A Panel-Based Mutational Signature of Mismatch Repair Deficiency is Associated With Durable Response to Pembrolizumab in Metastatic Castration-Resistant Prostate Cancer

INTRODUCTION/BACKGROUND

Immune checkpoint inhibitors (ICIs) have limited efficacy in prostate cancer (PCa). Better biomarkers are needed to predict responses to ICIs. We sought to demonstrate that a panel-based mutational signature identifies mismatch repair (MMR) deficient (MMRd) PCa and is a biomarker of response to pembrolizumab.

PATIENTS AND METHODS

Clinico-genomic data was obtained for 2664 patients with PCa sequenced at Dana-Farber Cancer Institute (DFCI) and Memorial Sloan Kettering (MSK). Clinical outcomes were collected for patients with metastatic castration-resistant PCa (mCRPC) treated with pembrolizumab at DFCI. SigMA was used to characterize tumors as MMRd or MMR proficient (MMRp). The concordance between MMRd with microsatellite instability (MSI-H) was assessed. Radiographic progression-free survival (rPFS) and overall survival (OS) were collected for patients treated with pembrolizumab. Event-time distributions were estimated using Kaplan-Meier methodology.

RESULTS

Across both cohorts, 100% (DFCI: 12/12; MSK: 43/43) of MSI-H tumors were MMRd. However, 14% (2/14) and 9.1% (6/66) of MMRd tumors in the DFCI and MSK cohorts respectively were microsatellite stable (MSS), and 26% (17/66) were MSI-indeterminate in the MSK cohort. Among patients treated with pembrolizumab, those with MMRd (n = 5) versus MMRp (n = 14) mCRPC experienced markedly improved rPFS (HR = 0.088, 95% CI: 0.011-0.70; P = .0064) and OS (HR = 0.11, 95% CI: 0.014-0.80; P = .010) from start of treatment. Four patients with MMRd experienced remissions of >= 2.5 years.

CONCLUSION

SigMA detects additional cases of MMRd as compared to MSI testing in PCa and identifies patients likely to experience durable response to pembrolizumab.

Systematic comparison of genome information processing and boundary recognition tools used for genomic island detection

Genomic islands are fragments of foreign DNA that are found in bacterial and archaeal genomes, and are typically associated with symbiosis or pathogenesis. While numerous genomic island detection methods have been proposed, there has been limited evaluation of the efficiency of the genome information processing and boundary recognition tools. In this study, we conducted a review of the statistical methods involved in genomic signatures, host signature extraction, informative signature selection, divergence measures, and boundary detection steps in genomic island prediction. We compared the performances of these methods on simulated experiments using alien fragments obtained from both artificial and real genomes. Our results indicate that among the nine genomic signatures evaluated, genomic signature frequency and full probability performed the best. However, their performance declined when normalized to their expectations and variances, such as Z-score and composition vector. Based on our experiments of the E. coli genome, we found that the confidence intervals of the window variances achieved the best performance in the signature extraction of the host, with the best confidence interval being 1.5-2 times the standard error. Ordered kurtosis was most effective in selecting informative signatures from a single genome, without requiring prior knowledge from other datasets. Among the three divergence measures evaluated, the two-sample t-test was the most successful, and a non-overlapping window with a small eye window (size 2) was best suited for identifying compositionally distinct regions. Finally, the maximum of the Markovian Jensen-Shannon divergence score, in terms of GC-content bias, was found to make boundary detection faster while maintaining a similar error rate.

Identification of conserved genomic signatures specific to Bifidobacterium species colonising the human gut

Bifidobacterium species are known for their ability to inhabit various habitats and are often regarded as the first colonisers of the human gut. In the present work, we have used comparative genomics to identify conserved genomic signatures specific to Bifidobacterium species associated with the human gut. Our approach discovered five genomic signatures with varying lengths and confidence. Among the predicted five signatures, a 1790 bp multi-drug resistance (MDR) signature was found to be remarkably specific to only those species that can colonise the human gut. The signature codes for a membrane transport protein belonging to the major facilitator superfamily (MFS) generally involved in MDR. Phylogenetic analyses of the MDR signature suggest a lineage-specific evolution of the MDR signature in bifidobacteria colonising the human gut. Functional annotation led to the discovery of two conserved domains in the protein; a catalytic MFS domain involved in the efflux of drugs and toxins, and a regulatory cystathionine-β-synthase (CBS) domain that can interact with adenosyl-carriers. Molecular docking simulation performed with the modelled tertiary structure of the MDR signature revealed the putative functional role of the covalently linked domains. The MFS domain displayed a high affinity towards various protein synthesis inhibitor antibiotics and human bile acids, whereas the C-terminally linked CBS domain exhibited favourable binding with molecular structures of ATP and AMP. Therefore, we believe that the predicted signature represents a niche-specific survival trait involved in bile and antibiotic resistance, imparting an adaptive advantage to the Bifidobacterium species colonising the human gut.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-023-03492-4.

INSIDER: alignment-free detection of foreign DNA sequences

External DNA sequences can be inserted into an organism's genome either through natural processes such as gene transfer, or through targeted genome engineering strategies. Being able to robustly identify such foreign DNA is a crucial capability for health and biosecurity applications, such as anti-microbial resistance (AMR) detection or monitoring gene drives. This capability does not exist for poorly characterised host genomes or with limited information about the integrated sequence. To address this, we developed the INserted Sequence Information DEtectoR (INSIDER). INSIDER analyses whole genome sequencing data and identifies segments of potentially foreign origin by their significant shift in k-mer signatures. We demonstrate the power of INSIDER to separate integrated DNA sequences from normal genomic sequences on a synthetic dataset simulating the insertion of a CRISPR-Cas gene drive into wild-type yeast. As a proof-of-concept, we use INSIDER to detect the exact AMR plasmid in whole genome sequencing data from a Citrobacter freundii patient isolate. INSIDER streamlines the process of identifying integrated DNA in poorly characterised wild species or when the insert is of unknown origin, thus enhancing the monitoring of emerging biosecurity threats.

The Association between Immune Infiltration and Clinical Phenotypes and Prognosis of Prostate Cancer

Background:

Some evidences show that immune infiltration is closely related to the clinical outcomes in cancers such as colorectal cancer. However, previous studies have not explained the diversity of cell types that make up the immune response. In particular, although some studies and reviews have shown that immunotherapy is important for cancer treatment, few studies have elucidated the relationship between prostate cancer (PCa) phenotype and immune infiltration.

Objectives:

In this study, we analyzed whether different types of tumor-infiltrating immune cells would affect the clinical phenotypes and survival of PCa based on a deconvolution algorithm and annotated gene expression profiles.

Materials and Methods:

The 22 subsets of immune cells inferred by CIBERSORT and the infiltration abundance of 6 immune cells calculated by TIMER were used to determine the associations between them and the PCa traits and survival response. In addition, the survival tree models were constructed to classify PCa patients into four subtypes, and the traits and prognosis were compared among these subtypes.

Results:

As a result, we found that some PCa patients with high death risk lacking immune infiltration were related to the poor prognosis. For the cell subsets studied and subtypes analysis, a low proportion of mast resting cells and T-cells follicular helper exhibited the obvious association with poor outcome.

Conclusions:

In summary, our study suggested the differences in the cellular composition of the immune infiltrate in PCa, and these differences might be important determinants for PCa traits and prognosis.

Placental genomic and epigenomic signatures associated with infant birth weight highlight mechanisms involved in collagen and growth factor signaling

Birth weight (BW) represents an important clinical and toxicological measure, indicative of the overall health of the newborn as well as potential risk for later-in-life outcomes. BW can be influenced by endogenous and exogenous factors and is known to be heavily impacted in utero by the health and function of the placenta. An aspect that remains understudied is the influence of genomic and epigenomic programming within the placenta on infant BW. To address this gap, we set out to test the hypothesis that genes involved in critical placental cell signaling are associated with infant BW, and are likely regulated, in part, through epigenetic mechanisms based on microRNA (miRNA) mediation. This study leveraged a robust dataset based on 390 infants born at low gestational age (ranged 23-27 weeks) to evaluate genome-wide expression profiles of both mRNAs and miRNAs in placenta tissues and relate these to infant BW. A total of 254 mRNAs and 268 miRNAs were identified as associated with BW, the majority of which showed consistent associations across placentas derived from both males and females. BW-associated mRNAs were found to be enriched for important biological pathways, including glycoprotein VI (the major receptor for collagen), human growth, and hepatocyte growth factor signaling, a portion of which were predicted to be regulated by BW-associated miRNAs. These miRNA-regulated pathways highlight key mechanisms potentially linking endogenous/exogenous factors to changes in birth outcomes that may be deleterious to infant and later-in-life health.

2SigFinder: the combined use of small-scale and large-scale statistical testing for genomic island detection from a single genome

BACKGROUND

Genomic islands are associated with microbial adaptations, carrying genomic signatures different from the host. Some methods perform an overall test to identify genomic islands based on their local features. However, regions of different scales will display different genomic features.

RESULTS

We proposed here a novel method "2SigFinder ", the first combined use of small-scale and large-scale statistical testing for genomic island detection. The proposed method was tested by genomic island boundary detection and identification of genomic islands or functional features of real biological data. We also compared the proposed method with the comparative genomics and composition-based approaches. The results indicate that the proposed 2SigFinder is more efficient in identifying genomic islands.

CONCLUSIONS

From real biological data, 2SigFinder identified genomic islands from a single genome and reported robust results across different experiments, without annotated information of genomes or prior knowledge from other datasets. 2SigHunter identified 25 Pathogenicity, 1 tRNA, 2 Virulence and 2 Repeats from 27 Pathogenicity, 1 tRNA, 2 Virulence and 2 Repeats, and detected 101 Phage and 28 HEG out of 130 Phage and 36 HEGs in S. enterica Typhi CT18, which shows that it is more efficient in detecting functional features associated with GIs.

Identification of an immunotherapy-responsive molecular subtype of bladder cancer

Background

Although various molecular subtypes of bladder cancer (BC) have been investigated, most of these studies have focused on muscle-invasive BC (MIBC). A few studies have investigated non-muscle-invasive BC (NMIBC) or NMIBC and MIBC together, but none has classified progressive NMIBC or immune checkpoint inhibitor (ICI)-based therapeutic responses in early-stage BC patients.

Methods

A total of 1,934 samples from seven patient cohorts were used. We performed unsupervised hierarchical clustering to stratify patients into distinct subgroups and constructed a classifier by applying SAM/PAM algorithms. We then investigated the association between molecular subtypes and immunotherapy responsiveness using various statistical methods.

Findings

We explored large-scale genomic datasets encompassing NMIBC and MIBC, redefining four distinct molecular subtypes, including a subgroup containing progressive NMIBC and MIBC with poor prognosis that would benefit from ICI treatment. This subgroup showed poor progression-free survival with the distinct features of high mutation load, activated cell cycle, and inhibited TGFβ signalling. Importantly, we verified that BC patients with this subtype were significantly responsive to an anti-PD-L1 agent in the IMvigor210 cohort.

Interpretation

Our results reveal an immunotherapeutic option for ICI treatment of highly progressive NMIBC and MIBC with poor prognosis.

Funding

This research was supported by the National Research Foundation of Korea grant funded by the Korean government, a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute, funded by the Ministry of Health and Welfare, Republic of Korea, and a grant from the KRIBB Research Initiative Program.

ML-DSP: Machine Learning with Digital Signal Processing for ultrafast, accurate, and scalable genome classification at all taxonomic levels

Background

Although software tools abound for the comparison, analysis, identification, and classification of genomic sequences, taxonomic classification remains challenging due to the magnitude of the datasets and the intrinsic problems associated with classification. The need exists for an approach and software tool that addresses the limitations of existing alignment-based methods, as well as the challenges of recently proposed alignment-free methods.

Results

We propose a novel combination of supervised Machine Learning with Digital Signal Processing, resulting in ML-DSP: an alignment-free software tool for ultrafast, accurate, and scalable genome classification at all taxonomic levels. We test ML-DSP by classifying 7396 full mitochondrial genomes at various taxonomic levels, from kingdom to genus, with an average classification accuracy of >97%.

A quantitative comparison with state-of-the-art classification software tools is performed, on two small benchmark datasets and one large 4322 vertebrate mtDNA genomes dataset. Our results show that ML-DSP overwhelmingly outperforms the alignment-based software MEGA7 (alignment with MUSCLE or CLUSTALW) in terms of processing time, while having comparable classification accuracies for small datasets and superior accuracies for the large dataset. Compared with the alignment-free software FFP (Feature Frequency Profile), ML-DSP has significantly better classification accuracy, and is overall faster.

We also provide preliminary experiments indicating the potential of ML-DSP to be used for other datasets, by classifying 4271 complete dengue virus genomes into subtypes with 100% accuracy, and 4,710 bacterial genomes into phyla with 95.5% accuracy.

Lastly, our analysis shows that the “Purine/Pyrimidine”, “Just-A” and “Real” numerical representations of DNA sequences outperform ten other such numerical representations used in the Digital Signal Processing literature for DNA classification purposes.

Conclusions

Due to its superior classification accuracy, speed, and scalability to large datasets, ML-DSP is highly relevant in the classification of newly discovered organisms, in distinguishing genomic signatures and identifying their mechanistic determinants, and in evaluating genome integrity.

Genomic signature of early T-cell response is associated with lower antibody titer threshold for sterilizing immunity

Vaccination is an effective approach to reduce disease burden. High vaccination coverage blocks pathogen transmission to ensure herd immunity. However, the concept of herd immunity assumes that vaccinated individuals cannot be infected and mediate silent pathogen transmission. While the correlates of vaccine-mediated protection against disease have been examined, the correlates of sterilizing immunity that prevents infection have not been systematically defined. Here, we used full genome expression profiling to explore the molecular correlates of serological response and non-response to measles, mumps and rubella (MMR) vaccination as surrogates of infection and sterilizing immunity, respectively. We observed that the antibody titers needed to sterilize infection with the vaccine strains were higher than current WHO disease protection thresholds. In subjects with baseline antibodies below such sterilizing immunity thresholds, serological non-response to MMR vaccination was associated with gene expression profile indicative of early T-cell activation and signalling. Specifically, genes that regulate T-cell function and response were induced at day 1 post-vaccination in non-responders but not in responders. These findings suggest that rapid T-cell response prevented MMR vaccine infection to limit antigenic presentation and hence serological response. Collectively, our findings suggest an important role for T-cells in engendering sterilizing immunity.

Colon Cancer Tumor Location Defined by Gene Expression May Disagree With Anatomic Tumor Location

BACKGROUND

Cancers of the right colon have been shown to differ from left-side colon cancers in prognosis, response to epithelial growth factor receptor inhibitors, microsatellite instability and BRAF mutation status, and other molecular characteristics. Clinical application of these differences will benefit from a deeper understanding of how tumor location defines and is defined by gene expression.

MATERIALS AND METHODS

This study was carried out using Affymetrix microarray datasets (Cohort A: training set, n = 352; validation set, n = 519) and samples from The Cancer Genome Atlas Colon Adenocarcinoma database (Cohort B: n = 408), in which tumor location was reported. Gene expression patterns characteristic of tumor side were identified in a manner unbiased by statistical classification method.

RESULTS

In the Cohort A validation set, the anatomic locations of 75% of tumors agree with the locations predicted by gene expression (so-called genomic location), whereas 8% of tumors had genomic locations discordant with their anatomic locations, and 17% of tumors had ambiguous genomic locations. Genomic location was a better predictor of microsatellite instability, CpG island methylator phenotype status, and BRAF mutation status than anatomic location. Tumors with ambiguous genomic location were significantly (P = 1.3 × 10^-7) more likely to have the mesenchymal consensus molecular subtype (40%) than those with a specific genomic location (18%). A genomic signature to predict genomic location was defined.

CONCLUSION

Tumor location is increasingly considered in deciding treatment of a colon tumor. We showed that genomic location was superior to anatomic location as a predictor of molecular characteristics, suggesting that it may be a more accurate predictor of response.

An efficient genomic signature ranking method for genomic island prediction from a single genome

Genomic islands that are associated with microbial adaptations and carry genomic signatures different from that of the host, and thus many methods have been proposed to select the informative genomic signatures from a range of organisms and discriminate genomic islands from the rest of the genome in terms of these signature biases. However, they are of limited use when closely related genomes are unavailable. In the present work, we proposed a kurtosis-based ranking method to select the informative genomic signatures from a single genome. In simulations with alien fragments from artificial and real genomes, the proposed kurtosis-based ranking method efficiently selected the informative genomic signatures from a single genome, without annotated information of genomes or prior knowledge from other datasets. This understanding can be useful to design more powerful method for genomic island detection.

The genetic landscape of gliomas arising after therapeutic radiation

Radiotherapy improves survival for common childhood cancers such as medulloblastoma, leukemia, and germ cell tumors. Unfortunately, long-term survivors suffer sequelae that can include secondary neoplasia. Gliomas are common secondary neoplasms after cranial or craniospinal radiation, most often manifesting as high-grade astrocytomas with poor clinical outcomes. Here, we performed genetic profiling on a cohort of 12 gliomas arising after therapeutic radiation to determine their molecular pathogenesis and assess for differences in genomic signature compared to their spontaneous counterparts. We identified a high frequency of TP53 mutations, CDK4 amplification or CDKN2A homozygous deletion, and amplifications or rearrangements involving receptor tyrosine kinase and Ras-Raf-MAP kinase pathway genes including PDGFRA, MET, BRAF, and RRAS2. Notably, all tumors lacked alterations in IDH1, IDH2, H3F3A, HIST1H3B, HIST1H3C, TERT (including promoter region), and PTEN, which genetically define the major subtypes of diffuse gliomas in children and adults. All gliomas in this cohort had very low somatic mutation burden (less than three somatic single nucleotide variants or small indels per Mb). The ten high-grade gliomas demonstrated markedly aneuploid genomes, with significantly increased quantity of intrachromosomal copy number breakpoints and focal amplifications/homozygous deletions compared to spontaneous high-grade gliomas, likely as a result of DNA double-strand breaks induced by gamma radiation. Together, these findings demonstrate a distinct molecular pathogenesis of secondary gliomas arising after radiation therapy and identify a genomic signature that may aid in differentiating these tumors from their spontaneous counterparts.

Genetic assessment of inbred chicken lines indicates genomic signatures of resistance to Marek's disease

Background

Marek’s disease (MD) is a highly contagious pathogenic and oncogenic disease primarily affecting chickens. However, the mechanisms of genetic resistance for MD are complex and not fully understood. MD-resistant line 6₃ and MD-susceptible line 7₂ are two highly inbred progenitor lines of White Leghorn. Recombinant Congenic Strains (RCS) were developed from these two lines, which show varied susceptibility to MD.

Results

We investigated genetic structure and genomic signatures across the genome, including the line 6₃ and line 7₂, six RCSs, and two reciprocally crossed flocks between the lines 6₃ and 7₂ (F1 6₃ × 7₂ and F1 7₂ × 6₃) using Affymetrix® Axiom® HD 600 K genotyping array. We observed 18 chickens from RCS lines were specifically clustered into resistance sub-groups distributed around line 6₃. Additionally, homozygosity analysis was employed to explore potential genetic components related to MD resistance, while runs of homozygosity (ROH) are regions of the genome where the identical haplotypes are inherited from each parent. We found several genes including SIK, SOX1, LIG4, SIK1 and TNFSF13B were contained in ROH region identified in resistant group (line 6₃ and RCS), and these genes have been reported that are contribute to immunology and survival. Based on F_ST based population differential analysis, we also identified important genes related to cell death and anti-apoptosis, including AKT1, API5, CDH13, CFDP and USP15, which could be involved in divergent selection during inbreeding process.

Conclusions

Our findings offer valuable insights for understanding the genetic mechanism of resistance to MD and the identified genes could be considered as candidate biomarkers in further evaluation.

Electronic supplementary material

The online version of this article (10.1186/s40104-018-0281-x) contains supplementary material, which is available to authorized users.

Results of a phase II trial for high-risk neuroblastoma treatment protocol JN-H-07: a report from the Japan Childhood Cancer Group Neuroblastoma Committee (JNBSG)

BACKGROUND

The Japanese Children's Cancer Group (JCCG) Neuroblastoma Committee (JNBSG) conducted a phase II clinical trial for high-risk neuroblastoma treatment. We report the result of the protocol treatment and associated genomic aberration studies.

METHODS

JN-H-07 was a single-arm, late phase II trial for high-risk neuroblastoma treatment with open enrollment from June 2007 to February 2009. Eligible patients underwent five courses of induction chemotherapy followed by high-dose chemotherapy with hematopoietic stem cell rescue. Surgery for the primary tumor was scheduled after three or four courses of induction chemotherapy. Radiotherapy was administered to the primary tumor site and to any bone metastases present at the end of induction chemotherapy.

RESULTS

The estimated 3-year progression-free and overall survival rates of the 50 patients enrolled were 36.5 ± 7.0 and 69.5 ± 6.6%, respectively. High-dose chemotherapy caused severe toxicity including three treatment-related deaths. In response to this, the high-dose chemotherapy regimen was modified during the trial by infusing melphalan before administering carboplatin and etoposide. The modified high-dose chemotherapy regimen was less toxic. Univariate analysis revealed that patients younger than 547 days and patients whose tumor showed a whole chromosomal gains / losses pattern had a significantly poor prognosis. Notably, the progression-free survival of cases with MYCN amplification were not inferior to those without MYCN amplification.

CONCLUSIONS

The outcome of patients treated with the JN-H-07 protocol showed improvement over the results reported by previous studies conducted in Japan. Molecular and genetic profiling may enable a more precise stratification of the high-risk cohort.

Morphology and genomic hallmarks of breast tumours developed by ATM deleterious variant carriers

Background

The ataxia telangiectasia mutated (ATM) gene is a moderate-risk breast cancer susceptibility gene; germline loss-of-function variants are found in up to 3% of hereditary breast and ovarian cancer (HBOC) families who undergo genetic testing. So far, no clear histopathological and molecular features of breast tumours occurring in ATM deleterious variant carriers have been described, but identification of an ATM-associated tumour signature may help in patient management.

Methods

To characterise hallmarks of ATM-associated tumours, we performed systematic pathology review of tumours from 21 participants from ataxia-telangiectasia families and 18 participants from HBOC families, as well as copy number profiling on a subset of 23 tumours. Morphology of ATM-associated tumours was compared with that of 599 patients with no BRCA1 and BRCA2 mutations from a hospital-based series, as well as with data from The Cancer Genome Atlas. Absolute copy number and loss of heterozygosity (LOH) profiles were obtained from the OncoScan SNP array. In addition, we performed whole-genome sequencing on four tumours from ATM loss-of-function variant carriers with available frozen material.

Results

We found that ATM-associated tumours belong mostly to the luminal B subtype, are tetraploid and show LOH at the ATM locus at 11q22–23. Unlike tumours in which BRCA1 or BRCA2 is inactivated, tumours arising in ATM deleterious variant carriers are not associated with increased large-scale genomic instability as measured by the large-scale state transitions signature. Losses at 13q14.11-q14.3, 17p13.2-p12, 21p11.2-p11.1 and 22q11.23 were observed. Somatic alterations at these loci may therefore represent biomarkers for ATM testing and harbour driver mutations in potentially ‘druggable’ genes that would allow patients to be directed towards tailored therapeutic strategies.

Conclusions

Although ATM is involved in the DNA damage response, ATM-associated tumours are distinct from BRCA1-associated tumours in terms of morphological characteristics and genomic alterations, and they are also distinguishable from sporadic breast tumours, thus opening up the possibility to identify ATM variant carriers outside the ataxia-telangiectasia disorder and direct them towards effective cancer risk management and therapeutic strategies.

Electronic supplementary material

The online version of this article (10.1186/s13058-018-0951-9) contains supplementary material, which is available to authorized users.

A novel genomic signature predicting FDG uptake in diverse metastatic tumors

BACKGROUND

Building a universal genomic signature predicting the intensity of FDG uptake in diverse metastatic tumors may allow us to understand better the biological processes underlying this phenomenon and their requirements of glucose uptake.

METHODS

A balanced training set (n = 71) of metastatic tumors including some of the most frequent histologies, with matched PET/CT quantification measurements and whole human genome gene expression microarrays, was used to build the signature. Selection of microarray features was carried out exclusively on the basis of their strong association with FDG uptake (as measured by SUVmean35) by means of univariate linear regression. A thorough bioinformatics study of these genes was performed, and multivariable models were built by fitting several state of the art regression techniques to the training set for comparison.

RESULTS

The 909 probes with the strongest association with the SUVmean35 (comprising 742 identifiable genes and 62 probes not matched to a symbol) were used to build the signature. Partial least squares using three components (PLS-3) was the best performing model in the training dataset cross-validation (root mean square error, RMSE = 0.443) and was validated further in an independent validation dataset (n = 13) obtaining a performance within the 95% CI of that obtained in the training dataset (RMSE = 0.645). Significantly overrepresented biological processes correlating with the SUVmean35 were identified beyond glycolysis, such as ribosome biogenesis and DNA replication (correlating with a higher SUVmean35) and cytoskeleton reorganization and autophagy (correlating with a lower SUVmean35).

CONCLUSIONS

PLS-3 is a signature predicting accurately the intensity of FDG uptake in diverse metastatic tumors. FDG-PET might help in the design of specific targeted therapies directed to counteract the identified malignant biological processes more likely activated in a tumor as inferred from the SUVmean35 and also from its variations in response to antineoplastic treatments.

A Genomic Signature for Genotyping Mycobacterium tuberculosis

Mycobacterium tuberculosis (MTB), the causative agent of tuberculosis (TB), has a vast diversity of genotypes including Beijing, CAS, EAI, Haarlem, LAM, X, Ural, T, AFRI1 and AFRI2. However, genotyping can be expensive, time consuming and in some cases, results may vary depending on methodology used. Here, we proposed a new set of 10 SNPs using a total of 249 MTB genomes, and selected by first the inclusion/ exclusion (IE) criteria using spoligotyping and phylogenies, followed by the selection of the nonsynonymous SNPs present in the most conserved cluster of orthologous groups (COG) of each genotype of MTB. Genotype assignment of the new set of 10 SNPs was validated using an additional of 34 MTB genomes and results showed 100% correlation with their known genotypes. Our set of 10 SNPs have not been previously reported and cover the MTB genotypes that are prevalent worldwide. This set of SNPs could be used for molecular epidemiology with drug resistant markers.

Screening of KRAS Mutation in Pre- and Post-Surgery Serum of Patients Suffering from Colon Cancer by COLD-PCR HRM

Genomic characterization of cell-free circulating tumour DNA (ctDNA) may offer an opportunity to assess clonal dynamics throughout the course of a patient's illness. The existence of KRAS driver mutations in colon cancer patients is determinant to decide their treatment and to predict their outcome. DNA is extracted automatically from 400 μL of serum using the MagNa Pure Compact with the Nucleic Acid Isolation Kit I. DNA amplification, COLD-PCR and HRM were performed in the same run in the Light Cycler 480.We found three different situations: pre- and post-surgical samples grouped with the negative control, pre-surgical samples appear to group with the positive control and the post-surgical samples appear to group with the negative control and finally both samples, pre- and post-surgical ones, appear to be grouped with the positive control. COLD-PCR HRM is a cost-effective way for screening one of the most common driver mutations to predict the worst prognosis in colorectal cancer.

Reducing chemotherapy use in clinically high-risk, genomically low-risk pN0 and pN1 early breast cancer patients: five-year data from the prospective, randomised phase 3 West German Study Group (WSG) PlanB trial

Background

The prospective phase 3 PlanB trial used the Oncotype DX^® Recurrence Score^® (RS) to define a genomically low-risk subset of clinically high-risk pN0-1 early breast cancer (EBC) patients for treatment with adjuvant endocrine therapy (ET) alone. Here, we report five-year data evaluating the prognostic value of RS, Ki-67, and other traditional clinicopathological parameters.

Methods

A central tumour bank was prospectively established within PlanB. Following an early amendment, hormone receptor (HR)+ , pN0-1 RS ≤ 11 patients were recommended to omit chemotherapy. Patients with RS ≥ 12, pN2-3, or HR-negative/HER2-negative disease were randomised to anthracycline-containing or anthracycline-free chemotherapy. Primary endpoint: disease-free survival (DFS). PlanB Clinicaltrials.gov identifier: NCT01049425.

Findings

From 2009 to 2011, PlanB enrolled 3198 patients (central tumour bank, n = 3073) with the median age of 56 years, 41.1% pN+, and 32.5% grade 3 EBC. Chemotherapy was omitted in 348/404 (86.1%) eligible RS ≤ 11 patients. After 55 months of median follow-up, five-year DFS in ET-treated RS ≤ 11 patients was 94% (in both pN0 and pN1) versus 94% (RS 12–25) and 84% (RS > 25) in chemotherapy-treated patients (p < 0.001); five-year overall survival (OS) was 99 versus 97% and 93%, respectively (p < 0.001). Nodal status, central/local grade, tumour size, continuous Ki-67, progesterone receptor (PR), IHC4, and RS were univariate prognostic factors for DFS. In a multivariate analysis including all univariate prognostic markers, only pN2-3, central and local grade 3, tumour size >2 cm, and RS, but not IHC4 or Ki-67 were independent adverse factors. If RS was excluded, IHC4 or both Ki-67 and PR entered the model. The impact of RS was particularly pronounced in patients with intermediate Ki-67 (>10%, <40%) tumours.

Interpretation

The excellent five-year outcomes in clinically high-risk, genomically low-risk (RS ≤ 11) pN0-1 patients without adjuvant chemotherapy support using RS with standardised pathology for treatment decisions in HR+ HER2-negative EBC. Ki-67 has the potential to support patient selection for genomic testing.

Bioinformatics strategies for taxonomy independent binning and visualization of sequences in shotgun metagenomics

One of main steps in a study of microbial communities is resolving their composition, diversity and function. In the past, these issues were mostly addressed by the use of amplicon sequencing of a target gene because of reasonable price and easier computational postprocessing of the bioinformatic data. With the advancement of sequencing techniques, the main focus shifted to the whole metagenome shotgun sequencing, which allows much more detailed analysis of the metagenomic data, including reconstruction of novel microbial genomes and to gain knowledge about genetic potential and metabolic capacities of whole environments. On the other hand, the output of whole metagenomic shotgun sequencing is mixture of short DNA fragments belonging to various genomes, therefore this approach requires more sophisticated computational algorithms for clustering of related sequences, commonly referred to as sequence binning. There are currently two types of binning methods: taxonomy dependent and taxonomy independent. The first type classifies the DNA fragments by performing a standard homology inference against a reference database, while the latter performs the reference-free binning by applying clustering techniques on features extracted from the sequences. In this review, we describe the strategies within the second approach. Although these strategies do not require prior knowledge, they have higher demands on the length of sequences. Besides their basic principle, an overview of particular methods and tools is provided. Furthermore, the review covers the utilization of the methods in context with the length of sequences and discusses the needs for metagenomic data preprocessing in form of initial assembly prior to binning.

Additive methods for genomic signatures

Background

Studies exploring the potential of Chaos Game Representations (CGR) of genomic sequences to act as “genomic signatures” (to be species- and genome-specific) showed that CGR patterns of nuclear and organellar DNA sequences of the same organism can be very different. While the hypothesis that CGRs of mitochondrial DNA sequences can act as genomic signatures was validated for a snapshot of all sequenced mitochondrial genomes available in the NCBI GenBank sequence database, to our knowledge no such extensive analysis of CGRs of nuclear DNA sequences exists to date.

Results

We analyzed an extensive dataset, totalling 1.45 gigabase pairs, of nuclear/nucleoid genomic sequences (nDNA) from 42 different organisms, spanning all major kingdoms of life. Our computational experiments indicate that CGR signatures of nDNA of two different origins cannot always be differentiated, especially if they originate from closely-related species such as H. sapiens and P. troglodytes or E. coli and E. fergusonii. To address this issue, we propose the general concept of additive DNA signature of a set (collection) of DNA sequences. One particular instance, the composite DNA signature, combines information from nDNA fragments and organellar (mitochondrial, chloroplast, or plasmid) genomes. We demonstrate that, in this dataset, composite DNA signatures originating from two different organisms can be differentiated in all cases, including those where the use of CGR signatures of nDNA failed or was inconclusive. Another instance, the assembled DNA signature, combines information from many short DNA subfragments (e.g., 100 basepairs) of a given DNA fragment, to produce its signature. We show that an assembled DNA signature has the same distinguishing power as a conventionally computed CGR signature, while using shorter contiguous sequences and potentially less sequence information.

Conclusions

Our results suggest that, while CGR signatures of nDNA cannot always play the role of genomic signatures, composite and assembled DNA signatures (separately or in combination) could potentially be used instead. Such additive signatures could be used, e.g., with raw unassembled next-generation sequencing (NGS) read data, when high-quality sequencing data is not available, or to complement information obtained by other methods of species identification or classification.

Electronic supplementary material

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Evaluation of the State of Transplanted Liver Health by Monitoring of Organ-Specific Genomic Marker in Circulating DNA from Receptor

The evaluation of the transplanted liver health by non-invasive approaches may offer an improvement in early clinical intervention. As transplanted organs have genomes that are distinct from the host's genome, the quantification of the specific DNA of the donated liver in the patient serum will allow us to obtain information about its damage. We evaluated the state of transplanted liver health by monitoring the RH gene in serum circulating DNA (cirDNA) from 17 recipient and donor mismatched for this gene. cirDNA RH gene was quantified by RT- PCR before, at the moment of transplantation (day 0) and during the stay at the intensive care unit. Beta-globin cirDNA was quantified as a general cellular damage marker. Patients were grouped based on clinical outcomes: (A) patients with no complication; (B) patients that accepted the organ but suffered other complications; (C) patients that suffered organ rejection. All patients showed an increased cirDNA levels at day 0 that decreased until patient stabilization. Patients from groups A and B showed low levels of the RH gene cDNA during the follow-up, with an increase of beta-globin gene at the moment of any clinical complication. Patients from group C showed an increase in the RH gene during rejection.

Intrinsic correlation of oligonucleotides: a novel genomic signature for metagenome analysis

Because a vast majority (99%) of microbes in a given community is likely to be non-cultivable, metagenomics has gradually entered the mainstream of microbial research methods. With the development of high-throughput sequencing techniques, an increasing number of sequencing read data sets of metagenomes from various microbial communities have become available. For these data sets, metagenomic analysis based on mapping reads to microbial genomes has been hampered by the limited number of microbial genomes that are available. Further, this type of analysis is computationally intensive. Thus alignment-free methods, which characterize the sequencing reads with a genomic signature instead of with genomic alignments, can be applied. However, the main requirement of these alignment-free methods is a stable genomic signature that performs reliably. Here, we propose a novel genomic signature of microbial genomes called the intrinsic correlation of oligonucleotides (ICOs). This signature represents the quantification of an intrinsic relationship between any two oligonucleotides. We analyzed microbial genomes at different taxonomic levels using ICO profiles and confirmed the wide availability of useful ICOs. We used intra-genomic and inter-genomic distances and relational grades to evaluate the performance of ICOs as a genomic signature. The results of these experiments showed that ICOs can characterize microbial genomes well, and ICOs were better at distinguishing species than tetranucleotide composition, not only in terms of whole genomes but also in terms of sequence fragments. In addition, we evaluated the performance of a hybrid feature that combined ICOs and tetranucleotide composition. The experimental results showed that the hybrid feature performed better than ICOs or tetranucleotide composition alone. ICOs can characterize microbial genomes successfully and are capable of distinguishing organisms at different taxonomic levels. ICOs perform better than tetranucleotide composition in characterizing microbial genomes. The hybrid feature that used a combination of the two kinds of sequence features had advantages over a single sequence feature.

GSTaxClassifier: a genomic signature based taxonomic classifier for metagenomic data analysis

GSTaxClassifier (Genomic Signature based Taxonomic Classifier) is a program for metagenomics analysis of shotgun DNA sequences. The program includes a simple but effective algorithm, a modification of the Bayesian method, to predict the most probable genomic origins of sequences at different taxonomical ranks, on the basis of genome databases;a function to generate genomic profiles of reference sequences with tri-, tetra-, penta-, and hexa-nucleotide motifs for setting a user-defined database; two different formats (tabular- and tree-based summaries) to display taxonomic predictions with improved analytical methods; and effective ways to retrieve, search, and summarize results by integrating the predictions into the NCBI tree-based taxonomic information.GSTaxClassifier takes input nucleotide sequences and using a modified Bayesian model evaluates the genomic signatures between metagenomic query sequences and reference genome databases. The simulation studies of a numerical data sets showed that GSTaxClassifier could serve as a useful program for metagenomics studies, which is freely available at http://helix2.biotech.ufl.edu:26878/metagenomics/.