Citations

Background. Study of myeloid neoplasms (MN) has been rapidly transformed by genome characterization. Gene mutations have been reported to influence disease phenotype and risk of disease progression, and the evaluation of mutational status can provide valuable insights to improve the decision-making process. In this international study, we evaluated the clinical impact of mutational screening in patients with CMML, which is characterized by significant clinical and genomic heterogeneity, including a substantial proportion of patients with poor outcomes and unmet needs. Currently, only limited molecular information has been incorporated into CMML classification and prognostication. Methods. We retrospectively collected 3182 CMML patients. We used MOSAIC, an AI-based framework for multimodal analysis in rare cancers (PMID: 38875514) to develop innovative, molecular-based tools for classification and prognostication. An independent validation of the results on 516 prospectively collected patients was performed. Results. Bayesian Networs and Hierarchical Dirichlet Processes were used to identify genomic associations and define a CMML molecular classification. We identified 11 clusters with distinct clinical and genomic features, including splicing machinery, signal transduction and tyrosine kinase pathways aberrations, and high-risk molecular signatures (i.e., AML-like and TP53 mutations). Up to 15% of patients showed clear genomic overlap with other MN. Importantly, extensive multi-color flow cytometry on T lymphocytes, NK and myeloid cells (BD FACSymphony A5 Cell Analyzer) revealed specific immunologic and inflammation signatures associated with molecular subgroups. We then integrated molecular and clinical information to build an international CMML Prognostic Scoring System (iCPSS). We assessed the performance of different statistical and AI-based methods; L1-penalized Cox Model resulted as the best performing method. Selected features included hematological parameters (WBC, Hb, PLT and marrow blasts), cytogenetic abnormalities, and mutations in 10 genes (ASXL1, DNMT3A, EZH2, NRAS, RUNX1, SETBP1, STAG2, TET2, TP53, U2AF1). By Bayesian thresholds optimization, we identified 5 risk classes (i.e., very-low, low, intermediate, high and very-high) with median overall survival (OS) ranging from 99 to 9 months (P<.001), and median leukemia-free survival (LFS) ranging from 210 to 18 months (P<.001). iCPSS provided better patient discrimination across all clinical endpoints compared to currently available prognostic tools, with a Concordance Index (CI) for OS of 0.75 versus 0.62-0.64, respectively. Notably, compared to available scoring systems, up to 40% of patients were reassigned to higher or lower risk classes by the iCPSS. External validation on an independent prospective cohort confirmed that the performance of iCPSS is superior to existing prognostic models (CI for OS 0.71 vs 0.54-0.61, respectively) To demonstrate the clinical utility of iCPSS, we focused on 753 patients who underwent allogeneic stem cell transplantation (HSCT). In multivariable analysis, iCPSS stratified the probability of OS post-HSCT (P<.001) and identified groups of patients with different probabilities of disease relapse, ranging from 9% to 62% (P<.001). We employed a clinical- and genomic-based decision support system (PMID: 38723212) to determine the optimal timing of HSCT. Preliminary analyses indicated that higher-risk patients according to iCPSS (i.e., intermediate, high, and very high) benefit from an immediate HSCT strategy. Lower-risk patients (i.e., very low and low) had life expectancy maximized with delayed HSCT. Modeling decision analysis using iCPSS compared to conventional scores, resulted in a change in transplantation policy for a significant proportion of patients (up to 25%). Conclusion. Molecular information significantly improves the classification of CMML patients, providing a basis for refining diagnostic boundaries with other MN and for a more rational inclusion of patients in clinical trials. The iCPSS demonstrated superior performance over currently available scoring systems and improved HSCT decision making process.

Abstract BACKGROUND Recurrent anaplastic astrocytoma (AA), a grade 3 brain cancer, remains an unmet medical need. STELLAR was a phase III randomized, open-label trial of eflornithine (ornithine decarboxylase inhibitor) with lomustine versus lomustine alone, originally for patients with recurrent AA. However, as the WHO definition of AA evolved during trial conduct (e.g., excluding IDH wild-type disease), we determined results in both the original ITT population and by revised molecularly defined WHO 2021 diagnoses in pre-planned analyses. METHOD Key eligibility: age ≥ 18, AA (2016 WHO criteria), first recurrence ≥ 6 months after radiation and temozolomide, KPS ≥ 70, no imaging findings consistent with grade 4 glioblastoma. Stratification: IDH, age, resection extent, and geography. Patients were randomized to Arm A: eflornithine (2.8 g/m2 TID Q 2/3 weeks) with lomustine (90 mg/m2 Q 6 weeks) or Arm B: lomustine (110 mg/m2 Q 6 weeks). Primary endpoint was OS. The pre-specified hazard ratio (HR) goal was 0.667 based on projected median OS (mOS) increase to 18 months from 12 months. RESULTS 343 pts (172/171 Arms A/B) were randomized (105 sites, 8 countries). ITT analysis found no difference between arms (mOS 23.4 vs. 20.3 months, HR = 0.94). However, a pre-specified subset analysis in IDH mutant, 2021 WHO-defined grade 3 astrocytoma (n=194), showed significant improvement with eflornithine (mOS 34.9 vs. 23.5 months, HR = 0.64, log rank p = 0.016) and PFS (15.8 vs. 7.2 months, HR = 0.58, log rank p = 0.015). No difference was observed in the IDH wild-type subset. AEs were consistent with historic data. CONCLUSION There was no difference in OS between arms in the ITT analysis. However, medically meaningful and statistically significant OS and PFS benefits were observed with eflornithine in the pre-planned analysis of patients with molecularly defined 2021 WHO CNS grade 3 astrocytoma.

Abstract GBM AGILE(NCT03970447) is a phase 2/3 Bayesian adaptive registration platform trial testing multiple therapies against a common control. Paxalisib, a PI3K/mTOR inhibitor, is the 3rd arm in the trial to conclude evaluation. METHODS Paxalisib was open to patients with newly diagnosed unmethylated (NDU) and recurrent (RD) glioblastoma, with three possible signatures: NDU, RD, and All(NDU+RD). Arm enrollment occurred Dec2020 through May2022. Control patients were enrolled from study initiation (July2019) and were treated with temozolomide(NDU) or lomustine(RD). GBM AGILE investigational arms have 1 or 2 stages, with adaptive randomization in stage 1 and fixed randomization if arms continue to stage 2. Efficacy is based on OS hazard ratio(HR) of Arm/Control. Efficacy goal is final Bayesian probability ≥ 98% for HR<1.00 in combined Stages. An Arm continues to Stage 2 if Bayesian predictive power (PP) ≥ 0.8. An Arm stops accruing in Stage 1 if it reaches maximal sample size(N) or does not meet a minimum efficacy threshold (PP<0.25 for all signatures when N>50). Clinical cut-off is 12 months after accrual stops. The maximum N for paxalisib was approximately 150(Stage 1) and 50(Stage 2). RESULTS After paxalisib reached >150 patients in Stage 1, accrual stopped [Paxalisib/control N 54/75(NDU), 100/188(RD)]. Neither PP thresholds for moving to Stage 2 nor stopping for minimum efficacy were met. Following an unplanned public disclosure that the arm did not continue to stage 2, clinical cut-off for final analysis (planned May2023) was updated to public disclosure date (August2022). At final analysis mean HRs were 0.89(NDU), 1.25(RD), 1.05(All), probabilities of HR<1.00 of 0.72(NDU), 0.076(RD), 0.398(All). Model estimated median OS paxalisib/control (months) were 14.77/13.84(NDU) and 8.58/10.06(RD). CONCLUSION Paxalisib did not show survival benefit over cumulative control in final primary analysis; additional secondary analyses are being considered. GBM AGILE continues to rapidly and efficiently assess therapies in ND and RD glioblastoma.

ABSTRACTBackgroundIntraductal papillary mucinous neoplasms (IPMNs) are the most commonly identified pancreatic cystic neoplasms. Incidentally detected IPMNs are common among liver transplant recipients. The risk of IPMN progression to pancreatic cancer in transplant recipients and the impact of immunosuppression on the risk of malignant transformation of IPMN are unclear.MethodsIn this retrospective study of consecutive liver transplant recipients across Mayo Clinic over a 13‐year period, patients were assessed for possible IPMN by automated chart review. Pancreatic cystic lesions were characterized as suspected IPMNs based on imaging criteria. Cox proportional hazards models were used to determine the association between IPMN progression (the development of cancer or worrisome features) and clinical and immunosuppression regimen characteristics.ResultsOf 146 patients with suspected IPMNs, progression occurred in 7 patients (2 cases of IPMN‐associated cancer and 5 cases of worrisome features) over an average follow‐up of 66.6 months. Immunosuppression type, medication number, and tacrolimus trough levels were not associated with IPMN progression (p > 0.05). Combined kidney and liver transplantation (p = 0.005) and pretransplant cholangiocarcinoma (p = 0.012) were associated with IPMN progression.ConclusionIPMN progression is rare after liver transplantation even over an extended follow‐up period. The findings were notable for the absence of an association between IPMN progression and immunosuppression regimen. Larger studies are needed given the low incidence.

Abstract The disease course and clinical outcome for brain tumor patients depend not only on the molecular and histological features of the tumor but also on the patient’s demographics and social determinants of health. While current investigations in neuro-oncology have broadly utilized artificial intelligence (AI) to enrich tumor diagnosis and more accurately predict treatment response, postoperative complications, and survival, equity-driven applications of AI have been limited. However, AI applications to advance health equity in the broader medical field have the potential to serve as practical blueprints to address known disparities in neuro-oncologic care. In this consensus review, we will describe current applications of AI in neuro-oncology, postulate viable AI solutions for the most pressing inequities in neuro-oncology based on broader literature, propose a framework for the effective integration of equity into AI-based neuro-oncology research, and close with the limitations of AI.

Liver fibrosis is characterized by the activation of perivascular hepatic stellate cells (HSCs), the release of fibrogenic nanosized extracellular vesicles (EVs), and increased HSC glycolysis. Nevertheless, how glycolysis in HSCs coordinates fibrosis amplification through tissue zone-specific pathways remains elusive. Here, we demonstrate that HSC-specific genetic inhibition of glycolysis reduced liver fibrosis. Moreover, spatial transcriptomics revealed a fibrosis-mediated up-regulation of EV-related pathways in the liver pericentral zone, which was abrogated by glycolysis genetic inhibition. Mechanistically, glycolysis in HSCs up-regulated the expression of EV-related genes such as Ras-related protein Rab-31 ( RAB31 ) by enhancing histone 3 lysine 9 acetylation on the promoter region, which increased EV release. Functionally, these glycolysis-dependent EVs increased fibrotic gene expression in recipient HSC. Furthermore, EVs derived from glycolysis-deficient mice abrogated liver fibrosis amplification in contrast to glycolysis-competent mouse EVs. In summary, glycolysis in HSCs amplifies liver fibrosis by promoting fibrogenic EV release in the hepatic pericentral zone, which represents a potential therapeutic target.

Background and Aims: Endoscopic ultrasound shear wave elastography (EUS-SWE) is a novel modality for liver stiffness measurement. The aims of this study are to evaluate the performance and reliability of EUS-SWE for detecting advanced liver disease in a prospective cohort. Methods: EUS-SWE measurements were prospectively obtained from patients undergoing EUS between August 2020 and March 2023. Liver stiffness measurements were compared between patients with and without advanced liver disease (ALD), defined as stage ≥3, to determine diagnostic accuracy for advanced fibrosis and portal hypertension. Logistic regression was performed to identify variables that impact the reliability of EUS-SWE readings. Select patients underwent paired magnetic resonance elastography (MRE) for liver fibrosis correlation. Results: Patients with ALD demonstrated higher liver stiffness compared to healthy controls (left lobe: 17.6 vs. 12.7 kPa, P <0.001; median right lobe: 24.8 vs. 11.0 kPa, P <0.001). The area under the receiver operator characteristic (AUROC) for the detection of ALD was 0.73 and 0.80 for left and right lobe measurements, respectively. General anesthesia was associated with reliable EUS-SWE liver readings (odds ratio: 2.73, 95% CI: 1.07-7.39, P =0.040). Left lobe measurements correlated significantly with MRE with an increase of 0.11 kPa (95% CI: 0.05-0.17 kPA) for every 1 kPa increase on EUS-SWE. D. Conclusions: SWE is a promising technology that can readily be incorporated into standard EUS examinations for the assessment of ALD.

Abstract DNA damage response (DDR) mechanisms are critical to maintenance of overall genomic stability, and their dysfunction can contribute to oncogenesis. Significant advances in our understanding of DDR pathways have raised the possibility of developing therapies that exploit these processes. In this expert-driven consensus review, we examine mechanisms of response to DNA damage, progress in development of DDR inhibitors in IDH-wild-type glioblastoma and IDH-mutant gliomas, and other important considerations such as biomarker development, preclinical models, combination therapies, mechanisms of resistance and clinical trial design considerations.

Abstract Meningiomas are the most common intracranial neoplasms in adults. While most meningiomas are cured by resection, further treatment by radiotherapy may be needed, particularly in WHO grades 2 and 3 tumors which have an increased risk of recurrence, even after conventional therapies. Still, there is an urgent need for novel therapeutic strategies after the exhaustion of local treatment approaches. Radionuclide therapies combine the specificity of tumor-specific antibodies or ligands with the cytotoxic activity of radioactive emitters. Alongside this, integrated molecular imaging allows for a noninvasive assessment of predictive biomarkers as treatment targets. Whereas the concept of “theranostics” has initially evolved in extracranial tumors such as thyroid diseases, neuroendocrine tumors, and prostate cancer, data from retrospective case series and early phase trials underscore the potential of this strategy in meningioma. This review aims to explore the available evidence of radionuclide treatments and ongoing clinical trial initiatives in meningioma. Moreover, we discuss optimal clinical trial design and future perspectives in the field, including compound- and host-specific determinants of the efficacy of “theranostic” treatment approaches.

Abstract Brain tumor diagnostics have significantly evolved with the use of positron emission tomography (PET) and advanced magnetic resonance imaging (MRI) techniques. In addition to anatomical MRI, these modalities may provide valuable information for several clinical applications such as differential diagnosis, delineation of tumor extent, prognostication, differentiation between tumor relapse and treatment-related changes, and the evaluation of response to anticancer therapy. In particular, joint recommendations of the Response Assessment in Neuro-Oncology (RANO) Group, the European Association of Neuro-oncology, and major European and American Nuclear Medicine societies highlighted that the additional clinical value of radiolabeled amino acids compared to anatomical MRI alone is outstanding and that its widespread clinical use should be supported. For advanced MRI and its steadily increasing use in clinical practice, the Standardization Subcommittee of the Jumpstarting Brain Tumor Drug Development Coalition provided more recently an updated acquisition protocol for the widely used dynamic susceptibility contrast perfusion MRI. Besides amino acid PET and perfusion MRI, other PET tracers and advanced MRI techniques (e.g. MR spectroscopy) are of considerable clinical interest and are increasingly integrated into everyday clinical practice. Nevertheless, these modalities have shortcomings which should be considered in clinical routine. This comprehensive review provides an overview of potential challenges, limitations, and pitfalls associated with PET imaging and advanced MRI techniques in patients with gliomas or brain metastases. Despite these issues, PET imaging and advanced MRI techniques continue to play an indispensable role in brain tumor management. Acknowledging and mitigating these challenges through interdisciplinary collaboration, standardized protocols, and continuous innovation will further enhance the utility of these modalities in guiding optimal patient care.