Utilizing genomic signatures to gain insights into the dynamics of SARS-CoV-2 through Machine and Deep Learning techniques.

The global spread of the SARS-CoV-2 pandemic, originating in Wuhan, China, has had profound consequences on both health and the economy. Traditional alignment-based phylogenetic tree methods for tracking epidemic dynamics demand substantial computational power due to the growing number of sequenced strains. Consequently, there is a pressing need for an alignment-free approach to characterize these strains and monitor the dynamics of various variants. In this work, we introduce a swift and straightforward tool named GenoSig, implemented in C++. The tool exploits the Di and Tri nucleotide frequency signatures to delineate the taxonomic lineages of SARS-CoV-2 by employing diverse machine learning (ML) and deep learning (DL) models. Our approach achieved a tenfold cross-validation accuracy of 87.88% (± 0.013) for DL and 86.37% (± 0.0009) for Random Forest (RF) model, surpassing the performance of other ML models. Validation using an additional unexposed dataset yielded comparable results. Despite variations in architectures between DL and RF, it was observed that later clades, specifically GRA, GRY, and GK, exhibited superior performance compared to earlier clades G and GH. As for the continental origin of the virus, both DL and RF models exhibited lower performance than in predicting clades. However, both models demonstrated relatively higher accuracy for Europe, North America, and South America compared to other continents, with DL outperforming RF. Both models consistently demonstrated a preference for cytosine and guanine over adenine and thymine in both clade and continental analyses, in both Di and Tri nucleotide frequencies signatures. Our findings suggest that GenoSig provides a straightforward approach to address taxonomic, epidemiological, and biological inquiries, utilizing a reductive method applicable not only to SARS-CoV-2 but also to similar research questions in an alignment-free context.

iGenSig-Rx: an integral genomic signature based white-box tool for modeling cancer therapeutic responses using multi-omics data.

Multi-omics sequencing is poised to revolutionize clinical care in the coming decade. However, there is a lack of effective and interpretable genome-wide modeling methods for the rational selection of patients for personalized interventions. To address this, we present iGenSig-Rx, an integral genomic signature-based approach, as a transparent tool for modeling therapeutic response using clinical trial datasets. This method adeptly addresses challenges related to cross-dataset modeling by capitalizing on high-dimensional redundant genomic features, analogous to reinforcing building pillars with redundant steel rods. Moreover, it integrates adaptive penalization of feature redundancy on a per-sample basis to prevent score flattening and mitigate overfitting. We then developed a purpose-built R package to implement this method for modeling clinical trial datasets. When applied to genomic datasets for HER2 targeted therapies, iGenSig-Rx model demonstrates consistent and reliable predictive power across four independent clinical trials. More importantly, the iGenSig-Rx model offers the level of transparency much needed for clinical application, allowing for clear explanations as to how the predictions are produced, how the features contribute to the prediction, and what are the key underlying pathways. We anticipate that iGenSig-Rx, as an interpretable class of multi-omics modeling methods, will find broad applications in big-data based precision oncology. The R package is available: https://github.com/wangxlab/iGenSig-Rx .

Genomic landscape defines peritoneal metastatic pattern and related target of peritoneal metastasis in colorectal cancer.

The primary objective of this study is to develop a prediction model for peritoneal metastasis (PM) in colorectal cancer by integrating the genomic features of primary colorectal cancer, along with clinicopathological features. Concurrently, we aim to identify potential target implicated in the peritoneal dissemination of colorectal cancer through bioinformatics exploration and experimental validation. By analyzing the genomic landscape of primary colorectal cancer and clinicopathological features from 363 metastatic colorectal cancer patients, we identified 22 differently distributed variables, which were used for subsequent LASSO regression to construct a PM prediction model. The integrated model established by LASSO regression, which incorporated two clinicopathological variables and seven genomic variables, precisely discriminated PM cases (AUC 0.899; 95% CI 0.860-0.937) with good calibration (Hosmer-Lemeshow test p = .147). Model validation yielded AUCs of 0.898 (95% CI 0.896-0.899) and 0.704 (95% CI 0.622-0.787) internally and externally, respectively. Additionally, the peritoneal metastasis-related genomic signature (PGS), which was composed of the seven genes in the integrated model, has prognostic stratification capability for colorectal cancer. The divergent genomic landscape drives the driver genes of PM. Bioinformatic analysis concerning these driver genes indicated SERINC1 may be associated with PM. Subsequent experiments indicate that knocking down of SERINC1 functionally suppresses peritoneal dissemination, emphasizing its importance in CRCPM. In summary, the genomic landscape of primary cancer in colorectal cancer defines peritoneal metastatic pattern and reveals the potential target of SERINC1 for PM in colorectal cancer.

Identification of Genomic Signatures for Colorectal Cancer Survival Using Exploratory Data Mining.

Clinicopathological presentations are critical for establishing a postoperative treatment regimen in Colorectal Cancer (CRC), although the prognostic value is low in Stage 2 CRC. We implemented a novel exploratory algorithm based on artificial intelligence (explainable artificial intelligence, XAI) that integrates mutational and clinical features to identify genomic signatures by repurposing the FoundationOne Companion Diagnostic (F1CDx) assay. The training data set (n = 378) consisted of subjects with recurrent and non-recurrent Stage 2 or 3 CRC retrieved from TCGA. Genomic signatures were built for identifying subgroups in Stage 2 and 3 CRC patients according to recurrence using genomic parameters and further associations with the clinical presentation. The summarization of the top-performing genomic signatures resulted in a 32-gene genomic signature that could predict tumor recurrence in CRC Stage 2 patients with high precision. The genomic signature was further validated using an independent dataset (n = 149), resulting in high-precision prognosis (AUC: 0.952; PPV = 0.974; NPV = 0.923). We anticipate that our genomic signatures and NCCN guidelines will improve recurrence predictions in CRC molecular stratification.

Tumor microenvironment in renal cell carcinoma: what is the current impact?

Tweetable abstract Tumor microenvironment plays a critical role in tumor progression and response to therapy. Recent studies show the potential of gene expression signatures and T cells to predict response to immunotherapy in renal cell carcinoma.

A computational approach to biological pathogenicity.

The current pandemic (COVID-19) has made evident the need to approach pathogenicity from a deeper and more systematic perspective that might lead to methodologies to quickly predict new strains of microbes that could be pathogenic to humans. Here we propose as a solution a general and principled definition of pathogenicity that can be practically implemented in operational ways in a framework for characterizing and assessing the (degree of) potential pathogenicity of a microbe to a given host (e.g., a human individual) just based on DNA biomarkers, and to the point of predicting its impact on a host a priori to a meaningful degree of accuracy. The definition is based on basic biochemistry, the Gibbs free Energy of duplex formation between oligonucleotides and some deep structural properties of DNA revealed by an approximation with certain properties. We propose two operational tests based on the nearest neighbor (NN) model of the Gibbs Energy and an approximating metric (the h-distance.) Quality assessments demonstrate that these tests predict pathogenicity with an accuracy of over 80%, and sensitivity and specificity over 90%. Other tests obtained by training machine learning models on deep features extracted from DNA sequences yield scores of 90% for accuracy, 100% for sensitivity and 80% for specificity. These results hint towards the possibility of an operational, objective, and general conceptual framework for prior identification of pathogens and their impact without the cost of death or sickness in a host (e.g., humans.) Consequently, a reasonable prediction of possible pathogens might pave the way to eventually transform the way we handle and prepare for future pandemic events and mitigate the adverse impact on human health, while reducing the number of clinical trials to obtain similar results.

Di-genic inheritance of germline POLE and PMS2 pathogenic variants causes a unique condition associated with pediatric cancer predisposition.

Polymerase proofreading-associated polyposis (PPAP) and Lynch syndrome, caused by mutated POLE and mismatch repair (MMR) genes, respectively, are associated with adult-onset cancer. PPAP and MMR-deficient tumors are both hypermutated, and each has a unique mutational signature. We describe a 4.5-year-old boy with multiple café au lait spots who presented with metastatic Sonic Hedgehog-activated medulloblastoma, with partial response to intensive chemotherapy and immunotherapy. The tumor showed microsatellite stability, loss of PMS2 nuclear expression, and an exceptionally high tumor mutational burden of 276 Mut/Mb. Germline molecular analysis revealed an inherited heterozygous pathogenic POLE variant and a de novo heterozygous PMS2 pathogenic variant. The tumor featured the MMR, POLE, and POLE+MMR mutational signatures. This is the first description of a di-genic condition, which we named "POL-LYNCH syndrome," manifested by an aggressive ultra-mutant pediatric medulloblastoma with a unique genomic signature.

Classifying single nucleotide polymorphisms in humans.

Single nucleotide polymorphisms (SNPs) are the most common form of genetic variation amongst the human population and are key to personalized medicine. New tests are presented to distinguish pathogenic/malign (i.e., likely to contribute to or cause a disease) from nonpathogenic/benign SNPs, regardless of whether they occur in coding (exon) or noncoding (intron) regions in the human genome. The tests are based on the nearest neighbor (NN) model of Gibbs free energy landscapes of DNA hybridization and on deep structural properties of DNA revealed by an approximating metric (the h-distance) in DNA spaces of oligonucleotides of a common size. The quality assessments show that the newly defined PNPG test can classify a SNP with an accuracy about 73% for the required parameters. The best performance among machine learning models is a feed-forward neural network with fivefold cross-validation accuracy of at least 73%. These results may provide valuable tools to solve the SNP classification problem, where tools are lacking, to assess the likelihood of disease causing in unclassified SNPs. These tests highlight the significance of hybridization chemistry in SNPs. They can be applied to further the effectiveness of research in the areas of genomics and metabolomics.

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.

Interstitial chromosomal deletion of the tuberous sclerosis complex 2 locus is a signature for radiation-associated renal tumors in Eker rats.

Ionizing radiation can damage DNA and, therefore, is a risk factor for cancer. Eker rats, which carry a heterozygous germline mutation in the tumor-suppressor gene tuberous sclerosis complex 2 (Tsc2), are susceptible to radiation-induced renal carcinogenesis. However, the molecular mechanisms involved in Tsc2 inactivation are unclear. We subjected Fischer 344 × Eker (Long Evans Tsc2+/- ) F1 hybrid rats to gamma-irradiation (2 Gy) at gestational day 19 (GD19) or postnatal day 5 (PND5) and investigated the patterns of genomic alterations in the Tsc2 allele of renal tumors that developed at 1 year after irradiation (N = 24 tumors for GD19, N = 10 for PND5), in comparison with spontaneously developed tumors (N = 8 tumors). Gamma-irradiation significantly increased the multiplicity of renal tumors. The frequency of LOH at the chromosome 10q12 region, including the Tsc2 locus, was 38%, 29% and 60% in renal carcinomas developed from the nonirradiated, GD19 and PND5 groups, respectively. Array comparative genomic hybridization analysis revealed that the LOH patterns on chromosome 10 in renal carcinomas were classified into chromosomal missegregation, mitotic recombination and chromosomal deletion types. LOH of the interstitial chromosomal deletion type was observed only in radiation-associated carcinomas. Sequence analysis for the wild-type Tsc2 allele in the LOH-negative carcinomas identified deletions (nonirradiated: 26%; GD19: 21%) and base-substitution mutations (GD19: 4%). Reduced expression of Tsc2 was also observed in the majority of the LOH-negative carcinomas. Our results suggest that interstitial chromosomal deletion is a characteristic mutagenic event caused by ionizing radiation, and it may contribute to the assessment of radiation-induced cancer risk.

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 Signatures Among Acanthamoeba polyphaga Entoorganisms Unveil Evidence of Coevolution.

The definition of a genomic signature (GS) is "the total net response to selective pressure". Recent isolation and sequencing of naturally occurring organisms, hereby named entoorganisms, within Acanthamoeba polyphaga, raised the hypothesis of a common genomic signature despite their diverse and unrelated evolutionary origin. Widely accepted and implemented tests for GS detection are oligonucleotide relative frequencies (OnRF) and relative codon usage (RCU) surveys. A common pattern and strong correlations were unveiled from OnRFs among A. polyphaga's Mimivirus and virophage Sputnik. RCU showed a common A-T bias at third codon position. We expanded tests to the amoebal mitochondrial genome and amoeba-resistant bacteria, achieving strikingly coherent results to the aforementioned viral analyses. The GSs in these entoorganisms of diverse evolutionary origin are coevolutionarily conserved within an intracellular environment that provides sanctuary for species of ecological and biomedical relevance.

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.

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.

Squamous cell lung cancer: genomic evolution and personalized therapy.

OBJECTIVE: To review the state-of-the-art in relation to the current information on squamous cell lung cancer (SCLC). We describe the genetic anomalies reported, their effect, and finally the most promising therapeutic agents. MATERIALS AND METHODS: We reviewed published articles in peer-reviewed journals as well as current treatment guidelines from local and international resources. RESULTS: SCLC represents a smaller proportion of the global burden of disease for lung cancer compared to its more frequent presentation, the adenocarcinoma. However, more than 400 000 cases are reported annually, a substantial population for whom therapeutic options are scarce and with limited efficacy. Several groups have been given the task of elucidating the mechanisms that lead to the development of SCLC, including molecular anomalies that can be used as targets for drug design. CONCLUSIONS: There are potential therapeutic targets for SCLC, which must be studied in clinical trials for validation.

OBJETIVO: Revisar el estado del arte en relación con la información actual sobre el cáncer de pulmón de células escamosas (CPCE) y describir las anomalías genéticas reportadas, su efecto y los agentes terapéuticos más prometedores. MATERIAL Y MÉTODOS: Se realizó una revisión de artículos publicados en revistas indizadas, así como las guías de tratamiento publicadas por instancias locales e internacionales. RESULTADOS: El CPCE representa una proporción menor de la carga mundial de la enfermedad por cáncer pulmonar en comparación con su presentación más frecuente, el adenocarcinoma. Sin embargo, más de 400 000 casos son reportados anualmente, una población sustancial para quienes las opciones terapéuticas son escasas y con una eficacia limitada. Diversos grupos se han dado a la tarea de elucidar los mecanismos que conllevan al desarrollo del CPCE, incluyendo anomalías moleculares que puedan servir como blancos para el diseño de fármacos. CONCLUSIONES: Existen blancos terapéuticos potenciales para el CPCE que deben ser estudiados en ensayos clínicos para ser validados.

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.

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.

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.

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.

Information theory applications for biological sequence analysis.

Information theory (IT) addresses the analysis of communication systems and has been widely applied in molecular biology. In particular, alignment-free sequence analysis and comparison greatly benefited from concepts derived from IT, such as entropy and mutual information. This review covers several aspects of IT applications, ranging from genome global analysis and comparison, including block-entropy estimation and resolution-free metrics based on iterative maps, to local analysis, comprising the classification of motifs, prediction of transcription factor binding sites and sequence characterization based on linguistic complexity and entropic profiles. IT has also been applied to high-level correlations that combine DNA, RNA or protein features with sequence-independent properties, such as gene mapping and phenotype analysis, and has also provided models based on communication systems theory to describe information transmission channels at the cell level and also during evolutionary processes. While not exhaustive, this review attempts to categorize existing methods and to indicate their relation with broader transversal topics such as genomic signatures, data compression and complexity, time series analysis and phylogenetic classification, providing a resource for future developments in this promising area.