The genomic basis of childhood T-lineage acute lymphoblastic leukaemia.

T-lineage acute lymphoblastic leukaemia (T-ALL) is a high-risk tumour1 that has eluded comprehensive genomic characterization, which is partly due to the high frequency of noncoding genomic alterations that result in oncogene deregulation2,3. Here we report an integrated analysis of genome and transcriptome sequencing of tumour and remission samples from more than 1,300 uniformly treated children with T-ALL, coupled with epigenomic and single-cell analyses of malignant and normal T cell precursors. This approach identified 15 subtypes with distinct genomic drivers, gene expression patterns, developmental states and outcomes. Analyses of chromatin topology revealed multiple mechanisms of enhancer deregulation that involve enhancers and genes in a subtype-specific manner, thereby demonstrating widespread involvement of the noncoding genome. We show that the immunophenotypically described, high-risk entity of early T cell precursor ALL is superseded by a broader category of 'early T cell precursor-like' leukaemia. This category has a variable immunophenotype and diverse genomic alterations of a core set of genes that encode regulators of hematopoietic stem cell development. Using multivariable outcome models, we show that genetic subtypes, driver and concomitant genetic alterations independently predict treatment failure and survival. These findings provide a roadmap for the classification, risk stratification and mechanistic understanding of this disease.

Stochastic EM algorithm for partially observed stochastic epidemics with individual heterogeneity.

We develop a stochastic epidemic model progressing over dynamic networks, where infection rates are heterogeneous and may vary with individual-level covariates. The joint dynamics are modeled as a continuous-time Markov chain such that disease transmission is constrained by the contact network structure, and network evolution is in turn influenced by individual disease statuses. To accommodate partial epidemic observations commonly seen in real-world data, we propose a stochastic EM algorithm for inference, introducing key innovations that include efficient conditional samplers for imputing missing infection and recovery times which respect the dynamic contact network. Experiments on both synthetic and real datasets demonstrate that our inference method can accurately and efficiently recover model parameters and provide valuable insight at the presence of unobserved disease episodes in epidemic data.

Diagnosis, management, and outcomes of immune checkpoint inhibitor induced acute interstitial nephritis: A single-center experience.

BACKGROUND: Immune checkpoint inhibitor (ICI)-associated acute interstitial nephritis (AIN) is a recognized complication of immunotherapy (IO), but literature on its management and outcomes is limited. METHODS: We retrospectively reviewed patients who received ICIs and developed biopsy-proven or clinically-suspected ICI-associated AIN at the University of Virginia Comprehensive Cancer Center from 2012-2023. We analyzed baseline characteristics and clinical outcomes, including treatment interruption and rechallenge rates. Acute kidney injury (AKI) was defined as a ≥ 1.5-fold increase in baseline creatinine under seven days, a two-fold increase above the upper limit of normal, or an increase by ≥0.3 mg/dL. Kidney function returning to within 0.3 mg/dL or less than twice baseline was considered complete (CRc) and partial (PRc) recovery, respectively. RESULTS: We identified 12 cases of ICI-AIN: four by biopsy (33%) and eight (67%) by clinical suspicion. Two patients received anti-CTLA-4 and anti-PD1, six received anti-PD1 alone, and four received chemo-immunotherapy. The majority (58%) of patients developed AIN within the first 5 cycles. Eight patients developed ≥ Grade 3 AKI, and six developed multiple irAEs. ICI was permanently discontinued in seven patients (58%) and temporarily interrupted in four (30%). The CRc and PRc rates were 67% and 8%, respectively. Upon AIN onset, the best disease response was stable disease in five patients, partial response in three, and progressive disease in three. Median overall survival was 4.87 years, and progression-free survival was 1.5 years. CONCLUSIONS: Rechallenge with IO after kidney irAE may be possible in some patients but requires careful evaluation on an individual basis.

Enrollment Trends Among Patients with Melanoma Brain Metastasis in Active Clinical Trials.

The CNS is a common site for distant metastasis and treatment failure in melanoma patients. This study aimed to evaluate the inclusion rate of patients with melanoma brain metastases (MBM) in prospective clinical trials. 69.3% of trials excluded MBM patients based on their CNS disease. In univariate analysis, trials not employing immunotherapy (p = 0.0174), inclusion of leptomeningeal disease (p < 0.0001) and non-pharmaceutical sponsor trials (p = 0.0461) were more likely to enroll patients with MBM. Thoughtful reconsideration of clinical trial designs is needed to give patients with MBMs access to promising investigational agents and improve outcomes for patients with MBM.

Multiplexed Imaging Mass Cytometry Analysis Characterizes the Vascular Niche in Pancreatic Cancer.

Oncogenesis and progression of pancreatic ductal adenocarcinoma (PDAC) are driven by complex interactions between the neoplastic component and the tumor microenvironment, which includes immune, stromal, and parenchymal cells. In particular, most PDACs are characterized by a hypovascular and hypoxic environment that alters tumor cell behavior and limits the efficacy of chemotherapy and immunotherapy. Characterization of the spatial features of the vascular niche could advance our understanding of inter- and intratumoral heterogeneity in PDAC. In this study, we investigated the vascular microenvironment of PDAC by applying imaging mass cytometry using a 26-antibody panel on 35 regions of interest across 9 patients, capturing more than 140,000 single cells. The approach distinguished major cell types, including multiple populations of lymphoid and myeloid cells, endocrine cells, ductal cells, stromal cells, and endothelial cells. Evaluation of cellular neighborhoods identified 10 distinct spatial domains, including multiple immune and tumor-enriched environments as well as the vascular niche. Focused analysis revealed differential interactions between immune populations and the vasculature and identified distinct spatial domains wherein tumor cell proliferation occurs. Importantly, the vascular niche was closely associated with a population of CD44-expressing macrophages enriched for a proangiogenic gene signature. Taken together, this study provides insights into the spatial heterogeneity of PDAC and suggests a role for CD44-expressing macrophages in shaping the vascular niche. Significance: Imaging mass cytometry revealed that pancreatic ductal cancers are composed of 10 distinct cellular neighborhoods, including a vascular niche enriched for macrophages expressing high levels of CD44 and a proangiogenic gene signature.

Mapping the cellular biogeography of human bone marrow niches using single-cell transcriptomics and proteomic imaging.

Non-hematopoietic cells are essential contributors to hematopoiesis. However, heterogeneity and spatial organization of these cells in human bone marrow remain largely uncharacterized. We used single-cell RNA sequencing (scRNA-seq) to profile 29,325 non-hematopoietic cells and discovered nine transcriptionally distinct subtypes. We simultaneously profiled 53,417 hematopoietic cells and predicted their interactions with non-hematopoietic subsets. We employed co-detection by indexing (CODEX) to spatially profile over 1.2 million cells. We integrated scRNA-seq and CODEX data to link predicted cellular signaling with spatial proximity. Our analysis revealed a hyperoxygenated arterio-endosteal neighborhood for early myelopoiesis, and an adipocytic localization for early hematopoietic stem and progenitor cells (HSPCs). We used our CODEX atlas to annotate new images and uncovered mesenchymal stromal cell (MSC) expansion and spatial neighborhoods co-enriched for leukemic blasts and MSCs in acute myeloid leukemia (AML) patient samples. This spatially resolved, multiomic atlas of human bone marrow provides a reference for investigation of cellular interactions that drive hematopoiesis.

Mapping the Cellular Biogeography of Human Bone Marrow Niches Using Single-Cell Transcriptomics and Proteomic Imaging.

The bone marrow is the organ responsible for blood production. Diverse non-hematopoietic cells contribute essentially to hematopoiesis. However, these cells and their spatial organization remain largely uncharacterized as they have been technically challenging to study in humans. Here, we used fresh femoral head samples and performed single-cell RNA sequencing (scRNA-Seq) to profile 29,325 enriched non-hematopoietic bone marrow cells and discover nine transcriptionally distinct subtypes. We next employed CO-detection by inDEXing (CODEX) multiplexed imaging of 18 individuals, including both healthy and acute myeloid leukemia (AML) samples, to spatially profile over one million single cells with a novel 53-antibody panel. We discovered a relatively hyperoxygenated arterio-endosteal niche for early myelopoiesis, and an adipocytic, but not endosteal or perivascular, niche for early hematopoietic stem and progenitor cells. We used our atlas to predict cell type labels in new bone marrow images and used these predictions to uncover mesenchymal stromal cell (MSC) expansion and leukemic blast/MSC-enriched spatial neighborhoods in AML patient samples. Our work represents the first comprehensive, spatially-resolved multiomic atlas of human bone marrow and will serve as a reference for future investigation of cellular interactions that drive hematopoiesis.

All-trans retinoic acid induces durable tumor immunity in IDH-mutant gliomas by rescuing transcriptional repression of the CRBP1-retinoic acid axis.

Diffuse gliomas are epigenetically dysregulated, immunologically cold, and fatal tumors characterized by mutations in isocitrate dehydrogenase (IDH). Although IDH mutations yield a uniquely immunosuppressive tumor microenvironment, the regulatory mechanisms that drive the immune landscape of IDH mutant (IDHm) gliomas remain unknown. Here, we reveal that transcriptional repression of retinoic acid (RA) pathway signaling impairs both innate and adaptive immune surveillance in IDHm glioma through epigenetic silencing of retinol binding protein 1 (RBP1) and induces a profound anti-inflammatory landscape marked by loss of inflammatory cell states and infiltration of suppressive myeloid phenotypes. Restorative retinoic acid therapy in murine glioma models promotes clonal CD4 + T cell expansion and induces tumor regression in IDHm, but not IDH wildtype (IDHwt), gliomas. Our findings provide a mechanistic rationale for RA immunotherapy in IDHm glioma and is the basis for an ongoing investigator-initiated, single-center clinical trial investigating all-trans retinoic acid (ATRA) in recurrent IDHm human subjects.

The FDA Reclassification of Cervical Pedicle and Lateral Mass Screws: A Case Study in Regulatory History.

The classification of medical devices by the Food and Drug Administration (FDA) involves rigorous scrutiny from specialized panels that designate devices as Class I, II, or III depending on their levels of relative risk to patient health. Posterior rigid pedicle screw systems were first classified by the FDA in 1984 and have since revolutionized the treatment of many spine pathologies. Despite this early classification by the FDA, posterior cervical pedicle and lateral mass screws were not reclassified from unclassified to Class III and then to Class II until 2019, nearly 35 years after their initial classification. This reclassification process involved a decades-long interplay between the FDA, formal panels, manufacturers, academic leaders, practicing physicians, and patients. It was delayed by lawsuits and a paucity of data demonstrating the ability to improve outcomes for cervical spinal pathologies. The off-label use of thoracolumbar pedicle screw rigid fixation systems by early adopters assisted manufacturers and professional organizations in providing the necessary data for the reclassification process. This case study highlights the collaboration between physicians and professional organizations in facilitating FDA reclassification and underscores changes to the current classification process that could avoid the prolonged dichotomy between common medical practice and FDA guidelines.

Multiomics Analysis of PCB126's Effect on a Mouse Chronic-Binge Alcohol Feeding Model.

BACKGROUND: Environmental pollutants, including polychlorinated biphenyls (PCBs) have been implicated in the pathogenesis of liver disease. Our group recently demonstrated that PCB126 promoted steatosis, hepatomegaly, and modulated intermediary metabolism in a rodent model of alcohol-associated liver disease (ALD). OBJECTIVE: To better understand how PCB126 promoted ALD in our previous model, the current study adopts multiple omics approaches to elucidate potential mechanistic hypotheses. METHODS: Briefly, male C57BL/6J mice were exposed to 0.2mg/kg polychlorinated biphenyl (PCB) 126 or corn oil vehicle prior to ethanol (EtOH) or control diet feeding in the chronic-binge alcohol feeding model. Liver tissues were collected and prepared for mRNA sequencing, phosphoproteomics, and inductively coupled plasma mass spectrometry for metals quantification. RESULTS: Principal component analysis showed that PCB126 uniquely modified the transcriptome in EtOH-fed mice. EtOH feeding alone resulted in >4,000 differentially expressed genes (DEGs), and PCB126 exposure resulted in more DEGs in the EtOH-fed group (907 DEGs) in comparison with the pair-fed group (503 DEGs). Top 20 significant gene ontology (GO) biological processes included "peptidyl tyrosine modifications," whereas top 25 significantly decreasing GO molecular functions included "metal/ion/zinc binding." Quantitative, label-free phosphoproteomics and western blot analysis revealed no major significant PCB126 effects on total phosphorylated tyrosine residues in EtOH-fed mice. Quantified hepatic essential metal levels were primarily significantly lower in EtOH-fed mice. PCB126-exposed mice had significantly lower magnesium, cobalt, and zinc levels in EtOH-fed mice. DISCUSSION: Previous work has demonstrated that PCB126 is a modifying factor in metabolic dysfunction-associated steatotic liver disease (MASLD), and our current work suggests that pollutants also modify ALD. PCB126 may, in part, be contributing to the malnutrition aspect of ALD, where metal deficiency is known to contribute and worsen prognosis. https://doi.org/10.1289/EHP14132.

Chronic Aroclor 1260 exposure alters the mouse liver proteome, selenoproteins, and metals in steatotic liver disease.

The prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) continues to increase due in part to the obesity epidemic and to environmental exposures to metabolism disrupting chemicals. A single gavage exposure of male mice to Aroclor 1260 (Ar1260), an environmentally relevant mixture of non-dioxin-like polychlorinated biphenyls (PCBs), resulted in steatohepatitis and altered RNA modifications in selenocysteine tRNA 34 weeks post-exposure. Unbiased approaches identified the liver proteome, selenoproteins, and levels of 25 metals. Ar1260 altered the abundance of 128 proteins. Enrichment analysis of the liver Ar1260 proteome included glutathione metabolism and translation of selenoproteins. Hepatic glutathione peroxidase 4 (GPX4) and Selenoprotein O (SELENOO) were increased and Selenoprotein F (SELENOF), Selenoprotein S (SELENOS), Selenium binding protein 2 (SELENBP2) were decreased with Ar1260 exposure. Increased copper, selenium (Se), and zinc and reduced iron levels were detected. These data demonstrate that Ar1260 exposure alters the (seleno)proteome, Se, and metals in MASLD-associated pathways.

A longitudinal single-cell and spatial multiomic atlas of pediatric high-grade glioma.

Pediatric high-grade glioma (pHGG) is an incurable central nervous system malignancy that is a leading cause of pediatric cancer death. While pHGG shares many similarities to adult glioma, it is increasingly recognized as a molecularly distinct, yet highly heterogeneous disease. In this study, we longitudinally profiled a molecularly diverse cohort of 16 pHGG patients before and after standard therapy through single-nucleus RNA and ATAC sequencing, whole-genome sequencing, and CODEX spatial proteomics to capture the evolution of the tumor microenvironment during progression following treatment. We found that the canonical neoplastic cell phenotypes of adult glioblastoma are insufficient to capture the range of tumor cell states in a pediatric cohort and observed differential tumor-myeloid interactions between malignant cell states. We identified key transcriptional regulators of pHGG cell states and did not observe the marked proneural to mesenchymal shift characteristic of adult glioblastoma. We showed that essential neuromodulators and the interferon response are upregulated post-therapy along with an increase in non-neoplastic oligodendrocytes. Through in vitro pharmacological perturbation, we demonstrated novel malignant cell-intrinsic targets. This multiomic atlas of longitudinal pHGG captures the key features of therapy response that support distinction from its adult counterpart and suggests therapeutic strategies which are targeted to pediatric gliomas.

Detecting changes in the transmission rate of a stochastic epidemic model.

Throughout the course of an epidemic, the rate at which disease spreads varies with behavioral changes, the emergence of new disease variants, and the introduction of mitigation policies. Estimating such changes in transmission rates can help us better model and predict the dynamics of an epidemic, and provide insight into the efficacy of control and intervention strategies. We present a method for likelihood-based estimation of parameters in the stochastic susceptible-infected-removed model under a time-inhomogeneous transmission rate comprised of piecewise constant components. In doing so, our method simultaneously learns change points in the transmission rate via a Markov chain Monte Carlo algorithm. The method targets the exact model posterior in a difficult missing data setting given only partially observed case counts over time. We validate performance on simulated data before applying our approach to data from an Ebola outbreak in Western Africa and COVID-19 outbreak on a university campus.

Inferring HIV transmission patterns from viral deep-sequence data via latent typed point processes.

Viral deep-sequencing data play a crucial role toward understanding disease transmission network flows, providing higher resolution compared to standard Sanger sequencing. To more fully utilize these rich data and account for the uncertainties in outcomes from phylogenetic analyses, we propose a spatial Poisson process model to uncover human immunodeficiency virus (HIV) transmission flow patterns at the population level. We represent pairings of individuals with viral sequence data as typed points, with coordinates representing covariates such as gender and age and point types representing the unobserved transmission statuses (linkage and direction). Points are associated with observed scores on the strength of evidence for each transmission status that are obtained through standard deep-sequence phylogenetic analysis. Our method is able to jointly infer the latent transmission statuses for all pairings and the transmission flow surface on the source-recipient covariate space. In contrast to existing methods, our framework does not require preclassification of the transmission statuses of data points, and instead learns them probabilistically through a fully Bayesian inference scheme. By directly modeling continuous spatial processes with smooth densities, our method enjoys significant computational advantages compared to previous methods that rely on discretization of the covariate space. We demonstrate that our framework can capture age structures in HIV transmission at high resolution, bringing valuable insights in a case study on viral deep-sequencing data from Southern Uganda.

A unified analysis of convex and non­convex 𝓛p­ball projection problems.

The task of projecting onto ℓp norm balls is ubiquitous in statistics and machine learning, yet the availability of actionable algorithms for doing so is largely limited to the special cases of p∈{0, 1, 2, ∞}. In this paper, we introduce novel, scalable methods for projecting onto the ℓp-ball for general p>0. For p≥1, we solve the univariate Lagrangian dual via a dual Newton method. We then carefully design a bisection approach For p<1, presenting theoretical and empirical evidence of zero or a small duality gap in the non-convex case. The success of our contributions is thoroughly assessed empirically, and applied to large-scale regularized multi-task learning and compressed sensing. The code implementing our methods is publicly available on Github.