Semantic parsing is a key NLP task that maps natural language to structured meaning representations. As in many other NLP tasks, SOTA performance in semantic parsing is now attained by fine-tuning a large pretrained language model (PLM). While effective, this approach is inefficient in the presence of multiple downstream tasks, as a new set of values for all parameters of the PLM needs to be stored for each task separately. Recent work has explored methods for adapting PLMs to downstream tasks while keeping most (or all) of their parameters frozen. We examine two such promising techniques, prefix tuning and bias-term tuning, specifically on semantic parsing. We compare them against each other on two different semantic parsing datasets, and we also compare them against full and partial fine-tuning, both in few-shot and conventional data settings. While prefix tuning is shown to do poorly for semantic parsing tasks off the shelf, we modify it by adding special token embeddings, which results in very strong performance without compromising parameter savings.

Task-oriented parsing (TOP) aims to convert natural language into machine-readable representations of specific tasks, such as setting an alarm. A popular approach to TOP is to apply seq2seq models to generate linearized parse trees. A more recent line of work argues that pretrained seq2seq2 models are better at generating outputs that are themselves natural language, so they replace linearized parse trees with canonical natural-language paraphrases that can then be easily translated into parse trees, resulting in so-called naturalized parsers. In this work we continue to explore naturalized semantic parsing by presenting a general reduction of TOP to abstractive question answering that overcomes some limitations of canonical paraphrasing. Experimental results show that our QA-based technique outperforms state-of-the-art methods in full-data settings while achieving dramatic improvements in few-shot settings.

The largest, multicenter, prospective analysis of patients with high-risk nonmetastatic ccRCC demonstrates the utility of CAIX score as a statistically significant prognostic biomarker for survival. It recommends that CAIX score be quantified for all patients with high-risk disease after nephrectomy.

Predictive model consisting of smoking history (p=0.040), T stage (p<0.0001), Fuhrman grade (p<0.0001), Eastern Cooperative Oncology Group performance status (p<0.0001), and microvascular invasion (p<0.0001) was independently associated with lymphatic spread. After adjustment with these clinical variables, low carbonic anhydrase IX (CAIX) (p=0.043) and high epithelial vascular endothelial growth factor receptor 2 (p=0.033) protein expression were associated with a higher risk of lymphatic spread, and loss of chromosome 3p (p<0.0001) with a lower risk.

In patients with RCC, a history of smoking was associated with worse pathologic features and survival outcomes and with an increased risk of having mutated p53. Further investigation of the genetic and molecular mechanisms associated with decreased CSS in patients with RCC who have a history of smoking is indicated.

CAIX was expressed differentially in noninvasive versus invasive tumors, in low-grade versus high-grade bladder cancer, and in primary tumors versus metastases. The current results indicated that CAIX is a strong predictor of recurrence, progression, and overall survival of patients with bladder cancer; and the integration of CAIX expression into conventional prognostic models significantly improved their predictive accuracy. The data suggest a tripartite role of CAIX as a diagnostic, prognostic, and therapeutic molecular marker in bladder cancer.

Among patients with locoregional clear-cell renal-cell carcinoma at high risk for tumor recurrence after nephrectomy, the median duration of disease-free survival was significantly longer in the sunitinib group than in the placebo group, at a cost of a higher rate of toxic events.

The unique structure of darolutamide is characterized by a high affinity for androgen receptors and detainment of antagonist activity in mutant isoforms of androgen receptors. In clinical practice, this is the main reason that makes darolutamide exceptional in terms of safety and efficacy compared to other drugs in this category. Darolutamide is considered to have the lowest probability for adverse events (AEs) compared to apalutamide and enzalutamide. Future studies, along with real-world clinical data are warranted to improve personalized treatment strategies as well as sequencing treatment between approved novel drugs.

Development of tumor-specific algorithms for the risk of VTEs is advisable. Patients with aUTC and a history of vascular events are at high risk for VTE development, and prophylaxis should be prospectively studied in this group.

Prostate cancer is the most common cancer in men. Regardless of the initial treatment of localized disease, almost all patients develop castration resistant prostate cancer (CRPC). A better understanding of the molecular mechanisms behind castration resistance has led to the approval of novel oral androgen receptor (AR) antagonists, such as enzalutamide and apalutamide. Indeed, research has accelerated with numerous agents being studied for the management of CRPC. Areas covered: Herein, the authors present currently used and emerging AR antagonists for the treatment of CRPC. Emerging agents include darolutamide, EZN-4176, AZD-3514, and AZD-5312, apatorsen, galeterone, ODM-2014, TRC-253, BMS-641988, and proxalutamide. Expert opinion: Further understanding of the mechanisms leading to castration resistance in prostate cancer can reveal potential targets for the development of novel AR antagonists. Current novel agents are associated with modest clinical and survival benefit, while acquired resistance and safety issues are under continuous evaluation. The combination of AR antagonists used and ideal sequencing strategies are key tasks ahead, along with the investigation of molecular biomarkers for future personalized targeted therapies. In the future, the challenge will be to determine an AR antagonist with the best combination of outcome and tolerability.

The aim of the study was to identify prognostic molecular profiles in patients with mRCC treated with sunitinib, we performed immunohistochemical analysis for VEGF and PI3K/Akt/mTOR pathway components. Immunohistochemistry for VEGF and p-mTOR proteins may discriminate patients refractory to first-line sunitinib with poor prognosis.

Further understanding of the mechanisms leading to castration resistance in prostate cancer can reveal potential targets for the development of novel anti-cancer agents. Except for the development of novel antiandrogens and CYP-17 modulators, bipolar androgen therapy is an interesting therapeutic approach. The combinations of the novel agents tested in Phase I and II studies with established agents is another field of interest. The real challenge is to distinguish a novel anti-cancer agent with acceptable tolerability and the best outcome.