Biology subjects

Porkka, K.

Publications and source records attributed to Porkka, K..

5 recordsLinked to original sources

bioRxiv2026

RUNX1 aberrations in blast-phase CML induce the RBP SPATS2L which promotes growth, survival and stress granule assembly

The RUNX1 transcription factor is a critical regulator of hematopoiesis and frequently mutated in myeloid malignancies. In the myeloproliferative neoplasm, chronic myeloid leukemia (CML), secondary somatic RUNX1 mutations and RUNX1::MECOM/EVI1, are associated with tyrosine kinase inhibitor (TKI) resistance and progression to the blast-phase (BP-CML). Research has predominantly focussed on transcriptional dysregulation mediated by RUNX1 mutations in myeloid malignancies, whilst post-transcriptional dysregulation remains comparatively unexplored. To address this, we used orthogonal organic phase separation (OOPS), to characterise the RNA-binding proteome of RUNX1 deficient BP-CML cells. RUNX1 depleted BP-CML cells exhibited significant alterations to RBP abundance involved in stress response pathways and translation/ribosome-biogenesis (RiBi). Furthermore, RUNX1 depletion or expression of RUNX1::EVI1 in BP-CML cells induced expression and RNA binding activity of SPATS2L, a component of stress granules (SG); membraneless cytoplasmic condensates protecting mRNAs from degradation, promoting survival under stress. Whilst RUNX1 depletion increased SG-assembly, SPATS2L depletion reduced SG-assembly in BP-CML cells and inhibited the growth and survival of multiple BP-CML cell lines. The translation inhibitor homoharringtonine (HHT), used historically in TKI-resistant CML, ablated SG-assembly in BP-CML cells with RUNX1 depletion, and, primary BP-CML cells with LOF/hypomorphic RUNX1 mutations (characterised by defective DNA-binding/CBF{beta}-interaction) were preferentially sensitised to HHT. Finally, suppressing SPATS2L expression induced by RUNX1 depletion, increased the HHT-sensitivity of RUNX1 depleted BP-CML cells, suggesting SPATS2L contributes to therapeutic resistance in CML with RUNX1 mutations. This study suggests that SPATS2L and SG induction could be critical to RUNX1-mutant leukemias, and, provides preliminary evidence for a mutationally-targeted approach in CML with RUNX1 aberrations.

cancer biology↗

bioRxiv2025

Sensitivity to ATR-CHK1 pathway inhibition in AML/MDS is enhanced by SRSF2 mutations and reduced by RUNX1 loss

SRSF2 mutations occur in up to 25% of acute myeloid leukemia (AML) and 17% of myelodysplastic syndrome (MDS) cases and are associated with poor prognosis, yet no mutation-directed therapy exists. Here, we aimed to identify therapeutically targetable vulnerabilities in MDS/AML with SRSF2 mutations. Ex vivo drug-sensitivity testing of bone marrow cells from AML patients and healthy donors showed that SRSF2-mutant cells are sensitive to inhibitors of CHK1, and WEE1 DNA damage response (DDR) kinases. To test causality, we engineered isogenic K562 cell line clones expressing SRSF2P95H/L/R mutations. RNA sequencing confirmed splicing aberrations characteristic of MDS/AML in these clones. We found that SRSF2P95H/L/R sensitize leukemia cells to ATR-CHK1-WEE1 inhibition. Bone marrow progenitors from Srsf2P95H and U2AF1S34F knock-in mice showed heightened sensitivity to CHK1 inhibition, corroborating the human data. In contrast, RUNX1 mutations were linked to resistance against CHK1 and WEE1 inhibition in SRSF2-mutant AML samples. Runx1 loss also caused resistance to CHK1 inhibitors in knock-in mouse progenitors harboring Srsf2P95H or U2AF1S34F, indicating that RUNX1 loss is a mechanism of resistance. In conclusion, SRSF2 and U2AF1 mutations are biomarkers of sensitivity to ATR-CHK1 pathway inhibitors, while RUNX1 mutations cause resistance. These biomarkers can support patient stratification in MDS/AML.

cancer biology↗

bioRxiv2023

Single-cell transcriptomes identify patient-tailored therapies for selective co-inhibition of cancer clones

Intratumoral cellular heterogeneity necessitates multi-targeting therapies for improved clinical benefits in patients with advanced malignancies. However, systematic identification of patient-specific treatments that selectively co-inhibit cancerous cell populations poses a combinatorial challenge, since the number of possible drug-dose combinations vastly exceeds what could be tested in scarce patient cells. Here, we developed scTherapy, a machine learning model that leverages single-cell transcriptomic profiles to prioritize multi-targeting treatment options for individual patients with hematological cancers or solid tumors.

bioinformatics↗

bioRxiv2020

Patient-tailored design of AML cell subpopulation-selective drug combinations

The extensive primary and secondary drug resistance in acute myeloid leukemia (AML) requires rational approaches to design personalized combinatorial treatments that exploit patient-specific therapeutic vulnerabilities to optimally target disease-driving AML cell subpopulations. However, the large number of AML-relevant drug combinations makes the testing impossible in scarce primary patient cells. This combinatorial problem is further exacerbated by the translational challenge of how to design such personalized and selective drug combinations that do not only show synergistic effect in overall AML cell killing but also result in minimal toxic side effects on non-malignant cells. To solve these challenges, we implemented a systematic computational-experimental approach for identifying potential drug combinations that have a desired synergy-efficacy-toxicity balance. Our mechanism-agnostic approach combines single-cell RNA-sequencing (scRNA-seq) with ex vivo single-agent viability testing in primary patient cells. The data integration and predictive modelling are carried out at a single-cell resolution by means of a machine learning model that makes use of compound-target interaction networks to narrow down the massive search space of potentially effective drug combinations. When applied to two diagnostic and two refractory AML patient cases, each having a different genetic background, our integrated approach predicted a number of patient-specific combinations that were shown to result not only in synergistic cancer cell inhibition but were also capable of targeting specific AML cell subpopulations that emerge in differing stages of disease pathogenesis or treatment regimens. Overall, 53% of the 59 predicted combinations were experimentally confirmed to show synergy, and 83% were non-antagonistic, as validated with viability assays, which is a significant improvement over the success rate of randomly guessing a synergistic drug combination (5%). Importantly, 67% of the predicted combinations showed low toxicity to non-malignant cells, as validated with flow-based population assays, suggesting their selective killing of AML cell populations. Our data-driven approach provides an unbiased means for systematic prioritization of patient-specific drug combinations that selectively inhibit AML cells and avoid co-inhibition of non-malignant cells, thereby increasing their likelihood for clinical translation. The approach uses only a limited number of patient primary cells, and it is widely applicable to hematological cancers that are accessible for scRNA-seq profiling and ex vivo compound testing.

systems biology↗

bioRxiv2020

Histopathological Landscape of Molecular Genetics and Clinical Determinants in MDS Patients

In myelodysplastic syndrome (MDS), bone marrow (BM) histopathology is visually assessed to identify dysplastic cellular morphology, cellularity, and blast excess. Yet, many morphological findings elude the human eye. Here, we extracted visual features of 236 MDS, 87 MDS/MPN, and 10 control BM biopsies with convolutional neural networks. Unsupervised analysis distinguished underlying correlations between tissue composition, leukocyte metrics, and clinical characteristics. We applied morphological features in elastic net-regularized regression models to predict genetic and cytogenetic aberrations, prognosis, and clinical variables. By parallelizing tile, pixel, and leukocyte-level image analysis, we deconvoluted each model to texture and cellular composition to dissect their pathobiological context. Model-based mutation predictions correlated with variant allele frequency and number of affected genes per pathway, demonstrating the models ability to identify relevant visual patterns. In summary, this study highlights the potential of deep histopathology in hematology by unveiling the fundamental association of BM morphology with genetic and clinical determinants.

bioinformatics↗