Rodriguez, E. M. | Search bioRxiv

bioRxiv2025

Archetype analysis of lung adenocarcinoma premalignancy links heterogeneity in premalignant lesions to diverging features of invasive disease

ABSTRACTLung adenocarcinoma (LUAD), the most common form of lung cancer, is a heterogeneous disease with highly variable histopathologic features and clinical outcomes. LUAD premalignant lesions (PMLs) are localized proliferative lesions of atypical pneumocytes that proliferate along the alveolar walls. In this study, we characterized the molecular heterogeneity across PMLs, regardless of histologic designation, and identified four unique groups or "archetypes" of PMLs using bulk RNA and exome sequencing data from laser captured microdissected PMLs, normal, and tumor tissue from LUAD resection cases. The archetypes were defined based on expression of recurrent gene co-expression modules discovered using the LUAD PMLs we profiled and three publicly available datasets. One PML archetype, termed "proliferation", was associated with increased expression of genes involved in cell proliferation, a pro-tumor immune environment, and enrichment for EGFR driver mutations. Another archetype, termed "normal-like", was associated with similar features to normal tissue, an anti-tumor immune response, and lacked enrichment for driver mutations compared to other archetypes. We projected the PML archetypes into independent gene expression datasets profiling LUAD and found that tumors closest to the proliferation archetype were enriched for multiple features of aggressive LUAD, including high tumor grade and lymph node invasion, and had significantly lower disease-free survival. Proliferation archetype PMLs may represent a subset of lesions with more aggressive features that could improve patient stratification and suggest novel targets for lung cancer interception.

cancer biology↗

bioRxiv2019

Identifying Women at Risk for Polycystic Ovary Syndrome Using a Mobile Health Application

BackgroundPolycystic ovary syndrome (PCOS) is an endocrine disrupting disorder affecting at least 10 percent of reproductive-aged women. Women with PCOS are at increased risk for diabetes and cardiovascular disease. In North America and Europe, the diagnosis of PCOS may be delayed several years and may require multiple doctors resulting in lost time for risk-reducing interventions. Menstrual tracking applications are one potential tool to alert women of their risk for PCOS while also prompting them to seek evaluation from a medical professional.\n\nObjectiveThe objective of this study was to develop the Irregular Cycles Feature (ICF), an adaptive questionnaire, on the mobile phone application (app) Clue(R) to generate a probability of a virtual test subjects risk for PCOS. The secondary objective was to assess the accuracy of the ICF by comparing the probability of risk generated by the app to a probability generated by a physician.\n\nMethodsFirst, a literature review was conducted to generate a list of signs and symptoms of PCOS, termed variables. These include, but are not limited to, hirsutism, acne, and alopecia. Probabilities were assigned to each variable and built into a Bayesian network. The network served as the backbone of the ICF, which identified potential subjects through self-reported menstrual cycles and answers to medical history questions. Upon completion of the questionnaire, a Result Screen summarizing the virtual test subjects probability of having PCOS is displayed. For each eligible virtual test subject, a Doctors Report containing information regarding tracked menstrual cycles and self-reported medical history is generated. Both of these documents share information about PCOS and detailed explanations for facilitating a diagnosis by a medical provider. Virtual test subjects were assigned probabilities by a) the ICF and b) a board-certified reproductive endocrinology/infertility physician-scientist, which served as the gold standard. The ICF was set to recommend individuals with a score greater than or equal to 25% to follow-up with their physician. Differences between the network and physician probability scores were assessed using a t-test and a Pearson correlation coefficient. An additional iteration was performed to improve the ICFs prediction capability.\n\nResultsThe first iteration of the ICF produced only one false positive compared to the physician screening score and had an absolute mean difference of 15.5% (SD= 15.1%) amongst virtual test subjects. Upon modification of the ICF, the second iteration had two false positives as compared to the physician screening score and had an absolute mean difference of 18.8% (SD = 13.6%). The majority of virtual test subjects had an ICF score that over predicted PCOS when compared to the physician. However, there was strong positive significant correlation between the ICF and the physician score (Pearson correlation coefficient= 0.69; p < 0.01). The second iteration performed worse with a Pearson correlation coefficient of 0.54; p > 0.01).\n\nConclusionThe first iteration ICF, as compared to the second, was better able to predict the probability of PCOS and can potentially be used as a screening tool to prompt a high-risk subject to seek evaluation by a medical professional.

bioinformatics↗