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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.

Because histologic types are subjective and difficult to reproduce between pathologists, tissue morphology often takes a back seat to molecular testing for the selection of breast cancer treatments. This work explores whether a deep-learning algorithm can learn objective histologic H&E features that predict the clinical subtypes of breast cancer, as assessed by immunostaining for estrogen, progesterone, and Her2 receptors (ER/PR/Her2). Translating deep learning to this and related problems in histopathology presents a challenge due to the lack of large, well-annotated data sets, which are typically required for the algorithms to learn statistically significant discriminatory patterns. To overcome this limitation, we introduce the concept of "tissue fingerprints," which leverages large, unannotated datasets in a label-free manner to learn H&E features that can distinguish one patient from another. The hypothesis is that training the algorithm to learn the morphological differences between patients will implicitly teach it about the biologic variation between them. Following this training internship, we used the features the network learned, which we call "fingerprints," to predict ER, PR, and Her2 status in two datasets. Despite the discovery dataset being relatively small by the standards of the machine learning community (n = 939), fingerprints enabled the determination of ER, PR, and Her2 status from whole slide H&E images with 0.89 AUC (ER), 0.81 AUC (PR), and 0.79 AUC (Her2) on a large, independent test set (n = 2531). Tissue fingerprints are concise but meaningful histopathologic image representations that capture biological information and may enable machine learning algorithms that go beyond the traditional ER/PR/Her2 clinical groupings by directly predicting theragnosis.

Live cell imaging has improved our ability to measure phenotypic heterogeneity. However, bottlenecks in imaging and image processing often make it difficult to differentiate interesting biological behavior from technical artifact. Thus there is a need for new methods that improve data quality without sacrificing throughput. Here we present a 3-step workflow to improve dynamic phenotype measurements of heterogeneous cell populations. We provide guidelines for image acquisition, phenotype tracking, and data filtering to remove erroneous cell tracks using the novel Tracking Aberration Measure (TrAM). Our workflow is broadly applicable across imaging platforms and analysis software. By applying this workflow to cancer cell assays, we reduced aberrant cell track prevalence from 17% to 2%. The cost of this improvement was removing 15% of the well-tracked cells. This enabled detection of significant motility differences between cell lines. Similarly, we avoided detecting a false change in translocation kinetics by eliminating the true cause: varied proportions of unresponsive cells. Finally, by systematically seeking heterogeneous behaviors, we detected subpopulations that otherwise could have been missed, including early apoptotic events and pre-mitotic cells. We provide optimized protocols for specific applications and step-by-step guidelines for adapting them to a variety of biological systems.

Tumor progression results from a complex interplay between cellular heterogeneity, treatment response, microenvironment and heterocellular interactions. Existing approaches to characterize this interplay suffer from an inability to distinguish between multiple cell types, often lack environmental context, and are unable to perform multiplex phenotypic profiling of cell populations. Here we present a high-throughput platform for characterizing, with single-cell resolution, the dynamic phenotypic responses (i.e. morphology changes, proliferation, apoptosis) of heterogeneous cell populations both during standard growth and in response to multiple, co-occurring selective pressures. The speed of this platform enables a thorough investigation of the impacts of diverse selective pressures including genetic alterations, therapeutic interventions, heterocellular components and microenvironmental factors. The platform has been applied to both 2D and 3D culture systems and readily distinguishes between (1) cytotoxic versus cytostatic cellular responses; and (2) changes in morphological features over time and in response to perturbation. These important features can directly influence tumor evolution and clinical outcome. Our image-based approach provides a deeper insight into the cellular dynamics and heterogeneity of tumors (or other complex systems), with reduced reagents and time, offering advantages over traditional biological assays.

The androgen receptor (AR) pathway plays a central role in prostate cancer (PCa) growth and progression and is a validated therapeutic target. In response to ligand binding AR translocates to the nucleus, though the molecular mechanism is not well understood. We therefore developed multimodal Image Correlation Spectroscopy (mICS) to measure anisotropic molecular motion across a live cell. We applied mICS to AR translocation dynamics to reveal its multimodal motion. By integrating fluorescence imaging methods we observed evidence for diffusion, confined movement, and binding of AR within both the cytoplasm and nucleus of PCa cells. Our findings suggest that in presence of cytoplasmic diffusion, the probability of AR crossing the nuclear membrane is an important factor in determining the AR distribution between cytoplasm and the nucleus, independent of functional microtubule transport. These findings may have implications for the future design of novel therapeutics targeting the AR pathway in PCa.

To investigate the transition from non-cancerous to metastatic from a physical sciences perspective, the Physical Sciences-Oncology Centers (PS-OC) Network performed molecular and biophysical comparative studies of the non-tumorigenic MCF-10A and metastatic MDA-MB-231 breast epithelial cell lines, commonly used as models of cancer metastasis. Experiments were performed in 20 laboratories from 12 PS-OCs. Each laboratory was supplied with identical aliquots and common reagents and culture protocols. Analyses of these measurements revealed dramatic differences in their mechanics, migration, adhesion, oxygen response, and proteomic profiles. Model-based multi-omics approaches identified key differences between these cells' regulatory networks involved in morphology and survival. These results provide a multifaceted description of cellular parameters of two widely used cell lines and demonstrate the value of the PS-OC Network approach for integration of diverse experimental observations to elucidate the phenotypes associated with cancer metastasis.

We have identified a gene that is highly expressed in the androgen-dependent prostate cancer cell line, LNCaP. Sequence analysis revealed that it was identical to a recently cloned gene designated TMEFF2, which encodes a transmembrane protein containing an epidermal growth factor (EGF)-like motif and two follistatin domains. This gene was highly expressed only in primary samples of normal prostate and prostate cancer as well as normal brain. Expression of the gene was controlled by androgen as shown by dihydrotestosterone markedly increasing TMEFF2 expression in LNCaP cells. Also, androgen-dependent human prostate cancer xenografts (CWR22) expressed high levels of TMEFF2 and these levels markedly decreased by day 10 after castration of the mice. Furthermore, a large number of androgen-dependent xenografts (CWR22, LuCaP-35, LAPC-4AD, LAPC-9AD) exhibited higher levels of TMEFF2 mRNA than androgen-independent xenografts (CWR22R, LAPC-3AI, LAPC-4AI, LAPC-9AI). Ectopic expression of TMEFF2 in DU145 and PC3 cells resulted in their prominent inhibition of growth. Taken together, the results demonstrate that TMEFF2 is a androgen-regulated gene, which can suppress growth of prostate cancer cells and our xenograft data show that escape of prostate cancer cells from androgen modulation causes them to decrease their expression of this gene, which may result in their more malignant behavior.