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Anttila, E.

Publications and source records attributed to Anttila, E..

3 recordsLinked to original sources
bioRxiv2024

Single-cell spatial atlas of high-grade serous ovarian cancer unveils MHC class II as a key driver of spatial tumor ecosystems and clinical outcomes

The tumor microenvironment (TME) is a complex network of interactions between malignant and host cells, yet its orchestration in advanced high-grade serous ovarian carcinoma (HGSC) remains poorly understood. We present a comprehensive single-cell spatial atlas of 280 metastatic HGSCs, integrating high-dimensional imaging, genomics, and transcriptomics. Using 929 single-cell maps, we identify distinct spatial domains associated with phenotypically heterogeneous cellular compositions, and demonstrate that immune cell co-infiltration at the tumor-stroma interface significantly influences clinical outcomes. To uncover the key drivers of the tumor ecosystem, we developed CEFIIRA (Cell Feature Importance Identification by RAndom forest), which identified tumor cell-intrinsic MHC-II expression as a critical predictor of prolonged survival, independent of clinicomolecular profiles. Validation with external datasets confirmed that MHC-II-expressing cancer cells drive immune infiltration and orchestrate spatial tumor-immune interactions. Our atlas offers novel insights into immune surveillance mechanisms across HGSC clinicomolecular groups, paving the way for improved therapeutic strategies and patient stratification.

cancer biology↗
bioRxiv2024

Tribus: Semi-automated discovery of cell identities and phenotypes from multiplexed imaging and proteomicdata

MotivationMultiplexed imaging and single-cell analysis are increasingly applied to investigate the tissue spatial ecosystems in cancer and other complex diseases. Accurate single-cell phenotyping based on marker combinations is a critical but challenging task due to (i) low reproducibility across experiments with manual thresholding, and, (ii) labor-intensive ground-truth expert annotation required for learning-based methods. ResultsWe developed Tribus, an interactive knowledge-based classifier for multiplexed images and proteomic datasets that avoids hard-set thresholds and manual labeling. We demonstrated that Tribus recovers fine-grained cell types, matching the gold standard annotations by human experts. Additionally, Tribus can target ambiguous populations and discover phenotypically distinct cell subtypes. Through benchmarking against three similar methods in four public datasets with ground truth labels, we show that Tribus outperforms other methods in accuracy and computational efficiency, reducing runtime by an order of magnitude. Finally, we demonstrate the performance of Tribus in rapid and precise cell phenotyping with two large in-house whole-slide imaging datasets. AvailabilityTribus is available at https://github.com/farkkilab/tribus as an open-source Python package.

bioinformatics↗
bioRxiv2023

Single-cell spatial atlas of tertiary lymphoid structures in ovarian cancer

BackgroundRecent advances in highly-multiplexed tissue technologies and image analysis tools have enabled a more detailed investigation of the tumor microenvironment (TME) and its spatial features, including tertiary lymphoid structures (TLSs), at single-cell resolution. TLSs play a major part in antitumor immune responses, however, their role in antitumor immunity in ovarian cancer remains largely unexplored. MethodsIn this study, we generated a comprehensive single-cell spatial atlas of TLSs in ovarian cancer by extracting spatial topology information from in-situ highly-multiplexed cellular imaging using tissue cyclic immunofluorescence (CyCIF). Our analysis included 44 patients with high-grade serous ovarian cancer (HGSC) from the TOPACIO Phase II clinical trial. We combined spatial and phenotypic features from 302,545 single-cells with histopathology, targeted sequencing-based tumor molecular groups, and Nanostring gene expression data. ResultsWe find that TLSs are associated with a distinct TME composition and gene expression profile, characterized by elevated levels of the chemokines CCL19, CCL21, and CXCL13 correlating with the number of TLSs in the tumors. Using single-cell feature quantification and spatial mapping, we uncover enriched germinal center (GC) B cell infiltration and selective spatial attraction to follicular helper T and follicular regulatory T cells in the TLSs from chemo-exposed and BRCA1 mutated HGSCs. Importantly, spatial statistics reveal three main groups of cell-to-cell interactions; significantly enriched structural compartments of CD31+ cells, myeloid, and stromal cell types, homotypic cancer cell- and cancer cell to IBA1+ myeloid cell crosstalk, and enriched selective Tfh, Tfr, and Tfc communities with predominant Tfh - GC B cell interactions. Finally, we report spatiotemporal gradients of GC-B cell interactions during TLS maturation, with enriched non-GC B cell attraction towards the GC B cells in early TLSs, and avoidance patterns with selective GC B-cell communities in the TLSs with GCs. ConclusionsOur single-cell multi-omics analyses of TLSs showed evidence of active adaptive immunity with spatial and phenotypic variations among distinct clinical and molecular subtypes of HGSC. Overall, our findings provide new insights into the spatial biology of TLSs and have the potential to improve immunotherapeutic targeting of ovarian cancer. What is already known on this topicTLSs play a major part in antitumor immune responses, however, their exact role and mechanisms in antitumor immunity are widely unexplored. What this study addsOur results deepen the understanding of TLS biology including cell-cell interactions and shows how the presence of TLSs is characterized with a distinct TME composition and gene expression profile. How this study might affect research, practice or policyOverall, our findings provide new insights into the spatial biology of TLSs and have the potential to improve therapeutic options for ovarian cancer.

cancer biology↗