tbuckley/cabot-search huggingface dataset:
The impact of <i>SPOP</i> gene alterations in men with metastatic prostate cancer: Results from the Prostate Cancer Precision Medicine Multi-Institutional Collaborative Effort (PROMISE) consortium.
Authors: Kyra Lee Yamamoto, Nicholas Henderson, Clara Hwang, Pedro C. Barata, Mehmet Asim Bilen, Deepak Kilari, Laura Graham, Rohan Garje, Shoshana Rothstein, Vadim S Koshkin, Abhishek Tripathi, Frank Cameron Cackowski, Jones T. Nauseef, Michael Thomas Schweizer, Andrew J. Armstrong, Tanya B. Dorff, Ajjai Shivaram Alva, Rana R. McKay
Journal: Journal of Clinical Oncology (2024)

225 Background: Inactivating mutations in the SPOP gene, encoding speckled-type poxvirus and zinc-finger protein, are common among men with localized and metastatic prostate cancer, occurring at a frequency of 6-15%. Previous studies have suggested the presence of an inactivating mutation in SPOP results in increased sensitivity to androgen deprivation therapy (ADT). In this multi-institutional clinical-genomic database, we evaluated the impact of SPOP alterations on survival outcomes in men with metastatic prostate cancer. Methods: Retrospective data from the PROMISE Consortium were utilized for this analysis. Eligible patients had metastatic prostate cancer and had undergone standard of care next-generation sequencing (NGS). Patients with inactivating mutations in SPOP were defined as SPOP mutated, where. as those lacking such alterations were defined as SPOP wild-type. The primary endpoint was overall survival defined as the time from diagnosis of metastatic prostate cancer to death from any cause, censored at the date of last follow-up. Secondary endpoints included time from metastatic disease to castration resistance and time from castration resistance to death. Results: Of the 2097 patients with available NGS testing, 5.5% (n=115) had SPOP alterations. The median age at diagnosis was 63 years, 427 were Black, and 83 were Hispanic. At last assessment, 66% had bone metastases, 7% lung metastases, and 5% liver metastases. The most frequent co-occurring alterations in the SPOP-mutated group were: TP53 (30.4%), APC (25.2%), and AR (21.7%). 1832 patients were included in the survival analysis [n=96 (5.2%) with and n=1736 (94.8%) without SPOP alterations]. Median overall survival was numerically longer, though not statistically significant, in the SPOP-mutated compared SPOP-wild-type group (75.9 versus 59.5 months, p=0.12). In patients with metastatic disease, median time to castration resistance was 14.6 months in the SPOP-mutated group versus 12.2 months in the SPOP-wild-type group (p=0.30). Median time from metastatic castration resistant to death was 45.0 months in the SPOP-mutated group and 40.2 months in the SPOP-wild type group. Conclusions: Our hypothesis generating data support that SPOP-mutated prostate cancer likely represents a unique molecular subtype of prostate cancer that may confer prolonged survival. Future studies of novel androgen-receptor targeting treatments should be tested in this molecular population.
Apalutamide toxicity and tolerability: A real-world multicentre experience in metastatic hormone-sensitive prostate cancer (MHSPC) in the United Kingdom (UK).
Authors: Sindhu Ramamurthy, Thiraviyam Elumalai, Rachel Pearson, Iakov Bolnykh, Tehreem Muhammad-Afzal, Gbenro Olukiran, Ananya Arora, Gay Bradley, Kate Taft, Nayan Lin Myint, Ei The Htike, Will Ince, Xue Yan Jiang
Journal: Journal of Clinical Oncology (2024)

107 Background: Apalutamide became available for mHSPC along with androgen deprivation treatment (ADT) in the UK in November 2021. We present here, to our best knowledge the first real world multicentre experience of Apalutamide use in mHSPC patients from three tertiary cancer centres from the UK. Our primary aim was to assess for toxicity and tolerability at this stage. Methods: We collected retrospective data from hospital prescription registries including all patients with mHSPC receiving Apalutamide between November 2021 and January 2023. Median period of follow up in our cohort was 14 months. Results: Two hundreds and ten patients received Apalutamide for mHSPC from three centres. The median age was 73 years. A total of 16.4% of the patients had recurrent mHSPC previously treated for localised PC and 83% were de novo metastatic. Of all patients, 43.3% had high-volume disease, and 56.7% had low-volume disease. The median presenting Prostate Specific Antigen (PSA) was 62 ng/l at diagnosis (range 0.87 to 5580). Undetectable PSA achieved in 43.3% of patient after starting Apalutamide and ADT. For those who have detectable PSA, median value was 0.22. Ninety percent of patients maintained their performance status (PS) at 0 or 1 on treatment. Most common side effects were fatigue (35.7%) and rash (11.4%). Cardiac adverse effects were recorded in 1.9% and thromboembolic events in 0.4%. Overall grade 3 and 4 toxicity was 5.2% across all symptoms assessed. To date, 83.3% patients continue on Apalutamide, 7.6 % discontinued due to toxicity and 9.1% had progressive disease (PD). Three deaths were recorded and all due to PD. Conclusions: In this multicentre retrospective analysis we found that Apalutamide and ADT is well tolerated in the real-world setting. Our patients are older and had lower volume metastasis compared with patients from the registry Trial (TITAN). Fatigue is more prevalent. Majority patients continue on Apalutamide with manageable side effects and good PS.
Comparison of pathologist and deep learning–based prostate cancer grading for prediction of metastatic outcomes in primary prostate cancer.
Authors: Lia DePaula Oliveira, Jiayun Lu, Eric Erak, Adrianna Mendes, Saikiran Bonthu, Uttara Joshi, Chaith Kondragunta, Corinne E Joshu, Nitin Singhal, Angelo M De Marzo, Tamara L. Lotan
Journal: Journal of Clinical Oncology (2024)

345 Background: Gleason grading is the most potent prognostic variable in primary prostate cancer, however inter-observer variability remains a major issue, particularly where subspecialty-trained pathologists are not available. Artificial intelligence algorithms for prostate cancer grading may improve health care equity by ensuring widespread access to standardized, high quality grading, however most algorithms have not been tested for performance with respect to oncologic outcomes. Here, we compared deep learning-based and pathologist-based Gleason grading for prediction of metastatic outcome in three large radical prostatectomy cohorts. Methods: Three previously published radical prostatectomy cohorts from Johns Hopkins were utilized for this study. The Natural History Cohort (n=318, PMID: 26058959) and Case-Cohort (n=231, PMID: 36006048) both utilized a case-cohort design on the outcome of metastasis, while the Race Cohort (n=335, PMID: 30851334) was designed for a grade-matched comparison of self-identified White and Black patients. For each cohort, a single representative H&amp;E-stained slide from the dominant tumor nodule was scanned and Gleason Grade Group (GG) assessed by a modified deep learning-based grading algorithm (PMID: 35233002), or by a subspecialty-trained pathologist. Harrell’s C-indices based on unadjusted Cox models for time to metastasis were compared for the pathologist- and deep learning-assigned GG in each cohort. Results: The C-index for deep learning-assigned GG in the Natural History Cohort was 0.757 (95% confidence interval [CI]: 0.703-0.811) compared to 0.748 (95% CI: 0.692-0.804) for the pathologist-assigned GG. The C-index for deep learning-assigned GG in the Case-Cohort was 0.848 (95% CI: 0.727-0.969) compared to 0.843 (95% CI: 0.722-0.964) for the pathologist-assigned GG. The C-index for deep learning-assigned GG in the Race Cohort was 0.754 (95% CI: 0.697-0.810) compared to 0.780 (95% CI: 0.717-0.844) for the pathologist-assigned GG. In a combined analysis of all three cohorts, the C-index for the deep learning-assigned GG was 0.765 (95% CI: 0.725-0.805) compared to 0.772 (95% CI: 0.730-0.814) for the pathologist-assigned grade group. In the combined cohorts, the historical pathologist-assigned GG for the entire case had a C-index of 0.796 (95% CI: 0.757-0.836). Conclusions: Deep learning and subspecialty-trained pathologist grading were highly comparable for association with metastatic outcome across three large radical prostatectomy cohorts.
Clinical outcomes with split-dose cisplatin-based regimens in patients (pts) with locally advanced or metastatic urothelial carcinoma (la/mUC): Results of a systematic literature review (SLR) and network meta-analysis (NMA).
Authors: Richard Thomas O'Dwyer, Mairead Kearney, Mihaela Musat, Ioana Gulas, Silke Guenther, Elizabeth Hubscher, Hoora Moradian, Srikala S. Sridhar
Journal: Journal of Clinical Oncology (2024)

589 Background: Gemcitabine + cisplatin (GC) is one of the most effective and commonly used regimens in la/mUC. In GC, cisplatin is dosed at 70 mg/m 2 on day 1 of a 3-week cycle; however, for many pts, impaired renal or cardiac function, neuropathy, or poor performance status can preclude use of cisplatin. A promising alternative is split-dose GC, where the cisplatin dose is divided over 2 days. We conducted an SLR and NMA to better understand outcomes, toxicity, and comparative effectiveness of split-dose GC vs GC, GCarboplatin (GCa), and methotrexate + vinblastine + doxorubicin + cisplatin (MVAC). Methods: An SLR, performed on March 8, 2023, followed Cochrane guidelines to identify observational studies of treated pts with la/mUC and randomized and nonrandomized trials investigating split-dose cisplatin regimens for this indication. No limits were placed on publication year or geography, and studies reported in English, Spanish, French, and German were included. An NMA was conducted for objective response rate (ORR), progression-free survival (PFS), and overall survival (OS). Results were reported as odds ratios or hazard ratios (HRs) with 95% credible intervals (Crls). Results: Among 120 studies that met SLR inclusion criteria, 16 (6 clinical trials, 10 retrospective studies) conducted in the US, Europe, and Asia published from 1999-2023, representing 1,767 pts, included split-dose GC. Common reasons for choosing split-dose GC were impaired renal function (creatinine clearance ≤60 mL/min), age &gt;70 years, comorbidities, and physician preference. Most often, cisplatin was split (35 mg/m 2 ) on days 1+8 or 1+2, and pts received a median of 3-5 cycles. In studies reporting outcomes with split-dose GC (12 studies), ORRs were 39-80% (11 studies), median PFS was 3.5-9.9 months (7 studies), and median OS was 8.5-18.1 months (11 studies). Discontinuation rates due to adverse events were 8-38% (5 studies). In the NMA, ORR for split-dose GC was significantly higher than GCa but not GC and MVAC. PFS and OS for split-dose GC were not significantly different from GCa, GC, or MVAC (Table). Conclusions: This is the first SLR and NMA of split-dose GC in la/mUC. Despite heterogeneity in the limited studies included, survival with split-dose GC was numerically better than GC and worse versus MVAC but not statistically different in either case. Split-dose GC has the potential to extend the la/mUC population eligible to receive cisplatin-based regimens and warrants further study. [Table: see text]
Patient-reported outcomes (PRO) in patients (pts) with <i>BRCA1/2</i>-altered metastatic castration-resistant prostate cancer (mCRPC) receiving niraparib (NIRA) with abiraterone acetate and prednisone (AAP): Results from MAGNITUDE study.
Authors: Dana E. Rathkopf, Guilhem Roubaud, Kim N. Chi, Eleni Efstathiou, Gerhardt Attard, David Olmos, Eric J. Small, Marniza Saad, Elena Castro, Won Kim, Daphne Wu, Kristi Bertzos, Shiva Dibaj, Jenny Zhang, Peter St. John Francis, Matthew Raymond Smith
Journal: Journal of Clinical Oncology (2024)

105 Background: MAGNITUDE, an international phase 3 randomized double-blind study, demonstrated that mCRPC pts with BRCA1/2 alterations receiving NIRA + AAP had significantly improved radiographic progression-free survival, and clinically relevant prolongations in time to symptomatic progression and time to cytotoxic chemotherapy compared with placebo (PBO) + AAP. Here, we report PRO results (pain, health-related quality of life [HRQoL], and side effect bother) in the BRCA1/2 subset of mCRPC pts in the final analysis of MAGNITUDE. Methods: Pts were screened prospectively for homologous recombination repair (HRR) gene alterations. Eligible pts had ECOG status ≤1 and a Brief Pain Inventory–Short Form (BPI-SF) worst pain score ≤3 (scale of 0-10), and were randomized 1:1 to NIRA + AAP or PBO + AAP orally daily in 28-day cycles. PRO assessments on day 1 of specified cycles included BPI-SF and Functional Assessment of Cancer Therapy–Prostate (FACT-P). Time to deterioration (TTD) in pain (BPI-SF worst, average, and pain interference, and FACT-P pain-related scale [PRS]) were compared between treatment arms using proportional hazards regression models. Changes from baseline in HRQoL (FACT-P total, scale of 0-156) were compared using repeated measures analysis, and side-effect bother was assessed in both arms as a single item from FACT-P (GP5). Results: PRO compliance for FACT-P and BPI-SF was &gt;85% in 225 pts with BRCA1/2-altered mCRPC. At baseline, mean BPI-SF pain scores was 1.09 (SD, 1.57) in NIRA + AAP and 1.35 (SD, 1.98) in PBO + AAP. Mean FACT-P Total in NIRA + AAP and PBO + AAP was 116.33 (SD, 18.42) and 114.8 (SD, 18.9), respectively. Median TTD in BPI-SF worst pain, pain interference, average pain, and FACT-P PRS were numerically longer for NIRA + AAP vs. PBO + AAP (Table). Repeated measures results showed HRQoL was maintained on treatment for the BRCA subgroup with no clinically meaningful differences in the FACT-P total score over time or between treatment arms. Analysis of FACT-P item GP5 in the BRCA subset showed side effect bother was rated “not at all” or “a little bit” by 87% of NIRA + AAP and 92% of PBO + AAP subjects across treatment cycles. Conclusions: In pts with BRCA1/2-altered mCRPC, NIRA + AAP maintained HRQoL. Pts experienced minimal bother from side effects associated with treatment. These data further support the benefit-risk profile of NIRA + AAP for the treatment of mCRPC pts with BRCA1/2 alterations. Clinical trial information: NCT03748641 . [Table: see text]
The utility of ChatGPT in subspecialty consultation for patients (pts) with metastatic genitourinary (GU) cancer.
Authors: Ayana Srivastava, Gliceida Galarza Fortuna, Beverly Chigarira, Emre Dal, Chadi Hage Chehade, Georges Gebrael, Arshit Narang, Neeraj Agarwal, Umang Swami, Haoran Li
Journal: Journal of Clinical Oncology (2024)

227 Background: Cancer management requires a multidisciplinary approach, often requiring medical consultation from subspecialists. With the advent of artificial intelligence (AI) technologies like ChatGPT, it is hypothesized that these tools may help expedite the consultation process. This study aimed to assess the efficacy of ChatGPT in providing guideline-based sub-speciality recommendations for managing pts with metastatic GU cancer. Methods: In this single-institution, IRB-approved, retrospective, proof-of-concept study, pts with metastatic GU cancer over the past 3 years were screened. Those with at least one consultation referral to subspecialty clinics were randomly selected. ChatGPT 3.5 was given the most recent clinic note that triggered sub-specialty consultation. The AI tool was then asked to provide an assessment and plan. Two physicians independently assessed the accuracy of diagnoses made by ChatGPT and subspecialty physicians. The primary outcome was the consistency of ChatGPT recommendations with those of subspecialty physicians. Secondary Outcomes included potential time saved by using ChatGPT and comparison of medical decision-making (MDM) complexity levels between ChatGPT and subspecialty physicians. Results: A total of 39 pts were included. Their primary diagnoses included prostate cancer (51.3%), bladder cancer (23.1%) and kidney cancer (15.4%). The referred subspecialty clinic included cardiology (33.3%), hematology (17.9%), hepatology (2.6 %), hospice (10.3%), neurology (12.8%), pulmonary (15.4 %), and rheumatology (7.7%). The average waiting time for pts to be seen in subspecialty clinics was 44.9 days (SD = 42.4). Of 39 patient’s charts reviewed by ChatGPT, 30/39 (76.9%) had the same diagnosis with consultant sub-specialties. The average diagnoses made by ChatGPT were 8.2, compared with 3.4 made by sub-specialty physicians (p &lt; 0.0001). The accuracy of diagnoses made by ChatGPT was the same, higher, and lower than human physicians in 10 (33.3%), 3 (10%), and 17 (56.7%) cases, respectively. Consistency of treatment plans between ChatGPT and physicians was found in 18 cases (46.2%). ChatGPT recommended additional workup in 32 cases (85.1%). The average number of words written in consultation notes by ChatGPT was 362.7 (SD = 72.9), which was significantly greater than subspecialty physicians (n = 224.7, p &lt; 0.0001). Conclusions: These hypothesis-generating data suggest the potential utility of ChatGPT to assist medical oncologists in managing increasingly complex pts with metastatic cancer. Further studies are needed to validate our findings.
Risk of early non-cancer deaths in adults diagnosed with metastatic genitourinary (mGU) cancers: A retrospective cohort study.
Authors: Ahsan Ayaz, Syed Arsalan Ahmed Naqvi, Zaryab Bin Riaz, Arifa Bibi, Kaneez Zahra Rubab Khakwani, Ewan Kemar Cobran, Parminder Singh, Alan Haruo Bryce, Irbaz Bin Riaz, Sajid Ahmad Mir
Journal: Journal of Clinical Oncology (2024)

229 Background: We aim to investigate non-cancer cause of death in adults within 2 years after initial diagnosis of mGU cancers including bladder cancer (BC), kidney cancer (KC), and prostate cancer (PC). Methods: Surveillance, Epidemiology, and End Results (SEER) database (2004-2020) was searched to obtain data on death caused by cardiovascular diseases (CVD), kidney diseases (KD), respiratory diseases (RD), liver diseases (LD), diabetes mellitus (DM), and sepsis in adults (&gt;20y of age) diagnosed with mGU cancers. Standardized mortality ratios (SMRs) with 95% confidence interval (CI) were calculated within 2y of initial diagnosis. SMR was defined as the observed deaths from each category divided by the expected number of deaths in the age-matched US population for the same period. SMR &gt;1 indicated increased while &lt;1 indicated decreased mortality. Stratification factors included age (&lt;50y, 50-70y, &gt;70y), race (White, Black, Asian/Pacific Islander), ethnicity (Hispanic, non-Hispanic), chemotherapy, and radiotherapy. Results: This study including 77,246 mGU cancer patients showed that the greatest risk of early death was observed to be caused by sepsis (SMR: 4.21, 95% CI: 3.64-4.84) followed by CVD (1.70, 1.62-1.80), KD (1.58, 1.31-1.90), LD (1.58, 1.16-2.09), DM (1.56, 1.32-1.82), and RD (1.38, 1.22-1.56). Patients aged &lt;50y observed the highest risk of mortality caused by sepsis (61.76, 30.83-110.5) followed by KD (28.06, 9.11-65.48) and CVD (5.63, 3.60- 8.84). However, patients aged 50-70y were more likely to die by RD (2.47, 1.94-3.09) and LD (2.01, 1.38-2.82). The results were consistent for Black patients; sepsis (6.59, 5.04-8.46) followed by RD (2.01, 1.41-2.78) and KD (1.64, 1.04-2.46). Asian/Pacific Islanders were more likely to die from CVD (2.52, 2.13- 3.00) and DM (2.21, 1.21-3.71). Hispanics were more likely to die from LD (3.11, 1.66-5.32) and non-Hispanics from sepsis (4.28, 3.68-4.96). This risk of death due to sepsis was increased with chemotherapy (7.06, 5.22-9.33) and radiotherapy (4.92, 3.58-6.61). Detailed results are available in Table. Conclusions: Sepsis remained the leading cause of early non-cancer-deaths in mGU cancers consistently across all subgroups. Younger adults were more likely to die from CVD and KD, Black patients from RD, and Hispanics from LD. [Table: see text]
Spatial analysis of the tumor immune microenvironment in papillary renal cell carcinoma.
Authors: Mitch Hayes, Kirill Sabitov, Alex C. Soupir, Taylor Peak, Jonathan Nguyen, Carlos Moran-Segura, Daryoush Saeed-Vafa, Neale Lopez-Blanco, Paola Ramos Echevarria, Christopher Guske, Jodi Balasi, Jasreman Dhillon, Youngchul Kim, James J Mule, Brooke Fridley, Brandon J. Manley
Journal: Journal of Clinical Oncology (2024)

471 Background: Spatial analysis of the tumor immune microenvironment (TIME) has yet to be explored in papillary renal cell carcinoma (pRCC). We utilized multiplex immunofluorescence (mIF) and spatial transcriptomics using spatial molecular imaging (SMI) to evaluate TIME properties in pRCC and contrasted these results with clear cell RCC (ccRCC). Methods: Tumor specimens were obtained from localized RCC tumors. mIF was performed on regions of interest (ROIs) selected from matched compartments from tumor, stroma, and tumor/stromal subsets of the interface. Two antibody panels were used for markers against T cells and B cells/macrophages. Marker abundance and clustering differences between pRCC and ccRCC were evaluated across ROIs. Select markers were also explored across pathologic tumor staging in pRCC. The SMI platform used for validation utilized probes against 959 transcripts. Cells were phenotyped using InSituType using the Kidney Cell Atlas as a reference. Cell clustering was quantified by univariate and bivariate Ripley’s K using the spatialTIME package in R. Results: mIF was performed on 1178 ROIs from 16 pRCC tumors and 70 ccRCC tumors. Compared to ccRCC, pRCC immune cell abundance was statistically lower amongst many T cell types and M2-like macrophages (Figure). M1-like macrophages were the only cell line seen at higher levels in interface compartments only. Increased macrophage clustering was observed in pRCC, including doubly positive M2-like macrophages in interface compartments (p=0.001 and 0.007). Higher abundance of CD8+ and FOXP3+ T cells in pRCC was associated with worse clinical stage, but no trend was seen with marker clustering. Four ROIs from 2 pRCC patients underwent SMI validation. On SMI of the tumor compartment, T cells were clustered with other T cells, B cells, and M1 macrophages. Conclusions: Compared to ccRCC, pRCC has fewer T cells and macrophages but more macrophage clustering. Using spatial transcriptomics, we found significant clustering between T cells, macrophages, and B cells in pRCC.