PerPAS: Topology-Based Single Sample Pathway Analysis Method

Identification of intracellular pathways that play key roles in cancer progression and drug resistance is a prerequisite for developing targeted cancer treatments. The era of personalized medicine calls for computational methods that can function with one sample or very small set of samples. Developing such methods is challenging because standard statistical approaches pose several limiting assumptions, such as number of samples, that prevent their application when n approaches to one. We have developed a novel pathway analysis method called PerPAS to estimate pathway activity at a single sample level by integrating pathway topology and transcriptomics data. In addition, PerPAS is able to identify altered pathways between cancer and control samples as well as to identify key nodes that contribute to the pathway activity. In our case study using breast cancer data, we show that PerPAS can identify highly altered pathways that are associated with patient survival. PerPAS identified four pathways that were associated with patient survival and were successfully validated in three independent breast cancer cohorts. In comparison to two other pathway analysis methods that function at a single sample level, PerPAS had superior performance in both synthetic and breast cancer expression datasets. PerPAS is a free R package (http://csbi.ltdk.helsinki.fi/pub/czliu/perpas/).Peer reviewe

Fumaraasi-geenin merkitys tuumorien muodostuksessa

In this study, a predisposing gene for a recently characterized cancer syndrome, hereditary leiomyomatosis and renal cell cancer (HLRCC), was identified and the role of the gene was investigated in other familial cancers and in nonsyndromic tumorigenesis. HLRCC is a dominantly inherited disorder predisposing predominantly to uterine and skin leiomyomas, and also to renal cell cancer and uterine leiomyosarcoma. The disease gene was recently localized in Finnish families to 1q42-q43 by a genome-wide linkage search. Independently in the UK, a clinically similar condition, multiple cutaneous and uterine leiomyomata (MCUL), was linked to the same chromosomal region, strongly suggesting that HLRCC and MCUL are actually a single syndrome. Linkage results were confirmed by detecting loss of heterozygosity (LOH) at the disease locus in most of the patients' tumors, suggesting that this predisposing gene acts as a tumor suppressor. Through detailed investigation by genotyping of microsatellite markers and haplotype construction in Finnish and UK HLRCC/MCUL families we were able to narrow the disease locus down to 1.6 Mb. Extensive mutation screening of known and predicted transcripts in the target region resulted in identification of the HLRCC predisposing gene, fumarase (fumarate hydratase, FH). FH is a key enzyme in energy metabolism, catalyzing fumarate to malate in the tricarboxylic acid cycle (TCAC) in mitochondria. Germline alterations in FH segregating with the disease were detected in 25 of 42 HLRCC/MCUL families including whole-gene deletions, truncating small deletions/insertions and nonsense mutations, as well as substitutions or deletions of highly conserved amino acids. Biallelic inactivation was detected in almost all studied tumors of HLRCC patients. Furthermore, FH enzyme activity was reduced in the patients' normal tissues and was completely or virtually absent from tumors. Based on these findings, we extensively demonstrated that mutations in FH underlie the HLRCC/MCUL syndrome. In our studies of other familial cancers, evidence for involvement of FH defects was not found in familial prostate and breast cancers. To investigate the role of FH in sporadic tumorigenesis, we analyzed 652 lesions, including a series of 353 nonsyndromic counterparts of tumor types associated with HLRCC. Mutations in nonsyndromic tumors were rare and appeared to be limited to tumor types observed in the hereditary form of the disease. Biallelic inactivation of FH was detected in a uterine leiomyosarcoma, a cutaneous leiomyoma, a soft-tissue sarcoma, and in two uterine leiomyomas. In the uterine leiomyosarcoma and the cutaneous lesion FH mutations originated from the germline whereas the soft-tissue sarcoma harbored purely somatic changes. In uterine leiomyomas somatic mutations were detected in the two out of five tumors with LOH at the FH locus. Our findings demonstrate that FH inactivation is also involved in nonhereditary tumor development, and further support the hypothesis that FH acts as a tumor suppressor. The role of FH in predisposition to malignancies, renal cell carcinoma and leiomyosarcoma is important in the diagnosis and prevention of cancer among HLRCC patients. This study is of general clinical interest, because prior to our findings, little was known about the molecular genetics of uterine leiomyomas, the most common tumors of women.Perheet, joissa esiintyy poikkeuksellisen nuorella iällä paljon samankaltaisia syöpätyyppejä, ovat erittäin tärkeitä tutkimuskohteita syöpägeenien paikannus ja tunnistamistutkimuksissa. Syöpäperheissä tunnistetut syövälle altistavat geenit ovat usein kasvainalttiuden takana myös satunnaisesti esiintyvissä syövissä. Tässä työssä paikannettiin ja tunnistettiin aikaisemmin kuvattuun syöpäsyndroomaan, nimeltään perinnöllinen kohdun leiomyomatoosi ja munuaissyöpä (hereditary leiomyomatosis and renal cell cancer, HLRCC, tunnetaan myös nimellä multiple cutaneous and uterine leiomyomata, MCUL) syntyyn altistava geeni. Näissä perheissä esiintyy tyypillisesti hyvänlaatuisia kohdun ja ihon leiomyoomaa sekä joissain tapauksissa pahanlaatuista kohdun leiomyosarkoomaa sekä erittäin aggressiivista munuaissyöpää. Kasvaimet on havaittu varsin nuorella iällä, myoomat 10-47 -vuotiailla ja pahanlaatuiset syövät 16-48 -vuotiailla. Lisäksi tutkimme altistavan geenin merkitystä muissa perinnöllisissä syövissä sekä satunnaisesti esiintyvissä (sporadisissa) kasvaimissa. HLRCC syndroomalle altistavat geenivirheet löydettiin sitruunahappokierron entsyymiä koodaavasta geenistä, fumaraatti hydrataasista (fumaraasi, FH). Selvittääksemme fumaraasin yleistä roolia kasvainten kehityksessä analysoimme 652 ei-syndromista kasvainta, joista 353 olivat kasvaintyyppejä, joita esiintyy HLRCC syndroomassa. Fumaraasin mutaatiot olivat harvinaisia ja näyttivät rajoittuvan kasvaintyyppeihin, joita esiintyy HLRCC-syndroomassa. Molempien fumaraasi-alleelien inaktivoituminen havaittiin yhdessä kohdun leiomyosarkoomassa, yhdessä ihon leiomyoomassa, yhdessä pehmytkudossarkoomasssa ja kahdessa kohdun leiomyoomassa. Fumaarasin mutaatioiden tunnistaminen on tärkeää geenivirheen kantajien diagnoosin sekä HLRCC-sukujen riskihenkilöiden tunnistamiseksi sekä tarvittavan seurannan järjestämiseksi. Jos perheessä esiintyy erittäin paljon kohdun tai ihon myoomia, taustalla voi esiintyä fumaraasin mutaatio. Koska fumaraasin mutaation kantajilla on kohonnut riski saada munuaissyöpä, voidaan pahanlaatuisen munuaissyövän leviäminen mahdollisesti estää, jos se voidaan tunnistaa riittävän ajoissa. Tutkimustuloksilla on yleisempää kliinistä painoarvoa myös siksi, että nyt on ensimmäisen kerran tunnistettu geeni, joka altistaa sekä familiaalisille (perheittäin esiintyville) ja satunnaisille kohdun leiomyoomille. Kohdun leiomyoomat ovat lisääntymisiässä olevien naisten yleisimpiä kasvaimia. Vaikka nämä kasvaimet ovatkin hyvänlaatuisia, on niiden kliininen merkitys huomattava johtuen niiden yleisyydestä. Vähintään 25 % naisista on havaittu kliinisesti merkittäviä kohdun myoomia, ja joidenkin arvioiden mukaan esiintyvyys voi olla jopa 77 %. Vaikka useimmat myoomat ovat oireettomia, monet naiset kuitenkin kärsivät voimakkaista kuukautisvuodoista, kivusta ja jopa lisääntymishäiriöistä. Lisäksi on mahdollista, että myös satunnaisissa, yksittäisissä myoomissa, joissa on fumaraasin mutaatio, voi esiintyä kohonnutta pahanlaatuisuuden riski

Genetic effects on life-history traits in the Glanville fritillary butterfly

Background: Adaptation to local habitat conditions may lead to the natural divergence of populations in life-history traits such as body size, time of reproduction, mate signaling or dispersal capacity. Given enough time and strong enough selection pressures, populations may experience local genetic differentiation. The genetic basis of many life-history traits, and their evolution according to different environmental conditions remain however poorly understood. Methods: We conducted an association study on the Glanville fritillary butterfly, using material from five populations along a latitudinal gradient within the Baltic Sea region, which show different degrees of habitat fragmentation. We investigated variation in 10 principal components, cofounding in total 21 life-history traits, according to two environmental types, and 33 genetic SNP markers from 15 candidate genes. Results: We found that nine SNPs from five genes showed strong trend for trait associations (p-values under 0.001 before correction). These associations, yet nonsignificant after multiple test corrections, with a total number of 1,086 tests, were consistent across the study populations. Additionally, these nine genes also showed an allele frequency difference between the populations from the northern fragmented versus the southern continuous landscape. Discussion: Our study provides further support for previously described trait associations within the Glanville fritillary butterfly species across different spatial scales. Although our results alone are inconclusive, they are concordant with previous studies that identified these associations to be related to climatic changes or habitat fragmentation within the angstrom land population.Peer reviewe

qSNE: quadratic rate t-SNE optimizer with automatic parameter tuning for large datasets

Peer reviewe

Effects of ambient and preceding temperatures and metabolic genes on flight metabolism in the Glanville fritillary butterfly

Flight is essential for foraging, mate searching and dispersal in many insects, but flight metabolism in ectotherms is strongly constrained by temperature. Thermal conditions vary greatly in natural populations and may hence restrict fitness-related activities. Working on the Glanville fritillary butterfly (Melitaea cinxia), we studied the effects of temperature experienced during the first 2 days of adult life on flight metabolism, genetic associations between flight metabolic rate and variation in candidate metabolic genes, and genotype–temperature interactions. The maximal flight performance was reduced by 17% by 2 days of low ambient temperature (15 °C) prior to the flight trial, mimicking conditions that butterflies commonly encounter in nature. A SNP in phosphoglucose isomerase (Pgi) had a significant association on flight metabolic rate in males and a SNP in triosephosphate isomerase (Tpi) was significantly associated with flight metabolic rate in females. In the Pgi SNP, AC heterozygotes had higher flight metabolic rate than AA homozygotes following low preceding temperature, but the trend was reversed following high preceding temperature, consistent with previous results on genotype–temperature interaction for this SNP. We suggest that these results on 2-day old butterflies reflect thermal effect on the maturation of flight muscles. These results highlight the consequences of variation in thermal conditions on the time scale of days, and they contribute to a better understanding of the complex dynamics of flight metabolism and flight-related activities under conditions that are relevant for natural populations living under variable thermal conditions.Peer reviewe

Identification of sample-specific regulations using integrative network level analysis

Background: Histologically similar tumors even from the same anatomical position may still show high variability at molecular level hindering analysis of genome-wide data. Leveling the analysis to a gene regulatory network instead of focusing on single genes has been suggested to overcome the heterogeneity issue although the majority of the network methods require large datasets. Network methods that are able to function at a single sample level are needed to overcome the heterogeneity and sample size issues. Methods: We present a novel network method, Differentially Expressed Regulation Analysis (DERA) that integrates expression data to biological network information at a single sample level. The sample-specific networks are subsequently used to discover samples with similar molecular functions by identification of regulations that are shared between samples or are specific for a subgroup. Results: We applied DERA to identify key regulations in triple negative breast cancer (TNBC), which is characterized by lack of estrogen receptor, progesterone receptor and HER2 expression and has poorer prognosis than the other breast cancer subtypes. DERA identified 110 core regulations consisting of 28 disconnected subnetworks for TNBC. These subnetworks are related to oncogenic activity, proliferation, cancer survival, invasiveness and metastasis. Our analysis further revealed 31 regulations specific for TNBC as compared to the other breast cancer subtypes and thus form a basis for understanding TNBC. We also applied DERA to high-grade serous ovarian cancer (HGS-OvCa) data and identified several common regulations between HGS-OvCa and TNBC. The performance of DERA was compared to two pathway analysis methods GSEA and SPIA and our results shows better reproducibility and higher sensitivity in a small sample set. Conclusions: We present a novel method called DERA to identify subnetworks that are similarly active for a group of samples. DERA was applied to breast cancer and ovarian cancer data showing our method is able to identify reliable and potentially important regulations with high reproducibility. R package is available at http://csbi.ltdk.helsinki.fi/pub/czliu/DERA/.Peer reviewe

Multiple components of PKA and TGF-beta pathways are mutated in pseudomyxoma peritonei

Pseudomyxoma peritonei (PMP) is a subtype of mucinous adenocarcinoma mainly restricted to the peritoneal cavity and most commonly originating from the appendix. The genetic background of PMP is poorly understood and no targeted treatments are currently available for this fatal disease. While RAS signaling pathway is affected in most if not all PMP cases and over half of them also have a mutation in the GNAS gene, other genetic alterations and affected pathways are, to a large degree, poorly known. In this study, we sequenced whole coding genome of nine PMP tumors and paired normal tissues in order to identify additional, commonly mutated genes and signaling pathways affected in PMP. These exome sequencing results were validated with an ultra-deep amplicon sequencing method, leading to 14 validated variants. The validated results contain seven genes that contribute to the protein kinase A (PKA) pathway. PKA pathway, which also contains GNAS, is a major player of overproduction of mucin, which is the characteristic feature of PMP. In addition to PKA pathway, we identified mutations in six genes that belong to the transforming growth factor beta (TGF-beta) pathway, which is a key regulator of cell proliferation. Since either GNAS mutation or an alternative mutation in the PKA pathway was identified in 8/9 patients, inhibition of the PKA pathway might reduce mucin production in most of the PMP patients and potentially suppress disease progression.Peer reviewe

SePIA: RNA and small RNA sequence processing, integration, and analysis

Abstract Background Large-scale sequencing experiments are complex and require a wide spectrum of computational tools to extract and interpret relevant biological information. This is especially true in projects where individual processing and integrated analysis of both small RNA and complementary RNA data is needed. Such studies would benefit from a computational workflow that is easy to implement and standardizes the processing and analysis of both sequenced data types. Results We developed SePIA (Sequence Processing, Integration, and Analysis), a comprehensive small RNA and RNA workflow. It provides ready execution for over 20 commonly known RNA-seq tools on top of an established workflow engine and provides dynamic pipeline architecture to manage, individually analyze, and integrate both small RNA and RNA data. Implementation with Docker makes SePIA portable and easy to run. We demonstrate the workflow’s extensive utility with two case studies involving three breast cancer datasets. SePIA is straightforward to configure and organizes results into a perusable HTML report. Furthermore, the underlying pipeline engine supports computational resource management for optimal performance. Conclusion SePIA is an open-source workflow introducing standardized processing and analysis of RNA and small RNA data. SePIA’s modular design enables robust customization to a given experiment while maintaining overall workflow structure. It is available at http://anduril.org/sepia

Drug screening approach combines epigenetic sensitization with immunochemotherapy in cancer

Background The epigenome plays a key role in cancer heterogeneity and drug resistance. Hence, a number of epigenetic inhibitors have been developed and tested in cancers. The major focus of most studies so far has been on the cytotoxic effect of these compounds, and only few have investigated the ability to revert the resistant phenotype in cancer cells. Hence, there is a need for a systematic methodology to unravel the mechanisms behind epigenetic sensitization. Results We have developed a high-throughput protocol to screen non-simultaneous drug combinations, and used it to investigate the reprogramming potential of epigenetic inhibitors. We demonstrated the effectiveness of our protocol by screening 60 epigenetic compounds on diffuse large B-cell lymphoma (DLBCL) cells. We identified several histone deacetylase (HDAC) and histone methyltransferase (HMT) inhibitors that acted synergistically with doxorubicin and rituximab. These two classes of epigenetic inhibitors achieved sensitization by disrupting DNA repair, cell cycle, and apoptotic signaling. The data used to perform these analyses are easily browsable through our Results Explorer. Additionally, we showed that these inhibitors achieve sensitization at lower doses than those required to induce cytotoxicity. Conclusions Our drug screening approach provides a systematic framework to test non-simultaneous drug combinations. This methodology identified HDAC and HMT inhibitors as successful sensitizing compounds in treatment-resistant DLBCL. Further investigation into the mechanisms behind successful epigenetic sensitization highlighted DNA repair, cell cycle, and apoptosis as the most dysregulated pathways. Altogether, our method adds supporting evidence in the use of epigenetic inhibitors as sensitizing agents in clinical settings.Peer reviewe

Data integration to prioritize drugs using genomics and curated data

Background: Genomic alterations affecting drug target proteins occur in several tumor types and are prime candidates for patient-specific tailored treatments. Increasingly, patients likely to benefit from targeted cancer therapy are selected based on molecular alterations. The selection of a precision therapy benefiting most patients is challenging but can be enhanced with integration of multiple types of molecular data. Data integration approaches for drug prioritization have successfully integrated diverse molecular data but do not take full advantage of existing data and literature. Results: We have built a knowledge-base which connects data from public databases with molecular results from over 2200 tumors, signaling pathways and drug-target databases. Moreover, we have developed a data mining algorithm to effectively utilize this heterogeneous knowledge-base. Our algorithm is designed to facilitate retargeting of existing drugs by stratifying samples and prioritizing drug targets. We analyzed 797 primary tumors from The Cancer Genome Atlas breast and ovarian cancer cohorts using our framework. FGFR, CDK and HER2 inhibitors were prioritized in breast and ovarian data sets. Estrogen receptor positive breast tumors showed potential sensitivity to targeted inhibitors of FGFR due to activation of FGFR3. Conclusions: Our results suggest that computational sample stratification selects potentially sensitive samples for targeted therapies and can aid in precision medicine drug repositioning. Source code is available from http://csblcanges.fimm.fi/GOPredict/.Peer reviewe