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Pellinen, T.

Publications and source records attributed to Pellinen, T..

4 recordsLinked to original sources

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↗

Colorectal cancer heterogeneity co-evolves with tumor architecture to determine disease outcome

Intratumoral heterogeneity, originating from genetic, epigenetic, and phenotypic cellular diversity, is pervasive in cancer. As these heterogeneous states employ diverse mechanisms to promote tumor progression, metastasis, and therapy resistance, emergence of cancer heterogeneity is one of the most significant barriers to curative treatment. Here we leverage deep learning approaches to develop a high-throughput image-analysis paradigm with subcellular resolution that quantifies and predicts colorectal cancer (CRC) patient outcome based on tissue architecture and nuclear morphology. We further combine this approach with spatial transcriptomics, multiplex immunohistochemistry, and patient-derived organoids to uncover a dynamic, co-evolutionary relationship between tumor architecture and cell states. We identify clinically relevant architectural interfaces in CRC tissue that diversify cellular identities by favoring distinct cancer stem cell states and thus promote evolution of tumor heterogeneity. Specifically, tissue fragmentation and associated compressive forces promote loss of classic stem cell signature and acquisition of fetal/regenerative stem cell states, which initially emerges as a hybrid state with features of epithelial-to-mesenchyme (EMT) transition. Additional tumor stroma communication then diversifies these states into distinct stem cell and EMT states at the invasive margins of tumors. Reciprocally, Wnt signaling state of tumor cells tunes their responsiveness to tissue architecture. Collectively, this work uncovers a feedback loop between cell states and tissue architecture that drives cancer heterogeneity and cell state diversification. Machine learning-based analyses harness this co-dependency, independently of mutational status or cancer stage, to predict patient outcome.

cancer biology↗

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↗

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↗