bluuebunny/crossref_metadata_2025_split huggingface dataset:
Real world data of apalutamid in the treatment of prostate cancer: Data of the German multicenter AmPel trial.
Authors: Hegele Axel , Schultheis Stefan , Skrobek Lennart , Hollwegs Sebastian , Weber Jost , Ludwig Martin , Bartsch-Polle Anke , Maywurm Manfred , Huwe Petra , Khalil Samuel , Häußermann Rainer , Thiemer Markus , Varughese Denny 
Published: June 2023

e17073 Background: Apalutamide (Apa) is a competitive inhibitor of the androgen receptor binding directly to the ligand-binding domain. In metastatic castration-sensitive prostate cancer (mCSPC) as well as non-metastatic castration-resistant prostate cancer (nmCRPC) Apa demonstrated a survival benefit in Phase-III trials. Nevertheless Real World data are still lacking. Methods: In the retrospective, multicenter AmPel trial (Apalutamid in prostate cancer in middle-Hessen) data of mCSPC and nmCRPC pat were collected before and during Apa treatment in routine clinical practice. Beside pat and tumor specific data side-effects, dose-modifications, response and subsequent therapies were assessed. Results: To date 100 pat were included since 11/2021. General data are summarized in table 1. mCSPC (n = 74): mFU 10 month (1-30). 75% showed mets at initial diagnosis: 56% with osseous, 15% with viszeral and 29% with oss/visz mets. PSA before therapy 13.5 ng/ml decreased to 0.2 ng/ml (-98.9%) after 3 mo. 77.4% showed a PSA decrease > 90% and 49% a PSA level of < 0.2 ng/ml after 6 mo. AEs ≥ 3: 12.3%. In 12.3% dose reduction was necessary. In 21% therapy was stopped due to toxicity (10.9%), progression (5.5%) and patient will (5.5%). 3 patients died: 1 due to PCA progression. PFS is not reached. nmCRPC (n = 26): mFU 15 month (3-30). Time after initial diagnosis: 6 y (1-22). PSA-Doubling time 3.9 mo (1-10). PSA before therapy 7.2 ng/ml with a decrease of 94.7% after 3 mo (0.45 ng/ml) and 62.5% with a PSA decrease > 90%. AEs ≥3: 5%. In 15.4% dose-reduction was necessary. In 24% therapy was stopped due to toxicity (5%), progression (9.5%) and patient will (9.5%). MFS is not reached. No increased adverse events were observed in case of statin and anticoagulant medication. Conclusions: Compared to Phase III trials this first real-world data of the AmPel-trial showed treatment of older and more co-morbid mCSPC and nmCRPC patients in clinical practice. Apa treatment resulted in a rapid PSA-response, was safe and effective with tolerable and manageable side effects in real world unselected patients. PSMA-PET/CT is not used in daily practice as well as Conference decisions before therapy. Updated data of the ongoing AmPel trial will be presented. [Table: see text]
Darolutamide and time to pain progression by disease volume in ARASENS.
Authors: Smith Matthew Raymond , Tombal Bertrand F. , Saad Fred , Fizazi Karim , Sternberg Cora N. , Crawford E. David , Aragon-Ching Jeanny B. , Schostak Martin , Tutrone Ronald , Mohamed Ateesha F. , Littleton Natasha , Li Rui , Srinivasan Shankar , Hussain Maha H. A. 
Published: June 2023

e17075 Background: In ARASENS (NCT02799602), darolutamide (DARO) + androgen-deprivation therapy (ADT) + docetaxel significantly reduced risk of death by 32.5% (hazard ratio [HR] 0.68, 95% confidence interval [CI[ 0.57–0.80; P< 0.0001) and significantly delayed time to pain progression (HR 0.79, 95% CI 0.66–0.95; P= 0.006) vs placebo (PBO) + ADT + docetaxel in patients with metastatic hormone-sensitive prostate cancer (mHSPC). We report time to pain progression in patients (pts) with high/low disease volume (HV/LV). Methods: Pts with mHSPC were randomized to oral DARO 600 mg twice daily or PBO, both + ADT + docetaxel. Pain was assessed by the Brief Pain Inventory short form “pain at its worst” score (WPS). Pain progression was defined as WPS increase ≥2 points from nadir (and absolute WPS ≥4 if > 0 at baseline) or initiation of opioid therapy for ≥7 consecutive days. A sensitivity analysis assessed pain progression after completion of docetaxel. HV/LV subgroups were defined per CHAARTED. Results: 1305 pts (DARO 651, PBO 654) were analyzed. At baseline, the mean WPS was 1.5 (standard deviation 1.9) vs 1.4 (1.8) in the DARO vs PBO groups; 258 (40%) vs 274 (42%) pts had no pain (WPS 0). DARO + ADT + docetaxel had a robust impact on delaying time to pain progression vs PBO + ADT + docetaxel in the post-docetaxel sensitivity analysis (stratified HR 0.75, 95% CI 0.62–0.90) and in the HV subgroup (unstratified HR 0.75, 95% CI 0.61–0.93) (Table). Overall, median time to pain progression was longer in the LV vs HV subgroup. WPS change from nadir was the main driver of pain progression events. Conclusions: In pts with mHSPC, the addition of DARO to ADT and docetaxel provided clinically meaningful benefit through delayed time to pain progression, notably in pts with HV disease. Increased overall survival, a delay in time to pain progression, and a favorable safety profile set DARO + ADT + docetaxel as a new standard of care for pts with mHSPC. Clinical trial information: NCT02799602 . [Table: see text]
Transcriptomic heterogeneity of metastatic disease timing within metastatic castration-sensitive prostate cancer.
Authors: Sutera Philip , Shetty Amol , Hakansson Alexander K. , Van der Eecken Kim , Song Yang , Liu Yang , Fonteyne Valerie , Verbeke Sofie , Song Daniel Y. , Ross Ashley , Feng Felix Y , Gillessen Silke , Attard Gerhardt , James Nicholas D. , Lotan Tamara L. , Davicioni Elai , Sweeney Christopher , Tran Phuoc T. , Deek Matthew Pierre , Ost Piet 
Published: June 2023

e17083 Background: Metastatic castration-sensitive prostate cancer (mCSPC) is commonly partitioned into high- and low-volume subgroups which have demonstrated differential biology, prognosis, and response to therapy. Timing of metastasis has similarly demonstrated differences in clinical outcomes, however less is known about any potential underlying biologic differences between these disease states. Herein we aim to compare transcriptomic differences between synchronous and metachronous mCSPC and identify any differential responses to therapy. Methods: We performed an international multi-institutional retrospective review of men with mCSPC who completed RNA expression profiling evaluation of their primary tumor. Patients were stratified according to disease timing (synchronous vs metachronous). The primary endpoint was to identify differences in transcriptomic profiles between disease time. Median genomic scores between groups were compared with Mann-Whitney U test. Secondary analyses included determining clinical and transcriptomic variables associated with overall survival (OS) from time of metastasis. Survival analysis was performed with the Kaplan-Meier Method and Multivariable Cox regression. Results: 252 patients were included with a median follow-up of 39.6 months. Patients with synchronous disease experienced worse 5-yr OS (39% vs 79%, p<0.01) and demonstrated lower median Androgen Receptor Activity (AR-A) (11.78 vs 12.64, p<0.01) and Hallmark Androgen Response (HAR) (3.15 vs 3.32; p<0.01). Multivariable cox-regression identified only high-volume disease (HR=4.97, 95%CI 2.71-9.10; p<0.01) and HAR score (HR=0.51, 95%CI 0.28-0.88; p=0.02 significantly associated with OS. Finally, patients with synchronous (HR=0.47, 95%CI 0.30-0.72; <0.01) but not metachronous (HR=1.37, 95%CI 0.50-3.92; p=0.56) disease were found to have better OS with Androgen Receptor (AR) + non-AR combination therapy as compared to monotherapy (p value for interaction = 0.05). Conclusions: We have demonstrated a potential biologic difference between metastatic timing of mCSPC. Specifically, for patients with low volume disease, those with metachronous low volume disease have a more hormone dependent transcriptional profile and exhibit a better prognosis than synchronous low volume disease. [Table: see text]
Relationship between androgen biosynthesis linked to 3βHSD1 and resistance to radiotherapy: A germline biomarker for combined androgen blockade with radiation in high-risk prostate cancer.
Authors: Ganguly Shinjini , Lone Zaeem M. , Muskara Andrew , Imamura Jarell , Gupta Shilpa , Ornstein Moshe Chaim , Sharifi Nima , Mian Omar Y. 
Published: June 2023

e17084 Background: Around 50% of men with advanced prostate cancer (PCa) have at least one germline copy of the adrenal-permissive (1245C) HSD3B1 allele. This allele leads to higher levels of the steroid biosynthesis enzyme, 3β-hydroxysteroid dehydrogenase (3βHSD1). Inheriting this allele is linked to worse outcomes in men with advanced PCa. To determine if (1245C) HSD3B1 could be causing early resistance to combined androgen deprivation and radiotherapy in localized PCa, we studied its role in modulating radioresistance. Methods: PCa cell lines were used to investigate the reciprocal effects of 3βHSD1 knockdown and overexpression in intrinsically high and low 3βHSD1 lines, respectively. PCa xenografts were used to confirm the results in vivo. The connection between androgen receptor (AR) expression and elevated DNA Damage Response (DDR) gene expression was validated using transcriptomic data from 680 radical prostatectomy specimens The ability of enzalutamide, a non-steroidal anti-androgen, to restore radiosensitivity in 1245C expressing lines was interrogated in vitro and in vivo. Results: 1245C HSD3B1 expressing PCa lines displayed increased clonogenic survival after ionizing radiation. Effects were dependent on the 3βHSD1 substrate, DHEA, confirming intratumoral androgen metabolism was required for radioresistance. Resistant lines showed enhanced DNA double-strand break (DSB) repair and heightened DDR gene expression. PCa xenografts with 1245C HSD3B1 were similarly more resistant to radiation using murine models that faithfully mimic human adrenal physiology. A correlation between AR expression and increased DDR gene expression was confirmed in 680 patient samples. Enzalutamide pretreatment resulted in a decrease in DSB repair ability and re-sensitized 1245C HSD3B1 PCa cells to radiation. Conclusions: 1245C HSD3B1 fuels prostate cancer treatment resistance by elevating regional androgen biosynthesis from adrenal steroid precursors, leading to DDR gene overexpression and enhanced DNA DSB repair kinetics. This work supports clinical validation of biomarker informed selective intensification strategies, e.g., combined androgen blockade, for high-risk men with the 1245C HSD3B1 allele receiving curative intent radiotherapy.
Impact of social determinants of health (SDOH) measures on prescribing advanced androgen blockade (AAB) in patients with metastatic hormone sensitive prostate cancer (mHSPC).
Authors: Ravilla Rahul , Wilson Thomas , Wentworth Chuck , Sundarrajan Sudha , Espirito Janet L. , Robert Nicholas J. 
Published: June 2023

e17086 Background: Use of AAB (abiraterone acetate, apalutamide or enzalutamide) has been shown to improve survival in mHSPC, but many patients are not prescribed this treatment. There is substantial interest in the broader role of SDOH in cancer. We explored how SDOH influences prescribing patterns of AAB in patients with mHSPC. Methods: Patients diagnosed with mHSPC between 1/1/2017 and 12/31/2021 were identified using the iKnowMed electronic health record database from The US Oncology Network of community oncology practices. Records were searched for prescriptions for AAB (based on intent to treat). Individual level measures were age, diagnosis year, ECOG, race and type of health insurance. Area level measures were Area Deprivation Index (ADI) at national and state level, and rural status. ADI is a validated metric based on demographic variables from census block groups; high ADI scores for state ( > 8) and national ( > 80) level are markers of low socioeconomic status. Logistic regression models were run on each SDOH variable and adjusted for confounding variables, including a joint distribution model of African American (AA) and ADI (significance at p < 0.05). Results: There were 3,855 patients identified with mHSPC: 40% had a prescription for AAB. Summary measures overall were mean age: 71 years, AA: 10%, Medicaid: 5%, ECOG 0-1 and 2+: 60% and 14% respectively; and diagnosis year 2017-21: 15%, 20%, 21%, 22%, and 21% respectively. Area level scores: National ADI high: 8%, State ADI high: 18%, rural: 5%. Interaction variable: AA + State ADI (high): 2%. Statistical differences between those with/without AAB (not shown) were diagnosis year, ECOG and age. Multivariate logistic models were adjusted for these 3 variables with the single SDOH measure as the primary independent variable and the binary variable AAB as the dependent variable (Table). Conclusions: There was no significant association between AAB prescriptions and AA race, rural status, ADI (state or national), or Medicaid status. These data indicate that SDOH measures do not appear to influence prescribing of these treatments. We believe this study is among the first to examine these particular SDOH measures and their relationship to the prescribing of oncology treatments. Further research should be conducted into the impact of SDOH measures on the fulfillment and compliance of these drugs. [Table: see text]
Darolutamide observational (DAROL) study in patients with nonmetastatic castration-resistant prostate cancer: Second interim analysis.
Authors: Yu Evan Y. , Pieczonka Christopher Michael , Suzuki Hiroyoshi , Bailen James L. , Gotto Geoffrey T. , Hamilton Joelle P. , Uemura Hiroji , Murphy Declan G. , Lebret Thierry , Luz Murilo de Almeida , Ortiz Jorge A. , Adorjan Patrick , Khan Javeed , Briganti Alberto , Armstrong Andrew J. 
Published: June 2023

e17095 Background: In ARAMIS (NCT02200614), darolutamide (DARO) significantly improved metastasis-free survival (MFS) by ~2 years (HR 0.41, 95% CI 0.34–0.50) and reduced the risk of death by 31% (HR 0.69, 95% CI 0.53–0.88) compared with placebo in men with nonmetastatic castration-resistant prostate cancer (nmCRPC), with a favorable tolerability profile. The ongoing DAROL study (NCT04122976) aims to understand the real-world safety and effectiveness of DARO in patients (pts) with nmCRPC. We report results from the pre-planned second interim analysis (IA). Methods: DAROL is a global, open-label, single-arm, non-interventional study in pts aged ≥18 years with confirmed nmCRPC for whom the decision to be treated with DARO is decided pre-enrollment. The primary endpoint is safety, including incidence, severity, and frequency of treatment-emergent adverse events (TEAEs). Secondary endpoints are patient demographics and disease characteristics, treatment duration, MFS, overall survival (OS), time to prostate-specific antigen (PSA) progression, and PSA response. Primary and secondary endpoints are reported after 300 pts completed ≥6 mo of treatment. All treated pts were assessed for safety while efficacy was evaluated in pts who did not violate any eligibility criteria and had ≥1 post-baseline assessment (cut-off October 11, 2022). Results: Of the 300 treated pts, the percentage enrolled in North America/Europe/Asia Pacific/Latin America was 45%/30%/25%/ < 1%, respectively. Median age was 80 years; 51% had a reported Gleason score > 7; 92% had a reported ECOG performance status of 0/1. Median baseline (BL) PSA was 3.9 ng/mL (range 0–248.0), with 21% of pts reporting PSA < 2 ng/mL. Median BL PSA doubling time (PSADT) was 5.3 mo (range 0–36.2), with 42% of pts reporting PSADT > 6 mo. At the data cut-off, median follow-up time was 14.8 mo (interquartile range [IQR] 10.9–19.3) and median treatment duration was 13.4 mo (IQR 9.3–17.8). The incidences of TEAEs and DARO-related TEAEs were generally low (Table), consistent with the safety profile of DARO reported in ARAMIS. Fatigue occurred in ≥5% of pts. For the 263 pts eligible for efficacy analysis, median time to PSA progression was 17.6 mo (95% CI 13.2–19.0); MFS/OS data remain immature. PSA declines from baseline of ≥30%, ≥50%, and ≥90% at any time were observed in 80%, 76%, and 54% of pts, respectively. Conclusions: In the DAROL second IA, under real-world conditions, DARO continued to show a favorable safety profile, consistent with the clinical profile of ARAMIS, with no new safety signals. Clinical trial information: NCT04122976 . [Table: see text]
Algorithm-based histologic grade and tumor ratio for radical prostatectomy: Comparison with pathology reports.
Authors: Kwak Tae-Yeong , Lee JaeHeon , Cho Joonyoung , Kim Sun Woo , Chang Hyeyoon , Ha Hong Koo 
Published: June 2023

e17096 Background: The histologic grade (Gleason score) as well as the tumor ratio of the resected specimen plays a significant role in assessing the clinical risk of prostate cancer patients who underwent radical prostatectomy. Gleason scoring is known to be prone to inter- and intra-observer discordance, and eyeballing measurement of tumor ratio is possibly coarse and inaccurate. Several deep learning-based tissue image analysis algorithms that perform prostate cancer diagnosis have been developed and are about to be applied clinically. We evaluated the utility of one of those algorithms by comparing its output with the pathology reports. Methods: A total of 29681 H&E-stained tissue slides were collected for 1001 radical prostatectomy cases during 2010-2021 at Pusan National University Hospital and scanned at 40x magnification into whole-slide images. On each slide, we utilized a tissue detection algorithm to identify tissue regions and measure the area. A deep learning-based algorithm was used to identify prostate cancer lesions, measure the area, and determine the grades. The area measurement was converted into the volume figure assuming that the tissue slice thickness was 5mm. The slide-wise algorithm outputs were then aggregated for each case, resulting in the specimen volume, tumor ratio, and Gleason score. In the evaluation, the algorithm-based Gleason scores were compared with the ones in the hospital pathology reports on the ISUP grade group basis. The correlation analysis was performed for the tumor ratio. The specimen density values were calculated from the volume figures and the hospital weight measurement and utilized to exclude the cases with extreme density values. Results: The min, max, and average number of slides per case were 1, 67, and 29.7. For two cases with a single slide collected, both the algorithm and the expert pathologist found no cancer. The pathologist also confirmed that 7 had residual tumors among 15 cases where there was no Gleason score in the pathology reports while the algorithm found cancer. For the remaining 984 cases, the algorithm provided the grade groups equal to, higher than, and lower than the pathology reports for 482, 384, and 118 cases respectively, showing moderate agreement. The min, max, and mean values of the specimen volume (mL) were 0.01, 75.3, and 32.7, while the corresponding values of the specimen density (g/mL) were 0.11, 3001.79, and 4.38. The density values at the lower and upper 2.5% were 0.42 and 1.40 respectively, and the mean of the values between them (inner 95%) was 0.92. The degree of tumor ratio correlation between the algorithm and the pathology reports was high with the coefficient 0.813 (95% CI: 0.791-0.833), which went up to 0.826 (95% CI: 0.805-0.845) for the inner 95% density cases. Conclusions: Our findings support the usefulness of the algorithm-based analysis of prostatectomy specimens, which can benefit clinicians in the hospital.
Machine learning to predict future PSA in patients with prostate cancer managed with active surveillance.
Authors: Ayed Aziz , Saillard Claire-Alix , Onofrey John A. , Moon Intae , Chang Steven Lee , Feldman Adam Scott , Nayan Madhur 
Published: June 2023

e17098 Background: Active surveillance (AS) for prostate cancer (PCa) requires serial assessments and there is a need to optimize resource utilization by personalizing AS. Evaluating serial PSA values is fundamental in AS and a model predicting future PSA may aid decision-making on optimizing the timing of the subsequent assessment. In this study, we use a machine learning (ML) approach to predict future PSA in AS patients. Methods: We searched electronic health record data at two tertiary academic centers for patients with a diagnosis of PCa or reason for visit related to PCa (e.g. elevated PSA) between 1990 and 2020. Within this group, we identified patients diagnosed with PCa and managed with AS. We trained different ML models (Table 1) to predict the next PSA value given a sequence of historical PSAs and expected date of next PSA assessment, and compared this prediction to the ground truth (actual PSA measured). We augmented the model with clinical features (age, prostate volume, and BMI). We compared the ML models to a baseline method that considers a patient-specific constant slope based on prior PSA values. The test set consisted of AS patients only (10% of all AS patients), not used in training. The dataset was split patient-wise. The primary performance metric was root-mean-square error (RMSE). We compared training using AS patients only to using all patients, still testing on AS patients only. We evaluated feature importance to facilitate model interpretability. Results: We identified 4269 patients (totaling 33371 PSA values) with at least 3 PSA values and complete data for the clinical features. Of these, 1134 were on AS (totaling 10358 PSA values). On average, AS patients were 65 years old (± 7 [standard deviation]), with a mean PSA of 6.5 ng/mL (± 6.4), prostate volume of 48g (± 27) and BMI of 28 kg/m2 (± 5), at diagnosis. The best performing model was the Gradient Boosting Regression model that was trained in the heterogeneous sample of all patients (including those not on AS) (Table 1). Evaluation of feature importance found that increasing the number of previous PSA measurements as input to the model further reduced the RMSE. Conclusions: Our study used machine learning to predict future PSA in PCa patients on AS. We also demonstrate that for our best performing model, a heterogeneous training sample further improved performance. While our model requires further validation, a robust similar model may be useful as a decision-aid tool to personalize AS and optimize resource-utilization. We have deployed our model at https://psa-evolution.streamlit.app/ . [Table: see text]