Biology subjects

Mirtti, T.

Publications and source records attributed to Mirtti, T..

10 recordsLinked to original sources

bioRxiv2026

Pathway-centric multi-omics and functional precision medicine reveal shared drug vulnerabilities in heterogeneous adult Wilms tumor

Wilms tumor, i.e., nephroblastoma, is rare in adults and lacks standardized treatment, complicating clinical decision-making. Within the functional precision medicine study (DEDUCER), we profiled two spatially distinct tumor regions (T1 and T2) of an adult Wilms tumor patient using integrated histopathology, whole-exome sequencing, FFPE transcriptomics, and ex vivo drug screening of short-term cultured patient-derived cancer cells (PDCs) with 528 compounds. Genomic profiling revealed a truncal ASXL1 frameshift and shared F7, UBA1, COL21A1, and ATM variants alongside region-specific alterations: a TP53 mutation and broad copy-number (CN) gains in T1, versus ARID1A and KMT2D stop-gains in copy-neutral T2. Transcriptomics of tumor areas identified convergent activation of the G2/M checkpoint, E2F targets, and mitotic spindle programs across regions, consistent with high proliferation and partially comparable biomarker signatures to those observed in an open-source pediatric Wilms tumor dataset (n = 130). Functional assays uncovered distinct and shared drug vulnerabilities: although ATM alterations were present in both tumors, T1 PDCs showed selective sensitivity to topoisomerase I and BCL-2 inhibition in the context of an additional T1-specific TP53 alteration, while broader single-agent sensitivity and stronger drug synergies were observed in T2. Pathway-centric data integration indicated that differential gene expression and copy-number gains, rather than single mutations alone, better predicted ex vivo drug responses, revealing actionable shared dependencies despite pronounced spatial heterogeneity and establishing a translational framework for individualized management in this rare disease. HIGHLIGHTS- In the adult Wilms tumor, multi-region genomics revealed a truncal ASXL1 frameshift together with F7, UBA1, COL21A1 and ATM mutations across two tumor regions (T1 and T2), as well as region-specific alterations: TP53 mutation and widespread copy-number gains in T1, versus ARID1A and KMT2D stop-gains in copy-neutral T2, illustrating spatial heterogeneity. - Transcriptomics showed convergent activation of E2F targets, G2/M checkpoint, and mitotic spindle programs in both regions, consistent with high proliferation and aligning with Wilms tumor signatures (TARGET dataset); these pathways were associated with higher ex vivo drug sensitivity scores. - Functional drug sensitivity testing of patient -derived cancer cells ex vivo uncovered distinct and shared vulnerabilities: Although both tumors shared an ATM mutation, T1-specific TP53 alteration and death-pathway/stress-response alterations may underlie selective sensitivity to topoisomerase I inhibitors and BCL-2 inhibition. - Clinically relevant combinations, including vincristine plus dactinomycin and doxorubicin plus dactinomycin, showed ex vivo synergy. These findings are consistent with the patients more than five-year relapse-free outcome following vincristine, doxorubicin, and dactinomycin treatment combined with surgery, supporting the translational relevance of the ex vivo drug testing approach. - Pathway-centric integration (copy-number gains and differential expression) predicted drug response better than single-gene biomarkers. Overall, pathway-level dependencies provide robust, actionable targets despite genomic and phenotypic heterogeneity in adult Wilms tumor.

Cancer Biology↗

bioRxiv2026

Site-Dependent Decoupling of Drug-Biomarker Associations in Clear Cell Renal Cell Carcinoma Revealed by Functional Profiling of Patient-Derived Cell Models

Clear cell renal cell carcinoma (ccRCC) frequently exhibits primary and acquired resistance to standard-of-care therapies, underscoring the need for improved strategies to predict therapeutic response and prioritize patient-specific treatments. Although recent multi-omic and single-cell studies have provided insight into the molecular landscape of ccRCC, molecular alterations alone incompletely predict drug sensitivity. We prospectively profiled tumors from 28 patients with localized and metastatic ccRCC by integrating molecular characterization, functional drug sensitivity and resistance testing in patient-derived cell models, and longitudinal clinical data. Although genomic biomarkers suggested potentially actionable therapies in 27/28 patients, functional testing revealed discordance between genomic actionability and ex vivo drug sensitivity, whereas linear mixed-effects modelling uncovered 16 novel copy-number-based features associated with sensitivity to 11 drugs. Genotype-drug response associations were largely preserved between primary tumors and vena cava thrombi but frequently disrupted in distant metastases. Integrating functional drug testing with multi-omic profiling reveals vulnerabilities not apparent from genomic data alone, refines therapeutic actionability, captures interpatient and intersite heterogeneity in ccRCC, and provides a scalable framework for individualized treatment prioritization.

cancer biology↗

bioRxiv2025

Self-supervised learning enables unbiased patient characterization from multiplexed microscopy images

Multiplexed immunofluorescence microscopy offers detailed insights into the spatial architecture of cancer tissue. However, classical single-cell analysis approaches are limited by segmentation accuracy, reliance on predefined features, and the inability to capture spatial interrelationships among cells. We developed a hierarchical self-supervised deep learning framework that learns spatial protein marker patterns from multiplexed microscopy images by encoding the tissue at both local (cellular) and global (tissue architecture) levels. Applied to lung, prostate, and renal cancer tissue microarray cohorts, our method stratified patients into prognostically distinct groups with significantly different survival outcomes. These groupings were consistent with prior expert-driven single-cell analyses, demonstrating the validity of our approach. Furthermore, attention maps extracted from these models highlighted biologically relevant tissue regions associated with specific marker patterns. Overall, our framework effectively profiles complex multiplexed microscopy images and offers a scalable, interpretable tool for improved biomarker discovery, with potential to support more informed cancer treatment decisions.

bioinformatics↗

bioRxiv2024

Fibroblast activation protein defines aggressive EMT-associated cancer cell state and prognosis in localized ccRCC

Clear cell renal cell carcinoma (ccRCC) is among the most immune-infiltrated cancers, where high inflammation correlates with poor clinical outcomes. While epithelial-to-mesenchymal transition (EMT), immunosuppression, and cancer-associated fibroblasts (CAFs) have been linked with poor prognosis in metastatic ccRCC, their relevance and interplay in localized disease remain uncertain. To address this gap, we performed single-cell spatial profiling of highly inflamed localized tumors to characterize interactions between EMT-associated cancer cells and immune subsets, aiming to identify tumor microenvironment cell subsets and states responsible for the poor prognosis in highly inflamed ccRCC. Multiplexed immunofluorescence imaging with a 33-marker panel was applied to 1,728 tissue cores collected from tumor centers, invasive borders, and adjacent benign tissue from 435 localized ccRCC patients. Independent discovery (n=196) and validation (n=239) cohorts were used to confirm the significance of identified prognostic markers. Hierarchical clustering revealed TME subsets enriched for CD45 immune cells, CD31 endothelial cells, or fibroblasts. High CD45 cell density significantly correlated with poor recurrence-free survival across tumor regions, including tumor center, invasive border, and adjacent benign tissue. Within tumors exhibiting high CD45 infiltration, EMT-associated cancer cells strongly expressed fibroblast activation protein (FAP) at invasive borders, independently predicting worse prognosis and increased risk of liver metastasis. Furthermore, CD45high tumors with elevated FAP expression featured immunosuppressive FAP CAFs, M2-like macrophages, exhausted T cells, and regulatory T cells. Tumor-cell-specific FAP expression was validated as an independent prognostic biomarker in both early-stage localized ccRCC (pT1-2) and patients subsequently developing metastases who received sunitinib, highlighting its potential for improved patient stratification. SignificanceFAP expression identifies an aggressive, immunosuppressive EMT-associated subtype of localized ccRCC, uncovering critical interactions between cancer cells and the TME and revealing novel biological insights and therapeutic vulnerabilities driving early-stage ccRCC progression. Graphical AbstractO_ST_ABSTumor-cell FAP Expression Defines an Aggressive, Immunosuppressive EMT Subtype in Highly Inflamed Localized ccRCCC_ST_ABS O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=197 SRC="FIGDIR/small/620479v2_ufig1.gif" ALT="Figure 1"> View larger version (55K): org.highwire.dtl.DTLVardef@d25831org.highwire.dtl.DTLVardef@19c0de7org.highwire.dtl.DTLVardef@54bd9eorg.highwire.dtl.DTLVardef@16d0387_HPS_FORMAT_FIGEXP M_FIG C_FIG Localized ccRCC tumors were stratified by CD45 immune infiltration, revealing that CD45high tumors were associated with significantly poorer recurrence-free survival (5-year RFS rate: 39%). Among these, tumor cell-specific expression of fibroblast activation protein (FAP) identified a particularly aggressive subtype marked by epithelial-to-mesenchymal transition (EMT), reduced endothelial content, and an immunosuppressive tumor microenvironment enriched with M2-like macrophages, regulatory T cells, exhausted T cells, and FAP cancer-associated fibroblasts (CAFs). FAP tumors had an even lower 5-year RFS rate of 26%, compared to 50% in FAP- CD45high tumors. Tumor-cell FAP thus provides an additional layer of risk stratification within the CD45high subgroup and reflects a biologically distinct, immune-evasive tumor state. These findings highlight FAP as both a potential therapeutic target and imaging biomarker in localized ccRCC.

cancer biology↗

bioRxiv2024

Androgen deprivation therapy-resistant club cells are linked to myeloid cell-driven immunosuppression in the prostate tumor microenvironment

Prostate cancer treatment resistance is a significant challenge facing the field. Genomic and transcriptomic profiling have partially elucidated the mechanisms through which cancer cells escape treatment, but their relation toward the tumor microenvironment (TME) remains elusive. Here we present a comprehensive transcriptomic landscape of the prostate TME at multiple points in the standard treatment timeline employing single-cell RNA-sequencing and spatial transcriptomics data from 110 patients. We identify club-like cells as a key epithelial cell subtype that acts as an interface between the prostate and the immune system. Tissue areas enriched with club-like cells have depleted androgen signaling and upregulated expression of luminal progenitor cell markers. Club-like cells display a senescence-associated secretory phenotype and their presence is linked to increased polymorphonuclear myeloid-derived suppressor cell (PMN-MDSC) activity. Our results indicate that club-like cells partake in inducing myeloid inflammation previously associated with androgen deprivation therapy resistance, providing a rationale for their therapeutic targeting.

cancer biology↗

bioRxiv2022

A Community Challenge to Predict Clinical Outcomes After Immune Checkpoint Blockade in Non-Small Cell Lung Cancer

PurposePredictive biomarkers of immune checkpoint inhibitors (ICIs) efficacy are currently lacking for non-small cell lung cancer (NSCLC). Here, we describe the results from the Anti-PD-1 Response Prediction DREAM Challenge, a crowdsourced initiative that enabled the assessment of predictive models by using data from two randomized controlled clinical trials (RCTs) of ICIs in first-line metastatic NSCLC. MethodsParticipants developed and trained models using public resources. These were evaluated with data from the CheckMate 026 trial (NCT02041533), according to the model-to-data paradigm to maintain patient confidentiality. The generalizability of the models with the best predictive performance was assessed using data from the CheckMate 227 trial (NCT02477826). Both trials were phase III RCTs with a chemotherapy control arm, which supported the differentiation between predictive and prognostic models. Isolated model containers were evaluated using a bespoke strategy that considered the challenges of handling transcriptome data from clinical trials. ResultsA total of 59 teams participated, with 417 models submitted. Multiple predictive models, as opposed to a prognostic model, were generated for predicting overall survival, progression-free survival, and progressive disease status with ICIs. Variables within the models submitted by participants included tumor mutational burden (TMB), programmed death ligand 1 (PD-L1) expression, and gene-expression-based signatures. The bestperforming models showed improved predictive power over reference variables, including TMB or PD-L1. ConclusionThis DREAM Challenge is the first successful attempt to use protected phase III clinical data for a crowdsourced effort towards generating predictive models for ICIs clinical outcomes and could serve as a blueprint for similar efforts in other tumor types and disease states, setting a benchmark for future studies aiming to identify biomarkers predictive of ICIs efficacy. Context summaryO_ST_ABSKey objectiveC_ST_ABSNot all patients with non-small cell lung cancer (NSCLC) eligible for immune checkpoint inhibitor (ICIs) respond to treatment, but accurate predictive biomarkers of ICIs clinical outcomes are currently lacking. This crowdsourced initiative enabled the robust assessment of predictive models using data from two randomized clinical trials of first-line ICI in metastatic NSCLC. Knowledge generatedModels submitted indicate that a combination of programmed death ligand 1 (PD-L1), tumor mutational burden (TMB), and immune gene signatures might be able to identify patients more likely to respond to ICIs. TMB and PD-L1 seemed important to predict progression-free survival and overall survival. Mechanisms including apoptosis, T-cell crosstalk, and adaptive immune resistance appeared essential to predict response. Relevance

cancer biology↗

bioRxiv2022

IRF7 impacts on prostate cancer cell survival in response to radiation

Understanding the impact of radiotherapy on the evolution of treatment resistant prostate cancer is critical for selecting effective treatment combinations. Whilst activation of Type 1 interferon signalling is a hallmark of how cells respond to viral infection, in cancer cells, multiple stresses are known to activate this same response. In this study we have evaluated for the first time the changes in the interferon response induced by culturing prostate cancer cells under sphere- forming conditions and following irradiation. We report a conserved upregulated transcript profile for both conditions that is strongly associated with therapeutic resistance and cell survival in vitro and in vivo. The profile includes and is regulated by the Type 1 interferon master regulator IRF7 which, when depleted, delays tumour re-growth following irradiation. We immuno-stained two independent prostate cohorts for IRF7 and found that increased expression, particularly in cases with low PTEN expression, correlated with poor prognosis. To more comprehensively characterise the impact of IRF7 and radiation on cells, RNA-Seq was performed on IRF7 knockdown cells at different radiation doses. We identified a number of biological processes that were IRF7-dependent, including the formation of stem-like cell populations and also therapeutic vulnerabilities. For example, irradiation sensitised surviving cells to either a combination of an IKK{varepsilon}/TBK1 and a MEK inhibitor or treatment with an inhibitor of IDO1, an IRF7- dependent gene. Translationally our work suggests that IRF7 expression can be used to stratify patients who may not benefit from receiving radiotherapy alone but rather may benefit from treatment combinations. In two cohorts treated with radical intent, strong IRF7 staining was associated with disease-specific death implicating this pathway as a convergence point for therapeutic resistance in prostate and potentially other cancer types.

cancer biology↗

bioRxiv2022

OSCAR: Optimal subset cardinality regression using the L0-pseudonorm with applications to prognostic modelling of prostate cancer

In many real-world applications, such as those based on patient electronic health records, prognostic prediction of patient survival is based on heterogeneous sets of clinical laboratory measurements. To address the trade-off between the predictive accuracy of a prognostic model and the costs related to its clinical implementation, we propose an optimized L0-pseudonorm approach to learn sparse solutions in multivariable regression. The model sparsity is maintained by restricting the number of nonzero coefficients in the model with a cardinality constraint, which makes the optimization problem NP-hard. In addition, we generalize the cardinality constraint for grouped feature selection, hence making it possible to identify key sets of predictors that may be measured together in a kit in clinical practice. We demonstrate the operation of our cardinality constraint-based feature subset selection method, named OSCAR, in the context of prognostic modelling of prostate cancer, where it enabled one to determine the key explanatory predictors at different levels of model sparsity, and to explore how the model sparsity affects the model accuracy and implementation cost. Author summaryFeature selection has become a crucial part in building biomedical models, due to the abundance of available predictors in many applications, yet there remains an uncertainty of their importance and generalization ability. Regularized regression methods have become popular approaches to tackle this challenge by balancing the model goodness-of-fit against the increasing complexity of the model in terms of coefficients that deviate from zero. Regularization norms are pivotal in formulating the model complexity, and currently L1 (LASSO), L2 (Ridge Regression) and their hybrid (Elastic Net) norms dominate the field. In this paper, we present a novel methodology using the L0-pseudonorm, also known as the best subset selection, which has largely gone overlooked due to its challenging discrete nature. Our methodology makes use of a continuous transformation of the discrete optimization problem, and provides effective solvers implemented in a user friendly R software package. We exemplify the use of oscar-package in the context of prostate cancer prognostic prediction using both real-world hospital registry and clinical cohort data. By benchmarking the methodology against related regularization methods, we illustrate the advantages of the L0-pseudonorm for better clinical applicability and selection of grouped features.

bioinformatics↗

bioRxiv2021

The spatial landscape of clonal somatic mutations in benign and malignant tissue

Defining the transition from benign to malignant tissue is fundamental to improve early diagnosis of cancer. Here, we provide an unsupervised approach to study spatial genome integrity in situ to gain molecular insight into clonal relationships. We employed spatially resolved transcriptomics to infer spatial copy number variations in >120 000 regions across multiple organs, in benign and malignant tissues. We demonstrate that genome-wide copy number variation reveals distinct clonal patterns within tumours and in nearby benign tissue. Our results suggest a model for how genomic instability arises in histologically benign tissue that may represent early events in cancer evolution. We highlight the power of an unsupervised approach to capture the molecular and spatial continuums in a tissue context and challenge the rationale for treatment paradigms, including focal therapy.

genomics↗

bioRxiv2019

PP2A inhibitor PME-1 suppresses anoikis, and is associated with therapy relapse of PTEN-deficient prostate cancers

While organ-confined PCa is mostly therapeutically manageable, metastatic progression of PCa remains an unmet clinical challenge. Resistance to anoikis, a form of cell death initiated by cell detachment from the surrounding extracellular matrix, is one of the cellular processes critical for PCa progression towards aggressive disease. Therefore, further understanding of anoikis regulation in PCa might provide therapeutic opportunities. Here, we discover that PCa tumors with concomitantly compromised function of two tumor suppressor phosphatases, PP2A and PTEN, are particularly aggressive, having less than 50% 5-year secondary-therapy free patient survival. Functionally, overexpression of PME-1, a PP2A inhibitor protein, inhibits anoikis in PTEN-deficient PCa cells. In vivo, PME-1 inhibition increased apoptosis in in ovo PCa tumor xenografts, and attenuated PCa cell survival in zebrafish circulation. Molecularly, PME-1 deficient PCa cells display increased trimethylation at lysines 9 and 27 of histone H3 (H3K9me3 and H3K27me3), a phenotype corresponding to increased apoptosis sensitivity. In summary, we discover that PME-1 overexpression supports anoikis resistance in PTEN-deficient PCa cells. Clinically, the results identify PME-1 as a candidate biomarker for a subset of particularly aggressive PTEN-deficient PCa.

cancer biology↗