Cemiplimab as First Line Therapy in Advanced Penile Squamous Cell Carcinoma: A Real-World Experience.

In the treatment of cancer, immune checkpoint inhibitors (ICIs) have demonstrated significantly greater effectiveness compared to conventional cytotoxic or platinum-based chemotherapies. To assess the efficacy of ICI's in penile squamous cell carcinoma (pSCC) we performed a retrospective observational study. We reviewed electronic medical records of patients with penile squamous cell carcinoma (SCC), diagnosed between January 2020 and February 2023. Nine patients were screened, of whom three were ineligible for chemotherapy and received immunotherapy, cemiplimab, in a first-line setting. Each of the three immunotherapy-treated patients achieved almost a complete response (CR) after only a few cycles of therapy. The first patient had cerebral arteritis during treatment and received a high-dose steroid treatment with resolution of the symptoms of arteritis. After tapering down the steroids dose, the patient continued cemiplimab without further toxicity. The other two patients did not have any toxic side effects of the treatment. To the best of our knowledge, this is the first real world report of near CR with cemiplimab as a first-line treatment in penile SCC.

TP73-AS1 is induced by YY1 during TMZ treatment and highly expressed in the aging brain.

Aging is a factor associated with poor prognosis in glioblastoma (GBM). It is therefore important to understand the molecular features of aging contributing to GBM morbidity. TP73-AS1 is a long noncoding RNA (lncRNA) over expressed in GBM tumors shown to promote resistance to the chemotherapeutic temozolomide (TMZ), and tumor aggressiveness. How the expression of TP73-AS1 is regulated is not known, nor is it known if its expression is associated with aging. By analyzing transcriptional data obtained from natural and pathological aging brain, we found that the expression of TP73-AS1 is high in pathological and naturally aging brains. YY1 physically associates with the promoter of TP73-AS1 and we found that along with TP73-AS1, YY1 is induced by TMZ. We found that the TP73-AS1 promoter is activated by TMZ, and by YY1 over expression. Using CRISPRi to deplete YY1, we found that YY1 promotes up regulation of TP73-AS1 and the activation of its promoter during TMZ treatment. In addition, we identified two putative YY1 binding sites within the TP73-AS1 promoter, and used mutagenesis to find that they are essential for TMZ mediated promoter activation. Together, our data positions YY1 as an important TP73-AS1 regulator, demonstrating that TP73-AS1 is expressed in the natural and pathological aging brain, including during neurodegeneration and cancer. Our findings advance our understanding of TP73-AS1 expression, bringing forth a new link between TMZ resistance and aging, both of which contribute to GBM morbidity.

Cooperation of cancer drivers with regulatory germline variants shapes clinical outcomes.

Pediatric malignancies including Ewing sarcoma (EwS) feature a paucity of somatic alterations except for pathognomonic driver-mutations that cannot explain overt variations in clinical outcome. Here, we demonstrate in EwS how cooperation of dominant oncogenes and regulatory germline variants determine tumor growth, patient survival and drug response. Binding of the oncogenic EWSR1-FLI1 fusion transcription factor to a polymorphic enhancer-like DNA element controls expression of the transcription factor MYBL2 mediating these phenotypes. Whole-genome and RNA sequencing reveals that variability at this locus is inherited via the germline and is associated with variable inter-tumoral MYBL2 expression. High MYBL2 levels sensitize EwS cells for inhibition of its upstream activating kinase CDK2 in vitro and in vivo, suggesting MYBL2 as a putative biomarker for anti-CDK2-therapy. Collectively, we establish cooperation of somatic mutations and regulatory germline variants as a major determinant of tumor progression and highlight the importance of integrating the regulatory genome in precision medicine.

HYDRA: Hybrid deep magnetic resonance fingerprinting.

PURPOSE: Magnetic resonance fingerprinting (MRF) methods typically rely on dictionary matching to map the temporal MRF signals to quantitative tissue parameters. Such approaches suffer from inherent discretization errors, as well as high computational complexity as the dictionary size grows. To alleviate these issues, we propose a HYbrid Deep magnetic ResonAnce fingerprinting (HYDRA) approach, referred to as HYDRA. METHODS: HYDRA involves two stages: a model-based signature restoration phase and a learning-based parameter restoration phase. Signal restoration is implemented using low-rank based de-aliasing techniques while parameter restoration is performed using a deep nonlocal residual convolutional neural network. The designed network is trained on synthesized MRF data simulated with the Bloch equations and fast imaging with steady-state precession (FISP) sequences. In test mode, it takes a temporal MRF signal as input and produces the corresponding tissue parameters. RESULTS: We validated our approach on both synthetic data and anatomical data generated from a healthy subject. The results demonstrate that, in contrast to conventional dictionary matching-based MRF techniques, our approach significantly improves inference speed by eliminating the time-consuming dictionary matching operation, and alleviates discretization errors by outputting continuous-valued parameters. We further avoid the need to store a large dictionary, thus reducing memory requirements. CONCLUSIONS: Our approach demonstrates advantages in terms of inference speed, accuracy, and storage requirements over competing MRF methods.

The long noncoding RNA TP73-AS1 promotes tumorigenicity of medulloblastoma cells.

Medulloblastoma is the most common malignant brain cancer in children. Since previous studies have mainly focused on alterations in the coding genome, our understanding of the contribution of long noncoding RNAs (lncRNAs) to medulloblastoma biology is just emerging. Using patient-derived data, we show that the promoter of lncRNA TP73-AS1 is hypomethylated and that the transcript is highly expressed in the SHH subgroup. Furthermore, high expression of TP73-AS1 is correlated with poor outcome in patients with TP53 wild-type SHH tumors. Silencing TP73-AS1 in medulloblastoma tumor cells induced apoptosis, while proliferation and migration were inhibited in culture. In vivo, silencing TP73-AS1 in medulloblastoma tumor cells resulted in reduced tumor growth, reduced proliferation of tumor cells, increased apoptosis and led to prolonged survival of tumor-bearing mice. Together, our study suggests that the lncRNA TP73-AS1 is a prognostic marker and therapeutic target in medulloblastoma tumors and serves as a proof of concept that lncRNAs are important factors in the disease.

The lncRNA TP73-AS1 is linked to aggressiveness in glioblastoma and promotes temozolomide resistance in glioblastoma cancer stem cells.

Glioblastoma multiform (GBM) is the most common brain tumor characterized by a dismal prognosis. GBM cancer stem cells (gCSC) or tumor-initiating cells are the cell population within the tumor-driving therapy resistance and recurrence. While temozolomide (TMZ), an alkylating agent, constitutes the first-line chemotherapeutic significantly improving survival in GBM patients, resistance against this compound commonly leads to GBM recurrence and treatment failure. Although the roles of protein-coding transcripts, proteins and microRNA in gCSC, and therapy resistance have been comprehensively investigated, very little is known about the role of long noncoding RNAs (lncRNAs) in this context. Using nonoverlapping, independent RNA sequencing and gene expression profiling datasets, we reveal that TP73-AS1 constitutes a clinically relevant lncRNA in GBM. Specifically, we demonstrate significant overexpression of TP73-AS1 in primary GBM samples, which is particularly increased in the gCSC. More importantly, we demonstrate that TP73-AS1 comprises a prognostic biomarker in glioma and in GBM with high expression identifying patients with particularly poor prognosis. Using CRISPRi to downregulate our candidate lncRNA in gCSC, we demonstrate that TP73-AS1 promotes TMZ resistance in gCSC and is linked to regulation of the expression of metabolism- related genes and ALDH1A1, a protein known to be expressed in cancer stem cell markers and protects gCSC from TMZ treatment. Taken together, our results reveal that high TP73-AS1 predicts poor prognosis in primary GBM cohorts and that this lncRNA promotes tumor aggressiveness and TMZ resistance in gCSC.

Low-rank magnetic resonance fingerprinting.

PURPOSE: Magnetic resonance fingerprinting (MRF) is a relatively new approach that provides quantitative MRI measures using randomized acquisition. Extraction of physical quantitative tissue parameters is performed offline, without the need of patient presence, based on acquisition with varying parameters and a dictionary generated according to the Bloch equation simulations. MRF uses hundreds of radio frequency (RF) excitation pulses for acquisition, and therefore, a high undersampling ratio in the sampling domain (k-space) is required for reasonable scanning time. This undersampling causes spatial artifacts that hamper the ability to accurately estimate the tissue's quantitative values. In this work, we introduce a new approach for quantitative MRI using MRF, called magnetic resonance fingerprinting with low rank (FLOR). METHODS: We exploit the low-rank property of the concatenated temporal imaging contrasts, on top of the fact that the MRF signal is sparsely represented in the generated dictionary domain. We present an iterative recovery scheme that consists of a gradient step followed by a low-rank projection using the singular value decomposition. RESULTS: Experimental results consist of retrospective sampling that allows comparison to a well defined reference, and prospective sampling that shows the performance of FLOR for a real-data sampling scenario. Both experiments demonstrate improved parameter accuracy compared to other compressed-sensing and low-rank based methods for MRF at 5% and 9% sampling ratios for the retrospective and prospective experiments, respectively. CONCLUSIONS: We have shown through retrospective and prospective experiments that by exploiting the low-rank nature of the MRF signal, FLOR recovers the MRF temporal undersampled images and provides more accurate parameter maps compared to previous iterative approaches.

Low rank magnetic resonance fingerprinting.

Magnetic Resonance Fingerprinting (MRF) is a relatively new approach that provides quantitative MRI using randomized acquisition. Extraction of physical quantitative tissue values is preformed off-line, based on acquisition with varying parameters and a dictionary generated according to the Bloch equations. MRF uses hundreds of radio frequency (RF) excitation pulses for acquisition, and therefore high under-sampling ratio in the sampling domain (k-space) is required. This under-sampling causes spatial artifacts that hamper the ability to accurately estimate the quantitative tissue values. In this work, we introduce a new approach for quantitative MRI using MRF, called Low Rank MRF. We exploit the low rank property of the temporal domain, on top of the well-known sparsity of the MRF signal in the generated dictionary domain. We present an iterative scheme that consists of a gradient step followed by a low rank projection using the singular value decomposition. Experiments on real MRI data demonstrate superior results compared to conventional implementation of compressed sensing for MRF at 15% sampling ratio.