Absolute quantification of somatic DNA alterations in human cancer

Ovarian cancer-associated mutations disable catalytic activity of CDK12, a kinase that promotes homologous recombination repair and resistance to cisplatin and poly(ADP-ribose) polymerase inhibitors

Mutations in the tumor suppressors BRCA1 and BRCA2, which encode proteins that are key participants in homologous recombination (HR) repair, occur in ∼20% of high grade serous ovarian cancers. Although only 20% of these tumors have mutations in BRCA1 and BRCA2, nearly 50% of these tumors have defects in HR. Notably, however, the underlying genetic defects that give rise to HR defects in the absence of BRCA1 and BRCA2 mutations have not been fully elucidated. Here we show that the recurrent somatic CDK12 mutations identified in ovarian cancers impair the catalytic activity of this kinase, which is involved in the transcription of a subset of genes, including BRCA1 and other DNA repair genes. Furthermore, we show that disabling CDK12 function in ovarian cancer cells reduces BRCA1 levels, disrupts HR repair, and sensitizes these cells to the cross-linking agents melphalan and cisplatin and to the poly(ADP-ribose) polymerase (PARP) inhibitor veliparib (ABT-888). Taken together, these findings suggest that many CDK12 mutations are an unrecognized cause of HR defects in ovarian cancers.

BET bromodomain inhibition of MYC-amplified medulloblastoma

PURPOSE

MYC-amplified medulloblastomas are highly lethal tumors. Bromodomain and extraterminal (BET) bromodomain inhibition has recently been shown to suppress MYC-associated transcriptional activity in other cancers. The compound JQ1 inhibits BET bromodomain-containing proteins, including BRD4. Here, we investigate BET bromodomain targeting for the treatment of MYC-amplified medulloblastoma.

EXPERIMENTAL DESIGN

We evaluated the effects of genetic and pharmacologic inhibition of BET bromodomains on proliferation, cell cycle, and apoptosis in established and newly generated patient- and genetically engineered mouse model (GEMM)-derived medulloblastoma cell lines and xenografts that harbored amplifications of MYC or MYCN. We also assessed the effect of JQ1 on MYC expression and global MYC-associated transcriptional activity. We assessed the in vivo efficacy of JQ1 in orthotopic xenografts established in immunocompromised mice.

RESULTS

Treatment of MYC-amplified medulloblastoma cells with JQ1 decreased cell viability associated with arrest at G1 and apoptosis. We observed downregulation of MYC expression and confirmed the inhibition of MYC-associated transcriptional targets. The exogenous expression of MYC from a retroviral promoter reduced the effect of JQ1 on cell viability, suggesting that attenuated levels of MYC contribute to the functional effects of JQ1. JQ1 significantly prolonged the survival of orthotopic xenograft models of MYC-amplified medulloblastoma (P < 0.001). Xenografts harvested from mice after five doses of JQ1 had reduced the expression of MYC mRNA and a reduced proliferative index.

CONCLUSION

JQ1 suppresses MYC expression and MYC-associated transcriptional activity in medulloblastomas, resulting in an overall decrease in medulloblastoma cell viability. These preclinical findings highlight the promise of BET bromodomain inhibitors as novel agents for MYC-amplified medulloblastoma.

SInC: an accurate and fast error-model based simulator for SNPs, Indels and CNVs coupled with a read generator for short-read sequence data

Background

The rapid advancements in the field of genome sequencing are aiding our understanding on many biological systems. In the last five years, computational biologists and bioinformatics specialists have come up with newer, better and more efficient tools towards the discovery, analysis and interpretation of different genomic variants from high-throughput sequencing data. Availability of reliable simulated dataset is essential and is the first step towards testing any newly developed analytical tools for variant discovery. Although there are tools currently available that can simulate variants, none present the possibility of simulating all the three major types of variations (Single Nucleotide Polymorphisms, Insertions and Deletions and Copy Number Variations) and can generate reads taking a realistic error-model into consideration. Therefore, an efficient simulator and read generator is needed that can simulate variants taking the error rates of true biological samples into consideration.

Results

We report SInC (Snp, Indel and Cnv) an open-source variant simulator and read generator capable of simulating all the three common types of biological variants taking into account a distribution of base quality score from a most commonly used next-generation sequencing instrument from Illumina. SInC is capable of generating single- and paired-end reads with user-defined insert size and with high efficiency compared to the other existing tools. SInC, due to its multi-threaded capability during read generation, has a low time footprint. SInC is currently optimised to work in limited infrastructure setup and can efficiently exploit the commonly used quad-core desktop architecture to simulate short sequence reads with deep coverage for large genomes.

Conclusions

We have come up with a user-friendly multi-variant simulator and read-generator tools called SInC. SInC can be downloaded from http://sourceforge.net/projects/sincsimulator.

The evolution of the unstable cancer genome

Cancer next-generation sequencing and genomics studies published over the last five years have provided unprecedented insights into the forces shaping cancer genome evolution. In particular, these studies have revealed a high level of heterogeneity not only between different tumours, but also within individual tumours; the 'cancer genome' may evolve along several independent trajectories within a single tumour. There is an increasing appreciation of the importance of intratumour genetic heterogeneity in determining disease progression and clinical outcome in cancer medicine, and thus an increasing awareness of the need to understand the processes that both generate genetic diversity and shape genome evolution in human tumours.

Inferring clonal evolution of tumors from single nucleotide somatic mutations

Background

High-throughput sequencing allows the detection and quantification of frequencies of somatic single nucleotide variants (SNV) in heterogeneous tumor cell populations. In some cases, the evolutionary history and population frequency of the subclonal lineages of tumor cells present in the sample can be reconstructed from these SNV frequency measurements. But automated methods to do this reconstruction are not available and the conditions under which reconstruction is possible have not been described.

Results

We describe the conditions under which the evolutionary history can be uniquely reconstructed from SNV frequencies from single or multiple samples from the tumor population and we introduce a new statistical model, PhyloSub, that infers the phylogeny and genotype of the major subclonal lineages represented in the population of cancer cells. It uses a Bayesian nonparametric prior over trees that groups SNVs into major subclonal lineages and automatically estimates the number of lineages and their ancestry. We sample from the joint posterior distribution over trees to identify evolutionary histories and cell population frequencies that have the highest probability of generating the observed SNV frequency data. When multiple phylogenies are consistent with a given set of SNV frequencies, PhyloSub represents the uncertainty in the tumor phylogeny using a “partial order plot”. Experiments on a simulated dataset and two real datasets comprising tumor samples from acute myeloid leukemia and chronic lymphocytic leukemia patients demonstrate that PhyloSub can infer both linear (or chain) and branching lineages and its inferences are in good agreement with ground truth, where it is available.

Conclusions

PhyloSub can be applied to frequencies of any “binary” somatic mutation, including SNVs as well as small insertions and deletions. The PhyloSub and partial order plot software is available from https://github.com/morrislab/phylosub/.

Glioblastomas are composed of genetically divergent clones with distinct tumourigenic potential and variable stem cell-associated phenotypes

Glioblastoma (GBM) is known to be a heterogeneous disease; however, the genetic composition of the cells within a given tumour is only poorly explored. In the advent of personalised medicine the understanding of intra-tumoural heterogeneity at the cellular and the genetic level is mandatory to improve treatment and clinical outcome. By combining ploidy-based flow sorting with array-comparative genomic hybridization we show that primary GBMs present as either mono- or polygenomic tumours (64 versus 36 %, respectively). Monogenomic tumours were limited to a pseudodiploid tumour clone admixed with normal stromal cells, whereas polygenomic tumours contained multiple tumour clones, yet always including a pseudodiploid population. Interestingly, pseudodiploid and aneuploid fractions carried the same aberrations as defined by identical chromosomal breakpoints, suggesting that evolution towards aneuploidy is a late event in GBM development. Interestingly, while clonal heterogeneity could be recapitulated in spheroid-based xenografts, we find that genetically distinct clones displayed different tumourigenic potential. Moreover, we show that putative cancer stem cell markers including CD133, CD15, A2B5 and CD44 were present on genetically distinct tumour cell populations. These data reveal the clonal heterogeneity of GBMs at the level of DNA content, tumourigenic potential and stem cell marker expression, which is likely to impact glioma progression and treatment response. The combined knowledge of intra-tumour heterogeneity at the genetic, cellular and functional level is crucial to assess treatment responses and to design personalized treatment strategies for primary GBM.

Genome-wide identification of somatic aberrations from paired normal-tumor samples

Genomic copy number alteration and allelic imbalance are distinct features of cancer cells, and recent advances in the genotyping technology have greatly boosted the research in the cancer genome. However, the complicated nature of tumor usually hampers the dissection of the SNP arrays. In this study, we describe a bioinformatic tool, named GIANT, for genome-wide identification of somatic aberrations from paired normal-tumor samples measured with SNP arrays. By efficiently incorporating genotype information of matched normal sample, it accurately detects different types of aberrations in cancer genome, even for aneuploid tumor samples with severe normal cell contamination. Furthermore, it allows for discovery of recurrent aberrations with critical biological properties in tumorigenesis by using statistical significance test. We demonstrate the superior performance of the proposed method on various datasets including tumor replicate pairs, simulated SNP arrays and dilution series of normal-cancer cell lines. Results show that GIANT has the potential to detect the genomic aberration even when the cancer cell proportion is as low as 5∼10%. Application on a large number of paired tumor samples delivers a genome-wide profile of the statistical significance of the various aberrations, including amplification, deletion and LOH. We believe that GIANT represents a powerful bioinformatic tool for interpreting the complex genomic aberration, and thus assisting both academic study and the clinical treatment of cancer.

Identifying driver mutations in sequenced cancer genomes: computational approaches to enable precision medicine

High-throughput DNA sequencing is revolutionizing the study of cancer and enabling the measurement of the somatic mutations that drive cancer development. However, the resulting sequencing datasets are large and complex, obscuring the clinically important mutations in a background of errors, noise, and random mutations. Here, we review computational approaches to identify somatic mutations in cancer genome sequences and to distinguish the driver mutations that are responsible for cancer from random, passenger mutations. First, we describe approaches to detect somatic mutations from high-throughput DNA sequencing data, particularly for tumor samples that comprise heterogeneous populations of cells. Next, we review computational approaches that aim to predict driver mutations according to their frequency of occurrence in a cohort of samples, or according to their predicted functional impact on protein sequence or structure. Finally, we review techniques to identify recurrent combinations of somatic mutations, including approaches that examine mutations in known pathways or protein-interaction networks, as well as de novo approaches that identify combinations of mutations according to statistical patterns of mutual exclusivity. These techniques, coupled with advances in high-throughput DNA sequencing, are enabling precision medicine approaches to the diagnosis and treatment of cancer.

Whole-genome sequencing identifies genomic heterogeneity at a nucleotide and chromosomal level in bladder cancer

Using complete genome analysis, we sequenced five bladder tumors accrued from patients with muscle-invasive transitional cell carcinoma of the urinary bladder (TCC-UB) and identified a spectrum of genomic aberrations. In three tumors, complex genotype changes were noted. All three had tumor protein p53 mutations and a relatively large number of single-nucleotide variants (SNVs; average of 11.2 per megabase), structural variants (SVs; average of 46), or both. This group was best characterized by chromothripsis and the presence of subclonal populations of neoplastic cells or intratumoral mutational heterogeneity. Here, we provide evidence that the process of chromothripsis in TCC-UB is mediated by nonhomologous end-joining using kilobase, rather than megabase, fragments of DNA, which we refer to as "stitchers," to repair this process. We postulate that a potential unifying theme among tumors with the more complex genotype group is a defective replication-licensing complex. A second group (two bladder tumors) had no chromothripsis, and a simpler genotype, WT tumor protein p53, had relatively few SNVs (average of 5.9 per megabase) and only a single SV. There was no evidence of a subclonal population of neoplastic cells. In this group, we used a preclinical model of bladder carcinoma cell lines to study a unique SV (translocation and amplification) of the gene glutamate receptor ionotropic N-methyl D-aspertate as a potential new therapeutic target in bladder cancer.

Tolerance of whole-genome doubling propagates chromosomal instability and accelerates cancer genome evolution

The contribution of whole-genome doubling to chromosomal instability (CIN) and tumor evolution is unclear. We use long-term culture of isogenic tetraploid cells from a stable diploid colon cancer progenitor to investigate how a genome-doubling event affects genome stability over time. Rare cells that survive genome doubling demonstrate increased tolerance to chromosome aberrations. Tetraploid cells do not exhibit increased frequencies of structural or numerical CIN per chromosome. However, the tolerant phenotype in tetraploid cells, coupled with a doubling of chromosome aberrations per cell, allows chromosome abnormalities to evolve specifically in tetraploids, recapitulating chromosomal changes in genomically complex colorectal tumors. Finally, a genome-doubling event is independently predictive of poor relapse-free survival in early-stage disease in two independent cohorts in multivariate analyses [discovery data: hazard ratio (HR), 4.70, 95% confidence interval (CI), 1.04-21.37; validation data: HR, 1.59, 95% CI, 1.05-2.42]. These data highlight an important role for the tolerance of genome doubling in driving cancer genome evolution.

SIGNIFICANCE

Our work sheds light on the importance of whole-genome–doubling events in colorectal cancer evolution. We show that tetraploid cells undergo rapid genomic changes and recapitulate the genetic alterations seen in chromosomally unstable tumors. Furthermore, we demonstrate that a genome-doubling event is prognostic of poor relapse-free survival in this disease type.

Widespread genetic heterogeneity in multiple myeloma: implications for targeted therapy

We performed massively parallel sequencing of paired tumor/normal samples from 203 multiple myeloma (MM) patients and identified significantly mutated genes and copy number alterations and discovered putative tumor suppressor genes by determining homozygous deletions and loss of heterozygosity. We observed frequent mutations in KRAS (particularly in previously treated patients), NRAS, BRAF, FAM46C, TP53, and DIS3 (particularly in nonhyperdiploid MM). Mutations were often present in subclonal populations, and multiple mutations within the same pathway (e.g., KRAS, NRAS, and BRAF) were observed in the same patient. In vitro modeling predicts only partial treatment efficacy of targeting subclonal mutations, and even growth promotion of nonmutated subclones in some cases. These results emphasize the importance of heterogeneity analysis for treatment decisions.

CGARS: cancer genome analysis by rank sums

MOTIVATION

Cancer genomes are characterized by the accumulation of point mutations and structural alterations such as copy-number alterations and genomic rearrangements. Among structural changes, systematic analyses of copy-number alterations have provided deeper insight into the architecture of cancer genomes and had led to new potential treatment opportunities. During the course of cancer genome evolution, selection mechanisms are leading to a non-random pattern of mutational events contributing to fitness benefits of the cancer cells. We therefore developed a new method to dissect random from non-random patterns in copy-number data and thereby to assess significantly enriched somatic copy-number aberrations across a set of tumor specimens or cell lines. In contrast to existing approaches, the method is invariant to any strictly monotonous transformation of the input data which results to an insensitivity of differences in tumor purity, array saturation effects and copy-number baseline levels.

RESULTS

We applied our approach to recently published datasets of small-cell lung cancer and squamous cell lung cancer and validated its performance by comparing the results to an orthogonal approach. In addition, we found a new deletion peak containing the HLA-A gene in squamous cell lung cancer.

Emergence of constitutively active estrogen receptor-α mutations in pretreated advanced estrogen receptor-positive breast cancer

PURPOSE

We undertook this study to determine the prevalence of estrogen receptor (ER) α (ESR1) mutations throughout the natural history of hormone-dependent breast cancer and to delineate the functional roles of the most commonly detected alterations.

EXPERIMENTAL DESIGN

We studied a total of 249 tumor specimens from 208 patients. The specimens include 134 ER-positive (ER(+)/HER2(-)) and, as controls, 115 ER-negative (ER(-)) tumors. The ER(+) samples consist of 58 primary breast cancers and 76 metastatic samples. All tumors were sequenced to high unique coverage using next-generation sequencing targeting the coding sequence of the estrogen receptor and an additional 182 cancer-related genes.

RESULTS

Recurring somatic mutations in codons 537 and 538 within the ligand-binding domain of ER were detected in ER(+) metastatic disease. Overall, the frequency of these mutations was 12% [9/76; 95% confidence interval (CI), 6%-21%] in metastatic tumors and in a subgroup of patients who received an average of 7 lines of treatment the frequency was 20% (5/25; 95% CI, 7%-41%). These mutations were not detected in primary or treatment-naïve ER(+) cancer or in any stage of ER(-) disease. Functional studies in cell line models demonstrate that these mutations render estrogen receptor constitutive activity and confer partial resistance to currently available endocrine treatments.

CONCLUSIONS

In this study, we show evidence for the temporal selection of functional ESR1 mutations as potential drivers of endocrine resistance during the progression of ER(+) breast cancer.

Discovery and saturation analysis of cancer genes across 21 tumour types

Although a few cancer genes are mutated in a high proportion of tumours of a given type (>20%), most are mutated at intermediate frequencies (2-20%). To explore the feasibility of creating a comprehensive catalogue of cancer genes, we analysed somatic point mutations in exome sequences from 4,742 human cancers and their matched normal-tissue samples across 21 cancer types. We found that large-scale genomic analysis can identify nearly all known cancer genes in these tumour types. Our analysis also identified 33 genes that were not previously known to be significantly mutated in cancer, including genes related to proliferation, apoptosis, genome stability, chromatin regulation, immune evasion, RNA processing and protein homeostasis. Down-sampling analysis indicates that larger sample sizes will reveal many more genes mutated at clinically important frequencies. We estimate that near-saturation may be achieved with 600-5,000 samples per tumour type, depending on background mutation frequency. The results may help to guide the next stage of cancer genomics.

AbsCN-seq: a statistical method to estimate tumor purity, ploidy and absolute copy numbers from next-generation sequencing data

MOTIVATION

Detection and quantification of the absolute DNA copy number alterations in tumor cells is challenging because the DNA specimen is extracted from a mixture of tumor and normal stromal cells. Estimates of tumor purity and ploidy are necessary to correctly infer copy number, and ploidy may itself be a prognostic factor in cancer progression. As deep sequencing of the exome or genome has become routine for characterization of tumor samples, in this work, we aim to develop a simple and robust algorithm to infer purity, ploidy and absolute copy numbers in whole numbers for tumor cells from sequencing data.

RESULTS

A simulation study shows that estimates have reasonable accuracy, and that the algorithm is robust against the presence of segmentation errors and subclonal populations. We validated our algorithm against a panel of cell lines with experimentally determined ploidy. We also compared our algorithm with the well-established single-nucleotide polymorphism array-based method called ABSOLUTE on three sets of tumors of different types. Our method had good performance on these four benchmark datasets for both purity and ploidy estimates, and may offer a simple solution to copy number alteration quantification for cancer sequencing projects.

AVAILABILITY AND IMPLEMENTATION

The R package absCNseq is available from http://biostats.mcc.ucsd.edu/files/absCNseq_1.0.tar.gz CONTACT: kmesser@ucsd.edu Supplementary information: Supplementary data are available at Bioinformatics online.

The '-omics' revolution and oesophageal adenocarcinoma

Oesophageal adenocarcinoma (OAC) is the eighth most common cancer type worldwide with a dismal 5-year survival. Barrett oesophagus, the replacement of the normal squamous epithelia with glandular cells, is the first step in the pathway towards OAC. Although most patients with OAC present de novo, the presence of the easily detectable OAC precursor lesion, Barrett oesophagus, enables the possibility of early detection of high-risk patients who are more likely to progress. Currently, identification of high-risk patients depends on histopathological assessment of dysplasia with no regards to molecular pathogenesis. In the future, screening and risk stratification initiatives for Barrett oesophagus that incorporate molecular profiles might permit improved early diagnosis and intervention strategies with the possibility of preventing OAC. For the majority of patients presenting de novo at an advanced stage, combining so-called -omics datasets with current clinical staging algorithms might enable OACs to be better classified according to distinct molecular programmes, thereby leading to better targeted treatment strategies as well as cancer monitoring regimes. This Review discusses how the latest advances in -omics technologies have improved our understanding of the development and biology of OAC, and how this development might alter patient management in the future.

Studying cancer genomics through next-generation DNA sequencing and bioinformatics

Cancer is a complex disease driven by multiple mutations acquired over the lifetime of the cancer cells. These alterations, termed somatic mutations to distinguish them from inherited germline mutations, can include single-nucleotide substitutions, insertions, deletions, copy number alterations, and structural rearrangements. A patient's cancer can contain a combination of these aberrations, and the ability to generate a comprehensive genetic profile should greatly improve patient diagnosis and treatment. Next-generation sequencing has become the tool of choice to uncover multiple cancer mutations from a single tumor source, and the falling costs of this rapid high-throughput technology are encouraging its transition from basic research into a clinical setting. However, the detection of mutations in sequencing data is still an evolving area and cancer genomic data requires some special considerations. This chapter discusses these aspects and gives an overview of current bioinformatics methods for the detection of somatic mutations in cancer sequencing data.

Landscape of genomic alterations in cervical carcinomas

Cervical cancer is responsible for 10-15% of cancer-related deaths in women worldwide. The aetiological role of infection with high-risk human papilloma viruses (HPVs) in cervical carcinomas is well established. Previous studies have also implicated somatic mutations in PIK3CA, PTEN, TP53, STK11 and KRAS as well as several copy-number alterations in the pathogenesis of cervical carcinomas. Here we report whole-exome sequencing analysis of 115 cervical carcinoma-normal paired samples, transcriptome sequencing of 79 cases and whole-genome sequencing of 14 tumour-normal pairs. Previously unknown somatic mutations in 79 primary squamous cell carcinomas include recurrent E322K substitutions in the MAPK1 gene (8%), inactivating mutations in the HLA-B gene (9%), and mutations in EP300 (16%), FBXW7 (15%), NFE2L2 (4%), TP53 (5%) and ERBB2 (6%). We also observe somatic ELF3 (13%) and CBFB (8%) mutations in 24 adenocarcinomas. Squamous cell carcinomas have higher frequencies of somatic nucleotide substitutions occurring at cytosines preceded by thymines (Tp*C sites) than adenocarcinomas. Gene expression levels at HPV integration sites were statistically significantly higher in tumours with HPV integration compared with expression of the same genes in tumours without viral integration at the same site. These data demonstrate several recurrent genomic alterations in cervical carcinomas that suggest new strategies to combat this disease.

An extensive evaluation of read trimming effects on Illumina NGS data analysis

Next Generation Sequencing is having an extremely strong impact in biological and medical research and diagnostics, with applications ranging from gene expression quantification to genotyping and genome reconstruction. Sequencing data is often provided as raw reads which are processed prior to analysis 1 of the most used preprocessing procedures is read trimming, which aims at removing low quality portions while preserving the longest high quality part of a NGS read. In the current work, we evaluate nine different trimming algorithms in four datasets and three common NGS-based applications (RNA-Seq, SNP calling and genome assembly). Trimming is shown to increase the quality and reliability of the analysis, with concurrent gains in terms of execution time and computational resources needed.

Integrated genomic analysis identifies recurrent mutations and evolution patterns driving the initiation and progression of follicular lymphoma

Follicular lymphoma is an incurable malignancy, with transformation to an aggressive subtype representing a critical event during disease progression. Here we performed whole-genome or whole-exome sequencing on 10 follicular lymphoma-transformed follicular lymphoma pairs followed by deep sequencing of 28 genes in an extension cohort, and we report the key events and evolutionary processes governing tumor initiation and transformation. Tumor evolution occurred through either a 'rich' or 'sparse' ancestral common progenitor clone (CPC). We identified recurrent mutations in linker histone, JAK-STAT signaling, NF-κB signaling and B cell developmental genes. Longitudinal analyses identified early driver mutations in chromatin regulator genes (CREBBP, EZH2 and KMT2D (MLL2)), whereas mutations in EBF1 and regulators of NF-κB signaling (MYD88 and TNFAIP3) were gained at transformation. Collectively, this study provides new insights into the genetic basis of follicular lymphoma and the clonal dynamics of transformation and suggests that personalizing therapies to target key genetic alterations in the CPC represents an attractive therapeutic strategy.

Molecular profiling of the residual disease of triple-negative breast cancers after neoadjuvant chemotherapy identifies actionable therapeutic targets

Neoadjuvant chemotherapy (NAC) induces a pathologic complete response (pCR) in approximately 30% of patients with triple-negative breast cancers (TNBC). In patients lacking a pCR, NAC selects a subpopulation of chemotherapy-resistant tumor cells. To understand the molecular underpinnings driving treatment-resistant TNBCs, we performed comprehensive molecular analyses on the residual disease of 74 clinically defined TNBCs after NAC, including next-generation sequencing (NGS) on 20 matched pretreatment biopsies. Combined NGS and digital RNA expression analysis identified diverse molecular lesions and pathway activation in drug-resistant tumor cells. Ninety percent of the tumors contained a genetic alteration potentially treatable with a currently available targeted therapy. Thus, profiling residual TNBCs after NAC identifies targetable molecular lesions in the chemotherapy-resistant component of the tumor, which may mirror micrometastases destined to recur clinically. These data can guide biomarker-driven adjuvant studies targeting these micrometastases to improve the outcome of patients with TNBC who do not respond completely to NAC.

SIGNIFICANCE

This study demonstrates the spectrum of genomic alterations present in residual TNBC after NAC. Because TNBCs that do not achieve a CR after NAC are likely to recur as metastatic disease at variable times after surgery, these alterations may guide the selection of targeted therapies immediately after mastectomy before these metastases become evident.

The somatic genomic landscape of glioblastoma

We describe the landscape of somatic genomic alterations based on multidimensional and comprehensive characterization of more than 500 glioblastoma tumors (GBMs). We identify several novel mutated genes as well as complex rearrangements of signature receptors, including EGFR and PDGFRA. TERT promoter mutations are shown to correlate with elevated mRNA expression, supporting a role in telomerase reactivation. Correlative analyses confirm that the survival advantage of the proneural subtype is conferred by the G-CIMP phenotype, and MGMT DNA methylation may be a predictive biomarker for treatment response only in classical subtype GBM. Integrative analysis of genomic and proteomic profiles challenges the notion of therapeutic inhibition of a pathway as an alternative to inhibition of the target itself. These data will facilitate the discovery of therapeutic and diagnostic target candidates, the validation of research and clinical observations and the generation of unanticipated hypotheses that can advance our molecular understanding of this lethal cancer.

Estimation of the fraction of cancer cells in a tumor DNA sample using DNA methylation

Contamination of normal cells is almost always present in tumor samples and affects their molecular analyses. DNA methylation, a stable epigenetic modification, is cell type-dependent, and different between cancer and normal cells. Here, we aimed to demonstrate that DNA methylation can be used to estimate the fraction of cancer cells in a tumor DNA sample, using esophageal squamous cell carcinoma (ESCC) as an example. First, by an Infinium HumanMethylation450 BeadChip array, we isolated three genomic regions (TFAP2B, ARHGEF4, and RAPGEFL1) i) highly methylated in four ESCC cell lines, ii) hardly methylated in a pooled sample of non-cancerous mucosae, a pooled sample of normal esophageal mucosae, and peripheral leukocytes, and iii) frequently methylated in 28 ESCCs (TFAP2B, 24/28; ARHGEF4, 20/28; and RAPGEFL1, 19/28). Second, using eight pairs of cancer and non-cancer cell samples prepared by laser capture microdissection, we confirmed that at least one of the three regions was almost completely methylated in ESCC cells, and all the three regions were almost completely unmethylated in non-cancer cells. We also confirmed that DNA copy number alterations of the three regions in 15 ESCC samples were rare, and did not affect the estimation of the fraction of cancer cells. Then, the fraction of cancer cells in a tumor DNA sample was defined as the highest methylation level of the three regions, and we confirmed a high correlation between the fraction assessed by the DNA methylation fraction marker and the fraction assessed by a pathologist (r=0.85; p<0.001). Finally, we observed that, by correction of the cancer cell content, CpG islands in promoter regions of tumor-suppressor genes were almost completely methylated. These results demonstrate that DNA methylation can be used to estimate the fraction of cancer cells in a tumor DNA sample.

Chromothripsis and beyond: rapid genome evolution from complex chromosomal rearrangements

Recent genome sequencing studies have identified several classes of complex genomic rearrangements that appear to be derived from a single catastrophic event. These discoveries identify ways that genomes can be altered in single large jumps rather than by many incremental steps. Here we compare and contrast these phenomena and examine the evidence that they arise "all at once." We consider the impact of massive chromosomal change for the development of diseases such as cancer and for evolution more generally. Finally, we summarize current models for underlying mechanisms and discuss strategies for testing these models.

Corrections for mRNA extraction and sample normalization errors find increased mRNA levels may compensate for cancer haplo-insufficiency

The relative mRNA levels of differentially expressed (DE) and housekeeping (HK) genes of six aneuploid cancer lines with large-scale genomic changes identified by SNP/SKY analysis were compared with similar genes in diploid cells. The aneuploid cancer lines had heterogeneous genomic landscapes with subdiploid, diploid, and supradiploid regions and higher overall gene copy numbers compared with diploid cells. The mRNA levels of the haploid, diploid, and triploid HK genes were found to be higher after correction of easily identifiable mRNA measurement errors. Surprisingly, diploid and aneuploid HK gene mRNA levels were the same by standard expression array analyses, despite the higher copy numbers of the cancer cell HK genes. This paradoxical result proved to be due to inaccurate inputs of true intra-cellular mRNAs for analysis. These errors were corrected by analyzing the expression intensities of DE and HK genes in mRNAs extracted from equal cell numbers (50:50) of intact cancer cell and lymphocyte mixtures. Correction for both mRNA extraction/sample normalization errors and total gene copy numbers found the SUIT-2 and PC-3 cell lines' cancer genes both had ∼50% higher mRNA levels per single allele than lymphocyte gene alleles. These increased mRNA levels for single transcribed cancer alleles may restore functional mRNA levels to cancer genes rendered haplo-insufficient by the genetic instability of cancer. © 2013 Wiley Periodicals, Inc.

AISAIC: a software suite for accurate identification of significant aberrations in cancers

Accurate identification of significant aberrations in cancers (AISAIC) is a systematic effort to discover potential cancer-driving genes such as oncogenes and tumor suppressors. Two major confounding factors against this goal are the normal cell contamination and random background aberrations in tumor samples. We describe a Java AISAIC package that provides comprehensive analytic functions and graphic user interface for integrating two statistically principled in silico approaches to address the aforementioned challenges in DNA copy number analyses. In addition, the package provides a command-line interface for users with scripting and programming needs to incorporate or extend AISAIC to their customized analysis pipelines. This open-source multiplatform software offers several attractive features: (i) it implements a user friendly complete pipeline from processing raw data to reporting analytic results; (ii) it detects deletion types directly from copy number signals using a Bayes hypothesis test; (iii) it estimates the fraction of normal contamination for each sample; (iv) it produces unbiased null distribution of random background alterations by iterative aberration-exclusive permutations; and (v) it identifies significant consensus regions and the percentage of homozygous/hemizygous deletions across multiple samples. AISAIC also provides users with a parallel computing option to leverage ubiquitous multicore machines.

AVAILABILITY AND IMPLEMENTATION

 AISAIC is available as a Java application, with a user's guide and source code, at https://code.google.com/p/aisaic/.

Induction of endocycles represses apoptosis independently of differentiation and predisposes cells to genome instability

The endocycle is a common developmental cell cycle variation wherein cells become polyploid through repeated genome duplication without mitosis. We previously showed that Drosophila endocycling cells repress the apoptotic cell death response to genotoxic stress. Here, we investigate whether it is differentiation or endocycle remodeling that promotes apoptotic repression. We find that when nurse and follicle cells switch into endocycles during oogenesis they repress the apoptotic response to DNA damage caused by ionizing radiation, and that this repression has been conserved in the genus Drosophila over 40 million years of evolution. Follicle cells defective for Notch signaling failed to switch into endocycles or differentiate and remained apoptotic competent. However, genetic ablation of mitosis by knockdown of Cyclin A or overexpression of fzr/Cdh1 induced follicle cell endocycles and repressed apoptosis independently of Notch signaling and differentiation. Cells recovering from these induced endocycles regained apoptotic competence, showing that repression is reversible. Recovery from fzr/Cdh1 overexpression also resulted in an error-prone mitosis with amplified centrosomes and high levels of chromosome loss and fragmentation. Our results reveal an unanticipated link between endocycles and the repression of apoptosis, with broader implications for how endocycles may contribute to genome instability and oncogenesis.

Temporal and spatial evolution of somatic chromosomal alterations: a case-cohort study of Barrett's esophagus

All cancers are believed to arise by dynamic, stochastic somatic genomic evolution with genome instability, generation of diversity, and selection of genomic alterations that underlie multistage progression to cancer. Advanced esophageal adenocarcinomas have high levels of somatic copy number alterations. Barrett's esophagus is a risk factor for developing esophageal adenocarcinoma, and somatic chromosomal alterations (SCA) are known to occur in Barrett's esophagus. The vast majority (∼95%) of individuals with Barrett's esophagus do not progress to esophageal adenocarcinoma during their lifetimes, but a small subset develop esophageal adenocarcinoma, many of which arise rapidly even in carefully monitored patients without visible endoscopic abnormalities at the index endoscopy. Using a well-designed, longitudinal case-cohort study, we characterized SCA as assessed by single-nucleotide polymorphism arrays over space and time in 79 "progressors" with Barrett's esophagus as they approach the diagnosis of cancer and 169 "nonprogressors" with Barrett's esophagus who did not progress to esophageal adenocarcinoma over more than 20,425 person-months of follow-up. The genomes of nonprogressors typically had small localized deletions involving fragile sites and 9p loss/copy neutral LOH that generate little genetic diversity and remained relatively stable over prolonged follow-up. As progressors approach the diagnosis of cancer, their genomes developed chromosome instability with initial gains and losses, genomic diversity, and selection of SCAs followed by catastrophic genome doublings. Our results support a model of differential disease dynamics in which nonprogressor genomes largely remain stable over prolonged periods, whereas progressor genomes evolve significantly increased SCA and diversity within four years of esophageal adenocarcinoma diagnosis, suggesting a window of opportunity for early detection.

Genome-wide profiling of genetic synthetic lethality identifies CDK12 as a novel determinant of PARP1/2 inhibitor sensitivity

Small-molecule inhibitors of PARP1/2, such as olaparib, have been proposed to serve as a synthetic lethal therapy for cancers that harbor BRCA1 or BRCA2 mutations. Indeed, in clinical trials, PARP1/2 inhibitors elicit sustained antitumor responses in patients with germline BRCA gene mutations. In hypothesizing that additional genetic determinants might direct use of these drugs, we conducted a genome-wide synthetic lethal screen for candidate olaparib sensitivity genes. In support of this hypothesis, the set of identified genes included known determinants of olaparib sensitivity, such as BRCA1, RAD51, and Fanconi's anemia susceptibility genes. In addition, the set included genes implicated in established networks of DNA repair, DNA cohesion, and chromatin remodeling, none of which were known previously to confer sensitivity to PARP1/2 inhibition. Notably, integration of the list of candidate sensitivity genes with data from tumor DNA sequencing studies identified CDK12 deficiency as a clinically relevant biomarker of PARP1/2 inhibitor sensitivity. In models of high-grade serous ovarian cancer (HGS-OVCa), CDK12 attenuation was sufficient to confer sensitivity to PARP1/2 inhibition, suppression of DNA repair via homologous recombination, and reduced expression of BRCA1. As one of only nine genes known to be significantly mutated in HGS-OVCa, CDK12 has properties that should confirm interest in its use as a biomarker, particularly in ongoing clinical trials of PARP1/2 inhibitors and other agents that trigger replication fork arrest.

SomatiCA: identifying, characterizing and quantifying somatic copy number aberrations from cancer genome sequencing data

Whole genome sequencing of matched tumor-normal sample pairs is becoming routine in cancer research. However, analysis of somatic copy-number changes from sequencing data is still challenging because of insufficient sequencing coverage, unknown tumor sample purity and subclonal heterogeneity. Here we describe a computational framework, named SomatiCA, which explicitly accounts for tumor purity and subclonality in the analysis of somatic copy-number profiles. Taking read depths (RD) and lesser allele frequencies (LAF) as input, SomatiCA will output 1) admixture rate for each tumor sample, 2) somatic allelic copy-number for each genomic segment, 3) fraction of tumor cells with subclonal change in each somatic copy number aberration (SCNA), and 4) a list of substantial genomic aberration events including gain, loss and LOH. SomatiCA is available as a Bioconductor R package at http://www.bioconductor.org/packages/2.13/bioc/html/SomatiCA.html.

The causes and consequences of genetic heterogeneity in cancer evolution

Recent studies have revealed extensive genetic diversity both between and within tumours. This heterogeneity affects key cancer pathways, driving phenotypic variation, and poses a significant challenge to personalized cancer medicine. A major cause of genetic heterogeneity in cancer is genomic instability. This instability leads to an increased mutation rate and can shape the evolution of the cancer genome through a plethora of mechanisms. By understanding these mechanisms we can gain insight into the common pathways of tumour evolution that could support the development of future therapeutic strategies.

WaveCNV: allele-specific copy number alterations in primary tumors and xenograft models from next-generation sequencing

Motivation: Copy number variations (CNVs) are a major source of genomic variability and are especially significant in cancer. Until recently microarray technologies have been used to characterize CNVs in genomes. However, advances in next-generation sequencing technology offer significant opportunities to deduce copy number directly from genome sequencing data. Unfortunately cancer genomes differ from normal genomes in several aspects that make them far less amenable to copy number detection. For example, cancer genomes are often aneuploid and an admixture of diploid/non-tumor cell fractions. Also patient-derived xenograft models can be laden with mouse contamination that strongly affects accurate assignment of copy number. Hence, there is a need to develop analytical tools that can take into account cancer-specific parameters for detecting CNVs directly from genome sequencing data.

Results: We have developed WaveCNV, a software package to identify copy number alterations by detecting breakpoints of CNVs using translation-invariant discrete wavelet transforms and assign digitized copy numbers to each event using next-generation sequencing data. We also assign alleles specifying the chromosomal ratio following duplication/loss. We verified copy number calls using both microarray (correlation coefficient 0.97) and quantitative polymerase chain reaction (correlation coefficient 0.94) and found them to be highly concordant. We demonstrate its utility in pancreatic primary and xenograft sequencing data.

Availability and implementation: Source code and executables are available at https://github.com/WaveCNV. The segmentation algorithm is implemented in MATLAB, and copy number assignment is implemented Perl.

Contact:lakshmi.muthuswamy@gmail.com

Supplementary information:Supplementary data are available at Bioinformatics online.

Resistance to CDK2 inhibitors is associated with selection of polyploid cells in CCNE1-amplified ovarian cancer

PURPOSE

Amplification of cyclin E1 (CCNE1) is associated with poor outcome in breast, lung, and other solid cancers, and is the most prominent structural variant associated with primary treatment failure in high-grade serous ovarian cancer (HGSC). We have previously shown that CCNE1-amplified tumors show amplicon-dependent sensitivity to CCNE1 suppression. Here, we explore targeting CDK2 as a novel therapeutic strategy in CCNE1-amplified cancers and mechanisms of resistance.

EXPERIMENTAL DESIGN

We examined the effect of CDK2 suppression using RNA interference and small-molecule inhibitors in SK-OV-3, OVCAR-4, and OVCAR-3 ovarian cancer cell lines. To identify mechanisms of resistance, we derived multiple, independent resistant sublines of OVCAR-3 to CDK2 inhibitors. Resistant cells were extensively characterized by gene expression and copy number analysis, fluorescence-activated cell sorting profiling and conventional karyotyping. In addition, we explored the relationship between CCNE1 amplification and polyploidy using data from primary tumors.

RESULTS

We validate CDK2 as a therapeutic target in CCNE1-amplified cells by showing selective sensitivity to suppression, either by gene knockdown or using small-molecule inhibitors. In addition, we identified two resistance mechanisms, one involving upregulation of CDK2 and another novel mechanism involving selection of polyploid cells from the pretreatment tumor population. Our analysis of genomic data shows that polyploidy is a feature of cancer genomes with CCNE1 amplification.

CONCLUSIONS

These findings suggest that cyclinE1/CDK2 is an important therapeutic target in HGSC, but that resistance to CDK2 inhibitors may emerge due to upregulation of CDK2 target protein and through preexisting cellular polyploidy.

Development and validation of a clinical cancer genomic profiling test based on massively parallel DNA sequencing

As more clinically relevant cancer genes are identified, comprehensive diagnostic approaches are needed to match patients to therapies, raising the challenge of optimization and analytical validation of assays that interrogate millions of bases of cancer genomes altered by multiple mechanisms. Here we describe a test based on massively parallel DNA sequencing to characterize base substitutions, short insertions and deletions (indels), copy number alterations and selected fusions across 287 cancer-related genes from routine formalin-fixed and paraffin-embedded (FFPE) clinical specimens. We implemented a practical validation strategy with reference samples of pooled cell lines that model key determinants of accuracy, including mutant allele frequency, indel length and amplitude of copy change. Test sensitivity achieved was 95-99% across alteration types, with high specificity (positive predictive value >99%). We confirmed accuracy using 249 FFPE cancer specimens characterized by established assays. Application of the test to 2,221 clinical cases revealed clinically actionable alterations in 76% of tumors, three times the number of actionable alterations detected by current diagnostic tests.

EXPANDS: expanding ploidy and allele frequency on nested subpopulations

Motivation: Several cancer types consist of multiple genetically and phenotypically distinct subpopulations. The underlying mechanism for this intra-tumoral heterogeneity can be explained by the clonal evolution model, whereby growth advantageous mutations cause the expansion of cancer cell subclones. The recurrent phenotype of many cancers may be a consequence of these coexisting subpopulations responding unequally to therapies. Methods to computationally infer tumor evolution and subpopulation diversity are emerging and they hold the promise to improve the understanding of genetic and molecular determinants of recurrence.

Results: To address cellular subpopulation dynamics within human tumors, we developed a bioinformatic method, EXPANDS. It estimates the proportion of cells harboring specific mutations in a tumor. By modeling cellular frequencies as probability distributions, EXPANDS predicts mutations that accumulate in a cell before its clonal expansion. We assessed the performance of EXPANDS on one whole genome sequenced breast cancer and performed SP analyses on 118 glioblastoma multiforme samples obtained from TCGA. Our results inform about the extent of subclonal diversity in primary glioblastoma, subpopulation dynamics during recurrence and provide a set of candidate genes mutated in the most well-adapted subpopulations. In summary, EXPANDS predicts tumor purity and subclonal composition from sequencing data.

Availability and implementation: EXPANDS is available for download at http://code.google.com/p/expands (matlab version - used in this manuscript) and http://cran.r-project.org/web/packages/expands (R version).

Contact: claudia.petritsch@ucsf.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Proteins pinpoint double strand breaks

Combining green fluorescent protein with a protein that only binds to double strand breaks in DNA allows these breaks-which are an important form of DNA damage-to be detected with high efficiency in living bacteria.

Computational deconvolution: extracting cell type-specific information from heterogeneous samples

The quanta unit of the immune system is the cell, yet analyzed samples are often heterogeneous with respect to cell subsets which can mislead result interpretation. Experimentally, researchers face a difficult choice whether to profile heterogeneous samples with the ensuing confounding effects, or a priori focus on a few cell subsets of interest, potentially limiting new discoveries. An attractive alternative solution is to extract cell subset-specific information directly from heterogeneous samples via computational deconvolution techniques, thereby capturing both cell-centered and whole system level context. Such approaches are capable of unraveling novel biology, undetectable otherwise. Here we review the present state of available deconvolution techniques, their advantages and limitations, with a focus on blood expression data and immunological studies in general.

Pan-cancer patterns of somatic copy number alteration

Determining how somatic copy number alterations (SCNAs) promote cancer is an important goal. We characterized SCNA patterns in 4,934 cancers from The Cancer Genome Atlas Pan-Cancer data set. Whole-genome doubling, observed in 37% of cancers, was associated with higher rates of every other type of SCNA, TP53 mutations, CCNE1 amplifications and alterations of the PPP2R complex. SCNAs that were internal to chromosomes tended to be shorter than telomere-bounded SCNAs, suggesting different mechanisms underlying their generation. Significantly recurrent focal SCNAs were observed in 140 regions, including 102 without known oncogene or tumor suppressor gene targets and 50 with significantly mutated genes. Amplified regions without known oncogenes were enriched for genes involved in epigenetic regulation. When levels of genomic disruption were accounted for, 7% of region pairs were anticorrelated, and these regions tended to encompass genes whose proteins physically interact, suggesting related functions. These results provide insights into mechanisms of generation and functional consequences of cancer-related SCNAs.

Emerging patterns of somatic mutations in cancer

Recent advances in technological tools for massively parallel, high-throughput sequencing of DNA have enabled the comprehensive characterization of somatic mutations in a large number of tumour samples. In this Review, we describe recent cancer genomic studies that have assembled emerging views of the landscapes of somatic mutations through deep-sequencing analyses of the coding exomes and whole genomes in various cancer types. We discuss the comparative genomics of different cancers, including mutation rates and spectra, as well as the roles of environmental insults that influence these processes. We highlight the developing statistical approaches that are used to identify significantly mutated genes, and discuss the emerging biological and clinical insights from such analyses, as well as the future challenges of translating these genomic data into clinical impacts.

A simple consensus approach improves somatic mutation prediction accuracy

Differentiating true somatic mutations from artifacts in massively parallel sequencing data is an immense challenge. To develop methods for optimal somatic mutation detection and to identify factors influencing somatic mutation prediction accuracy, we validated predictions from three somatic mutation detection algorithms, MuTect, JointSNVMix2 and SomaticSniper, by Sanger sequencing. Full consensus predictions had a validation rate of >98%, but some partial consensus predictions validated too. In cases of partial consensus, read depth and mapping quality data, along with additional prediction methods, aided in removing inaccurate predictions. Our consensus approach is fast, flexible and provides a high-confidence list of putative somatic mutations.

The roles of telomerase in the generation of polyploidy during neoplastic cell growth

Polyploidy contributes to extensive intratumor genomic heterogeneity that characterizes advanced malignancies and is thought to limit the efficiency of current cancer therapies. It has been shown that telomere deprotection in p53-deficient mouse embryonic fibroblasts leads to high rates of polyploidization. We now show that tumor genome evolution through whole-genome duplication occurs in ∼15% of the karyotyped human neoplasms and correlates with disease progression. In a panel of human cancer and transformed cell lines representing the two known types of genomic instability (chromosomal and microsatellite), as well as the two known pathways of telomere maintenance in cancer (telomerase activity and alternative lengthening of telomeres), telomere dysfunction-driven polyploidization occurred independently of the mutational status of p53. Depending on the preexisting context of telomere maintenance, telomerase activity and its major components, human telomerase reverse transcriptase (hTERT) and human telomerase RNA component (hTERC), exert both reverse transcriptase-related (canonical) and noncanonical functions to affect tumor genome evolution through suppression or induction of polyploidization. These new findings provide a more complete mechanistic understanding of cancer progression that may, in the future, lead to novel therapeutic interventions.

Virmid: accurate detection of somatic mutations with sample impurity inference

Detection of somatic variation using sequence from disease-control matched data sets is a critical first step. In many cases including cancer, however, it is hard to isolate pure disease tissue, and the impurity hinders accurate mutation analysis by disrupting overall allele frequencies. Here, we propose a new method, Virmid, that explicitly determines the level of impurity in the sample, and uses it for improved detection of somatic variation. Extensive tests on simulated and real sequencing data from breast cancer and hemimegalencephaly demonstrate the power of our model. A software implementation of our method is available at http://sourceforge.net/projects/virmid/.

Clonal evolution in hematological malignancies and therapeutic implications

The ability of cancer to evolve and adapt is a principal challenge to therapy in general and to the paradigm of targeted therapy in particular. This ability is fueled by the co-existence of multiple, genetically heterogeneous subpopulations within the cancer cell population. Increasing evidence has supported the idea that these subpopulations are selected in a Darwinian fashion, by which the genetic landscape of the tumor is continuously reshaped. Massively parallel sequencing has enabled a recent surge in our ability to study this process, adding to previous efforts using cytogenetic methods and targeted sequencing. Altogether, these studies reveal the complex evolutionary trajectories occurring across individual hematological malignancies. They also suggest that while clonal evolution may contribute to resistance to therapy, treatment may also hasten the evolutionary process. New insights into this process challenge us to understand the impact of treatment on clonal evolution and inspire the development of novel prognostic and therapeutic strategies.

THetA: inferring intra-tumor heterogeneity from high-throughput DNA sequencing data

Tumor samples are typically heterogeneous, containing admixture by normal, non-cancerous cells and one or more subpopulations of cancerous cells. Whole-genome sequencing of a tumor sample yields reads from this mixture, but does not directly reveal the cell of origin for each read. We introduce THetA (Tumor Heterogeneity Analysis), an algorithm that infers the most likely collection of genomes and their proportions in a sample, for the case where copy number aberrations distinguish subpopulations. THetA successfully estimates normal admixture and recovers clonal and subclonal copy number aberrations in real and simulated sequencing data. THetA is available at http://compbio.cs.brown.edu/software/.

Genomics of lymphoid malignancies reveal major activation pathways in lymphocytes

Breakdown of tolerance leads to autoimmunity due to emergence of autoreactive T or B cell clones. Autoimmune diseases predispose to lymphoid malignancies and lymphoid malignancies, conversely, can manifest as autoimmune diseases. While it has been clear for a long time that a competitive advantage and uncontrolled growth of lymphocytes contribute to the pathogenesis of both lymphoid malignancies as well as autoimmune diseases, the overlap of the underlying mechanisms has been less well described. Next generation sequencing has led to massive expansion of the available genomic data in many diseases over the last five years. These data allow for comparison of the molecular pathogenesis between autoimmune diseases and lymphoid malignancies. Here, we review the similarities between autoimmune diseases and lymphoid malignancies: 1) Both, autoimmune diseases and lymphoid malignancies are characterized by activation of the same T and B cell signaling pathways, and dysregulation of these pathways can occur through genetic or epigenetic events. 2) In both scenarios, clonal and subclonal evolution of lymphocytes contribute to disease. 3) Development of both diseases not only depends on T or B cell intrinsic factors, such as germline or somatic mutations, but also on environmental factors. These include infections, the presence of other immune cells in the microenvironment, and the cytokine milieu. A better mechanistic understanding of the parallels between lymphomagenesis and autoimmunity may help the development of precision treatment strategies with rationally designed therapeutic agents.

Progression from ductal carcinoma in situ to invasive breast cancer: revisited

Ductal carcinoma in situ (DCIS) is an intraductal neoplastic proliferation of epithelial cells that is separated from the breast stroma by an intact layer of basement membrane and myoepithelial cells. DCIS is a non-obligate precursor of invasive breast cancer, and up to 40% of these lesions progress to invasive disease if untreated. Currently, it is not possible to predict accurately which DCIS would be more likely to progress to invasive breast cancer as neither the significant drivers of the invasive transition have been identified, nor has the clinical utility of tests predicting the likelihood of progression been demonstrated. Although molecular studies have shown that qualitatively, synchronous DCIS and invasive breast cancers are remarkably similar, there is burgeoning evidence to demonstrate that intra-tumor genetic heterogeneity is observed in a subset of DCIS, and that the process of progression to invasive disease may constitute an 'evolutionary bottleneck', resulting in the selection of subsets of tumor cells with specific genetic and/or epigenetic aberrations. Here we review the clinical challenge posed by DCIS, the contribution of the microenvironment and genetic aberrations to the progression from in situ to invasive breast cancer, the emerging evidence of the impact of intra-tumor genetic heterogeneity on this process, and strategies to combat this heterogeneity.