Multi-omic dataset of patient-derived tumor organoids of neuroendocrine neoplasms.

BACKGROUND: Organoids are 3-dimensional experimental models that summarize the anatomical and functional structure of an organ. Although a promising experimental model for precision medicine, patient-derived tumor organoids (PDTOs) have currently been developed only for a fraction of tumor types. RESULTS: We have generated the first multi-omic dataset (whole-genome sequencing [WGS] and RNA-sequencing [RNA-seq]) of PDTOs from the rare and understudied pulmonary neuroendocrine tumors (n = 12; 6 grade 1, 6 grade 2) and provide data from other rare neuroendocrine neoplasms: small intestine (ileal) neuroendocrine tumors (n = 6; 2 grade 1 and 4 grade 2) and large-cell neuroendocrine carcinoma (n = 5; 1 pancreatic and 4 pulmonary). This dataset includes a matched sample from the parental sample (primary tumor or metastasis) for a majority of samples (21/23) and longitudinal sampling of the PDTOs (1 to 2 time points), for a total of n = 47 RNA-seq and n = 33 WGS. We here provide quality control for each technique and the raw and processed data as well as all scripts for genomic analyses to ensure an optimal reuse of the data. In addition, we report gene expression data and somatic small variant calls and describe how they were generated, in particular how we used WGS somatic calls to train a random forest classifier to detect variants in tumor-only RNA-seq. We also report all histopathological images used for medical diagnosis: hematoxylin and eosin-stained slides, brightfield images, and immunohistochemistry images of protein markers of clinical relevance. CONCLUSIONS: This dataset will be critical to future studies relying on this PDTO biobank, such as drug screens for novel therapies and experiments investigating the mechanisms of carcinogenesis in these understudied diseases.

Altered splicing machinery in lung carcinoids unveils NOVA1, PRPF8 and SRSF10 as novel candidates to understand tumor biology and expand biomarker discovery.

BACKGROUND: Lung neuroendocrine neoplasms (LungNENs) comprise a heterogeneous group of tumors ranging from indolent lesions with good prognosis to highly aggressive cancers. Carcinoids are the rarest LungNENs, display low to intermediate malignancy and may be surgically managed, but show resistance to radiotherapy/chemotherapy in case of metastasis. Molecular profiling is providing new information to understand lung carcinoids, but its clinical value is still limited. Altered alternative splicing is emerging as a novel cancer hallmark unveiling a highly informative layer. METHODS: We primarily examined the status of the splicing machinery in lung carcinoids, by assessing the expression profile of the core spliceosome components and selected splicing factors in a cohort of 25 carcinoids using a microfluidic array. Results were validated in an external set of 51 samples. Dysregulation of splicing variants was further explored in silico in a separate set of 18 atypical carcinoids. Selected altered factors were tested by immunohistochemistry, their associations with clinical features were assessed and their putative functional roles were evaluated in vitro in two lung carcinoid-derived cell lines. RESULTS: The expression profile of the splicing machinery was profoundly dysregulated. Clustering and classification analyses highlighted five splicing factors: NOVA1, SRSF1, SRSF10, SRSF9 and PRPF8. Anatomopathological analysis showed protein differences in the presence of NOVA1, PRPF8 and SRSF10 in tumor versus non-tumor tissue. Expression levels of each of these factors were differentially related to distinct number and profiles of splicing events, and were associated to both common and disparate functional pathways. Accordingly, modulating the expression of NOVA1, PRPF8 and SRSF10 in vitro predictably influenced cell proliferation and colony formation, supporting their functional relevance and potential as actionable targets. CONCLUSIONS: These results provide primary evidence for dysregulation of the splicing machinery in lung carcinoids and suggest a plausible functional role and therapeutic targetability of NOVA1, PRPF8 and SRSF10.

Druggable growth dependencies and tumor evolution analysis in patient-derived organoids of neuroendocrine neoplasms from multiple body sites.

Neuroendocrine neoplasms (NENs) comprise well-differentiated neuroendocrine tumors (NETs) and poorly differentiated neuroendocrine carcinomas (NECs). Treatment options for patients with NENs are limited, in part due to lack of accurate models. We establish patient-derived tumor organoids (PDTOs) from pulmonary NETs and derive PDTOs from an understudied subtype of NEC, large cell neuroendocrine carcinoma (LCNEC), arising from multiple body sites. PDTOs maintain the gene expression patterns, intra-tumoral heterogeneity, and evolutionary processes of parental tumors. Through hypothesis-driven drug sensitivity analyses, we identify ASCL1 as a potential biomarker for response of LCNEC to treatment with BCL-2 inhibitors. Additionally, we discover a dependency on EGF in pulmonary NET PDTOs. Consistent with these findings, we find that, in an independent cohort, approximately 50% of pulmonary NETs express EGFR. This study identifies an actionable vulnerability for a subset of pulmonary NETs, emphasizing the utility of these PDTO models.

Kidney Function and Risk of Renal Cell Carcinoma.

BACKGROUND: We evaluated the temporal association between kidney function, assessed by estimated glomerular filtration rate (eGFR), and the risk of incident renal cell carcinoma (RCC). We also evaluated whether eGFR could improve RCC risk discrimination beyond established risk factors. METHODS: We analyzed the UK Biobank cohort, including 463,178 participants of whom 1,447 were diagnosed with RCC during 5,696,963 person-years of follow-up. We evaluated the temporal association between eGFR and RCC risk using flexible parametric survival models, adjusted for C-reactive protein and RCC risk factors. eGFR was calculated from creatinine and cystatin C levels. RESULTS: Lower eGFR, an indication of poor kidney function, was associated with higher RCC risk when measured up to 5 years prior to diagnosis. The RCC HR per SD decrease in eGFR when measured 1 year before diagnosis was 1.26 [95% confidence interval (95% CI), 1.16-1.37], and 1.11 (95% CI, 1.05-1.17) when measured 5 years before diagnosis. Adding eGFR to the RCC risk model provided a small improvement in risk discrimination 1 year before diagnosis with an AUC of 0.73 (95% CI, 0.67-0.84) compared with the published model (0.69; 95% CI, 0.63-0.79). CONCLUSIONS: This study demonstrated that kidney function markers are associated with RCC risk, but the nature of these associations are consistent with reversed causality. Markers of kidney function provided limited improvements in RCC risk discrimination beyond established risk factors. IMPACT: eGFR may be of potential use to identify individuals in the extremes of the risk distribution.

Impact of diabetes and chronic kidney disease on active surveillance outcomes for small renal masses: A cohort study.

Introduction: The American Cancer Society estimates 79,000 individuals will be diagnosed with kidney cancer in 2022, most of which are initially found as small renal masses (SRMs). Proper management of SRM patients includes careful evaluation of risk factors such as medical comorbidities and renal function. To investigate the importance of these risk factors, we examined their effect on crossover to delayed intervention (DI) and overall survival (OS) in patients undergoing active surveillance (AS) for SRMs. Methods: This is an Institutional Review Board-approved retrospective analysis of AS patients presented at kidney tumor conferences with SRMs between 2007 and 2017. Univariable and multivariable logistic regression analyses were performed to determine how factors including estimated glomerular filtration rate (eGFR), diabetes, and chronic kidney disease are associated with DI and OS. Results: A total of 111 cases were reviewed. In general, AS patients were elderly and had significant comorbidities. On univariate analysis, intervention was more likely to occur in patients with a younger age (P = 0.01), better kidney function (P = 0.01), and higher tumor growth rates (GRs) (P = 0.02). Higher eGFR was associated with better survival (P = 0.03), while higher tumor GRs (P = 0.014), greater Charlson Comorbidity Index (P = 0.01), and larger tumors (P = 0.01) were associated with worse OS. Of the comorbidities, diabetes was found to be an independent predictor of worse OS (P = 0.01). Conclusions: Patient-level factors - such as diabetes and eGFR - are associated with the rate of DI and OS among SRM patients. Consideration of these factors may facilitate better AS protocols and improve patient outcomes for those with SRMs.

Multiomic analysis of malignant pleural mesothelioma identifies molecular axes and specialized tumor profiles driving intertumor heterogeneity.

Malignant pleural mesothelioma (MPM) is an aggressive cancer with rising incidence and challenging clinical management. Through a large series of whole-genome sequencing data, integrated with transcriptomic and epigenomic data using multiomics factor analysis, we demonstrate that the current World Health Organization classification only accounts for up to 10% of interpatient molecular differences. Instead, the MESOMICS project paves the way for a morphomolecular classification of MPM based on four dimensions: ploidy, tumor cell morphology, adaptive immune response and CpG island methylator profile. We show that these four dimensions are complementary, capture major interpatient molecular differences and are delimited by extreme phenotypes that-in the case of the interdependent tumor cell morphology and adapted immune response-reflect tumor specialization. These findings unearth the interplay between MPM functional biology and its genomic history, and provide insights into the variations observed in the clinical behavior of patients with MPM.

Mohs microsurgery for localized penile carcinoma: 10 year retrospective review of local recurrence rates and surgical complications.

PURPOSE: To investigate the local recurrence rates of men treated with Mohs microsurgery (MMS) for penile carcinoma. The secondary outcome was surgical complications from the MMS procedure or the subsequent reconstructive procedures. MATERIALS AND METHODS: All patients from 2010 to 2020 with penile carcinoma at our institution were seen in a multidisciplinary setting. Patients with Ta, Tis, T1, and T2 disease were considered candidates for MMS. Clinical and pathologic data were collected for analysis. Local recurrence rates were stratified by stage and complications reported per the Clavien-Dindo Grade. RESULTS: A total of 43 patients met inclusion criteria. The median age at diagnosis was 64 years. Stage distribution was Ta in 4.7%, Tis in 58.1%, T1a in 14.0%, T1b in 7.0%, and T2 in 16.3%. No patients had a positive surgical margin after MMS. The overall local recurrence rate was 2% (n = 1) at a median of 47 months. Local recurrence rates at 1, 3, and 5 years for Ta, Tis, and T1 patients were 0%. Local recurrence rates for T2 patients were 14% at 1 year. Complications occurred in 12% (n = 5), all of which were Clavien-Dindo ≤ III. CONCLUSIONS: MMS provides effective local control for Ta, Tis, and T1 penile cancer with an overall local recurrence rate of 2% and an acceptable complication rate. A multi-disciplinary team involving urologic oncology, reconstructive urology, and MMS is essential to patient management.

FSTruct: An FST -based tool for measuring ancestry variation in inference of population structure.

In model-based inference of population structure from individual-level genetic data, individuals are assigned membership coefficients in a series of statistical clusters generated by clustering algorithms. Distinct patterns of variability in membership coefficients can be produced for different groups of individuals, for example, representing different predefined populations, sampling sites or time periods. Such variability can be difficult to capture in a single numerical value; membership coefficient vectors are multivariate and potentially incommensurable across predefined groups, as the number of clusters over which individuals are distributed can vary among groups of interest. Further, two groups might share few clusters in common, so that membership coefficient vectors are concentrated on different clusters. We introduce a method for measuring the variability of membership coefficients of individuals in a predefined group, making use of an analogy between variability across individuals in membership coefficient vectors and variation across populations in allele frequency vectors. We show that in a model in which membership coefficient vectors in a population follow a Dirichlet distribution, the measure increases linearly with a parameter describing the variance of a specified component of the membership vector and does not depend on its mean. We apply the approach, which makes use of a normalized FST statistic, to data on inferred population structure in three example scenarios. We also introduce a bootstrap test for equivalence of two or more predefined groups in their level of membership coefficient variability. Our methods are implemented in the r package FSTruct.

Mathematical constraints on FST: multiallelic markers in arbitrarily many populations.

Interpretations of values of the FST measure of genetic differentiation rely on an understanding of its mathematical constraints. Previously, it has been shown that FST values computed from a biallelic locus in a set of multiple populations and FST values computed from a multiallelic locus in a pair of populations are mathematically constrained as a function of the frequency of the allele that is most frequent across populations. We generalize from these cases to report here the mathematical constraint on FST given the frequency M of the most frequent allele at a multiallelic locus in a set of multiple populations. Using coalescent simulations of an island model of migration with an infinitely-many-alleles mutation model, we argue that the joint distribution of FST and M helps in disentangling the separate influences of mutation and migration on FST. Finally, we show that our results explain a puzzling pattern of microsatellite differentiation: the lower FST in an interspecific comparison between humans and chimpanzees than in the comparison of chimpanzee populations. We discuss the implications of our results for the use of FST. This article is part of the theme issue 'Celebrating 50 years since Lewontin's apportionment of human diversity'.

Differential Orthopedia Homeobox expression in pulmonary carcinoids is associated with changes in DNA methylation.

Limited number of tumor types have been examined for Orthopedia Homeobox (OTP) expression. In pulmonary carcinoids, loss of expression is a strong indicator of poor prognosis. Here, we investigated OTP expression in 37 different tumor types, and the association between OTP expression and DNA methylation levels in lung neuroendocrine neoplasms. We analyzed publicly available multi-omics data (whole-exome-, whole-genome-, RNA sequencing and Epic 850K-methylation array) of 58 typical carcinoids, 27 atypical carcinoids, 69 large cell neuroendocrine carcinoma and 51 small cell lung cancer patients and TCGA (The Cancer Genome Atlas) data of 33 tumor types. 850K-methylation analysis was cross-validated using targeted pyrosequencing on 35 carcinoids. We report bimodality of OTP expression in carcinoids (OTPhigh vs OTPlow group, likelihood-ratio test P = 1.5 × 10-2 ), with the OTPhigh group specific to pulmonary carcinoids while absent from all other cohorts analyzed. Significantly different DNA methylation levels were observed between OTPhigh and OTPlow carcinoids in 12/34 OTP infinium probes (FDR < 0.05 and ß-value effect size > .2). OTPlow carcinoids harbor high DNA methylation levels as compared to OTPhigh carcinoids. OTPlow carcinoids showed a significantly worse overall survival (log-rank test P = .0052). Gene set enrichment analysis for somatically mutated genes associated with hallmarks of cancer showed robust enrichment of three hallmarks in the OTPlow group, that is, sustaining proliferative signaling, evading growth suppressor and genome instability and mutation. Together our data suggest that high OTP expression is a unique feature of pulmonary carcinoids with a favorable prognosis and that in poor prognostic patients, OTP expression is lost, most likely due to changes in DNA methylation levels.

A molecular phenotypic map of malignant pleural mesothelioma.

BACKGROUND: Malignant pleural mesothelioma (MPM) is a rare understudied cancer associated with exposure to asbestos. So far, MPM patients have benefited marginally from the genomics medicine revolution due to the limited size or breadth of existing molecular studies. In the context of the MESOMICS project, we have performed the most comprehensive molecular characterization of MPM to date, with the underlying dataset made of the largest whole-genome sequencing series yet reported, together with transcriptome sequencing and methylation arrays for 120 MPM patients. RESULTS: We first provide comprehensive quality controls for all samples, of both raw and processed data. Due to the difficulty in collecting specimens from such rare tumors, a part of the cohort does not include matched normal material. We provide a detailed analysis of data processing of these tumor-only samples, showing that all somatic alteration calls match very stringent criteria of precision and recall. Finally, integrating our data with previously published multiomic MPM datasets (n = 374 in total), we provide an extensive molecular phenotype map of MPM based on the multitask theory. The generated map can be interactively explored and interrogated on the UCSC TumorMap portal (https://tumormap.ucsc.edu/?p=RCG_MESOMICS/MPM_Archetypes ). CONCLUSIONS: This new high-quality MPM multiomics dataset, together with the state-of-art bioinformatics and interactive visualization tools we provide, will support the development of precision medicine in MPM that is particularly challenging to implement in rare cancers due to limited molecular studies.

Challenges in lung and thoracic pathology: molecular advances in the classification of pleural mesotheliomas.

The diagnosis and classification of malignant pleural mesothelioma (MPM) is extremely challenging; obtaining an accurate histopathological diagnosis of the different types and subtypes requires expert assessment and suitable biopsies that are not always available, which can leave doctors uncertain about the patient's diagnosis, sometimes resulting in a delay in the start of treatment. In this review, we discuss recent major advances in the molecular characterisation of MPM and their implications for histological classification. We detail what is known of the molecular landscape of MPM at the genomic, transcriptomic, and epigenomic levels, describe the similarities and dissimilarities of the multiple molecular classifications that have been proposed, and provide an overview of the current state of knowledge regarding inter- and intra-tumour heterogeneity. We also highlight the current gaps in knowledge and how addressing them would benefit classification, as well as the patients in general.

Epidemiology and survival outcome of adult kidney, bladder, and prostate rhabdomyosarcoma: A SEER database analysis.

Rhabdomyosarcoma (RMS) is rare in adulthood, accounting for 2%-5% of adult soft tissue tumors, and less than 20% occur in genitourinary organs. Given its rarity, survival data on adult kidney, bladder, and prostate RMSs is limited. In this population-based analysis, we performed an analysis of all adult RMS cases reported in Surveillance, Epidemiology, and End Results (SEER) database to understand prognostic factors among kidney, bladder, and prostate RMS. A query of the SEER database was performed from 1973 to 2016 for patients >18 of age with RMS. The final cohort consisted of 14 kidney, 35 bladder, and 21 prostate RMS cases in the adult population. Demographic, treatment, and survival data were obtained. Analysis was performed using Fisher's exact test, survival analysis, and model. The median (range) age of diagnosis for adult bladder RMS was 65 years old (19-84) compared to 52.5 (28-68) and 42 (19-87) for kidney and prostate (p = 0.007). About 78.6% of patients underwent surgical intervention. Five-year overall survival (OS) for adult kidney, bladder, and prostate RMS are 17.1% (2.9-41.6%), 22.2% (9.4-38.4%), and 33.0 (12.8-55.0%), respectively. OS was not statistically associated with primary site (p = 0.209). On multivariable analysis, compared to adult bladder RMS, kidney RMS had a higher incidence of mortality (HR: 2.16, 95% CI 1.03-4.53, p = 0.041). Incidence of mortality from prostate RMS was not significantly different from bladder RMS (HR: 0.70, 95% CI 0.30-1.65, p = 0.411). Extent of disease (HR: 5.17, 95% CI 2.09-12.79, p < 0.001) and older age (HR 1.03, 95% CI 1.01-1.04, p = 0.002) were adverse prognostic factors for OS. Overall survival at 5 years for adult kidney, bladder, and prostate RMS is poor. Localized disease and younger age are prognostic factors for improved outcomes in adult RMS. Hence, early diagnosis and intervention appear paramount to improved survival for this rare malignancy in adulthood.

A molecular map of lung neuroendocrine neoplasms.

BACKGROUND: Lung neuroendocrine neoplasms (LNENs) are rare solid cancers, with most genomic studies including a limited number of samples. Recently, generating the first multi-omic dataset for atypical pulmonary carcinoids and the first methylation dataset for large-cell neuroendocrine carcinomas led us to the discovery of clinically relevant molecular groups, as well as a new entity of pulmonary carcinoids (supra-carcinoids). RESULTS: To promote the integration of LNENs molecular data, we provide here detailed information on data generation and quality control for whole-genome/exome sequencing, RNA sequencing, and EPIC 850K methylation arrays for a total of 84 patients with LNENs. We integrate the transcriptomic data with other previously published data and generate the first comprehensive molecular map of LNENs using the Uniform Manifold Approximation and Projection (UMAP) dimension reduction technique. We show that this map captures the main biological findings of previous studies and can be used as reference to integrate datasets for which RNA sequencing is available. The generated map can be interactively explored and interrogated on the UCSC TumorMap portal (https://tumormap.ucsc.edu/?p=RCG_lungNENomics/LNEN). The data, source code, and compute environments used to generate and evaluate the map as well as the raw data are available, respectively, in a Nextjournal interactive notebook (https://nextjournal.com/rarecancersgenomics/a-molecular-map-of-lung-neuroendocrine-neoplasms/) and at the EMBL-EBI European Genome-phenome Archive and Gene Expression Omnibus data repositories. CONCLUSIONS: We provide data and all resources needed to integrate them with future LNENs transcriptomic studies, allowing meaningful conclusions to be drawn that will eventually lead to a better understanding of this rare understudied disease.

Needlestack: an ultra-sensitive variant caller for multi-sample next generation sequencing data.

The emergence of next-generation sequencing (NGS) has revolutionized the way of reaching a genome sequence, with the promise of potentially providing a comprehensive characterization of DNA variations. Nevertheless, detecting somatic mutations is still a difficult problem, in particular when trying to identify low abundance mutations, such as subclonal mutations, tumour-derived alterations in body fluids or somatic mutations from histological normal tissue. The main challenge is to precisely distinguish between sequencing artefacts and true mutations, particularly when the latter are so rare they reach similar abundance levels as artefacts. Here, we present needlestack, a highly sensitive variant caller, which directly learns from the data the level of systematic sequencing errors to accurately call mutations. Needlestack is based on the idea that the sequencing error rate can be dynamically estimated from analysing multiple samples together. We show that the sequencing error rate varies across alterations, illustrating the need to precisely estimate it. We evaluate the performance of needlestack for various types of variations, and we show that needlestack is robust among positions and outperforms existing state-of-the-art method for low abundance mutations. Needlestack, along with its source code is freely available on the GitHub platform: https://github.com/IARCbioinfo/needlestack.

EURACAN/IASLC Proposals for Updating the Histologic Classification of Pleural Mesothelioma: Towards a More Multidisciplinary Approach.

INTRODUCTION: Molecular and immunologic breakthroughs are transforming the management of thoracic cancer, although advances have not been as marked for malignant pleural mesothelioma where pathologic diagnosis has been essentially limited to three histologic subtypes. METHODS: A multidisciplinary group (pathologists, molecular biologists, surgeons, radiologists, and oncologists), sponsored by European Network for Rare Adult Solid Cancers/International Association for the Study of Lung Cancer, met in 2018 to critically review the current classification. RESULTS: Recommendations include: (1) classification should be updated to include architectural patterns and stromal and cytologic features that refine prognostication; (2) subject to data accrual, malignant mesothelioma in situ could be an additional category; (3) grading of epithelioid malignant pleural mesotheliomas should be routinely undertaken; (4) favorable/unfavorable histologic characteristics should be routinely reported; (5) clinically relevant molecular data (programmed death ligand 1, BRCA 1 associated protein 1 [BAP1], and cyclin dependent kinase inhibitor 2A) should be incorporated into reports, if undertaken; (6) other molecular data should be accrued as part of future trials; (7) resection specimens (i.e., extended pleurectomy/decortication and extrapleural pneumonectomy) should be pathologically staged with smaller specimens being clinically staged; (8) ideally, at least three separate areas should be sampled from the pleural cavity, including areas of interest identified on pre-surgical imaging; (9) image-acquisition protocols/imaging terminology should be standardized to aid research/refine clinical staging; (10) multidisciplinary tumor boards should include pathologists to ensure appropriate treatment options are considered; (11) all histologic subtypes should be considered potential candidates for chemotherapy; (12) patients with sarcomatoid or biphasic mesothelioma should not be excluded from first-line clinical trials unless there is a compelling reason; (13) tumor subtyping should be further assessed in relation to duration of response to immunotherapy; and (14) systematic screening of all patients for germline mutations is not recommended, in the absence of a family history suspicious for BAP1 syndrome. CONCLUSIONS: These multidisciplinary recommendations for pathology classification and application will allow more informative pathologic reporting and potential risk stratification, to support clinical practice, research investigation and clinical trials.

Redefining malignant pleural mesothelioma types as a continuum uncovers immune-vascular interactions.

BACKGROUND: Malignant Pleural Mesothelioma (MPM) is an aggressive disease related to asbestos exposure, with no effective therapeutic options. METHODS: We undertook unsupervised analyses of RNA-sequencing data of 284 MPMs, with no assumption of discreteness. Using immunohistochemistry, we performed an orthogonal validation on a subset of 103 samples and a biological replication in an independent series of 77 samples. FINDINGS: A continuum of molecular profiles explained the prognosis of the disease better than any discrete model. The immune and vascular pathways were the major sources of molecular variation, with strong differences in the expression of immune checkpoints and pro-angiogenic genes; the extrema of this continuum had specific molecular profiles: a "hot" bad-prognosis profile, with high lymphocyte infiltration and high expression of immune checkpoints and pro-angiogenic genes; a "cold" bad-prognosis profile, with low lymphocyte infiltration and high expression of pro-angiogenic genes; and a "VEGFR2+/VISTA+" better-prognosis profile, with high expression of immune checkpoint VISTA and pro-angiogenic gene VEGFR2. We validated the gene expression levels at the protein level for a subset of five selected genes belonging to the immune and vascular pathways (CD8A, PDL1, VEGFR3, VEGFR2, and VISTA), in the validation series, and replicated the molecular profiles as well as their prognostic value in the replication series. INTERPRETATION: The prognosis of MPM is best explained by a continuous model, which extremes show specific expression patterns of genes involved in angiogenesis and immune response.

Coalescent Theory of Migration Network Motifs.

Natural populations display a variety of spatial arrangements, each potentially with a distinctive impact on genetic diversity and genetic differentiation among subpopulations. Although the spatial arrangement of populations can lead to intricate migration networks, theoretical developments have focused mainly on a small subset of such networks, emphasizing the island-migration and stepping-stone models. In this study, we investigate all small network motifs: the set of all possible migration networks among populations subdivided into at most four subpopulations. For each motif, we use coalescent theory to derive expectations for three quantities that describe genetic variation: nucleotide diversity, FST, and half-time to equilibrium diversity. We describe the impact of network properties on these quantities, finding that motifs with a high mean node degree have the largest nucleotide diversity and the longest time to equilibrium, whereas motifs with low density have the largest FST. In addition, we show that the motifs whose pattern of variation is most strongly influenced by loss of a connection or a subpopulation are those that can be split easily into disconnected components. We illustrate our results using two example data sets-sky island birds of genus Sholicola and Indian tigers-identifying disturbance scenarios that produce the greatest reduction in genetic diversity; for tigers, we also compare the benefits of two assisted gene flow scenarios. Our results have consequences for understanding the effect of geography on genetic diversity, and they can assist in designing strategies to alter population migration networks toward maximizing genetic variation in the context of conservation of endangered species.

GST' , Jost's D, and FST are similarly constrained by allele frequencies: A mathematical, simulation, and empirical study.

Statistics GST' and Jost's D have been proposed for replacing FST as measures of genetic differentiation. A principal argument in favour of these statistics is the independence of their maximal values with respect to the subpopulation heterozygosity HS , a property not shared by FST . Nevertheless, it has been unclear if these alternative differentiation measures are constrained by other aspects of the allele frequencies. Here, for biallelic markers, we study the mathematical properties of the maximal values of GST' and D, comparing them to those of FST . We show that GST' and D exhibit the same peculiar frequency-dependence phenomena as FST , including a maximal value as a function of the frequency of the most frequent allele that lies well below one. Although the functions describing GST' , D, and FST in terms of the frequency of the most frequent allele are different, the allele frequencies that maximize them are identical. Moreover, we show using coalescent simulations that when taking into account the specific maximal values of the three statistics, their behaviours become similar across a large range of migration rates. We use our results to explain two empirical patterns: the similar values of the three statistics among North American wolves, and the low D values compared to GST' and FST in Atlantic salmon. The results suggest that the three statistics are often predictably similar, so that they can make quite similar contributions to data analysis. When they are not similar, the difference can be understood in relation to features of genetic diversity.

Use of stochastic patch occupancy models in the California red-legged frog for Bayesian inference regarding past events and future persistence.

Assessing causes of population decline is critically important to management of threatened species. Stochastic patch occupancy models (SPOMs) are popular tools for examining spatial and temporal dynamics of populations when presence-absence data in multiple habitat patches are available. We developed a Bayesian Markov chain method that extends existing SPOMs by focusing on past environmental changes that may have altered occupancy patterns prior to the beginning of data collection. Using occupancy data from 3 creeks, we applied the method to assess 2 hypothesized causes of population decline-in situ die-off and residual impact of past source population loss-in the California red-legged frog. Despite having no data for the 20-30 years between the hypothetical event leading to population decline and the first data collected, we were able to discriminate among hypotheses, finding evidence that in situ die-off increased in 2 of the creeks. Although the creeks had comparable numbers of occupied segments, owing to different extinction-colonization dynamics, our model predicted an 8-fold difference in persistence probabilities of their populations to 2030. Adding a source population led to a greater predicted persistence probability than did decreasing the in situ die-off, emphasizing that reversing the deleterious impacts of a disturbance may not be the most efficient management strategy. We expect our method will be useful for studying dynamics and evaluating management strategies of many species.

Uso de Modelos Estocásticos de Ocupación de Fragmentos de la Rana Californiana de Patas Rojas para la Inferencia Bayesiana con Respecto a Eventos Pasados y su Persistencia en el Futuro Resumen La evaluación de las causas de la declinación poblacional es de importancia crítica para el manejo de especies amenazadas. Los modelos estocásticos de ocupación de parches (SPOMs, en inglés) son herramientas populares para examinar las dinámicas espaciales y temporales de las poblaciones cuando están disponibles los datos de presencia-ausencia para múltiples parches de hábitat. Desarrollamos un método bayesiano de cadena de Markov que extiende a los SPOMs existentes al enfocarse en los cambios ambientales pasados que podrían haber alterado los patrones de ocupación previos al inicio de la recolección de datos. Con los datos de ocupación de tres arroyos, aplicamos este método para evaluar dos causas hipotéticas de la declinación poblacional - muerte in situ e impacto residual de causas anteriores de pérdida de una poblacion fuente - de la rana californiana de patas rojas. A pesar de no tener datos para 20 - 30 años entre el evento hipotético que derivó en la declinación poblacional y los primeros datos recolectados, pudimos discriminar entre las hipótesis, encontrando evidencia de que la muerte in situ incrementó en dos de los arroyos. Aunque los arroyos tuvieron un número comparable de segmentos ocupados, debido a diferentes dinámicas de colonización - extinción, nuestro modelo predijo una diferencia ocho veces mayor en las probabilidades de persistencia de las poblaciones hasta el 2030. La suma de una población fuente resultó en una mayor probabilidad de persistencia pronosticada que con la reducción de la muerte in situ, enfatizando que la reversión de los impactos dañinos de una perturbación puede no ser la mejor estrategia de manejo. Esperamos que nuestro método sea útil para el estudio de las dinámicas y para la evaluación de las estrategias de manejo de muchas especies.