Bradley E. Aouizerat

Abstract

Objective: To determine if microbiome differences exist in head and neck squamous cell carcinoma (HNSCC) based on high-risk pathologic features, smoking, and outcomes using The Cancer Microbiome Atlas (TCMA). Study Design: Database study. Setting: Database review. Methods: TCMA is a publicly available database containing curated, decontaminated microbial profiles for tumors from 1772 patients. The data were limited to microbiome profiles, survival, and clinicopathologic features for HNSCC patients. Phyloseq objects were created, low-read samples were removed, and differential abundance analysis (DAA) using Analysis of Compositions of Microbiomes with Bias Correction 2 (ANCOM-BC2) was performed. Statistical analysis was done in R (v4.3.1). Results: One hundred fifty-six patients with HNSCC were included from TCMA with a mean age of 59 (std 13, min 19, and max 90), 72% male (n = 113), and 91% white (n = 140). Primary sites encompassed oral cavity (n = 106, 68%), oropharynx (n = 26, 17%), and larynx/hypopharynx (n = 24, 15%). For all HNSCC in TCMA, rates of lymphovascular invasion were 17% (n = 26), perineural invasion, 34% (n = 53), and microscopic or gross extranodal extension (ENE), 19% (n = 30). DAA revealed significant changes in bacterial genera based on high-risk pathologic features, smoking status, vital status, and disease-specific survival (DSS). Genera observed with ANCOM-BC2 include Scardovia, Alloscardovia, Lactobacillus, and Corynebacterium genera for vital status and DSS. Conclusion: Changes in the relative abundance of select intratumoral bacterial genera are associated with adverse pathologic features, DSS, and vital status in HNSCC. Shifts in the microbiome need further investigation to determine if they can provide any mechanistic insight or predictive role.

Abstract

While immunotherapy tends to be ineffective against pancreatic ductal adenocarcinoma (PDAC), this cancer type often elicits B-cell immunity. However, the exact antigens responsible for these spontaneous immune responses are still unclear. This study used a unique high-dimensional ELISA to analyze IgG responses to 93 post-translational modifications and other chemical determinants in PDAC patients at the time of diagnosis and before therapy. Results identified 13 specific targets of serum IgG that distinguished PDAC patients from healthy donors. Phosphorylated-serine, -threonine, and -tyrosine emerged as the primary targets, with most patients showing high-titer IgG, predominantly of the IgG1 and IgG3 subclasses. Moreover, serum reactivity to these phosphorylated residues was higher in patients with metastatic disease, suggesting a relation between B cell immunity and tumor burden. Lastly, immunofluorescence staining and phosphoproteomic analysis provided evidence of the accumulation of phosphorylated amino acids in PDAC cells and identified a series of consensus abnormal phosphosites. Overall, our findings reveal for the first time the development of robust antibody responses targeting phosphorylated residues in PDAC.

Abstract

Objective: Past correlational research has shown that minority stress has direct and indirect effects on the biology of sexual minority people. This pilot randomized controlled trial (RCT) examined the potential of AWARENESS, a nine-session cognitive behavioral intervention to reduce intersectional minority stress, to alter gene expression related to immune function, inflammation, and HIV disease progression. Method: Between 2016 and 2019, 25 sexual minority men living with HIV with recent substance use (n=12 in AWARENESS and n= 13 in control) were enrolled, a subset with complete gene expression data among the 41 individuals within the parent RCT. Blood samples were taken prior to the intervention, at the 9-week conclusion of the intervention, and at 4 months postrandomization, and leukocyte RNA was sequenced for all samples. The authors examined differential expression analyses of single genes and overrepresentation analysis of gene sets. Results: Neither AWARENESS nor the control condition was related to the differential expression of single genes. Overrepresentation analysis suggested that AWARENESS was related to changes over time in gene expression in leukocyte RNA in 52 gene sets (q,.05), many of which are related to immune function, while the active control condition was related to changes in gene expression among genes in only one gene set. When AWARENESS was compared to the control condition, four gene sets evidenced an overrepresentation of genes reflecting change over time. Conclusions: This RCT suggests that AWARENESS is associated with changes in gene expression, primarily focused on changes in genes associated with immune processes.

Abstract

Background: Depression affects 33% of women with type 2 diabetes (T2D) and leads to increased risks of premature mortality. Fluctuation and variation of depressive presentations can hinder clinical identification. Purpose: We aimed to identify and examine subgroups characterized by distinct depressive symptom trajectories among women with T2D. Methods: This retrospective analysis leveraged the Women’s Interagency HIV Study data to identify depressive symptom trajectories based on the Center for Epidemiological Studies Depression scores (2014-2019) among women with and without HIV. Descriptive statistics characterized sample demographics (eg, age, race, income), clinical indices (eg, hemoglobin A1C [HbA1c], BMI, HIV status), and psychosocial experiences (eg, discrimination, social support, anxiety, pain). We used growth mixture modeling to identify groups defined by distinct depressive symptom trajectories and parametric and non-parametric tests to examine demographic, clinical, and psychosocial differences across subgroups. Results: Among the 630 women included, the mean age was 50.4 (SD = 8.3) years, 72.4% identified as Black and non-Hispanic, and 68.2% were living with HIV. Five subgroups were identified and distinguished by severity and symptom type. Participants with lower incomes (P = .01), lower employment (P < .0001), lower social support (P = .0001), and experiences of discrimination (P < .0001) showed greater membership in threshold, moderate, and severe depressive subgroups. Subgroup membership was not associated with metabolic indices (BMI, HbA1c) or HIV status. Anxiety, pain, and loneliness (all P = .0001) were worse in subgroups with higher depressive symptoms. Conclusions: Among women with T2D, depressive symptom trajectories differ across clinical and social contexts. This study advances precision by delineating subgroups within a broad clinical category.

Abstract

Objective: This cross-sectional study investigated the associations of neighborhood-level factors with immune activation, systemic inflammation, and leukocyte telomere length in 110 sexual minority men with human immunodeficiency virus. Method: From 2013 to 2017, sexual minority men with human immunodeficiency virus who used stimulants were recruited in San Francisco, California and provided blood samples to measure the markers of immune activation, systemic inflammation, and leukocyte telomere length. To measure neighborhood-level indices, the home address for each participant was geocoded and linked to data from the Centers for Disease Control and Prevention. Hierarchical linear modeling was employed to investigate the associations of neighborhood-level factors with systemic inflammation and leukocyte telomere length. Results: After adjusting for age, stimulant use, self-reported income, level of education, and race and ethnicity, residing in neighborhoods with greater percentages of poverty (β=.33, p,.001) and a higher proportion of racial/ethnic minority residents (β =.26, p,.05) were independently associated with higher levels of interleukin-6. Additionally, residing in neighborhoods with higher percentage of uninsured individuals was independently associated with higher tumor necrosis factor-alpha (β =.24, p,.05). Indices of neighborhood-level adversity were additionally associated with providing a urine sample that was reactive for stimulants (OR= 1.31, p=.002), which was, in turn, associated with shorter leukocyte telomere length (β=−.31, p,.05). Conclusions: Future longitudinal research should examine the biobehavioral pathways linking neighborhood-level factors and stimulant use with systemic inflammation and cellular aging.

Abstract

Background:Poor sleep and frailty are prevalent among aging women with HIV (WWH). Although poor sleep quality has been associated with frailty in general aging populations, these relationships are not well characterized among WWH.Methods:Among 1001 WWH and 371 women without HIV (WWoH) over age 40 years with Pittsburgh Sleep Quality Index (PSQI) and Fried Frailty Phenotype data, we analyzed relationships of poor sleep quality (PSQI>5) and sleep quality components with frailty. Separate hierarchical regression models evaluated associations between sleep and frailty status (prefrail vs. robust, frail vs robust) adjusting for: (1) study site and HIV status, (2) demographics, (3) substance use/Central Nervous System active medications, (4) comorbidities, and (5) depressive symptoms.Results:Median age was 53 years; 9.2% were frail while 52.8% were prefrail. Poor sleep quality was frequent (52% WWH vs. 47% WWoH; p=0.07) and associated with double the frailty odds independent of HIV status, after adjusting for depressive symptoms (fully adjusted odds ratio AOR 1.99, 95% CI:1.14, 3.50, p=0.016). Sleep-associated daytime dysfunction and very poor sleep efficiency were independently associated with being frail. Poor self-rated sleep quality and higher use of sleep medications were independently associated with being prefrail.Conclusions:Among midlife WWH and WWoH, poor subjective sleep measures are independently associated with higher frailty odds. Longitudinal studies are needed to understand how aspects of sleep may impact progression from prefrailty to frailty after accounting for comorbidities and to elucidate the complex relationships between comorbidities and frailty, with sleep quality among midlife PWH.

Abstract

Hypermutated proviruses, which arise in a single Human Immunodeficiency Virus (HIV) replication cycle when host antiviral APOBEC3 proteins introduce extensive guanine to adenine mutations throughout the viral genome, persist in all people living with HIV receiving antiretroviral therapy (ART). However, hypermutated sequences are routinely excluded from phylogenetic trees because their extensive mutations complicate phylogenetic inference, and as a result, we know relatively little about their within-host evolutionary origins and dynamics. Using >1400 longitudinal single-genome-amplified HIV env-gp120 sequences isolated from six women over a median of 18 years of follow-up—including plasma HIV RNA sequences collected over a median of 9 years between seroconversion and ART initiation, and >500 proviruses isolated over a median of 9 years on ART—we evaluated three approaches for masking hypermutation in nucleotide alignments. Our goals were to (i) reconstruct phylogenies that can be used for molecular dating and (ii) phylogenetically infer the integration dates of hypermutated proviruses persisting during ART. Two of the approaches (stripping all positions containing putative APOBEC3 mutations from the alignment or replacing individual putative APOBEC3 mutations in hypermutated sequences with the ambiguous base R) consistently normalized tree topologies, eliminated erroneous clustering of hypermutated proviruses, and brought env-intact and hypermutated proviruses into comparable ranges with respect to multiple tree-based metrics. Importantly, these corrected trees produced integration date estimates for env-intact proviruses that were highly concordant with those from benchmark trees that excluded hypermutated sequences, supporting the use of these corrected trees for molecular dating. Subsequent molecular dating of hypermutated proviruses revealed that these sequences spanned a wide within-host age range, with the oldest ones dating to shortly after infection. This indicates that hypermutated proviruses, like other provirus types, begin to be seeded into the proviral pool immediately following infection and can persist for decades. In two of the six participants, hypermutated proviruses differed from env-intact ones in terms of their age distributions, suggesting that different provirus types decay at heterogeneous rates in some hosts. These simple approaches to reconstruct hypermutated provirus’ evolutionary histories reveal insights into their in vivo origins and longevity toward a more comprehensive understanding of HIV persistence during ART.

Abstract

Oral squamous cell carcinoma (OSCC) is a highly unpredictable disease with devastating mortality rates that have not changed over the past decades, in the face of advancements in treatments and biomarkers, which have improved survival for other cancers. Delays in diagnosis are frequent, leading to more disfiguring treatments and poor outcomes for patients. The clinical challenge lies in identifying those patients at the highest risk of developing OSCC. Oral epithelial dysplasia (OED) is a precursor of OSCC with highly variable behavior across patients. There is no reliable clinical, pathological, histological, or molecular biomarker to determine individual risk in OED patients. Similarly, there are no robust biomarkers to predict treatment outcomes or mortality in OSCC patients. This review aims to highlight advancements in artificial intelligence (AI)-based methods to develop predictive biomarkers of OED transformation to OSCC or predictive biomarkers of OSCC mortality and treatment response. Biomarkers such as S100A7 demonstrate promising appraisal for the risk of malignant transformation of OED. Machine learning-enhanced multiplex immunohistochemistry workflows examine immune cell patterns and organization within the tumor immune microenvironment to generate outcome predictions in immunotherapy. Deep learning (DL) is an AI-based method using an extended neural network or related architecture with multiple “hidden” layers of simulated neurons to combine simple visual features into complex patterns. DL-based digital pathology is currently being developed to assess OED and OSCC outcomes. The integration of machine learning in epigenomics aims to examine the epigenetic modification of diseases and improve our ability to detect, classify, and predict outcomes associated with epigenetic marks. Collectively, these tools showcase promising advancements in discovery and technology, which may provide a potential solution to addressing the current limitations in predicting OED transformation and OSCC behavior, both of which are clinical challenges that must be addressed in order to improve OSCC survival.

Abstract

Oral squamous cell carcinoma (OSCC) biomarker studies rarely employ multi-omic biomarker strategies and pertinent clinicopathologic characteristics to predict mortality. In this study we determine for the first time a combined epigenetic, gene expression, and histology signature that differentiates between patients with different tobacco use history (heavy tobacco use with ≥10 pack years vs. no tobacco use). Using The Cancer Genome Atlas (TCGA) cohort (n = 257) and an internal cohort (n = 40), we identify 3 epigenetic markers (GPR15, GNG12, GDNF) and 13 expression markers (IGHA2, SCG5, RPL3L, NTRK1, CD96, BMP6, TFPI2, EFEMP2, RYR3, DMTN, GPD2, BAALC, and FMO3), which are dysregulated in OSCC patients who were never smokers vs. those who have a ≥ 10 pack year history. While mortality risk prediction based on smoking status and clinicopathologic covariates alone is inaccurate (c-statistic = 0.57), the combined epigenetic/expression and histologic signature has a c-statistic = 0.9409 in predicting 5-year mortality in OSCC patients.

Abstract

Substance use disparities among sexual and gender minority (SGM) people are attributed to minority stress, but few studies have examined minority stress and cannabis use over time or investigated differences in cannabis use trajectories by less-studied gender subgroups. We examined if longitudinal cannabis use trajectories are related to baseline minority stressors and if gender differences persisted after accounting for minority stress. Cannabis use risk was measured annually over four years (2017–2021) within a longitudinal cohort study of SGM adults in the United States (N = 11,813). Discrimination and victimization, internalized stigma, disclosure and concealment, and safety and acceptance comprised minority stress (n = 5,673). Latent class growth curve mixture models identified five cannabis use trajectories: ‘low or no risk’, ‘low moderate risk’, ‘high moderate risk’, ‘steep risk increase’, and ‘highest risk’. Participants who reported past-year discrimination and/or victimization at baseline had greater odds of membership in any cannabis risk category compared to the ‘low risk’ category (odds ratios [OR] 1.17–1.33). Internalized stigma was related to ‘high moderate’ and ‘highest risk’ cannabis use (ORs 1.27–1.38). After accounting for minority stress, compared to cisgender men, gender expansive people and transgender men had higher odds of ‘low moderate risk’ (ORs 1.61, 1.67) or ‘high moderate risk’ (ORs 2.09, 1.99), and transgender men had higher odds of ‘highest risk’ (OR 2.36) cannabis use. This study indicates minority stress is related to prospective cannabis use risk trajectories among SGM people, and transgender men and gender expansive people have greater odds of trajectories reflecting cannabis use risk.