Pancreatoblastoma: Cytologic and histologic analysis of 12 adult cases reveals helpful criteria in their diagnosis and distinction from common mimics

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/152894/1/cncy22187_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/152894/2/cncy22187.pd

Hashimoto’s Thyroiditis among Patients with Thyroid Disorders Visiting a Tertiary Care Centre

Introduction: Hashimoto’s thyroiditis is a chronic autoimmune lymphocytic thyroiditis characterised by thyroid autoantibodies. Early detection and treatment of this condition help in reducing the morbidity and mortality associated with it. The aim of the study was to find out the prevalence of Hashimoto’s thyroiditis among patients with thyroid disorders visiting a tertiary care centre. Methods: A descriptive cross-sectional study was conducted among patients visiting the outpatient department of a tertiary care centre. Data from 14 April 2017 to 13 April 2019 was collected between 30 June 2022 to 15 September 2022 from medical records. Ethical approval was obtained from the Nepal Health Research Council. Hashimoto’s thyroiditis was diagnosed based on clinical presentation and positive antibodies to thyroid antigens. Convenience sampling method was used. The point estimate was calculated at a 95% Confidence Interval. Results: Among 813 patients with thyroid disorders, 393 (48.33%) (44.89-51.77, 95% Confidence Interval) had Hashimoto’s thyroiditis. The manifestation of the spectrum of Hashimoto’s thyroiditis were euthyroid in 215 (54.70%), subclinical hypothyroidism in 102 (25.95%), subclinical hyperthyroidism in 23 (5.85%), overt hyperthyroidism in 9 (2.30%) and overt hypothyroidism in 4 (1.02%). Conclusions: The prevalence of Hashimoto’s thyroiditis among patients with thyroid disorders was higher than in other studies done in similar settings

Combined HER3-EGFR score in triple-negative breast cancer provides prognostic and predictive significance superior to individual biomarkers

© 2020, The Author(s). Epidermal growth factor receptor (EGFR) and human epidermal growth factor receptor 3 (HER3) have been investigated as triple-negative breast cancer (TNBC) biomarkers. Reduced EGFR levels can be compensated by increases in HER3; thus, assaying EGFR and HER3 together may improve prognostic value. In a multi-institutional cohort of 510 TNBC patients, we analyzed the impact of HER3, EGFR, or combined HER3-EGFR protein expression in pre-treatment samples on breast cancer-specific and distant metastasis-free survival (BCSS and DMFS, respectively). A subset of 60 TNBC samples were RNA-sequenced using massive parallel sequencing. The combined HER3-EGFR score outperformed individual HER3 and EGFR scores, with high HER3-EGFR score independently predicting worse BCSS (Hazard Ratio [HR] = 2.30, p = 0.006) and DMFS (HR = 1.78, p = 0.041, respectively). TNBCs with high HER3-EGFR scores exhibited significantly suppressed ATM signaling and differential expression of a network predicted to be controlled by low TXN activity, resulting in activation of EGFR, PARP1, and caspases and inhibition of p53 and NFκB. Nuclear PARP1 protein levels were higher in HER3-EGFR-high TNBCs based on immunohistochemistry (p = 0.036). Assessing HER3 and EGFR protein expression in combination may identify which adjuvant chemotherapy-treated TNBC patients have a higher risk of treatment resistance and may benefit from a dual HER3-EGFR inhibitor and a PARP1 inhibitor

Prognostic Role of Androgen Receptor in Triple Negative Breast Cancer: A Multi-Institutional Study

Background: Androgen Receptor (AR) has emerged as a potential therapeutic target for AR-positive triple-negative breast cancer (TNBC). However, conflicting reports regarding AR’s prognostic role in TNBC are putting its usefulness in question. Some studies conclude that AR positivity indicates a good prognosis in TNBC whereas others suggest the opposite, and some show that AR status has no significant bearing on the patients’ prognosis. Methods: We evaluated the prognostic value of AR in resected primary tumors from TNBC patients from six international cohorts {US (n=420), UK (n=239), Norway (n=104), Ireland (n=222), Nigeria (n=180), and India (n=242); total n=1407}. All TNBC samples were stained with the same anti-AR antibody using the same immunohistochemistry protocol, and samples with ≥1% of AR-positive nuclei were deemed AR-positive TNBCs. Results: AR status shows population-specific patterns of association with patients’ overall survival after controlling for age, grade, population, and chemotherapy. We found AR-positive status to be a marker of good prognosis in US and Nigerian cohorts, a marker of poor prognosis in Norway, Ireland and Indian cohorts, and neutral in UK cohort. Conclusion: AR status, on its own, is not a reliable prognostic marker. More research to investigate molecular subtype composition among the different cohorts is warranted

A Case Series Exploration of Multi-Regional Expression Heterogeneity in Triple-Negative Breast Cancer Patients

Background: Extensive intratumoral heterogeneity (ITH) is believed to contribute to therapeutic failure and tumor recurrence, as treatment-resistant cell clones can survive and expand. However, little is known about ITH in triple-negative breast cancer (TNBC) because of the limited number of single-cell sequencing studies on TNBC. In this study, we explored ITH in TNBC by evaluating gene expression-derived and imaging-derived multi-region differences within the same tumor. Methods: We obtained tissue specimens from 10 TNBC patients and conducted RNA sequencing analysis of 2-4 regions per tumor. We developed a novel analysis framework to dissect and characterize different types of variability: between-patients (inter-tumoral heterogeneity), between-patients across regions (inter-tumoral and region heterogeneity), and within-patient, between-regions (regional intratumoral heterogeneity). We performed a Bayesian changepoint analysis to assess and classify regional variability as low (convergent) versus high (divergent) within each patient feature (TNBC and PAM50 subtypes, immune, stroma, tumor counts and tumor infiltrating lymphocytes). Results: Gene expression signatures were categorized into three types of variability: between-patients (108 genes), between-patients across regions (183 genes), and within-patients, between-regions (778 genes). Based on the between-patient gene signature, we identified two distinct patient clusters that differed in menopausal status. Significant intratumoral divergence was observed for PAM50 classification, tumor cell counts, and tumor-infiltrating T cell abundance. Other features examined showed a representation of both divergent and convergent results. Lymph node stage was significantly associated with divergent tumors. Conclusions: Our results show extensive intertumoral heterogeneity and regional ITH in gene expression and image-derived features in TNBC. Our findings also raise concerns regarding gene expression based TNBC subtyping. Future studies are warranted to elucidate the role of regional heterogeneity in TNBC as a driver of treatment resistance

Machine learning-based prediction of breast cancer growth rate in-vivo

BackgroundDetermining the rate of breast cancer (BC) growth in vivo, which can predict prognosis, has remained elusive despite its relevance for treatment, screening recommendations and medicolegal practice. We developed a model that predicts the rate of in vivo tumour growth using a unique study cohort of BC patients who had two serial mammograms wherein the tumour, visible in the diagnostic mammogram, was missed in the first screen.MethodsA serial mammography-derived in vivo growth rate (SM-INVIGOR) index was developed using tumour volumes from two serial mammograms and time interval between measurements. We then developed a machine learning-based surrogate model called Surr-INVIGOR using routinely assessed biomarkers to predict in vivo rate of tumour growth and extend the utility of this approach to a larger patient population. Surr-INVIGOR was validated using an independent cohort.ResultsSM-INVIGOR stratified discovery cohort patients into fast-growing versus slow-growing tumour subgroups, wherein patients with fast-growing tumours experienced poorer BC-specific survival. Our clinically relevant Surr-INVIGOR stratified tumours in the discovery cohort and was concordant with SM-INVIGOR. In the validation cohort, Surr-INVIGOR uncovered significant survival differences between patients with fast-growing and slow-growing tumours.ConclusionOur Surr-INVIGOR model predicts in vivo BC growth rate during the pre-diagnostic stage and offers several useful applications

QNBC Is Associated with High Genomic Instability Characterized by Copy Number Alterations and miRNA Deregulation

Triple-negative breast cancer (TNBC) can be further classified into androgen receptor (AR)-positive TNBC and AR-negative TNBC or quadruple-negative breast cancer (QNBC). Here, we investigated genomic instability in 53 clinical cases by array-CGH and miRNA expression profiling. Immunohistochemical analysis revealed that 64% of TNBC samples lacked AR expression. This group of tumors exhibited a higher level of copy number alterations (CNAs) and a higher frequency of cases affected by CNAs than TNBCs. CNAs in genes of the chromosome instability 25 (CIN25) and centrosome amplification (CA) signatures were more frequent in the QNBCs and were similar between the groups, respectively. However, expression levels of CIN25 and CA20 genes were higher in QNBCs. miRNA profiling revealed 184 differentially expressed miRNAs between the groups. Fifteen of these miRNAs were mapped at cytobands with CNAs, of which eight (miR-1204, miR-1265, miR-1267, miR-23c, miR-548ai, miR-567, miR-613, and miR-943), and presented concordance of expression and copy number levels. Pathway enrichment analysis of these miRNAs/mRNAs pairings showed association with genomic instability, cell cycle, and DNA damage response. Furthermore, the combined expression of these eight miRNAs robustly discriminated TNBCs from QNBCs (AUC = 0.946). Altogether, our results suggest a significant loss of AR in TNBC and a profound impact in genomic instability characterized by CNAs and deregulation of miRNA expression

Prognostic and Predictive Biomarkers in Breast Cancer

Breast cancer (BC) is a heterogeneous disease consisting of distinct subtypes that vary in prognosis. Routine diagnosis is limited to the assessment of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2), there are just a few others that have been clinically validated to guide chemotherapy and medicolegal decisions for patients with BC. Although androgen receptor (AR) has recently emerged as a promising predictive and prognostic biomarker for BC, especially triple negative breast cancers (TNBCs), there is still an unmet need to risk-stratify risk BC patients and predict response to therapy. Thus, we hypothesis that a biomarker-guided deeper stratification of patients will improve prognostication; aid in tailored therapy and decision making in medicolegal cases. My research has primarily focused on evaluating biomarkers that can determine in vivo tumor growth rate, predict response to neoadjuvant in BC patients, and risk-stratify TNBC patients using a combination of in silico analysis, in vitro assays and RNA- sequencing. Our clinically relevant growth rate model derived from Ki67, histological tumor size and mitotic index, stratified tumors into fast-growing versus slow-growing tumor subgroups, wherein patients with fast-growing tumors experienced poorer BC-specific survival. Evaluation of different biomarkers to predict pCR in BC patients revealed that HER2+ and TNBC subtypes had higher pCR rates compared with the luminal subtype. ER and PR negativity, HER2 positivity, Nottingham grade 3, increased TLI and SLI, high mitotic count and Ki67 score correlated significantly with pCR. Evaluating AR status shows population-specific patterns of association with patients’ overall survival after controlling for age, grade, population, and chemotherapy. My study validates the striking association of AR loss with worse clinical outcome. The collective data offers compelling evidence to support misregulation of oncogenic Wnt/β-catenin in AR negative scenario. Collectively, my work has revealed a prognostic model that can predict the in vivo breast tumor growth rate and offers several useful application; identified immunohistochemical and clinicopathological biomarkers that are independent predictors of neoadjuvant chemotherapy; stratify risk in TNBC patients based on AR status; and uncovered molecular pathways that can optimize targeted therapy to combat TNBCs that lack AR

Sleep Pattern and Problems in Young Children Visiting Outpatient Department of a Tertiary Level Hospital in Kathmandu, Nepal

Background. Sleep is an important parameter of a child’s growth and development. The pattern and duration of sleep varies with age. Sleep problems are a common occurrence during childhood days, and these problems that establish in childhood are presumed to continue later in life. Many times, parental concerns regarding their child’s sleep problems like difficulty in putting to sleep, frequent night time awakening, and waking up early are overlooked during their visits to the hospital. Objective. The aim of this study was to find out the sleep patterns and problems of children aged six to thirty-six months. Methodology. A cross-sectional study was conducted at the pediatric outpatient department of Kathmandu Medical College Teaching Hospital from October, 2019 till March, 2020. Two hundred and forty-nine respondents were chosen purposively and were given questionnaires to be filled out. Research instrument was a standard, Nepali version of a structured questionnaire called Brief Infant Sleep Questionnaire (BISQ) which contained questions related to sleep parameters and sleep problems existing among young children of 6-36 months. Mean, standard deviation, frequencies, and Kruskal Wallis test were used for statistical analysis. Results. The mean duration of total sleep was 12.12±2.00 hours, while that of night sleep was 9.22±1.19 hours and mean daytime nap was 2.90±1.66 hours. Most of the children (96%) coslept with their parents, and 55% of the children had feeding as a bedtime ritual. Overall, 19.6% of the children had sleep problems as identified by BISQ although only 5.6% of the parents perceived that their children had it. Conclusions. Sleep problems were present among young Nepalese children included in our study, and sleep assessment should be a part of every health checkup for children

Binding Analysis Using Accelerated Molecular Dynamics Simulations and Future Perspectives

Biomolecular recognition such as binding of small molecules, nucleic acids, peptides and proteins to their target receptors plays key roles in cellular function and has been targeted for therapeutic drug design. Molecular dynamics (MD) is a computational approach to analyze these binding processes at an atomistic level, which provides valuable understandings of the mechanisms of biomolecular recognition. However, the rather slow biomolecular binding events often present challenges for conventional MD (cMD), due to limited simulation timescales (typically over hundreds of nanoseconds to tens of microseconds). In this regard, enhanced sampling methods, particularly accelerated MD (aMD), have proven useful to bridge the gap and enable all-atom simulations of biomolecular binding events. Here, we will review the recent method developments of Gaussian aMD (GaMD), ligand GaMD (LiGaMD) and peptide GaMD (Pep-GaMD), which have greatly expanded our capabilities to simulate biomolecular binding processes. Spontaneous binding of various biomolecules to their receptors has been successfully simulated by GaMD. Microsecond LiGaMD and Pep-GaMD simulations have captured repetitive binding and dissociation of small-molecule ligands and highly flexible peptides, and thus enabled ligand/peptide binding thermodynamics and kinetics calculations. We will also present relevant application studies in simulations of important drug targets and future perspectives for rational computer-aided drug design