Citations

Soft tissue sarcomas (STS) are rare malignancies of mesenchymal cell origin that display a heterogenous mix of clinical and pathologic characteristics. STS can develop from fat, muscle, nerves, blood vessels, and other connective tissues. The evaluation and treatment of patients with STS requires a multidisciplinary team with demonstrated expertise in the management of these tumors. The complete NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines) for Soft Tissue Sarcoma provide recommendations for the diagnosis, evaluation, and treatment of extremity/superficial trunk/head and neck STS, as well as retroperitoneal/intra-abdominal STS, desmoid tumors, and rhabdomyosarcoma. This portion of the NCCN Guidelines discusses general principles for the diagnosis and treatment of retroperitoneal/intra-abdominal STS, outlines treatment recommendations, and reviews the evidence to support the guidelines recommendations.

Abstract Purpose: To identify molecular subclasses of clear cell ovarian carcinoma (CCOC) and assess their impact on clinical presentation and outcomes. Experimental Design: We profiled 421 primary CCOCs that passed quality control using a targeted deep sequencing panel of 163 putative CCOC driver genes and whole transcriptome sequencing of 211 of these tumors. Molecularly defined subgroups were identified and tested for association with clinical characteristics and overall survival. Results: We detected a putative somatic driver mutation in at least one candidate gene in 95% (401/421) of CCOC tumors including ARID1A (in 49% of tumors), PIK3CA (49%), TERT (20%), and TP53 (16%). Clustering of cancer driver mutations and RNA expression converged upon two distinct subclasses of CCOC. The first was dominated by ARID1A-mutated tumors with enriched expression of canonical CCOC genes and markers of platinum resistance; the second was largely comprised of tumors with TP53 mutations and enriched for the expression of genes involved in extracellular matrix organization and mesenchymal differentiation. Compared with the ARID1A-mutated group, women with TP53-mutated tumors were more likely to have advanced-stage disease, no antecedent history of endometriosis, and poorer survival, driven by their advanced stage at presentation. In women with ARID1A-mutated tumors, there was a trend toward a lower rate of response to first-line platinum-based therapy. Conclusions: Our study suggests that CCOC consists of two distinct molecular subclasses with distinct clinical presentation and outcomes, with potential relevance to both traditional and experimental therapy responsiveness. See related commentary by Lheureux, p. 4838

OBJECTIVE One of the key metrics that is used to predict the likelihood of success of MR-guided focused ultrasound (MRgFUS) thalamotomy is the overall calvarial skull density ratio (SDR). However, this measure does not fully predict the sonication parameters that would be required or the technical success rates. The authors aimed to assess other skull characteristics that may also contribute to technical success. METHODS The authors retrospectively studied consecutive patients with essential tremor who were treated by MRgFUS at their center between 2017 and 2021. They evaluated the correlation between the different treatment parameters, particularly maximum power and energy delivered, with a range of patients’ skull metrics and demographics. Machine learning algorithms were applied to investigate whether sonication parameters could be predicted from skull density metrics alone and whether including combined local transducer SDRs with overall calvarial SDR would increase model accuracy. RESULTS A total of 62 patients were included in the study. The mean age was 77.1 (SD 9.2) years, and 78% of treatments (49/63) were performed in males. The mean SDR was 0.51 (SD 0.10). Among the evaluated metrics, SDR had the highest correlation with the maximum power used in treatment (ρ = −0.626, p < 0.001; proportion of local SDR values ≤ 0.8 group also had ρ = +0.626, p < 0.001) and maximum energy delivered (ρ = −0.680, p < 0.001). Machine learning algorithms achieved a moderate ability to predict maximum power and energy required from the local and overall SDRs (accuracy of approximately 80% for maximum power and approximately 55% for maximum energy), and high ability to predict average maximum temperature reached from the local and overall SDRs (approximately 95% accuracy). CONCLUSIONS The authors compared a number of skull metrics against SDR and showed that SDR was one of the best indicators of treatment parameters when used alone. In addition, a number of other machine learning algorithms are proposed that may be explored to improve its accuracy when additional data are obtained. Additional metrics related to eventual sonication parameters should also be identified and explored.

Abstract Glial fibrillary acidic protein (GFAP) is a promising candidate blood-based biomarker for Alzheimer’s disease (AD) diagnosis and prognostication. The timing of its disease-associated changes, its clinical correlates, and biofluid-type dependency will influence its clinical utility. We evaluated plasma, serum, and CSF GFAP in families with autosomal dominant AD (ADAD), leveraging the predictable age at symptom onset to determine changes by stage of disease. Plasma GFAP elevations appear a decade before expected symptom onset, after β-amyloid accumulation and prior to neurodegeneration and cognitive decline. Plasma GFAP distinguished β-amyloid-positive from β-amyloid-negative ADAD participants and showed a stronger relationship with β-amyloid load in asymptomatic than symptomatic ADAD. Higher plasma GFAP was associated with the degree and rate of neurodegeneration and cognitive impairment. Serum GFAP showed similar relationships, but these were less pronounced for CSF GFAP. Our findings support a role for plasma GFAP as a clinical biomarker for β-amyloid-associated cognitive deterioration in AD.

A subset of individuals who recover from coronavirus disease 2019 (COVID-19) develop post-acute sequelae of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (PASC), but the mechanistic basis of PASC-associated lung abnormalities suffers from a lack of longitudinal tissue samples. The mouse-adapted SARS-CoV-2 strain MA10 produces an acute respiratory distress syndrome in mice similar to humans. To investigate PASC pathogenesis, studies of MA10-infected mice were extended from acute to clinical recovery phases. At 15 to 120 days after virus clearance, pulmonary histologic findings included subpleural lesions composed of collagen, proliferative fibroblasts, and chronic inflammation, including tertiary lymphoid structures. Longitudinal spatial transcriptional profiling identified global reparative and fibrotic pathways dysregulated in diseased regions, similar to human COVID-19. Populations of alveolar intermediate cells, coupled with focal up-regulation of profibrotic markers, were identified in persistently diseased regions. Early intervention with antiviral EIDD-2801 reduced chronic disease, and early antifibrotic agent (nintedanib) intervention modified early disease severity. This murine model provides opportunities to identify pathways associated with persistent SARS-CoV-2 pulmonary disease and test countermeasures to ameliorate PASC.

Abstract Somatic mutations in UBA1 cause vacuoles, E1 ubiquitin-activating enzyme, X-linked, autoinflammatory somatic (VEXAS) syndrome, an adult-onset inflammatory disease with an overlap of hematologic manifestations. VEXAS syndrome is characterized by a high mortality rate and significant clinical heterogeneity. We sought to determine independent predictors of survival in VEXAS and to understand the mechanistic basis for these factors. We analyzed 83 patients with somatic pathogenic variants in UBA1 at p.Met41 (p.Met41Leu/Thr/Val), the start codon for translation of the cytoplasmic isoform of UBA1 (UBA1b). Patients with the p.Met41Val genotype were most likely to have an undifferentiated inflammatory syndrome. Multivariate analysis showed ear chondritis was associated with increased survival, whereas transfusion dependence and the p.Met41Val variant were independently associated with decreased survival. Using in vitro models and patient-derived cells, we demonstrate that p.Met41Val variant supports less UBA1b translation than either p.Met41Leu or p.Met41Thr, providing a molecular rationale for decreased survival. In addition, we show that these 3 canonical VEXAS variants produce more UBA1b than any of the 6 other possible single-nucleotide variants within this codon. Finally, we report a patient, clinically diagnosed with VEXAS syndrome, with 2 novel mutations in UBA1 occurring in cis on the same allele. One mutation (c.121 A&gt;T; p.Met41Leu) caused severely reduced translation of UBA1b in a reporter assay, but coexpression with the second mutation (c.119 G&gt;C; p.Gly40Ala) rescued UBA1b levels to those of canonical mutations. We conclude that regulation of residual UBA1b translation is fundamental to the pathogenesis of VEXAS syndrome and contributes to disease prognosis.

Background: With the improvement in outcomes for acute coronary syndrome (ACS), the practice of routine admission to cardiac intensive care units (CICUs) is evolving. We aimed to describe the epidemiology of patients with ACS admitted to contemporary CICUs. Methods: Using the CCCTN (Critical Care Cardiology Trials Network) Registry for consecutive medical CICU admissions across 26 advanced CICUs in North America between 2017 and 2020, we identified patients with a primary diagnosis of ACS at CICU admission and compared patient characteristics, resource utilization, and outcomes to patients admitted with a non-ACS diagnosis and across sub-populations of patients with ACS, including by indication for CICU admission. Results: Of 10 118 CICU admissions, 29.4% (n=2978) were for a primary diagnosis of ACS, with significant interhospital variability (range, 13.4%–56.6%). Compared with patients admitted with a diagnosis other than ACS, patients with ACS had fewer comorbidities, lower acute severity of illness with less utilization of advanced CICU therapies (41.3% versus 66.1%, P <0.0001), and lower CICU mortality (5.4% versus 9.9%, P <0.0001). Monitoring alone, without another CICU indication at the time of admission, was the most frequent admission indication in patients with ACS (53.8%); less common indications in patients with ACS included respiratory insufficiency, shock, or the need for vasoactive therapy. Of patients with ACS admitted for monitoring alone, 94.8% did not subsequently require advanced intensive care unit therapies and had a low CICU length of stay (1.5 days [0.9–2.4] versus 2.6 [1.4–5.1], P <0.0001) and CICU mortality (0.6% versus 11.0%, P <0.0001), compared with patients with ACS with an admission indication beyond monitoring. Conclusions: In a registry of tertiary care CICUs, ACS represent ≈1/3 of all admissions with significant variability across hospitals. More than half of the ACS admissions to the CICU were for routine monitoring alone, with a low rate of complications and mortality. This observation highlights an opportunity for prospective studies to refine triage strategies for lower risk patients with ACS.

Abstract Case reports of patients infected with COVID-19 and influenza virus (“flurona”) have raised questions around the prevalence and severity of coinfection. Using data from HHS Protect Public Data Hub, NCBI Virus, and CDC FluView, we analyzed trends in SARS-CoV-2 and influenza hospitalized coinfection cases and strain prevalences. We also characterized coinfection cases across the Mayo Clinic Enterprise from January 2020 to April 2022. We compared expected and observed coinfection case counts across different waves of the pandemic and assessed symptoms and outcomes of coinfection and COVID-19 monoinfection cases after propensity score matching on clinically relevant baseline characteristics. From both the Mayo Clinic and nationwide datasets, the observed coinfection rate for SARS-CoV-2 and influenza has been higher during the Omicron era (2021 December 14 to 2022 April 2) compared to previous waves, but no higher than expected assuming infection rates are independent. At the Mayo Clinic, only 120 coinfection cases were observed among 197,364 SARS-CoV-2 cases. Coinfected patients were relatively young (mean age: 26.7 years) and had fewer serious comorbidities compared to monoinfected patients. While there were no significant differences in 30-day hospitalization, ICU admission, or mortality rates between coinfected and matched COVID-19 monoinfection cases, coinfection cases reported higher rates of symptoms including congestion, cough, fever/chills, headache, myalgia/arthralgia, pharyngitis, and rhinitis. While most coinfection cases observed at the Mayo Clinic occurred among relatively healthy individuals, further observation is needed to assess outcomes among subpopulations with risk factors for severe COVID-19 such as older age, obesity, and immunocompromised status.

Abstract The classification of myeloid neoplasms and acute leukemias was last updated in 2016 within a collaboration between the World Health Organization (WHO), the Society for Hematopathology, and the European Association for Haematopathology. This collaboration was primarily based on input from a clinical advisory committees (CACs) composed of pathologists, hematologists, oncologists, geneticists, and bioinformaticians from around the world. The recent advances in our understanding of the biology of hematologic malignancies, the experience with the use of the 2016 WHO classification in clinical practice, and the results of clinical trials have indicated the need for further revising and updating the classification. As a continuation of this CAC-based process, the authors, a group with expertise in the clinical, pathologic, and genetic aspects of these disorders, developed the International Consensus Classification (ICC) of myeloid neoplasms and acute leukemias. Using a multiparameter approach, the main objective of the consensus process was the definition of real disease entities, including the introduction of new entities and refined criteria for existing diagnostic categories, based on accumulated data. The ICC is aimed at facilitating diagnosis and prognostication of these neoplasms, improving treatment of affected patients, and allowing the design of innovative clinical trials.