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OBJECTIVE Stereotactic radiosurgery (SRS) offers excellent tumor control for pituitary adenoma (PA); however, treatment failure occasionally necessitates salvage surgery. Comprehensive studies on salvage surgical outcomes for recurrent PA after SRS remain scarce. This study aimed to elucidate the outcomes of salvage endonasal transsphenoidal surgery (sETS) for progressive PA failing SRS. METHODS This retrospective, two institution–based cohort study analyzed data from patients who underwent sETS for progressive PA failing SRS. Progression-free survival (PFS), disease-specific survival (DSS), and neurological and endocrinological outcomes in the sETS group were analyzed and compared with those in the primary ETS (pETS) group after propensity score matching using the following variables: age at surgery, maximum tumor diameter, highest Knosp–Steiner classification, and tumor type. RESULTS Thirteen sETS patients (8 males [62%], median age at surgery of 56 years) with 5 nonfunctioning (39%), 6 corticotropic (46%), and 2 other functioning (15%) PAs who received median (range) follow-up of 125 ( 23–169) months were included. None of the patients experienced new neurological deficits or death after sETS. The median (range) tumor resection rate was 90% (80%–100%). The 5-year PFS and DSS rates were 55% and 77%, respectively. All 4 patients (31%) who experienced recurrence after sETS had corticotropic tumors. In the matched cohort analysis between the sETS group with 12 patients and the pETS group with 12 patients, no significant differences were observed in surgical outcomes. PFS rates were marginally higher in the sETS group than in the pETS group (80% vs 49% at 3 years, p = 0.216, log-rank test), and DSS was similar between the two groups (p = 0.543, log-rank test). CONCLUSIONS The authors’ results indicate that ETS can be safely performed as a salvage treatment after failed SRS with low complication rates and satisfactory tumor control in treatment-resistant PA.

nema

Microglia actively survey the brain and dynamically interact with neurons to maintain brain homeostasis. Microglial Gi protein–coupled receptors (Gi-GPCRs) play a critical role in microglia-neuron communications. However, the impact of temporally activating microglial Gi signaling on microglial dynamics and neuronal activity in the homeostatic brain remains largely unknown. In this study, we used Gi-based designer receptors exclusively activated by designer drugs (Gi-DREADD) to selectively and temporally modulate microglial Gi signaling pathway. By integrating this chemogenetic approach with in vivo two-photon imaging, we observed that exogenous activation of microglial Gi signaling transiently inhibited microglial process dynamics, reduced neuronal activity, and impaired neuronal synchronization. These altered neuronal functions were associated with a decrease in interactions between microglia and neuron somata. Together, this study demonstrates that acute, exogenous activation of microglial Gi signaling regulates neuronal circuit function, offering a potential pharmacological target for the neuromodulation through microglia.

Abstract Purpose: Monitoring disease progression in patients with high-grade gliomas (HGG) is challenging due to treatment-related changes in imaging and the requirement for neurosurgical intervention to obtain diagnostic tissue. DNA junctions in HGG often amplify oncogenes, making these DNA fragments potentially more abundant in blood than monoallelic mutations. In this study, we piloted a cell-free DNA approach for disease detection in the plasma of patients with HGG by leveraging patient-specific DNA junctions associated with oncogene amplifications. Experimental Design: Whole-genome sequencing of grade 3 or 4 isocitrate dehydrogenase–mutant or wild-type astrocytomas was utilized to identify amplified junctions. Individualized qPCR assays were developed using patient-specific primers designed for the amplified junction. ctDNA levels containing these junctions were measured in patient plasma samples. Results: Unique amplified junctions were evaluated by individualized semi-qPCR assays in presurgical plasma of 18 patients, 15 with tumor-associated focal amplifications and three without tumor-associated focal amplifications. high copy-number junctions were robustly detected in the plasma of 14 of 15 (93.3%) patients with amplified junctions and none of the controls. Changes in junction abundance correlated with disease trajectory in serial plasma samples from five patients, including increased abundance of amplified junctions preceding radiographic disease progression. Conclusions: In patients with grade 3 or 4 astrocytomas who had tumor-associated amplifications, patient-specific amplified junctions were successfully detected in assayed plasma from most patients. Longitudinal analysis of plasma samples correlated with disease trajectory, including cytoreduction and progression.

Abstract Human chromosomal anomalies, notably trisomies, disrupt gene expression, leading to diverse cellular and organ phenotypes. Increased cellular senescence (SEN) and oxidative stress in trisomies have gained recent attention. We assessed SEN, SEN-associated secretory phenotype (SASP), and oxidative stress on trisomy 13 (T13), T18, and T21 human fetal lung tissues and isolated primary human fetal lung fibroblasts. Telomerase-associated foci staining showed DNA damage primarily within T21 and T18 lungs. These results were confirmed by real-time quantitative PCR showing an increase of the SEN marker CDKN2B and SASP markers IL-6 and CXCL8. In contrast, lung tissues from T13 showed an upregulation of CDKN2A, whereas no significant changes in SASP marker genes were observed. γ-H2AX (H2A histone family member X) was upregulated in each genotype, particularly in T21. Isolated fibroblasts demonstrated a strong association between T21 and several SEN markers. An increase of γ-H2AX–positive cells were observed in fibroblasts from T21, T18, and T13, but only T21 exhibited an increase in P21 expression. Only T21 fibroblasts displayed a significant increase in reactive oxygen species levels, as indicated by MitoSOX and CellROX. This study provides the first evidence of a link between SEN and trisomy anomalies during prenatal human lung development, particularly in T21.

Purpose: Palliative care focuses on providing relief from the stress and symptoms of serious illness. Obstetrician gynecologists (OBGYNs) manage a variety of complex clinical situations ranging from delivering a cancer diagnosis to caring for a neonatal demise to symptom management, all of which fall within the realm of palliative care. Palliative care is relevant to the practice of OBGYNs, yet residents rarely receive formal primary palliative care training. We sought to design, implement, and evaluate a dedicated curriculum on palliative care for OBGYN residents. Methods: We performed a needs assessment of an OBGYN residency at a single institution. This information was used to develop a multidisciplinary palliative care curriculum. Post-curriculum surveys were distributed and analyzed. Descriptive statistics were utilized. Results: Our needs assessment identified a lack of knowledge and competency in palliative care concepts and skills. Using this information, we created a multidisciplinary palliative care curriculum, including clinical experiences, chalk talks, and online didactics. Post-curriculum surveys revealed a marked improvement in both knowledge base and perceived competence. Feedback was overwhelmingly positive; residents indicated that the curriculum was both a valuable use of time and would impact their provision of care in the future. Conclusion: A dedicated palliative care curriculum is an impactful addition to OBGYN training.

Abstract Objective The fast accumulation of vast pharmacogenomics data of cancer cell lines provide unprecedented opportunities for drug sensitivity prediction (DSP), a crucial prerequisite for the advancement of precision oncology. Recently, Generative Large Language Models (LLM) have demonstrated performance and generalization prowess across diverse tasks in the field of natural language processing (NLP). However, the structured format of the pharmacogenomics data poses challenge for the utility of LLM in DSP. Therefore, the objective of this study is multi-fold: to adapt prompt engineering for structured pharmacogenomics data toward optimizing LLM’s DSP performance, to evaluate LLM’s generalization in real-world DSP scenarios, and to compare LLM’s DSP performance against that of state-of-the-science baselines. Methods We systematically investigated the capability of the Generative Pre-trained Transformer (GPT) as a DSP model on four publicly available benchmark pharmacogenomics datasets, which are stratified by five cancer tissue types of cell lines and encompass both oncology and non-oncology drugs. Essentially, the predictive landscape of GPT is assessed for effectiveness on the DSP task via four learning paradigms: zero-shot learning, few-shot learning, fine-tuning and clustering pretrained embeddings. To facilitate GPT in seamlessly processing the structured pharmacogenomics data, domain-specific novel prompt engineering is employed by implementing three prompt templates (i.e., Instruction, Instruction-Prefix, Cloze) and integrating pharmacogenomics-related features into the prompt. We validated GPT’s performance in diverse real-world DSP scenarios: cross-tissue generalization, blind tests, and analyses of drug-pathway associations and top sensitive/resistant cell lines. Furthermore, we conducted a comparative evaluation of GPT against multiple Transformer-based pretrained models and existing DSP baselines. Results Extensive experiments on the pharmacogenomics datasets across the five tissue cohorts demonstrate that fine-tuning GPT yields the best DSP performance (28% F1 increase, p-value= 0.0003) followed by clustering pretrained GPT embeddings (26% F1 increase, p-value= 0.0005), outperforming GPT in-context learning (i.e., few-shot). However, GPT in the zero-shot setting had a big F1 gap, resulting in the worst performance. Within the scope of prompt engineering, performance enhancement was achieved by directly instructing GPT about the DSP task and resorting to a concise context format (i.e., instruction-prefix), leading to F1 performance gain of 22% (p-value=0.02); while incorporation of drug-cell line prompt context derived from genomics and/or molecular features further boosted F1 score by 2%. Compared to state-of-the-science DSP baselines, GPT significantly asserted superior mean F1 performance (16% gain, p-value<0.05) on the GDSC dataset. In the crosstissue analysis, GPT showcased comparable generalizability to the within-tissue performances on the GDSC and PRISM datasets, while statistically significant F1 performance improvements on the CCLE (8%, p-value=0.001) and DrugComb (19%, p-value=0.009) datasets. Evaluation on the challenging blind tests suggests GPT’s competitiveness on the CCLE and DrugComb datasets compared to random splitting. Furthermore, analyses of the drug-pathway associations and log probabilities provided valuable insights that align with previous DSP findings. Conclusion The diverse experiment setups and in-depth analysis underscore the importance of generative LLM, such as GPT, as a viable in silico approach to guide precision oncology. Availability https://github.com/bioIKEA/SensitiveCancerGPT