Top 5 gene ontology pathways enriched in each cell type upon chemoradiation related to Figure 3D

Breast and prostate cancers are both hormone-driven adenocarcinomas that undergo analogous invasion programs. Using lightsheet microscopy on intact tumors, we identified transitional junctions between precancerous and invasive regions. We then developed a multimodal serial-section workflow integrating volumetric reconstruction with spatial transcriptomics. Analysis of 319 spatial assays from 51 cases revealed gene expression features and novel structural insights defining the shift from precancer to invasive disease. In breast cancer, loss of MGP and PLAT was associated with invasive transition and promoted tumorigenesis in functional assays. In prostate cancer, GDF15, ALDH1A3, ANPEP, and FASN were upregulated along invasive progression, and their knockdown in PC-3 cells suppressed proliferation and migration. Enrichment of tumor-associated macrophages (SPP1+ and MS4A6A+) along non-triple-negative breast cancer breast cancer transitions highlights immune involvement as a potential driver of invasiveness. SIGNIFICANCE:Our method of defining precise spatial locations of invasive transition allows for the direct interrogation of transition drivers, presenting new therapeutic targets for the two most prevalent cancers and providing a framework for studying spatially defined mechanisms of tumor progression. See related commentary by Jing and Li, p. 1720.

Pancreatic ductal adenocarcinoma (PDAC) is unresponsive to standard immunotherapies despite harboring cancer neoantigens capable of eliciting T cell responses. We completed two phase 1 clinical trials (NCT03956056 and NCT03122106) evaluating safety and immunogenicity of synthetic long peptide (SLP) and DNA personalized cancer vaccines (PCVs). PCVs were administered after resection and adjuvant chemotherapy. Tumor/normal whole-exome sequencing, RNA sequencing, and pVACtools were used to identify and prioritize candidate PCV neoantigens. PCVs were well tolerated without any grade ≥3 adverse events. Neoantigen-specific responses were demonstrated by interferon-γ enzyme-linked immunospot and intracellular cytokine staining. Expanded T cell receptor clonotypes were sequenced and transduced into autologous peripheral blood mononuclear cells to confirm neoantigen specificity. When compared with a contemporaneous institutional propensity-matched cohort, PCV patients demonstrated a trend toward prolonged median overall survival (4.4 versus 3.5 years, log-rank P = 0.23). Overall, PDAC PCVs are safe and feasible and elicit polyclonal T cell responses, linking prioritized cancer neoantigens to functional antitumor immunity.

Cellular senescence, a stress-induced program causing stable cell-cycle arrest, is a hallmark of liver aging, fibrosis, and cancer. However, the cell-type-specific mechanisms, spatial organization, and cancer-associated alterations in the liver remain unclear. We profiled 43 normal human livers spanning ages and fibrosis stages using a single-cell multiome, Xenium spatial transcriptomics, and CODEX, complemented by fibrotic mouse models and 24 colorectal cancer liver metastases. We found CDKN1A+ senescent hepatocytes, fibroblasts, cholangiocytes, and endothelial cells associated with age, liver disease, or cancer. Senescence differed between aged and fibrotic livers, with similar patterns in mice. Spatially, CDKN1A+ hepatocytes localized periportally, while SERPINE1+ aging-associated hepatocytes formed spatial clusters, potentially mediated by Claudins and THBS1. Fibrotic regions contained CXCL12+ senescent fibroblasts interacting with CXCR4+ immune cells. Chemotherapy intensified senescence in hepatocytes by 5-fold relative to aging and led to unique CDKN2A+ populations. Across conditions, senescent cells shared AP-1 activation, pro-inflammatory cytokines, and apoptosis resistance, suggesting therapeutic opportunities.

Advancements in immunogenomics and immuno-oncology have enabled the development of personalized cancer vaccines (PCVs) that target cancer cell-specific somatic variants. A subset of these variants produce neoantigens that, when presented on tumor cells by MHC molecules, have the potential to elicit a robust and specific immune response. To date, there are over one hundred interventional studies listed on clinicaltrials.gov that explore the use of PCVs. We have supported a number of these trials through the creation of bioinformatic pipelines, tools, and procedures for the identification of patient-specific neoantigen candidates. While many of these steps have been automated, the final selection of neoantigen candidates often relies on expert manual review, creating a bottleneck that limits scalability and full automation of PCV workflows. Addressing this challenge, we introduce NEAT (Neoantigen Evaluation & Automated Triage), a machine learning-based approach that enables automated neoantigen candidate prioritization and supports the transition toward more scalable and reproducible PCV design. We implemented a prediction model trained and tested on existing vaccine design results from 33 patients and 1,943 peptides, across 3 clinical trials, including 439 peptides prioritized for PCV inclusion. This model uses features such as tumor variant allele frequency, RNA expression, driver gene status, binding/presentation scores, and transcript support level to automatically predict whether a peptide will be accepted, rejected, or require further human review before inclusion in a vaccine. The model achieved a sensitivity of 0.847 and specificity of 0.924, with an area under the curve of 0.955. The model predictions have been incorporated in pVACtools v7.0.0. By integrating this model into the vaccine development pipeline, we foresee a significant reduction in the time required to transition from patient sample collection to vaccine manufacturing, thereby enhancing the efficiency and scalability of PCV production.

Cytoscape node information related to Fig. 1l