Spatial transcriptomics workflows often span separate tools for cell typing, neighborhoods, and cell-cell communication, yielding fragmented outputs that hinder scalability, interpretation, and reproducibility. STAPLE systematizes analyses across distinct methods into a modular framework, unifying data structures and cross-tool interoperability. End-to-end analyses are performed unassisted with a single invocation, fostering rigorous, reproducible spatial transcriptomics analysis. Its novel, AI-enabled reporting layer synthesizes quantitative results into summaries of biological findings, facilitating analysis interpretation.

Motivation:The Functional Expansion of Specific T cell (FEST)-based assays combine short-term peptide stimulation with TCR sequencing to identify clonotypes that expand in response to specific antigens. These approaches have proven invaluable for detecting neoantigen-specific T cell responses, guiding vaccine development, and assessing checkpoint blockade efficacy. However, variability introduced by biological and technical replicates poses challenges for reproducibility and interpretation, and existing computational tools do not address replicate-level analysis in these assays. Results:We developed replicateFest, a computational framework implemented as an R package and Shiny web application, to analyze FEST-based TCR-seq data with and without replicates. replicateFest applies Fisher's exact test for non-replicate datasets and negative binomial modeling for replicate experiments, returning adjusted p-values and odds ratios to identify clonotypes significantly expanded in antigen-stimulated conditions. The framework distinguishes FEST-expanded clonotypes (relative to a no-antigen control) and FEST-positive clonotypes (expanded compared to all other conditions). Validation using synthetic datasets confirmed accurate detection of antigen-specific clonotypes. Application to published HIV-1 epitope stimulation data reproduced original findings and demonstrated replicateFest's utility for reproducibility assessment and quality control. Availability and Implementation:replicateFest is freely available under the Apache-2.0 license as an R package at https://github.com/OncologyQS/replicateFest and as an interactive Shiny application at http://www.stat-apps.onc.jhmi.edu/FEST/.

The analysis of the interaction matrix between two distinct sets is essential across diverse fields, from pharmacovigilance to transcriptomics. Not all interactions are equally informative: a marker gene associated with a few specific biological processes is more informative than a highly expressed non-specific gene associated with most observed processes. Identifying these interactions is challenging due to background connections. Furthermore, data heterogeneity across sources precludes universal identification criteria. To address this challenge, we introduce \textsf{friends.test}, a method for identifying specificity by detecting structural breaks in entity interactions. Rank-based representation of the interaction matrix ensures invariance to heterogeneous data and allows for integrating data from diverse sources. To automatically locate the boundary between specific interactions and background activity, we employ model fitting. We demonstrate the applicability of \textsf{friends.test} on the GSE112026 -- transnational data from head and neck cancer. A computationally efficient \textsf{R} implementation is available at https://github.com/favorov/friends.test.

Supplementary Table S1. TCGA cancer study abbreviations and names.

Successful pancreatic ductal adenocarcinoma (PDAC) immunotherapy requires therapeutic combinations that induce quality T cells. Tumor microenvironment (TME) analysis following therapeutic interventions can identify response mechanisms, informing design of effective combinations. We provide a reference single-cell dataset from tumor-infiltrating leukocytes (TILs) from a human neoadjuvant clinical trial comparing the granulocyte-macrophage colony-stimulating factor (GM-CSF)-secreting allogeneic PDAC vaccine GVAX alone, in combination with anti-PD1, or with both anti-PD1 and CD137 agonist. Treatment with GVAX and anti-PD-1 led to increased CD8+ T cell activation and expression of cytoskeletal and extracellular matrix (ECM)-interacting components. Addition of CD137 agonist increased abundance of clonally expanded CD8+ T cells and increased immunosuppressive TREM2 signaling in tumor associated macrophages (TAMs), identified by comparison of ligand-receptor networks, corresponding to changes in metabolism and ECM interactions. These findings associate therapy with GVAX, anti-PD1, and CD137 agonist with enhanced CD8+ T cell function while inducing alternative immunosuppressive pathways in patients with PDAC.

Comparison of predicted splicing antigens from SpliceMutr to proteomics datasets. A, The log10-transformed total number of MHC-binding kmers found with and without reference kmer filtering in all samples of the breast cancer TCGA cohort (BRCA). B, The percentage of IEAtlas-validated immunogenic kmers between SpliceMutr with and without reference kmer filtering in all samples of the breast cancer TCGA cohort (BRCA).

We present a major update of the HOCOMOCO collection that provides DNA binding specificity patterns of 949 human transcription factors and 720 mouse orthologs. To make this release, we performed motif discovery in peak sets that originated from 14 183 ChIP-Seq experiments and reads from 2554 HT-SELEX experiments yielding more than 400 thousand candidate motifs. The candidate motifs were annotated according to their similarity to known motifs and the hierarchy of DNA-binding domains of the respective transcription factors. Next, the motifs underwent human expert curation to stratify distinct motif subtypes and remove non-informative patterns and common artifacts. Finally, the curated subset of 100 thousand motifs was supplied to the automated benchmarking to select the best-performing motifs for each transcription factor. The resulting HOCOMOCO v12 core collection contains 1443 verified position weight matrices, including distinct subtypes of DNA binding motifs for particular transcription factors. In addition to the core collection, HOCOMOCO v12 provides motif sets optimized for the recognition of binding sites in vivo and in vitro, and for annotation of regulatory sequence variants. HOCOMOCO is available at https://hocomoco12.autosome.org and https://hocomoco.autosome.org.

Background Current experimental practices typically produce large multidimensional datasets. Distance matrix calculation between elements (e.g., samples) for such data, although being often necessary in preprocessing for statistical inference or visualization, can be computationally demanding. Data sparsity, which is often observed in various experimental data modalities, such as single-cell sequencing in bioinformatics or collaborative filtering in recommendation systems, may pose additional algorithmic challenges.Results We present GPU-Assisted Distance Estimation Software (GADES), a graphical processing unit (GPU)-enhanced package that allows for massively paralleled Kendall-$\tau$ distance matrices computation. The package's architecture involves specific memory management, which lifts the limits for the data size imposed by GPU memory capacity. Additional algorithmic solutions provide a means to address the data sparsity problem and reinforce the acceleration effect for sparse datasets. Benchmarking against available central processing unit-based packages on simulated and real experimental single-cell RNA sequencing or single-cell ATAC sequencing datasets demonstrated significantly higher speed for GADES compared to other methods for both sparse and dense data processing, with additional performance boost for the sparse data.Conclusions This work significantly contributes to the development of computational strategies for high-performance Kendall distance matrices computation and allows for the efficient processing of Big Data with the power of GPU. GADES is freely available at https://github.com/lab-medvedeva/GADES-main.

Per splice junction normalized SA for responders compared with baseline samples. The SA averaged across samples for the subset of splice junctions derived from the top 20 genes with the highest SA per sample. The black horizontal line is the median SA for baseline samples. *, P value ≤ 0.05; **, P value ≤ 0.005; ***, P value ≤ 0.0005; ****, P value ≤ 0.00005. SA, splicing antigenicity.

ABSTRACT:Aberrant alternative splicing can generate neoantigens, which can themselves stimulate immune responses and surveillance. Previous methods for quantifying splicing-derived neoantigens are limited by independent references and potential batch effects. Here, we introduce SpliceMutr, a bioinformatics approach and pipeline for identifying splicing-derived neoantigens from tumor and normal data. SpliceMutr facilitates the identification of tumor-specific antigenic splice variants, predicts MHC-binding affinity, and estimates splicing antigenicity scores per gene. By applying this tool to transcriptomic data from The Cancer Genome Atlas, we generate splicing-derived neoantigens and neoantigenicity scores per sample and across all cancer types and find numerous correlations between splicing antigenicity and well-established biomarkers of antitumor immunity. Notably, carriers of mutations within splicing machinery genes have higher splicing antigenicity, which provides support for our approach. Further analysis of splicing antigenicity in cohorts of patients with melanoma treated with mono- or combined immune checkpoint inhibition suggests that the abundance of splicing antigens is reduced post-treatment from baseline in patients who progress. We also observe increased splicing antigenicity in responders to immunotherapy, which may relate to an increased capacity to mount an immune response to splicing-derived antigens. We find the splicing antigenicity to be higher in tumor samples when compared with normal, that mutations in the splicing machinery result in increased splicing antigenicity in some cancers, and higher splicing antigenicity is associated with positive response to immune checkpoint inhibitor therapies. Furthermore, this new computational pipeline provides novel analytical capabilities for splicing antigenicity and is openly available for further immuno-oncology analysis. SIGNIFICANCE:SpliceMutr shows that splicing antigenicity changes in response to ICI therapies and that native modulation of the splicing machinery through mutations increases the contribution of splicing to the neoantigen load of some The Cancer Genome Atlas cancer subtypes. Future studies of the relationship between splicing antigenicity and immune checkpoint inhibitor response pan-cancer are essential to establish the interplay between antigen heterogeneity and immunotherapy regimen on patient response.

<p>Figure S1. Experimental Figure Scheme. Figure S2. ChIP-Seq quality assessment for different samples and histone marks. Figure S3. Comparable ChIP-Seq and ChIP-qRT-PCR detection of histone enrichment for different histone marks near three reference genes. Figure S4. Comparable expression of reference genes. Figure S5. H3K9ac histone mark enrichment distribution near transcription start sites. Figure S6. H3K27ac histone mark enrichment distribution near transcription start sites. Figure S7. H3K9me3 histone mark enrichment distribution near transcription start sites. Figure S8. Correlation between tissue specific H3K9ac histone peak enrichment and expression of nearby genes. Figure S9. Correlation between tissue specific H3K9me3 histone peak enrichment and expression of nearby genes. Figure S10. Correlation between tissue specific H3K4me3 histone peak enrichment and expression of nearby genes for six study samples and two PDX-parental tissues. Figure S11. Correlation between tissue specific H3K27ac histone peak enrichment and expression of nearby genes for six study samples and two PDX-parental tissues. Figure S12. Correlation between tissue specific H3K9ac histone peak enrichment and expression of nearby genes for six study samples and two PDX-parental tissues. Figure S13. Correlation between tissue specific H3K9me3 histone peak enrichment and expression of nearby genes for six study samples and two PDX-parental tissues. Figure S14. Scheme for integration of ChIP-Seq Specific Histone Peaks with Expression Variation Analysis (EVA). Figure S15. Correlation of H3K27ac-enriched with genes differentially regulated in mesenchymal and classical HPV+ HNSCC subtypes.</p>

140 Illumina probes corresponding to138 unique genes differentially expressed in regressing (R) vs. progressing (P) cutaneous metastases based on whole genome microarray analysis (fold change magnitude {greater than or equal to}1.7, p value {less than or equal to}0.05 )

Supplemental Data S1 provides description on the ACC-01-XP xenograft development using ACC-01 cell line and shows list of PCR primers and other reagents used in the study. Supplemental Data S2 shows list of alternative splicing events identified in ACC compared to normal salivary gland tissue. Supplemental Data S3 shows nucleotides and corresponding amino acid composition of the three novel FGFR1 splice variants. Figure S1 shows ASE genes in PI3K-AKT signaling pathway enrichment using the Enrichr web tool. Figure S2 shows characterization of FGFR1v and impact on cell growth following siRNA-mediated knockdown. Figure S3 shows FGFR1v stability analysis and its effect on signaling and resistance to FGFR1 inhibitor. Figure S4 shows FGFR1v-mediated AKT signaling is independent on EGFR but supports the involvement of AXL. Figure S5 shows supportive expression and activities of FGFR1v in HACC-2A cells.

SFigure 1. Distribution of ASEs present within each tumor. SFigure2. AKT3 splice variant expression by qRT-PCR in a panel of 20 cell lines. SFigure 3. Western blot analysis of canonical AKT1 pathway after transient knockdown of AKT3 splice variant. SFigure 4. Correlation of AKT3 splice variant with other PI3K/AKT pathway mutations in TCGA.

<p>Table S1. Clinical data for patient-derived PDX1, PDX2, UPPP1, and UPPP2 samples used for ChIP-based analysis. Table S2. ChIP-DNA qRT-PCR primers-probe sequence Table S3. Sample-specific enrichment of H3K4me3 histone mark at 5''UTR of individual genes. Table S4. Sample-specific enrichment of H3K27ac histone mark at 5''UTR of individual genes. Table S5. Sample-specific enrichment of H3K9ac histone mark at 5''UTR of individual genes. Table S6. Sample-specific enrichment of H3K9me3 histone mark at 5''UTR of individual genes. Table S7. Gene set enrichment analysis of genes linked to H3K27ac-enrichment specific for tumor samples. Table S8. Gene set enrichment analysis of genes linked to H3K27ac-enrichment specific for normal samples. Table S9. Gene set enrichment analysis of genes linked to H3K4me3-enrichment specific for tumor samples. Table S10. Gene set enrichment analysis of genes linked to H3K4me3-enrichment specific for normal samples. Table S11. Correlation of H3K27ac-enriched with genes differentially regulated in HPV-KRT and HPV-IMU.</p>

Designed primer sequences for wild type AKT3 and variant AKT3 for qRT-PCR analysis as well as custom siRNA sequences

The script for ChIP-Seq and RNA-Seq data integration

R code for splice variant analysis and outlier statistics for identification of significant splice variants

Cell line authentication for SCC 61