Inhibition of the Sterol Regulatory Element Binding Protein SREBF-1 Overcomes Docetaxel Resistance in Advanced Prostate Cancer.

Resistance to antiandrogens and chemotherapy (Cx) limits therapeutic options for patients with hormone-sensitive and hormone-resistant prostate cancer (mCRPC). In this context, up-regulation of the glucocorticoid receptor has been identified as a potential bypass mechanism in mCRPC, and a combination of docetaxel and mifepristone (Doc + RU-486), an inhibitor of the glucocorticoid receptor, re-sensitized docetaxel-resistant cell models to Cx. This study was designed to elucidate the molecular mechanisms responsible for this phenomenon. RNA sequencing was performed in docetaxel-resistant prostate cancer cell models after Doc + RU-486 treatment with consecutive functional assays. Expression of selected proteins was verified in prostatic tissue from prostate cancer patients with progressive disease. Treatment with Doc + RU-486 significantly reduced cancer cell viability, and RNA sequencing revealed sterol regulatory element of binding transcription factor 1 (SREBF-1), a transcription factor of cholesterol and lipid biosynthesis, as a significantly down-regulated target. Functional assays confirmed that SREBF-1 down-regulation is partially responsible for this observation. In concordance, SREBF-1 knockdown and pharmacologic sterol regulatory element binding protein inhibition, together with other key enzymes in the cholesterol pathway, showed similar results. Furthermore, SREBF-1 expression is significantly elevated in advanced prostate cancer tissues, showing its potential involvement in tumor progression and emerging therapy resistance. Therefore, specific inhibition of cholesterol and lipid biosynthesis might also target Cx-resistant cancer cells and represents a potential additive future therapeutic option to improve mCRPC therapy.

Inhibition of the Cyclin K-CDK12 complex induces DNA damage and increases the effect of androgen deprivation therapy in prostate cancer.

Androgen deprivation therapy (ADT) is the mainstay of the current first-line treatment concepts for patients with advanced prostate carcinoma (PCa). However, due to treatment failure and recurrence investigation of new targeted therapeutics is urgently needed. In this study, we investigated the suitability of the Cyclin K-CDK12 complex as a novel therapeutic approach in PCa using the new covalent CDK12/13 inhibitor THZ531. Here we show that THZ531 impairs cellular proliferation, induces apoptosis, and decreases the expression of selected DNA repair genes in PCa cell lines, which is associated with an increasing extent of DNA damage. Furthermore, combination of THZ531 and ADT leads to an increase in these anti-tumoral effects in androgen-sensitive PCa cells. The anti-proliferative and pro-apoptotic activity of THZ531 in combination with ADT was validated in an ex vivo PCa tissue culture model. In a retrospective immunohistochemical analysis of 300 clinical tissue samples we show that Cyclin K (CycK) but not CDK12 expression correlates with a more aggressive type of PCa. In conclusion, this study demonstrates the clinical relevance of the CycK-CDK12 complex as a promising target for combinational therapy with ADT in PCa and its importance as a prognostic biomarker for patients with PCa.

Deep Learning Predicts Molecular Subtype of Muscle-invasive Bladder Cancer from Conventional Histopathological Slides.

BACKGROUND: Muscle-invasive bladder cancer (MIBC) is the second most common genitourinary malignancy, and is associated with high morbidity and mortality. Recently, molecular subtypes of MIBC have been identified, which have important clinical implications. OBJECTIVE: In the current study, we tried to predict the molecular subtype of MIBC samples from conventional histomorphology alone using deep learning. DESIGN, SETTING, AND PARTICIPANTS: Two cohorts of patients with MIBC were used: (1) The Cancer Genome Atlas Urothelial Bladder Carcinoma dataset including 407 patients and (2) our own cohort including 16 patients with treatment-naïve, primary resected MIBC. This resulted in a total of 423 digital whole slide images of tumor tissue to train, validate, and test the deep learning algorithm to predict the molecular subtype. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Various accuracy measurements including the area under the receiver operating characteristic curves were used to evaluate the deep learning model. A sliding window approach to visualize classification was used. Class activation maps were used to identify image features that are most relevant to call a specific class. RESULTS AND LIMITATIONS: The deep learning model showed great performance in the prediction of the molecular subtype of MIBC patients from hematoxylin and eosin (HE) slides alone-similar to or better than pathology experts. Using different visualization techniques, we identified new histopathological features that were most relevant to our model. CONCLUSIONS: Deep learning can be used to predict important molecular features in MIBC patients from HE slides alone, potentially improving the clinical management of this disease significantly. PATIENT SUMMARY: In patients with bladder cancer, a computer program found changes in the appearance of tumor tissue under the microscope and used these to predict genetic alterations. This could potentially benefit patients.

Combined human papillomavirus typing and TP53 mutation analysis in distinguishing second primary tumors from lung metastases in patients with head and neck squamous cell carcinoma.

BACKGROUND: In head and neck squamous cell carcinoma (HNSCC), the occurrence of concurrent lung malignancies poses a significant diagnostic challenge because metastatic HNSCC is difficult to discern from second primary lung squamous cell carcinoma (SCC). However, this differentiation is crucial because the recommended treatments for metastatic HNSCC and second primary lung SCC differ profoundly. METHODS: We analyzed the origin of lung tumors in 32 patients with HNSCC using human papillomavirus (HPV) typing and targeted next generation sequencing of all coding exons of tumor protein 53 (TP53). RESULTS: Lung tumors were clearly identified as HNSCC metastases or second primary tumors in 29 patients, thus revealing that 16 patients had received incorrect diagnoses based on clinical and morphological data alone. CONCLUSION: The HPV typing and mutation analysis of all TP53 coding exons is a valuable diagnostic tool in patients with HNSCC and concurrent lung SCC, which can help to ensure that patients receive the most suitable treatment.