Grace Lu-Yao

Background: T-cell redirecting therapies, including T-cell engagers (TCEs) and Chimeric Antigen Receptor T-cell (CAR-T), are under investigation in metastatic castration-resistant prostate cancer (mCRPC). Despite their promising therapeutic potential, these therapies remain in early stages of development due to treatment-related adverse events (TRAEs). This study aims to quantify the frequency and severity of adverse events in clinical trials with a pooled analysis. Materials and Methods: A systematic search of PubMed, CINHAL, Scopus, and Ovid was conducted to identify clinical trials evaluating TCEs or CAR-T in patients with mCRPC. Types and frequencies of TRAEs were collected and analyzed using a random-effects model. Results: Thirteen TCEs trials involving 861 patients and 5 CAR-T trials involving 55 patients were identified. Cytokine release syndrome occurred in 59% (95% confidence interval: 25, 86) and 43% (28, 59) with TCEs and CAR-T, respectively. Neurologic TRAEs occurred in 39% (22, 60) with CAR-T compared to 9% (4, 17) with TCEs (P < 0.0001). Hematologic and hepatic TRAEs occurred in 34% (8, 75) and 25% (12, 44) with CAR-T, compared to 23% (7, 56) and 33% (12, 64) with TCEs, respectively. Musculoskeletal/dermatologic TRAEs occurred in 33% (24, 42) and 29% (8, 58) with TCEs and CAR-T, respectively. Renal and gastrointestinal TRAEs occurred in 21% (5, 51) and 14% (2, 43) with CAR-T, compared to 15% (8, 25) and 21% (12, 36) with TCEs, respectively. Conclusions: These data underscore the TRAE profiles associated with these therapies and emphasize the importance of differentiating off-target toxicities from immune effector cell—toxicities to inform the development of effective TRAE mitigation strategies.

This population-based study aimed to quantify fracture risk after ARPIs in PCa patients by pre-existing health conditions. Patients were identified from the SEER-Medicare files who received abiraterone with prednisone (AAP) or enzalutamide (ENZA) between 1/1/2013 and 12/31/2020. Health and fracture history were based on claims one year before ARPI with follow-up through 12/31/2020. The main outcome of the study was the cumulative fracture risk after first date of ARPI. Fine and Gray’s sub-distribution hazard model was used to obtain adjusted relative risks with confounding factors. This study included 10,463 patients (6,037- AAP; 4,426-ENZA). The 3-year fracture risk after ARPI was high, exceeding 25% among those without a prior fracture. Among 1,445 men with a fracture the year before ARPI, 3-year fracture risk exceeded 50%, and remained high (above 44%) despite using bone health agents (BHA). A recent history of fracture was associated with a 2.84-fold fracture risk (aHR: 2.84, 95% CI 2.58-3.12). Pre-existing osteoporosis and a comorbidity score ≥ 2 were associated with 15% (aHR : 1.15, 95% CI 1.03-1.29) and 11% (aHR : 1.11, 95% CI 1.00 to 1.24) higher fracture risks. Bone health agent (BHA) use was associated with a 23% lower fracture risk (aHR: 0.77, 95% CI 0.70-0.83). Fracture risk after ARPI was high, exceeding 44% within 3 years in those with prior fractures despite BHA, suggesting limited benefit in patients with poor bone quality. Early identification and intervention for patients at high risk of fractures is critical.

Cancer staging is critical for patient prognosis and treatment planning, yet extracting pathologic TNM staging from unstructured pathology reports poses a persistent challenge. Existing natural language processing (NLP) and machine learning (ML) strategies often depend on large annotated datasets, limiting their scalability and adaptability. In this study, we introduce two Knowledge Elicitation methods designed to overcome these limitations by enabling large language models (LLMs) to induce and apply domain-specific rules for cancer staging. The first, Knowledge Elicitation with Long-Term Memory (KEwLTM), uses an iterative prompting strategy to derive staging rules directly from unannotated pathology reports, without requiring ground-truth labels. The second, Knowledge Elicitation with Retrieval-Augmented Generation (KEwRAG), employs a variation of RAG where rules are pre-extracted from relevant guidelines in a single step and then applied, enhancing interpretability and avoiding repeated retrieval overhead. We leverage the ability of LLMs to apply broad knowledge learned during pre-training to new tasks. Using breast cancer pathology reports from the TCGA dataset, we evaluate their performance in identifying T and N stages, comparing them against various baseline approaches on two open-source LLMs. Our results indicate that KEwLTM outperforms KEwRAG when Zero-Shot Chain-of-Thought (ZSCOT) inference is effective, whereas KEwRAG achieves better performance when ZSCOT inference is less effective. Both methods offer transparent, interpretable interfaces by making the induced rules explicit. These findings highlight the promise of our Knowledge Elicitation methods as scalable, high-performing solutions for automated cancer staging with enhanced interpretability, particularly in clinical settings with limited annotated data.

Background/objectives: Multiple systemic treatments are available for metastatic castration-resistant prostate cancer (mCRPC), with unclear safety profiles. This study seeks to describe the safety determined in randomized clinical trials of a systemic treatment for mCRPC and whether safety differs by age. Methods: We utilized individual patient data from industry-funded phase 2/3 trials in mCRPC on abiraterone acetate (AA). Vivli, a clinical trial repository site, was used. One investigator independently performed screening. Relative effects of treatment were assessed with frequencies and odds of serious adverse events (SAEs). The Preferred Reporting Items for Systematic Reviews and Meta-analyses guideline was used. Subgroup analysis measured odds of SAEs as modified by age. Results: We identified 14 trials with 4296 patients. The median age of participants was 69 years. Nearly all participants experienced at least one adverse event (98.4% abiraterone, 97.3% standard of care [SOC]). More serious adverse events (grade 3 or 4) and deaths (grade 5) occurred in those receiving SOC (71.8%) compared to abiraterone (64.1%). The most frequent adverse event category was “Musculoskeletal and Connective Tissue Disorders”. The most frequent event types included anemia, back pain, hypertension, fatigue, hypokalemia, and bone pain. The odds of all events were lower in those receiving abiraterone compared to SOC. Odds of a serious musculoskeletal event were lower in older subjects by 22% (OR 0.78; 95% CI 0.63, 0.96). Conclusions: In this IPD meta-analysis, abiraterone acetate provides no greater risk of SAE in those receiving abiraterone than those receiving SOCs. Patients in the RCTs are younger and healthier than those in the general population; consequently, the results of RCTS might not be applied to the general population, especially those under-represented in the RCTs. There is a need to further evaluate abiraterone-related fractures and neuromuscular toxicities (NMTs) as key outcomes to gain insight into risk factors related to these adverse events. A real-world prospective study is warranted to examine the overall risks and benefits associated with treatment.

Background: Older adults with advanced prostate cancer and type 2 diabetes mellitus are underrepresented in trials of androgen receptor pathway inhibitors. This study examined changes in unplanned hospitalization rates in patients receiving androgen receptor pathway inhibitors by type 2 diabetes mellitus status and assessed if unplanned hospitalization varies according to androgen receptor pathway inhibitors. Methods: This population-based study of advanced prostate cancer patients aged older than 66 years used Surveillance, Epidemiology, and End Results–Medicare data. Prepost androgen receptor pathway inhibitor initiation changes and androgen receptor pathway inhibitor differences in unplanned hospitalization rates were estimated by adjusted incidence rate ratio with considerations for interactions between period, androgen receptor pathway inhibitor, and type 2 diabetes mellitus status. Linear contrasts were used to estimate and test conditional incidence rate ratios. Tests were 2-sided, and a P value less than .05 was considered statistically significant. Results: The study included 12 240 patients: 3160 (25.8%) with type 2 diabetes mellitus, 7191 (58.8%) received abiraterone acetate with prednisone, and 5049 (41.2%) received enzalutamide. Unplanned hospitalization rates increased after androgen receptor pathway inhibitor initiation by 65% among patients with type 2 diabetes mellitus complications (adjusted incidence rate ratio = 1.65, 95% confidence interval [CI] = 1.37 to 1.98) and 109% in nondiabetics (adjusted incidence rate ratio = 2.09, 95% CI = 1.94 to 2.26). Among patients with type 2 diabetes mellitus without complications, the increase in unplanned hospitalization rates depended on the androgen receptor pathway inhibitor initiated: 103% after abiraterone acetate with prednisone (adjusted incidence rate ratio = 2.03, 95% CI = 1.70 to 2.43) and 47% after enzalutamide (adjusted incidence rate ratio = 1.47, 95% CI = 1.21 to 1.80) and a 38% greater increase in unplanned hospitalization rates after abiraterone acetate with prednisone than enzalutamide (ratio of abiraterone acetate with prednisone adjusted incidence rate ratio divided by enzalutamide adjusted incidence rate ratio = 1.38, 95% CI = 1.06 to 1.80). Conclusions: All patients had higher unplanned hospitalization rates after androgen receptor pathway inhibitor. Our findings highlight the importance of using real-world data to better understand the interplay between preexisting health conditions and treatment outcomes, a critical step toward precision medicine.

Breast cancer therapies have dramatically improved survival rates, but their long-term effects, especially on aging survivors, need careful consideration. This review delves into how breast cancer treatments and aging intersect, focusing on the epigenetic changes triggered by chemotherapy, radiation, hormonal treatments, and targeted therapies. Treatments can speed up biological aging by altering DNA methylation, histone modifications, and chromatin remodeling, affecting gene expression without changing the DNA sequence itself. The review explains the double-edged sword effect of therapy-induced epigenetic modifications, which help fight cancer but also accelerate aging. Chemotherapy and targeted therapies, in particular, impact DNA methylation and histone modifications, promoting chronic inflammation and shortening telomeres. These changes increase biological age, as seen in epigenetic clocks and biomarkers like p21, which also play roles in drug resistance and therapeutic decisions. Chronic inflammation, driven by higher levels of inflammatory cytokines such as TNF-α and IL-6 as well as telomere shortening, significantly contributes to the aging characteristics of breast cancer survivors. Non-coding RNAs, including microRNAs and long non-coding RNAs, are crucial in regulating gene expression and aging pathways altered by these treatments. This review explores new therapies targeting these epigenetic changes, like DNA methylation inhibitors, histone deacetylase inhibitors, and microRNA-based treatments, to reduce the aging effects of cancer therapy. Non-drug approaches, such as dietary changes and lifestyle modifications, also show promise in combating therapy-induced aging. It also highlights the clinical signs of aging-related side effects, such as heart and lung problems, endocrine and reproductive issues, and reduced quality of life. The development of comprehensive methods like the CHEMO-RADIAT score to predict major cardiovascular events after therapy is discussed. Understanding the epigenetic changes caused by breast cancer therapies offers valuable insights for creating interventions to enhance the health span and quality of life for survivors. Continued research is crucial to fully understand these epigenetic alterations and their long-term health impacts.

Background: Racial disparities exist in prostate cancer care and outcomes. Ultrasound-guided biopsy may miss a sizeable portion of clinically significant prostate cancer, while magnetic resonance imaging (MRI) improves its detection. This study aimed to investigate demographic and socioeconomic status–related factors influencing MRI use for prostate cancer detection. Methods: Surveillance, Epidemiology, and End Results Program–Medicare data were used to assess use of prediagnostic MRI in 90 908 patients with a diagnosis of primary prostate cancer (2012-2019). Modified Poisson regression models adjusted for socioeconomic factors such as income, education, Medicare buy-in, and dual eligibility, were used to examine factors associated with MRI use. All statistical tests were 2 sided. Results: Prediagnostic MRI use increased substantially between 2012 (3.8%) and 2019 (32.6%). The disparity in utilization between non-Hispanic Black and non-Hispanic White patients decreased by more than half, from 43% (relative risk [RR] = 0.57, 95% confidence interval [CI] = 0.48 to 0.67) in 2012 to 20% (RR = 0.80, 95% CI = 0.74 to 0.86) in 2019. Rural residents were 35% less likely (RR = 0.65, 95% CI = 0.61 to 0.69) to undergo MRI, while individuals in the US Census Central (vs West) region of the United States were 49% less likely (RR = 0.49, 95% CI = 0.48 to 0.51). No significant disparities in MRI use were identified between individuals 75 years of age and older and individuals aged 64 to 75 years. Socioeconomic status factors associated with MRI use were income, education, Medicare buy-in, and dual eligibility. Conclusions: This study revealed increased MRI utilization over time, including among individuals 75 years of age and older. Racial disparities decreased, while wide urban-rural disparities remained. Targeted public health interventions should focus on geographical factors because urban or rural designations and US region were associated with the most prominent disparities. Future research should explore pathways contributing to these disparities by using a multidisciplinary approach, including geographical studies, to help eliminate health-care inequities.