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There is conflicting data regarding the role of PBAF complex mutations and response to immune checkpoint blockade (ICB) therapy in clear cell renal cell carcinoma (ccRCC) and other solid tumors. We assess the prevalence of PBAF complex mutations from two large cohorts including the pan-cancer TCGA project ( n  = 10,359) and the MSK-IMPACT pan-cancer immunotherapy cohort ( n  = 3700). Across both cohorts, PBAF complex mutations, predominantly PBRM1 mutations, are most common in ccRCC. In multivariate models of ccRCC patients treated with ICB ( n  = 189), loss-of-function (LOF) mutations in PBRM1 are not associated with overall survival (OS) (HR = 1.24, p  = 0.47) or time to treatment failure (HR = 0.85, p  = 0.44). In a series of 11 solid tumors ( n  = 2936), LOF mutations are not associated with improved OS in a stratified multivariate model (HR = 0.9, p  = 0.7). In a current series of solid tumors treated with ICB, we are unable to demonstrate favorable response to ICB in patients with PBAF complex mutations. The clinical benefit from immunotherapy response in patients with mutations of genes forming the chromatin remodelling complex PBAF remains controversial. Here the authors show that PBAF complex mutations are not associated with favourable response in pan-cancer cohorts of patients treated with immune-checkpoint blockade.

Immunotherapy is used to treat almost all patients with advanced non-small cell lung cancer (NSCLC); however, identifying robust predictive biomarkers remains challenging. Here we show the predictive capacity of integrating medical imaging, histopathologic and genomic features to predict immunotherapy response using a cohort of 247 patients with advanced NSCLC with multimodal baseline data obtained during diagnostic clinical workup, including computed tomography scan images, digitized programmed death ligand-1 immunohistochemistry slides and known outcomes to immunotherapy. Using domain expert annotations, we developed a computational workflow to extract patient-level features and used a machine-learning approach to integrate multimodal features into a risk prediction model. Our multimodal model (area under the curve (AUC) = 0.80, 95% confidence interval (CI) 0.74–0.86) outperformed unimodal measures, including tumor mutational burden (AUC = 0.61, 95% CI 0.52–0.70) and programmed death ligand-1 immunohistochemistry score (AUC = 0.73, 95% CI 0.65–0.81). Our study therefore provides a quantitative rationale for using multimodal features to improve prediction of immunotherapy response in patients with NSCLC using expert-guided machine learning.

Databases that link molecular data to clinical outcomes can inform precision cancer research into novel prognostic and predictive biomarkers. However, outside of clinical trials, cancer outcomes are typically recorded only in text form within electronic health records (EHRs). Artificial intelligence (AI) models have been trained to extract outcomes from individual EHRs. However, patient privacy restrictions have historically precluded dissemination of these models beyond the centers at which they were trained. In this study, the vulnerability of text classification models trained directly on protected health information to membership inference attacks is confirmed. A teacher-student distillation approach is applied to develop shareable models for annotating outcomes from imaging reports and medical oncologist notes. ‘Teacher’ models trained on EHR data from Dana-Farber Cancer Institute (DFCI) are used to label imaging reports and discharge summaries from the Medical Information Mart for Intensive Care (MIMIC)-IV dataset. ‘Student’ models are trained to use these MIMIC documents to predict the labels assigned by teacher models and sent to Memorial Sloan Kettering (MSK) for evaluation. The student models exhibit high discrimination across outcomes in both the DFCI and MSK test sets. Leveraging private labeling of public datasets to distill publishable clinical AI models from academic centers could facilitate deployment of machine learning to accelerate precision oncology research. AI models can extract clinical outcomes from electronic health records, but it is critical to ensure that such models preserve patient privacy. Here, the authors develop a teacher-student approach to produce shareable models for annotating cancer outcomes from imaging reports and oncologist notes while protecting patient privacy.

Mucinous carcinomas can arise in any organ with epithelial cells that produce mucus. While mucinous tumors from different organs are histologically similar, it remains to be elucidated whether they share molecular alterations. Here we analyzed a total of 902 patients across six cancer types by comparing mucinous and non-mucinous samples, integrating text mining of pathology reports, gene expression, methylation, mutational and copy-number profiling. We found that, in addition to genes involved in mucin processing and secretion, MUC2 up-regulation is a multi-cancer biomarker of mucinous histology and is regulated by DNA methylation in colorectal, breast and stomach cancer. The majority of carcinomas with mucinous differentiation had fewer DNA copy-number alterations than non-mucinous tumors. The tumor mutational burden was lower in breast and lung with mucinous differentiation compared to their non-mucinous counterparts. We found several differences in the frequency of oncogenic gene and pathway alterations between mucinous and non-mucinous carcinomas, including a lower frequency of p53 pathway alterations in colorectal and lung cancer, and a lower frequency of PI-3-Kinase/Akt pathway alterations in breast and stomach cancer with mucinous differentiation. This study shows that carcinomas with mucinous differentiation originating from different organs share transcriptomic and genomic similarities. These results might pave the way for a more biologically relevant taxonomy for these rare cancers.

We describe the first case of antisynthetase syndrome (ASS) with antibodies to anti‐glycyl tRNA synthetase (EJ‐1) with antiphospholipid syndrome (APLS). A 66‐year‐old man presented with progressive dyspnoea, fever, dry cough and proximal muscle weakness over several months on a background of cryptogenic organizing pneumonia. Examination revealed bibasal fine chest crackles, proximal muscle weakness of the upper and lower limbs, digital skin thickening and facial telangiectasias. Creatine kinase was elevated and autoimmune screening was positive for anti‐EJ‐1, anti‐beta‐2‐glycoprotein, anti‐Ro and anti‐La antibodies. Computed tomography of the chest revealed a usual interstitial pneumonia pattern and a ventilation–perfusion scan demonstrated scintigraphic evidence of bilateral pulmonary emboli. A diagnosis of ASS and APLS was made. Immunosuppressive therapy including pulsed methylprednisolone, rituximab and mycophenolate was commenced with improvement in symptoms. This case highlights the importance of evaluation for ASS in idiopathic interstitial pneumonia, and APLS in ASS patients. To our knowledge, we describe the first case of antisynthetase syndrome with antibodies to anti‐glycyl tRNA synthetase (EJ‐1) with antiphospholipid syndrome.

A 61 year old male presented with clinical signs of Eosinophilic fasciitis (EF), a rare connective tissue disease. Early recognition of the diagnosis of EF is essential. Common examination findings are prayer sign and distal limb swelling, induration, venous guttering, and peau d'orange. CT PET scan can be helpful in supporting the diagnosis of EF.

Spherulitic concretions are very rare among carbonate concretions that generally consist of micritic carbonate. The occurrence of spherulitic concretions in Cambro-Ordovician black shales of unknown stratigraphic age on a mid-channel island in the St Lawrence Estuary in Quebec is a new example in addition to only three hitherto reported occurrences of spherulitic carbonate concretions. Their origin is still poorly understood. These concretions occur in close association with, and show various transitions to, cone-in-cone structure. The spherules, measuring 0.5 to 12 mm in diameter, consist of intergrown fine fibres of ferroan calcite and quartzine, pointing to the formation of the concretions below the sulfate-reduction zone. A phenomenological theory of spherulitic crystallization relates the thickness δ of an impurity-rich layer in front of impurity-rejecting growing crystals to the impurity-diffusion coefficient D and the growth velocity G of the crystal by δ = D/G. In spherulite-forming environments, extremely small values of δ (in the order of <10−4 cm) in conjunction with cellulation lead to spherulitic fibre growth. The theory of spherulitic crystallization is here applied to sedimentary deposits for the first time. The intimate association of calcite and quartzine in the concretions requires a chemical change from alkaline to acidic conditions, which occurs below the carbonate-reduction zone owing to the dissolution of sponge spicules or radiolarians. The transition from spherulite to the silica-free cone-in-cone structure occurs when the silica reservoir that acted as an impurity is exhausted in the crystallization process.

Human cancers arise from environmental, heritable and somatic factors, but how these mechanisms interact in tumorigenesis is poorly understood. Studying 17,152 prospectively sequenced patients with cancer, we identified pathogenic germline variants in cancer predisposition genes, and assessed their zygosity and co-occurring somatic alterations in the concomitant tumors. Two major routes to tumorigenesis were apparent. In carriers of pathogenic germline variants in high-penetrance genes (5.1% overall), lineage-dependent patterns of biallelic inactivation led to tumors exhibiting mechanism-specific somatic phenotypes and fewer additional somatic oncogenic drivers. Nevertheless, 27% of cancers in these patients, and most tumors in patients with pathogenic germline variants in lower-penetrance genes, lacked particular hallmarks of tumorigenesis associated with the germline allele. The dependence of tumors on pathogenic germline variants is variable and often dictated by both penetrance and lineage, a finding with implications for clinical management. A study of 17,152 patients with cancer identified pathogenic germline variants in cancer predisposition genes. Although tumors showed biallelic inactivation for high-penetrance genes, this was not the case in most patients with pathogenic variants in low-penetrance genes, suggesting alternative routes to tumorigenesis.

BACKGROUND: Alcohol use disorder (AUD) and posttraumatic stress disorder (PTSD) commonly co-occur, and together are associated with worse clinical outcomes than either disorder alone. While previous research has made clear that each disorder is associated with a reliable set of neural characteristics, it is unknown if the co-occurrence of the disorders correlates with a specific pattern of neural morphology. Such a pattern may help explain the exacerbated clinical manifestations observed in those with overlapping trauma- and alcohol-related problems and point to new intervention opportunities. This study sought to investigate if links between brain structural features and clinical phenotypes could serve as biomarkers for the co-occurrence of PTSD and AUD.METHODS: Structural Magnetic Resonance Imaging (sMRI) scans and self-report measures assessing harmful drinking and posttraumatic stress symptoms from the UK Biobank dataset were utilized to group participants with posttraumatic stress symptoms and problematic alcohol use, resulting in a relatively large, balanced, under-sampled dataset (N=1,748, n=437/group) of participants forming four groups (co-occurring posttraumatic stress symptoms and problematic alcohol use (PTSD+AUD), posttraumatic stress symptoms (PTSD) only, problematic alcohol use (AUD) only, and controls (CTL)). The groups were used in classification analyses employing machine learning (Boosted Random Forest and Support Vector Machines) and multivariate analyses (Multivariate omnibus test) to assess if sMRI measures are sensitive classification biomarkers for the PTSD+AUD group.RESULTS: Analyses across all three methods did not provide substantial evidence that sMRI measures could be used as sensitive classification biomarkers for differentiation of PTSD+AUD from the other three groups. The MOSTest analysis resulted in the greatest area under the curve (AUC) of the receiver-operating characteristic curve for PTSD+AUD classification from AUD, PTSD, and CTL (largest AUC: PTSD+AUD vs PTSD AUC=0.68 (95% CI=[0.65,0.72]).CONCLUSIONS: The absence of evidence for convincing biomarkers suggests that behavioral rather than structural-neural mechanisms may be more related to poor outcomes in the co-occurrence of PTSD+AUD. Future work, including additional imaging modalities (e.g. diffusion and functional MRI) with sMRI may account for additional variance that would more accurately classify PTSD+AUD from the other three groups and further elucidate the underlying brain-behavior phenotypes observed in patients with co-occurring PTSD+AUD.

BACKGROUND: Alcohol use disorder (AUD) and posttraumatic stress disorder (PTSD) commonly co-occur, and together are associated with worse clinical outcomes than either disorder alone. While previous research has made clear that each disorder is associated with a reliable set of neural characteristics, it is unknown if the co-occurrence of the disorders correlates with a specific pattern of neural morphology. Such a pattern may help explain the exacerbated clinical manifestations observed in those with overlapping trauma- and alcohol-related problems and point to new intervention opportunities. This study sought to investigate if links between brain structural features and clinical phenotypes could serve as biomarkers for the co-occurrence of PTSD and AUD.METHODS: Structural Magnetic Resonance Imaging (sMRI) scans and self-report measures assessing harmful drinking and posttraumatic stress symptoms from the UK Biobank dataset were utilized to group participants with posttraumatic stress symptoms and problematic alcohol use, resulting in a relatively large, balanced, under-sampled dataset (N=1,748, n=437/group) of participants forming four groups (co-occurring posttraumatic stress symptoms and problematic alcohol use (PTSD+AUD), posttraumatic stress symptoms (PTSD) only, problematic alcohol use (AUD) only, and controls (CTL)). The groups were used in classification analyses employing machine learning (Boosted Random Forest and Support Vector Machines) and multivariate analyses (Multivariate omnibus test) to assess if sMRI measures are sensitive classification biomarkers for the PTSD+AUD group.RESULTS: Analyses across all three methods did not provide substantial evidence that sMRI measures could be used as sensitive classification biomarkers for differentiation of PTSD+AUD from the other three groups. The MOSTest analysis resulted in the greatest area under the curve (AUC) of the receiver-operating characteristic curve for PTSD+AUD classification from AUD, PTSD, and CTL (largest AUC: PTSD+AUD vs PTSD AUC=0.68 (95% CI=[0.65,0.72]).CONCLUSIONS: The absence of evidence for convincing biomarkers suggests that behavioral rather than structural-neural mechanisms may be more related to poor outcomes in the co-occurrence of PTSD+AUD. Future work, including additional imaging modalities (e.g. diffusion and functional MRI) with sMRI may account for additional variance that would more accurately classify PTSD+AUD from the other three groups and further elucidate the underlying brain-behavior phenotypes observed in patients with co-occurring PTSD+AUD.