Predisposing, Enabling, and Need Factors Influencing Health-related Quality of Life Among People with Metabolic Syndrome.

INTRODUCTION: Metabolic syndrome (MetS) continues to impact the health-related quality of life (HRQoL) of patients despite various available therapeutic interventions. There is a dearth of information on how patient-centered factors holistically predict HRQoL to provide more insights on addressing MetS. OBJECTIVES: To predict the HRQoL of patients with MetS in the Southern states, using the predisposing, enabling, and need factors. METHOD: The study adopted a cross-sectional approach in collecting 706 complete surveys on HRQoL assessment using the EQ-5D-5L survey and demographic characteristics based on the predisposing, enabling, and need factors of Andersen's Behavioral model. The study focused on people with MetS in the southern states of the United States. Multinomial logistic regression was conducted to investigate the relationship between the number of comorbidities and each HRQoL dimension. Ordinal regression was used to explore factors predicting HRQoL. Sensitivity analysis was conducted using bootstrapping analysis to evaluate the regression's robustness. RESULTS: Over 70% were female and 30% had at least a bachelor's degree, while 47% were married. Most respondents (71.1%) had no problem with self-care. However, 20.0% had severe problems with pain, while the highest proportion (8.6%) was observed for extreme problems with anxiety or depression. A unit increase in comorbidities resulted in higher odds of having extreme problems with mobility (OR = 1.95), usual activities (OR = 1.73), and pain (OR = 1.70). Only 40.8% of the respondents had good HRQoL, compared to 26.2% with poor HRQoL. Age, race, geographical area, marital status, household income, number of prescription drugs, comorbidities, and body mass index were predictors of HRQoL. CONCLUSION: An increase in comorbidities significantly increased the odds of having challenges with the HRQoL dimensions. Demographic, socioeconomic, and health-related factors significantly predicted HRQoL. Therefore, healthcare providers must consider these factors as a component of patient-centered care to address health disparities and promote optimal health outcomes among people with MetS.

Measurable imaging-based changes in enhancement of intrahepatic cholangiocarcinoma after radiotherapy reflect physical mechanisms of response.

Background: Although escalated doses of radiation therapy (RT) for intrahepatic cholangiocarcinoma (iCCA) are associated with durable local control (LC) and prolonged survival, uncertainties persist regarding personalized RT based on biological factors. Compounding this knowledge gap, the assessment of RT response using traditional size-based criteria via computed tomography (CT) imaging correlates poorly with outcomes. We hypothesized that quantitative measures of enhancement would more accurately predict clinical outcomes than size-based assessment alone and developed a model to optimize RT. Methods: Pre-RT and post-RT CT scans of 154 patients with iCCA were analyzed retrospectively for measurements of tumor dimensions (for RECIST) and viable tumor volume using quantitative European Association for Study of Liver (qEASL) measurements. Binary classification and survival analyses were performed to evaluate the ability of qEASL to predict treatment outcomes, and mathematical modeling was performed to identify the mechanistic determinants of treatment outcomes and to predict optimal RT protocols. Results: Multivariable analysis accounting for traditional prognostic covariates revealed that percentage change in viable volume following RT was significantly associated with OS, outperforming stratification by RECIST. Binary classification identified ≥33% decrease in viable volume to optimally correspond to response to RT. The model-derived, patient-specific tumor enhancement growth rate emerged as the dominant mechanistic determinant of treatment outcome and yielded high accuracy of patient stratification (80.5%), strongly correlating with the qEASL-based classifier. Conclusion: Following RT for iCCA, changes in viable volume outperformed radiographic size-based assessment using RECIST for OS prediction. CT-derived tumor-specific mathematical parameters may help optimize RT for resistant tumors.

VMAP: Vaginal Microbiome Atlas during Pregnancy.

Objectives: To enable interactive visualization of the vaginal microbiome across the pregnancy and facilitate discovery of novel insights and generation of new hypotheses. Material and Methods: Vaginal Microbiome Atlas during Pregnancy (VMAP) was created with R shiny to generate visualizations of structured vaginal microbiome data from multiple studies. Results: VMAP (http://vmapapp.org) visualizes 3880 vaginal microbiome samples of 1402 pregnant individuals from 11 studies, aggregated via open-source tool MaLiAmPi. Visualized features include diversity measures, VALENCIA community state types, and composition (phylotypes, taxonomy) that can be filtered by various categories. Discussion: This work represents one of the largest and most geographically diverse aggregations of the vaginal microbiome in pregnancy to date and serves as a user-friendly resource to further analyze vaginal microbiome data and better understand pregnancies and associated outcomes. Conclusion: VMAP can be obtained from https://github.com/msirota/vmap.git and is currently deployed as an online app for non-R users.

Dissecting the oncogenic mechanisms of POT1 cancer mutations through deep scanning mutagenesis.

Mutations in the shelterin protein POT1 are associated with diverse cancers, but their role in cancer progression remains unclear. To resolve this, we performed deep scanning mutagenesis in POT1 locally haploid human stem cells to assess the impact of POT1 variants on cellular viability and cancer-associated telomeric phenotypes. Though POT1 is essential, frame-shift mutants are rescued by chemical ATR inhibition, indicating that POT1 is not required for telomere replication or lagging strand synthesis. In contrast, a substantial fraction of clinically-validated pathogenic mutations support normal cellular proliferation, but still drive ATR-dependent telomeric DNA damage signaling and ATR-independent telomere elongation. Moreover, this class of cancer-associated POT1 variants elongates telomeres more rapidly than POT1 frame-shifts, indicating they actively drive oncogenesis and are not simple loss-of-function mutations.

Assessing predictions on fitness effects of missense variants in HMBS in CAGI6.

This paper presents an evaluation of predictions submitted for the "HMBS" challenge, a component of the sixth round of the Critical Assessment of Genome Interpretation held in 2021. The challenge required participants to predict the effects of missense variants of the human HMBS gene on yeast growth. The HMBS enzyme, critical for the biosynthesis of heme in eukaryotic cells, is highly conserved among eukaryotes. Despite the application of a variety of algorithms and methods, the performance of predictors was relatively similar, with Kendall's tau correlation coefficients between predictions and experimental scores around 0.3 for a majority of submissions. Notably, the median correlation (≥ 0.34) observed among these predictors, especially the top predictions from different groups, was greater than the correlation observed between their predictions and the actual experimental results. Most predictors were moderately successful in distinguishing between deleterious and benign variants, as evidenced by an area under the receiver operating characteristic (ROC) curve (AUC) of approximately 0.7 respectively. Compared with the recent two rounds of CAGI competitions, we noticed more predictors outperformed the baseline predictor, which is solely based on the amino acid frequencies. Nevertheless, the overall accuracy of predictions is still far short of positive control, which is derived from experimental scores, indicating the necessity for considerable improvements in the field. The most inaccurately predicted variants in this round were associated with the insertion loop, which is absent in many orthologs, suggesting the predictors still heavily rely on the information from multiple sequence alignment.

Cardiac amyloidosis worsens prognosis in patients with heart failure: findings from the PREVAMIC study.

BACKGROUND AND OBJECTIVES: Cardiac amyloidosis (CA) is a common pathology in elderly patients that usually presents as heart failure (HF). However, it is not clear whether CA associated with HF has a worse prognosis compared with HF due to other etiologies. MATERIAL AND METHODS: Prospective, observational cohort study that recruited patients ≥65 years of age with HF in 30 Spanish centers. The cohort was divided according to whether the patients had AC or not. Patients were followed for 1 year. RESULTS: A total of 484 patients were included in the analysis. The population was elderly (median 86 years) and 49% were women CA was present in 23.8 % of the included patients. In the CA group, there was a lower prevalence of diabetes mellitus and valvular disease. At one year of follow-up, mortality was significantly more frequent in patients with CA compared to those without (33.0 vs.14.9%, p < 0.001). However, there were no differences between both groups in visits to the emergency room or readmissions. In the multivariate analysis, the variables that were shown to predict all-cause mortality at one year of follow-up were chronic kidney disease (HR 1.75 (1.01-3.05) p 0.045), NT-proBNP levels (HR 2.51 (1.46-4.30) p < 0.001), confusion (HR 2.05 (1.01-4.17), p 0.048), and the presence of CA (HR 1.77 (1.11-2.84), p 0.017). CONCLUSION: The presence of CA in elderly patients with HF is related to a worse prognosis at one year of follow-up. Early diagnosis of the pathology and multidisciplinary management can help improve patient outcomes.

Genomic Language Models: Opportunities and Challenges.

Large language models (LLMs) are having transformative impacts across a wide range of scientific fields, particularly in the biomedical sciences. Just as the goal of Natural Language Processing is to understand sequences of words, a major objective in biology is to understand biological sequences. Genomic Language Models (gLMs), which are LLMs trained on DNA sequences, have the potential to significantly advance our understanding of genomes and how DNA elements at various scales interact to give rise to complex functions. To showcase this potential, we highlight key applications of gLMs, including functional constraint prediction, sequence design, and transfer learning. Despite notable recent progress, however, developing effective and efficient gLMs presents numerous challenges, especially for species with large, complex genomes. Here, we discuss major considerations for developing and evaluating gLMs.

Perceptions of metabolic syndrome management utilization in relation to patient experience and health-related quality of life.

Background: One factor for the poor health outcomes among adult people with metabolic syndrome (MetS) is poor utilization of disease management resources, which may be attributable to prior experience with pharmacists (PEwP) and perceptions of disease management resource utilization (PMU). Therefore, understanding patients' experience could be critical to improving their perceptions and promoting health outcomes. Objectives: The study explored the influence of PEwP and PMU on the health-related quality of life (HRQoL) of people with MetS. Methods: Data on perceptions of healthcare, medication, and pharmacy services utilization, PEwP, and HRQoL were collected using validated tools via an electronic survey. Chi-square and ordinal regression tests were used to predict the association between PMU, PEwP, and HRQoL. Also, mediation analysis through Haye's model 4 explored the direct and indirect relationship of PMU and PEwP on HRQoL. Results: A total of 706 completed surveys were collected and used for analyses. On average, respondents reported three comorbidities. Of the respondents, 72.0% had good PEwP, while 32.6% had good PMU. Comparatively, 38.4% of those with good PEwP had good PMU, compared to 17.3% of those with poor PEwP. Also, 47.0% of those with good PMU had good HRQoL compared to 35.3% with poor PMU. The odds of having fair or good PMU were nearly triple (OR = 2.97, p < 0.001) among those with good PEwP compared to those with poor PEwP. Also, respondents with good PMU had 58% (OR = 1.58, p = 0.008) higher odds of having fair or good HRQoL. Analysis through bootstrap indicated a significant relationship (BootCI = -0.072, -0.022) between PEwP and HRQoL via respondents' PMU. Conclusions: MetS individuals with good experience and PMU were more likely to have good HRQoL. Prior experience with pharmacists influenced PMU and indirectly impacted HRQoL. Therefore, pharmacists must consider patients' experience and management utilization perceptions to promote health outcome among people with MetS, while implementing interventions.

A SIMPLE AND FLEXIBLE TEST OF SAMPLE EXCHANGEABILITY WITH APPLICATIONS TO STATISTICAL GENOMICS.

In scientific studies involving analyses of multivariate data, basic but important questions often arise for the researcher: Is the sample exchangeable, meaning that the joint distribution of the sample is invariant to the ordering of the units? Are the features independent of one another, or perhaps the features can be grouped so that the groups are mutually independent? In statistical genomics, these considerations are fundamental to downstream tasks such as demographic inference and the construction of polygenic risk scores. We propose a non-parametric approach, which we call the V test, to address these two questions, namely, a test of sample exchangeability given dependency structure of features, and a test of feature independence given sample exchangeability. Our test is conceptually simple, yet fast and flexible. It controls the Type I error across realistic scenarios, and handles data of arbitrary dimensions by leveraging large-sample asymptotics. Through extensive simulations and a comparison against unsupervised tests of stratification based on random matrix theory, we find that our test compares favorably in various scenarios of interest. We apply the test to data from the 1000 Genomes Project, demonstrating how it can be employed to assess exchangeability of the genetic sample, or find optimal linkage disequilibrium (LD) splits for downstream analysis. For exchangeability assessment, we find that removing rare variants can substantially increase the p-value of the test statistic. For optimal LD splitting, the V test reports different optimal splits than previous approaches not relying on hypothesis testing. Software for our methods is available in R (CRAN: flintyR) and Python (PyPI: flintyPy).

The longitudinal behavioral effects of acute exposure to galactic cosmic radiation in female C57BL/6J mice: implications for deep space missions, female crews, and potential antioxidant countermeasures.

Galactic cosmic radiation (GCR) is an unavoidable risk to astronauts that may affect mission success. Male rodents exposed to 33-beam-GCR (33-GCR) show short-term cognitive deficits but reports on female rodents and long-term assessment is lacking. Here we asked: What are the longitudinal behavioral effects of 33-GCR on female mice? Also, can an antioxidant/anti-inflammatory compound mitigate the impact of 33-GCR? Mature (6-month-old) C57BL/6J female mice received the antioxidant CDDO-EA (400 µg/g of food) or a control diet (vehicle, Veh) for 5 days and either Sham-irradiation (IRR) or whole-body 33-GCR (0.75Gy) on the 4th day. Three-months post-IRR, mice underwent two touchscreen-platform tests: 1) location discrimination reversal (which tests behavior pattern separation and cognitive flexibility, two abilities reliant on the dentate gyrus) and 2) stimulus-response learning/extinction. Mice then underwent arena-based behavior tests (e.g. open field, 3-chamber social interaction). At the experiment end (14.25-month post-IRR), neurogenesis was assessed (doublecortin-immunoreactive [DCX+] dentate gyrus neurons). Female mice exposed to Veh/Sham vs. Veh/33-GCR had similar pattern separation (% correct to 1st reversal). There were two effects of diet: CDDO-EA/Sham and CDDO-EA/33-GCR mice had better pattern separation vs. their respective control groups (Veh/Sham, Veh/33-GCR), and CDDO-EA/33-GCR mice had better cognitive flexibility (reversal number) vs. Veh/33-GCR mice. Notably, one radiation effect/CDDO-EA countereffect also emerged: Veh/33-GCR mice had worse stimulus-response learning (days to completion) vs. all other groups, including CDDO-EA/33-GCR mice. In general, all mice show normal anxiety-like behavior, exploration, and habituation to novel environments. There was also a change in neurogenesis: Veh/33-GCR mice had fewer DCX+ dentate gyrus immature neurons vs. Veh/Sham mice. Our study implies space radiation is a risk to a female crew's longitudinal mission-relevant cognitive processes and CDDO-EA is a potential dietary countermeasure for space-radiation CNS risks.

Efficacy and safety of shunt surgery in patients with idiopathic normal-pressure hydrocephalus: can we predict shunt response by preoperative magnetic resonance imaging (MRI)?

AIM: The aim of this study was to identify preoperative magnetic resonance imaging (MRI) findings that can predict the shunt responsiveness in idiopathic normal-pressure hydrocephalus (iNPH) patients and to investigate postoperative outcome and complications. MATERIALS AND METHODS: A total of 192 patients with iNPH who underwent shunt at our hospital between 2000 and 2021 were included to investigate complications. Of these, after exclusion, 127 (1-month postoperative follow-up) and 77 (1-year postoperative follow-up) patients were evaluated. The preoperative MRI features (the presence of tightness of the high-convexity subarachnoid space, Sylvian fissure enlargement, Evans' index, and callosal angle) of the shunt-response and nonresponse groups were compared, and a systematic review was conducted to evaluate whether preoperative MRI findings could predict shunt response. RESULTS: Postoperative complications within one month after surgery were observed in 6.8% (13/192), and the most common complication was hemorrhage. Changes in corpus callosum were observed in 4.2% (8/192). The shunt-response rates were 83.5% (106/127) in the 1-month follow-up group and 70.1% (54/77) in 1-year follow-up group. In the logistic regression analysis, only Evans' index measuring >0.4 had a significant negative relationship with shunt response at 1-month follow-up; however, no significant relationship was observed at 1-year follow-up. According to our systematic review, it is still controversial whether preoperative MRI findings could predict shunt response. CONCLUSION: Evans' index measure of >0.4 had a significant relationship with the shunt response in the 1-month follow-up group. In systematic reviews, there is ongoing debate about whether preoperative MRI findings can accurately predict responses to shunt surgery. Postoperative corpus callosal change was observed in 4.2% of iNPH patients.

Reproducibility and efficiency of liver volumetry using manual method and liver analysis software.

BACKGROUND: For liver volumetry, manual tracing on computed tomography (CT) images is time-consuming and operator dependent. To overcome these disadvantages, several three-dimensional simulation software programs have been developed; however, their efficacy has not fully been evaluated. METHODS: Three physicians performed liver volumetry on preoperative CT images on 30 patients who underwent formal right hepatectomy, using manual tracing volumetry and two simulation software programs, SYNAPSE and syngo.via. The future liver remnant (FLR) was calculated using each method of volumetry. The primary endpoint was reproducibility and secondary outcomes were calculation time and learning curve. RESULTS: The mean FLR was significantly lower for manual volumetry than for SYNAPSE or syngo.via; there was no significant difference in mean FLR between the two software-based methods. Reproducibility was lower for the manual method than for the software-based methods. Mean calculation time was shortest for SYNAPSE. For the two physicians unfamiliar with the software, no obvious learning curve was observed for using SYNAPSE, whereas learning curves were observed for using syngo.via. CONCLUSIONS: Liver volumetry was more reproducible and faster with three-dimensional simulation software, especially SYNAPSE software, than with the conventional manual tracing method. Software can help even inexperienced physicians learn quickly how to perform liver volumetry.

Clinical suspicion, diagnosis and management of cardiac amyloidosis: update document and executive summary.

In recent years, the interest in cardiac amyloidosis has grown exponentially. However, there is a need to improve our understanding of amyloidosis in order to optimise early detection systems. Therefore, it is crucial to incorporate solutions to improve the suspicion, diagnosis and follow-up of cardiac amyloidosis. In this sense, we designed a tool following the different phases to reach the diagnosis of cardiac amyloidosis, as well as an optimal follow-up: a) clinical suspicion, where the importance of the "red flags" to suspect it and activate the diagnostic process is highlighted; 2) diagnosis, where the diagnostic algorithm is mainly outlined; and 3) follow-up of confirmed patients. This is a practical resource that will be of great use to all professionals caring for patients with suspected or confirmed cardiac amyloidosis, to improve its early detection, as well as to optimise its accurate diagnosis and optimal follow-up.

Exact and efficient phylodynamic simulation from arbitrarily large populations.

Many biological studies involve inferring the evolutionary history of a sample of individuals from a large population and interpreting the reconstructed tree. Such an ascertained tree typically represents only a small part of a comprehensive population tree and is distorted by survivorship and sampling biases. Inferring evolutionary parameters from ascertained trees requires modeling both the underlying population dynamics and the ascertainment process. A crucial component of this phylodynamic modeling involves tree simulation, which is used to benchmark probabilistic inference methods. To simulate an ascertained tree, one must first simulate the full population tree and then prune unobserved lineages. Consequently, the computational cost is determined not by the size of the final simulated tree, but by the size of the population tree in which it is embedded. In most biological scenarios, simulations of the entire population are prohibitively expensive due to computational demands placed on lineages without sampled descendants. Here, we address this challenge by proving that, for any partially ascertained process from a general multi-type birth-death-mutation-sampling model, there exists an equivalent process with complete sampling and no death, a property which we leverage to develop a highly efficient algorithm for simulating trees. Our algorithm scales linearly with the size of the final simulated tree and is independent of the population size, enabling simulations from extremely large populations beyond the reach of current methods but essential for various biological applications. We anticipate that this unprecedented speedup will significantly advance the development of novel inference methods that require extensive training data.

Improving the accuracy of nitrogen estimates from nonpoint source in a river catchment with multi-isotope tracers.

Climate change can affect precipitation patterns, temperature, and the hydrological cycle, consequently influencing the dynamics of nitrogen (N) within aquatic ecosystems. In this study, multiple stable isotopes (15N-NO3/18O-NO3 and 2H-H2O/18O-H2O) were used to investigate the N sources and flowpath within the Bogang stream in South Korea. Within the vicinity of the stream with complex land use where various N sources were present, four end-members (rainfall, soil, sewage, and livestock) were sampled and examined. Consequently, spatial-temporal variations of the N sources were observed dependent on the type of land use. During the dry season, sewage accounted for the dominant N source, ranging from 62.2 % to 80.2 %. In contrast, nonpoint sources increased significantly across most sites during the wet season (10.3-41.6 % for soil; 6.3-35.2 % for livestock) compared to the dry season (7.7-28.5 % for soil; 6-13.2 % for livestock). However, sewage (78.7 %) remains dominant, representing the largest ratio at the site downstream of the wastewater treatment plant during the wet season. This ratio showed a notable difference from the calculated N loading ratio of 52.2 %, especially for livestock. This suggests that a significant potential for N legacy effects, given that groundwater flow is likely to be the primary hydrological pathway delivering N to rivers. This study will help to develop water resource management strategies by understanding how the interaction between N sources and hydrological process responds to climate change within sub-basins.

Highly parameterized polygenic scores tend to overfit to population stratification via random effects.

Polygenic scores (PGSs), increasingly used in clinical settings, frequently include many genetic variants, with performance typically peaking at thousands of variants. Such highly parameterized PGSs often include variants that do not pass a genome-wide significance threshold. We propose a mathematical perspective that renders the effects of many of these non-significant variants random rather than causal, with the randomness capturing population structure. We devise methods to assess variant effect randomness and population stratification bias. Applying these methods to 141 traits from the UK Biobank, we find that, for many PGSs, the effects of non-significant variants are considerably random, with the extent of randomness associated with the degree of overfitting to population structure of the discovery cohort. Our findings explain why highly parameterized PGSs simultaneously have superior cohort-specific performance and limited generalizability, suggesting the critical need for variant randomness tests in PGS evaluation. Supporting code and a dashboard are available at https://github.com/songlab-cal/StratPGS.

GPN-MSA: an alignment-based DNA language model for genome-wide variant effect prediction.

Whereas protein language models have demonstrated remarkable efficacy in predicting the effects of missense variants, DNA counterparts have not yet achieved a similar competitive edge for genome-wide variant effect predictions, especially in complex genomes such as that of humans. To address this challenge, we here introduce GPN-MSA, a novel framework for DNA language models that leverages whole-genome sequence alignments across multiple species and takes only a few hours to train. Across several benchmarks on clinical databases (ClinVar, COSMIC, OMIM), experimental functional assays (DMS, DepMap), and population genomic data (gnomAD), our model for the human genome achieves outstanding performance on deleteriousness prediction for both coding and non-coding variants.

Microbiome preterm birth DREAM challenge: Crowdsourcing machine learning approaches to advance preterm birth research.

Every year, 11% of infants are born preterm with significant health consequences, with the vaginal microbiome a risk factor for preterm birth. We crowdsource models to predict (1) preterm birth (PTB; <37 weeks) or (2) early preterm birth (ePTB; <32 weeks) from 9 vaginal microbiome studies representing 3,578 samples from 1,268 pregnant individuals, aggregated from public raw data via phylogenetic harmonization. The predictive models are validated on two independent unpublished datasets representing 331 samples from 148 pregnant individuals. The top-performing models (among 148 and 121 submissions from 318 teams) achieve area under the receiver operator characteristic (AUROC) curve scores of 0.69 and 0.87 predicting PTB and ePTB, respectively. Alpha diversity, VALENCIA community state types, and composition are important features in the top-performing models, most of which are tree-based methods. This work is a model for translation of microbiome data into clinically relevant predictive models and to better understand preterm birth.

ConvexML: Scalable and accurate inference of single-cell chronograms from CRISPR/Cas9 lineage tracing data.

CRISPR/Cas9 gene editing technology has enabled lineage tracing for thousands of cells in vivo. However, most of the analysis of CRISPR/Cas9 lineage tracing data has so far been limited to the reconstruction of single-cell tree topologies, which depict lineage relationships between cells, but not the amount of time that has passed between ancestral cell states and the present. Time-resolved trees, known as chronograms, would allow one to study the evolutionary dynamics of cell populations at an unprecedented level of resolution. Indeed, time-resolved trees would reveal the timing of events on the tree, the relative fitness of subclones, and the dynamics underlying phenotypic changes in the cell population - among other important applications. In this work, we introduce the first scalable and accurate method to refine any given single-cell tree topology into a single-cell chronogram by estimating its branch lengths. To do this, we leverage a statistical model of CRISPR/Cas9 cutting with missing data, paired with a conservative version of maximum parsimony that reconstructs only the ancestral states that we are confident about. As part of our method, we propose a novel approach to represent and handle missing data - specifically, double-resection events - which greatly simplifies and speeds up branch length estimation without compromising quality. All this leads to a convex maximum likelihood estimation (MLE) problem that can be readily solved in seconds with off-the-shelf convex optimization solvers. To stabilize estimates in low-information regimes, we propose a simple penalized version of MLE using a minimum branch length and pseudocounts. We benchmark our method using simulations and show that it performs well on several tasks, outperforming more naive baselines. Our method, which we name 'ConvexML', is available through the cassiopeia open source Python package.

Tracing cancer evolution and heterogeneity using Hi-C.

Chromosomal rearrangements can initiate and drive cancer progression, yet it has been challenging to evaluate their impact, especially in genetically heterogeneous solid cancers. To address this problem we developed HiDENSEC, a new computational framework for analyzing chromatin conformation capture in heterogeneous samples that can infer somatic copy number alterations, characterize large-scale chromosomal rearrangements, and estimate cancer cell fractions. After validating HiDENSEC with in silico and in vitro controls, we used it to characterize chromosome-scale evolution during melanoma progression in formalin-fixed tumor samples from three patients. The resulting comprehensive annotation of the genomic events includes copy number neutral translocations that disrupt tumor suppressor genes such as NF1, whole chromosome arm exchanges that result in loss of CDKN2A, and whole-arm copy-number neutral loss of homozygosity involving PTEN. These findings show that large-scale chromosomal rearrangements occur throughout cancer evolution and that characterizing these events yields insights into drivers of melanoma progression.