Gregory E. Holt

Background: Lung cancer is a leading cause of cancer-related mortality, with disparities in incidence and outcomes observed across different racial and sex groups. Identifying both patient-specific and cohort-specific disparity biomarkers is critical for developing targeted treatments. The lung cancer dataset is highly imbalanced across races, leading to biased results in disparity information if classification is based on race. Method: This study developed an explainable artificial intelligence-based framework, TILDA-X, which designs classification models based on disease conditions instead of races to mitigate racial imbalance in the dataset and applies explainable AI to delineate patient-specific disparity information. A lung cancer transcriptome dataset with three disease conditions—lung adenocarcinoma, lung squamous cell carcinoma, and healthy samples—was used to develop classification models. Applying a bottom-up approach from patient-specific disparity information, the cohort-specific disparity information is discovered for different racial and sex groups, African American males, European American males, African American females, and European American females. Results: Classification based on disease conditions achieved accuracy between 88% and 100% for minority groups (African American males and females), whereas it was only between 0% and 16% for race-based classification, which underscores the significance of the proposed approach. Functional analysis of sub-cohort-specific biomarker genes revealed unique pathways associated with lung cancers in different races and sexes. Among the significant pathways identified, over ~63% overlapped with previously reported lung cancer-related studies, supporting the biological validity of our findings. Overall, combining disease conditions-based classification with explainable AI, this study provides a robust, interpretable framework for characterizing race- and sex-specific disparities in lung cancer, offering a foundation for precision oncology and equitable therapeutic development based on transcriptome profile only.

Background: Sarcoidosis is a granulomatous disease affecting multiple organ systems and poses a diagnostic challenge due to its diverse clinical manifestations and absence of specific diagnostic tests. Currently, blood biomarkers such as ACE, sIL-2R, CD163, CCL18, serum amyloid A, and CRP are employed to aid in the diagnosis and monitoring of sarcoidosis. Metabolomics holds promise for identifying highly sensitive and specific biomarkers. This study aimed to leverage metabolomics for the early diagnosis of sarcoidosis and to identify metabolic phenotypes associated with disease progression. Methods: Serum samples from patients with sarcoidosis (n = 40, including stage 1 to stage 4), were analyzed for metabolite levels by semi-untargeted liquid chromatography–mass spectrometry (LC-MS). Metabolomics data from patients with sarcoidosis were compared with those from patients with COVID-19 and healthy controls to identify distinguishing metabolic biosignatures. Univariate and multivariate analyses were applied to obtain diagnostic and prognostic metabolic phenotypes. Results: Significant changes in metabolic profiles distinguished stage 1 sarcoidosis from healthy controls, with potential biomarkers including azelaic acid, itaconate, and glutarate. Distinct metabolic phenotypes were observed across the stages of sarcoidosis, with stage 2 exhibiting greater heterogeneity compared with stages 1, 3, and 4. Conclusions: we explored immunometabolic phenotypes by comparing patients with sarcoidosis with patients with COVID-19 and healthy controls, revealing potential metabolic pathways associated with acute and chronic inflammation across the stages of sarcoidosis.

Background: Long non-coding RNA plays a vital role in changing the expression profiles of various target genes that lead to cancer development. Thus, identifying prognostic lncRNAs related to different cancers might help in developing cancer therapy. Method: To discover the critical lncRNAs that can identify the origin of different cancers, we propose the use of the state-of-the-art deep learning algorithm concrete autoencoder (CAE) in an unsupervised setting, which efficiently identifies a subset of the most informative features. However, CAE does not identify reproducible features in different runs due to its stochastic nature. We thus propose a multi-run CAE (mrCAE) to identify a stable set of features to address this issue. The assumption is that a feature appearing in multiple runs carries more meaningful information about the data under consideration. The genome-wide lncRNA expression profiles of 12 different types of cancers, with a total of 4768 samples available in The Cancer Genome Atlas (TCGA), were analyzed to discover the key lncRNAs. The lncRNAs identified by multiple runs of CAE were added to a final list of key lncRNAs that are capable of identifying 12 different cancers. Results: Our results showed that mrCAE performs better in feature selection than single-run CAE, standard autoencoder (AE), and other state-of-the-art feature selection techniques. This study revealed a set of top-ranking 128 lncRNAs that could identify the origin of 12 different cancers with an accuracy of 95%. Survival analysis showed that 76 of 128 lncRNAs have the prognostic capability to differentiate high- and low-risk groups of patients with different cancers. Conclusion: The proposed mrCAE, which selects actual features, outperformed the AE even though it selects the latent or pseudo-features. By selecting actual features instead of pseudo-features, mrCAE can be valuable for precision medicine. The identified prognostic lncRNAs can be further studied to develop therapies for different cancers.

Introduction: An increased risk of sarcoidosis and sarcoid-like reactions in subjects with a history of malignancy has been suggested. We assessed the incidence and clinical characteristics of cancer patients with biopsies containing sarcoid-like granulomas on cancer metastasis and patient survival.Methods: This is a retrospective, multicentre, observational study involving endobronchial ultrasound transbronchial needle aspiration and a melanoma patient dataset at the University of Miami, USA, and a sarcoidosis patient database at Chiba University, Japan. Subjects with a confirmed diagnosis of cancer and who subsequently developed granulomas in different organs were enrolled. The study was registered at Clinicaltrials.gov (NCT03844698).Results: 133 patients met the study's criteria. The most common primary cancer sites were the skin (22.5%), breast (20.3%) and lymph node (12.8%). 24 (18%) patients developed sarcoid-like granulomas within 1 year of cancer diagnosis, 54 (40.6%) between 1 and 5 years and 49 (36.8%) after 5 years. Imaging showed possible sarcoid-like granulomas in lymph nodes in 51 cases (38.3%) and lung tissue and mediastinal lymph nodes in 73 cases (54.9%); some parenchymal reticular opacity and fibrosis was found in 5 (3.7%) and significant parenchymal fibrosis in 2 (1.5%) subjects. According to logistic regression analysis, the frequency of metastatic cancer was significantly lower in patients with sarcoid-like granulomas than in controls. Moreover, multivariate Cox proportional hazard analysis showed a significant survival advantage in those with sarcoid-like granuloma.Conclusion: Sarcoid-like granulomas are uncommon pathology findings in cancer patients. There is a significant association between the presence of granulomas and reduced metastasis and increased survival. Further study is warranted to understand the protective mechanism involved.

Growth hormone releasing hormone (GHRH) is a potent stimulator of GH secretion from the pituitary gland. Although GHRH is essential for the growth of immune cells, the regulatory effects of its antagonist in granulomatous disease remains unknown. Here, we report expression of GHRH receptor (R) in human tissue with sarcoidosis granuloma and demonstrate the anti-inflammatory effects of MIA602 (a GHRH antagonist) in two in vitro human granuloma models and an in vivo granuloma model. MIA602 decreases levels of IL2, IL12, and IL17A in in vitro granuloma model. We show further that the anti-inflammatory effect of MIA602 appears to be mediated by reduction in CD45 ⁺ +CD68 ⁺ cells in granulomatous tissue and upregulation in PD-1 expression in macrophages. In analysis of expression of proteins involved in the mitochondrial stage of apoptosis, we show that MIA602 increases the levels of caspase 3, BCL-xL/BAK dimer, and MCl-1/Bak dimer in granuloma. These findings indicate that MIA602 may not induce apoptosis. The clinical relevance of our findings further suggest that HGRH-R is potentially a target for treatment of granulomatous disease and MIA602 possibly a novel therapeutic agent for sarcoidosis.

Tobacco smoke negatively affects human bronchial epithelial (HBE) cells and is directly implicated in the etiology of smoking related respiratory diseases. Smoke exposure causes double-stranded DNA breaks and DNA damage activates PARP-1, the key mediator of the parthanatos pathway of cell death. We hypothesize that smoke exposure activates the parthanatos pathway in HBE cells and represents a cell death mechanism that contributes to smoking related lung diseases. We exposed fully differentiated, primary HBE cells grown at the air liquid interface to cigarette smoke and evaluated them for parthanatos pathway activation. Smoke exposure induced mitochondrial to nuclear translocation of Apoptosis-Inducing Factor (AIF) and Endonuclease G (EndoG) within the first three hours characteristic of the parthanatos pathway. Exposing cells to an increasing number of cigarettes revealed that significant activation of the parthanatos pathway occurs after exposure to higher levels of smoke. Use of the specific PARP-1 inhibitor, BMN673, abrogated the effect of smoke induced activation of the parthanatos pathway. Smoke-mediated activation of the parthanatos pathway is increased in HBE cells originating from habitual smokers compared to non-smokers. This suggests that chronic smoke exposure leads to an increase in smoke-mediated activation of the parthanatos pathway and implicates its contribution in the pathogenesis of smoke-related lung diseases.

Tattoos and medallions are examples of nonstandard do-not-resuscitate (DNR) orders that some people use to convey end-of-life wishes. These DNR orders are neither universally accepted nor understood for reasons discussed within this manuscript. Studies show both providers and patients confuse the meaning and implication of DNR orders. In the United States, out-of-hospital DNR orders are legislated at the state level. Most states standardized out-of-hospital DNR orders so caregivers can immediately recognize and accept the order and act on its behalf. These out-of-hospital orders are complicated by the need to be printed on paper that does not always accompany the individual. Oregon created an online system whereby individuals recorded their end-of-life wishes that medical personnel can access with an Internet connection. This system improved communication of end-of-life wishes in patients who selected comfort care only. To improve conveyance of an individual's wishes for end-of-life care, the authors discuss nationwide adoption of Oregon's online registry where a person's account could comprehensively document end-of-life wishes, be universally available in all healthcare institutions, and be searchable by common patient identifiers. Facial recognition software could identify unconscious patients who present without identification.

Aim: Lung cancer gene therapies require reagents to selectively transfect lung tumors after systemic administration. Materials & methods: We created a reagent called NSCLC-NP by attaching a peptide with binding affinity for lung cancer to polyamidoamine dendrimers. The positively charged dendrimers electrostatically bind negatively charged nucleic acids, inhibit endogenous nucleases and transfect cells targeted by the attached peptide. Results:In vitro, NSCLC-NP complexed to DNA plasmids bound and transfected three human lung cancer cell lines producing protein expression of the plasmid's gene. In vivo, systemically administered NSCLC-NP selectively transfected lung cancer cells growing in RAG1KO mice. Conclusion: The capability of NSCLC-NP to selectively transfect lung cancer allows its future use as a vehicle to implement human lung cancer gene therapy strategies.