Purpose We propose two software tools for non-rigid registration of MRI and transrectal ultrasound (TRUS) images of the prostate. Our ultimate goal is to develop an open-source solution to support MRI–TRUS fusion image guidance of prostate interventions, such as targeted biopsy for prostate cancer detection and focal therapy. It is widely hypothesized that image registration is an essential component in such systems. Methods The two non-rigid registration methods are: (1) a deformable registration of the prostate segmentation distance maps with B-spline regularization and (2) a finite element-based deformable registration of the segmentation surfaces in the presence of partial data. We evaluate the methods retrospectively using clinical patient image data collected during standard clinical procedures. Computation time and Target Registration Error (TRE) calculated at the expert-identified anatomical landmarks were used as quantitative measures for the evaluation. Results The presented image registration tools were capable of completing deformable registration computation within 5 min. Average TRE was approximately 3 mm for both methods, which is comparable with the slice thickness in our MRI data. Both tools are available under nonrestrictive open-source license. Conclusions We release open-source tools that may be used for registration during MRI–TRUS-guided prostate interventions. Our tools implement novel registration approaches and produce acceptable registration results. We believe these tools will lower the barriers in development and deployment of interventional research solutions and facilitate comparison with similar tools.

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BACKGROUND Prostate cancer remains a significant health problem for men in the Western world. Although treatment modalities are available, these do not confer long-term benefit and are accompanied by substantial side effects. Adoptive immunotherapy represents an attractive alternative to conventional treatments as a means to control tumor growth. METHODS To selectively target the tumor-expressed form of Muc1 we constructed a retroviral vector encoding a chimeric antigen receptor (CAR) directed against the aberrantly-expressed extracellular portion of Muc1 called the ‘variable number of tandem repeats’. RESULTS We now demonstrate that T cells can be genetically engineered to express a CAR targeting the tumor-associated antigen Muc1. CAR-Muc1 T cells were able to selectively kill Muc1-expressing human prostate cancer cells. However, we noted that heterogeneous expression of the Muc1 antigen on tumor cells facilitated immune escape and the outgrowth of target-antigen loss variants of the tumor. Given the importance of androgen ablation therapy in the management of metastatic prostate cancer, we therefore also tested the value of combining conventional (anti-androgen) and experimental (CAR-Muc1 T cells) approaches. We show that CAR-Muc1 T cells were not adversely impacted by anti-androgen therapy and subsequently demonstrate the feasibility of combining the approaches to produce additive anti-tumor effects in vitro. CONCLUSIONS Adoptive transfer of CAR-Muc1 T cells alone or in combination with other luteinizing hormone-releasing hormone analogs or antagonists should be tested in human clinical trials.

In surface-based registration for image-guided interventions, the presence of missing data can be a significant issue. This often arises with real-time imaging modalities such as ultrasound, where poor contrast can make tissue boundaries difficult to distinguish from surrounding tissue. Missing data poses two challenges: ambiguity in establishing correspondences; and extrapolation of the deformation field to those missing regions. To address these, we present a novel non-rigid registration method. For establishing correspondences, we use a probabilistic framework based on a Gaussian mixture model (GMM) that treats one surface as a potentially partial observation. To extrapolate and constrain the deformation field, we incorporate biomechanical prior knowledge in the form of a finite element model (FEM). We validate the algorithm, referred to as GMM-FEM, in the context of prostate interventions. Our method leads to a significant reduction in target registration error (TRE) compared to similar state-of-the-art registration algorithms in the case of missing data up to 30%, with a mean TRE of 2.6 mm. The method also performs well when full segmentations are available, leading to TREs that are comparable to or better than other surface-based techniques. We also analyze robustness of our approach, showing that GMM-FEM is a practical and reliable solution for surface-based registration.

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Numerous cancers, including prostate cancer (PCa), are addicted to transcription programs driven by superenhancers (SEs). The transcription of genes at SEs is enabled by the formation of phase-separated condensates by transcription factors and co-activators with intrinsically disordered regions. The androgen receptor (AR), main oncogenic driver in PCa, contains large disordered regions and is co-recruited with the co-activator MED1 to SEs to promote oncogenic programs. In this work, we show that dynamic AR-rich, liquid-like foci form in PCa models upon androgen stimulation and correlate with AR transcriptional activity. The co-activator MED1 plays an essential role in the formation of AR foci while AR antagonists hinder their formation. These results suggest that enhanced compartmentalization of AR and co-activators at SEs may play an important role in the activation of oncogenic transcription programs in PCa. A better understanding of the assembly and the regulation of these AR-rich compartments may provide novel therapeutic options.

Resistance to drug treatments is common in prostate cancer (PCa), and the gain-of-function mutations in human androgen receptor (AR) represent one of the most dominant drivers of progression to resistance to AR pathway inhibitors (ARPI). Previously, we evaluated the in vitro response of 24 AR mutations, identified in men with castration-resistant PCa, to five AR antagonists. In the current work, we evaluated 44 additional PCa-associated AR mutants, reported in the literature, and thus expanded the study of the effect of darolutamide to a total of 68 AR mutants. Unlike other AR antagonists, we demonstrate that darolutamide exhibits consistent efficiency against all characterized gain-of-function mutations in a full-length AR. Additionally, the response of the AR mutants to clinically used bicalutamide and enzalutamide, as well as to major endogenous steroids (DHT, estradiol, progesterone and hydrocortisone), was also investigated. As genomic profiling of PCa patients becomes increasingly feasible, the developed “AR functional encyclopedia” could provide decision-makers with a tool to guide the treatment choice for PCa patients based on their AR mutation status.

Figure 1: Our unified geometric skinning method for rigid and deformable bodies with two-way force coupling (left), is well suited for anatomical models that have a mix of hard and soft tissues, such as dynamic simulations of the tongue, jaw, skull, and vocal tract (right). AbstractWe propose a novel geometric skinning approach that unifies geometric blending for rigid-body models with embedded surfaces for finite-element models. The resulting skinning method provides flexibility for modelers and animators to select the desired dynamic degrees-of-freedom through a combination of coupled rigid and deformable structures connected to a single skin mesh that is influenced by all dynamic components. The approach is particularly useful for anatomical models that include a mix of hard structures (bones) and soft tissues (muscles, tendons). We demonstrate our skinning method for an upper airway model and create first-of-itskind simulations of swallowing and speech acoustics that are generated by muscle-driven biomechanical models of the oral anatomy.