Citations

Background: Evaluation of the performance of Cobas ® Pulse (Roche Diagnostics GmbH, Mannheim, Germany), a new blood glucose (BG) monitoring system (BGMS; referred to here as BGMSA) intended for point-of-care testing using samples from patients in diverse clinical settings by intended point-of-care test operators. Methods: Arterial, capillary, venous, or heel stick whole blood (WB) samples from patients in non-critical and critical care settings were collected and analyzed using BGMSA and the Nova StatStrip ® BGMS (Nova Biomedical, Waltham, MA; referred to here as BGMSB), and a hexokinase comparator (Cobas 6000 Analyzer Series; Roche Diagnostics GmbH). The blood glucose measurement accuracy was assessed by the Food and Drug Administration guidance criteria. Results: Two studies are presented. In the first, 2678 samples (622 arterial, 706 capillary, 1203 venous, 147 heel stick) were collected from 1577 patients in 14 US and three European sites. All accuracy criteria were met for arterial and venous samples considering all data combined. BGMSA showed better accuracy than BGMSB for arterial, venous, and heel stick, and similar results to BGMSB for capillary WB vs venous comparator. No endogenous interference from pO 2 , hematocrit, and sodium was identified. BGMSA was also accurate when analyzing contrived samples used to show accuracy over a wide range of glucose concentrations, alone and when combined with extreme hematocrit. In the second study, 117 capillary fingerstick samples collected at one US site were measured on both BGMSA and the comparator, and all accuracy criteria were met. No clinically significant medical risks were observed via Diabetes Technology Society Error Grids. Conclusions: BGMSA was effective for determining blood glucose in venous, arterial, neonatal arterial, neonatal heel stick, and capillary WB samples.

Palliative care (PC) has been shown to improve quality of life (QoL), reduce symptom burden, and align care with patient goals in those with serious illnesses. However, its integration into the emergency department (ED), where many patients with advanced illness present, remains underexplored and inconsistently applied. This narrative review evaluates several recent studies on ED-based palliative care, aiming to clarify whether the early benefits in symptom management and QoL are associated with reduced healthcare utilization and cost. A comprehensive literature search identified five new key studies with significant methodological differences, including variations in patient populations, outcome measures, and study designs. Some studies demonstrated short-term symptom relief, yet only one showed significant improvements in QoL at long-term follow-up. For healthcare utilization, still only one of these new studies reported substantial cost savings, underscoring the need for further research to validate financial benefits. We suggest how future work should be designed to explore both clinical and economic outcomes in tandem to establish a clear value proposition for ED-based palliative care. By addressing this lack of cohesion, we can determine the merit of ED-palliative care integrations with the goal of improving patient care and optimizing resource utilization.

The liver stores substantial numbers of neutral lipid organelles termed lipid droplets (LDs) that accumulate within hepatocytes in response to chronic ethanol (EtOH) consumption leading to hepatic steatosis. Mass spectrometry analysis of LDs isolated from EtOH-damaged rat livers revealed a substantial reduction in the valosin-containing protein ATPase (VCP/p97) that acts to remove targeted proteins from cellular membranes for degradation. Experimental disruption of VCP function resulted in an increase in LD content in hepatocytes and mouse livers along with a marked increase in LD-associated hydroxysteroid dehydrogenase (HSD17β13) known to contribute to hepatic steatosis. Surprisingly, treatment of hepatocytes with the proteasome inhibitor MG132 had no effect on HSD17β13 levels, while a disruption of lysosome function and chaperone-mediated autophagy increased cellular HSD17β13 levels substantially. These findings provide new insights into the cellular mechanisms by which the liver regulates its lipid stores and how this is disrupted by chronic EtOH exposure.

Background: When missing data are present in clinical outcomes studies, complete-case analysis (CCA) is often performed, whereby patients with missing data are excluded. While simple, CCA analysis may impart selection bias and reduce statistical power, leading to erroneous statistical results in some cases. However, there exist more rigorous statistical approaches, such as single and multiple imputation, which approximate the associations that would have been present in a full dataset and preserve the study’s power. The purpose of this study is to evaluate how statistical results differ when performed after CCA analysis versus imputation methods. Methods: This simulation study analyzed a sample dataset consisting of 2204 shoulders, with complete datapoints from a larger multicenter total shoulder arthroplasty database. From the sampled dataset of demographics, surgical characteristics, and clinical outcomes, we created five test datasets, ranging from 100 to 2000 shoulders, and simulated 10–50% missingness in the postoperative American Shoulder and Elbow Surgeons (ASES) score and range of motion in four planes in missing completely at random (MCAR), missing at random (MAR), and not missing at random (NMAR) patterns. Missingness in outcomes was remedied using CCA, three single imputation techniques, and two multiple imputation techniques. The imputation performance was evaluated relative to the native complete dataset using the root mean squared error (RMSE) and the mean absolute percentage error (MAPE). We also compared the mean and standard deviation (SD) of the postoperative ASES score and the results of multivariable linear and logistic regression to understand the effects of imputation on the study results. Results: The average overall RMSE and MAPE were similar for MCAR (22.6 and 27.2%) and MAR (19.2 and 17.7%) missingness patterns, but were substantially poorer for NMAR (37.5 and 79.2%); the sample size and the percentage of data missingness minimally affected RMSE and MAPE. Aggregated mean postoperative ASES scores were within 5% of the true value when missing data were remedied with CCA, and all candidate imputation methods for nearly all ranges of sample size and data missingness when data were MCAR or MAR, but not when data were NMAR. When data were MAR, CCA resulted in overestimates of the SD. When data were MCAR or MAR, the accuracy of the regression estimate (β or OR) and its corresponding 95% CI varied substantially based on the sample size and proportion of missing data for multivariable linear regression, but not logistic regression. When data were MAR, the width of the 95% CI was up to 300% larger when CCA was used, whereas most imputation methods maintained the width of the 95% CI within 50% of the true value. Single imputation with k-nearest neighbor (kNN) method and multiple imputation with predictive mean matching (MICE-PMM) best-reproduced point estimates and intervariable relationships resembling the native dataset. Availability of correlated outcome scores improved the RMSE, MAPE, accuracy of the mean postoperative ASES score, and multivariable linear regression model estimates. Conclusions: Complete-case analysis can introduce selection bias when data are MAR, and it results in loss of statistical power, resulting in loss of precision (i.e., expansion of the 95% CI) and predisposition to false-negative findings. Our data demonstrate that imputation can reliably reproduce missing clinical data and generate accurate population estimates that closely resemble results derived from native primary shoulder arthroplasty datasets (i.e., prior to simulated data missingness). Further study of the use of imputation in clinical database research is critical, as the use of CCA may lead to different conclusions in comparison to more rigorous imputation approaches.

TPS1143 Background: Poly(ADP-ribose) polymerase (PARP) inhibitors (PARPi) have revolutionized the treatment of patients (pts) with germline BRCA1/2 (gBRCA)-associated HER2-negative BC. However, resistance eventually occurs in almost all pts. Tumor-associated macrophages (TAMs), a key component of the BC tumor microenvironment, are highly immunosuppressive and associated with poor clinical outcomes. In preclinical immunocompetent models of BRCA-associated BC, PARP inhibition induces suppressive CSF-1R+ TAMs, contributing to resistance, so that combining anti-CSF-1R therapy with PARPi significantly enhances progression free survival (PFS) compared to PARPi monotherapy (190d vs 92d). Axatilimab (SNDX-6352; Ab969.g2), a humanized IgG4 monoclonal antibody targeting CSF-1R, reduces TAMs, potentially slowing tumor growth and enhancing anti-tumor immunity. In the Phase I SNDX-6352-0502 study for advanced solid tumors, axatilimab showed tolerability at the highest dose (6 mg/kg), with biomarker modulation observed at doses as low as 1 mg/kg. Axatilimab is FDA-approved for chronic graft-versus-host disease (cGVHD). Methods: AXALAP (NCT06488378) is a non-randomized open-label, proof-of-concept phase 1 study designed to evaluate axatilimab 1mg/kg or 3mg/kg every 2 weeks in combination with olaparib 300 mg twice daily in pts with somatic or germline BRCA1/2 - and PALB2 -associated HER2-negative metastatic BC. Patients must be PARPi naïve, or have not progressed on prior PARPi, and have received up to 2 prior lines of chemotherapy for metastatic disease. Pts will receive a two-week lead-in of olaparib monotherapy, followed by combined olaparib and axatilimab. Pts will undergo mandatory tumor biopsies pre-treatment, after the 2-week olaparib lead-in, and after 2 cycles of olaparib/axatilimab, with an optional biopsy at time-of-progression. Primary objectives are to establish the maximum tolerated dose (MTD) and recommended phase 2 dose and to assess the safety and tolerability of axatilimab and olaparib. Secondary objectives include assessment of changes in CSF-1R+ CD163+ macrophage levels after olaparib monotherapy and after 2 cycles of combination treatment at the MTD; to determine the objective response rate and the median PFS of the combination per RECIST 1.1 criteria. The MTD will be determined by Bayesian Optimal Interval (BOIN) design. Pts will be treated in cohorts of 3 with a maximum of 10 at each dose. If the MTD is identified as 3mg/kg, we will complete enrollment of 10 pts at 1 mg/kg to assess biological effectiveness and clinical efficacy of the lower dose. We expect to treat up to 20 pts, who will receive study treatment until development of unacceptable toxicity or disease progression. Enrollment began on 5/2024 at Dana-Farber Cancer Institute and the trial will also open at Beth Israel Deaconess Medical Center and Mayo Clinic-Rochester. Clinical trial information: NCT03604692 .

8513 Background: The MET pathway is a known mediator of EGFR-TKI resistance and represents a therapeutic vulnerability to select MET-TKIs. Savo is an oral, highly selective MET-TKI that, when combined with osi, has the potential to overcome MET-driven resistance after progressive disease (PD) on osi. The Phase 2 SAVANNAH study has demonstrated clinically meaningful activity with the combination of savo + osi in patients (pts) with EGFR-mutated (EGFRm) advanced NSCLC and MET overexpression/amplification (NCT03778229). Isolating the efficacy of savo in the use of savo + osi is a key question for this combination. Methods: A subset of SAVANNAH included eligible pts who had EGFRm advanced NSCLC with MET overexpression (IHC 3+ intensity in ≥90% of tumor cells [IHC3+/≥90%]) and/or amplification (≥10 MET gene copies by FISH [FISH10+]) after PD on first-line (1L) osi; asymptomatic stable brain metastases (treated/untreated) were allowed. Pts were randomized 2:1 (double-blind) to savo 300 mg BID + osi 80 mg QD, or savo 300 mg BID + PBO (stratified by investigator [INV] assessed baseline [BL] brain metastases [yes/no]), until INV-assessed PD per RECIST 1.1. Brain imaging occurred at BL and PD; pts with brain metastases were re-imaged at each tumor assessment to PD. Endpoints included objective response rate (ORR), duration of response (DoR), and progression-free survival (PFS) by BICR and INV; CNS PFS, and presence/absence of CNS lesions at PD by BICR. Results: Overall, 73 pts were randomized (savo + osi n=48; savo + PBO n=25). At BL, median age: 67 vs 65 years, female: 73% vs 64%, White: 73% vs 52% in the savo + osi and savo + PBO arms, respectively. Efficacy outcomes (ORR, DoR, and PFS) were higher with savo + osi than savo + PBO (Table). CNS PFS events by CNS BICR occurred in 5/14 (36% savo + osi) and 2/4 pts (50% savo + PBO). In pts without BL brain metastases, none of the 13 pts (savo + osi) with RECIST PD by BICR had a new CNS lesion; 6/11 pts in the savo + PBO arm developed a new CNS lesion. Conclusions: In EGFRm advanced NSCLC with MET IHC3+/≥90% and/or FISH10+ status after PD on 1L osi, efficacy of savo 300 mg BID + osi was numerically greater than savo + PBO and showed promising CNS activity. To date, this is one of the largest randomized data sets presented evaluating an oral MET-TKI in EGFRm NSCLC. Efficacy findings from SAVANNAH suggest that targeting both EGFR and MET is key and support further investigation of savo + osi and CNS activity in the Phase 3 SAFFRON study. Clinical trial information: NCT03778229 . Assessment Savo + osi (n=48) Savo + placebo (n=25) ORR, % (95% CI) BICR 58 (43, 72) 16 (5, 36) INV 54 (39, 69) 24 (9, 45) DoR, mo, median (95% CI) BICR 11.8 (6.0, NC) 4.5 (2.6, NC) INV 8.0 (4.9, 11.7) 4.2 (2.6, NC) PFS, mo, median (95% CI) BICR 8.3 (5.8, 15.1) 3.6 (1.4, 5.7) INV 7.6 (5.6, 11.0) 2.7 (1.4, 4.1) BICR, blinded independent central review; CI, confidence interval; mo, months; NC, not calculable.

e14626 Background: Despite advancements in neoadjuvant therapy and surgical techniques, patients with pancreatic ductal adenocarcinoma (PDAC) often experience high recurrence rates after total neoadjuvant therapy (TNT) and resection. Existing tumor response scoring systems, such as the College of American Pathologists (CAP) guidelines, fail to stratify recurrence risk effectively, emphasizing the need for novel, objective approaches. Methods: This study evaluated spatial characteristics of residual cancer and stroma in a multi-site cohort of 203 patients with resected PDAC following TNT, not having achieved a major pathologic response (CAP 2 or 3). Whole slide images (WSI) of hematoxylin and eosin-stained sections were digitized and analyzed using artificial intelligence to perform tissue segmentation. Metrics quantifying spatial morphology and configuration, inspired by principles of landscape ecology, were computed to explore spatial features of the tumor microenvironment (TME). Statistical models (recursive partitioning and regression tree [RPRT] model and Cox model with backwards eliminations) incorporating these features were developed to stratify recurrence risk. Results: Five metrics were used for risk classification model build. Three models successfully classified patients into high- and low-risk groups based on spatial metrics. Models demonstrated moderate discriminatory power (C-statistics: 0.560–0.566) and showed that increased tumor fragmentation, like stromal patch density and cancer patch shape index, were strongly associated with improved outcomes. Conclusions: Integrating spatial metrics of cancer-stroma configuration enhances risk stratification beyond conventional scoring systems. Given increased tumor fragmentation is associated with improved outcomes, these data indirectly suggest ecological mechanisms of treatment resistance. This approach offers a pathway toward personalized post-operative management strategies to improve outcomes in PDAC. Risk classification models. Model Description Event/n Median DFS (95% CI) HR(95% CI) p-value C-statistics 1(RPRT) High risk: log standard deviation of the stroma shape index > 0.651 87 / 101 8.22(5.72 – 10.1) 1.64(1.20 – 2.25) 0.002 0.562 Low risk: log standard deviation of the stroma shape index ≤ 0.651 71 / 102 11.90(9.24 – 25.1) Ref 2(RPRT) High risk: log standard deviation of the stroma shape index > 0.651 & mean cancer shape index ≤ 1.775 58 / 63 7.23(5.19 – 9.63) 1.82(1.31 – 2.52) 0.0003 0.560 Low risk: (log standard deviation of the stroma shape index > 0.651 & mean cancer shape index > 1.775) or log standard deviation of the stroma shape index ≤ 0.651 100 / 140 11.57(9.24 – 17.92) Ref 3(Backwards elimination) High risk: PI * > median 86 / 101 8.02(6.08 – 9.86) 1.73(1.26 – 2.37) 0.0007 0.566 Low risk:PI * ≤ median 72 / 102 15.72(9.97 – 25.05) Ref *PI: (0.2701 * log mean stroma area) + (0.3905 * log edge density).

e12568 Background: Modern preventive screening have made it possible to identify ductal carcinoma in situ (DCIS). Progression of DCIS to invasive disease is known to be driven by somatic evolution and affected by clinical interventions. Yet studies indicate that only 20–30% of the cases of DCIS will progress further if not treated, implying that a large portion of patients are over treated. Recognizing which pre-cancerous tumors are likely to progress to cancer or recur as DCIS after initial treatment is of great importance to optimize treatment options, lower associated risks and allocate healthcare efforts to patients that most benefit from early treatment. Methods: This study consists of a retrospective cohort of 158 patients, 57 of which had a DCIS recurrence over 2 to 228 months after treatment, and 45 progressed to invasive disease over 12 to 180 months. We generated a digital pathology library consisting of hematoxylin and eosin (H&E) stained whole slide images (WSI), and applied machine learning methods to perform duct and nuclear segmentation followed by cell classification. We then computed a set of spatial statistics from landscape ecology to characterize each sample. We used LASSO on half of the data (the training set) to select a Cox proportional hazards model that predicted recurrence of DCIS or cancer and used the other half of the data (the test set) to validate the predictors in the Cox model. Results: When considering the time to recurrence as disease-free survival (DFS), the type of DCIS treatment stratifies patients between Lumpectomy Only and More than Lumpectomy groups (log-rank test p-value < 0.0001), recapitulating previous results. When the subset of patients that had more than a lumpectomy was analyzed using spatial landscape ecology metrics, a decrease in the interaction between tumor cells and fibroblasts and between tumor cells and lymphocytes, measured by the mean nearest neighbor distance, were associated with improved DFS (concordance = 0.717) in the model validation set. Stratification of samples by the median risk is discriminatory with a p-value < 0.001. Alternatively, decreased nearest neighbor distance between lymphocytes and fibroblasts was associated with the time to invasive disease (p-value < 0.05). Conclusions: Integrating spatial ecological statistics of cell distributions enhances risk stratification of DCIS samples beyond the known effects of early treatment. Our results highlight the importance of ecological mechanisms in recurrence, progression and treatment resistance. The risk associated with spatial interactions of lymphocytes and fibroblasts suggests previously unknown ecological interactions may be associated with early micro-metastases that result in breast cancer even after mastectomy or lumpectomy with radiation in patients with DCIS.

5060 Background: Rucaparib significantly improved radiographic progression-free survival (rPFS) in men with BRCA-mutated chemotherapy-naïve metastatic castration-resistant prostate cancer (mCRPC) vs a control arm of physician’s choice of therapy (docetaxel or androgen-receptor pathway inhibitor [ARPI] therapy: abiraterone acetate or enzalutamide) in the randomized, multicenter, open-label, phase 3 TRITON3 (NCT02975934) study. Herein, we conducted an analysis to determine the impact of germline vs somatic mutation status on the efficacy of rucaparib. Methods: Patients were randomized 2:1 to receive rucaparib 600 mg BID or physician’s choice of docetaxel or ARPI following progression while on 1 prior second-generation ARPI in any setting. The primary endpoint was rPFS. Color Health did genetic testing. Treatment-emergent adverse events (TEAEs) were reported for the BRCA subgroup. Results: In the rucaparib arm, 201/270 patients had BRCA mutations, and in the physician’s choice arm, 101/135 patients had BRCA mutations. Of patients with BRCA mutations: in the rucaparib arm, 80/201 (40%) were germline and 116/201 (58%) were somatic with 5/201 (2%) BRCA mutation status unknown, while in the physician’s choice arm, 39/101 (39%) were germline and 48/101 (48%) were somatic with 14/101 (14%) BRCA mutation status unknown. rPFS was significantly improved with rucaparib treatment vs physician’s choice in both the germline and somatic mutation groups (germline: HR, 0.52 [95% CI, 0.32–0.84]; somatic: HR, 0.38 [95% CI, 0.25–0.59]). Incidence rates of TEAEs were similar overall between patients with germline and somatic BRCA mutations in both arms. Conclusions: Rucaparib improves progression-free survival for patients with mCRPC with either germline or somatic BRCA mutations with a manageable safety profile. These data support the use of rucaparib as a beneficial treatment option for patients with BRCA-mutated mCRPC with germline or somatic mutations. Clinical trial information: NCT02975934 .