Citations

AbstractBackgroundDespite recent approval of several new agents, relapsed acute lymphoblastic leukemia (ALL) remains challenging to treat. Sapanisertib (MLN0128/TAK‐228) is an oral TORC1/2 inhibitor that exhibited preclinical activity against ALL.MethodsWe conducted a single‐arm multi‐center Phase II study of sapanisertib monotherapy (3 mg orally daily of the milled formulation for 21 days every 28 days) in patients with ALL through the Experimental Therapeutics Clinical Trials Network (NCI‐9775).ResultsSixteen patients, 15 of whom were previously treated (median 3 prior lines of therapy), were enrolled. Major grade 3–4 non‐hematologic toxicities included mucositis (3 patients) and hyperglycemia (2 patients) as well as hepatic failure, seizures, confusion, pneumonitis, and anorexia (1 patient each). Grade >2 hematological toxicity included leukopenia (3), lymphopenia (2), thrombocytopenia, and neutropenia (1). The best response was stable disease in 2 patients (12.5%), while only 3 patients (19%) were able to proceed to Cycle 2. Pharmacokinetic analysis demonstrated drug exposures similar to those observed in solid tumor patients. Immunoblotting in serially collected samples indicated limited impact of treatment on phosphorylation of mTOR pathway substrates such as 4EBP1, S6, and AKT.ConclusionIn summary, single‐agent sapanisertib had a good safety profile but limited target inhibition or efficacy in ALL as a single agent. This trial was registered at ClinicalTrials.gov as NCT02484430.

OBJECTIVE Chordomas are rare tumors that often recur regardless of surgery with negative margins and postoperative radiotherapy. The predictive accuracy of widely used immunohistochemical (IHC) markers in addressing the recurrence of skull base chordomas (SBCs) is yet to be determined. This study aimed to investigate IHC markers in the prediction of recurrence after SBC resection with adjuvant radiation therapy. METHODS The authors reviewed the records of patients who had treatment for SBC between January 2017 and June 2021 across the Mayo Clinic in Minnesota, Florida, and Arizona. Exclusion criteria included patients who had no histopathology or recurrence as an outcome. Histopathological markers included cytokeratin A1/A3 only, epithelial membrane antigen (EMA), S100 protein, pan-cytokeratin, IN1, GATA3, CAM5.2, OSCAR, and chondroid. Information from patient records was abstracted, including treatment, clinical and radiological follow-up duration, demographics, and histopathological factors. Decision tree and random forest classifiers were trained and tested to predict the recurrence based on unseen data using an 80/20 split. RESULTS A total of 38 patients with a diagnosis of SBC who underwent resection (gross-total resection: 42.1%; and subtotal resection: 57.9%) and radiation therapy were extracted from the medical records. The mean patient age was 48.2 (SD 19.6) years; most patients were male (n = 23; 60.5%) and White (n = 36; 94.7%). Pan-cytokeratin was associated with an increased risk of postoperative recurrence (OR 14.67, 95% CI 2.44–88.13; p = 0.00517) after resection and adjuvant radiotherapy. The decision tree analysis found pan-cytokeratin–positive tumors to have a 78% chance of being classified as a recurrence, with an accuracy of 75%. The distribution of minimal depth in the prediction of postoperative recurrence indicates that the most important variables were pan-cytokeratin, followed by cytokeratin A1/A3 and EMA. CONCLUSIONS The authors’ machine learning algorithm identified pan-cytokeratin as the largest contributor to recurrence among other IHC markers after SBC resection. Machine learning may facilitate the prediction of outcomes in rare tumors, such as chordomas.

AbstractBackgroundCardiac arrest (CA) is a leading cause of death in the United States (US). Social determinants of health may impact CA outcomes. We aimed to assess mortality trends, disparities, and the influence of the social vulnerability index (SVI) on CA outcomes in the young.MethodsWe conducted a cross‐sectional analysis of age‐adjusted mortality rates (AAMRs) related to CA in the United States from the Years 1999 to 2020 in individuals aged 35 years and younger. Data were obtained from death certificates and analyzed using log‐linear regression models. We examined disparities in mortality rates based on demographic variables. We also explored the impact of the SVI on CA mortality.ResultsA total of 4792 CA deaths in the young were identified. Overall AAMR decreased from 0.20 in 1999 to 0.14 in 2020 with an average annual percentage change of −1.3% (p = .001). Black (AAMR: 0.30) and male populations (AAMR: 0.14) had higher AAMR compared with White (AAMR: 0.11) and female (AAMR: 0.11) populations, respectively. Nonmetropolitan (AAMR: 0.29) and Southern (AAMR: 0.26) regions were also impacted by higher AAMR compared with metropolitan (AAMR: 0.11) and other US census regions, respectively. A higher SVI was associated with greater mortality risks related to CA (risk ratio: 1.82 [95% CI, 1.77–1.87]).ConclusionsOur analysis of CA in the young revealed disparities based on demographics, with a decline in AAMR from 1999 to 2020. There is a correlation between a higher SVI and increased CA mortality risk, highlighting the importance of targeted interventions to address these disparities effectively.

Abstract Struma ovarii comprises 1% of all ovarian tumors and 3% of ovarian teratomas. It occurs in older females. Struma ovarii is often asymptomatic, unilateral, and accidentally detected through abdominal ultrasound or computed tomography. It presents with palpable abdominal pain or irregular menstrual cycles. Generally, it is treated with surgical resection, even though the best procedure in these cases remains under discussion. In this study, we present a case of a 28-year-old female with severe pain in the right iliac fossa. Physical examination and radiological images showed a large mass. A bilateral salpingo-oophorectomy with omentectomy, a total mass resection, and an abdominal hysterectomy were performed. A biopsy confirmed the diagnosis of a follicular thyroid tumor. The management decision is based on clinical and pathological data. This is particularly challenging due to its rarity and the insufficient guidelines regarding the management of this type of cancer.

Purpose of review This review delves into the potential of artificial intelligence (AI), particularly machine learning (ML), in enhancing graft-versus-host disease (GVHD) risk assessment, diagnosis, and personalized treatment. Recent findings Recent studies have demonstrated the superiority of ML algorithms over traditional multivariate statistical models in donor selection for allogeneic hematopoietic stem cell transplantation. ML has recently enabled dynamic risk assessment by modeling time-series data, an upgrade from the static, “snapshot” assessment of patients that conventional statistical models and older ML algorithms offer. Regarding diagnosis, a deep learning model, a subset of ML, can accurately identify skin segments affected with chronic GVHD with satisfactory results. ML methods such as Q-learning and deep reinforcement learning have been utilized to develop adaptive treatment strategies (ATS) for the personalized prevention and treatment of acute and chronic GVHD. Summary To capitalize on these promising advancements, there is a need for large-scale, multicenter collaborations to develop generalizable ML models. Furthermore, addressing pertinent issues such as the implementation of stringent ethical guidelines is crucial before the widespread introduction of AI into GVHD care.

Nonalcoholic steatohepatis (NASH) is characterized by excess circulating toxic lipids, along with hepatic steatosis, and recruited myeloid cells-mediated liver inflammation. Myeloid cells adhesion to the liver sinusoidal endothelial cells (LSEC) and transendothelial migration are crucial elements in the inflammatory process, and LSEC under lipotoxic stress develop a pro-inflammatory phenotype known as endotheliopathy. However, the exact mechanisms of this phenomenon are obscure. To define the molecular mediators of LSEC endotheliopathy, we isolated primary mouse LSECs from C57BL/6J mice on chow or a NASH-inducing diet rich in fat, fructose, and cholesterol (FFC), and subjected them to multi-omics studies. An integrated pathway analysis of the mouse LSEC proteomics and transcriptomics indicates that leukocyte transendothelial migration and focal adhesion are the major pathways altered in NASH LSEC. To explore the LSEC intracellular signaling networks, we performed kinome profiling of the LSEC phospho-proteomics and identified Glycogen Synthase Kinase 3 (GSK3)β as the major hub, and the top kinase in NASH. We then confirmed that GSK3β is activated in primary human LSEC treated with the toxic lipid Palmitate (PA). PA also promoted the LSEC pro-inflammatory response by upregulating CXCL2 and intracellular adhesion molecule 1 (ICAM-1) , all of which were attenuated with the GSK3 inhibitor LY2090314 (LY). Using a collagen gel-based transmigration assay, we demonstrated that THP-1 monocyte migration through an LSEC monolayer treated with PA was reduced by LY. In FFC-fed mice, LY treatment ameliorated liver inflammation, injury, and fibrosis as assessed by reduced F4/80, MPO, and ICAM-1 immuno-staining, ALT serum levels and hepatic TUNEL-positive cells, Sirius red and alpha smooth muscle actin staining of the liver. Mice with NASH-induced diet had reduced haptic neutrophil and proinflammatory monocyte infiltration when treated with ICAM1 neutralizing antibody. Furthermore, we confirmed increased LSEC expression of ICAM1 in human with NASH. In conclusion, GSK3 inhibition attenuates toxic lipid induced LSEC pro-inflammatory phenotype and may serve as a potential therapeutic strategy in human NASH.

Abstract In the pre–novel agent era, the median postprogression overall survival (PPS) of patients with classic Hodgkin lymphoma (cHL) who progress after autologous stem cell transplant (ASCT) was 2 to 3 years. Recently, checkpoint inhibitors (CPI) and brentuximab vedotin (BV) have improved the depth and durability of response in this population. Here, we report the estimate of PPS in patients with relapsed cHL after ASCT in the era of CPI and BV. In this multicenter retrospective study of 15 participating institutions, adult patients with relapsed cHL after ASCT were included. Study objective was postprogression overall survival (PPS), defined as the time from posttransplant progression to death or last follow-up. Of 1158 patients who underwent ASCT, 367 had progressive disease. Median age was 34 years (range, 27-46) and 192 were male. Median PPS was 114.57 months (95% confidence interval [CI], 91-not achieved) or 9.5 years. In multivariate analysis, increasing age, progression within 6 months, and pre-ASCT positive positron emission tomography scan were associated with inferior PPS. When adjusted for these features, patients who received CPI, but not BV, as first treatment for post-ASCT progression had significantly higher PPS than the no CPI/no BV group (hazard ratio, 3.5; 95% CI, 1.6-7.8; P = .001). Receipt of allogeneic SCT (Allo-SCT) did not improve PPS. In the era of novel agents, progressive cHL after ASCT had long survival that compares favorably with previous reports. Patients who receive CPI as first treatment for progression had higher PPS. Receipt to Allo-SCT was not associated with PPS in this population.

Transplant pathology plays a critical role in ensuring that transplanted organs function properly and the immune systems of the recipients do not reject them. To improve outcomes for transplant recipients, accurate diagnosis and timely treatment are essential. Recent advances in artificial intelligence (AI)-empowered digital pathology could help monitor allograft rejection and weaning of immunosuppressive drugs. To explore the role of AI in transplant pathology, we conducted a systematic search of electronic databases from January 2010 to April 2023. The PRISMA checklist was used as a guide for screening article titles, abstracts, and full texts, and we selected articles that met our inclusion criteria. Through this search, we identified 68 articles from multiple databases. After careful screening, only 14 articles were included based on title and abstract. Our review focuses on the AI approaches applied to four transplant organs: heart, lungs, liver, and kidneys. Specifically, we found that several deep learning-based AI models have been developed to analyze digital pathology slides of biopsy specimens from transplant organs. The use of AI models could improve clinicians’ decision-making capabilities and reduce diagnostic variability. In conclusion, our review highlights the advancements and limitations of AI in transplant pathology. We believe that these AI technologies have the potential to significantly improve transplant outcomes and pave the way for future advancements in this field.

Verteporfin and 5-ALA are used for visualizing malignant tissue components in different body tumors and as photodynamic therapy in treating isocitrate dehydrogenase (IDH) wild-type glioblastoma (GBM). Additionally, verteporfin interferes with Yes-associated protein 1 (YAP)/Transcriptional coactivator with PDZ-binding motif - TEA domain (TAZ-TEAD) pathway, thus inhibiting the downstream effect of these oncogenes and reducing the malignant properties of GBM. Animal studies have shown verteporfin to be successful in increasing survival rates, which have led to the conduction of phase 1 and 2 clinical trials to further investigate its efficacy in treating GBM. In this article, we aimed to review the novel mechanism of verteporfin’s action, the impact of its interaction with YAP/TAZ-TEAD, its effect on glioblastoma stem cells, and its role in inducing ferroptosis.