A Fast, Reproducible, High-throughput Variant Calling Workflow for Population Genomics.

The increasing availability of genomic resequencing data sets and high-quality reference genomes across the tree of life present exciting opportunities for comparative population genomic studies. However, substantial challenges prevent the simple reuse of data across different studies and species, arising from variability in variant calling pipelines, data quality, and the need for computationally intensive reanalysis. Here, we present snpArcher, a flexible and highly efficient workflow designed for the analysis of genomic resequencing data in nonmodel organisms. snpArcher provides a standardized variant calling pipeline and includes modules for variant quality control, data visualization, variant filtering, and other downstream analyses. Implemented in Snakemake, snpArcher is user-friendly, reproducible, and designed to be compatible with high-performance computing clusters and cloud environments. To demonstrate the flexibility of this pipeline, we applied snpArcher to 26 public resequencing data sets from nonmammalian vertebrates. These variant data sets are hosted publicly to enable future comparative population genomic analyses. With its extensibility and the availability of public data sets, snpArcher will contribute to a broader understanding of genetic variation across species by facilitating the rapid use and reuse of large genomic data sets.

Contemporary Incidence of Synchronous Multiple Primary Lung Cancers and Survival in the Era of Lung Cancer Screening.

OBJECTIVE: Up to 15% of lung cancer patients have multiple suspicious nodules. While some of these nodules may represent metastatic lung cancer, others represent synchronous multiple primary lung cancer (SMPLC). The incidence of SMPLC ranges from 0.8% to 8.4% and appears to be increasing. Inconsistent identification of SMPLC can be detrimental for patients who are misdiagnosed as having intrapulmonary metastasis and not offered stage-based treatment. We sought to identify the contemporary incidence of SMPLC at a tertiary institution. METHODS: From January 2018 to September 2019, patients who underwent lung cancer resection were retrospectively reviewed. Patients with SMPLC were identified using the modified Martini-Melamed criteria. RESULTS: During the 21-month period, 227 patients underwent lung cancer resection. There were 47 patients (20.7%) who had 119 pathologically confirmed SMPLC. Most patients had ipsilateral tumors (n = 24, 51.1%) with at least 1 adenocarcinoma (n = 40, 85.1%). Considering histologic subtyping, 38 (80.9%) had histologically distinct tumors. Overall and cancer-specific survival at 4 years was 86% and 90%, respectively. Only patients with 3 or more SMPLC had poor 4-year overall (P = 0.002) and cancer-specific survival (P = 0.043) compared with those with 2 SMPLC. Patient demographics, histology, tumor location, and highest pathologic staging did not affect survival outcomes. CONCLUSIONS: Using a strict inclusion criterion, the incidence of SMPLC is higher than previously reported. SMPLC patients have favorable survival outcomes, suggesting that they behave like primary lung cancer, not intrapulmonary metastasis. Awareness of SMPLC by thoracic surgeons is critical in optimizing outcomes in this patient population.

Subclonal Somatic Copy-Number Alterations Emerge and Dominate in Recurrent Osteosarcoma.

Multiple large-scale genomic profiling efforts have been undertaken in osteosarcoma to define the genomic drivers of tumorigenesis, therapeutic response, and disease recurrence. The spatial and temporal intratumor heterogeneity could also play a role in promoting tumor growth and treatment resistance. We conducted longitudinal whole-genome sequencing of 37 tumor samples from 8 patients with relapsed or refractory osteosarcoma. Each patient had at least one sample from a primary site and a metastatic or relapse site. Subclonal copy-number alterations were identified in all patients except one. In 5 patients, subclones from the primary tumor emerged and dominated at subsequent relapses. MYC gain/amplification was enriched in the treatment-resistant clones in 6 of 7 patients with multiple clones. Amplifications in other potential driver genes, such as CCNE1, RAD21, VEGFA, and IGF1R, were also observed in the resistant copy-number clones. A chromosomal duplication timing analysis revealed that complex genomic rearrangements typically occurred prior to diagnosis, supporting a macroevolutionary model of evolution, where a large number of genomic aberrations are acquired over a short period of time followed by clonal selection, as opposed to ongoing evolution. A mutational signature analysis of recurrent tumors revealed that homologous repair deficiency (HRD)-related SBS3 increases at each time point in patients with recurrent disease, suggesting that HRD continues to be an active mutagenic process after diagnosis. Overall, by examining the clonal relationships between temporally and spatially separated samples from patients with relapsed/refractory osteosarcoma, this study sheds light on the intratumor heterogeneity and potential drivers of treatment resistance in this disease. SIGNIFICANCE: The chemoresistant population in recurrent osteosarcoma is subclonal at diagnosis, emerges at the time of primary resection due to selective pressure from neoadjuvant chemotherapy, and is characterized by unique oncogenic amplifications.

Mapping the topography of spatial gene expression with interpretable deep learning.

Spatially resolved transcriptomics technologies provide high-throughput measurements of gene expression in a tissue slice, but the sparsity of this data complicates the analysis of spatial gene expression patterns such as gene expression gradients. We address these issues by deriving a topographic map of a tissue slice-analogous to a map of elevation in a landscape-using a novel quantity called the isodepth. Contours of constant isodepth enclose spatial domains with distinct cell type composition, while gradients of the isodepth indicate spatial directions of maximum change in gene expression. We develop GASTON, an unsupervised and interpretable deep learning algorithm that simultaneously learns the isodepth, spatial gene expression gradients, and piecewise linear functions of the isodepth that model both continuous gradients and discontinuous spatial variation in the expression of individual genes. We validate GASTON by showing that it accurately identifies spatial domains and marker genes across several biological systems. In SRT data from the brain, GASTON reveals gradients of neuronal differentiation and firing, and in SRT data from a tumor sample, GASTON infers gradients of metabolic activity and epithelial-mesenchymal transition (EMT)-related gene expression in the tumor microenvironment.

HATCHet2: clone- and haplotype-specific copy number inference from bulk tumor sequencing data.

Multi-region DNA sequencing of primary tumors and metastases from individual patients helps identify somatic aberrations driving cancer development. However, most methods to infer copy-number aberrations (CNAs) analyze individual samples. We introduce HATCHet2 to identify haplotype- and clone-specific CNAs simultaneously from multiple bulk samples. HATCHet2 introduces a novel statistic, the mirrored haplotype B-allele frequency (mhBAF), to identify mirrored-subclonal CNAs having different numbers of copies of parental haplotypes in different tumor clones. HATCHet2 also has high accuracy in identifying focal CNAs and extends the earlier HATCHet method in several directions. We demonstrate HATCHet2's improved accuracy using simulations and a single-cell sequencing dataset. HATCHet2 analysis of 50 prostate cancer samples from 10 patients reveals previously-unreported mirrored-subclonal CNAs affecting cancer genes.

Subclonal somatic copy number alterations emerge and dominate in recurrent osteosarcoma.

Multiple large-scale tumor genomic profiling efforts have been undertaken in osteosarcoma, however, little is known about the spatial and temporal intratumor heterogeneity and how it may drive treatment resistance. We performed whole-genome sequencing of 37 tumor samples from eight patients with relapsed or refractory osteosarcoma. Each patient had at least one sample from a primary site and a metastatic or relapse site. We identified subclonal copy number alterations in all but one patient. We observed that in five patients, a subclonal copy number clone from the primary tumor emerged and dominated at subsequent relapses. MYC gain/amplification was enriched in the treatment-resistant clone in 6 out of 7 patients with more than one clone. Amplifications in other potential driver genes, such as CCNE1, RAD21, VEGFA, and IGF1R, were also observed in the resistant copy number clones. Our study sheds light on intratumor heterogeneity and the potential drivers of treatment resistance in osteosarcoma.

Metrics to relate COVID-19 wastewater data to clinical testing dynamics.

Wastewater surveillance has emerged as a useful tool in the public health response to the COVID-19 pandemic. While wastewater surveillance has been applied at various scales to monitor population-level COVID-19 dynamics, there is a need for quantitative metrics to interpret wastewater data in the context of public health trends. 24-hour composite wastewater samples were collected from March 2020 through May 2021 from a Massachusetts wastewater treatment plant and SARS-CoV-2 RNA concentrations were measured using RT-qPCR. The relationship between wastewater copy numbers of SARS-CoV-2 gene fragments and COVID-19 clinical cases and deaths varies over time. We demonstrate the utility of three new metrics to monitor changes in COVID-19 epidemiology: (1) the ratio between wastewater copy numbers of SARS-CoV-2 gene fragments and clinical cases (WC ratio), (2) the time lag between wastewater and clinical reporting, and (3) a transfer function between the wastewater and clinical case curves. The WC ratio increases after key events, providing insight into the balance between disease spread and public health response. Time lag and transfer function analysis showed that wastewater data preceded clinically reported cases in the first wave of the pandemic but did not serve as a leading indicator in the second wave, likely due to increased testing capacity, which allows for more timely case detection and reporting. These three metrics could help further integrate wastewater surveillance into the public health response to the COVID-19 pandemic and future pandemics.

Horizontal gene transfer and adaptive evolution in bacteria.

Horizontal gene transfer (HGT) is arguably the most conspicuous feature of bacterial evolution. Evidence for HGT is found in most bacterial genomes. Although HGT can considerably alter bacterial genomes, not all transfer events may be biologically significant and may instead represent the outcome of an incessant evolutionary process that only occasionally has a beneficial purpose. When adaptive transfers occur, HGT and positive selection may result in specific, detectable signatures in genomes, such as gene-specific sweeps or increased transfer rates for genes that are ecologically relevant. In this Review, we first discuss the various mechanisms whereby HGT occurs, how the genetic signatures shape patterns of genomic variation and the distinct bioinformatic algorithms developed to detect these patterns. We then discuss the evolutionary theory behind HGT and positive selection in bacteria, and discuss the approaches developed over the past decade to detect transferred DNA that may be involved in adaptation to new environments.

Ivory poaching and the rapid evolution of tusklessness in African elephants.

Understanding the evolutionary consequences of wildlife exploitation is increasingly important as harvesting becomes more efficient. We examined the impacts of ivory poaching during the Mozambican Civil War (1977 to 1992) on the evolution of African savanna elephants (Loxodonta africana) in Gorongosa National Park. Poaching resulted in strong selection that favored tusklessness amid a rapid population decline. Survey data revealed tusk-inheritance patterns consistent with an X chromosome­linked dominant, male-lethal trait. Whole-genome scans implicated two candidate genes with known roles in mammalian tooth development (AMELX and MEP1a), including the formation of enamel, dentin, cementum, and the periodontium. One of these loci (AMELX) is associated with an X-linked dominant, male-lethal syndrome in humans that diminishes the growth of maxillary lateral incisors (homologous to elephant tusks). This study provides evidence for rapid, poaching-mediated selection for the loss of a prominent anatomical trait in a keystone species.

Metrics to relate COVID-19 wastewater data to clinical testing dynamics.

Wastewater surveillance has emerged as a useful tool in the public health response to the COVID-19 pandemic. While wastewater surveillance has been applied at various scales to monitor population-level COVID-19 dynamics, there is a need for quantitative metrics to interpret wastewater data in the context of public health trends. We collected 24-hour composite wastewater samples from March 2020 through May 2021 from a Massachusetts wastewater treatment plant and measured SARS-CoV-2 RNA concentrations using RT-qPCR. We show that the relationship between wastewater viral titers and COVID-19 clinical cases and deaths varies over time. We demonstrate the utility of three new metrics to monitor changes in COVID-19 epidemiology: (1) the ratio between wastewater viral titers and clinical cases (WC ratio), (2) the time lag between wastewater and clinical reporting, and (3) a transfer function between the wastewater and clinical case curves. We find that the WC ratio increases after key events, providing insight into the balance between disease spread and public health response. We also find that wastewater data preceded clinically reported cases in the first wave of the pandemic but did not serve as a leading indicator in the second wave, likely due to increased testing capacity. These three metrics could complement a framework for integrating wastewater surveillance into the public health response to the COVID-19 pandemic and future pandemics.

Deeply Altered Genome Architecture in the Endoparasitic Flowering Plant Sapria himalayana Griff. (Rafflesiaceae).

Despite more than 2,000-fold variation in genome size, key features of genome architecture are largely conserved across angiosperms. Parasitic plants have elucidated the many ways in which genomes can be modified, yet we still lack comprehensive genome data for species that represent the most extreme form of parasitism. Here, we present the highly modified genome of the iconic endophytic parasite Sapria himalayana Griff. (Rafflesiaceae), which lacks a typical plant body. First, 44% of the genes conserved in eurosids are lost in Sapria, dwarfing previously reported levels of gene loss in vascular plants. These losses demonstrate remarkable functional convergence with other parasitic plants, suggesting a common genetic roadmap underlying the evolution of plant parasitism. Second, we identified extreme disparity in intron size among retained genes. This includes a category of genes with introns longer than any so far observed in angiosperms, nearing 100 kb in some cases, and a second category of genes with exceptionally short or absent introns. Finally, at least 1.2% of the Sapria genome, including both genic and intergenic content, is inferred to be derived from host-to-parasite horizontal gene transfers (HGTs) and includes genes potentially adaptive for parasitism. Focused phylogenomic reconstruction of HGTs reveals a hidden history of former host-parasite associations involving close relatives of Sapria's modern hosts in the grapevine family. Our findings offer a unique perspective into how deeply angiosperm genomes can be altered to fit an extreme form of plant parasitism and demonstrate the value of HGTs as DNA fossils to investigate extinct symbioses.

Frequency-dependent selection can forecast evolution in Streptococcus pneumoniae.

Predicting how pathogen populations will change over time is challenging. Such has been the case with Streptococcus pneumoniae, an important human pathogen, and the pneumococcal conjugate vaccines (PCVs), which target only a fraction of the strains in the population. Here, we use the frequencies of accessory genes to predict changes in the pneumococcal population after vaccination, hypothesizing that these frequencies reflect negative frequency-dependent selection (NFDS) on the gene products. We find that the standardized predicted fitness of a strain, estimated by an NFDS-based model at the time the vaccine is introduced, enables us to predict whether the strain increases or decreases in prevalence following vaccination. Further, we are able to forecast the equilibrium post-vaccine population composition and assess the invasion capacity of emerging lineages. Overall, we provide a method for predicting the impact of an intervention on pneumococcal populations with potential application to other bacterial pathogens in which NFDS is a driving force.

Fine-Scale Haplotype Structure Reveals Strong Signatures of Positive Selection in a Recombining Bacterial Pathogen.

Identifying genetic variation in bacteria that has been shaped by ecological differences remains an important challenge. For recombining bacteria, the sign and strength of linkage provide a unique lens into ongoing selection. We show that derived alleles <300 bp apart in Neisseria gonorrhoeae exhibit more coupling linkage than repulsion linkage, a pattern that cannot be explained by limited recombination or neutrality as these couplings are significantly stronger for nonsynonymous alleles than synonymous alleles. This general pattern is driven by a small fraction of highly diverse genes, many of which exhibit evidence of interspecies horizontal gene transfer and an excess of intermediate frequency alleles. Extensive simulations show that two distinct forms of positive selection can create these patterns of genetic variation: directional selection on horizontally transferred alleles or balancing selection that maintains distinct haplotypes in the presence of recombination. Our results establish a framework for identifying patterns of selection in fine-scale haplotype structure that indicate specific ecological processes in species that recombine with distantly related lineages or possess coexisting adaptive haplotypes.

Genome-wide epistasis and co-selection study using mutual information.

Covariance-based discovery of polymorphisms under co-selective pressure or epistasis has received considerable recent attention in population genomics. Both statistical modeling of the population level covariation of alleles across the chromosome and model-free testing of dependencies between pairs of polymorphisms have been shown to successfully uncover patterns of selection in bacterial populations. Here we introduce a model-free method, SpydrPick, whose computational efficiency enables analysis at the scale of pan-genomes of many bacteria. SpydrPick incorporates an efficient correction for population structure, which adjusts for the phylogenetic signal in the data without requiring an explicit phylogenetic tree. We also introduce a new type of visualization of the results similar to the Manhattan plots used in genome-wide association studies, which enables rapid exploration of the identified signals of co-evolution. Simulations demonstrate the usefulness of our method and give some insight to when this type of analysis is most likely to be successful. Application of the method to large population genomic datasets of two major human pathogens, Streptococcus pneumoniae and Neisseria meningitidis, revealed both previously identified and novel putative targets of co-selection related to virulence and antibiotic resistance, highlighting the potential of this approach to drive molecular discoveries, even in the absence of phenotypic data.

Pervasive population genomic consequences of genome duplication in Arabidopsis arenosa.

Ploidy-variable species allow direct inference of the effects of chromosome copy number on fundamental evolutionary processes. While an abundance of theoretical work suggests polyploidy should leave distinct population genomic signatures, empirical data remains sparse. We sequenced ~300 individuals from 39 populations of Arabidopsis arenosa, a naturally diploid-autotetraploid species. We find that the impacts of polyploidy on population genomic processes are subtle yet pervasive, such as reduced efficiency of purifying selection, differences in linked selection and rampant gene flow from diploids. Initial masking of deleterious mutations, faster rates of nucleotide substitution and interploidy introgression likely conspire to shape the evolutionary potential of polyploids.

Quality Versus Quantity: The Potential Impact of Public Reporting of Hospital Safety for Complex Cancer Surgery.

OBJECTIVE: To estimate the potential mortality reduction if patients chose the safest hospitals for complex cancer surgery. BACKGROUND: Mortality after complex oncologic surgery is highly variable across hospitals, and directing patients away from unsafe hospitals could potentially improve survivorship. Hospital quality measures are becoming increasingly accessible at a time when patients are more engaged in choosing providers. It is currently unclear what information to share with patients to maximally capitalize on patient-centered realignment. METHODS: The National Cancer Database was queried for adults undergoing 5 complex cancer surgeries (pulmonary lobectomy, pneumonectomy, esophagectomy, gastrectomy, and colectomy) for a primary cancer between 2008 and 2012. Risk-standardized mortality rate (RSMR) methodology, currently used by Medicare-based hospital rating systems, was used to classify hospitals as "safest" and "least safe" by procedure. Patients were modeled moving from "least safe" to "safest" hospitals and the potential number of lives saved through patient realignment determined. As surgical volume has historically been used to distinguish safe hospitals, comparisons were made to models moving patients from low-volume to high-volume hospitals. RESULTS: A total of 292,040 patients were analyzed. In an optimally modeled scenario, realignment using RSMR would result in a greater number of lives saved (3592 vs 2161, P < 0.01) and require only 15 patients to change hospitals to save a life, compared to 78 patients using volume models (P < 0.01). CONCLUSIONS: Public reporting of hospital safety, specifically based on RSMR instead of volume, has the potential to lead to meaningful reductions in surgical mortality after complex cancer surgery, even in the setting of a modest patient realignment.

Defining the learning curve in robot-assisted thoracoscopic lobectomy.

BACKGROUND: Robot-assisted thoracoscopic lobectomy has been shown to be a safe approach to pulmonary lobectomy. This study sought to define, mathematically, the learning curve for RATS lobectomy. METHODS: Patients undergoing robot-assisted thoracoscopic lobectomy at a single institution from 2010 through 2016 were considered. Covariates included patient demographics, comorbidities, operating time, length of stay, estimated blood loss, and postoperative complications. A cumulative sum analysis of operating time was performed to define the learning curve. RESULTS: A total of 101 patients were included. Three distinct phases of the learning curve were identified: cases 1-22, cases 23-63, and cases 64-101. There was a statistically significant difference in operating time and estimated blood loss between phases 1 and 2 (P < .05, P = .016, respectively) and between phases 1 and 3 (P < .05, P = .006, respectively). There was no statistically significant difference in comorbidities, chest tube duration, length of stay, postoperative complications, or conversion rate across the learning curve. CONCLUSION: Based on operating time, the learning curve for robot-assisted thoracoscopic lobectomy is 22 cases, with mastery achieved after 63 cases. No differences in length of stay, chest tube duration, conversion rate, or complication rate were observed in the learning curve. Other factors not measured in this study may play a role in the learning process and warrant further study.

Prophylactic Ureteral Stenting in Laparoscopic Colectomy: Revisiting Traditional Practice.

BACKGROUND: Prophylactic placement of ureteral stents is performed during open colectomy to aid in ureteral identification and to enhance detection of injury. The effects of this practice in laparoscopic colectomy are unknown. This study compares outcomes of patients undergoing laparoscopic colectomy with and without prophylactic ureteral stenting. METHODS: A retrospective cohort study at a tertiary academic medical center was performed. The primary outcome measure was the incidence of ureteral injury. Secondary outcomes evaluated included mortality, length of stay, procedural duration, and new-onset urinary complication (hematuria, dysuria, and urinary tract infection). RESULTS: In 702 consecutive patients undergoing elective laparoscopic colectomy from 2013 to 2016, prophylactic stents were placed in 261 (37%) patients. Two ureteral injuries occurred (0.3%), both in patients who underwent ureteral stent placement (P = 0.07) and were found and repaired intraoperatively. There was no in-hospital mortality. When accounting for age-adjusted Charlson comorbidity score, procedural indication, gender, BMI, and extent of resection, no difference in hospital length of stay (P = 0.79) was noted comparing patients with and without stenting. However, stent placement prolonged operating time (P = 0.03) and increased the risk of new-onset urinary complications (P = 0.04). CONCLUSIONS: In this study, ureteral injuries only occurred in those with stent placement. Prophylactic ureteral stents in laparoscopic colectomy are associated with increased operative time and urologic morbidity. Owing to the low prevalence of ureteral injury in the elective setting and the increased risk of urinary complications, use of prophylactic ureteral stenting should be highly selective.