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Background: A goal of developmental genetics is to identify functional interactions that underlie phenotypes caused by mutations. We sought to identify functional interactors of Vsx2, which when mutated, disrupts early retinal development. We utilized the Vsx2 loss‐of‐function mouse, ocular retardation J (orJ), to assess interactions based on principles of positive and negative epistasis as applied to bulk transcriptome data. This was first tested in vivo with Mitf, a target of Vsx2 repression, and then to cultures of orJ retina treated with inhibitors of Retinoid‐X Receptors (RXR) to target Rxrg, an up‐regulated gene in the orJ retina, and gamma‐Secretase, an enzyme required for Notch signaling, a key mediator of retinal proliferation and neurogenesis. Results: Whereas Mitf exhibited robust positive epistasis with Vsx2, it only partially accounts for the orJ phenotype, suggesting other functional interactors. RXR inhibition yielded minimal evidence for epistasis between Vsx2 and Rxrg. In contrast, gamma‐Secretase inhibition caused hundreds of Vsx2‐dependent genes associated with proliferation to deviate further from wild‐type, providing evidence for convergent negative epistasis with Vsx2 in regulating tissue growth. Conclusions: Combining in vivo and ex vivo testing with transcriptome analysis revealed quantitative and qualitative characteristics of functional interaction between Vsx2, Mitf, RXR, and gamma‐Secretase activities. Key Findings: The MItf‐mi allele exhibits positive epistasis with the Vsx2‐orJ allele, but Mitf activity only partially accounts for disrupted early retinal development in Vsx2 mutant mice.Rxrg is highly upregulated in the Vsx2 mutant retina, but RXR activity is not a strong driver of the early retinal phenotype.Gamma secretase activity regulates a large cohort of Vsx2‐dependent genes associated with proliferation, providing evidence for convergent negative epistasis, and could account for the continued but slow rate of proliferation in Vsx2 mutant retinal progenitor cells.Bulk RNA sequencing combined with tests of functional interaction can provide sufficient resolution to assess phenotypic changes and epistasis of complex developmental phenotypes [ABSTRACT FROM AUTHOR]

Assessing the clinical utility of biomarkers is a critical step before clinical implementation. The reclassification of patients across clinically relevant subgroups is considered one of the best methods to estimate clinical utility. However, there are important limitations with this methodology. We recently proposed the intervention probability curve (IPC) which models the likelihood that a provider will choose an intervention as a continuous function of the probability, or risk, of disease. To assess the potential impact of a new biomarker for lung cancer using the IPC. The IPC derived from the National Lung Screening Trial was used to assess the potential clinical utility of a biomarker for suspected lung cancer. The summary statistics of the change in likelihood of intervention over the population can be interpreted as the expected clinical impact of the added biomarker. The IPC analysis of the novel biomarker estimated that 8% of the benign nodules could avoid an invasive procedure while the cancer nodules would largely remain unchanged (0.1%). We showed the benefits of this approach compared to traditional reclassification methods based on thresholds. The IPC methodology can be a valuable tool for assessing biomarkers prior to clinical implementation. [ABSTRACT FROM AUTHOR]

Background: Diagnostic prediction models are useful guides when considering lesions suspicious for cancer, as they provide a quantitative estimate of the probability that a lesion is malignant. However, the decision to intervene ultimately rests on patient and physician preferences. The appropriate intervention in many clinical situations is typically defined by clinically relevant, actionable subgroups based upon the probability of malignancy. However, the "all-or-nothing" approach of threshold-based decisions is in practice incorrect. Methods: Here, we present a novel approach to understanding clinical decision-making, the intervention probability curve (IPC). The IPC models the likelihood that an intervention will be chosen as a continuous function of the probability of disease. We propose the cumulative distribution function as a suitable model. The IPC is explored using the National Lung Screening Trial and the Prostate Lung Colorectal and Ovarian Screening Trial datasets. Results: Fitting the IPC results in a continuous curve as a function of pretest probability of cancer with high correlation (R² > 0.97 for each) with fitted parameters closely aligned with professional society guidelines. Conclusions: The IPC allows analysis of intervention decisions in a continuous, rather than threshold-based, approach to further understand the role of biomarkers and risk models in clinical practice. [ABSTRACT FROM AUTHOR]

Lung adenocarcinoma (LUAD) is a heterogeneous group of tumors associated with different survival rates, even when detected at an early stage. Here, we aim to investigate the biological determinants of early LUAD indolence or aggressiveness using radiomics as a surrogate of behavior. We present a set of 92 patients with LUAD with data collected across different methodologies. Patients were risk-stratified using the CT-based Score Indicative of Lung cancer Aggression (SILA) tool (0 = least aggressive, 1 = most aggressive). We grouped the patients as indolent ( x ≤ 0.4, n = 14), intermediate (0.4 > x ≤ 0.6, n = 27), and aggressive (0.6 > x ≤ 1, n = 52). Using Cytometry by time of flight (CyTOF), we identified subpopulations with high HLA-DR expression that were associated with indolent behavior. In the RNA sequencing (RNA-seq) dataset, pathways related to immune response were associated with indolent behavior, while pathways associated with cell cycle and proliferation were associated with aggressive behavior. We extracted quantitative radiomics features from the CT scans of the patients. Integrating these datasets, we identified four feature signatures and four patient clusters that were associated with survival. Using single-cell RNA-seq, we found that indolent tumors had significantly more T cells and less B cells than aggressive tumors, and that the latter had a higher abundance of regulatory T cells and Th cells. In conclusion, we were able to uncover a correspondence between radiomics and tumor biology, which could improve the discrimination between indolent and aggressive LUAD tumors, enhance our knowledge in the biology of these tumors, and offer novel and personalized avenues for intervention., Significance: This study provides a comprehensive profiling of LUAD indolence and aggressiveness at the biological bulk and single-cell levels, as well as at the clinical and radiomics levels. This hypothesis generating study uncovers several potential future research avenues. It also highlights the importance and power of data integration to improve our systemic understanding of LUAD and to help reduce the gap between basic science research and clinical practice., (© 2023 The Authors; Published by the American Association for Cancer Research.)

Rationale: Patients with indeterminate pulmonary nodules (IPNs) at risk of cancer undergo high rates of invasive, costly, and morbid procedures. Objectives: To train and externally validate a risk prediction model that combined clinical, blood, and imaging biomarkers to improve the noninvasive management of IPNs. Methods: In this prospectively collected, retrospective blinded evaluation study, probability of cancer was calculated for 456 patient nodules using the Mayo Clinic model, and patients were categorized into low-, intermediate-, and high-risk groups. A combined biomarker model (CBM) including clinical variables, serum high sensitivity CYFRA 21-1 level, and a radiomic signature was trained in cohort 1 ( n  = 170) and validated in cohorts 2-4 (total n  = 286). All patients were pooled to recalibrate the model for clinical implementation. The clinical utility of the CBM compared with current clinical care was evaluated in 2 cohorts. Measurements and Main Results: The CBM provided improved diagnostic accuracy over the Mayo Clinic model with an improvement in area under the curve of 0.124 (95% bootstrap confidence interval, 0.091-0.156; P  < 2 × 10 -16 ). Applying 10% and 70% risk thresholds resulted in a bias-corrected clinical reclassification index for cases and control subjects of 0.15 and 0.12, respectively. A clinical utility analysis of patient medical records estimated that a CBM-guided strategy would have reduced invasive procedures from 62.9% to 50.6% in the intermediate-risk benign population and shortened the median time to diagnosis of cancer from 60 to 21 days in intermediate-risk cancers. Conclusions: Integration of clinical, blood, and image biomarkers improves noninvasive diagnosis of patients with IPNs, potentially reducing the rate of unnecessary invasive procedures while shortening the time to diagnosis.