautaniemi@helsinki.fi Supplementary informationSupplementary material is available and FCS files are hosted at flowrepository.org/id/FR-FCM-Z2LW

bioinformatics↗

bioRxiv2019

PRISM: Recovering cell type specific expression profilesfrom composite RNA-seq data

A major challenge in analyzing cancer patient transcriptomes is that the tumors are inherently heterogeneous and evolving. We analyzed 214 bulk RNA samples of a longitudinal, prospective ovarian cancer cohort and found that the sample composition changes systematically due to chemotherapy and between the anatomical sites, preventing direct comparison of treatment-naive and treated samples. To overcome this, we developed PRISM, a latent statistical framework to simultaneously extract the sample composition and cell type specific whole-transcriptome profiles adapted to each individual sample. Our results indicate that the PRISM-derived composition-free transcriptomic profiles and signatures derived from them predict the patient response better than the composite raw bulk data. We validated our findings in independent ovarian cancer and melanoma cohorts, and verified that PRISM accurately estimates the composition and cell type specific expression through whole-genome sequencing and RNA in situ hybridization experiments. PRISM is freely available with full source code and documentation.

bioinformatics↗

bioRxiv2018

Identifying differentially methylated sites in samples with varying tumor purity

DNA methylation aberrations are common in many cancer types. A major challenge hindering comparison of patient-derived samples is that they comprise of heterogeneous collection of cancer and microenvironment cells. We present a computational method that allows comparing cancer methylomes in two or more heterogeneous tumor samples featuring differing, unknown fraction of cancer cells. The method is unique in that it allows comparison also in the absence of normal cell control samples and without prior tumor purity estimates, as these are often unavailable or unreliable in clinical samples. We use simulations and next-generation methylome, RNA, and whole-genome sequencing data from two cancer types to demonstrate that the method is accurate and outperforms alternatives. The results show that our method adapts well to various cancer types and to a wide range of tumor content, and works robustly without a control or with controls derived from various sources. The method is freely available at https://bitbucket.org/anthakki/dmml.

bioinformatics↗