All cancers are caused by somatic mutations; however, understanding of the biological processes generating these mutations is limited. The catalogue of somatic mutations from a cancer genome bears the signatures of the mutational processes that have been operative. Here we analysed 4,938,362 mutations from 7,042 cancers and extracted more than 20 distinct mutational signatures. Some are present in many cancer types, notably a signature attributed to the APOBEC family of cytidine deaminases, whereas others are confined to a single cancer class. Certain signatures are associated with age of the patient at cancer diagnosis, known mutagenic exposures or defects in DNA maintenance, but many are of cryptic origin. In addition to these genome-wide mutational signatures, hypermutation localized to small genomic regions, 'kataegis', is found in many cancer types. The results reveal the diversity of mutational processes underlying the development of cancer, with potential implications for understanding of cancer aetiology, prevention and therapy.

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Signatures of mutational processes in human cancer

BackgroundSomatic mutations have the potential to encode “non-self” immunogenic antigens. We hypothesized that tumors with a large number of somatic mutations due to mismatch-repair defects may be susceptible to immune checkpoint blockade. MethodsWe conducted a phase 2 study to evaluate the clinical activity of pembrolizumab, an anti–programmed death 1 immune checkpoint inhibitor, in 41 patients with progressive metastatic carcinoma with or without mismatch-repair deficiency. Pembrolizumab was administered intravenously at a dose of 10 mg per kilogram of body weight every 14 days in patients with mismatch repair–deficient colorectal cancers, patients with mismatch repair–proficient colorectal cancers, and patients with mismatch repair–deficient cancers that were not colorectal. The coprimary end points were the immune-related objective response rate and the 20-week immune-related progression-free survival rate. ResultsThe immune-related objective response rate and immune-related progression-free survival ...

/pdf/pd-1-blockade-in-tumors-with-mismatch-repair-deficiency-1eb0ztv7j7.pdf

PD-1 Blockade in Tumors with Mismatch-Repair Deficiency

The Cancer Genome Atlas (TCGA) Research Network has profiled and analyzed large numbers of human tumors to discover molecular aberrations at the DNA, RNA, protein and epigenetic levels. The resulting rich data provide a major opportunity to develop an integrated picture of commonalities, differences and emergent themes across tumor lineages. The Pan-Cancer initiative compares the first 12 tumor types profiled by TCGA. Analysis of the molecular aberrations and their functional roles across tumor types will teach us how to extend therapies effective in one cancer type to others with a similar genomic profile.

/pdf/the-cancer-genome-atlas-pan-cancer-analysis-project-gw3srsikvs.pdf

The cancer genome atlas pan-cancer analysis project

Infiltrating stromal and immune cells form the major fraction of normal cells in tumour tissue and not only perturb the tumour signal in molecular studies but also have an important role in cancer biology. Here we describe 'Estimation of STromal and Immune cells in MAlignant Tumours using Expression data' (ESTIMATE)--a method that uses gene expression signatures to infer the fraction of stromal and immune cells in tumour samples. ESTIMATE scores correlate with DNA copy number-based tumour purity across samples from 11 different tumour types, profiled on Agilent, Affymetrix platforms or based on RNA sequencing and available through The Cancer Genome Atlas. The prediction accuracy is further corroborated using 3,809 transcriptional profiles available elsewhere in the public domain. The ESTIMATE method allows consideration of tumour-associated normal cells in genomic and transcriptomic studies. An R-library is available on https://sourceforge.net/projects/estimateproject/.

/pdf/inferring-tumour-purity-and-stromal-and-immune-cell-3qvnp398w9.pdf

Inferring tumour purity and stromal and immune cell admixture from expression data

The Cancer Genome Atlas Pan-Cancer analysis project

Network Component Analysis (NCA) has shown its effectiveness inregulator identification by inferring the transcription factor activity (TFA) whenboth microarray data and ChIP-on-chip data are available. However, the NCAscheme is not applicable to many biological studies due to the lack of completeChIP-on-chip data. In this paper, we propose an integrative NCA (iNCA) approachto combine motif information, limited ChIP-on-chip data, and geneexpression data for regulatory network inference. Specifically, a Bayesian frameworkis adopted to develop a novel strategy, namely stability analysis with topologicalsampling, to infer key TFAs and their downstream gene targets. TheiNCA approach with stability analysis reduces the computational cost by avoidinga direct estimation of the high-dimensional distribution in a traditionalBayesian approach. Stability indices are designed to measure the goodness of theestimated TFAs and their connectivity strengths. The approach can also be usedto evaluate the confidence level of different data sources, considering the inevitableinconsistency among the data sources. The iNCA approach has beenapplied to a time course microarray data set of muscle regeneration. The experimentalresults show that iNCA can effectively integrate motif information, ChIP-on-chip data and microarray data to identify key regulators and their gene targetsin muscle regeneration. In particular, several identified TFAs like those ofMyoD, myogenin and YY1 are well supported by biological experiments.

Integrative network component analysis for regulatory network reconstruction

A simple and effective blind approach is proposed for digital communication signals' separation task By adopting deterministic contrasts, the general statistical independence assumption hold by conventional blind separation schemes is relaxed Simplex genetic algorithm is applied in optimization procedure to avoid falling into local minima or saddle points, and could easily incorporate priori into fitness function to simplify optimization procedure Moreover, GA based approach is a general framework and need little modification for different modulation mode, comparing with algebra or gradient based realizations Presented simulations illustrate its effectiveness both in MIMO separation problem of correlated sources and blind STBC detection scheme

A deterministic blind separation approach based on GA for digital communication signals

Shallow-depth whole-genome sequencing (WGS) of circulating cell-free DNA (ccfDNA) is a popular approach for non-invasive genomic screening assays, including liquid biopsy for early detection of invasive tumors as well as non-invasive prenatal screening (NIPS) for common fetal trisomies. In contrast to nuclear DNA WGS, ccfDNA WGS exhibits extensive inter- and intra- sample coverage variability that is not fully explained by typical sources of variation in WGS, such as GC content. This variability may inflate false positive and false negative screening rates of copy-number alterations and aneuploidy, particularly if these features are present at a relatively low proportion of total sequenced content. Herein, we propose an empirically-driven coverage correction strategy that leverages prior annotation information in a multi-distance learning context to improve within-sample coverage profile correction. Specifically, we train a weighted k-nearest neighbors-style method on non-pregnant female donor ccfDNA WGS samples, and apply it to NIPS samples to evaluate coverage profile variability reduction. We additionally characterize improvement in the discrimination of positive fetal trisomy cases relative to normal controls, and compare our results against a more traditional regression-based approach to profile coverage correction based on GC content and mappability. Under cross-validation, performance measures indicated benefit to combining the two feature sets relative to either in isolation. We also observed substantial improvement in coverage profile variability reduction in leave-out clinical NIPS samples, with variability reduced by 26.5-53.5% relative to the standard regression-based method as quantified by median absolute deviation. Finally, we observed improvement discrimination for screening positive trisomy cases reducing ccfDNA WGS coverage variability while additionally improving NIPS trisomy screening assay performance. Overall, our results indicate that machine learning approaches can substantially improve ccfDNA WGS coverage profile correction and downstream analyses.

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Coverage profile correction of shallow-depth circulating cell-free DNA sequencing via multidistance learning.

Serous borderline ovarian tumor (SBOT) and high-grade serous ovarian cancer (HGSOC) are two distinct subtypes of epithelial ovarian tumors, with markedly different biologic background, behavior, prognosis, and treatment. However, the histologic diagnosis of serous ovarian tumors can be subjectively variable and labor-intensive as multiple tumor slides/blocks need to be thoroughly examined to search for these features. In this study, we aimed to evaluate technical feasibility of using digital pathological approaches to facilitate objective and scalable diagnosis screening for SBOT and HGSOC. Based on Groovy scripts and QuPath, a novel informatics system was developed to facilitate interactive annotation and imaging data exchange for machine learning purposes. Through this developed system, cellular boundaries were detected and expanded set of cellular features were extracted to represent cell- and tissue-level characteristics. According to our evaluation, cell-level classification was accurately achieved for both tumor and stroma cells with greater than 90% accuracy. Upon further re-examinations, 44.2% of the misclassified cells were due to over-/under-segmentations or low-quality of imaging areas. For a total number of 6,485 imaging patches with sufficient tumor and stroma cells (ten of each at least), we achieved 91-95% accuracy to differentiate HGSOC v. SBOT. When all the patches were considered for a WSI to make consensus prediction, 97% accuracy was achieved for accurately classifying all patients, indicating that cellular features digitally extracted from pathological images can be used for cell classification and SBOT v. HGSOC differentiation. Introducing digital pathology into ovarian cancer research could be beneficial to discover potential clinical implications.

Digital pathology-based study of cell- and tissue-level morphologic features in serous borderline ovarian tumor and high-grade serous ovarian cancer

Background This study is aimed at exploring the prognostic value of preoperative lymphocyte-to-monocyte ratio (LMR), an index of systemic inflammation before operation, in ankle lateral ligament repair (ALLR). Methods A total of 213 I-III degrees injuries of lateral ankle ligament patients received ALLR and were followed up for more than 2 years. Univariate and multivariable linear regression analysis was used to determine the relationship between preoperative LMR and postoperative recovery. The evaluations of postoperative recovery include American Orthopaedic Foot and Ankle Society (AOFAS) score, Karlsson-Peter ankle score (KPAS), Cumberland Ankle Instability Tool (CAIT) score, Visual Analog Scale (VAS) score, and range of motion (ROM). The prognostic value of preoperative LMR was measured by receiver operating characteristic (ROC) curve. Results 178 patients (178 ankles) were followed up successfully, with a follow-up of 2.82 ± 1.54 years. Overall, the mean AOFAS, KPAS, CAIT and VAS scores, and ankle varus angle were significantly improved at the final follow-up. Univariate and multiple linear regression analysis showed that preoperative LMR was the only independent factor associated with postoperative function, ROM, and pain. The preoperative LMR of patients with poor recovery was significantly lower than that of patients with good recovery. Based on the ROC analysis, the cutoff value of preoperative LMR was 3.824. The clinical outcomes of patients with preoperative LMR < 3.824 were significantly lower than that of patients with preoperative LMR ≥ 3.824. The corresponding specificity and sensitivity were 84.6% and 71.4%. Conclusion The clinical outcomes of open or arthroscopic repair for ATFL injury are satisfactory. As a marker of systemic inflammation, preoperative LMR can be used as a prognostic indicator for ALLR.

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Chen Wang

Papers

Integrative network component analysis for regulatory network reconstruction

A deterministic blind separation approach based on GA for digital communication signals

Coverage profile correction of shallow-depth circulating cell-free DNA sequencing via multidistance learning.

Digital pathology-based study of cell- and tissue-level morphologic features in serous borderline ovarian tumor and high-grade serous ovarian cancer

Preoperative Lymphocyte-to-Monocyte Ratio Can Indicate the Outcomes in Repair of I-III Degree Injury of Lateral Ankle Ligament