Biology subjects

Solis Soto, L. M.

Publications and source records attributed to Solis Soto, L. M..

10 recordsLinked to original sources

bioRxiv2026

Distinct Spatial Immune Architectures in Tumor and Tumor-Adjacent Tissues of Early-Stage Non-Small Cell Lung Cancer

Background: Lung cancer remains the leading cause of cancer-related deaths in the United States with over 124,000 estimated deaths for 2026. Previous studies have found that tumor-adjacent lung tissues may provide additional insight into the immune microenvironment of early-stage NSCLC. Methods: Multiplex immunofluorescence (mIF) imaging was performed on 192 tissues from 101 early-stage non-small cell lung cancer patients including 91 matched tumor-adjacent pairs, using three mIF panels. Spatial analyses were performed to identify distinctions between tissues and identify associations with clinical and genomic features. Results: Tumor tissue showed significantly higher densities of T cells, macrophages, B cells, and memory/regulatory populations than adjacent tissue (p<0.001). Tumors exhibited greater spatial heterogeneity, with higher prevalence of spatial patterning (47.7% vs. 31.2%) and more consistently localized organization, whereas adjacent tissue showed stronger individual-cell clustering. Pairwise colocalization (Ripley's L-cross) revealed selective spatial segregation in tumors (including B-cells from memory/regulatory cells and among cytotoxic T-cell subsets) and reduced immune proximity to malignant cells relative to the strong immune-epithelial association in adjacent tissue. Spatial features were linked to genomic features or patient outcomes: tumor neoantigen burden exhibited a positive association with CD3+ T cells in the tumor, while colocalization between CD45RO+CD57+GZMB+ cells and CD57+GZMB+ cells was negatively associated with overall survival outside the tumor. Conclusion: Tumor and adjacent tissues harbor distinct spatial immune architectures, with spatial features displaying associations with genomic features or patient outcomes. These findings highlight immune microenvironment reorganization and the importance of incorporating spatial context from both compartments into risk assessment in NSCLC.

immunology↗

bioRxiv2026

DLL3 expression predicts response and long-term outcome in small cell lung cancer patients treated with tarlatamab

Background: Small cell lung cancer (SCLC) is an aggressive, high-grade neuroendocrine carcinoma (hgNEC) with poor prognosis. Tarlatamab, a DLL3-targeting T cell engager was approved in 2024 for relapsed SCLC, with almost doubled overall survival benefit compared to chemotherapy. However, over half of patients do not respond to tarlatamab and others develop resistance within months. With multiple DLL3-targeting therapies in clinical trials, there are currently no validated predictive biomarkers to identify patients most likely to benefit from tarlatamab. In this study, we evaluate baseline DLL3 IHC level in patients as a predictive biomarker to tarlatamab clinical outcome. Methods: We assembled a cohort of 138 patients within IRB-approved MD Anderson GEMINI database (PA13-0589) with DLL3 expression by CLIA-validated immunohistochemistry (IHC), and those treated with tarlatamab from 7/1/2024 to 3/30/2026. Only those with DLL3 IHC results and treated with tarlatamab were included in this predictive biomarker study. All DLL3 levels were reported as percentage cells with cytoplasmic or surface DLL3 expression, including 24 with additional intensity score and H-score calculation. demographic, clinical, and outcome data were collected. Systemic and intracranial response data were assessed by RECIST and mRANO BM criteria respectively. Longitudinal liquid biopsies were taken from a subset of patients for circulating tumor DNA (ctDNA) and (Cytometry by Time-of-Flight) CyTOF analyses. Results: In the predictive biomarker cohort of 54 patients with SCLC treated with tarlatamab, Median DLL3% level in tarlatamab treated cohort was 75%. Median follow-up time was 9.9 months, median time on tarlatamab treatment (ToT) was 5.8 months. At the data cut-off, 61% (33 out of 54) patients were alive. Kaplan-Meier curve stratified by DLL3 % of <=75% vs. >75 showed significant longer ToT and a trend toward longer median OS (mOS). In DLL3>75% compared to DLL3 <=75%: mToT was 10.1 months vs. 2.9 months (p=0.036), mOS: not reached vs. 9.5 months (p= 0.066). Among 41 patients who completed one cycle of tarlatamab treatment and were eligible for systemic (extra-cranial) response per RECIST criteria; we observed partial response (PR) in 51% (21) patients, stable disease (SD) in 22% (9) patients; and progressive disease (PD) in 27% (11) patients. The median DLL3 % in those with PR vs. PD were 80% vs. 40% (p=0.006); median DLL3% comparing PR and SD were 80% vs. 60% (p=0.051). Among 35 patients evaluable for intracranial response, best objective response prior to any brain radiation were 5 (14%) CR,11(31%) PR, 8 (23%) SD and 11 (31%) PD. 9 received brain radiation (8 SRS, 1 WBRT) during tarlatamab to achieve better intracranial control while systemic control is maintained. DLL3% does not appear to be correlated with intracranial response. Longitudinal ctDNA and CyTOF analyses showed persistent ctDNA positivity, as well as increasing NEUROD1 subtype cells in circulating tumor cells in post-tarlatamab progression samples. We additionally profiled DLL3 expression by IHC in 138 patients with SCLC and found heterogenous DLL3 expression, with Median DLL3% of 70% across different biopsy sites. Conclusions: Our data suggests DLL3 expression in patients with SCLC is heterogeneous and not ubiquitous, with median DLL3 IHC of 70%. DLL3 IHC predicts objective response and long-term outcome with tarlatamab. In addition, dynamic monitoring of ctDNA level and CTCs by CyTOF are suggestive of development of tarlatamab resistance, however further studies with larger cohort of paired longitudinal samples are needed to validate the novel blood-based biomarkers. To our knowledge, this study is the first to validate DLL3 IHC as a predictive biomarker for tarlatamab in SCLC, which leads the way for optimizing treatment selection and combinatorial therapies for the subset of patients less likely to respond to tarlatamab.

cancer biology↗

bioRxiv2026

A modular generalist-specialist AI framework for ROI selection across spatial profiling workflow

Selection of regions of interest (ROIs) is often a crucial step in spatial molecular profiling and many pathology tasks, with substantial implications for research reproducibility and biological interpretability. To provide a reproducible and adaptive framework for AI-guided ROI selection, we developed a modular generalist-specialist solution across spatial profiling platforms. In a cohort comprising 55 tumor types from 160 tissue donors profiled using NanoString Digital Spatial Profiling and multiplex immunofluorescence, we first established a protein-profiling reference atlas capturing compartment-specific immune, checkpoint, stromal, and proliferation patterns. We then developed an AI Specialist Task-Oriented Model for ROI Selection (ASTROS) and tested comprehensive benchmarks considering specialist-only (ASTROS), generalist-only (PLIP/GFM), and hybrid generalist-specialist strategies, showing that the latter provides a balanced tradeoff across slide-level signal preservation, pathologist-reference concordance, within-slide placement consistency, and large-slide computational efficiency. We further demonstrated the feasibility of virtual staining for ROI preview and modular ROI placement for other spatial omics technologies, Visium and Visium HD workflows. Together, these results support our proposed framework to enable ROI selection responding to unmet needs for reducing inter-rater variability, reproducibility, and versatility in spatial profiling experiments.

pathology↗

bioRxiv2026

UniST: A Unified Computational Framework for 3D Spatial Transcriptomics Reconstruction

Spatial transcriptomics (ST) enables the measurement of gene expression in its native spatial context, yet most ST datasets are acquired as two-dimensional (2D) sections. Consequently, the underlying three-dimensional (3D) organization of tissues is only partially observed, and 3D ST data generated from serial sections are typically sparse and heterogeneous, with substantial tissue loss and missing measurements. These limitations pose major analytical challenges for reconstructing coherent 3D tissue architecture, rather than issues of experimental scalability alone. Here, we present UniST, a unified generative artificial intelligence (AI) framework designed to computationally reconstruct dense and continuous 3D ST landscapes from sparse serial sections, without altering the underlying experimental ST technologies. UniST integrates three complementary modules: kernel point convolution with cross-attention layers for point cloud upsampling, optical flow-based interpolation for continuous slice reconstruction, and a graph autoencoder with implicit neural representations for gene expression imputation. Together, these components densify sparse slices, resolve discontinuities, and map spatial coordinates to high-dimensional transcriptomics. Across multiple ST platforms and tissue contexts, UniST accurately restored structural continuity and biologically meaningful expression patterns. In a mouse embryo dataset, UniST reconstructed a dense 3D heart architecture from sparsely sampled slices. In two 3D human cancer tissues, UniST recovered critical spatial features, including tumor-immune boundaries and tertiary lymphoid structures, that were fragmented in the original data. By providing a generalizable computational solution that complements existing ST acquisition protocols, UniST facilitates cost-efficient and scalable reconstruction of 3D ST landscapes, enabling more faithful investigation of tissue organization and disease biology.

bioinformatics↗

bioRxiv2026

Toward Computationally Complete Spatial Omics

Multimodal spatial omics has transformed biology by mapping molecular complexity within intact tissues, yet current technologies remain limited in the number of modalities measured simultaneously and often produce lower-quality data than single-modality assays. We present COSIE, a computational framework that generates high-resolution, multilayered molecular landscapes across tissue sections, individuals, and platforms. COSIE integrates histology, epigenome, transcriptome, proteome, and metabolome into a unified representation. Applied to 12 datasets spanning 10 spatial technologies, eight modalities, and nine tissue types, ranging from thousands of spots to millions of cells, COSIE outperforms existing methods. It resolves tissue structures, enhances noisy measurements, predicts unmeasured modalities, and captures dynamic processes. In human tumors, COSIE identifies invasive subregions linked to clinical outcomes and predicts spatial gene expression in TCGA samples using only histology images. By transforming fragmented data into comprehensive spatial maps, COSIE advances computationally complete spatial omics and the creation of digital tissue twins for biomedicine.

genomics↗

bioRxiv2025

CyTOF-based profiling of circulating tumor cells predicts aggressiveness and therapy response in SCLC liquid biopsies at a personalized level

Small-cell lung cancer (SCLC) is an aggressive neuroendocrine carcinoma characterized by high numbers of circulating tumor cells (CTCs). We applied CyTOF and a 20-marker antibody panel to detect and phenotype CTCs directly in liquid biopsies of 51 SCLC patients (treatment-naive, chemotherapy and immunotherapy-treated, and tarlatamab-treated), of which a subset were longitudinally tracked. Unsupervised clustering revealed distinct cell populations enriched in patient liquid biopsies compared to those from healthy donors. Further analysis identified CTC populations of the three established SCLC subtypes driven by the high expression of ASCL1, NeuroD1, and POU2F3 transcription factors respectively. Significant differences in CTC EMT markers, established therapeutic targets (e.g. DLL3), and subtype heterogeneity were observed between naive versus treated samples. Changes in subtype proportions were observed in longitudinally tracked samples in both treatment modalities. Our study demonstrates the utility of CyTOF for high-resolution CTC profiling, offering dynamic insights into CTC heterogeneity, treatment response, and resistance mechanisms. HighlightsO_LICTCs can be detected, subtyped and phenotyped in SCLC liquid biopsies using CyTOF C_LIO_LICTC subtypes and EMT states are differentially associated with treatment modality C_LIO_LICTC DLL3 levels and epithelial features increase following anti-DLL3 BiTE therapy C_LIO_LICyTOF CTC subtyping can predict disease aggressiveness C_LIO_LILongitudinal tracking reveals CTC plasticity and therapy response correlations C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=132 SRC="FIGDIR/small/694669v1_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@384cbcorg.highwire.dtl.DTLVardef@35dd37org.highwire.dtl.DTLVardef@4e7d61org.highwire.dtl.DTLVardef@1098e4f_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2025

YAP1 defines an emergent, plastic population of relapsed small cell lung cancer

Small cell lung cancer (SCLC) is an aggressive neuroendocrine malignancy characterized by rapid onset of chemoresistance and poor clinical outcomes. Transcriptional heterogeneity among treatment-naive SCLC tumors underlies four transcriptional subtypes, each with distinct clinical vulnerabilities. Though previously hypothesized to delineate a distinct subtype, expression of YAP1 is largely absent from treatment-naive, pure SCLC. To characterize relapsed SCLC, circulating tumor DNA, circulating tumor cells, and core needle biopsies from SCLC patients and preclinical models following resistance to standard-of-care therapies were analyzed. In contrast to treatment-naive SCLC, these analyses reveal an emergent YAP1-positive cell population that coincides with treatment resistance. These YAP1-positive cells exhibit characteristics of drug tolerant persister cells, including senescence, stemness, and plasticity, as YAP1 positive cells largely abandon features characteristic of SCLC to adopt those of large-cell neuroendocrine carcinoma (LCNEC). As a result of this SCLC-like to LCNEC-like evolution, YAP1-positive cells lack several clinically relevant SCLC surface targets (i.e., DLL3, SEZ6), but are enriched for others (i.e., B7-H3, TROP2). We propose a model where YAP1 expressing cells emerge with SCLC treatment resistance and characterize a tenacious subpopulation capable of diverging from the treatment naive lineage and adopting features to evade therapeutic response.

cancer biology↗

bioRxiv2025

Tissue Factor Expression in Penile Squamous Cell Carcinoma: A Marker of HPV-Independent Disease

In a series of 33-patients, we evaluated tissue factor (TF) expression in penile squamous cell carcinoma (PSCC). A tissue microarray (TMA) was constructed with 3 cores per patient tumor (99 total cores). Anti-TF antibody staining was performed by immunohistochemistry and H-scores for membrane and cytoplasm staining were assessed (range 0-300). Percentage of cores and patient tumors staining positive for TF ([≥]10% of tumor cells with at least 1+ intensity in cytoplasm and/or membrane) and H-scores were described and compared with HPV and p16 status. Association of TF expression with tumor grade, presence of metastatic disease, lymphovascular invasion (LVI), perineural invasion (PNI), aberrant p53 expression, recurrence free survival (RFS), and cancer specific survival (CSS) were assessed. Nectin-4 and TROP2 staining and their association with clinical/pathological data was determined in a similar manner. TF staining was more prominent in HPV-negative tumors in both the membrane (H-score 69.6 vs 18.8; p=0.003) and cytoplasm (H-score 59.2 vs. 17.7, p=0.007). Cytoplasmic (H-score 61.7 vs 11.7, p=<0.001) and membrane TF staining (H-score 71.7 vs 15.0, p=<0.001) favored p16 negative tumors. The p53 status was more likely to be aberrant in the higher TF staining samples (cytoplasm H-score 61.7 vs 18.3, p=0.012; membrane H-score 67.5 vs 20.3, p=0.006). We observed an association with TROP2 staining and positive p16 status (membrane H-score 120.3 vs. 85, p=0.052; cytoplasmic H-score 135 vs. 107.5, p=0.041). We observed an association of TROP2 staining with positive LVI (membrane H-score 136.7 vs. 66.7, p=0.014; cytoplasmic H-score 110 vs. 93.3, p=0.04). We found no association between TF, TROP2, or nectin-4 staining with CSS or RFS; however, we suspect that this is due to our small sample size. Our results indicate that TF expression could be positive biomarker for HPV-independent, p53-aberrant PSCC, while TROP2 could be associated with HPV-associated PSCC.

pathology↗

bioRxiv2024

TLS_Finder: An algorithm for Identifying Tertiary Lymphoid Structures Using Immune Cell Spatial Coordinates

Tertiary lymphoid structures (TLS) are lymphoid formations that develop in non-lymphoid tissues during chronic inflammation, autoimmune diseases, and cancer. Accurate identification and quantification of TLS in tissue can provide crucial insights into the immune response of several disease processes including antitumor immune response. TLS are defined as aggregates of T cells, B cells and dendritic cells. In histological tissue sections stained with Hematoxylin and Eosin they are identified as aggregates of 50 or more lymphoid cells, however immunohistochemical analysis are required to confirm presence of distinct immune cell patterns. Assessment of lymphoid aggregates can be done in H&E slides or in slides stained with single or multiplex immunohistochemistry or other tissue based high-plex approaches with key biomarkers such as CD3 (T cells) and CD20 (B cells), however manual assessment of them is time consuming thus limiting its full evaluation. To our knowledge, published algorithms that identify TLS within a tissue are based on histological assessment of H&E slides or through use of ML algorithms trained on images that show the TLS presence in tissues; and quantification and spatial analysis of TLS still remains a challenge. This study aims to develop a robust algorithm to recognize TLS using the spatial coordinates of immune cells in any given tissue. The algorithm uses X and Y coordinates of T and B cells in tissues identified by a pathologist-supervised digital image analysis of multiplex chromogenic immunohistochemistry of tissues stained with CD3 and CD20. The algorithm is flexible to be used for detailed analysis of TLS stages; including other cell types within the definition of TLS; such as dendritic cells (DC) and high endothelial venules (HEV); to assess different stages of TLS formation.

bioinformatics↗

bioRxiv2024

Comparison of imaging-based single-cell resolution spatial transcriptomics profiling platforms using formalin-fixed, paraffin-embedded tumor samples

Imaging-based spatial transcriptomics (ST) is evolving rapidly as a pivotal technology in studying the biology of tumors and their associated microenvironments. However, the strengths of the commercially available ST platforms in studying spatial biology have not been systematically evaluated using rigorously controlled experiments. In this study, we used serial 5-m sections of formalin-fixed, paraffin-embedded surgically resected lung adenocarcinoma and pleural mesothelioma tumor samples in tissue microarrays to compare the performance of the single cell ST platforms CosMx, MERFISH, and Xenium (uni/multi-modal) platforms in reference to bulk RNA sequencing, multiplex immunofluorescence, GeoMx Digital Spatial Profiler, and hematoxylin and eosin staining data for the same samples. In addition to objective assessment of automatic cell segmentation and phenotyping, we performed pixel-resolution manual evaluation of phenotyping to carry out pathologically meaningful comparison between ST platforms. Our study detailed the intricate differences between the ST platforms, revealed the importance of parameters such as tissue age and probe design in determining the data quality, and suggested reliable workflows for accurate spatial profiling and molecular discovery.

bioinformatics↗