Combining PARP Inhibitors and Androgen Receptor Signalling Inhibitors in Metastatic Prostate Cancer: A Quantitative Synthesis and Meta-analysis

Copyright \ua9 2023. Published by Elsevier B.V. CONTEXT: PARP inhibitors (PARPi) are established treatments for metastatic castration-resistant prostate cancer (mCRPC) with homologous recombination repair (HRR) deficiency after androgen receptor signalling inhibitor (ARSI) failure. New PARPi + ARSI combinations have been tested in all comers, although their clinical relevance in HRR-proficient tumours remains uncertain. OBJECTIVE: To quantitatively synthesise evidence from randomised trials assessing the efficacy and safety of PARPi + ARSI combinations for first-line treatment of mCRPC. EVIDENCE ACQUISITION: We searched the PubMed, EMBASE, SCOPUS, and Cochrane Library databases up to February 28, 2023. Randomised controlled trials (RCTs) comparing PARPi + ARSI versus placebo + ARSI for first-line treatment of mCRPC were eligible. Two reviewers independently performed screening and data extraction and assessed the risk of bias, while a third reviewer evaluated the eligibility criteria. EVIDENCE SYNTHESIS: Overall, three phase 3 RCTs were included in the systematic review: PROPEL, MAGNITUDE, and TALAPRO-2. A total of 2601 patients with mCRPC were enrolled. Two of these trials (PROPEL and TALAPRO-2) assessed the radiographic progression-free survival benefit of PARPi + ARSI for first-line treatment of mCRPC, independent of HRR status. The pooled hazard ratio was 0.62 (95% confidence interval 0.53-0.72). The pooled hazard ratio for overall survival was 0.84 (95% confidence interval 0.72-0.98), indicating a 16% reduction in the risk of death among patients who received the combination. CONCLUSIONS: Results from this meta-analysis support the use of ARSI + PARPi combinations in biomarker-unselected mCRPC. However, such combinations might be less clinically relevant in HRR-proficient cancers, especially considering the change in treatment landscape for mCRPC. PATIENT SUMMARY: We looked at outcomes from trials testing combinations of two classes of drugs (PARP inhibitors and ARSI) in advanced prostate cancer. We found that these combinations seem to work regardless of gene mutations identified as biomarkers of response to PARP inhibitors when used on their own

Opportunistic skeletal muscle metrics as prognostic tools in metastatic castration-resistant prostate cancer patients candidates to receive Radium-223

Objective: Androgen deprivation therapy alters body composition promoting a significant loss in skeletal muscle (SM) mass through inflammation and oxidative damage. We verified whether SM anthropometric composition and metabolism are associated with unfavourable overall survival (OS) in a retrospective cohort of metastatic castration-resistant prostate cancer (mCRPC) patients submitted to 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography (FDG PET/CT) imaging before receiving Radium-223. Patients and methods: Low-dose CT were opportunistically analysed using a cross-sectional approach to calculate SM and adipose tissue areas at the third lumbar vertebra level. Moreover, a 3D computational method was used to extract psoas muscles to evaluate their volume, Hounsfield Units (HU) and FDG retention estimated by the standardized uptake value (SUV). Baseline established clinical, lab and imaging prognosticators were also recorded. Results: SM area predicted OS at univariate analysis. However, this capability was not additive to the power of mean HU and maximum SUV of psoas muscles volume. These factors were thus combined in the Attenuation Metabolic Index (AMI) whose power was tested in a novel uni- and multivariable model. While Prostate-Specific Antigen (PSA), Alkaline Phosphatase (ALP), Lactate Dehydrogenase and Hemoglobin, Metabolic Tumor Volume, Total Lesion Glycolysis and AMI were associated with long-term OS at the univariate analyses, only PSA, ALP and AMI resulted in independent prognosticator at the multivariate analysis. Conclusion: The present data suggest that assessing individual 'patients' SM metrics through an opportunistic operator-independent computational analysis of FDG PET/CT imaging provides prognostic insights in mCRPC patients candidates to receive Radium-223. Graphical abstract: [Figure not available: see fulltext.

Somatostatin receptor PET/CT imaging for the detection and staging of pancreatic NET. A systematic review and meta-analysis

We investigated the diagnostic performance of Somatostatin Receptor Positron Emission Tomography/Computed Tomography (SSR-PET/CT) for the detection of primary lesion and initial staging of pancreatic neuroendocrine tumors (pNETs). A comprehensive literature search up to January 2020 was performed selecting studies in presence of: sample size ≥10 patients; index test (i.e., 68Ga-DOTATOC or 68Ga-DOTANOC or 68Ga-DOTATATE PET/CT); and outcomes (i.e., detection rate (DR), true positive, true negative, false positive, and false-negative). The methodological quality was evaluated with QUADAS-2. Pooled DR and pooled sensitivity and specificity for the identification of the primary tumor were assessed by a patient-based and a lesion-based analysis. Thirty-eight studies were selected for the qualitative analysis, while 18 papers were included in the meta-analysis. The number of pNET patients ranged from 10 to 142, for a total of 1143 subjects. At patient-based analysis, the pooled sensitivity and specificity for the assessment of primary pNET were 79.6% (95% confidence interval (95%CI): 71–87%) and 95% (95%CI: 75–100%) with a heterogeneity of 59.6% and 51.5%, respectively. Pooled DR for the primary lesion was 81% (95%CI: 65–90%) and 92% (95%CI: 80–97%), respectively, at patient-based and lesion-based analysis. In conclusion, SSR-PET/CT has high DR and diagnostic performances for primary lesion and initial staging of pNETs

Immune Checkpoint Inhibitors in Advanced Prostate Cancer: Current Data and Future Perspectives

In the last 10 years, many new therapeutic options have been approved in advanced prostate cancer (PCa) patients, granting a more prolonged survival in patients with metastatic disease, which, nevertheless, remains incurable. The emphasis on immune checkpoint inhibitors (ICIs) has led to many trials in this setting, with disappointing results until now. Therefore, we discuss the immunobiology of PCa, presenting ongoing trials and the available clinical data, to understand if immunotherapy could represent a valid option in this disease, and which subset of patients may be more likely to benefit. Current evidence suggests that the tumor microenvironment needs a qualitative rather than quantitative evaluation, along with the genomic determinants of prostate tumor cells. The prognostic or predictive value of immunotherapy biomarkers, such as PD-L1, TMB, or dMMR/MSI-high, needs further evaluation in PCa. Monotherapy with immune checkpoint inhibitors (ICIs) has been modestly effective. In contrast, combined strategies with other standard treatments (hormonal agents, chemotherapy, PARP inhibitors, radium-223, and TKIs) have shown some results. Immunotherapy should be better investigated in biomarker-selected patients, particularly with specific pathway aberrations (e.g., AR-V7 variant, HRD, CDK12 inactivated tumors, MSI-high tumors). Lastly, we present new possible targets in PCa that could potentially modulate the tumor microenvironment and improve antitumor activity with ICIs

Spinal cord hypermetabolism extends to skeletal muscle in amyotrophic lateral sclerosis: a computational approach to [18F]-fluorodeoxyglucose PET/CT images

Purpose: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease leading to neuromuscular palsy and death. We propose a computational approach to [18F]-fluorodeoxyglucose (FDG) PET/CT images to analyze the structure and metabolic pattern of skeletal muscle in ALS and its relationship with disease aggressiveness. Materials and methods: A computational 3D method was used to extract whole psoas muscle\u2019s volumes and average attenuation coefficient (AAC) from CT images obtained by FDG PET/CT performed in 62 ALS patients and healthy controls. Psoas average standardized uptake value (normalized on the liver, N-SUV) and its distribution heterogeneity (defined as N-SUV variation coefficient, VC-SUV) were also extracted. Spinal cord and brain motor cortex FDG uptake were also estimated. Results: As previously described, FDG uptake was significantly higher in the spinal cord and lower in the brain motor cortex, in ALS compared to controls. While psoas AAC was similar in patients and controls, in ALS a significant reduction in psoas volume (3.6 \ub1 1.02 vs 4.12 \ub1 1.33 mL/kg; p < 0.01) and increase in psoas N-SUV (0.45 \ub1 0.19 vs 0.29 \ub1 0.09; p < 0.001) were observed. Higher heterogeneity of psoas FDG uptake was also documented in ALS (VC-SUV 8 \ub1 4%, vs 5 \ub1 2%, respectively, p < 0.001) and significantly predicted overall survival at Kaplan\u2013Meier analysis. VC-SUV prognostic power was confirmed by univariate analysis, while the multivariate Cox regression model identified the spinal cord metabolic activation as the only independent prognostic biomarker. Conclusion: The present data suggest the existence of a common mechanism contributing to disease progression through the metabolic impairment of both second motor neuron and its effector

Adopting transfer learning for neuroimaging: a comparative analysis with a custom 3D convolution neural network model

Background: In recent years, neuroimaging with deep learning (DL) algorithms have made remarkable advances in the diagnosis of neurodegenerative disorders. However, applying DL in different medical domains is usually challenged by lack of labeled data. To address this challenge, transfer learning (TL) has been applied to use state-of-the-art convolution neural networks pre-trained on natural images. Yet, there are differences in characteristics between medical and natural images, also image classification and targeted medical diagnosis tasks. The purpose of this study is to investigate the performance of specialized and TL in the classification of neurodegenerative disorders using 3D volumes of 18F-FDG-PET brain scans. Results: Results show that TL models are suboptimal for classification of neurodegenerative disorders, especially when the objective is to separate more than two disorders. Additionally, specialized CNN model provides better interpretations of predicted diagnosis. Conclusions: TL can indeed lead to superior performance on binary classification in timely and data efficient manner, yet for detecting more than a single disorder, TL models do not perform well. Additionally, custom 3D model performs comparably to TL models for binary classification, and interestingly perform better for diagnosis of multiple disorders. The results confirm the superiority of the custom 3D-CNN in providing better explainable model compared to TL adopted ones

Adopting transfer learning for neuroimaging: a comparative analysis with a custom 3D convolution neural network model.

BACKGROUND In recent years, neuroimaging with deep learning (DL) algorithms have made remarkable advances in the diagnosis of neurodegenerative disorders. However, applying DL in different medical domains is usually challenged by lack of labeled data. To address this challenge, transfer learning (TL) has been applied to use state-of-the-art convolution neural networks pre-trained on natural images. Yet, there are differences in characteristics between medical and natural images, also image classification and targeted medical diagnosis tasks. The purpose of this study is to investigate the performance of specialized and TL in the classification of neurodegenerative disorders using 3D volumes of 18F-FDG-PET brain scans. RESULTS Results show that TL models are suboptimal for classification of neurodegenerative disorders, especially when the objective is to separate more than two disorders. Additionally, specialized CNN model provides better interpretations of predicted diagnosis. CONCLUSIONS TL can indeed lead to superior performance on binary classification in timely and data efficient manner, yet for detecting more than a single disorder, TL models do not perform well. Additionally, custom 3D model performs comparably to TL models for binary classification, and interestingly perform better for diagnosis of multiple disorders. The results confirm the superiority of the custom 3D-CNN in providing better explainable model compared to TL adopted ones

Metabolic network alterations as a supportive biomarker in dementia with Lewy bodies with preserved dopamine transmission

Purpose Metabolic network analysis of FDG-PET utilizes an index of inter-regional correlation of resting state glucose metabolism and has been proven to provide complementary information regarding the disease process in parkinsonian syndromes. The goals of this study were (i) to evaluate pattern similarities of glucose metabolism and network connectivity in dementia with Lewy bodies (DLB) subjects with subthreshold dopaminergic loss compared to advanced disease stages and to (ii) investigate metabolic network alterations of FDG-PET for discrimination of patients with early DLB from other neurodegenerative disorders (Alzheimer’s disease, Parkinson’s disease, multiple system atrophy) at individual patient level via principal component analysis (PCA). Methods FDG-PETs of subjects with probable or possible DLB (n = 22) without significant dopamine deficiency (z-score < 2 in putamen binding loss on DaT-SPECT compared to healthy controls (HC)) were scaled by global-mean, prior to volume-of-interest-based analyses of relative glucose metabolism. Single region metabolic changes and network connectivity changes were compared against HC (n = 23) and against DLB subjects with significant dopamine deficiency (n = 86). PCA was applied to test discrimination of patients with DLB from disease controls (n = 101) at individual patient level. Results Similar patterns of hypo- (parietal- and occipital cortex) and hypermetabolism (basal ganglia, limbic system, motor cortices) were observed in DLB patients with and without significant dopamine deficiency when compared to HC. Metabolic connectivity alterations correlated between DLB patients with and without significant dopamine deficiency (R2 = 0.597, p < 0.01). A PCA trained by DLB patients with dopamine deficiency and HC discriminated DLB patients without significant dopaminergic loss from other neurodegenerative parkinsonian disorders at individual patient level (area-under-the-curve (AUC): 0.912). Conclusion Disease-specific patterns of altered glucose metabolism and altered metabolic networks are present in DLB subjects without significant dopaminergic loss. Metabolic network alterations in FDG-PET can act as a supporting biomarker in the subgroup of DLB patients without significant dopaminergic loss at symptoms onset