Landscape dynamic network biomarker analysis reveals the tipping point of transcriptome reprogramming to prevent skin photodamage

Skin, as the outmost layer of human body, is frequently exposed to environmental stressors including pollutants and ultraviolet (UV), which could lead to skin disorders. Generally, skin response process to ultraviolet B (UVB) irradiation is a nonlinear dynamic process, with unknown underlying molecular mechanism of critical transition. Here, the landscape dynamic network biomarker (l-DNB) analysis of time series transcriptome data on 3D skin model was conducted to reveal the complicated process of skin response to UV irradiation at both molecular and network levels. The advanced l-DNB analysis approach showed that: (i) there was a tipping point before critical transition state during pigmentation process, validated by 3D skin model; (ii) 13 core DNB genes were identified to detect the tipping point as a network biomarker, supported by computational assessment; (iii) core DNB genes such as COL7A1 and CTNNB1 can effectively predict skin lightening, validated by independent human skin data. Overall, this study provides new insights for skin response to repetitive UVB irradiation, including dynamic pathway pattern, bi-phasic response, and DNBs for skin lightening change, and enables us to further understand the skin resilience process after external stress.

Dynamic Network Biomarker of Pre-Exhausted CD8+ T Cells Contributed to T Cell Exhaustion in Colorectal Cancer

Immunotherapy has achieved positive clinical responses in various cancers. However, in advanced colorectal cancer (CRC), immunotherapy is challenging because of the deterioration of T-cell exhaustion, the mechanism of which is still unclear. In this study, we depicted CD8+ T-cell developmental trajectories and characterized the pre-exhausted T cells isolated from CRC patients in the scRNA-seq data set using a dynamic network biomarker (DNB). Moreover, CCT6A identified by DNB was a biomarker for pre-exhausted T-cell subpopulation in CRC. Besides, TUBA1B expression was triggered by CCT6A as DNB core genes contributing to CD8+ T cell exhaustion, indicating that core genes serve as biomarkers in pre-exhausted T cells. Remarkably, both TUBA1B and CCT6A expressions were significantly associated with the overall survival of COAD patients in the TCGA database (p = 0.0082 and p = 0.026, respectively). We also observed that cellular communication between terminally differentiated exhausted T cells and pre-exhausted T cells contributes to exhaustion. These findings provide new insights into the mechanism of T-cell exhaustion and provide clue for targeted immunotherapy in CRC.

Patient Knowledge and Expectations About Return of Genomic Results in a Biomarker-Driven Master Protocol Trial (SWOG S1400GEN)

PURPOSE: Biomarker-driven master protocols represent a new paradigm in oncology clinical trials, but their complex designs and wide-ranging genomic results returned can be difficult to communicate to participants. The objective of this pilot study was to evaluate patient knowledge and expectations related to return of genomic results in the Lung Cancer Master Protocol (Lung-MAP). METHODS: Eligible participants with previously treated advanced non–small-cell lung cancer were recruited from patients enrolled in Lung-MAP. Participants completed a 38-item telephone survey ≤ 30 days from Lung-MAP consent. The survey assessed understanding about the benefits and risks of Lung-MAP participation and knowledge of the potential uses of somatic testing results returned. Descriptive statistics and odds ratios for associations between demographic factors and correct responses to survey items were assessed. RESULTS: From August 1, 2017, to June 30, 2019, we recruited 207 participants with a median age of 67, 57.3% male, and 94.2% White. Most participants “strongly/somewhat agreed” with statements that they “received enough information to understand” Lung-MAP benefits (82.6%) and risks (69.5%). In items asking about potential uses of Lung-MAP genomic results, 87.0% correctly indicated that the results help to select cancer treatment, but < 20% correctly indicated that the results are not used to confirm cancer diagnosis, would not reveal risk of developing diseases besides cancer, and would not indicate if family members had increased cancer risk. There were no associations between sociodemographic factors and proportions providing correct responses. CONCLUSION: In a large National Clinical Trials Network biomarker-driven master protocol, most participants demonstrated incorrect knowledge and expectations about the uses of genomic results provided in the study despite most indicating that they had enough information to understand benefits and risks.

A microstructural neural network biomarker for dystonia diagnosis identified by a DystoniaNet deep learning platform

Isolated dystonia is a neurological disorder of heterogeneous pathophysiology, which causes involuntary muscle contractions leading to abnormal movements and postures. Its diagnosis is remarkably challenging due to the absence of a biomarker or gold standard diagnostic test. This leads to a low agreement between clinicians, with up to 50% of cases being misdiagnosed and diagnostic delays extending up to 10.1 y. We developed a deep learning algorithmic platform, DystoniaNet, to automatically identify and validate a microstructural neural network biomarker for dystonia diagnosis from raw structural brain MRIs of 612 subjects, including 392 patients with three different forms of isolated focal dystonia and 220 healthy controls. DystoniaNet identified clusters in corpus callosum, anterior and posterior thalamic radiations, inferior fronto-occipital fasciculus, and inferior temporal and superior orbital gyri as the biomarker components. These regions are known to contribute to abnormal interhemispheric information transfer, heteromodal sensorimotor processing, and executive control of motor commands in dystonia pathophysiology. The DystoniaNet-based biomarker showed an overall accuracy of 98.8% in diagnosing dystonia, with a referral of 3.5% of cases due to diagnostic uncertainty. The diagnostic decision by DystoniaNet was computed in 0.36 s per subject. DystoniaNet significantly outperformed shallow machine-learning algorithms in benchmark comparisons, showing nearly a 20% increase in its diagnostic performance. Importantly, the microstructural neural network biomarker and its DystoniaNet platform showed substantial improvement over the current 34% agreement on dystonia diagnosis between clinicians. The translational potential of this biomarker is in its highly accurate, interpretable, and generalizable performance for enhanced clinical decision-making.