Selection and environmental adaptation along a path to speciation in the Tibetan frog Nanorana parkeri.

Tibetan frogs, Nanorana parkeri, are differentiated genetically but not morphologically along geographical and elevational gradients in a challenging environment, presenting a unique opportunity to investigate processes leading to speciation. Analyses of whole genomes of 63 frogs reveal population structuring and historical demography, characterized by highly restricted gene flow in a narrow geographic zone lying between matrilines West (W) and East (E). A population found only along a single tributary of the Yalu Zangbu River has the mitogenome only of E, whereas nuclear genes of W comprise 89-95% of the nuclear genome. Selection accounts for 579 broadly scattered, highly divergent regions (HDRs) of the genome, which involve 365 genes. These genes fall into 51 gene ontology (GO) functional classes, 14 of which are likely to be important in driving reproductive isolation. GO enrichment analyses of E reveal many overrepresented functional categories associated with adaptation to high elevations, including blood circulation, response to hypoxia, and UV radiation. Four genes, including DNAJC8 in the brain, TNNC1 and ADORA1 in the heart, and LAMB3 in the lung, differ in levels of expression between low- and high-elevation populations. High-altitude adaptation plays an important role in maintaining and driving continuing divergence and reproductive isolation. Use of total genomes enabled recognition of selection and adaptation in and between populations, as well as documentation of evolution along a stepped cline toward speciation

Identification of immune-related gene signatures to evaluate immunotherapeutic response in cancer patients using exploratory subgroup discovery

Phenotypic and genotypic heterogeneity are characteristic features of cancer patients. To tackle patients[trademark] heterogeneity, immune checkpoint inhibitors (ICIs) represent one of the most promising therapeutic approaches. However, approximately 50 percent of cancer patients that are eligible for treatment with ICIs will not respond well, which motivates the exploration of immunotherapy in combination with either targeted treatments or chemotherapy. Over the years, multiple patient stratification techniques have been developed to identify homogenous patient subgroups, although, matching patient subgroup to treatment option that can improve patients[trademark] health outcome remains a challenging task. We extend our exploratory subgroup discovery algorithm to identify patient subpopulations that can potentially benefit from immuno-targeted combination therapies or chemoimmunotherapy in five cancer types: Head and Neck Squamous Carcinoma (HNSC), Lung Adenocarcinoma (LUAD), Lung Squamous Carcinoma (LUSC), Skin Cutaneous Melanoma (SKCM) and Triple-Negative Breast Cancer (TNBC). We employ various regression models to identify immune-related gene signatures and drug targets that increase the likelihood of partial remission on combination therapies, either immunotargeted regimen or chemoimmunotherapy. Moreover, our pipelines can pinpoint adverse drug effects associated with predicted drug combinations. In addition, we uncovered distinct immune cell populations (T-cells, B-cells, Myeloid, NK-cells) for TNBC patients that differentiate patients with partial remission from patients with progressive disease after chemoimmunotherapy. Finally, we incorporate our methodological developments on Mutational Forks Formalism that enable an assessment of patient-specific flow by leveraging information from multiple single-nucleotide alterations to adjust the transitional likelihoods that are solely based on the canonical view of a disease. Our suit of methods can help to better select responders for combination therapies and improve health outcome for cancer patients with limited treatment options.Includes bibliographical references

Designing Rational Combination Strategies for Overcoming Drug Resistance in Breast Cancer

Drug resistance is a ubiquitous problem in the therapeutic management of breast cancer, even in the context of next-generation targeted therapies where only modest clinical improvements have been observed despite a tumors mutational load for a given target pathway or intrinsic subtype. To devise effective anti-cancer treatment strategies, new systems-based methods are needed to fully interpret factors underlying drug responses encompassing both genetic and non-genetic mechanisms. Here we developed two approaches towards designing novel combination strategies for overcoming drug resistance. First, using an unbiased chemoproteomics approach, we profiled kinome dynamics across breast cancer cells in response to various targeted therapies and identified signaling changes that correlate with drug sensitivity. This signaling map identified survival factors whose presence limits the efficacy of targeted therapies and revealed AURKA as a new co-targeting opportunity to enhance the therapeutic efficacy of PI3K-pathway inhibitors in breast cancer. Second, we used single-cell transcriptomics data and pharmacogenomic modeling as a way to inform upfront drug combinations based on systematic analysis of tumor subpopulation architectures. Using in silico and experimental approaches, our study provides an effective new framework to discover drug combinations capable of counteracting intrinsic cell variability by predicting drug responses of single cells within tumor cell subpopulations and systematically links transcriptional heterogeneity with drug actionability to optimize therapy combinations

Explainable artificial intelligence for patient stratification and drug repositioning

Enabling precision medicine requires developing robust patient stratification methods as well as drugs tailored to homogeneous subgroups of patients from a heterogeneous population. Developing de novo drugs is expensive and time consuming with an ultimately low FDA approval rate. These limitations make developing new drugs for a small portion of a disease population unfeasible. Therefore, drug repositioning is an essential alternative for developing new drugs for a disease subpopulation. There is a crucial need to develop data-driven approaches that find druggable homogeneous subgroups within the disease population and reposition the drugs for these subgroups. In this study, we developed an explainable AI approach for patient stratification and drug repositioning. Exploratory mining mimicking the trial recruitment process as well as network analysis were used to discover homogeneous subgroups within a disease population. For each subgroup, a biomedical network analysis was done to find the drugs that are most relevant to a given subgroup of patients. The set of candidate drugs for each subgroup was ranked using an aggregated drug score assigned to each drug. The method represents a human-in-the-loop framework, where medical experts use data-driven results to generate hypotheses and obtain insights into potential therapeutic candidates for patients who belong to a subgroup. To examine the validity of our method, we implemented our method on individual cancer types and on pan-cancer data to consider the inter- and intra-heterogeneity within a cancer type and among cancer types. Patients' phenotypic and genotypic data was utilized with a heterogeneous knowledge base because it gives a multi-view perspective for finding new indications for drugs outside of their original use. Our analysis of the top candidate drugs for the subgroups showed that most of these drugs are FDA-approved drugs for cancer, and others are non-cancer related, but have the potential to be repurposed for cancer. We have discovered novel cancer-related mechanisms that these drugs can target in different cancer types to reduce cancer treatment costs and improve patient survival. Further wet lab experiments to validate these findings are required prior to initiating clinical trials using these repurposed therapies.Includes bibliographical references

INVESTIGATING INVASION IN DUCTAL CARCINOMA IN SITU WITH TOPOGRAPHICAL SINGLE CELL GENOME SEQUENCING

Synchronous Ductal Carcinoma in situ (DCIS-IDC) is an early stage breast cancer invasion in which it is possible to delineate genomic evolution during invasion because of the presence of both in situ and invasive regions within the same sample. While laser capture microdissection studies of DCIS-IDC examined the relationship between the paired in situ (DCIS) and invasive (IDC) regions, these studies were either confounded by bulk tissue or limited to a small set of genes or markers. To overcome these challenges, we developed Topographic Single Cell Sequencing (TSCS), which combines laser-catapulting with single cell DNA sequencing to measure genomic copy number profiles from single tumor cells while preserving their spatial context. We applied TSCS to sequence 1,293 single cells from 10 synchronous DCIS patients. We also applied deep-exome sequencing to the in situ, invasive and normal tissues for the DCIS-IDC patients. Previous bulk tissue studies had produced several conflicting models of tumor evolution. Our data support a multiclonal invasion model, in which genome evolution occurs within the ducts and gives rise to multiple subclones that escape the ducts into the adjacent tissues to establish the invasive carcinomas. In summary, we have developed a novel method for single cell DNA sequencing, which preserves spatial context, and applied this method to understand clonal evolution during the transition between carcinoma in situ to invasive ductal carcinoma

Investigating Variation in Learning Processes in a FutureLearn MOOC

Studies on engagement and learning design in Massive Open Online Courses (MOOCs) have laid the groundwork for understanding how people learn in this relatively new type of informal learning environment. To advance our understanding of how people learn in MOOCs, we investigate the intersection between learning design and the temporal process of engagement in the course. This study investigates the detailed processes of engagement using educational process mining (EPM) in a FutureLearn science course (N = 2086 learners) and applying an established taxonomy of learning design to classify learning activities. The analyses were performed on three groups of learners categorised based upon their clicking behaviour. The process-mining results show at least one dominant pathway in each of the three groups, though multiple popular additional pathways were identified within each group. All three groups remained interested and engaged in the various learning and assessment activities. The findings from this study suggest that in the analysis of voluminous MOOC data there is value in first clustering learners and then investigating detailed progressions within each cluster that take the order and type of learning activities into account. The approach is promising because it provides insight into variation in behavioural sequences based on learners’ intentions for earning a course certificate. These insights can inform the targeting of analytics-based interventions to support learners and inform MOOC designers about adapting learning activities to different groups of learners based on their goals

Characterization of inter- and intratumoral heterogeneity and the differential immune microenvironment during malignant progression in meningiomas

Meningiomas are thought to arise from the arachnoid cells of the leptomeninx and make up the most common primary intracranial tumor in adults. They are usually benign, however in about 20 % of cases, tumors present with an aggressive phenotype and higher risk of recurrence. Risk stratification thereby remains challenging especially for NF2-mutated meningiomas, which make up about two thirds of all cases, as they can occur at the full spectrum of WHO grades in meningioma from 1 to 3. Recently, molecular profiling has gained importance for prognosis in meningioma with several classification systems that have been established mostly based on the DNA methylation of the tumors. However, the DNA methylation-based classification has not been extensively linked to phenotypic traits of the tumor. Nor have meningiomas been investigated regarding intratumoral subpopulations that may exist in parallel and may have different characteristics, especially regarding the stage of progression and ability to recur. In addition, the role of the immune microenvironment in meningiomas is poorly understood, despite the identification of an immune-enriched meningioma subgroup with beneficial outcome in two independent DNA methylation classification systems. In this dissertation, I investigated the consistency of subgroups initially defined on epigenomic level across molecular levels by comparison to transcriptomic and proteomic data. Further, I leveraged single nuclei transcriptomic profiling to dissect intertumoral differences in the expression profile specific to the tumor cell population depending on the tumor subgroup, and to investigate the abundance and phenotype of intratumoral tumor cell subpopulations across samples. Similarly, I analyzed the single nuclei transcriptomic data to characterize tumor-infiltrating immune cells with respect to their abundance and activation status. I furthermore correlated the differences in immune infiltration with the progression-free survival of patients by deconvoluting DNA methylation array data according to their cellular composition. These analyses underlined the coherence of epigenomic meningioma subgroups across transcriptome and proteome. Moreover, I identified six tumor cell subpopulations that were defined by distinct expression profiles und could be identified across samples at varying abundancies depending on the stage of progression. Similarly, I observed profound differences in infiltrating immune cells between tumor subgroups, with a significant enrichment of tumor-associated macrophages in a benign subgroup of NF2-mutated meningiomas as compared to more progressed tumors. In parallel to their abundancy, macrophages changed in activation between benign and malignant cases from an anti- to a pro-tumorigenic phenotype. The evaluation of progression-free survival data revealed a positive correlation to the proportion of infiltrating immune cells as estimated from epigenomic profiles. Altogether, these results highlight the role of multi-level molecular profiling for tumor grading in a paradigmatic, epidemiologically relevant tumor type. They further indicate an important role of tumor-infiltrating macrophages during meningioma progression with possible consequences for risk prediction as well as therapeutic targets in meningioma