Supp. Fig. S6 validates the functional characterization of survival-associated myeloid cells in an independent melanoma dataset.

Supplementary Table S1. The outcomes of all 46 clinical trials with racial comparisons. Supplementary Table S2. The list of clinically actionable genes. Supplementary Table S3. Summary of TCGA cancer types, patient samples and data types surveyed in this study. For each data type and each cancer type, the numbers of EUR, EAS and AFR patients available in the analysis are shown. NA: the data type in that cancer type was not included in the analysis. Cancer types are marked red for further analysis. Supplementary Table S4. Detailed information of independent mutation datasets Supplementary Table S5. Summary of TCGA cancer types with imputed drug data in this study. For each data type and each cancer type, the numbers of EUR, EAS and AFR patients available in the analysis are shown. NA: the data type in that cancer type was not included in the analysis. Cancer types are marked red for further analysis. Supplementary Table S6. FDA approved anti-cancer drugs during 1949 to 2023. Supplementary Table S7. Detailed information of independent cohorts.

ARID1A maintains the integrity of heterochromatin complex.

Abstract Introduction: Complete responses to PARP inhibitor (PARPi) monotherapy in recurrent high-grade serous ovarian cancer (HGSOC) are rare. However, preclinical data have demonstrated promising synergy between PARP and ATR inhibitors. Characterizing the immune contexture of the tumor microenvironment and the surrounding stroma during treatment may provide valuable biological insights into the efficacy of this combination therapy and inform future combinations. Methods: Patients with recurrent HGSOC received ceralasertib 160mg orally daily, days 1-7 and olaparib 300mg twice daily, days 1-28 of a 28-day cycle. 18 tissue samples were collected across archival (resection) and pre-treatment and on-treatment timepoints (core biopsies). Each sample was analyzed using a 25-plex multiplex immunohistochemistry (mIHC) assay, which interrogates cell composition and functional states of neoplastic and immune cell types, including all major lymphoid and myeloid populations. Segmented cells were assigned to either a tumor or stroma compartment using a PanCK mask that was uniformly expanded by 25μm, and average cell densities were calculated for each compartment. Results: Samples obtained during combination PARPi + ATRi treatment demonstrated widespread increases in immune cell densities including T cells (CD8+, Tregs, and Th1-like cells), B cells, dendritic cells, macrophages, and monocytes. Among the T-cell populations, higher densities of Granzyme B and PD-1 were observed, indicating enhanced cytotoxic activity and immune engagement. Concurrently, there was a decrease in proliferating neoplastic cells (PanCK+Ki67+), consistent with reduced tumor cell proliferation during treatment. Using the PanCK tumor mask, we observed that CD8+ T cells, Th1-like cells, B cells, and dendritic cells increased more prominently within the tumor compartment compared to the surrounding stroma. Samples obtained prior to treatment from patients with stable or progressive disease (SD/PD) exhibited higher macrophage densities, primarily attributable to elevated levels of M2-like (immunosuppressive) macrophages. Conclusions: The increased immune cell densities measured by mIHC indicate overall activation of the immune system following PARPi + ATRi treatment. Elevated levels of PD-1+ and Granzyme B+ T cells suggest enhanced immune activation and cytotoxic potential, while comparative analysis of the tumor versus stroma compartments demonstrates improved immune cell infiltration into the tumor. Notably, higher baseline densities of M2-like macrophages may influence or limit response to therapy. Collectively, these findings provide evidence that PARPi + ATRi combination therapy promotes anti-tumor immune activity. However, additional data is needed to correlate these immune changes with clinical outcomes. Citation Format: Elias Pavlatos, Benjamin Tate, Austin Nguyen, Ian S. Heller, Dimitrios Nasioudis, Janos L. Tanyi, Drew A. Torigian, Diego Rodriguez, Susan M. Domchek, Ronny I. Drapkin, Eric J. Brown, Gordon B. Mills, Fiona Simpkins. Increased immune activity in patients with high-grade serious ovarian cancer after combination PARPi + ATRi therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 7749.

INTRODUCTION:Homologous recombination (HR) deficiency (HRD) and replication stress (RS) are increasingly recognized as interconnected hallmarks of genomic instability in cancer, offering promising avenues for therapeutic targeting. As novel combination therapies targeting these hallmarks continue to emerge, understanding how these processes interact in therapeutic contexts and comparing the relative efficacy and toxicity of combination therapy is critical in advancing precision oncology. AREAS COVERED:We investigate current and emerging assays that assess HRD and RS in cancer treatment and provide an exploration of related pathways. We also provide a high-level comparative pan-cancer analysis from results available on clinicaltrials.gov for common mono poly(ADP-ribose) polymerase inhibitor (PARPi), RS, and immune checkpoint blockade (ICB) therapies as well as their pairwise combinations, demonstrating efficacy and toxicity of current combination therapies. EXPERT OPINION:Integrating functional assessment of HRD and RS with immune contexture and considering these processes as outputs of their interconnected pathways in response to targeted agents could establish a unified therapeutic axis. Targeting this collective axis comprehensively may provide a promising foundation for next-generation, functionally guided treatment strategies capable of achieving durable responses with acceptable toxicity across diverse cancer types.

Supplementary Fig. S1. The underrepresentation of Hispanic or Latino populations in clinical trials. (A) The summary of all clinical trials in Clinical Trials obtained on March 24, 2024. (B-C) The populations of Hispanic or Latino, non-Hispanic or Latino individuals in clinical trials in different phases (B) and (C) different years. Supplementary Fig. S2. The multi-omics molecular variations of clinically actionable genes. (A) The number of multi-omics molecular alterations of clinically actionable genes across ancestries. (B) The difference of EGFR mutation frequency in White vs. Asian patients with NSCLC. (C) The sample sizes of different racial populations across 23 metastatic cancer types with sample sizes ≥10 for at least two of White, Black, and Asian in PMID: 35120664 cohort. The y-axis denotes the sample size; the x-axis denotes the cancer types; the colors represent the different racial populations. (D) The mutation frequency differences of EGFR in White vs. Asian patients with mNSCLC. (E) The difference of PIK3CA mutation frequency in EUR vs. AFR patients with CRC. One-sided Fisher's exact tests were used for the estimation of the significance of the differences in proportions between two groups. Abbreviations: BLCA: Bladder Squamous cell carcinoma, BRCA: Breast invasive carcinoma, CESC: Cervical squamous cell carcinoma and endocervical adenocarcinoma, COAD: Colon adenocarcinoma, CRC: Colorectal cancer, ESCA: Esophageal carcinoma, HNSC: Head and neck squamous cell carcinoma, KIRC: Kidney renal clear cell carcinoma; LIHC: Liver hepatocellular carcinoma; LUAD: Lung adenocarcinoma; LUSC: Lung squamous cell carcinoma, MESO: Mesothelioma, NSCLC: Non-small cell lung cancer, OV: Ovarian serous cystadenocarcinoma, PAAD: Pancreatic adenocarcinoma, PRAD: Prostate adenocarcinoma, READ: Rectum adenocarcinoma, SARC: Sarcoma, SKCM: Skin cutaneous melanoma, STAD: Stomach adenocarcinoma, THCA: Thyroid carcinoma, UCEC: Uterine corpus endometrial carcinoma, UCS: Uterine carcinosarcoma. Supplementary Fig. S3. Landscape of drug response across multiple cancer types. (A) The landscape of ancestry-associated drug response of 359 drugs or small molecules across multiple cancer types. (B) The difference in imputed drug response to the afatinib in EUR vs. EAS patients with NSCLC. The y-axis denotes imputed ln(IC50). The boxes show the median ±1 quartile, with whiskers extending from the hinge to the smallest or largest value within 1.5 interquartile range from the box boundaries. FDR indicates the adjusted two-sided P value calculated from the propensity score algorithm. (C) The mutation frequency differences of EGFR in White vs. Asian patients with NSCLC. (D) The mutation frequency differences of ERBB4 in White vs. Asian patients with NSCLC. (E) The difference in imputed drug response to the lapatinib in EUR vs. EAS patients with BRCA. (F) The mutation frequency differences of ERBB2 in White vs. Asian patients with BRCA. (G) The difference in imputed drug response to the paclitaxel in EUR vs. EAS patients with STAD. (H) The gene set enrichment analysis of microtubule in ERU and EAS patients with STAD. FDR <0.25 is significant. Supplementary Fig. S4. The difference of ancestry-associated immune characteristics across multiple cancer types. (A) The gene set enrichment analysis of 16 immune pathways across different ancestral populations. (B) The differences of 6 stimulatory immune T cells in different ancestries. (C) The multi-omics molecular differences of immune checkpoint genes across ancestry in multiple cancer types within at least 7 comparisons. Supplementary Fig. S5. The differences of immune features and ICB response in BRCA and NSCLC. (A & B) The difference in lymphocyte infiltration scores (A), and indel neoantigens (B) between EUR and AFR patients with PRAD. (C-H) The difference in TCR richness (C), TCR Shannon (D), lymphocyte infiltration score (E), indel neoantigens (F), SNV-derived neoantigens (G) and TGF beta response (H) between EUR and EAS patients with NSCLC. (I) The difference of TMB between EUR and EAS patients with mNSCLC. Student's t-test was used for the estimation of the significance of the difference in proportions between two groups. Supplementary Fig. S6. The differences of immune features and ICB response in BLCA. (A-F) The difference of CYT (A), GEP (B), TMB (C), lymphocyte infiltration score (D), aneuploidy score (E), and SNV neoantigens (F) in EUR and EAS patients with BLCA. (G-I) The differences of immune checkpoint CTLA4 (G), PDCD1 (H) and CD274 (encoding PD-L1; I) in EUR vs. EAS with BLCA. (J) The difference of response to anti-PD-L1 avelumab in White vs. Asian in BLCA. One-sided Fisher's exact tests were used to estimate the difference significance of the difference in proportions between two groups. Supplementary Fig. S7. The difference of multi-omics molecular variations related to PROTACs. (A & B & C) The number of multi-omics molecular alterations of CART targets (A), PROTAC targets (B) and E3 ligase genes (C) across ancestries. (D) The multi-omics molecular alterations of E3 ligase genes within at least 10 comparisons.

Overview of ERα activity and association with progression-free interval outcomes across TCGA cohorts.

ERb activity levels are positively correlated with gene signatures of three prognostic gene signatures and TLS across 33 TCGA cohorts.

Supp. Fig. S1 shows cell-level attention, risk grouping and patient-level survival prediction in simulated data.

ARID1A interacts with TRIM28 independent of SWI/SNF.

Extended scRNA-seq analysis of mimicked cell states in the Pal and colleagues human breast tumor scRNA-seq dataset

AR activity level inversely correlates with response to ICB treatment in public datasets. A–P, Scatter plots showing Pearson correlations of AR activity and the Hallmark IFNγ pathway, T cell–inflamed GEP, ICR-20, and TLS gene signature activity scores for the phs000452.v2.p1 and GSE145996 combined melanoma dataset (A–D), the GSE135222 and GSE126044 combined NSCLC dataset (E–H), Yang and colleagues mixed tumors dataset (I–L), and the Guan and colleagues prostate cancer dataset (M–P). Q–T, Boxplots showing AR activity levels between responders and nonresponders to ICB treatment in the melanoma dataset (Q, P = 0.041), the NSCLC dataset (R, P = 0.018), and Guan and colleagues prostate cancer dataset (T, P = 0.13), as well as the high-sensitivity/clinical benefit (HS/CB) and low-sensitivity (LS) groups in mixed tumors dataset (S, P = 0.034). NR, nonresponder; R, responder.

Correlations between AR activity and six immune cell populations in males and females.

Association between AR/ESR1 ratio and progression-free interval (PFI) across TCGA cohorts.

Abstract Prostate cancer (PC) progresses from benign epithelium through pre-malignant lesions, localized tumors, metastatic dissemination, and castration-resistant stages, with some cases exhibiting phenotype plasticity under therapeutic pressure. However, high-resolution insights into how cellular phenotypes and ecological interactions shift across these successive stages remain incomplete. Here, we present the Prostate Cancer Cell Atlas (PCCAT) by integrating ∼710,000 single cells from 197 human samples covering a spectrum of tumor stages. By further incorporating bulk transcriptomic cohorts, spatial transcriptomics, and experimental validations, this comprehensive analysis dissects the multicellular landscape of PC and identifies key epithelial, stromal, and immune programs associated with disease progression. We highlight a dynamic and heterogeneous continuum of malignant and non-malignant epithelial states, and uncover several cell states strongly linked to aggressive progression and poor prognosis, including lineage plasticity-like malignant cells, neuroendocrine tumor cells, matrix cancer-associated fibroblasts (mCAFs), and SPP1-expressing macrophages (SPP1+ Mph). Furthermore, we identify shared immune-suppressive states enriched in advanced disease, including proliferative T cells, activated regulatory T cells, exhausted CD8+ T cells, and SPP1+ Mph, highlighting convergent microenvironmental mechanisms of immune evasion. Among these, SPP1+ Mph emerge as a key immunosuppressive population, progressively accumulate along PC progression, exhibiting an M2-like immunosuppressive phenotype, associating with bone metastasis, and spatially colocalizing with mCAFs at the tumor-stroma interface in NEPC. Functionally, SPP1 blockade restrains bone metastatic progression and alleviates cancer-induced bone pain by reprogramming tumor-associated macrophages and reshaping CAF and CD8+ T-cell phenotypes. Lastly, by integrating PCCAT with spatial transcriptomics, we identify distinct spatial cellular neighborhood (CNs) that transition from epithelial-dominated to stroma-enriched ecosystems along PC evolution. Among tumor-associated CNs, CN1 reflects neuroendocrine and lineage-plasticity features, whereas CN4 captures aggressive luminal malignant programs. Among microenvironmental CNs, mCAF-enriched CN7 expands with tumor progression and predicts adverse prognosis, while iCAF-enriched CN8 diminishes and associates with favorable outcome. Notably, a CN7-CN8-derived ecosystem index robustly predicts biochemical recurrence across multiple patient cohorts. Overall, our study provides a high-resolution reference of PC cellular ecosystems and reveals the dynamic cell states, spatial niches, and immunoregulatory interactions that shape disease progression, offering mechanistic insights and therapeutic opportunities. Citation Format: Faming Zhao, Han Zeng, Jianming Zeng, Canping Chen, Xiaofan Zhao, Tingting Zhang, Kunlun Wang, Gulsu Sener, Jingui Liu, George V. Thomas, Rosalie C. Sears, Joshi J. Alumkal, Amy Moran, Gordon B. Mills, Ece S. Eksi, Ji Zheng, Peter S. Nelson, Zheng Xia. Deciphering single-cell heterogeneity and cellular ecosystem dynamics during prostate cancer progression [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 1294.

Supp. Fig. S9 provides additional visualizations and evaluations of the results from scSurvival analysis of survival-associated T cells in the melanoma cohort.

Master regulator and pathway enrichment analyses reveal decreased AR activity, increased immune cell signature activity, and enrichment of the IFNγ response after enza treatment in 21 matched-biopsy patients with mCRPC. A, Study schematic. B, msVIPER plot depicting the top three TFs predicted to be most activated (red in activity) or deactivated (blue) in progression tumor samples compared with baseline tumor samples. Tick marks in red or blue lines represent targets of TFs that are positively or negatively regulated, respectively. C, GSEA of 28 immune cell signatures indicating immune infiltration in progressing tumor samples. D, GSEA of GO BP demonstrating the top 15 enriched pathways (with normalized enrichment score ranging from 2.71 to 2.82) in progression tumor samples associated with immune system processes. E, GSEA plot showing significantly activated three prognostic gene signatures after enza treatment. Adj., adjusted; NES, normalized enrichment score. (A, Created in BioRender. Xia, Z. (2025) https://BioRender.com/vnzdcsz.)