Exhausted intratumoral Vδ2- γδ T cells in human kidney cancer retain effector function.

Gamma delta (γδ) T cells reside within human tissues including tumors, but their function in mediating antitumor responses to immune checkpoint inhibition is unknown. Here we show that kidney cancers are infiltrated by Vδ2- γδ T cells, with equivalent representation of Vδ1+ and Vδ1- cells, that are distinct from γδ T cells found in normal human tissues. These tumor-resident Vδ2- T cells can express the transcriptional program of exhausted αß CD8+ T cells as well as canonical markers of terminal T-cell exhaustion including PD-1, TIGIT and TIM-3. Although Vδ2- γδ T cells have reduced IL-2 production, they retain expression of cytolytic effector molecules and co-stimulatory receptors such as 4-1BB. Exhausted Vδ2- γδ T cells are composed of three distinct populations that lack TCF7, are clonally expanded and express cytotoxic molecules and multiple Vδ2- T-cell receptors. Human tumor-derived Vδ2- γδ T cells maintain cytotoxic function and pro-inflammatory cytokine secretion in vitro. The transcriptional program of Vδ2- T cells in pretreatment tumor biopsies was used to predict subsequent clinical responses to PD-1 blockade in patients with cancer. Thus, Vδ2- γδ T cells within the tumor microenvironment can contribute to antitumor efficacy.

Intratumoral CD4+ T Cells Mediate Anti-tumor Cytotoxicity in Human Bladder Cancer.

Responses to anti-PD-1 immunotherapy occur but are infrequent in bladder cancer. The specific T cells that mediate tumor rejection are unknown. T cells from human bladder tumors and non-malignant tissue were assessed with single-cell RNA and paired T cell receptor (TCR) sequencing of 30,604 T cells from 7 patients. We find that the states and repertoires of CD8+ T cells are not distinct in tumors compared with non-malignant tissues. In contrast, single-cell analysis of CD4+ T cells demonstrates several tumor-specific states, including multiple distinct states of regulatory T cells. Surprisingly, we also find multiple cytotoxic CD4+ T cell states that are clonally expanded. These CD4+ T cells can kill autologous tumors in an MHC class II-dependent fashion and are suppressed by regulatory T cells. Further, a gene signature of cytotoxic CD4+ T cells in tumors predicts a clinical response in 244 metastatic bladder cancer patients treated with anti-PD-L1.

Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage.

Tissue fibrosis is a major cause of mortality that results from the deposition of matrix proteins by an activated mesenchyme. Macrophages accumulate in fibrosis, but the role of specific subgroups in supporting fibrogenesis has not been investigated in vivo. Here, we used single-cell RNA sequencing (scRNA-seq) to characterize the heterogeneity of macrophages in bleomycin-induced lung fibrosis in mice. A novel computational framework for the annotation of scRNA-seq by reference to bulk transcriptomes (SingleR) enabled the subclustering of macrophages and revealed a disease-associated subgroup with a transitional gene expression profile intermediate between monocyte-derived and alveolar macrophages. These CX3CR1+SiglecF+ transitional macrophages localized to the fibrotic niche and had a profibrotic effect in vivo. Human orthologs of genes expressed by the transitional macrophages were upregulated in samples from patients with idiopathic pulmonary fibrosis. Thus, we have identified a pathological subgroup of transitional macrophages that are required for the fibrotic response to injury.

Integration of transcription regulation and functional genomic data reveals lncRNA SNHG6's role in hematopoietic differentiation and leukemia.

BACKGROUND: Long non-coding RNAs (lncRNAs) are pivotal players in cellular processes, and their unique cell-type specific expression patterns render them attractive biomarkers and therapeutic targets. Yet, the functional roles of most lncRNAs remain enigmatic. To address the need to identify new druggable lncRNAs, we developed a comprehensive approach integrating transcription factor binding data with other genetic features to generate a machine learning model, which we have called INFLAMeR (Identifying Novel Functional LncRNAs with Advanced Machine Learning Resources). METHODS: INFLAMeR was trained on high-throughput CRISPR interference (CRISPRi) screens across seven cell lines, and the algorithm was based on 71 genetic features. To validate the predictions, we selected candidate lncRNAs in the human K562 leukemia cell line and determined the impact of their knockdown (KD) on cell proliferation and chemotherapeutic drug response. We further performed transcriptomic analysis for candidate genes. Based on these findings, we assessed the lncRNA small nucleolar RNA host gene 6 (SNHG6) for its role in myeloid differentiation. Finally, we established a mouse K562 leukemia xenograft model to determine whether SNHG6 KD attenuates tumor growth in vivo. RESULTS: The INFLAMeR model successfully reconstituted CRISPRi screening data and predicted functional lncRNAs that were previously overlooked. Intensive cell-based and transcriptomic validation of nearly fifty genes in K562 revealed cell type-specific functionality for 85% of the predicted lncRNAs. In this respect, our cell-based and transcriptomic analyses predicted a role for SNHG6 in hematopoiesis and leukemia. Consistent with its predicted role in hematopoietic differentiation, SNHG6 transcription is regulated by hematopoiesis-associated transcription factors. SNHG6 KD reduced the proliferation of leukemia cells and sensitized them to differentiation. Treatment of K562 leukemic cells with hemin and PMA, respectively, demonstrated that SNHG6 inhibits red blood cell differentiation but strongly promotes megakaryocyte differentiation. Using a xenograft mouse model, we demonstrate that SNHG6 KD attenuated tumor growth in vivo. CONCLUSIONS: Our approach not only improved the identification and characterization of functional lncRNAs through genomic approaches in a cell type-specific manner, but also identified new lncRNAs with roles in hematopoiesis and leukemia. Such approaches can be readily applied to identify novel targets for precision medicine.

Comparative Analysis of First-Line FOLFOX Treatment With and Without Anti-VEGF Therapy in Metastatic Colorectal Carcinoma: A Real-World Data Study.

BACKGROUND: FOLFOX (leucovorin calcium [folinic acid], fluorouracil, and oxaliplatin) combined with or without anti-VEGF therapy represents one of the primary first-line treatment options for metastatic colorectal carcinoma (mCRC). However, there is limited comparative data on the impact of anti-VEGF therapy on treatment effectiveness, survival outcomes, and tumor location. METHODS: This retrospective, comparative study utilized data from the AIM Cancer Care Quality Program and commercially insured patients treated at medical oncology clinics in the US. We analyzed 1652 mCRC patients who received FOLFOX, of which 1015 (61.4%) were also treated with anti-VEGF therapy (VEGF cohort). RESULTS: Patients in the VEGF cohort exhibited a higher frequency of lung (33% vs 23%; P < .001) and liver metastases (74% vs 62%; P < .001), underwent fewer liver surgeries prior to treatment (1.2% vs 3.6%; P = .002), and had a higher proportion of right-sided tumors (27% vs 18%; P = .001). Adjusted analysis revealed no significant difference in overall survival (OS) between patients treated with and without anti-VEGF (median survival: 25.4 vs 26.0 months; P = .4). FOLFOX-only treated patients experienced higher rates of post-treatment hospitalizations (22% vs 15%; P < .001). Notably, left-sided tumors treated with anti-VEGF showed a trend toward decreased OS (median survival: 26.8 vs 33 months; P = .09). CONCLUSION: Our real-world data analysis suggests that the addition of anti-VEGF to FOLFOX offers limited and short-lived benefits in the context of mCRC and may provide differential survival benefit based on tumor sidedness.

MAM: Flexible Monte-Carlo Agent based model for modelling COVID-19 spread.

In the three years since SARS-CoV-2 was first detected in China, hundreds of millions of people have been infected and millions have died. Along with the immediate need for treatment solutions, the COVID-19 epidemic has reinforced the need for mathematical models that can predict the spread of the pandemic in an ever-changing environment. The susceptible-infectious-removed (SIR) model has been widely used to model COVID-19 transmission, however, with limited success. Here, we present a novel, dynamic Monte-Carlo Agent-based Model (MAM), which is based on the basic principles of statistical physics. Using public aggregative data from Israel on three major outbreaks, we compare predictions made by SIR and MAM, and show that MAM outperforms SIR in all aspects. Furthermore, MAM is a flexible model and allows to accurately examine the effects of vaccinations in different subgroups, and the effects of the introduction of new variants.

Human splice factors contribute to latent HIV infection in primary cell models and blood CD4+ T cells from ART-treated individuals.

It is unclear what mechanisms govern latent HIV infection in vivo or in primary cell models. To investigate these questions, we compared the HIV and cellular transcription profile in three primary cell models and peripheral CD4+ T cells from HIV-infected ART-suppressed individuals using RT-ddPCR and RNA-seq. All primary cell models recapitulated the block to HIV multiple splicing seen in cells from ART-suppressed individuals, suggesting that this may be a key feature of HIV latency in primary CD4+ T cells. Blocks to HIV transcriptional initiation and elongation were observed more variably among models. A common set of 234 cellular genes, including members of the minor spliceosome pathway, was differentially expressed between unstimulated and activated cells from primary cell models and ART-suppressed individuals, suggesting these genes may play a role in the blocks to HIV transcription and splicing underlying latent infection. These genes may represent new targets for therapies designed to reactivate or silence latently-infected cells.

Heterogeneity in HIV and cellular transcription profiles in cell line models of latent and productive infection: implications for HIV latency.

BACKGROUND: HIV-infected cell lines are widely used to study latent HIV infection, which is considered the main barrier to HIV cure. We hypothesized that these cell lines differ from each other and from cells from HIV-infected individuals in the mechanisms underlying latency. RESULTS: To quantify the degree to which HIV expression is inhibited by blocks at different stages of HIV transcription, we employed a recently-described panel of RT-ddPCR assays to measure levels of 7 HIV transcripts ("read-through," initiated, 5' elongated, mid-transcribed/unspliced [Pol], distal-transcribed [Nef], polyadenylated, and multiply-sliced [Tat-Rev]) in bulk populations of latently-infected (U1, ACH-2, J-Lat) and productively-infected (8E5, activated J-Lat) cell lines. To assess single-cell variation and investigate cellular genes associated with HIV transcriptional blocks, we developed a novel multiplex qPCR panel and quantified single cell levels of 7 HIV targets and 89 cellular transcripts in latently- and productively-infected cell lines. The bulk cell HIV transcription profile differed dramatically between cell lines and cells from ART-suppressed individuals. Compared to cells from ART-suppressed individuals, latent cell lines showed lower levels of HIV transcriptional initiation and higher levels of polyadenylation and splicing. ACH-2 and J-Lat cells showed different forms of transcriptional interference, while U1 cells showed a block to elongation. Single-cell studies revealed marked variation between/within cell lines in expression of HIV transcripts, T cell phenotypic markers, antiviral factors, and genes implicated in latency. Expression of multiply-spliced HIV Tat-Rev was associated with expression of cellular genes involved in activation, tissue retention, T cell transcription, and apoptosis/survival. CONCLUSIONS: HIV-infected cell lines differ from each other and from cells from ART-treated individuals in the mechanisms governing latent HIV infection. These differences in viral and cellular gene expression must be considered when gauging the suitability of a given cell line for future research on HIV. At the same time, some features were shared across cell lines, such as low expression of antiviral defense genes and a relationship between productive infection and genes involved in survival. These features may contribute to HIV latency or persistence in vivo, and deserve further study using novel single cell assays such as those described in this manuscript.

Embryonic Stem Cell (ES)-Specific Enhancers Specify the Expression Potential of ES Genes in Cancer.

Cancers often display gene expression profiles resembling those of undifferentiated cells. The mechanisms controlling these expression programs have yet to be identified. Exploring transcriptional enhancers throughout hematopoietic cell development and derived cancers, we uncovered a novel class of regulatory epigenetic mutations. These epimutations are particularly enriched in a group of enhancers, designated ES-specific enhancers (ESSEs) of the hematopoietic cell lineage. We found that hematopoietic ESSEs are prone to DNA methylation changes, indicative of their chromatin activity states. Strikingly, ESSE methylation is associated with gene transcriptional activity in cancer. Methylated ESSEs are hypermethylated in cancer relative to normal somatic cells and co-localized with silenced genes, whereas unmethylated ESSEs tend to be hypomethylated in cancer and associated with reactivated genes. Constitutive or hematopoietic stem cell-specific enhancers do not show these trends, suggesting selective reactivation of ESSEs in cancer. Further analyses of a hypomethylated ESSE downstream to the VEGFA gene revealed a novel regulatory circuit affecting VEGFA transcript levels across cancers and patients. We suggest that the discovered enhancer sites provide a framework for reactivation of ES genes in cancer.

Unmasking risk loci: DNA methylation illuminates the biology of cancer predisposition: analyzing DNA methylation of transcriptional enhancers reveals missed regulatory links between cancer risk loci and genes.

Paradoxically, DNA sequence polymorphisms in cancer risk loci rarely correlate with the expression of cancer genes. Therefore, the molecular mechanism underlying an individual's susceptibility to cancer has remained largely unknown. However, recent evaluations of the correlations between DNA methylation and gene expression levels across healthy and cancerous genomes have revealed enrichment of disease-related DNA methylation variations within disease-associated risk loci. Moreover, it appears that transcriptional enhancers embedded in cancer risk loci often contain DNA methylation sites that closely define the expression of prominent cancer genes, despite the lack of significant correlations between gene expression levels and the surrounding disease-associated polymorphic sequences. We suggest that DNA methylation variations may obscure the effect of co-residing risk sequence alleles. Analysis of enhancer methylation data may help to reveal the regulatory circuits underlying predisposition to cancers and other common diseases.

Genome-wide survey reveals predisposing diabetes type 2-related DNA methylation variations in human peripheral blood.

Inter-individual DNA methylation variations were frequently hypothesized to alter individual susceptibility to Type 2 Diabetes Mellitus (T2DM). Sequence-influenced methylations were described in T2DM-associated genomic regions, but evidence for direct, sequence-independent association with disease risk is missing. Here, we explore disease-contributing DNA methylation through a stepwise study design: first, a pool-based, genome-scale screen among 1169 case and control individuals revealed an excess of differentially methylated sites in genomic regions that were previously associated with T2DM through genetic studies. Next, in-depth analyses were performed at selected top-ranking regions. A CpG site in the first intron of the FTO gene showed small (3.35%) but significant (P = 0.000021) hypomethylation of cases relative to controls. The effect was independent of the sequence polymorphism in the region and persists among individuals carrying the sequence-risk alleles. The odds of belonging to the T2DM group increased by 6.1% for every 1% decrease in methylation (OR = 1.061, 95% CI: 1.032-1.090), the odds ratio for decrease of 1 standard deviation of methylation (adjusted to gender) was 1.5856 (95% CI: 1.2824-1.9606) and the sensitivity (area under the curve = 0.638, 95% CI: 0.586-0.690; males = 0.675, females = 0.609) was better than that of the strongest known sequence variant. Furthermore, a prospective study in an independent population cohort revealed significant hypomethylation of young individuals that later progressed to T2DM, relative to the individuals who stayed healthy. Further genomic analysis revealed co-localization with gene enhancers and with binding sites for methylation-sensitive transcriptional regulators. The data showed that low methylation level at the analyzed sites is an early marker of T2DM and suggests a novel mechanism by which early-onset, inter-individual methylation variation at isolated non-promoter genomic sites predisposes to T2DM.

Transformer-based time-to-event prediction for chronic kidney disease deterioration.

OBJECTIVE: Deep-learning techniques, particularly the Transformer model, have shown great potential in enhancing the prediction performance of longitudinal health records. Previous methods focused on fixed-time risk prediction, however, time-to-event prediction is often more appropriate for clinical scenarios. Here, we present STRAFE, a generalizable survival analysis Transformer-based architecture for electronic health records. MATERIALS AND METHODS: The input for STRAFE is a sequence of visits with SNOMED-CT codes in OMOP-CDM format. A Transformer-based architecture was developed to calculate probabilities of the occurrence of the event in each of 48 months. Performance was evaluated using a real-world claims dataset of over 130 000 individuals with stage 3 chronic kidney disease (CKD). RESULTS: STRAFE showed improved mean absolute error (MAE) compared to other time-to-event algorithms in predicting the time to deterioration to stage 5 CKD. Additionally, STRAFE showed an improved area under the receiver operating curve compared to binary outcome algorithms. We show that STRAFE predictions can improve the positive predictive value of high-risk patients by 3-fold. Finally, we suggest a novel visualization approach to predictions on a per-patient basis. DISCUSSION: Time-to-event predictions are the most appropriate approach for clinical predictions. Our deep-learning algorithm outperformed not only other time-to-event prediction algorithms but also fixed-time algorithms, possibly due to its ability to train on censored data. We demonstrated possible clinical usage by identifying the highest-risk patients. CONCLUSIONS: The ability to accurately identify patients at high risk and prioritize their needs can result in improved health outcomes, reduced costs, and more efficient use of resources.

Defective interferon signaling in the circulating monocytes of type 2 diabetic mice.

Type 2 diabetes mellitus (T2DM) is associated with poor outcome after stroke. Peripheral monocytes play a critical role in the secondary injury and recovery of damaged brain tissue after stroke, but the underlying mechanisms are largely unclear. To investigate transcriptome changes and molecular networks across monocyte subsets in response to T2DM and stroke, we performed single-cell RNA-sequencing (scRNAseq) from peripheral blood mononuclear cells and bulk RNA-sequencing from blood monocytes from four groups of adult mice, consisting of T2DM model db/db and normoglycemic control db/+ mice with or without ischemic stroke. Via scRNAseq we found that T2DM expands the monocyte population at the expense of lymphocytes, which was validated by flow cytometry. Among the monocytes, T2DM also disproportionally increased the inflammatory subsets with Ly6C+ and negative MHC class II expression (MO.6C+II-). Conversely, monocytes from control mice without stroke are enriched with steady-state classical monocyte subset of MO.6C+II+ but with the least percentage of MO.6C+II- subtype. Apart from enhancing inflammation and coagulation, enrichment analysis from both scRNAseq and bulk RNAseq revealed that T2DM specifically suppressed type-1 and type-2 interferon signaling pathways crucial for antigen presentation and the induction of ischemia tolerance. Preconditioning by lipopolysaccharide conferred neuroprotection against ischemic brain injury in db/+ but not in db/db mice and coincided with a lesser induction of brain Interferon-regulatory-factor-3 in the brains of the latter mice. Our results suggest that the increased diversity and altered transcriptome in the monocytes of T2DM mice underlie the worse stroke outcome by exacerbating secondary injury and potentiating stroke-induced immunosuppression. Significance Statement: The mechanisms involved in the detrimental diabetic effect on stroke are largely unclear. We show here, for the first time, that peripheral monocytes have disproportionally altered the subsets and changed transcriptome under diabetes and/or stroke conditions. Moreover, genes in the IFN-related signaling pathways are suppressed in the diabetic monocytes, which underscores the immunosuppression and impaired ischemic tolerance under the T2DM condition. Our data raise a possibility that malfunctioned monocytes may systemically and focally affect the host, leading to the poor outcome of diabetes in the setting of stroke. The results yield important clues to molecular mechanisms involved in the detrimental diabetic effect on stroke outcome.

Replication timing-related and gene body-specific methylation of active human genes.

Understanding how the epigenetic blueprint of the genome shapes human phenotypes requires systematic evaluation of the complex interplay between gene activity and the different layers of the epigenome. Utilizing microarray-based techniques, we explored the relationships between DNA methylation, DNA replication timing and gene expression levels across a variety of human tissues and cell lines. The analyses revealed unequal methylation levels among early- and late-replicating fractions of the genome: late-replicating DNA was hypomethylated compared with early-replicating DNA. Moreover, late-replicating regions were gradually demethylated with cell divisions, whereas the methylation of early-replicating regions was better maintained. As active genes concentrate at early-replicating regions, they are overall hypermethylated relative to inactive genes. Accordingly, we show that the previously reported positive correlation between gene-body methylation (methylation of the transcribed portion of genes) and gene expression is restricted to proliferative tissues and cell lines, whereas in tissues containing few proliferating cells, active and inactive genes have similar methylation levels. We further show that active gene bodies are hypermethylated not only compared with inactive gene bodies, but also compared with their flanking sequences. This specific hypermethylation of the active gene bodies is severely disrupted in cells of an immunodeficiency, centromeric region instability, facial anomalies (ICF) syndrome patient bearing mutated DNA methyltransferase 3B (DNMT3B). Our data show that a high methylation level is preferentially maintained in active gene bodies through independent cellular processes. Rather than serving as a distinctive mark between active and inactive genes, gene-body methylation appears to serve a vital, currently unknown function in active genes.

Single-Cell RNA Sequencing for Studying Human Cancers.

Since the first publication a decade ago describing the use of single-cell RNA sequencing (scRNA-seq) in the context of cancer, over 200 datasets and thousands of scRNA-seq studies have been published in cancer biology. scRNA-seq technologies have been applied across dozens of cancer types and a diverse array of study designs to improve our understanding of tumor biology, the tumor microenvironment, and therapeutic responses, and scRNA-seq is on the verge of being used to improve decision-making in the clinic. Computational methodologies and analytical pipelines are key in facilitating scRNA-seq research. Numerous computational methods utilizing the most advanced tools in data science have been developed to extract meaningful insights. Here, we review the advancements in cancer biology gained by scRNA-seq and discuss the computational challenges of the technology that are specific to cancer research.

Comparison of FOLFIRINOX vs Gemcitabine Plus Nab-Paclitaxel as First-Line Chemotherapy for Metastatic Pancreatic Ductal Adenocarcinoma.

Importance: FOLFIRINOX (leucovorin calcium [folinic acid], fluorouracil, irinotecan hydrochloride, and oxaliplatin) and gemcitabine plus nab-paclitaxel are the 2 common first-line therapies for metastatic adenocarcinoma of the pancreas (mPC), but they have not been directly compared in a clinical trial, and comparative clinical data analyses on their effectiveness are limited. Objective: To compare the FOLFIRINOX and gemcitabine plus nab-paclitaxel treatments of mPC in clinical data and evaluate whether there are differences in overall survival and posttreatment complications between them. Design, Setting, and Participants: This retrospective, nonrandomized comparative effectiveness study used data from the AIM Specialty Health-Anthem Cancer Care Quality Program and from administrative claims of commercially insured patients, spanning 388 outpatient centers and clinics for medical oncology located in 44 states across the US. Effectiveness and safety of the treatments were analyzed by matching or adjusting for age, Charlson Comorbidity Index, ECOG performance status (PS) score, Social Deprivation Index (SDI), liver and lymph node metastasis, prior radiotherapy or surgical procedures, and year of treatment. Patients with mPC treated between January 1, 2016, and December 31, 2019, and followed up until June 30, 2020, were included in the analysis. Interventions: Initiation of treatment with FOLFIRINOX or gemcitabine plus nab-paclitaxel. Main Outcomes and Measures: Outcomes were overall survival and posttreatment costs and hospitalization. Median survival time was calculated using Kaplan-Meier estimates adjusted with inverse probability of treatment weighting and 1:1 matching. Results: Among the 1102 patients included in the analysis (618 men [56.1%]; median age, 60.0 [IQR, 55.5-63.7] years), those treated with FOLFIRINOX were younger (median age, 59.1 [IQR, 53.9-63.3] vs 61.2 [IQR, 57.2-64.3] years; P < .001), with better PS scores (226 [39.9%] with PS of 0 in the FOLFIRINOX group vs 176 [32.8%] in the gemcitabine plus nab-paclitaxel group; P = .02), fewer comorbidities (median Charlson Comorbidity Index, 0.0 [IQR, 0.0-1.0] vs 1.0 [IQR, 0.0-1.0]), and lower SDI (median, 36.0 [IQR, 16.2-61.0] vs 42.0 [IQR, 23.8-66.2]). After adjustments, the median overall survival was 9.27 (IQR, 8.74-9.76) and 6.87 (IQR, 6.41-7.66) months for patients treated with FOLFIRINOX and gemcitabine plus nab-paclitaxel, respectively (P < .001). This survival benefit was observed among all subgroups, including different ECOG PS scores, ages, SDIs, and metastatic sites. FOLFIRINOX-treated patients also had 17.3% fewer posttreatment hospitalizations (P = .03) and 20% lower posttreatment costs (P < .001). Conclusions and Relevance: In this comparative effectiveness cohort study, FOLFIRINOX was associated with improved survival of approximately 2 months compared with gemcitabine plus nab-paclitaxel and was also associated with fewer posttreatment complications. A randomized clinical trial comparing these first-line treatments is warranted to test the survival and posttreatment hospitalization (or complications) benefit of FOLFIRINOX compared with gemcitabine plus nab-paclitaxel.

Implementation of machine learning models for the prediction of vaginal birth after cesarean delivery.

OBJECTIVE: Accurate prediction of vaginal birth after cesarean is crucial for selecting women suitable for a trial of labor after cesarean (TOLAC). We sought to develop a machine learning (ML) model for prediction of TOLAC success and to compare its accuracy with that of the MFMU model. METHODS: All consecutive singleton TOLAC deliveries from a tertiary academic medical center between February 2017 and December 2018 were included. We developed models using the following ML algorithms: random forest (RF), regularized regression (GLM), and eXtreme gradient-boosted decision trees (XGBoost). For developing the ML models, we disaggregated BMI into height and weight. Similarly, we disaggregated prior arrest of progression into prior arrest of dilatation and prior arrest of descent. We applied a nested cross-validation approach, using 100 random splits of the data to training (80%, 792 samples) and testing sets (20%, 197 samples). We used the area under the precision-recall curve (AUC-PR) as a measure of accuracy. RESULTS: Nine hundred and eighty-nine TOLAC deliveries were included in the analysis with an observed TOLAC success rate of 85.6%. The AUC-PR in the RF, XGBoost and GLM models were 0.351±0.028, 0.350±0.028 and 0.336±0.024, respectively, compared to 0.325±0.067 for the MFMU-C. The algorithms performed significantly better than the MFMU-C (p-values = .0002, .0004, .0393 for RF, XGBoost, GLM respectively). In the XGBoost model, eight variables were sufficient for accurate prediction. In all ML models, previous vaginal delivery and height were among the three most important predictors of TOLAC success. Prior arrest of descent contributed to prediction more than prior arrest of dilatation, maternal height contributed more than weight. CONCLUSION: All ML models performed significantly better than the MFMU-C. In the XGBoost model, eight variables were sufficient for accurate prediction. Prior arrest of descent and maternal height contribute to prediction more than prior arrest of dilation and maternal weight.

Molecular programs of fibrotic change in aging human lung.

Lung fibrosis is increasingly detected with aging and has been associated with poor outcomes in acute lung injury or infection. However, the molecular programs driving this pro-fibrotic evolution are unclear. Here we profile distal lung samples from healthy human donors across the lifespan. Gene expression profiling by bulk RNAseq reveals both increasing cellular senescence and pro-fibrotic pathway activation with age. Quantitation of telomere length shows progressive shortening with age, which is associated with DNA damage foci and cellular senescence. Cell type deconvolution analysis of the RNAseq data indicates a progressive loss of lung epithelial cells and an increasing proportion of fibroblasts with age. Consistent with this pro-fibrotic profile, second harmonic imaging of aged lungs demonstrates increased density of interstitial collagen as well as decreased alveolar expansion and surfactant secretion. In this work, we reveal the transcriptional and structural features of fibrosis and associated functional impairment in normal lung aging.

CNS fibroblasts form a fibrotic scar in response to immune cell infiltration.

Fibrosis is a common pathological response to inflammation in many peripheral tissues and can prevent tissue regeneration and repair. Here, we identified persistent fibrotic scarring in the CNS following immune cell infiltration in the experimental autoimmune encephalomyelitis (EAE) mouse model of multiple sclerosis. Using lineage tracing and single-cell sequencing in EAE, we determined that the majority of the fibrotic scar is derived from proliferative CNS fibroblasts, not pericytes or infiltrating bone marrow-derived cells. Ablating proliferating fibrotic cells using cell-specific expression of herpes thymidine kinase led to an increase in oligodendrocyte lineage cells within the inflammatory lesions and a reduction in motor disability. We further identified that interferon-gamma pathway genes are enriched in CNS fibrotic cells, and the fibrotic cell-specific deletion of Ifngr1 resulted in reduced fibrotic scarring in EAE. These data delineate a framework for understanding the CNS fibrotic response.