Diane Esposito

Tumour evolution inferred by single-cell sequencing

Genomic analysis provides insights into the role of copy number variation in disease, but most methods are not designed to resolve mixed populations of cells. In tumours, where genetic heterogeneity is common, very important information may be lost that would be useful for reconstructing evolutionary history. Here we show that with flow-sorted nuclei, whole genome amplification and next generation sequencing we can accurately quantify genomic copy number within an individual nucleus. We apply single-nucleus sequencing to investigate tumour population structure and evolution in two human breast cancer cases. Analysis of 100 single cells from a polygenomic tumour revealed three distinct clonal subpopulations that probably represent sequential clonal expansions. Additional analysis of 100 single cells from a monogenomic primary tumour and its liver metastasis indicated that a single clonal expansion formed the primary tumour and seeded the metastasis. In both primary tumours, we also identified an unexpectedly abundant subpopulation of genetically diverse ‘pseudodiploid’ cells that do not travel to the metastatic site. In contrast to gradual models of tumour progression, our data indicate that tumours grow by punctuated clonal expansions with few persistent intermediates.

DBC2, a candidate for a tumor suppressor gene involved in breast cancer

A previously uncharacterized gene, DBC2 (deleted in breast cancer), was cloned from a homozygously deleted region at human chromosome 8p21. DBC2 contains a highly conserved RAS domain and two putative protein interacting domains. Our analyses indicate that DBC2 is the best candidate tumor suppressor gene from this region. It lies within the epicenter of the deletions and is homozygously deleted in 3.5% (7/200) of breast tumors. Mutation analysis of DBC2 led to discovery of two instances of somatic missense mutations in breast tumor specimens, whereas no missense mutations were found in other candidates from the region. Unlike other genes in the region, expression of DBC2 is often extinguished in breast cancer cells or tissues. Moreover, our functional analysis revealed that DBC2 expression in breast cancer cells lacking DBC2 transcripts causes growth inhibition. By contrast, expression of a somatic mutant discovered in a breast cancer specimen does not suppress the growth of breast cancer cells.

Abnormalities of the large ribosomal subunit protein, Rpl35A, in diamond-blackfan anemia

Diamond-Blackfan anemia (DBA) is an inherited bone marrow failure syndrome characterized by anemia, congenital abnormalities, and cancer predisposition. Small ribosomal subunit genes RPS19, RPS24, and RPS17 are mutated in approximately one-third of patients. We used a candidate gene strategy combining high-resolution genomic mapping and gene expression microarray in the analysis of 2 DBA patients with chromosome 3q deletions to identify RPL35A as a potential DBA gene. Sequence analysis of a cohort of DBA probands confirmed involvement RPL35A in DBA. shRNA inhibition shows that Rpl35a is essential for maturation of 28S and 5.8S rRNAs, 60S subunit biogenesis, normal proliferation, and cell survival. Analysis of pre-rRNA processing in primary DBA lymphoblastoid cell lines demonstrated similar alterations of large ribosomal subunit rRNA in both RPL35A-mutated and some RPL35A wild-type patients, suggesting additional large ribosomal subunit gene defects are likely present in some cases of DBA. These data demonstrate that alterations of large ribosomal subunit proteins cause DBA and support the hypothesis that DBA is primarily the result of altered ribosomal function. The results also establish that haploinsufficiency of large ribosomal subunit proteins contributes to bone marrow failure and potentially cancer predisposition.

Genome-wide copy number analysis of single cells

Copy number variation (CNV) is increasingly recognized as an important contributor to phenotypic variation in health and disease. Most methods for determining CNV rely on admixtures of cells in which information regarding genetic heterogeneity is lost. Here we present a protocol that allows for the genome-wide copy number analysis of single nuclei isolated from mixed populations of cells. Single-nucleus sequencing (SNS), combines flow sorting of single nuclei on the basis of DNA content and whole-genome amplification (WGA); this is followed by next-generation sequencing to quantize genomic intervals in a genome-wide manner. Multiplexing of single cells is discussed. In addition, we outline informatic approaches that correct for biases inherent in the WGA procedure and allow for accurate determination of copy number profiles. All together, the protocol takes ∼3 d from flow cytometry to sequence-ready DNA libraries.

Novel genomic alterations and clonal evolution in chronic lymphocytic leukemia revealed by representational oligonucleotide microarray analysis (ROMA)

We examined copy number changes in the genomes of B cells from 58 patients with chronic lymphocytic leukemia (CLL) by using representational oligonucleotide microarray analysis (ROMA), a form of comparative genomic hybridization (CGH), at a resolution exceeding previously published studies. We observed at least 1 genomic lesion in each CLL sample and considerable variation in the number of abnormalities from case to case. Virtually all abnormalities previously reported also were observed here, most of which were indeed highly recurrent. We observed the boundaries of known events with greater clarity and identified previously undescribed lesions, some of which were recurrent. We profiled the genomes of CLL cells separated by the surface marker CD38 and found evidence of distinct subclones of CLL within the same patient. We discuss the potential applications of high-resolution CGH analysis in a clinical setting.

Rare De Novo Germline Copy-Number Variation in Testicular Cancer

Although heritable factors are an important determinant of risk of early-onset cancer, the majority of these malignancies appear to occur sporadically without identifiable risk factors. Germline de novo copy-number variations (CNVs) have been observed in sporadic neurocognitive and cardiovascular disorders. We explored this mechanism in 382 genomes of 116 early-onset cancer case-parent trios and unaffected siblings. Unique de novo germline CNVs were not observed in 107 breast or colon cancer trios or controls but were indeed found in 7% of 43 testicular germ cell tumor trios; this percentage exceeds background CNV rates and suggests a rare de novo genetic paradigm for susceptibility to some human malignancies.

Reducing system noise in copy number data using principal components of self-self hybridizations

Genomic copy number variation underlies genetic disorders such as autism, schizophrenia, and congenital heart disease. Copy number variations are commonly detected by array based comparative genomic hybridization of sample to reference DNAs, but probe and operational variables combine to create correlated system noise that degrades detection of genetic events. To correct for this we have explored hybridizations in which no genetic signal is expected, namely “self-self” hybridizations (SSH) comparing DNAs from the same genome. We show that SSH trap a variety of correlated system noise present also in sample-reference (test) data. Through singular value decomposition of SSH, we are able to determine the principal components (PCs) of this noise. The PCs themselves offer deep insights into the sources of noise, and facilitate detection of artifacts. We present evidence that linear and piecewise linear correction of test data with the PCs does not introduce detectable spurious signal, yet improves signal-to-noise metrics, reduces false positives, and facilitates copy number determination.

Corrigendum: Genome-wide copy number analysis of single cells.

Nat. Protoc. 7, 1024–1041 (2012); published online 3 May 2012; corrected after print 24 February 2016 In the version of this article initially published, the units for the concentration of NaCl in the NST buffer described in the Reagent Setup section were incorrect. The correct unit should be mM. The error has been corrected in the HTML and PDF versions of the article.