Publications by authors named "Kim-Anh Do"

Metastatic duodenopancreatic neuroendocrine tumors (dpNETs) are the primary cause of mortality among patients with Multiple Endocrine Neoplasia Type 1 (MEN1). Emerging evidence implicates the microbiome and microbial-derived secreted factors in promoting cancer development and progression. In the current study, we report that the circulating microbial-associated uremic toxins trimethylamine N-oxide (TMAO), indoxyl sulfate (IS), cresol sulfate (CS), cresol glucuronide (CG), and phenol sulfate (PS) are elevated in MEN1 patients with metastatic dpNETs.

View Article and Find Full Text PDF

In the current study, we assessed whether repeated measurements of a panel of protein biomarkers with relevance to pancreatic ductal adenocarcinoma (PDAC) improves lead time performance for earlier detection over a single timepoint measurement. Specifically, CA125, CEA, LRG1, REG3A, THBS2, TIMP1, TNRFSF1A as well as CA19-9 were assayed in serially collected pre-diagnostic plasma from 242 PDAC cases and 242 age- and sex-matched non-case control participants in the PLCO cohort. We compared performance estimates of a parametric empirical Bayes (PEB) algorithm, which incorporates participant biomarker history, to that of a single-threshold (ST) method.

View Article and Find Full Text PDF

In cell line perturbation experiments, a collection of cells is perturbed with external agents and responses such as protein expression measured. Due to cost constraints, only a small fraction of all possible perturbations can be tested in vitro. This has led to the development of computational models that can predict cellular responses to perturbations in silico.

View Article and Find Full Text PDF

In the cancer early detection field, logistic regression (LR) is a frequently used approach to establish a combination rule that differentiates cancer from noncancer. However, the application of LR relies on a maximum likelihood approach, which may not yield optimal combination rules for maximizing sensitivity at a clinically desirable specificity and vice versa. In this article, we have developed an improved regression framework, sensitivity maximization at a given specificity (SMAGS), for binary classification that finds the linear decision rule, yielding the maximum sensitivity for a given specificity or the maximum specificity for a given sensitivity.

View Article and Find Full Text PDF
Article Synopsis
  • Cancer patients are at a higher risk for severe COVID-19, and identifying specific biomarkers related to this risk could help target early treatment.
  • A study analyzed plasma from 128 cancer patients with COVID-19 and found that lower levels of certain miRNAs (hsa-miR-150-5p and hsa-miR-93-5p) correlated with increased mortality, while hsa-miR-92b-3p linked to COVID-19 positivity.
  • The research suggests that monitoring these miRNAs could serve as a potential blood biomarker for predicting COVID-19 severity in cancer patients, improving clinical outcomes.
View Article and Find Full Text PDF

Background: To determine whether an algorithm based on repeated measurements of a panel of four circulating protein biomarkers (4 MP) for lung cancer risk assessment results in improved performance over a single time measurement.

Methods: We conducted data analysis of the 4 MP consisting of the precursor form of surfactant protein B, cancer antigen 125, carcinoembryonic antigen, and cytokeratin-19 fragment in pre-diagnostic sera from 2483 ever-smoker participants (389 cases and 2094 randomly selected non-cases) in the Prostate, Lung, Colorectal, Ovarian (PLCO) Study who had at least two sequential blood collections over 6 years. A parametric empirical Bayes (PEB) algorithm, which incorporates participant biomarker history at each time point, was compared to a single-threshold (ST) method.

View Article and Find Full Text PDF
Article Synopsis
  • A study in the UK showed that using CA125 blood tests and ultrasound for ovarian cancer didn't really lower deaths but helped find earlier cases.
  • Researchers tested blood samples from women with ovarian cancer and controls to create a new test using 7 specific chemicals plus CA125.
  • This new test was better at finding early-stage ovarian cancer, catching a good number of cases even when CA125 levels were low.
View Article and Find Full Text PDF

Summary: Advances in survival analysis have facilitated unprecedented flexibility in data modeling, yet there remains a lack of tools for illustrating the influence of continuous covariates on predicted survival outcomes. We propose the utilization of a colored contour plot to depict the predicted survival probabilities over time. Our approach is capable of supporting conventional models, including the Cox and Fine-Gray models.

View Article and Find Full Text PDF

The microbiome represents a hidden world of tiny organisms populating not only our surroundings but also our own bodies. By enabling comprehensive profiling of these invisible creatures, modern genomic sequencing tools have given us an unprecedented ability to characterize these populations and uncover their outsize impact on our environment and health. Statistical analysis of microbiome data is critical to infer patterns from the observed abundances.

View Article and Find Full Text PDF

This study aimed to assess a four-marker protein panel (4MP)'s performance, including the precursor form of surfactant protein B, cancer antigen 125, carcinoembryonic antigen, and cytokeratin-19, for predicting lung cancer in a cohort enriched with never- and ever-smokers. Blinded pre-diagnostic plasma samples collected within 2 years prior to a lung cancer diagnosis from 25 cases and 100 sex-, age-, and smoking-matched controls were obtained from the Physicians' Health Study (PHS). The 4MP yielded AUC performance estimates of 0.

View Article and Find Full Text PDF

Unlabelled: Logistic regression has demonstrated its utility in classifying binary labeled datasets through the maximum likelihood approach. However, in numerous biological and clinical contexts, the aim is often to determine coefficients that yield the highest sensitivity at the pre-specified specificity or vice versa. Therefore, the application of logistic regression is limited in such settings.

View Article and Find Full Text PDF

Tumors represent ecosystems where subclones compete during tumor growth. While extensively investigated, a comprehensive picture of the interplay of clonal lineages during dissemination is still lacking. Using patient-derived pancreatic cancer cells, we created orthotopically implanted clonal replica tumors to trace clonal dynamics of unperturbed tumor expansion and dissemination.

View Article and Find Full Text PDF

Purpose: Data indicates that clinicians might be under-prescribing opioids for patients with chronic cancer pain, and this could impact adequate pain management. Few studies have sought to understand healthcare provider (HCP) perceptions and practices regarding the prescription of opioids for chronic cancer pain. We assessed HCP perceptions and practices regarding opioid prescription for patients with chronic cancer pain since the onset of the COVID-19 pandemic.

View Article and Find Full Text PDF

Objective: The primary objective of this study is to assess factors that influence opioid prescribing by dentists and the role of these factors in the practice of dental pain control.

Design: A 25-question survey instrument was distributed to the study population for anonymous responses, covering dentist and practice demographics and opioid prescribing characteristics.

Setting: Private solo and group practice settings, including general practitioners and dental specialists.

View Article and Find Full Text PDF

Oral mucositis (OM) is a common and clinically impactful side effect of cytotoxic cancer treatment, particularly in patients with head and neck squamous cell carcinoma (HNSCC) who undergo radiotherapy with or without concomitant chemotherapy. The etiology and pathogenic mechanisms of OM are complex, multifaceted and elicit both direct and indirect damage to the mucosa. In this narrative review, we describe studies that use various omics methodologies (genomics, transcriptomics, microbiomics and metabolomics) in attempts to elucidate the biological pathways associated with the development or severity of OM.

View Article and Find Full Text PDF

Background: Accessible prebiotic foods hold strong potential to jointly target gut health and metabolic health in high-risk patients. The BE GONE trial targeted the gut microbiota of obese surveillance patients with a history of colorectal neoplasia through a straightforward bean intervention.

Methods: This low-risk, non-invasive dietary intervention trial was conducted at MD Anderson Cancer Center (Houston, TX, USA).

View Article and Find Full Text PDF

Purpose: Data indicates that clinicians might be under-prescribing opioids for patients with chronic cancer pain, and this could impact adequate chronic pain management. Few studies have sought to understand healthcare provider (HCP) perceptions and practices regarding the prescription of opioids for chronic pain. We assessed HCP perceptions and practices regarding opioid prescription for patients with chronic pain since the onset of the COVID-19 pandemic.

View Article and Find Full Text PDF
Article Synopsis
  • Emerging research suggests that gut bacteria (microbiome) may play a role in pancreatic cancer (PaCa) development.
  • The study analyzed blood samples from 172 individuals diagnosed with PaCa and 863 matched control samples to explore the relationship between microbial-related metabolites and PaCa risk.
  • A panel of microbial and non-microbial metabolites was created to enhance risk prediction for PaCa, identifying individuals at high risk who could benefit from closer monitoring and potential preventive strategies.
View Article and Find Full Text PDF
Article Synopsis
  • This study examined how oral bacteria affect the severity of oral mucositis (OM) in head and neck cancer patients during treatment.
  • Researchers analyzed buccal swabs over treatment periods and grouped patients based on their OM severity patterns, identifying four distinct groups.
  • The results indicated specific bacterial populations correlated with OM severity, suggesting a potential for personalized treatment plans based on a patient’s oral microbiome profile.
View Article and Find Full Text PDF

Background: The development of diverse spatial profiling technologies has provided an unprecedented insight into molecular mechanisms driving cancer pathogenesis. Here, we conducted the first integrated cross-species assessment of spatial transcriptomics and spatial metabolomics alterations associated with progression of intraductal papillary mucinous neoplasms (IPMN), cystic precursors of pancreatic ductal adenocarcinoma (PDAC).

Methods: Matrix Assisted Laster Desorption/Ionization (MALDI) mass spectrometry (MS)-based spatial imaging and Visium spatial transcriptomics (ST) (10X Genomics) was performed on human resected IPMN tissues (N= 23) as well as pancreata from a mutant mouse model of IPMN.

View Article and Find Full Text PDF
Article Synopsis
  • The study investigates the effectiveness of using a combination of a protein biomarker panel and a risk model to pinpoint individuals at high risk for lethal lung cancer.
  • Data from over 2,700 participants, including 552 lung cancer cases, were analyzed to assess the predictive ability of this combined approach.
  • Results showed a strong predictive capability, with a risk prediction area under the curve of 0.88, indicating that the biomarker and risk model can significantly identify high-risk individuals, emphasizing its potential for early detection.
View Article and Find Full Text PDF

Purpose: Patients with multiple endocrine neoplasia type 1 (MEN1) are predisposed to develop duodenopancreatic neuroendocrine tumors (dpNETs), and metastatic dpNET is the primary cause of disease-related mortality. Presently, there is a paucity of prognostic factors that can reliably identify patients with MEN1-related dpNETS who are at high risk of distant metastasis. In the current study, we aimed to establish novel circulating molecular protein signatures associated with disease progression.

View Article and Find Full Text PDF

There is a keen interest in characterizing variation in the microbiome across cancer patients, given increasing evidence of its important role in determining treatment outcomes. Here our goal is to discover subgroups of patients with similar microbiome profiles. We propose a novel unsupervised clustering approach in the Bayesian framework that innovates over existing model-based clustering approaches, such as the Dirichlet multinomial mixture model, in three key respects: we incorporate feature selection, learn the appropriate number of clusters from the data, and integrate information on the tree structure relating the observed features.

View Article and Find Full Text PDF

Motivation: Multilevel molecular profiling of tumors and the integrative analysis with clinical outcomes have enabled a deeper characterization of cancer treatment. Mediation analysis has emerged as a promising statistical tool to identify and quantify the intermediate mechanisms by which a gene affects an outcome. However, existing methods lack a unified approach to handle various types of outcome variables, making them unsuitable for high-throughput molecular profiling data with highly interconnected variables.

View Article and Find Full Text PDF

A PHP Error was encountered

Severity: Warning

Message: fopen(/var/lib/php/sessions/ci_sessionrrpm7mk0u5hmjbrt8j5t0cvm3kl10l20): Failed to open stream: No space left on device

Filename: drivers/Session_files_driver.php

Line Number: 177

Backtrace:

File: /var/www/html/index.php
Line: 316
Function: require_once

A PHP Error was encountered

Severity: Warning

Message: session_start(): Failed to read session data: user (path: /var/lib/php/sessions)

Filename: Session/Session.php

Line Number: 137

Backtrace:

File: /var/www/html/index.php
Line: 316
Function: require_once