Citations

Abstract Background: Previous studies demonstrated that obesity is associated with poor outcomes and significantly worse pathological complete response (pCR) in triple-negative breast cancer (TNBC) treated with conventional chemotherapy. Emerging studies described a phenomenon termed the obesity paradox, which referred to improved outcomes observed in obese patients treated with immune checkpoint inhibitors (ICIs). Our study aimed to determine distinct immune features in TNBC across body mass index (BMI). Methods: Characteristics and outcomes of TNBC patients treated with neoadjuvant pembrolizumab-based chemotherapy at Mayo Clinic were collected. Normal BMI was defined as 18-25 kg/m2, overweight 25-30, and obese BMI ≥ 30. NanoString CosMx, a spatial multi-omics single-cell imaging (SMI), was performed in 75 samples from the Mayo Clinic TNBC cohort (Leon-Ferre BCRT 2018). Differential expression as log 2-fold change (FC) was estimated from the linear mixed model with significance defined as two-sided p< 0.05. Results: Between 2021-2023, there were 108 TNBC patients treated with neoadjuvant pembrolizumab. 25.9% were obese, 32.4% were overweight, and 41.7% had normal BMI. In contrast to previous studies with conventional chemotherapy, obese patients numerically had a higher pCR rate of 71.4% with pembrolizumab-based chemotherapy compared to 55.6% in normal and 62.9% in overweight patients. However, this difference did not reach statistical significance (obese vs. normal weight, OR 2.26, 95%CI 0.89-6.06, p 0.094). We further evaluated spatially-resolved immune features associated with obesity using CosMx SMI in 75 samples from the MC TNBC cohort. Obese patients had significantly higher intratumoral B cells (log2FC 1.88, p 0.017), stromal B cells (log2FC 2.79, p 0.001), stromal plasmablasts (log2FC 1.94, p 0.047), and stromal macrophages (log2FC 1.80, p 0.017) compared to patients with normal BMI. Obese patients also had significantly lower intratumoral myeloid dendritic cells (mDC) (log2FC -1.27, p 0.037). Similar trends were observed after standardizing cell counts per 100 tumor or stromal cells, including higher intratumoral B cells (log2FC 3.00, p 0.001), intratumoral plasmablasts (log2FC 2.00, p 0.006), intratumoral natural killer (NK) cells (log2FC 1.66, p 0.044), and stromal B cells (log2FC 1.50, p 0.008). Differential gene analyses for intratumoral B cells revealed that genes (CD74, HLA-C, HLA-DRB1, HLA-DRA, HLA-DRB) associated with the positive regulation of type 2 immune or chemokine response were upregulated. Conclusions: Using in-depth analysis with spatially defined context, our study showed that obese TNBC patients had significantly higher B cell infiltration, which is associated with improved outcomes with ICIs. Future research on how obesity alters the immune microenvironment is needed to identify potential pathways to improve outcomes with ICIs in TNBC. Citation Format: Songhan Pang, Yi Liu, Yaohua Ma, Zhuo Li, Roberto A. Leon-Ferre, David Zahrieh, David W. Hillman, Judy C. Boughey, James N. Ingle, Krishna R. Kalari, Fergus J. Couch, Matthew P. Goetz, Keith L. Knutson, Saranya Chumsri, E. Aubrey Thompson. Tumor-immune microenvironment associated with obesity in triple-negative breast cancer in the Mayo Clinic cohort [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1162.

Abstract Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive and lethal type of pancreatic neoplasm. Unfortunately, most patients are diagnosed with unresectable or metastatic disease, making current treatments insufficient. As a result, the 5-year survival rate for PDAC is below 10%.Pancreatic neoplasms are primarily initiated by the KRAS mutation, accounting for 95% of cases. However, the progression of PDAC to a highly aggressive and therapy-resistant cancer is accelerated by chronic inflammation. The role of mutant KRAS signaling and NF-κB inflammatory signaling in promoting PDAC progression has been extensively studied. Yet, the transcriptional mechanism of their cooperation remains unclear. Additionally, the interaction between PDAC tumor cells and signals in the tumor microenvironment (TME) is not yet fully understood. Our study demonstrates that the simultaneous activation of KRAS/MAPK and NF-κB by epidermal growth factor (EGF) and TNFα, respectively, increases cell migration as shown through live-cell imaging analysis. An integrated analysis of epigenetic and transcriptomic data (ChIP-seq and RNA-seq) was performed after dual stimulation of both pathways. This revealed a high occupancy of the active mark, H3K27ac at transcription start sites of a subset of genes involved in cell migration and subtype switch. Through gain and loss-of-function studies, FOSL1 and RELA were identified as the key transcription factors. The epigenome mapping of FOSL1 and RELA after the stimulation of both pathways revealed their occupancy at RELA-dominant and FOSL1/RELA co-occupied regions. Inhibitor treatments and loss-of-function studies revealed that RELA binding controls RNA polymerase recruitment for transcription initiation and enhances cell migration. Further analysis of RNA-seq data on PDAC cell lines and cells from patient-derived xenografts highlighted that active inflammatory signaling contributes to a subtype switch from the classical to the more migratory basal subtype. Our study, combined with single-cell RNA-seq analysis of published data, shows that macrophages in the tumor microenvironment supply TNFα that activates NF-κB signaling, leading to changes in cell migration. Our study has provided a comprehensive understanding of the mechanism by which mutant KRAS and inflammatory signaling contribute to the progression of PDAC. These findings could offer a new perspective in the development of mechanism-based therapeutic approaches that can improve the efficacy of chemotherapeutic agents and make tumors more amenable to surgical resection. Citation Format: Joana E. Aggrey-Fynn, Meghana Manjunath, Steven A. Johnsen. MAPK and NF-kappaB signaling converge on the epigenome to transcriptionally activate genes involved in pancreatic cancer metastasis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1692.

Abstract Introduction Cholangiocarcinoma (CCA) is a highly lethal, heterogenous biliary malignancy. Despite the development of targeted therapies, treatment success has been hindered by primary or acquired resistance. New therapeutic strategies are necessary to improve outcomes in these patients. NXP800 is a GCN2 small-molecule activator that phosphorylates eIF2a inducing selective ATF4 transcription which stimulates stress-induced genes leading to apoptosis. NXP800 has been previously validated in ovarian cancers with ARID1A mutations, well-known players in cholangiocarcinoma. We sought to determine the treatment efficacy of novel NXP800 in CCA utilizing patient derived xenograft (PDX) preclinical models. Methods In our preliminary studies, four human and two murine CCA cell lines were tested for cell viability using CellTiter-Glo to determine the maximal inhibitory concentration (IC50). Immunoblot analysis of HuCCT-1 cells incubated with 1 mM of NXP800 for 6 hours vs. vehicle was performed. We also evaluated response to GCN2 activation in multiple RNA sequenced PDX models. Xenografts were expanded into the flank of NOD/SCID mice and treatment begun when tumors averaged 120mm3. Tumor bearing mice were treated with NXP800 (35 mg/kg) or vehicle via oral gavage five days on, two days off, for 28 days. Tumor volume and animal weight were collected twice weekly. Predicted sensitive signatures were determined using multi-omics. Results IC50 dose response curves showed nanomolar activity in all six cell lines, ranging from 5-170 nM. Immunoblotting demonstrated an upregulation of phosphorylated eIF2a and in turn upregulation of ATF4 with treatment of NXP800 compared to vehicle. Five PDX tumors from patients with intrahepatic or distal CCA was established and validated by histologic analysis. Molecular characterization of the tumor identified tumor mutation and a total tumor mutational burden. RNA sequencing was completed and identified 21-gene signatures related to YAP activity, which were cross-referenced against our existing PDX model tumor bank. Treatment with NXP800 was associated with a statistically significant decrease in tumor size in three of five PDX models tested, which included an ARID1A, FGFR1, and ATM mutants. Preliminary multi-omics on the PDX models revealed that sensitivity mechanisms were centered around EGFR signaling. Conclusion The novel GCN2 kinase activator, NXP800, has nanomolar efficacy in both human and murine CCA cell lines in vitro. Additionally, NXP800 demonstrated therapeutic activity in multiple cholangiocarcinoma PDX models in vivo. We are opening a Phase 1b clinical trial investigating the effects of NXP800 use in patients with advanced CCA. Future studies determining the cellular effect of GCN2 activation utilizing NXP800 are being conducted. Citation Format: Danielle Marie Carlson, Hendrien Kuipers, Amro Abdelrahman, Erik Jessen, Joshua Roldan Kalil, Jack Sample, Jennifer Tomlinson, Mark Truty, Rory Smoot. A novel GCN2 kinase activator demonstrates therapeutic efficacy in preclinical PDX models of human cholangiocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2098.

Abstract We demonstrate development and technical validation of a novel approach for analysis of plasma circulating-tumor DNA (ctDNA) for residual disease detection and monitoring that offers improvements over existing technologies. ctDNA in blood can be leveraged to monitor treatment response, detect cancer recurrence, and inform treatment decisions for cancer patients. However, accurate detection and quantification of ctDNA is technically challenging due to low concentration of plasma DNA, especially in the adjuvant setting. Most current assays rely on interrogating multiple patient-specific point mutations to achieve exquisite limits of detection but struggle with polymerase errors, sequencing artifacts and limited conversion of input DNA. To overcome these challenges, we developed an approach called Structural Variant Enrichment and Normalization (SVEN) that targets patient-specific genomic rearrangements to achieve sensitive detection and quantification of plasma ctDNA. This method leverages intrinsic features of tumor-derived structural variants (SVs) to improve enrichment and confidence of detection at low tumor fraction through multiplexed PCR and amplicon-based next generation sequencing. To evaluate the assay, we performed whole genome sequencing of DNA from two breast cancer cell lines, as well as matched FFPE tumor and buffy coat samples from 21 early-stage breast cancer patients. We identified consensus SVs, and designed multiplexed SV-specific primers for each subject. We then conducted PCR amplification of cell-free DNA followed by sequencing, amplicon identification, and quantification. Analytical validation was conducted using 284 replicates of cell line dilutions from 1% to 0.003%, and 5 to 40 ng of input. We developed multiplexed assays targeting 25 and 32 SVs across two cell lines, and 3 - 43 SVs across breast cancer patients (median 23 per patient). 91% and 42% of PCR targets were successfully validated for cell lines and FFPE samples respectively. Cell line dilutions demonstrated a sensitivity of 89% at 0.003% tumor fraction with 10 ng input or more. Tumor fractions measured using SVEN agreed with known values (R2 = 0.989). Application of validated panels to patient plasma samples and tumor/germline dilutions is ongoing. SVEN is a novel approach for tumor-guided ctDNA detection and quantification with potential relevance for residual disease detection. On-going studies are investigating the performance of this approach for ctDNA detection in patients with early and locally advanced breast cancer treated with neoadjuvant therapy. Citation Format: Bradon R. McDonald, Kirsten L. Dennison, Amanda L. Schussman, Stephanie M. McGregor, Barbara A. Pockaj, Muhammed Murtaza. Personalized minimal residual disease detection using tumor-derived structural variants in cell-free DNA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2416.

Abstract We used deep learning (DL) to predict multiple genetic biomarkers of colorectal cancer (CRC) from routine histopathology images, aiming to identify morphological changes associated with the mutational profile of tumors. A new Transformer-based prediction model was developed which can simultaneously predict multiple biomarkers from a single histopathology image. We compared the performance of the multi-target approach to conventional single-target models. We analyzed five patient cohorts from the Genetics and Epidemiology of CRC Consortium (GECCO) with colorectal cancer (N=1,385 patients). Our model was trained on an internal data set (N=739) to predict the presence of >200 genetic alterations, focusing on genes with non-silent mutations and mutational signatures, which were assessed using targeted sequencing. We used a 7-fold cross validation and validated the model on an independent, external data set (N=646). Morphological features of microsatellite instability (MSI) are detectable by DL models in histopathology images, making MSI a potential confounding factor. Thus, we also assessed whether our model can predict genetic alterations regardless of MSI status, or whether the model identifies the MSI phenotype. For the majority of biomarkers, the multi-target transformer reached a higher prediction performance as measured by the mean Area Under the Receiver Operating Characteristic (AUROC) Curve and lower standard deviation than single-target DL models. For the external validation set, the mean AUROC of the multi-target versus the single-target model was 0.78 (+/- 0.01) versus 0.72 (+/- 0.06) for BRAF mutations, 0.88 (+/- 0.01) versus 0.86 (+/- 0.03) for hypermutation status, 0.94 (+/- 0.01) versus 0.91 (+/- 0.02) for MSI, 0.86 (+/- 0.01) versus 0.80 (+/- 0.05) for RNF43 mutations and 0.72 (+/- 0.02) versus 0.69 (+/- 0.05) for TP53 mutations, respectively. However, our analysis of the individual prediction scores suggests that the model does not identify the phenotype of the alterations themselves. Instead, the model seems to determine the status of specific genetic alterations based on their correlation with MSI. Consequently, biomarkers which are associated with microsatellite stability have prediction scores negatively correlated with predicted MSI scores. Conversely, biomarkers which are associated with MSI have prediction scores positively correlated with predicted MSI scores. Our results demonstrate potential benefits of multi-target DL models in improving the predictive power and efficacy of histology-based biomarker extraction compared to single-target DL models. However, our results crucially highlight the importance of considering potential confounders, such as MSI status in CRC, and emphasize the need for extensive analysis beyond AUROC in studies focusing on DL-based biomarker detection. Citation Format: Marco Gustav, Marko van Treeck, Zunamys I. Carrero, Chiara M. Loeffler, Nic G. Reitsam, Bruno Märkl, Asier Rabasco Meneghetti, Lisa A. Boardman, Amy J. French, Ellen L. Goode, Andrea Gsur, Stefanie Brezina, Marc J. Gunter, Neil Murphy, Paul Limburg, Stephen Thibodeau, Sebastian Foersch, Robert Steinfelder, Tabitha Harrison, Ulrike Peters, Amanda Phipps, Jakob N. Kather. Assessing microsatellite instability dominance in colorectal cancer phenotype: A multi-study initiative using multi-target transformers for genomic biomarker prediction [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4924.

Abstract Chronic Lymphocytic Leukemia (CLL) exhibits a substantial familial risk that extends beyond CLL to other B-cell lymphomas (including follicular lymphoma and diffuse large B-cell lymphoma). Monoclonal B-cell Lymphocytosis (MBL), a precursor to CLL, is also associated with a significant risk for hematologic malignancies overall and for lymphoid malignancies, specifically. Moreover, previous research has shown an association between the CLL polygenic risk score (CLL-PRS) and common lymphoma subtypes. This study aims to further evaluate CLL-PRS associations with hematological cancer overall and by lineage while also considering MBL status a strong risk factor for lymphoid malignancies. Utilizing the Mayo Clinic Biobank, we selected individuals aged 40 years or older, residing in proximal counties, who had no prior hematologic malignancies, and with available biospecimens. MBL screening was done using 8-color flow cytometry, and MBL with CLL (CD5+/CD20dim) or atypical CLL (CD5+/CD20+) immunophenotype were included. CLL-PRS was calculated from 41 known susceptibility variants, with weights being the log of the odds ratios from the largest CLL genome-wide association study. Incident hematologic malignancies were ascertained through ICD codes and confirmed via medical record review. Hazard ratios (HR) and 95% confidence intervals (CIs) for associations with hematologic malignancies were estimated through Cox regression. Of 7,332 participants, 1,150 (16%) screened positive for MBL; median age was 67 years (range 40-101), and 37% male. Median follow-up was 2.9 years (range 0-13), and 56 hematologic cancers were identified. After adjusting for age and sex, the CLL-PRS was associated with increased risk of developing hematologic (HR=1.49, 95%CI=1.12-1.98, P=0.006) which was all attributable to lymphoid cancers (HR=1.79, 95%CI=1.26-2.56, P=0.001). When adjusting for baseline MBL status in the models, the CLL-PRS associations remained significant but were attenuated for hematological (HR=1.33, 95%CI=0.99-1.79, P=0.058) and lymphoid (HR=1.56, 95%CI=1.07-2.26, P=0.020) malignancies, while MBL status was also significantly associated with risk of hematologic (HR=2.76, 95%CI=1.55-4.91, P=0.001) and lymphoid (HR=3.61, 95%CI=1.78-3.32, P<0.001) malignancies. In this prospective cohort study of individuals screened for MBL, CLL-PRS was associated with an elevated risk of hematologic malignancies overall and within the lymphoid lineage, underscoring a shared genetic etiology across many B-cell lymphomas. However, the associations between CLL-PRS and hematologic malignancies were attenuated when accounting for MBL status or when analyzing among MBL-positive individuals, highlighting the role of MBL status in these associations. Further research with expanded cohorts is needed to unravel these complex genetic and clinical interactions. Citation Format: Raphael Mwangi, Dennis P. Robinson, Sara J. Achenbach, Geffen Kleinstern, Aaron D. Norman, Kari G. Rabe, Janet E. Olson, Neil E. Kay, Rosalie Griffin, Nicholas J. Boddicker, Esteban Braggio, Sameer A. Parikh, Curtis A. Hanson, Celine M. Vachon, James R. Cerhan, Tait D. Shanafelt, Susan L. Slager. Genetic associations with hematologic malignancies: An evaluation of CLL polygenic risk score and MBL status in the Mayo Clinic Biobank Cohort [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6152.

Abstract Magnetic resonance imaging (MRI) is a centerpiece of clinical management of brain tumor patients, yet the biology underlying even commonly interpreted imaging changes is unclear. The purpose of this project was to understand what biology is associated with brain tumor edema abundance and if that association was sex distinct. We have developed a two-compartment model of MRI signal intensity to quantitatively estimate relative edema abundance from T2-weighted MRIs. Multiparametric MRIs were preprocessed with bias field correction and intensity normalization. The images were co-registered and segmented for abnormal regions and normal tissue. The segmentations and preprocessed images were utilized in the edema mathematical model to estimate the fraction of extracellular space (Fecs) and fraction of intracellular space (Fics). Through an ongoing image-localized biopsy collection protocol, we analyzed 179 samples (Female: 75; Male: 104) from 55 patients (Female: 21; Male: 34) for bulk RNA-Seq. Samples were characterized by their edema Fecs scores and underwent differential expression, gene set enrichment analysis using MSigDB hallmarks and cellular deconvolution using CIBERSORTx. Through differential expression and gene set enrichment analyses of these spatial biopsies, we have identified sex-distinct gene expression and pathways corresponding to regions of high vs low edema. Within highly edematous regions, male samples were amplified for respiratory pathways, while female samples were suppressed. Connecting changes on MRIs with molecular markers from image-localized biopsies provides an opportunity to identify the biological drivers of brain tumor associated imageable edema. Specifically, elucidating the sex-distinct patterns connecting changes on MRI with cellular populations and molecular pathways could be used to better interpret imaging of patients and interpreting treatment response. Citation Format: Pamela R. Jackson, Lee Curtin, Sara Ranjbar, Kyle W. Singleton, Maciej M. Mrugala, Richard S. Zimmerman, Bernard R. Bendok, Peter Canoll, Kristin R. Swanson. Sex-distinct patterns of molecular pathways associated with brain tumor edema [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6168.

Abstract Background: Smoldering multiple myeloma (SMM) is a state where patients have more than 10% clonal plasma cells or a serum monoclonal protein > 3g/dl but no evidence of end organ damage. Patients with SMM are at risk of developing active multiple myeloma (defined by the CRAB criteria). The best stratification system with respect to the risk of progression to active myeloma is the 20/2/20 (20% plasma cells, M protein >2.0 g/dl, and involved to uninvolved immunoglobulin free light chain ratio >20). The presence of ≥2 criteria predicts a 50 - 60% risk of progression within 2 years. Thus, many patients considered ‘high-risk’ may not progress in this time frame, and patients with ≤1 risk factor can progress to active myeloma. Therefore, improved risk stratification methods are needed to correctly identify patients at high risk of progression. We hypothesized that Fourier Transform InfraRed (FTIR) spectroscopic analysis of serum combined with artificial intelligence (AI) can correctly risk stratify patients with SMM. Methods: We identified 192 SMM patients: 96 who progressed and 96 who did not progress within 2 years of diagnosis. We used stored sera from the time of diagnosis to perform FTIR to determine differences in the spectra of patients who did/did not progress and train an AI algorithm. A randomized 10-fold cross-validation analysis was performed using 172 samples (86 from each group) for training and the remaining 20 for testing to assess the model across 10 independent iterations. Subsequently, we blindly tested the trained model against an independent set of serum samples (N=24) from patients who progressed ≤3 years of diagnosis. Receiver operator characteristic (ROC) analysis was used to assess the FTIR AI algorithm output compared to the clinical outcome data. Results: From the 96 patients who progressed, 48 had complete data for the 20/2/20 risk stratification. 11 patients (23%) had ≤1 of the criteria and so would have been considered at low likelihood to progress. Of the 96 who did not progress within 2 years, 49 had complete data for risk stratification. Of these, 27 (55.1%) met the high-risk criteria of the 20/2/20 classification and would have been predicted to progress within 2 years. FTIR/AI model correctly identified the patients at risk of progression (95% accuracy) with AUC of 0.96. Similarly, it was able to classify patients who did not progress within 2 years of diagnosis (94% accuracy) with an AUC of 0.96. FTIR/AI correctly predicted the patients at risk of progression within 2 years (90%) and 3 years (87.5%) in the blinded validation group with an AUC of 0.89 and 0.82, respectively. Conclusion: Analysis of serum in patients with newly diagnosed SMM with FTIR is able to correctly identify those at risk of progression within 2 or 3 years of diagnosis with an accuracy of ~90% in a blinded external validation study. FTIR may, therefore, be used to assess SMM patient risk before committing to potentially toxic therapy. Citation Format: David Dingli, Darlyna Khonkhammy, Caden Gunnarson, Grant Schlauderaff, Dan Q. Pham, Ali Khammanivong. Risk stratification of smoldering multiple myeloma using fourier transform infrared spectroscopy of serum [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6170.

Abstract Benign breast disease (BBD) affects over one million women annually in the United States and is diagnosed in nearly half of all women during their lifetime. Pseudoangiomatous stromal hyperplasia (PASH), a common BBD subtype, is characterized by an overaccumulation of tissue fibroblasts. Current research suggests a pivotal role of the immune microenvironment in progression of BBD to breast cancer, suggesting its composition as a determinant of breast cancer risk. Our study evaluates whether fibroblast variations and immune microenvironment differences in BBD biopsies containing PASH lesions are associated with subsequent breast cancer development. We evaluated BBD biopsies from women who subsequently developed breast cancer (cases, N=5) or remained cancer-free (controls, N=5). Using Opal-based multiplex immunofluorescence (mIF) assays, we mapped expression of fibroblast markers linked to breast cancer associated fibroblasts (CAFs), including vimentin (VIM), smooth muscle actin (ACTA2), fibroblast specific protein-1 (S100A4/FSP1), fibroblast associated protein (FAP), integrin beta-1 (CD29/ITGB1), and platelet derived growth factor receptor beta (PDGFRβ) and markers of key immune cells (T cells: CD4, CD8; dendritic cells: CD11c; B cells: CD20; and macrophages: CD68) along with MKI67 as a proliferation marker. Our results revealed marked differences in fibroblast marker expression and immune cell populations between these groups in the normal lobules in the BBD biopsies. PASH cases showed elevated PDGFRβ and CD29/ITGB1 expression in VIM-positive fibroblasts, markers characteristic of CAF subtypes known to accumulate in more aggressive tumor microenvironments. Conversely, a higher percentage of FSP1+/VIM+ cells, similar to that of a different CAF subtype, was noted in the lobules of PASH controls. This difference in biomarker expression, along with corresponding variations in immune cell counts, suggests a complex interplay between fibroblast composition and immune response that ultimately modulate cancer risk in PASH-containing BBD tissues. Additionally, increased MKI67 expression in PASH cases potentially signals the higher likelihood of cancer development. This work advances our understanding of fibroblast heterogeneity in BBD and its implications for breast cancer risk and progression, and paves the way for innovative strategies to improve clinical outcomes for BBD patients to reduce their incidence of breast cancer. Citation Format: Nicole Cruz-Reyes, Matilde Rossi, Melody Stallings Mann, Bryan McCauley, Tanya Hoskin, Robert Vierkant, Stacey Winham, Amy Degnim, Mark E. Sherman, Derek C. Radisky. Fibroblast diversity and immune microenvironment in pseudoangiomatous stromal hyperplasia (PASH): Associations with breast cancer risk [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 283.

Steatohepatitis with diverse etiologies is the most common histological manifestation in patients with liver disease. However, there are currently no specific histopathological features pathognomonic for metabolic dysfunction–associated steatotic liver disease, alcohol-associated liver disease, or metabolic dysfunction–associated steatotic liver disease with increased alcohol intake. Digitizing traditional pathology slides has created an emerging field of digital pathology, allowing for easier access, storage, sharing, and analysis of whole-slide images. Artificial intelligence (AI) algorithms have been developed for whole-slide images to enhance the accuracy and speed of the histological interpretation of steatohepatitis and are currently employed in biomarker development. Spatial biology is a novel field that enables investigators to map gene and protein expression within a specific region of interest on liver histological sections, examine disease heterogeneity within tissues, and understand the relationship between molecular changes and distinct tissue morphology. Here, we review the utility of digital pathology (using linear and nonlinear microscopy) augmented with AI analysis to improve the accuracy of histological interpretation. We will also discuss the spatial omics landscape with special emphasis on the strengths and limitations of established spatial transcriptomics and proteomics technologies and their application in steatohepatitis. We then highlight the power of multimodal integration of digital pathology augmented by machine learning (ML)algorithms with spatial biology. The review concludes with a discussion of the current gaps in knowledge, the limitations and premises of these tools and technologies, and the areas of future research.