Novel autoantibodies help diagnose anti-SSA antibody negative Sjögren disease and predict abnormal labial salivary gland pathology.

OBJECTIVES: Sjögren disease (SjD) diagnosis often requires either positive anti-SSA antibodies or a labial salivary gland biopsy with a positive focus score (FS). One-third of patients with SjD lack anti-SSA antibodies (SSA-), requiring a positive FS for diagnosis. Our objective was to identify novel autoantibodies to diagnose 'seronegative' SjD. METHODS: IgG binding to a high-density whole human peptidome array was quantified using sera from SSA- SjD cases and matched non-autoimmune controls. We identified the highest bound peptides using empirical Bayesian statistical filters, which we confirmed in an independent cohort comprising SSA- SjD (n=76), sicca-controls without autoimmunity (n=75) and autoimmune-feature controls (SjD features but not meeting SjD criteria; n=41). In this external validation, we used non-parametric methods for binding abundance and controlled false discovery rate in group comparisons. For predictive modelling, we used logistic regression, model selection methods and cross-validation to identify clinical and peptide variables that predict SSA- SjD and FS positivity. RESULTS: IgG against a peptide from D-aminoacyl-tRNA deacylase (DTD2) bound more in SSA- SjD than sicca-controls (p=0.004) and combined controls (sicca-controls and autoimmune-feature controls combined; p=0.003). IgG against peptides from retroelement silencing factor-1 and DTD2 were bound more in FS-positive than FS-negative participants (p=0.010; p=0.012). A predictive model incorporating clinical variables showed good discrimination between SjD versus control (area under the curve (AUC) 74%) and between FS-positive versus FS-negative (AUC 72%). CONCLUSION: We present novel autoantibodies in SSA- SjD that have good predictive value for SSA- SjD and FS positivity.

Validation of Thermal Imaging and the ALT-70 Prediction Model to Differentiate Cellulitis From Pseudocellulitis.

Importance: Cellulitis is misdiagnosed in up to 30% of cases due to mimic conditions termed pseudocellulitis. The resulting overuse of antibiotics is a threat to patient safety and public health. Surface thermal imaging and the ALT-70 (asymmetry, leukocytosis, tachycardia, and age ≥70 years) prediction model have been proposed as tools to help differentiate cellulitis from pseudocellulitis. Objectives: To validate differences in skin surface temperatures between patients with cellulitis and patients with pseudocellulitis, assess the optimal temperature measure and cut point for differentiating cellulitis from pseudocellulitis, and compare the performance of skin surface temperature and the ALT-70 prediction model in differentiating cellulitis from pseudocellulitis. Design, Setting, and Participants: This prospective diagnostic validation study was conducted among patients who presented to the emergency department with acute dermatologic lower extremity symptoms from October 11, 2018, through March 11, 2020. Statistical analysis was performed from July 2020 to March 2021 with additional work conducted in September 2023. Main Outcomes and Measures: Temperature measures for affected and unaffected skin were obtained. Cellulitis vs pseudocellulitis was assessed by a 6-physician, independent consensus review. Differences in temperature measures were compared using the t test. Logistic regression was used to identify the temperature measure and associated cut point with the optimal performance for discriminating between cellulitis and pseudocellulitis. Diagnostic performance characteristics for the ALT-70 prediction model, surface skin temperature, and both combined were also assessed. Results: The final sample included 204 participants (mean [SD] age, 56.6 [16.5] years; 121 men [59.3%]), 92 (45.1%) of whom had a consensus diagnosis of cellulitis. There were statistically significant differences in all skin surface temperature measures (mean temperature, maximum temperature, and gradients) between cellulitis and pseudocellulitis. The maximum temperature of the affected limb for patients with cellulitis was 33.2 °C compared with 31.2 °C for those with pseudocellulitis (difference, 2.0 °C [95% CI, 1.3-2.7 °C]; P < .001). The maximum temperature was the optimal temperature measure with a cut point of 31.2 °C in the affected skin, yielding a mean (SD) negative predictive value of 93.5% (4.7%) and a sensitivity of 96.8% (2.3%). The sensitivity of all 3 measures remained above 90%, while specificity varied considerably (ALT-70, 22.0% [95% CI, 15.8%-28.1%]; maximum temperature of the affected limb, 38.4% [95% CI, 31.7%-45.1%]; combination measure, 53.9% [95% CI, 46.5%-61.2%]). Conclusions and Relevance: In this large diagnostic validation study, significant differences in skin surface temperature measures were observed between cases of cellulitis and cases of pseudocellulitis. Thermal imaging and the ALT-70 both demonstrated high sensitivity, but specificity was improved by combining the 2 measures. These findings support the potential of thermal imaging, alone or in combination with the ALT-70 prediction model, as a diagnostic adjunct that may reduce overdiagnosis of cellulitis.

The Importance of Outcomes Ascertainment for Accurate Assessment of the Mammography Screening Cancer Detection Rate: A Simulation Study.

PURPOSE: Cancer detection rate (CDR), an important metric in the mammography screening audit, is designed to ensure adequate sensitivity. Most practices use biopsy results as the reference standard; however, commonly ascertainment of biopsy results is incomplete. We used simulation to determine the relationship between the cancer ascertainment rate of biopsy (AR-biopsy), CDR estimation, and associated error rates in classifying whether practices and radiologists meet the established ACR benchmark of 2.5 per 1,000. MATERIALS AND METHODS: We simulated screening mammography volume, number of cancers detected, and CDR, using negative binomial and beta-binomial distributions, respectively. Simulations were performed at both the practice and radiologist level. Average CDR was based on linearly rescaling a published CDR by the AR-biopsy. CDR distributions were simulated for AR-biopsy between 5% and 100% in steps of five percentage points and were summarized with boxplots and smoothed histograms over the range of AR-biopsy, to quantify the proportion of practices and radiologists meeting the ACR benchmark at each level of AR-biopsy. RESULTS: Decreasing AR-biopsy led to an increasing probability of categorizing CDR performance as being below the ACR benchmark. Our simulation predicts that at the practice level, an AR-biopsy of 65% categorizes 17.6% below the benchmark (compared to 1.6% at an AR-biopsy of 100%), and at the radiologist level, an AR-biopsy of 65% categorizes 34.7% as being below the benchmark (compared to 11.6% at an AR-biopsy of 100%). CONCLUSIONS: Our simulation demonstrates that decreasing the AR-biopsy (in currently clinically relevant ranges) has the potential to artifactually lower the assessed CDR on both the practice and radiologist levels and may, in turn, increase the chance of erroneous categorization of underperformance per the ACR benchmark.

ATM inhibition augments type I interferon response and antitumor T-cell immunity when combined with radiation therapy in murine tumor models.

BACKGROUND: Radiation therapy (RT) elicits DNA double-strand breaks, resulting in tumor cytotoxicity and a type I interferon (IFN) response via stimulator of interferon genes (STING) activation. We investigated whether combining RT with an ataxia-telangiectasia mutated inhibitor promoted these effects and amplified tumor immunity. METHODS: Mice-bearing syngeneic flank tumors (MOC2 head and neck squamous cell carcinoma or B78 melanoma) were treated with tumor-directed RT and oral administration of AZD0156. Specific immune cell depletion, type 1 interferon receptor 1 knock-out mice (IFNAR1-KO), and STING-deficient tumor cells were used to investigate tumor-immune crosstalk following RT and AZD0156 treatment. RESULTS: Combining RT and AZD0156 reduced tumor growth compared with RT or AZD0156 alone in mice bearing MOC2 or B78 tumors. Low-dose AZD0156 (1-100 nM) alone did not affect tumor cell proliferation but suppressed tumor cell clonogenicity in combination with RT. Low-dose AZD0156 with RT synergistically increased IFN-ß, major histocompatibility complex (MHC)-I, and programmed death-ligand 1 (PD-L1) expression in tumor cells. In contrast to wild-type mice, IFNAR1-KO mice showed reduced CD8+T cell tumor infiltration and poor survival following RT+AZD0156 treatment. CD8+T cell depletion reduced antitumor response during RT+AZD0156 treatment. STING-deficient MOC2 (MOC2-STING+/-) or B78 (B78-STING-/-) tumors eliminated the effects of RT+AZD0156 on the expression of IFN-ß, MHC-I, and PD-L1, and reduced CD8+T cell infiltration and migration. Additional anti-PD-L1 therapy promoted antitumor response by elevation of tumor-MHC-I and lymphocyte activation. CONCLUSIONS: Combined radiation and AZD0156 increase STING-dependent antitumor response. Tumor-derived cell-autonomous IFN-ß amplification drives both MHC-I and PD-L1 induction at the tumor cell surface, which is required by anti-PD-L1 therapy to promote antitumor immune response following RT and AZD0156 combination therapy.

Novel autoantibodies help diagnose anti-SSA antibody negative Sjögren's disease and predict abnormal labial salivary gland pathology.

Objectives: Sj□gren's disease (SjD) diagnosis requires either positive anti-SSA antibodies or a labial salivary gland biopsy with a positive focus score (FS). One-third of SjD patients lack anti-SSA antibodies (SSA-), requiring a positive FS for diagnosis. Our objective was to identify novel autoantibodies to diagnose 'seronegative' SjD. Methods: IgG binding to a high density whole human peptidome array was quantified using sera from SSA- SjD cases and matched non-autoimmune controls. We identified the highest bound peptides using empirical Bayesian statistical filters, which we confirmed in an independent cohort comprising SSA- SjD (n=76), sicca controls without autoimmunity (n=75), and autoimmune controls (SjD features but not meeting SjD criteria; n=41). In this external validation, we used non-parametric methods for peptide abundance and controlled false discovery rate in group comparisons. For predictive modeling, we used logistic regression, model selection methods, and cross-validation to identify clinical and peptide variables that predict SSA- SjD and FS positivity. Results: IgG against a peptide from D-aminoacyl-tRNA deacylase (DTD2) was bound more in SSA- SjD than sicca controls (p=.004) and more than combined controls (sicca and autoimmune controls combined; p=0.003). IgG against peptides from retroelement silencing factor-1 (RESF1) and DTD2, were bound more in FS-positive than FS-negative participants (p=.010; p=0.012). A predictive model incorporating clinical variables showed good discrimination between SjD versus control (AUC 74%) and between FS-positive versus FS-negative (AUC 72%). Conclusion: We present novel autoantibodies in SSA- SjD that have good predictive value for SSA- SjD and FS-positivity. KEY MESSAGES: What is already known on this topic - Seronegative (anti-SSA antibody negative [SSA-]) Sjögren's disease (SjD) requires a labial salivary gland biopsy for diagnosis, which is challenging to obtain and interpret. What this study adds - We identified novel autoantibodies in SSA- SjD that, when combined with readily available clinical variables, provide good predictive ability to discriminate 1) SSA- SjD from control participants and 2) abnormal salivary gland biopsies from normal salivary gland biopsies. How this study might affect research, practice or policy - This study provides novel diagnostic antibodies addressing the critical need for improvement of SSA- SjD diagnostic tools.

Dual Axl/MerTK inhibitor INCB081776 creates a proinflammatory tumor immune microenvironment and enhances anti-PDL1 efficacy in head and neck cancer.

BACKGROUND: The tyrosine kinase receptors Axl and MerTK are highly overexpressed in head and neck cancer (HNC) cells, where they are critical drivers of survival, proliferation, metastasis, and therapeutic resistance. METHODS: We investigated the role of Axl and MerTK in creating an immunologically "cold" tumor immune microenvironment (TIME) by targeting both receptors simultaneously with a small molecule inhibitor of Axl and MerTK (INCB081776). Effects of INCB081776 and/or anti-PDL1 on mouse oral cancer (MOC) cell growth and on the TIME were evaluated. RESULTS: Targeting Axl and MerTK can reduce M2 and induce M1 macrophage polarization. In vivo, INCB081776 treatment alone or with anti-PDL1 appears to slow MOC tumor growth, increase proinflammatory immune infiltration, and decrease anti-inflammatory immune infiltration. CONCLUSIONS: This data indicates that simultaneous targeting of Axl and MerTK with INCB081776, either alone or in combination with anti-PDL1, slows tumor growth and creates a proinflammatory TIME in mouse models of HNC.

Beyond post-operative readmissions: analysis of the impact of unplanned readmissions during primary treatment of advanced-stage epithelial ovarian cancer on long-term oncology outcome.

BACKGROUND: Multiple studies have assessed post-operative readmissions in advanced ovarian cancer. OBJECTIVE: To evaluate all unplanned readmissions during the primary treatment period of advanced epithelial ovarian cancer, and the impact of readmission on progression-free survival. METHODS: This was a single institution retrospective study from January 2008 to October 2018. Χ2/Fisher's exact and t-test, or Kruskal-Wallis test were used. Multivariable Cox proportional hazard models were used to assess the effect of covariates in progression-free survival analysis. RESULTS: A total of 484 patients (279 primary cytoreductive surgery, 205 neoadjuvant chemotherapy) were analyzed. In total, 272 of 484 (56%; 37% primary cytoreductive surgery, 32% neoadjuvant chemotherapy, p=0.29) patients were readmitted during the primary treatment period. Overall, 42.3% of the readmissions were surgery related, 47.8% were chemotherapy related, and 59.6% were cancer related but not related to surgery or chemotherapy, and each readmission could qualify for more than one reason. Readmitted patients had a higher rate of chronic kidney disease (4.1% vs 1.0%, p=0.038). Post-operative, chemotherapy, and cancer-related readmissions were similar between the two groups. However, the percentage of inpatient treatment days due to unplanned readmission was twice as high for primary cytoreductive surgery at 2.2% vs 1.3% for neoadjuvant chemotherapy (p<0.001). Despite longer readmissions in the primary cytoreductive surgery group, Cox regression analysis demonstrated that readmissions did not affect progression-free survival (HR=1.22, 95% CI 0.98 to 1.51; p=0.08). Primary cytoreductive surgery, higher modified Frailty Index, grade 3 disease, and optimal cytoreduction were associated with longer progression-free survival. CONCLUSIONS: In this study, 35% of the women with advanced ovarian cancer had at least one unplanned readmission during the entire treatment time. Patients treated by primary cytoreductive surgery spent more days during readmission than those with neoadjuvant chemotherapy. Readmissions did not affect progression-free survival and may not be valuable as a quality metric.

Documentation from trained medical students has a low rate of relative downcoding for emergency medicine encounters.

Background: Since 2018, the Centers for Medicare & Medicaid Services (CMS) guidelines have allowed teaching physicians to bill for evaluation and management services based on medical student documentation. Limited previous data suggest that medical student documentation suffers from a high rate of downcoding relative to faculty documentation. We sought to compare the coding outcomes of documentation performed by medical students, and not edited by faculty, with documentation edited and submitted by faculty. Methods: A total of 104 randomly selected notes from real patient encounters written by senior medical students were compared to the revised notes submitted by faculty. The note pairs were then split and reviewed by blinded professional coders and assigned level of service (LoS) codes 1-5 (corresponding to E&M CPT codes 99281-99285). Results: We found that the LoS agreement between student and faculty note versions was 63%, with 23% of all student notes receiving lower LoS compared to faculty notes (downcoded). This was found to be similar to baseline variability in professional coder LoS designations. Conclusions: Notes from medical students who have completed a focused documentation curriculum have less LoS downcoding than in previous reports.

Emergency Medicine Residents' "Just World" Bias Is Not Associated with a Biased Case Mix.

INTRODUCTION: Belief in a just world is the cognitive bias that "one gets what they deserve." Stronger belief in a just world for others (BJW-O) has been associated with discrimination against individuals with low socioeconomic status (SES) or poor health status, as they may be perceived to have "deserved" their situation. Emergency medicine (EM) residents have been shown to "cherry pick" patients; in this study we sought to determine whether BJW-O is associated with a biased case mix seen in residency. METHODS: We assessed EM residents on their BJW-O using a scale with previous validity evidence and behavioral correlates. We identified chief complaints that residents may associate with low SES or poor health status, including psychiatric disease, substance use disorder (SUD); and patients with multidisciplinary care plans due to frequent ED visits. We then calculated the percentage of each of these patient types seen by each resident as well as correlations and a multiple linear regression. RESULTS: 38 of 48 (79%) residents completed the BJW-O, representing 98,825 total patient encounters. The median BJW-O score was 3.25 (interquartile range 2.81-3.75). There were no significant correlations observed between BJW-O and the percentage of patients with multidisciplinary care plans who were seen, or patients with psychiatric, SUD, dental or sickle cell chief complaints seen; and a multiple linear regression showed no significant association. CONCLUSION: Higher BJW-O scores in EM residents are not significantly associated with a biased case mix of patients seen in residency.

Patient Derived Models to Study Head and Neck Cancer Radiation Response.

Patient-derived model systems are important tools for studying novel anti-cancer therapies. Patient-derived xenografts (PDXs) have gained favor over the last 10 years as newer mouse strains have improved the success rate of establishing PDXs from patient biopsies. PDXs can be engrafted from head and neck cancer (HNC) samples across a wide range of cancer stages, retain the genetic features of their human source, and can be treated with both chemotherapy and radiation, allowing for clinically relevant studies. Not only do PDXs allow for the study of patient tissues in an in vivo model, they can also provide a renewable source of cancer cells for organoid cultures. Herein, we review the uses of HNC patient-derived models for radiation research, including approaches to establishing both orthotopic and heterotopic PDXs, approaches and potential pitfalls to delivering chemotherapy and radiation to these animal models, biological advantages and limitations, and alternatives to animal studies that still use patient-derived tissues.

Utility of Genetic Testing in Addition to Mammography for Determining Risk of Breast Cancer Depends on Patient Age.

While screening and treatment have sharply reduced breast cancer mortality in the past 50 years, more targeted diagnostic testing may improve the accuracy and efficiency of care. Our retrospective, age-matched, case-control study evaluated the differential value of mammography and genetic variants to predict breast cancer depending on patient age. We developed predictive models using logistic regression with group lasso comparing (1) diagnostic mammography findings, (2) selected genetic variants, and (3) a combination of both. For women older than 60, mammography features were most predictive of breast cancer risk (imaging AUC = 0.74, genetic variants AUC = 0.54, combined AUC = 0.71). For women younger than 60 there is additional benefit to obtaining genetic testing (imaging AUC = 0.69, genetic variants AUC = 0.70, combined AUC = 0.72). In summary, genetic testing supplements mammography in younger women while mammography appears sufficient in older women for breast cancer risk prediction.

Improving breast cancer risk prediction by using demographic risk factors, abnormality features on mammograms and genetic variants.

The predictive capability of combining demographic risk factors, germline genetic variants, and mammogram abnormality features for breast cancer risk prediction is poorly understood. We evaluated the predictive performance of combinations of demographic risk factors, high risk single nucleotide polymorphisms (SNPs), and mammography features for women recommended for breast biopsy in a retrospective case-control study (n = 768) with four logistic regression models. The AUC of the baseline demographic features model was 0.580. Both genetic variants and mammography abnormality features augmented the performance of the baseline model: demographics + SNP (AUC =0.668), demographics + mammography (AUC =0.702). Finally, we found that the demographics + SNP + mammography model (AUC = 0.753) had the greatest predictive power, with a significant performance improvement over the other models. The combination of demographic risk factors, genetic variants and imaging features improves breast cancer risk prediction over prior methods utilizing only a subset of these features.

Combined Plasma and Cerebrospinal Fluid Signature for the Prediction of Midterm Progression From Mild Cognitive Impairment to Alzheimer Disease.

Importance: A reliable method of detecting Alzheimer disease (AD) in its prodromal state is needed for patient stratification in clinical trials or for personalizing existing or potential upcoming therapies. Current cerebrospinal fluid (CSF)- or imaging-based single biomarkers for AD offer reliable identification of patients with underlying AD but insufficient prediction of the rate of AD progression. Objective: To optimize prediction of progression from mild cognitive impairment (MCI) to AD dementia by combining information from diverse patient variables. Design, Setting, and Participants: This cohort study from the Alzheimer Disease Neuroimaging Initiative (ADNI) enrolled 928 patients with MCI at baseline and 249 selected variables available in the ADNI data set. Variables included clinical and demographic data, cognitive scores, magnetic resonance imaging-based brain volumetric data, the apolipoprotein E (APOE) and translocase of outer mitochondrial membrane 40 homolog (TOMM40) genotypes, and analyte levels measured in the CSF and plasma. Data were collected in July 2012 and analyzed from July 1, 2012, to June 1, 2015. Main Outcomes and Measures: Progression from MCI to AD within 1 to 6 years. To determine whether combinations of markers could predict progression from MCI to AD within 1 to 6 years, the elastic net algorithm was used in an iterative resampling of a training- and test-based variable selection and modeling approach. Results: Among the 928 patients with MCI in the ADNI database, 94 had 224 of the required variables available for the modeling. The results showed the contributions of age, Clinical Dementia Rating Sum of Boxes composite test score, hippocampal volume, and multiple plasma and CSF factors in modeling progression to AD. A combination of apolipoprotein A-II and cortisol levels in plasma and fibroblast growth factor 4, heart-type fatty acid binding protein, calcitonin, and tumor necrosis factor-related apoptosis-inducing ligand receptor 3 (TRAIL-R3) in CSF allowed for reliable prediction of disease status 3 years from the time of sample collection (80% classification accuracy, 88% sensitivity, and 70% specificity). Conclusions and Relevance: These study findings suggest that a combination of markers measured in plasma and CSF, distinct from ß-amyloid and tau, could prove useful in predicting midterm progression from MCI to AD dementia. Such a large-scale, multivariable-based analytical approach could be applied to other similar large data sets involving AD and beyond.

A computational strategy to analyze label-free temporal bottom-up proteomics data.

Biological systems are in a continual state of flux, which necessitates an understanding of the dynamic nature of protein abundances. The study of protein abundance dynamics has become feasible with recent improvements in mass spectrometry-based quantitative proteomics. However, a number of challenges still remain related to how best to extract biological information from dynamic proteomics data, for example, challenges related to extraneous variability, missing abundance values, and the identification of significant temporal patterns. This paper describes a strategy that addresses these issues and demonstrates its values for analyzing temporal bottom-up proteomics data using data from a Rhodobacter sphaeroides 2.4.1 time-course study.

Class discovery and classification of tumor samples using mixture modeling of gene expression data--a unified approach.

MOTIVATION: The DNA microarray technology has been increasingly used in cancer research. In the literature, discovery of putative classes and classification to known classes based on gene expression data have been largely treated as separate problems. This paper offers a unified approach to class discovery and classification, which we believe is more appropriate, and has greater applicability, in practical situations. RESULTS: We model the gene expression profile of a tumor sample as from a finite mixture distribution, with each component characterizing the gene expression levels in a class. The proposed method was applied to a leukemia dataset, and good results are obtained. With appropriate choices of genes and preprocessing method, the number of leukemia types and subtypes is correctly inferred, and all the tumor samples are correctly classified into their respective type/subtype. Further evaluation of the method was carried out on other variants of the leukemia data and a colon dataset.