Comparison between diffuse and partial involvement of thoracic lymph nodes on the outcome of sarcoidosis patients.

BACKGROUND AND AIM: Sarcoidosis is a systemic disease of unknown etiology with diverse clinical manifestations. Disease may resolve spontaneously or require immunosuppression to control progression. Currently, there is no predictive model to direct treatment, and management is guided by symptoms and functional impairment. This study examines the association between biopsy features and prognosis. METHODS: This is a retrospective population-based cohort study. New cases of biopsy-proven sarcoidosis were divided into two groups: those with diffuse thoracic lymph nodes (TLN) involvement, versus partial TLN involvement (Defined as Non-necrotizing granuloma (NNG) found in some but not all sampled TLN). We compared outcomes one year after diagnosis. We assessed the need for immunosuppression, the number of hospitalizations, and lung function deterioration. RESULTS: 77 cases were included in the final analysis. 48.1% demonstrated extensive TLN involvement, and 51.9% demonstrated partial or non-involvement of sampled TLN. The partial positive group had a more aggressive disease, reflected by a significantly higher need for steroid therapy in the first year after diagnosis (45.0% vs. 18.9% p=0.015). The number of hospitalizations and lung functions were not significantly different between groups. CONCLUSIONS: Our findings demonstrate a significantly increased need for steroidal therapy among sarcoidosis patients with a partial positivity of TLN. These findings suggest that the degree of TLN involvement can help predict worse outcome and guide therapeutic decisions.

Personalizing non-small cell lung cancer treatment through patient-derived xenograft models: preclinical and clinical factors for consideration.

PURPOSE: In the pursuit of creating personalized and more effective treatment strategies for lung cancer patients, Patient-Derived Xenografts (PDXs) have been introduced as preclinical platforms that can recapitulate the specific patient's tumor in an in vivo model. We investigated how well PDX models can preserve the tumor's clinical and molecular characteristics across different generations. METHODS: A Non-Small Cell Lung Cancer (NSCLC) PDX model was established in NSG-SGM3 mice and clinical and preclinical factors were assessed throughout subsequent passages. Our cohort consisted of 40 NSCLC patients, which were used to create 20 patient-specific PDX models in NSG-SGM3 mice. Histopathological staining and Whole Exome Sequencing (WES) analysis were preformed to understand tumor heterogeneity throughout serial passages. RESULTS: The main factors that contributed to the growth of the engrafted PDX in mice were a higher grade or stage of disease, in contrast to the long duration of chemotherapy treatment, which was negatively correlated with PDX propagation. Successful PDX growth was also linked to poorer prognosis and overall survival, while growth pattern variability was affected by the tumor aggressiveness, primarily affecting the first passage. Pathology analysis showed preservation of the histological type and grade; however, WES analysis revealed genomic instability in advanced passages, leading to the inconsistencies in clinically relevant alterations between the PDXs and biopsies. CONCLUSIONS: Our study highlights the impact of multiple clinical and preclinical factors on the engraftment success, growth kinetics, and tumor stability of patient-specific NSCLC PDXs, and underscores the importance of considering these factors when guiding and evaluating prolonged personalized treatment studies for NSCLC patients in these models, as well as signaling the imperative for additional investigations to determine the full clinical potential of this technique.

Tumor- and circulating-free DNA methylation identifies clinically relevant small cell lung cancer subtypes.

Small cell lung cancer (SCLC) is an aggressive malignancy composed of distinct transcriptional subtypes, but implementing subtyping in the clinic has remained challenging, particularly due to limited tissue availability. Given the known epigenetic regulation of critical SCLC transcriptional programs, we hypothesized that subtype-specific patterns of DNA methylation could be detected in tumor or blood from SCLC patients. Using genomic-wide reduced-representation bisulfite sequencing (RRBS) in two cohorts totaling 179 SCLC patients and using machine learning approaches, we report a highly accurate DNA methylation-based classifier (SCLC-DMC) that can distinguish SCLC subtypes. We further adjust the classifier for circulating-free DNA (cfDNA) to subtype SCLC from plasma. Using the cfDNA classifier (cfDMC), we demonstrate that SCLC phenotypes can evolve during disease progression, highlighting the need for longitudinal tracking of SCLC during clinical treatment. These data establish that tumor and cfDNA methylation can be used to identify SCLC subtypes and might guide precision SCLC therapy.