Biology subjects

Vähärautio, A.

Publications and source records attributed to Vähärautio, A..

8 recordsLinked to original sources

bioRxiv2026

Single-Cell Transcriptomic Signatures Enable Stratified Combination Therapy for Platinum-Resistant Ovarian Cancer

In high-grade serous carcinoma (HGSC), extensive intra-tumoral heterogeneity hinders complete eradication and remains a major obstacle to developing combination therapies capable of eliminating subpopulations resistant to standard-of-care treatment. Using single-cell RNA sequencing of 72 samples from 54 HGSC patients spanning treatment-naive, post-neoadjuvant chemotherapy and relapse stages, we established a carboplatin-anchored framework that identifies transcriptional signatures of intrinsic (pre-existing) and adaptive (therapy-induced) resistance in individual tumors and prioritizes mechanistically matched drugs to potentiate carboplatin efficacy. Candidate compounds were ranked by integrating orthogonal resources--viability (GDSC, PRISM) and perturbational transcriptomics (L1000, Perturb-seq)--to reduce context bias. Among 64 candidates, three carboplatin adjuvants enhanced long-term efficacy in patient-derived organoids (PDOs), and pevonedistat further significantly reduced tumor burden in orthotopic xenografts. This tiered validation pipeline--from short-term and long-term PDOs and in vivo orthoptic xenografts--establishes a translational framework linking single cell resistance programs to actionable, tumor-specific, carboplatin-anchored combinations for HGSC.

cancer biology↗

bioRxiv2024

Uncovering pre-sensitizing agents to FLT3 inhibitors in acute myeloid leukemia with ReSisTrace lineage tracing

While FLT3 inhibitors have significantly improved the treatment of aggressive FLT3-mutated acute myeloid leukemia (AML), the emergence of resistance remains as a major challenge. Here, we applied our recently developed single-cell lineage-tracing method ReSisTrace to identify cells that are pre-resistant or pre-sensitive to FLT3 inhibitors midostaurin and quizartinib in FLT3-ITD-positive AML. By comparing the gene expression profiles of these cells, we unraveled the transcriptional pre-resistance signatures, including G1 to S phase transition 1 (GSPT1) gene. Targeting GSPT1 with the small molecule CC-90009 exhibited strong synergistic effect when combined with FLT3 inhibitors in the FLT3-ITD-mutated MOLM-13 and MV4-11 cell lines and primary AML patient samples. Further, we identified novel compounds that induced transcriptomic changes opposite to the pre-resistance signatures, thereby driving cells to FLT3 inhibitor-sensitive states. Vistusertib (mTOR inhibitor), linsitinib (IGF1R and insulin receptor inhibitor), and meisoindigo (IGF1R and Src family kinase inhibitor), all inhibiting pathways parallel to or downstream of oncogenic FLT3 signaling, were predicted and validated to pre-sensitize the FLT3-ITD-mutated cell lines and primary cells to FLT3 inhibitors. Collectively, these findings demonstrate the validity of our lineage-tracing method in unveiling pre-existing transcriptional features of treatment vulnerability in hematological cancers, and elucidate novel strategies for enhancing FLT3 inhibitor treatment efficacy in FLT3-ITD-positive AML by preventing the emergence of treatment resistance.

cancer biology↗

bioRxiv2024

Patient-derived functional immuno-oncology platform identifies responders to ATR inhibitor and immunotherapy combinations in ovarian cancer

Responses to single agent immunotherapies have remained modest in high-grade serous ovarian cancer (HGSC), suggesting the need for combination treatments. Identifying clinically effective immunotherapy combinations (IC) requires pre-clinical testing using models representing the patient-specific immune microenvironment. Here, we established a functional immuno-oncology platform for high-throughput and functional testing of IC using HGSC patient-derived immunocompetent cultures (iPDCs) established on patient-derived omentum gel matrix. We employed genomic and single-cell analysis to assess the intricate and functional characteristics of the iPDCs combined with tumor and immune cell-specific cytotoxic responses. Corroborating the clinical response to Poly (ADP-ribose) polymerase inhibitors (PARPi), iPDCs showed homologous recombination deficiency (HRD) - specific response to PARPi. Importantly, drug responses from iPDCs of chemotherapy and PARPi refractory patients corresponded with patient outcomes and aligned with distinct pathway activities from single-cell RNA sequencing analysis. Furthermore, iPDCs from HRD tumors showed response to anti-PD1 antibody as measured by decrease in tumor cells combined with augmented T cell activation. High-throughput drug testing followed by single cell-imaging from iPDCs revealed patient-specific responses to combination of ataxia telangiectasia and Rad3-related inhibitor (ATRi) with DNA damaging agents or immunotherapies. Integration of cytotoxic responses with immune cell states uncovered patient-specific immune activation with the combination of ATRi and a novel immunotherapy targeting Autotaxin (ATX), and this response was significantly associated with a tumor-cell replication stress biomarker in single-cell analysis of tCycIF highly multiplexed imaging. In conclusion, iPDCs provide a platform for high-throughput screening and functional testing of immuno-oncology agents for precision oncology in HGSC.

cancer biology↗

bioRxiv2022

Tracing back primed resistance in cancer via sister cells

Exploring non-genetic evolution of cell states during cancer treatments has become attainable by recent advances in lineage-tracing methods. However, transcriptional changes that drive cells into resistant fates may be subtle, necessitating high resolution analysis. We developed ReSisTrace that uses shared transcriptomic features of synchronised sister cells to predict the states that prime treatment resistance. We applied ReSisTrace in ovarian cancer cells perturbed with olaparib, carboplatin or natural killer (NK) cells. The pre-resistant phenotypes were defined by cell cycle and proteostatic features, reflecting the traits enriched in the upcoming subclonal selection. Furthermore, DNA repair deficiency rendered cells susceptible to both DNA damaging agents and NK killing in a context-dependent manner. Finally, we leveraged the pre-resistance profiles to predict and validate small molecules driving cells to sensitive states prior to treatment. In summary, ReSisTrace resolves pre-existing transcriptional features of treatment vulnerability, facilitating both molecular patient stratification and discovery of synergistic pre-sensitizing therapies.

cancer biology↗

bioRxiv2022

A platform for efficient establishment, expansion and drug response profiling of high-grade serous ovarian cancer organoids

The broad research use of organoids from high-grade serous ovarian carcinoma (HGSC) has been hampered by low culture success rates and limited availability of fresh tumor material. Here we describe a method for generation and long-term expansion of HGSC organoids with efficacy markedly improved over previous reports (55% vs. 23-38%). We established organoids from cryopreserved material, demonstrating the feasibility of using viably biobanked tissue for HGSC organoid derivation. Genomic, histologic and single-cell transcriptomic analyses revealed that organoids recapitulated genetic and phenotypic features of original tumors. Organoid drug responses correlated with clinical treatment outcomes, although in culture conditions-dependent manner and only in organoids maintained in human plasma-like medium (HPLM). Organoids from consenting patients are available to the research community through a public biobank and organoid genomic data explorable through an interactive online tool. Taken together, this resource facilitates the application of HGSC organoids in basic and translational ovarian cancer research.

cancer biology↗

bioRxiv2021

miQC: An adaptive probabilistic framework for quality control of single-cell RNA-sequencing data

MotivationSingle-cell RNA-sequencing (scRNA-seq) has made it possible to profile gene expression in tissues at high resolution. An important preprocessing step prior to performing downstream analyses is to identify and remove cells with poor or degraded sample quality using quality control (QC) metrics. Two widely used QC metrics to identify a low-quality cell are (i) if the cell includes a high proportion of reads that map to mitochondrial DNA (mtDNA) encoded genes and (ii) if a small number of genes are detected. Current best practices use these QC metrics independently with either arbitrary, uniform thresholds (e.g. 5%) or biological context-dependent (e.g. species) thresholds, and fail to jointly model these metrics in a data-driven manner. Current practices are often overly stringent and especially untenable on lower-quality tissues, such as archived tumor tissues. ResultsWe 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. We demonstrate how our 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. AvailabilitySoftware available at https://github.com/greenelab/miQC. The code used to download datasets, perform the analyses, and reproduce the figures is available at https://github.com/greenelab/mito-filtering. ContactStephanie C. Hicks (shicks19@jhu.edu) and Anna Vaharautio (anna.vaharautio@helsinki.fi)

genomics↗

bioRxiv2020

Myc-dependent cell competition and proliferative response requires induction of the ribosome biogenesis regulator Peter Pan

The transcription factor Myc is activated in most major forms of human cancer. Myc regulates a large set of target genes, and drives cell growth across animal phyla. However, it has not been clear which target genes are required for Myc-induced growth, and whether the targets are individually necessary or act in an additive fashion. Here, we have used comparative functional genomics to identify a core set of Myc target genes whose regulation is conserved between humans and Drosophila melanogaster. Most of these targets are essential genes involved in ribosome biogenesis and ribonucleotide metabolism. To identify Drosophila genes whose upregulation is necessary for Myc induced growth, we deleted the Myc binding sites (E-boxes) in the promoter regions of four genes using CRISPR/Cas9. All mutant flies were homozygous viable, indicating that E-box sequences are not required for basal expression of the Myc target genes. E-Box deletions in RpS20, RpS24 and Nop56 did not cause strong growth phenotypes. However, deletion of the E-box in the rRNA processing factor Peter Pan (ppan) made the flies resistant to Myc-induced cell growth, without affecting Myc-induced apoptosis. Despite their failure to respond to Myc, the ppanEbox-/- flies are healthy and display only a minor developmental delay, suggesting that it may be possible to treat or prevent tumorigenesis by targeting individual downstream targets of Myc.

cell biology↗

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↗