Zhang K, Erkan EP, Jamalzadeh S, Dai J, Andersson N, Kaipio K, Lamminen T, Mansuri N, Huhtinen K, Carpén O, Hietanen S, Oikkonen J, Hynninen J, Virtanen A, Häkkinen A, Hautaniemi S, Vähärautio A. Science Advances. 2022 Feb 23;8(8):eabm1831.

To characterize chemotherapy resistance processes in high-grade serous ovarian cancer, the authors prospectively collected tissue samples before and after chemotherapy and analyzed their transcriptomic profiles at a single-cell resolution. After removing patient-specific signals by a novel analysis approach, PRIMUS, they found a consistent increase in stress-associated cell state during chemotherapy, which was validated by RNA in situ hybridization and bulk RNA sequencing. The stress-associated state exists before chemotherapy, is subclonally enriched during the treatment, and associates with poor progression-free survival. Co-occurrence with an inflammatory cancer–associated fibroblast subtype in tumors implies that chemotherapy is associated with stress response in both cancer cells and stroma, driving a paracrine feed-forward loop. In summary, the authors have found a resistant state that integrates stromal signaling and subclonal evolution and offers targets to overcome chemotherapy resistance.

He L, Bulanova D, Oikkonen J, Häkkinen A, Zhang K, Zheng S, Wang W, Erkan EP, Carpén O, Joutsiniemi T, Hietanen S, Hynninen J, Huhtinen K, Hautaniemi S, Vähärautio A, Tang J, Wennerberg K, Aittokallio T. Briefings in bioinformatics. 2021 Nov;22(6):bbab272.

To address the intra- and inter-tumoral heterogeneity when designing combinatorial treatment regimens for cancer patients, the authors have implemented a machine learning-based platform to guide identification of safe and effective combinatorial treatments that selectively inhibit cancer-related dysfunctions or resistance mechanisms in individual patients. In this case study, they show how the platform enables prediction of cancer-selective drug combinations for patients with high-grade serous ovarian cancer using single-cell imaging cytometry drug response assay, combined with genome-wide transcriptomic and genetic profiles. The platform makes use of drug-target interaction networks to prioritize those combinations that warrant further preclinical testing in scarce patient-derived primary cells. During the case study in ovarian cancer patients, we investigated (i) the relative performance of various ensemble learning algorithms for drug response prediction, (ii) the use of matched single-cell RNA-sequencing data to deconvolute cell population-specific transcriptome profiles from bulk RNA-seq data, (iii) and whether multi-patient or patient-specific predictive models lead to better predictive accuracy. The general platform and the comparison results are expected to become useful for future studies that use similar predictive approaches also in other cancer types.

Hippen AA, Falco MM, Weber LM, Erkan EP, Zhang K, Doherty JA, Vähärautio A, Greene CS, Hicks SC. PLoS computational biology. 2021 Aug 24;17(8):e1009290.

In this study the authors propose a data-driven QC metric (miQC) that jointly models both the proportion of reads mapping to mtDNA genes and the number of detected genes with mixture models in a probabilistic framework to predict the low-quality cells in a given dataset. Then demonstrates how their QC metric easily adapts to different types of single-cell datasets to remove low-quality cells while preserving high-quality cells that can be used for downstream analyses. The software package is available at https://bioconductor.org/packages/miQC.

Häkkinen A, Zhang K, Alkodsi A, Andersson N, Erkan EP, Dai J, Kaipio K, Lamminen T, Mansuri N, Huhtinen K, Vähärautio A, Carpén O, Hynninen J, Hietanen S, Lehtonen R, Hautaniemi S. Bioinformatics. 2021 Sep 15;37(18):2882-8.

In this study the authors developed PRISM, a latent statistical framework to simultaneously extract the sample composition and cell-type-specific whole-transcriptome profiles adapted to each individual sample. The 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. The findings were validated 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.

Tumiati M, Hietanen S, Hynninen J, Pietilä E, Färkkilä A, Kaipio K, Roering P, Huhtinen K, Alkodsi A, Li Y, Lehtonen R, Pekcan Erkan E, M. Tuominen M, Lehti K, K. Hautaniemi S, Vähärautio A, Grénman S, Carpén O, Kauppi L. Clinical Cancer Research. 2018 Sep 15;24(18):4482-93.

In this study, the authors aimed to fill the gap of HRD diagnostic by developing a clinically relevant tool to detect functional HRD. Then established an ex vivo test in primary HGSOC and quantified the tumor HRD in an HR score. Finally demonstrated that a low HR score significantly predicts platinum sensitivity and correlates with improved overall survival.

Kivioja T*, Vähärautio A*, Karlsson K, Bonke M, Enge M, Linnarsson S, Taipale J. (2012) Nat Methods. 9, 72- 74. (*equal contribution) 

This paper describes a universal method that can be applied to counting the absolute number of molecules in a given sample. This stems from the idea that if each molecule in a sample is made unique prior amplification - for example with addition of a random sequence tag - one can simply count the number of unique molecules from an amplified sample to obtain the original number of molecules. The method completely eliminates PCR bias, a common problem in accurately determining the number of RNA or DNA molecules in a cell. In this paper, the method was applied to RNA-seq and the authors showed that it can be can be used to improve accuracy of almost any next generation sequencing method, including ChIP- sequencing, genome assembly, diagnostic applications and manufacturing process control and monitoring. and has become a golden standard in quantitative single-cell RNA-sequencing. For this paper, Anna developed a custom RNA-seq library preparation method to include UMIs and performed the RNA-seq experiments (Cited 339 times; Source: Google Scholar).

Sur IK, Hallikas O, Vähärautio A, Yan J, Turunen M, Enge M, Taipale M, Karhu A, Aaltonen LA, Taipale J. (2012)  Science. 338:1360-3.

In this paper,  the authors generated mice lacking Myc- 335, a putative Myc regulatory element that contains rs6983267. rs6983267 is a colon-cancer associated SNP that accounts for more human cancer-related morbidity than any other genetic variant or mutation. In Myc-335 null mice, Myc transcripts were expressed at modestly reduced levels with a pattern similar to that of wild-type mice. The mutant mice displayed no overt phenotype but when crossed with APCmin mice, mutant mice were markedly resistant to intestinal tumourigenesis. These results highlight the fact that although a disease-associated polymorphism typically has a relatively modest effect size, the element that it affects can be critically important for the underlying pathological process. Thus, we may harbor switches that might not compromise the normal development but can be critical for disease pathogenesis. For this paper, Anna analyzed transcriptomics data from which the authors identified a modest decrease in Myc exon expression (Cited 151 times, Source: Google Scholar).

Bonke M, Turunen M, Sokolova M, Vähärautio A, Kivioja T, Taipale M, Björklund M, Taipale J. (2013) G3 (Bethesda). 3:75-90. 

In this paper, the authors studied transcriptional networks that regulate the cell cycle in Drosophila melanogaster, and found two interconnected feedback circuits, of which one controls overall protein homeostasis and connects mribosome and proteasome, and another that controls protein synthesis capacity and connects the ribosome and Myc/Max. For this study, Anna developed the basic library preparation methodology that she later adapted to molecule counting and applied this initial version to a large number of custom RNA-seq libraries for Drosophila RNAi samples (Cited 21 times; Source: Google Scholar).