David B. Agus

Multiple epidemiological studies have established obesity as an adverse factor in breast cancer incidence, progression, and clinical outcomes. However, the biological mechanisms underlying these associations remain incompletely understood. In peritumoral adipose tissue, obesity is associated with chronic inflammation, macrophage recruitment, and broader alterations in the local microenvironment. Whether these obesity-associated changes are accompanied by distinct morphologic features within the tumor and non-adipose stromal compartments remains unclear. In this study, we investigated whether an AI-based pathology foundation model could support the development of machine learning algorithms capable of identifying obesity-associated morphologic patterns in histopathology images, with a particular focus on tumor and stromal regions. In a cohort of 195 patients with breast cancer, machine learning models using vector embeddings from non-adipose stromal regions classified high BMI (≥30 kg/m ² ) versus low BMI (2 ) with an average AUC of 0.78 (95% CI: 0.73-0.83), consistently outperforming models based on tumor-only or combined tumor-stroma regions. These obesity-associated stromal signatures were observed across subgroups defined by hormone-receptor status and age, indicating that they are not restricted to specific clinical populations. Complementary embedding-based dissimilarity analysis further showed that higher BMI was associated with reduced inter-patient heterogeneity in both tumor and stromal tissues. Overall, these findings suggest that obesity is associated with distinct morphologic alterations in both tumor and non-adipose stromal tissues. Notably, the stromal compartment emerged as a particularly informative indicator of obesity-associated morphology and demonstrated reduced inter-patient heterogeneity among individuals with high BMI.

Small molecules that target the androgen receptor (AR) are the mainstay of therapy for lethal castration-resistant prostate cancer (CRPC), yet existing drugs lose their efficacy during continued treatment. This evolution of resistance is due to heterogenous mechanisms which include AR mutations causing the identical drug to activate instead of inhibit the receptor. Understanding in molecular detail the paradoxical phenomenon wherein an AR antagonist is transformed into an agonist by structural mutations in the target receptor is thus of paramount importance. Herein, we describe a reciprocal paradox: opposing antagonist and agonist AR regulation determined uniquely by enantiomeric forms of the same drug structure. The antiandrogen BMS-641988, which has ( R )-chirality at C-5 encompasses a previously uncharacterized ( S )-stereoisomer that is, surprisingly, a potent agonist of AR, as demonstrated by transcriptional assays supported by cell imaging studies. This duality was reproduced in a series of novel compounds derived from the BMS-641988 scaffold. Coupled with in silico modeling studies, the results inform an AR model that explains the switch from potent antagonist to high-affinity agonist in terms of C-5 substituent steric interactions with helix 12 of the ligand binding site. They imply strategies to overcome AR drug resistance and demonstrate that insufficient enantiopurity in this class of AR antagonist can confound efforts to correlate structure with function.

Purpose Monoamine oxidase A (MAOA) influences prostate cancer growth and metastasis in pre-clinical models. We examined effects of phenelzine (a monoamine oxidase inhibitor) in patients with biochemical recurrent castrate-sensitive prostate cancer. Materials and methods An open-label single arm clinical trial enrolled subjects with biochemical recurrent prostate cancer defined by PSA ≥ 0.4 ng/ml (post prostatectomy) or PSA ≥ 2 ng/ml above nadir (post-radiation therapy); no evidence of metastasis on imaging; and normal androgen levels. Subjects received phenelzine 30 mg orally twice daily. Mood symptoms were assessed with the hospital anxiety depression score (HADS) questionnaire. The primary endpoint was the proportion of patients who achieved a PSA decline of ≥50% from baseline. Results Characteristics of the 20 eligible patients enrolled included: mean ± SD age 66.9 ± 4.8 years and PSA 4.7 ± 5.8 ng/dl. Maximal PSA declines ≥30% and ≥50% were observed in 25% (n = 5/20) and 10% (n = 2/20) of subjects, respectively. At 12 weeks, 17 subjects remained on treatment with PSA declines ≥30% and ≥50% of 24% (n = 4/17) and 6% (n = 1/17), respectively. Common toxicities observed included dizziness (grade 1 = 45%, grade 2 = 35%), hypertension (grade ≥ 2 = 30%), and edema (grade 1 = 25%, grade 2 = 10%). There was one episode of grade 4 hypertension (cycle 4) and two episodes of grade 3 syncope (cycle 12 and cycle 14) requiring treatment discontinuation. HADS questionnaires demonstrated a significant decrease in anxiety with no change in depressive symptoms on treatment. Conclusions Phenelzine demonstrated efficacy in patients with biochemical recurrent castrate-sensitive prostate cancer. Most treatment-related toxicities were mild, but rare significant and reversible cardiovascular toxicities were observed. Therapies directed at MAOA may represent a new avenue for treatment in patients with recurrent prostate cancer.

Cell-free DNA (cfDNA) is an attractive cancer biomarker, as it is thought to reflect a component of the underlying genetic makeup of the tumor and is readily accessible in serial fashion. Because chemotherapy regimens are expected to act rapidly on cancer and cfDNA is cleared from the blood within minutes, we hypothesized that cfDNA would reflect immediate effects of treatment. Here, we developed a method for monitoring long cfDNA fragments, and report dynamic changes in response to cytotoxic chemotherapy. Peripheral blood was obtained from 15 patients with metastatic castration-resistant prostate cancer (CRPC) immediately before and after cytotoxic chemotherapy infusion. cfDNA was extracted and quantified for long interspersed nuclear elements (LINE1; 297 bp) using qPCR. Targeted deep sequencing was performed to quantify the frequency of mutations in exon 8 of the androgen receptor (AR), a mutational hotspot region in CRPC. Single nucleotide mutations in AR exon 8 were found in 6 subjects (6/15 = 40%). Analytical variability was minimized by pooling independent PCR reactions for each library. In 5 patients, tumor-derived long cfDNA levels were found to change immediately after infusion. Detailed analysis of one subject suggests that cytotoxic chemotherapy can produce rapidly observable effects on cfDNA.

In this pilot study, we introduce a machine learning framework to identify relationships between cancer tissue morphology and hormone receptor pathway activation in breast cancer pathology hematoxylin and eosin (H&E)-stained samples. As a proof-of-concept, we focus on predicting clinical estrogen receptor (ER) status—defined as greater than one percent of cells positive for estrogen receptor by immunohistochemistry staining—from spatial arrangement of nuclear features. Our learning pipeline segments nuclei from H&E images, extracts their position, shape and orientation descriptors, and then passes them to a deep neural network to predict ER status. After training on 57 tissue cores of invasive ductal carcinoma (IDC), our pipeline predicted ER status in an independent test set of patient samples (AUC ROC = 0.72, 95%CI = 0.55–0.89, n = 56). This proof of concept shows that machine-derived descriptors of morphologic histology patterns can be correlated to signaling pathway status. Unlike other deep learning approaches to pathology, our system uses deep neural networks to learn spatial relationships between pre-defined biological features, which improves the interpretability of the system and sheds light on the features the neural network uses to predict ER status. Future studies will correlate morphometry to quantitative measures of estrogen receptor status and, ultimately response to hormonal therapy.

Cell biology is increasingly focused on cellular heterogeneity and multicellular systems. To make the fullest use of experimental, clinical, and computational efforts, we need standardized data formats, community-curated “public data libraries”, and tools to combine and analyze shared data. To address these needs, our multidisciplinary community created MultiCellDS (MultiCellular Data Standard): an extensible standard, a library of digital cell lines and tissue snapshots, and support software. With the help of experimentalists, clinicians, modelers, and data and library scientists, we can grow this seed into a community-owned ecosystem of shared data and tools, to the benefit of basic science, engineering, and human health.

Exchanging and understanding scientific data and their context represents a significant barrier to advancing research, especially with respect to information siloing. Maintaining information provenance and providing data curation and quality control help overcome common concerns and barriers to the effective sharing of scientific data. To address these problems in and the unique challenges of multicellular systems, we assembled a panel composed of investigators from several disciplines to create the MultiCellular Data Standard (MultiCellDS) with a use-case driven development process. The standard includes (1) digital cell lines, which are analogous to traditional biological cell lines, to record metadata, cellular microenvironment, and cellular phenotype variables of a biological cell line, (2) digital snapshots to consistently record simulation, experimental, and clinical data for multicellular systems, and (3) collections that can logically group digital cell lines and snapshots. We have created a MultiCellular DataBase (MultiCellDB) to store digital snapshots and the 200+ digital cell lines we have generated. MultiCellDS, by having a fixed standard, enables discoverability, extensibility, maintainability, searchability, and sustainability of data, creating biological applicability and clinical utility that permits us to identify upcoming challenges to uplift biology and strategies and therapies for improving human health.