Citations

Abstract Breast cancer is the leading cause of cancer death among women worldwide. Most cases occur in those without identifiable modifiable risk factors, highlighting critical gaps in our understanding of breast cancer biology including the influence of the gut microbiome. Here we studied the gut microbiome with shallow shotgun metagenomic sequencing from preoperative stool samples in a well-characterized clinical cohort of 131 women, median age 59 years, undergoing breast-conserving surgery for benign (n=23), high-risk (n=47), or malignant (n=61) breast disease. Taxonomic profiling was performed with Sourmasth 4.2.4 with GTDBv202 as reference database; functional profiling of metagenomic data employed HUMAnN 3.6 with identified gene families mapped to MetaCyc pathways. Differences in alpha diversity in terms of species richness were observed across diagnosis groups (benign, high-risk, malignant) and was lowest for high-risk lesions (p=0.031). Decreased alpha diversity was also associated with higher body mass index (BMI) (Shannon p=0.12, Inverse Simpson p=0.025). Significant beta diversity differences across diagnosis groups also were seen (Bray-Curtis p=0.044, Jaccard p=0.013) as were differences in association with BMI (Bray-Curtis, Jaccard, both p=0.002). No significant diversity differences associated with age, mammographic breast density or menopausal status were observed. Functional pathway profiling stratified by BMI showed a correlation between increasing BMI and greater relative abundance of genes in nicotinamide adenine dinucleotide (NAD) biosynthesis, folate metabolism, and aromatic amino acid metabolism pathways (p=0.0279, 0.0667 and 0.0860, respectively) - pathways implicated in breast cancer detection, survival and prognosis. Cross-referencing our significantly (FDR <5%) differentially abundant taxonomic findings with these functional pathway data identified enrichment of several Blautia species, linked to NAD biosynthesis via tryptophan metabolism, in patients with higher BMI. We also noted enrichment of Dorea formicigenerans, a species associated with increased breast cancer risk in obese women due to its pro-inflammatory effects, in individuals with a higher BMI. Here we report significant differences in gut microbiome diversity, taxonomy and functional pathway signals in association with BMI in women with breast disease. Differentially abundant species seen with rising BMI may contribute to functional pathways that drive increased breast cancer risk and may provide an actionable target for risk reduction independent of conventional risk reduction strategies. Citation Format: Jack W. Sample, Matteo Redaelli, Stephen Johnson, Tanya L. Hoskin, Marina R. Walther-Antonio, Jun Chen, Tina J. Hieken. Obesity alters cancer-related microbial pathways in women with benign and malignant breast disease [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2213.

Pathology remains a labor-intensive discipline, relying on workflows rooted in practices established over a century ago. Despite incremental technological advancements, the diagnostic process—from tissue preparation to interpretation—still heavily depends on thinly sliced tissue sections stained and examined under brightfield microscopy by skilled pathologists. Difficult cases often necessitate iterative staining or complex immunohistochemistry (IHC) analyses. However, the recent emergence of machine learning (ML) presents an opportunity to revolutionize traditional diagnostic workflows. One promising advancement is Digital Chemical Pathology (DCP), a paradigm that combines modern ML algorithms with infrared (IR) imaging to streamline pathology and enhance diagnostic accuracy. Infrared wavelengths inherently align with molecular vibrational frequencies, providing intrinsic contrast based on the chemical composition of samples. Mid-IR spectroscopic microscopes leverage this principle to spatially resolve molecular distributions at the pixel level, creating "chemical fingerprints" that ML algorithms can correlate with cellular identities and disease states. Recent advancements, particularly the development of broadly tunable quantum cascade lasers (QCLs), have enabled faster IR imaging, paving the way for clinical applications of IR spectroscopic microscopes. In this research, we demonstrate the potential of DCP using frozen tissue samples. Our approach focuses on two core tasks: (1) virtual staining, translating IR images into realistic, stained images interpretable by pathologists, and (2) tissue segmentation, enabling the automated detection of cancer and the distinction between normal and cancerous tissues. Furthermore, we incorporate statistical safety algorithms to generate uncertainty maps, ensuring the reliability of predictions. Our findings highlight that the integration of IR imaging and ML can facilitate automated diagnostics, providing pathologists with powerful tools for enhanced healthcare delivery. By leveraging DCP, we aim to modernize pathology workflows, reduce diagnostic turnaround times, and improve patient outcomes through precise and efficient analysis. Citation Format: Kianoush Falahkheirkhah, Kevin Yeh, Ji-Hun Oh, Yen-Ting Liu, Ruo-Jing Ho, Ishaan Sharma, Matthew Confer, Andres Orr, Mykolas Tamonis, John Cheville, Rohit Bhargava. Transforming cancer diagnosis with digital chemical pathology [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2470.

Abstract Text While the importance of estrogen in driving ER+ breast cancer progression is clear, data from pre-clinical studies exploring the role of tumoral progesterone (PR) and androgen (AR) receptors are conflicting, likely due in part to the promiscuity of steroid receptor ligands, as well as relative levels of steroid hormone receptor expression and of steroid hormones within the tumor milieu. While bone is usually the first site of metastases in ER+ metastatic breast cancer, the roles of tumoral PR and AR (present in 58 percent and 86 percent of clinical ER+ bone metastases [BMET], respectively) in ER+ breast cancer progression at this site have not been well studied. Studies were undertaken to examine the effects of ovariectomy (OVX), a model of surgical menopause that significantly reduces endogenous progesterone and androgen levels, in a pre-clinical model of human ER+ bone metastases that is dependent on exogenous 17ß-estradiol (E2) supplementation. Effects of OVX (at 9-weeks of age) were ascertained using skeletally mature female nude mice supplemented with a modest dose of E2 (0.36 mg 60 day pellets) prior to intracardiac inoculation (at 10 weeks of age) with bone metastasis-derived MCF7 cells (43.4M cells) that form robust E2- and TGFß-dependent osteolytic metastasis in vivo, and express PR and AR in vitro. Osteolytic bone metastatic lesions in E2-supplemented mice were reduced in both incidence and size in OVX (vs ovary intact) mice. Anti-tumoral effects of OVX were not limited to bone; E2-driven orthotopic tumor progression was similarly reduced. Histologic assessment of cytokeratin-positive breast cancer tumor cells within bone revealed a small but statistically significant reduction in Ki67-positivity in OVX (vs ovary -intact) mice that correlated with osteolytic lesion size. In addition, PR and AR expression in BMET lesions, which was readily detected by IHC in ovary-intact mice, was markedly reduced in OVX mice. While additional experiments are underway to query possible roles of specific reproductive hormones altered in OVX mice, these results are consistent with the postulate that tumoral AR and PR signaling may promote ER+ bone metastases progression in this model. In addition, while ovariectomy is a common pre-clinical means of reducing estrogen to model natural menopause in women, resultant changes in steroid receptor signaling are clearly much more complex. Funding: NIH 5R25CA275753, 5P30CA023074 Date of Presentation October 17, 2024

Abstract Text While the importance of estrogen in driving ER+ breast cancer progression is clear, data from pre-clinical studies exploring the role of tumoral progesterone (PR) and androgen (AR) receptors are conflicting, likely due in part to the promiscuity of steroid receptor ligands, as well as relative levels of steroid hormone receptor expression and of steroid hormones within the tumor milieu. While bone is usually the first site of metastases in ER+ metastatic breast cancer, the roles of tumoral PR and AR (present in 58 percent and 86 percent of clinical ER+ bone metastases [BMET], respectively) in ER+ breast cancer progression at this site have not been well studied. Studies were undertaken to examine the effects of ovariectomy (OVX), a model of surgical menopause that significantly reduces endogenous progesterone and androgen levels, in a pre-clinical model of human ER+ bone metastases that is dependent on exogenous 17ß-estradiol (E2) supplementation. Effects of OVX (at 9-weeks of age) were ascertained using skeletally mature female nude mice supplemented with a modest dose of E2 (0.36 mg 60 day pellets) prior to intracardiac inoculation (at 10 weeks of age) with bone metastasis-derived MCF7 cells (43.4M cells) that form robust E2- and TGFß-dependent osteolytic metastasis in vivo, and express PR and AR in vitro. Osteolytic bone metastatic lesions in E2-supplemented mice were reduced in both incidence and size in OVX (vs ovary intact) mice. Anti-tumoral effects of OVX were not limited to bone; E2-driven orthotopic tumor progression was similarly reduced. Histologic assessment of cytokeratin-positive breast cancer tumor cells within bone revealed a small but statistically significant reduction in Ki67-positivity in OVX (vs ovary -intact) mice that correlated with osteolytic lesion size. In addition, PR and AR expression in BMET lesions, which was readily detected by IHC in ovary-intact mice, was markedly reduced in OVX mice. While additional experiments are underway to query possible roles of specific reproductive hormones altered in OVX mice, these results are consistent with the postulate that tumoral AR and PR signaling may promote ER+ bone metastases progression in this model. In addition, while ovariectomy is a common pre-clinical means of reducing estrogen to model natural menopause in women, resultant changes in steroid receptor signaling are clearly much more complex. Funding: NIH 5R25CA275753, 5P30CA023074 Date of Presentation October 17, 2024

Abstract Background: Second generation androgen receptor signaling inhibitors (ARSIs), such as abiraterone, have demonstrated improved survival in metastatic castration-resistant prostate cancer (mCRPC) patients, however, acquired resistance to these agents often arises. Novel and alternative therapies are needed to combat the ARSI resistant tumor. In the last two decades patient-derived xenograft (PDX) and PDX organoids have become pivotal models in anticancer drug development and biomarker discovery due to their ability to faithfully recapitulate the complex and heterogeneous tumor biology. Methods: We analyzed RNAseq data from mCRPC patients and corresponding PDX models (PROMOTE study, NCT #01953640) and employed L1000 Chem Perturbation down/up signatures database to find candidate drugs to overcome abiraterone resistance by reverse signature search. We then performed proliferation assay using 49 compounds in three PROMOTE-PDX organoid models (2 abiraterone-resistant and 1 abiraterone-sensitive) using our semi high-throughput drug screening platform. Results: Overall, we identified a total of 32 compounds with IC50<10µM in at least one of the models, among which, 5 agents are active in all three models and 5 in at least two of the models. (Table 1). Within active compounds we find selective kinase inhibitors (BRAF, JAK2, AKT, FLT3) and histone deacetylase inhibitors (belinostat and pracinostat). Particularly interesting are two drugs that are only active in the abiraterone resistant setting but not the sensitive PDX. Conclusions: In summary, we leveraged RNAseq data from mCRPC patients and PDX models and performed a high-throughput drug screen in PDX-derived organoid models to identify potential drugs that could overcome abiraterone-resistance in lethal advanced prostate cancer. These data highlight potential drug combinations or subsequent therapeutic strategies for these patients. Citation Format: Irene Marín-Goñi, Huanyao Gao, Joachim L. Petit, Shreya Indulkar, Adam M. Kase, Cassandra N. Moore, Krishna R. Kalari, Michael T. Barrett, Richard Weinshilboum, Winston Tan, John A. Copland, Liewei Wang. Overcoming resistance in mCRPC: drug discovery using PDX models and high-throughput drug screening [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5658.

Abstract Introduction: High-grade gliomas (HGG) are aggressive brain tumors with heterogeneous cell populations, including invasive regions driving disease progression and recurrence. Our previous work integrated molecular profiling with spatially matched advanced multiparametric MRI in a cohort of HGG patients with multi-regional sampling, uncovering distinct biological signatures linked to individual advanced MRI phenotypes. Yet, the relationships between MRI phenotypes—and how this potentially informs underlying biology—remains unexplored. Here, we leverage an unsupervised approach to dimensional reduction for a broad array of MRI features and compare regional transcriptomic signatures in a cohort of image-localized HGG samples. Methods: From our existing data, we selected 20 HGG patients with 73 multi-regional tumor biopsies, each including 18 advanced MRI parameter calculations. With an iterative approach, data was projected using uniform manifold approximation (UMAP) at multiple resolutions and clustered, producing high-confidence stable clusters. We used random forest modeling to assess cluster associations with previously identified signatures. Cluster-specific transcriptomic profiles were detected by DESeq2 and subsequent gene-set enrichment analysis. We further evaluated Pearson correlations and statistical fit with MRI image parameters by performing pathway analysis on genes with significant correlations by image feature, across clusters. Results: 8 clusters emerged from our analyses. Most advanced MRI features exhibited statistically significant differences across clusters, forming unique patterns of feature behavior. Notably, while these image-based clusters did not correlate well with metabolic profiles, contrast enhancement, or genomics, they did demonstrate distinct and HGG-relevant transcriptomic profiles, with shared and divergent signatures. Across clusters, we also identified several correlations between specific MRI parameters and gene pathway trends. These highlighted combined patterns of multiple MRI features, offering a spectrum of regional phenotypes imperceptible with single feature analysis. We generalize these results to an image-based decision matrix to detect biologic signatures for validation. Conclusion: Our integrative analysis of MRI mapping stratifies imaged-based patterns to non-invasively assess molecular signatures within HGG. This approach will help elucidate MRI biomarkers that may predict glioma behavior and support future individualized treatment approaches. Citation Format: Matthew W. Kenaston, Matthew Flick, Kasthuri Kannan, Michele Ceccarelli, Nhan Tran, Leland S. Hu. Integrative analysis of regional MRI phenotypes in high-grade glioma: Dimensional reduction of multi-omics imaging and molecular profiles [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5776.

Abstract Squamous cell carcinoma (SCC) is the second most prevalent type of skin cancer after basal cell carcinoma, with the highest incidence occurring in sun-exposed regions of the body, particularly the head and neck. Approximately 300,000 new cases are diagnosed each year, imposing significant financial burdens on healthcare systems due to treatment costs and on the economy due to productivity losses. Surgery remains the primary treatment option; however, patients, particularly those with advanced-stage disease, may decline surgery due to concerns about functional and cosmetic outcomes. Other effective treatments include cryosurgery, radiotherapy, topical therapies, photodynamic therapy, and Mohs micrographic surgery. Recently, electrochemotherapy (ECT) has emerged as a novel treatment option. ECT involves administering bleomycin followed by electric pulses targeted at the tumor and surrounding margins, inducing transient cell membrane permeabilization. This enhances drug uptake and amplifies cytotoxic effects by over 1,000-fold. Since 2006, ECT has been a standard therapy in Europe and was introduced in Argentina in 2020, offering a new approach for patients who are unsuitable for or decline other treatments. In this study, we present outcomes from six patients with head and neck SCC treated with ECT on an outpatient basis. Most patients required only a single 60-minute treatment session. Observed side effects included grade 1-2 edema, pain, and flu-like symptoms per CTCAE 4.0. Tumor response was assessed using RECIST criteria, with results showing 71.4% complete response (5 of 7 lesions), 14.3% partial response (1 lesion), and 14.3% progressive disease, yielding an objective response rate of 85.7%.In conclusion, electrochemotherapy proved to be a safe and effective treatment for select head and neck SCC patients who are not candidates for surgery, providing a promising alternative in the therapeutic landscape. Citation Format: Felipe H. Maglietti, Sebastian D. Michinski, Lorna A. Evans, Raquel Lertora. Electrochemotherapy for the treatment of head and neck squamous cell carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 4707.

Abstract Despite advances in surgery and chemotherapy, patients with resectable pancreatic ductal adenocarcinoma (PDAC) face high recurrence rates (40-60%). Total neoadjuvant therapy (TNT) seeks to enhance margin-negative resections (R0) and address micrometastatic disease, with potential survival benefits. Yet, TNT still results in high recurrence rates, with 1-, 2-, and 3-year disease-free survival (DFS) at 65%, 48%, and 32%, respectively. Residual cancer extent is a key prognostic factor in PDAC after TNT. Tumor response scores show favorable outcomes only for patients achieving near-complete or complete pathologic response. In a cohort of 511 PDAC patients treated with TNT and surgery (2015-2022), improved DFS was only found in those with a complete pathologic response (pCR, 7.8%). This highlights the limited use of current response scoring in stratifying post-TNT patient risk, underscoring the need to better understand disease persistence and relapse. Here we developed a quantitative method to assess residual cancer. Using a digital pathology H&E library from 2180 PDAC cases (2000-2022), an AI platform segmented tissue into cancer epithelium, stroma, and normal regions. For 202 TNT-treated cases without pCR, we calculated a ratio of cancer area to stromal area from representative slides. Modeled after the histologic tumor regression grade (HTRG) system, cases were classified into <5%, 5-50%, or >50% cancer-to-stroma ratios. However, this measure alone showed no difference in DFS, suggesting tumor burden cannot fully explain differences in clinical outcomes. We hypothesized that the spatial organization of the tumor microenvironment (TME) is a determinant of patient outcomes. Drawing from landscape ecology, which studies spatial properties of ecosystems, we applied similar principles to analyze tissue architecture. Using an AI-based two-class segmentation of whole-slide images, we assessed spatial features like patch area, perimeter, density, and Shannon diversity. Python’s ‘pylandstats’ package calculated 21 spatial metrics, reduced via stepwise Cox regression using the Akaike information criterion (AIC). The final model included mean Shannon diversity, mean and standard deviation of stromal patch areas, and stromal patch density. Kaplan-Meier analysis of 202 samples stratified model predictions into quartiles, correlating with high (mean DFS 10.85 months), medium (19.32 months), and low risk (25.92 months) groups. These findings aligned with model expectations, underscoring the potential of spatial TME analysis to predict outcomes. Citation Format: Andrea V. Maraone, Zafar Siddiqui, Luis H. Cisneros, Merih D. Toruner, Mark Truty, Cornelius Thiels, Robert R. McWilliams, Steven R. Alberts, Rondell P. Graham, Carlo Maley, Qian Shi, Martin E. Fernandez-Zapico, Ryan M. Carr. Tumor spatial configuration predicts pancreatic cancer recurrence risk after total neoadjuvant therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5070.

Abstract Background and Objective: Glioblastoma (GBM) is most common primary brain tumor with dire need of novel therapeutics. Studies with intracranial tumors are required for robust characterization and development of novel drugs for brain cancer. Tumor harvest and processing is an important aspect to study pharmacokinetics, pharmacodynamics, drug delivery, and tumor evolution. Typically, delineating and dissecting tumor from surrounding brain for varied downstream processes is dependent on the distinct morphology of the tumor and/or expression of fluorescent marker. However, genetic modification of the tumor cells for the expression of fluorescent protein is not always possible, especially for patient-derived xenograft (PDX) tumor models. Thus, we have developed a dye-based staining method to confidently visualize tumor in the brain and assessed usability of isolated tumors for drug and DNA analysis. Methodology: Whole brains were harvested from mice with intracranial Mayo GBM PDX tumors and stored at -80oC freezer. Few days prior to staining, brains were transferred to -20oC freezer. Brain was cut into 1 mm thick coronal sections and transferred onto glass slides. Slices were covered with toluidine blue (TB) solution (0.05 to 10 mg/mL) for 45 s. Excess stain solution was removed, and slices were observed under white light. Regions of tumor became more transparent, while normal brain remained opaque. This enabled dissection of tumor from normal brain for downstream analyses. Results: TB concentration (0.05 to 10 mg/mL) was optimized to stain 1 mm thick sections of fresh frozen brain, and the best contrast between tumor to normal brain was observed at 0.1-0.3 mg/mL. TB aided differential visualization of tumors in several PDX models - GBM12, GBM43, GBM39 and GBM108 - suggesting broad applicability. To evaluate the accuracy of TB staining, brain with PDX tumor transduced with pSINeGFP/fLuc2 was observed by both TB staining and eGFP fluorescence. This analysis demonstrated excellent concordance across 6 animals with three distinct PDX models. Genomic DNA was isolated from tumor dissected under TB guidance and analyzed by PCR for primers specific for murine vs human PTGER2. 50-70% of the DNA in tumor samples was detected to be of human origin while normal brain tissue had 90-95% of the mouse DNA highlighting the specificity of TB stain for tumor detection. Tumors from drug treated GBM108 PDX were isolated under TB guidance and comparable drug levels were detected by LC-MS/MS as analyzed in GFP-assisted tumor samples. Conclusion: Staining fresh frozen thick brain sections with toluidine blue enables visualization of tumor from normal brain regions that is compatible with varied downstream processes. Development of such staining assays can help faster and easier tumor processing and is not dependent on genetic modification of tumor cells prior to experimentation. Citation Format: Sonia Jain, Anders Herfindahl-Quint, Caroline D. Doherty, Wenjuan Zhang, Danielle M. Burgenske, Jann N. Sarkaria. Novel staining method for visualization, isolation and processing of intracranial brain tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2531.