Seema Nagpal

Compared to hormone receptor (HR)-positive breast cancers, triple-negative and HER2+ subtypes demonstrate greater prevalence of central nervous system (CNS) metastasis, including its most aggressive form, leptomeningeal disease (LMD). In LMD, cancer cells infiltrate the leptomeninges of the brain and spine, resulting in rapid progression, with dismal prognosis and lack of effective treatment. In this retrospective analysis of 141 breast cancer patients with LMD treated at Stanford Hospital (2008–2020), we sought to provide a comprehensive evaluation of each HR (estrogen receptor (ER), progesterone receptor (PR)) and HER2 receptor status at multiple timepoints between primary diagnosis and CNS metastasis, with a particular focus on the impact of HR discordance events on disease outcomes. Receptor status was recorded at the primary tumor diagnosis, at subsequent local recurrence, non-CNS metastases, brain metastases and LMD. Linear and Cox regression analyses were performed to evaluate the impact of HR status and HR discordance between primary breast cancer and LMD on disease progression. HR status at local recurrences and distant metastases, assessed by immunohistochemistry and clinical notes, was incorporated as a time-dependent covariate. A systematic review and meta-analysis were performed to compare receptor status distributions and dynamics in the Stanford cohort with published CNS metastasis cohorts. For both ER and PR, negative HR status at primary breast cancer diagnosis was associated with higher CNS tropism, a shorter time to LMD diagnosis, and reduced survival following LMD diagnosis. Since initial diagnosis, receptor status switched in 29% (41 out of 141) of patients, with HRs switched in 26% (37/141) and HER2 switched in 7% (10/141) of patients. HR loss was a predominant change after initial diagnosis and was strongly associated with increased CNS tropism and LMD. Our results confirm the value of reassessing receptor status at recurrence and identify loss of ER or PR expression as a potential marker of CNS-directed disease progression that warrants further study.

Background: Leptomeningeal disease (LMD) is a devastating complication of metastatic breast cancer that leads to severe neurologic symptoms and poor prognosis. Current treatment options, including radiation and intrathecal chemotherapy, offer limited efficacy and are associated with significant toxicity. Generally, systemic therapy in LMD poorly penetrates the blood-brain barrier (BBB). Leveraging the L-type amino acid transporter 1 (LAT1) represents a unique therapeutic strategy, as LAT1 facilitates transport of medications across the BBB, and is also highly expressed in metastatic cancer cells. QBS72S is a first-in-class BBB-penetrant bifunctional molecule targeting overexpression of LAT1 to selectively eliminate cancer cells. It has shown preclinical efficacy in a mouse model of breast cancer LMD. Methods: This Phase 2a, single-arm, open-label clinical trial (NCT05305365) investigated the safety, cerebrospinal fluid (CSF) concentration, pharmacokinetics, and preliminary efficacy of QBS72S in participants with breast cancer brain metastases. Secondary endpoints included progression-free survival, overall survival, and duration of response. Correlative analyses included LAT1 immunohistochemistry and CSF cell-free RNA sequencing. Results: In this analysis, we report results from breast cancer participants with LMD (n = 10) treated with QBS72S. QBS72S was reliably detected in CSF, adequately tolerated, and two participants exhibited long-term radiographic stability or improvement lasting 6-7 months. Conclusions: These early findings support further investigation of QBS72S as a targeted therapy for LMD, addressing an urgent unmet need in metastatic breast cancer.

Leptomeningeal disease (LMD) is a devastating complication of cancer, and its incidence is rising. The inaugural LMD Think Tank Meeting took place in 2023 during the Society for Neuro-Oncology (SNO) and American Society of Clinical Oncology (ASCO) Central Nervous System (CNS) annual meeting, convening an international group of clinicians and scientists dedicated to understanding and treating LMD in a multidisciplinary fashion. While this meeting laid the foundation for a SNO/ASCO consensus review on the clinical management of LMD, the frequent exclusion of LMD patients from trials was identified as a clear and pervasive challenge. To address this gap, a follow-up assembly convened in 2024 with a goal of developing an SNO/ASCO consensus on best practices for clinical research; this included addressing the role of industry and basic scientists in clinical trials, providing guidance for improved clinical trial design, and accelerating the enrollment of LMD patients into clinical trials. Academic-industry partnerships and multi-institutional collaborations are crucial to advance our understanding of LMD, expand treatment access, and ultimately, improve survival and quality of life for our patients.

Background and purpose High-grade gliomas pose significant diagnostic imaging challenges in their initial diagnosis, evaluation of treatment-related change versus recurrent/progressive disease, and prognostication. 1-((2-fluoro-6-[¹⁸F]fluorophenyl)sulfonyl)-4-((4-methoxyphenyl)sulfonyl)piperazine ([¹⁸F]DASA-23) which binds pyruvate kinase M2 (PKM2), an important enzyme in glycolysis, was developed to probe the metabolic activity of high-grade gliomas. This pilot study sought to evaluate [¹⁸F]DASA-23 PET’s diagnostic performance in high-grade gliomas, how its uptake over time is associated with survival, and its relationship with conventional MR findings. Materials and methods This prospective pilot study enrolled healthy controls and patients with high-grade gliomas who received [¹⁸F]DASA-23 PET/MRI’s. In the latter group, scans were performed at the time of initial diagnosis or recurrence, with a sub-group also receiving follow-up scans during their therapy course. Three nuclear medicine physicians independently adjudicated the presence or absence of focal intracranial [¹⁸F]DASA-23 uptake, from which the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were determined. For patients with tumors, tumor-to-background SUVmax was calculated and correlated with progression-free survival (PFS). Correlation analysis between MRI perfusion parameters and [¹⁸F]DASA-23 TBRmax was also performed. As an exploratory analysis, the amount of [¹⁸F]DASA-23 uptake overlap with sites of both MRI T1 post-contrast enhancement and non-enhancing T2-FLAIR hyperintense signal about the resection cavity pre-therapy was determined. Results Four healthy controls and nine patients with high-grade gliomas were included in this study. The baseline [¹⁸F]DASA-23 PET demonstrated a sensitivity of 0.78, specificity of 1.00, and accuracy of 0.85 in the diagnosis of high-grade glioma. The follow-up [¹⁸F]DASA-23 PET exams in 5 patients at a mean of 46 days after initiation of therapy showed tumor to background ratio SUVmax (TBRmax) was significantly inversely correlated with PFS (r = -0.91, p = 0.03). MR dynamic contrast susceptibility (DSC) derived normalized cerebral blood volume (nCBV) and arterial spin labeling (ASL) derived normalized cerebral blood flow max (nCBFmax) were not significantly associated with [¹⁸F]DASA-23 uptake. Within the [¹⁸F]DASA-23 uptake regions of interest, 20.2% of the volume was occupied by T1 post-contrast enhancement, and 28.3% of the volume was non-enhancing T2 FLAIR hyperintensity, with no significant difference in the volume of enhancement and non-enhancing T2 FLAIR hyperintensity. Conclusion This pilot study demonstrated favorable [¹⁸F]DASA-23 PET diagnostic performance in the diagnosis of high-grade gliomas, and its uptake after initiation of therapy may with further research be useful as a marker to evaluate treatment efficacy and patient prognosis. Additionally, the absence of a significant relationship between [¹⁸F]DASA-23 uptake and MR perfusion markers, and [¹⁸F]DASA-23 PET’s limited overlap with enhancement and non-enhancing T2 FLAIR hyperintensity raises the possibility that future [¹⁸F]DASA-23 PET studies with larger patient samples could uncover clinically relevant information not fully captured with MR alone.

Brain metastases (BrM) are a frequent and devastating complication of non-small cell lung cancer (NSCLC), affecting up to 40% of patients during their disease course. Historically, treatment relied primarily on surgery and radiation, as systemic therapies were thought to have limited activity in the central nervous system (CNS). However, over the past two decades, the development of more targeted therapies has transformed the treatment landscape. Many of these newer agents have improved CNS penetration, which may offer patients the possibility of deferring or reducing the need for local therapies and their associated toxicities. Despite these advances, many challenges remain. There is still a lack of consistent inclusion of BrM patients in clinical trials, and when they are included, there are inconsistencies in measuring radiographic responses as well as in standardized CNS-specific endpoints, making it difficult to compare outcomes across therapies. As treatment options expand, the integration of systemic therapies with surgery and radiation requires nuanced, multidisciplinary decision-making tailored to individual patients. In this review, we aim to summarize the current treatment landscape of pertinent therapies for BrM in NSCLC patients while highlighting the need for broadening inclusion of these patients and creating well-designed prospective studies that intentionally include patients with active and untreated BrM alongside rigorously assess intracranial outcomes. Such efforts will be critical to developing treatment plans in order to ultimately improve survival and quality of life for NSCLC patients with BrM.

With more women entering the medical workforce, caregiving challenges and family-work conflicts are of growing importance to today's neurologists. The aim of this study was to assess the impact of caregiver (CG) status on academic achievements in neurology, analyze the division of labor and time devoted to domestic responsibilities, and measure family-work conflict in US academic neurology faculty. A total of 19 US neurology departments completed a survey on baseline demographics, academic achievements, CG status, division of domestic time and labor, and responses on a FWC scale. Variables were assessed using independent samples t tests (or Mann-Whitney U for non-normally distributed data) and X² analyses as appropriate, with CGs vs noncaregivers (N-CGs) serving as the independent groups. Women were twice as likely as men to categorize themselves as caregivers (p = 0.005). There were no significant differences in academic achievements between the CG and N-CG groups. However, CG men had a statistically significantly higher number of leadership positions (p = 0.022), number of publications as first or last author (p = 0.020), and number of awards (p = 0.004) when compared with CG women. The percentage of CG women faculty who reported spending more than 22 hours per week on child care was significantly higher than that of CG men (p = 0.003). Caregiver women also reported doing more work at home; taking care of children when they are sick; taking days off work when children are sick; handling more chores related to child care (e.g., managing activities/schedules); and doing laundry, cooking, and cleaning related to child care. Finally, the CG group experienced a higher level of FWC, as evidenced by responses on the FWC scale (p < 0.001). This finding was same for CG women compared with CG men (p = 0.034). Although the caregiving burden did not directly affect academic productivity, it significantly increased FWC in US academic neurology faculty surveyed. Female CG faculty disproportionately shouldered domestic and household responsibilities. Beyond recognizing CG challenges, advocating for a change in paradigm and providing solutions to these pervasive issues could be instrumental in preventing further attrition of professionals from our field, particularly women with caregiving roles.

Background Gliomas account for approximately 25.5% of all primary brain and central nervous system (CNS) tumors and 80.8% of malignant brain and CNS tumors. The prognosis varies considerably; patients with low-grade gliomas (LGGs) have 5-year survival rates of up to 80%, while patients with higher-grade gliomas (HGGs) often experience rates below 5%. Recurrence is a common challenge, occurring in 52% to 62% of patients with LGGs and 90% of patients with HGGs, complicating clinical management and treatment planning. Currently, no widely available models exist for reliably predicting early glioma recurrence, which is critical for optimizing patient outcomes. Machine learning (ML) and deep learning (DL) techniques have shown promise in predicting recurrence for various cancers, with those utilizing multimodal data sources showing increasing promise. Methods We developed a DL-based predictive model with attention mechanisms, gLioma recUrreNce Attention-based classifieR (LUNAR), to predict early vs. late glioma recurrence using clinical, mutation, and mRNA-expression data from patients with primary grade II-IV gliomas from The Cancer Genome Atlas (TCGA) and, as an external validation set, the Glioma Longitudinal Analysis Consortium (GLASS). Results Our model outperforms traditional ML models and non-attention counterparts, achieving area under the receiver operating characteristic curve (AUROC) of 82.84% and 82.54% on the TCGA and GLASS datasets, respectively. Conclusions Our results demonstrate the potential of multimodal DL classifiers for predicting early glioma recurrence. By integrating clinical, mutational, and transcriptomic data from patients, LUNAR enables improved risk stratification. Its consistent performance across two independent datasets underscores its robustness.