Re-evaluating experimental validation in the Big Data Era : a conceptual argument

Non peer reviewe

Deciphering signatures of mutational processes operative in human cancer.

The genome of a cancer cell carries somatic mutations that are the cumulative consequences of the DNA damage and repair processes operative during the cellular lineage between the fertilized egg and the cancer cell. Remarkably, these mutational processes are poorly characterized. Global sequencing initiatives are yielding catalogs of somatic mutations from thousands of cancers, thus providing the unique opportunity to decipher the signatures of mutational processes operative in human cancer. However, until now there have been no theoretical models describing the signatures of mutational processes operative in cancer genomes and no systematic computational approaches are available to decipher these mutational signatures. Here, by modeling mutational processes as a blind source separation problem, we introduce a computational framework that effectively addresses these questions. Our approach provides a basis for characterizing mutational signatures from cancer-derived somatic mutational catalogs, paving the way to insights into the pathogenetic mechanism underlying all cancers

Diagnostic value of H3F3A mutations in giant cell tumour of bone compared to osteoclast-rich mimics

Driver mutations in the two histone 3.3 (H3.3) genes, H3F3A and H3F3B, were recently identified by whole genome sequencing in 95% of chondroblastoma (CB) and by targeted gene sequencing in 92% of giant cell tumour of bone (GCT). Given the high prevalence of these driver mutations, it may be possible to utilise these alterations as diagnostic adjuncts in clinical practice. Here, we explored the spectrum of H3.3 mutations in a wide range and large number of bone tumours (n 5 412) to determine if these alterations could be used to distinguish GCT from other osteoclast-rich tumours such as aneurysmal bone cyst, nonossifying fibroma, giant cell granuloma, and osteoclast-rich malignant bone tumours and others. In addition, we explored the driver landscape of GCT through whole genome, exome and targeted sequencing (14 gene panel). We found that H3.3 mutations, namely mutations of glycine 34 in H3F3A, occur in 96% of GCT. We did not find additional driver mutations in GCT, including mutations in IDH1, IDH2, USP6, TP53. The genomes of GCT exhibited few somatic mutations, akin to the picture seen in CB. Overall our observations suggest that the presence of H3F3A p.Gly34 mutations does not entirely exclude malignancy in osteoclast-rich tumours. However, H3F3A p.Gly34 mutations appear to be an almost essential feature of GCT that will aid pathological evaluation of bone tumours, especially when confronted with small needle core biopsies. In the absence of H3F3A p.Gly34 mutations, a diagnosis of GCT should be made with caution

On global-local artificial neural networks for function approximation

We present a hybrid radial basis function (RBF) sigmoid neural network with a three-step training algorithm that utilizes both global search and gradient descent training. The algorithm used is intended to identify global features of an input-output relationship before adding local detail to the approximating function. It aims to achieve efficient function approximation through the separate identification of aspects of a relationship that are expressed universally from those that vary only within particular regions of the input space. We test the effectiveness of our method using five regression tasks; four use synthetic datasets while the last problem uses real-world data on the wave overtopping of seawalls. It is shown that the hybrid architecture is often superior to architectures containing neurons of a single type in several ways: lower mean square errors are often achievable using fewer hidden neurons and with less need for regularization. Our global-local artificial neural network (GL-ANN) is also seen to compare favorably with both perceptron radial basis net and regression tree derived RBFs. A number of issues concerning the training of GL-ANNs are discussed: the use of regularization, the inclusion of a gradient descent optimization step, the choice of RBF spreads, model selection, and the development of appropriate stopping criteria

APOBEC3 mutational signatures are associated with extensive and diverse genomic instability across multiple tumour types

Background: The APOBEC3 (apolipoprotein B mRNA editing enzyme catalytic polypeptide 3) family of cytidine deaminases is responsible for two mutational signatures (SBS2 and SBS13) found in cancer genomes. APOBEC3 enzymes are activated in response to viral infection, and have been associated with increased mutation burden and TP53 mutation. In addition to this, it has been suggested that APOBEC3 activity may be responsible for mutations that do not fall into the classical APOBEC3 signatures (SBS2 and SBS13), through generation of double strand breaks.Previous work has mainly focused on the effects of APOBEC3 within individual tumour types using exome sequencing data. Here, we use whole genome sequencing data from 2451 primary tumours from 39 different tumour types in the Pan-Cancer Analysis of Whole Genomes (PCAWG) data set to investigate the relationship between APOBEC3 and genomic instability (GI). Results and conclusions: We found that the number of classical APOBEC3 signature mutations correlates with increased mutation burden across different tumour types. In addition, the number of APOBEC3 mutations is a significant predictor for six different measures of GI. Two GI measures (INDELs attributed to INDEL signatures ID6 and ID8) strongly suggest the occurrence and error prone repair of double strand breaks, and the relationship between APOBEC3 mutations and GI remains when SNVs attributed to kataegis are excluded. We provide evidence that supports a model of cancer genome evolution in which APOBEC3 acts as a causative factor in the development of diverse and widespread genomic instability through the generation of double strand breaks. This has important implications for treatment approaches for cancers that carry APOBEC3 mutations, and challenges the view that APOBECs only act opportunistically at sites of single stranded DNA

Array-based evolution of DNA aptamers allows modelling of an explicit sequence-fitness landscape

Mapping the landscape of possible macromolecular polymer sequences to their fitness in performing biological functions is a challenge across the biosciences. A paradigm is the case of aptamers, nucleic acids that can be selected to bind particular target molecules. We have characterized the sequence-fitness landscape for aptamers binding allophycocyanin (APC) protein via a novel Closed Loop Aptameric Directed Evolution (CLADE) approach. In contrast to the conventional SELEX methodology, selection and mutation of aptamer sequences was carried out in silico, with explicit fitness assays for 44 131 aptamers of known sequence using DNA microarrays in vitro. We capture the landscape using a predictive machine learning model linking sequence features and function and validate this model using 5500 entirely separate test sequences, which give a very high observed versus predicted correlation of 0.87. This approach reveals a complex sequence-fitness mapping, and hypotheses for the physical basis of aptameric binding; it also enables rapid design of novel aptamers with desired binding properties. We demonstrate an extension to the approach by incorporating prior knowledge into CLADE, resulting in some of the tightest binding sequences

A Graph Based Neural Network Approach to Immune Profiling of Multiplexed Tissue Samples

Multiplexed immunofluorescence provides an unprecedented opportunity for studying specific cell-to-cell and cell microenvironment interactions. We employ graph neural networks to combine features obtained from tissue morphology with measurements of protein expression to profile the tumour microenvironment associated with different tumour stages. Our framework presents a new approach to analysing and processing these complex multi-dimensional datasets that overcomes some of the key challenges in analysing these data and opens up the opportunity to abstract biologically meaningful interactions

Distinct H3F3A and H3F3B driver mutations define chondroblastoma and giant cell tumor of bone

It is recognized that some mutated cancer genes contribute to the development of many cancer types, whereas others are cancer type specific. For genes that are mutated in multiple cancer classes, mutations are usually similar in the different affected cancer types. Here, however, we report exquisite tumor type specificity for different histone H3.3 driver alterations. In 73 of 77 cases of chondroblastoma (95%), we found p.Lys36Met alterations predominantly encoded in H3F3B, which is one of two genes for histone H3.3. In contrast, in 92% (49/53) of giant cell tumors of bone, we found histone H3.3 alterations exclusively in H3F3A, leading to p.Gly34Trp or, in one case, p.Gly34Leu alterations. The mutations were restricted to the stromal cell population and were not detected in osteoclasts or their precursors. In the context of previously reported H3F3A mutations encoding p.Lys27Met and p.Gly34Arg or p.Gly34Val alterations in childhood brain tumors, a remarkable picture of tumor type specificity for histone H3.3 driver alterations emerges, indicating that histone H3.3 residues, mutations and genes have distinct functions

Recurrent mutation of IGF signalling genes and distinct patterns of genomic rearrangement in osteosarcoma

Osteosarcoma is a primary malignancy of bone that affects children and adults. Here, we present the largest sequencing study of osteosarcoma to date, comprising 112 childhood and adult tumours encompassing all major histological subtypes. A key finding of our study is the identification of mutations in insulin-like growth factor (IGF) signalling genes in 8/112 (7%) of cases. We validate this observation using fluorescence in situ hybridization (FISH) in an additional 87 osteosarcomas, with IGF1 receptor (IGF1R) amplification observed in 14% of tumours. These findings may inform patient selection in future trials of IGF1R inhibitors in osteosarcoma. Analysing patterns of mutation, we identify distinct rearrangement profiles including a process characterized by chromothripsis and amplification. This process operates recurrently at discrete genomic regions and generates driver mutations. It may represent an age-independent mutational mechanism that contributes to the development of osteosarcoma in children and adults alike

Genomic evidence supports a clonal diaspora model for metastases of esophageal adenocarcinoma.

The poor outcomes in esophageal adenocarcinoma (EAC) prompted us to interrogate the pattern and timing of metastatic spread. Whole-genome sequencing and phylogenetic analysis of 388 samples across 18 individuals with EAC showed, in 90% of patients, that multiple subclones from the primary tumor spread very rapidly from the primary site to form multiple metastases, including lymph nodes and distant tissues-a mode of dissemination that we term 'clonal diaspora'. Metastatic subclones at autopsy were present in tissue and blood samples from earlier time points. These findings have implications for our understanding and clinical evaluation of EAC.MRC core grant (RG84369), an NIHR Research Professorship (RG67258) and Cancer Research UK (RG66287)