Genome evolution during progression to breast cancer

Unsupervised representation learning of chromatin images identifies changes in cell state and tissue organization in DCIS

Ductal carcinoma in situ (DCIS) is a pre-invasive tumor that can progress to invasive breast cancer, a leading cause of cancer death. We generate a large-scale tissue microarray dataset of chromatin images, from 560 samples from 122 female patients in 3 disease stages and 11 phenotypic categories. Using representation learning on chromatin images alone, without multiplexed staining or high-throughput sequencing, we identify eight morphological cell states and tissue features marking DCIS. All cell states are observed in all disease stages with different proportions, indicating that cell states enriched in invasive cancer exist in small fractions in normal breast tissue. Tissue-level analysis reveals significant changes in the spatial organization of cell states across disease stages, which is predictive of disease stage and phenotypic category. Taken together, we show that chromatin imaging represents a powerful measure of cell state and disease stage of DCIS, providing a simple and effective tumor biomarker.

Spatial Biology of Breast Cancer

Spatial findings have shaped on our understanding of breast cancer. In this review, we discuss how spatial methods, including spatial transcriptomics and proteomics and the resultant understanding of spatial relationships, have contributed to concepts regarding cancer progression and treatment. In addition to discussing traditional approaches, we examine how emerging multiplex imaging technologies have contributed to the field and how they might influence future research.

miR-125 in Breast Cancer Etiopathogenesis: An Emerging Role as a Biomarker in Differential Diagnosis, Regenerative Medicine, and the Challenges of Personalized Medicine

Breast Cancer (BC) is one of the most common cancer types worldwide, and it is characterized by a complex etiopathogenesis, resulting in an equally complex classification of subtypes. MicroRNA (miRNA or miR) are small non-coding RNA molecules that have an essential role in gene expression and are significantly linked to tumor development and angiogenesis in different types of cancer. Recently, complex interactions among coding and non-coding RNA have been elucidated, further shedding light on the complexity of the roles these molecules fulfill in cancer formation. In this context, knowledge about the role of miR in BC has significantly improved, highlighting the deregulation of these molecules as additional factors influencing BC occurrence, development and classification. A considerable number of papers has been published over the past few years regarding the role of miR-125 in human pathology in general and in several types of cancer formation in particular. Interestingly, miR-125 family members have been recently linked to BC formation as well, and complex interactions (competing endogenous RNA networks, or ceRNET) between this molecule and target mRNA have been described. In this review, we summarize the state-of-the-art about research on this topic.

Aneuploidy Landscape in Precursors of Ovarian Cancer

PURPOSE

Serous tubal intraepithelial carcinoma (STIC) is now recognized as the main precursor of ovarian high-grade serous carcinoma (HGSC). Other potential tubal lesions include p53 signatures and tubal intraepithelial lesions. We aimed to investigate the extent and pattern of aneuploidy in these epithelial lesions and HGSC to define the features that characterize stages of tumor initiation and progression.

EXPERIMENTAL DESIGN

We applied RealSeqS to compare genome-wide aneuploidy patterns among the precursors, HGSC (cases, n = 85), and histologically unremarkable fallopian tube epithelium (HU-FTE; control, n = 65). On the basis of a discovery set (n = 67), we developed an aneuploidy-based algorithm, REAL-FAST (Repetitive Element AneupLoidy Sequencing Fallopian Tube Aneuploidy in STIC), to correlate the molecular data with pathology diagnoses. We validated the result in an independent validation set (n = 83) to determine its performance. We correlated the molecularly defined precursor subgroups with proliferative activity and histology.

RESULTS

We found that nearly all p53 signatures lost the entire Chr17, offering a "two-hit" mechanism involving both TP53 and BRCA1 in BRCA1 germline mutation carriers. Proliferatively active STICs harbor gains of 19q12 (CCNE1), 19q13.2, 8q24 (MYC), or 8q arm, whereas proliferatively dormant STICs show 22q loss. REAL-FAST classified HU-FTE and STICs into 5 clusters and identified a STIC subgroup harboring unique aneuploidy that is associated with increased proliferation and discohesive growth. On the basis of a validation set, REAL-FAST showed 95.8% sensitivity and 97.1% specificity in detecting STIC/HGSC.

CONCLUSIONS

Morphologically similar STICs are molecularly distinct. The REAL-FAST assay identifies a potentially "aggressive" STIC subgroup harboring unique DNA aneuploidy that is associated with increased cellular proliferation and discohesive growth. REAL-FAST offers a highly reproducible adjunct technique to assist the diagnosis of STIC lesions.

Human Ductal Carcinoma In Situ: Advances and Future Perspectives

Due to widespread adoption of screening mammography, there has been a significant increase in new diagnoses of ductal carcinoma in situ (DCIS). However, DCIS outcomes remain unclear. A large fraction of human DCIS (>50%) may not need the multimodality treatment options currently offered to all DCIS patients. More importantly, while we may be overtreating many, we cannot identify those most at risk of invasion or metastasis following a DCIS diagnosis. This review summarizes the studies that have furthered our understanding of DCIS pathology and mechanisms of invasive progression by using advanced technologies including spatial genomics, transcriptomics, and multiplex proteomics. This review also highlights a need for rethinking DCIS with a more focused view on epithelial states and programs and their cross talk with the microenvironment.

Breast cancers as ecosystems: a metabolic perspective

Breast cancer (BC) is the most frequently diagnosed cancer and one of the major causes of cancer death. Despite enormous progress in its management, both from the therapeutic and early diagnosis viewpoints, still around 700,000 patients succumb to the disease each year, worldwide. Late recurrency is the major problem in BC, with many patients developing distant metastases several years after the successful eradication of the primary tumor. This is linked to the phenomenon of metastatic dormancy, a still mysterious trait of the natural history of BC, and of several other types of cancer, by which metastatic cells remain dormant for long periods of time before becoming reactivated to initiate the clinical metastatic disease. In recent years, it has become clear that cancers are best understood if studied as ecosystems in which the impact of non-cancer-cell-autonomous events-dependent on complex interaction between the cancer and its environment, both local and systemic-plays a paramount role, probably as significant as the cell-autonomous alterations occurring in the cancer cell. In adopting this perspective, a metabolic vision of the cancer ecosystem is bound to improve our understanding of the natural history of cancer, across space and time. In BC, many metabolic pathways are coopted into the cancer ecosystem, to serve the anabolic and energy demands of the cancer. Their study is shedding new light on the most critical aspect of BC management, of metastatic dissemination, and that of the related phenomenon of dormancy and fostering the application of the knowledge to the development of metabolic therapies.

Evolutionary histories of breast cancer and related clones

Recent studies have documented frequent evolution of clones carrying common cancer mutations in apparently normal tissues, which are implicated in cancer development1-3. However, our knowledge is still missing with regard to what additional driver events take place in what order, before one or more of these clones in normal tissues ultimately evolve to cancer. Here, using phylogenetic analyses of multiple microdissected samples from both cancer and non-cancer lesions, we show unique evolutionary histories of breast cancers harbouring der(1;16), a common driver alteration found in roughly 20% of breast cancers. The approximate timing of early evolutionary events was estimated from the mutation rate measured in normal epithelial cells. In der(1;16)(+) cancers, the derivative chromosome was acquired from early puberty to late adolescence, followed by the emergence of a common ancestor by the patient's early 30s, from which both cancer and non-cancer clones evolved. Replacing the pre-existing mammary epithelium in the following years, these clones occupied a large area within the premenopausal breast tissues by the time of cancer diagnosis. Evolution of multiple independent cancer founders from the non-cancer ancestors was common, contributing to intratumour heterogeneity. The number of driver events did not correlate with histology, suggesting the role of local microenvironments and/or epigenetic driver events. A similar evolutionary pattern was also observed in another case evolving from an AKT1-mutated founder. Taken together, our findings provide new insight into how breast cancer evolves.

Molecular classification and biomarkers of clinical outcome in breast ductal carcinoma in situ: Analysis of TBCRC 038 and RAHBT cohorts

Ductal carcinoma in situ (DCIS) is the most common precursor of invasive breast cancer (IBC), with variable propensity for progression. We perform multiscale, integrated molecular profiling of DCIS with clinical outcomes by analyzing 774 DCIS samples from 542 patients with 7.3 years median follow-up from the Translational Breast Cancer Research Consortium 038 study and the Resource of Archival Breast Tissue cohorts. We identify 812 genes associated with ipsilateral recurrence within 5 years from treatment and develop a classifier that predicts DCIS or IBC recurrence in both cohorts. Pathways associated with recurrence include proliferation, immune response, and metabolism. Distinct stromal expression patterns and immune cell compositions are identified. Our multiscale approach employed in situ methods to generate a spatially resolved atlas of breast precancers, where complementary modalities can be directly compared and correlated with conventional pathology findings, disease states, and clinical outcome.

Emerging Systemic Treatment Perspectives on Brain Metastases: Moving Toward a Better Outlook for Patients

The diagnosis of brain metastases has historically been a dreaded, end-stage complication of systemic disease. Additionally, with the increasing effectiveness of systemic therapies that prolong life expectancy and improved imaging tools, the incidence of intracranial progression is becoming more common. Within this context, there has been increasing attention directed at understanding the molecular underpinnings of intracranial progression. Exploring the unique features of brain metastases compared with their extracranial counterparts to identify aberrant signaling pathways, which can be targeted pharmacologically, may help lead to new treatments for this patient population. Additionally, critical discoveries outside the sphere of the central nervous system are increasingly being applied to brain metastases with the emergence of immune checkpoint inhibition, becoming a prevalent treatment option for patients with brain metastases across multiple histologies. As novel treatment strategies are considered, they require thoughtful incorporation of agents that can cross the blood-brain barrier and can synergize with pre-existing agents through rational combinations. Lastly, as clinicians and scientists continue to understand key molecular features of these tumors, they will continue to influence the treatment algorithms that are developing for the management of these patients. Due to the complexity of treatment decisions for patients with brain metastases, an emerging tool is the utilization of multidisciplinary brain metastasis tumor boards to ensure optimal treatment decisions are made and that patients are provided access to applicable clinical trials. Looking to the future, the collective effort to understand the various tumor-intrinsic and tumor-extrinsic factors that promote central nervous system seeding and propagation will have the potential to change the clinical trajectory for these patients.

The Unfolded Protein Response at the Tumor-Immune Interface

The tumor-immune interface has surged to primary relevance in an effort to understand the hurdles facing immune surveillance and cancer immunotherapy. Reports over the past decades have indicated a role for the unfolded protein response (UPR) in modulating not only tumor cell fitness and drug resistance, but also local immunity, with emphasis on the phenotype and altered function of immune cells such as myeloid cells and T cells. Emerging evidence also suggests that aneuploidy correlates with local immune dysregulation. Recently, we reported that the UPR serves as a link between aneuploidy and immune cell dysregulation in a cell nonautonomous way. These new findings add considerable complexity to the organization of the tumor microenvironment (TME) and the origin of its altered function. In this review, we summarize these data and also discuss the role of aneuploidy as a negative regulator of local immunity.

Evaluating statistical approaches to define clonal origin of tumours using bulk DNA sequencing: context is everything

Clonal analysis of tumour sequencing data enables the evaluation of the relationship of histologically distinct synchronous lesions, such as co-existing benign areas, and temporally distinct tumours, such as primary-recurrence comparisons. In this review, we summarise statistical approaches that are commonly employed to define tumour clonal relatedness using data from bulk DNA technologies. We discuss approaches using total copy number, allele-specific copy number and mutation data, and the relative genomic resolution required for analysis and summarise some of the current tools for inferring clonal relationships. We argue that the impact of the biological context is critical in selecting any particular approach, such as the relative genomic complexity of the lesions being compared, and we recommend considering this context before employing any method to a new dataset.

Transition to invasive breast cancer is associated with progressive changes in the structure and composition of tumor stroma

Ductal carcinoma in situ (DCIS) is a pre-invasive lesion that is thought to be a precursor to invasive breast cancer (IBC). To understand the changes in the tumor microenvironment (TME) accompanying transition to IBC, we used multiplexed ion beam imaging by time of flight (MIBI-TOF) and a 37-plex antibody staining panel to interrogate 79 clinically annotated surgical resections using machine learning tools for cell segmentation, pixel-based clustering, and object morphometrics. Comparison of normal breast with patient-matched DCIS and IBC revealed coordinated transitions between four TME states that were delineated based on the location and function of myoepithelium, fibroblasts, and immune cells. Surprisingly, myoepithelial disruption was more advanced in DCIS patients that did not develop IBC, suggesting this process could be protective against recurrence. Taken together, this HTAN Breast PreCancer Atlas study offers insight into drivers of IBC relapse and emphasizes the importance of the TME in regulating these processes.

The unfolded protein response links tumor aneuploidy to local immune dysregulation

Aneuploidy is a chromosomal abnormality associated with poor prognosis in many cancer types. Here, we tested the hypothesis that the unfolded protein response (UPR) mechanistically links aneuploidy and local immune dysregulation. Using a single somatic copy number alteration (SCNA) score inclusive of whole‐chromosome, chromosome arm, and focal alterations in a pan‐cancer analysis of 9,375 samples in The Cancer Genome Atlas (TCGA) database, we found an inverse correlation with a cytotoxicity (CYT) score across disease stages. Co‐expression patterns of UPR genes changed substantially between SCNA^low and SCNA^high groups. Pathway activity scores showed increased activity of multiple branches of the UPR in response to aneuploidy. The PERK branch showed the strongest association with a reduced CYT score. The conditioned medium of aneuploid cells transmitted XBP1 splicing and caused IL‐6 and arginase 1 transcription in receiver bone marrow‐derived macrophages and markedly diminished the production of IFN‐γ and granzyme B in activated human T cells. We propose the UPR as a mechanistic link between aneuploidy and immune dysregulation in the tumor microenvironment.

Molecular Profiles of Brain Metastases: A Focus on Heterogeneity

Simple Summary

Precision cancer medicine depends on the characterization of tumor samples, usually by a single-tumor biopsy, to administer an optimal therapeutic. However, primary tumors and their metastases are often heterogeneous. A metastatic lesion may harbor a completely different genetic makeup to that of its parent tumor, and a single tumor sampling may be ineffective in selecting the most efficient therapy. Brain metastases, due to their low availability and specific microenvironment, pose a particular challenge for precision medicine. In this review, we highlight the genetic landscape of brain metastases, with a particular focus on their heterogeneity. To illustrate this problem, we present phenotypic alterations in brain metastases originating from lung cancer, breast cancer, and melanoma. This article may help clinicians better understand alterations in brain metastases and the relevance of their heterogeneity.

Abstract

Brain metastasis is a common and devastating clinical entity. Intratumor heterogeneity in brain metastases poses a crucial challenge to precision medicine. However, advances in next-generation sequencing, new insight into the pathophysiology of driver mutations, and the creation of novel tumor models have allowed us to gain better insight into the genetic landscapes of brain metastases, their temporal evolution, and their response to various treatments. A plethora of genomic studies have identified the heterogeneous clonal landscape of tumors and, at the same time, introduced potential targets for precision medicine. As an example, we present phenotypic alterations in brain metastases originating from three malignancies with the highest brain metastasis frequency: lung cancer, breast cancer, and melanoma. We discuss the barriers to precision medicine, tumor heterogeneity, the significance of blood-based biomarkers in tracking clonal evolution, the phylogenetic relationship between primary and metastatic tumors, blood–brain barrier heterogeneity, and limitations to ongoing research.

Mutational profiling of micro-dissected pre-malignant lesions from archived specimens

BACKGROUND

Systematic cancer screening has led to the increased detection of pre-malignant lesions (PMLs). The absence of reliable prognostic markers has led mostly to over treatment resulting in potentially unnecessary stress, or insufficient treatment and avoidable progression. Importantly, most mutational profiling studies have relied on PML synchronous to invasive cancer, or performed in patients without outcome information, hence limiting their utility for biomarker discovery. The limitations in comprehensive mutational profiling of PMLs are in large part due to the significant technical and methodological challenges: most PML specimens are small, fixed in formalin and paraffin embedded (FFPE) and lack matching normal DNA.

METHODS

Using test DNA from a highly degraded FFPE specimen, multiple targeted sequencing approaches were evaluated, varying DNA input amount (3-200 ng), library preparation strategy (BE: Blunt-End, SS: Single-Strand, AT: A-Tailing) and target size (whole exome vs. cancer gene panel). Variants in high-input DNA from FFPE and mirrored frozen specimens were used for PML-specific variant calling training and testing, respectively. The resulting approach was applied to profile and compare multiple regions micro-dissected (mean area 5 mm²) from 3 breast ductal carcinoma in situ (DCIS).

RESULTS

Using low-input FFPE DNA, BE and SS libraries resulted in 4.9 and 3.7 increase over AT libraries in the fraction of whole exome covered at 20x (BE:87%, SS:63%, AT:17%). Compared to high-confidence somatic mutations from frozen specimens, PML-specific variant filtering increased recall (BE:85%, SS:80%, AT:75%) and precision (BE:93%, SS:91%, AT:84%) to levels expected from sampling variation. Copy number alterations were consistent across all tested approaches and only impacted by the design of the capture probe-set. Applied to DNA extracted from 9 micro-dissected regions (8 PML, 1 normal epithelium), the approach achieved comparable performance, illustrated the data adequacy to identify candidate driver events (GATA3 mutations, ERBB2 or FGFR1 gains, TP53 loss) and measure intra-lesion genetic heterogeneity.

CONCLUSION

Alternate experimental and analytical strategies increased the accuracy of DNA sequencing from archived micro-dissected PML regions, supporting the deeper molecular characterization of early cancer lesions and achieving a critical milestone in the development of biology-informed prognostic markers and precision chemo-prevention strategies.

Systematic analysis of breast atypical hyperplasia-associated hub genes and pathways based on text mining

The purpose of this study was to describe breast atypical hyperplasia (BAH)-related gene expression and to systematically analyze the functions, pathways, and networks of BAH-related hub genes. On the basis of natural language processing, gene data for BAH were extracted from the PubMed database using text mining. The enriched Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways were obtained using DAVID (http://david.abcc.ncifcrf.gov/). A protein–protein interaction network was constructed using the STRING database. Hub genes were identified as genes that interact with at least 10 other genes within the BAH-related gene network. In total, 138 BAH-associated genes were identified as significant (P < 0.05), and 133 pathways were identified as significant (P < 0.05, false discovery rate < 0.05). A BAH-related protein network that included 81 interactions was constructed. Twenty genes were determined to interact with at least 10 others (P < 0.05, false discovery rate < 0.05) and were identified as the BAH-related hub genes of this protein–protein interaction network. These 20 genes are TP53, PIK3CA, JUN, MYC, EGFR, CCND1, AKT1, ERBB2, CTNN1B, ESR1, IGF-1, VEGFA, HRAS, CDKN1B, CDKN1A, PCNA, HGF, HIF1A, RB1, and STAT5A. This study may help to disclose the molecular mechanisms of BAH development and provide implications for BAH-targeted therapy or even breast cancer prevention. Nevertheless, connections between certain genes and BAH require further exploration.

The genomic and epigenomic evolutionary history of papillary renal cell carcinomas

Intratumor heterogeneity (ITH) and tumor evolution have been well described for clear cell renal cell carcinomas (ccRCC), but they are less studied for other kidney cancer subtypes. Here we investigate ITH and clonal evolution of papillary renal cell carcinoma (pRCC) and rarer kidney cancer subtypes, integrating whole-genome sequencing and DNA methylation data. In 29 tumors, up to 10 samples from the center to the periphery of each tumor, and metastatic samples in 2 cases, enable phylogenetic analysis of spatial features of clonal expansion, which shows congruent patterns of genomic and epigenomic evolution. In contrast to previous studies of ccRCC, in pRCC, driver gene mutations and most arm-level somatic copy number alterations (SCNAs) are clonal. These findings suggest that a single biopsy would be sufficient to identify the important genetic drivers and that targeting large-scale SCNAs may improve pRCC treatment, which is currently poor. While type 1 pRCC displays near absence of structural variants (SVs), the more aggressive type 2 pRCC and the rarer subtypes have numerous SVs, which should be pursued for prognostic significance.

MIPUP: minimum perfect unmixed phylogenies for multi-sampled tumors via branchings and ILP

Motivation

Discovering the evolution of a tumor may help identify driver mutations and provide a more comprehensive view on the history of the tumor. Recent studies have tackled this problem using multiple samples sequenced from a tumor, and due to clinical implications, this has attracted great interest. However, such samples usually mix several distinct tumor subclones, which confounds the discovery of the tumor phylogeny.

Results

We study a natural problem formulation requiring to decompose the tumor samples into several subclones with the objective of forming a minimum perfect phylogeny. We propose an Integer Linear Programming formulation for it, and implement it into a method called MIPUP. We tested the ability of MIPUP and of four popular tools LICHeE, AncesTree, CITUP, Treeomics to reconstruct the tumor phylogeny. On simulated data, MIPUP shows up to a 34% improvement under the ancestor-descendant relations metric. On four real datasets, MIPUP’s reconstructions proved to be generally more faithful than those of LICHeE.

Availability and implementation

MIPUP is available at https://github.com/zhero9/MIPUP as open source.

Supplementary information

Supplementary data are available at Bioinformatics online.

Development and Analytical Validation of a DNA Dual-Strand Approach for the US Food and Drug Administration-Approved Next-Generation Sequencing-Based Praxis Extended RAS Panel for Metastatic Colorectal Cancer Samples

A next-generation sequencing method was developed that can distinguish single-stranded modifications from low-frequency somatic mutations present on both strands of DNA in formalin-fixed paraffin-embedded colorectal cancer samples. We applied this method for analytical validation of the Praxis Extended RAS Panel, a US Food and Drug Administration-approved companion diagnostic for panitumumab, on the Illumina MiSeqDx platform. With the use of the TruSeq amplicon workflow, both strands of DNA from the starting material were interrogated independently. Mutations were reported only if found on both strands; artifacts usually present on only one strand would not be reported. A total of 56 mutations were targeted within the KRAS and NRAS genes. A minimum read depth of 1800× per amplicon is required per sample but averaged >30,000× at maximum multiplexing levels. Analytical validation studies were performed to determine the simultaneous detection of mutations on both strands, reproducibility, assay detection level, precision of the assay across various factors, and the impact of interfering substances. In conclusion, this assay can clearly distinguish single-stranded artifacts from low-frequency mutations. Furthermore, the assay is accurate, precise, and reproducible, can achieve consistent detection of a mutation at 5% mutation frequency, exhibits minimal impact from tested interfering substances, and can simultaneously detect 56 mutations in a single run using 10 samples plus controls.

Reliability of Whole-Exome Sequencing for Assessing Intratumor Genetic Heterogeneity

Multi-region sequencing is used to detect intratumor genetic heterogeneity (ITGH) in tumors. To assess whether genuine ITGH can be distinguished from sequencing artifacts, we performed whole-exome sequencing (WES) on three anatomically distinct regions of the same tumor with technical replicates to estimate technical noise. Somatic variants were detected with three different WES pipelines and subsequently validated by high-depth amplicon sequencing. The cancer-only pipeline was unreliable, with about 69% of the identified somatic variants being false positive. Even with matched normal DNA for which 82% of the somatic variants were detected reliably, only 36%-78% were found consistently in technical replicate pairs. Overall, 34%-80% of the discordant somatic variants, which could be interpreted as ITGH, were found to constitute technical noise. Excluding mutations affecting low-mappability regions or occurring in certain mutational contexts was found to reduce artifacts, yet detection of subclonal mutations by WES in the absence of orthogonal validation remains unreliable.

Genomic landscape of ductal carcinoma in situ and association with progression

PURPOSE

The detection rate of breast ductal carcinoma in situ (DCIS) has increased significantly, raising the concern that DCIS is overdiagnosed and overtreated. Therefore, there is an unmet clinical need to better predict the risk of progression among DCIS patients. Our hypothesis is that by combining molecular signatures with clinicopathologic features, we can elucidate the biology of breast cancer progression, and risk-stratify patients with DCIS.

METHODS

Targeted exon sequencing with a custom panel of 223 genes/regions was performed for 125 DCIS cases. Among them, 60 were from cases having concurrent or subsequent invasive breast cancer (IBC) (DCIS + IBC group), and 65 from cases with no IBC development over a median follow-up of 13 years (DCIS-only group). Copy number alterations in chromosome 1q32, 8q24, and 11q13 were analyzed using fluorescence in situ hybridization (FISH). Multivariable logistic regression models were fit to the outcome of DCIS progression to IBC as functions of demographic and clinical features.

RESULTS

We observed recurrent variants of known IBC-related mutations, and the most commonly mutated genes in DCIS were PIK3CA (34.4%) and TP53 (18.4%). There was an inverse association between PIK3CA kinase domain mutations and progression (Odds Ratio [OR] 10.2, p < 0.05). Copy number variations in 1q32 and 8q24 were associated with progression (OR 9.3 and 46, respectively; both p < 0.05).

CONCLUSIONS

PIK3CA kinase domain mutations and the absence of copy number gains in DCIS are protective against progression to IBC. These results may guide efforts to distinguish low-risk from high-risk DCIS.

Genetic Alterations in Benign Breast Biopsies of Subsequent Breast Cancer Patients

Background: Fibrocystic changes are associated with an increased risk of breast cancer. Genetic alterations have been found in fibrocystic changes with or without epithelial changes, suggesting that critical oncogenic events are occurring at an early stage.

Methods: We investigated a unique collective of 17 breast cancer patients who, prior to the diagnosis of invasive breast cancer, underwent open surgical biopsy showing fibrocystic changes of the breast. The time span between biopsy for fibrocystic changes and invasive carcinoma ranged from 1 to 11 years (average 5.3 years). Ten (58.8%) of the patients had an ipsilateral invasive carcinoma, and 7 (41.2%) of the patients developed an invasive carcinoma of the contralateral breast. Massive parallel sequencing targeting genes frequently mutated in breast cancer was performed on the fibrocystic breast tissue as well as the ensuing cancer tissue.

Results: In 9 cases, somatic mutations were found in the tumor tissue, the most prevalent being PIK3CA mutations (n = 4), followed by TP53 mutations (n = 2). None of these mutations were present in the previously removed mastopathy tissue. In one of the cases, an ERBB3 E928G mutation was present in the mastopathy as well as in the tumor tissue, with the variant allele frequency in the mastopathy being <0.1%. In two patients, we found two mutations (MAP3K1 L380fs and PIK3CA I391M, respectively) present in the mastopathy as well as in the subsequent breast cancer. These two mutations, however, could also be due to fixation artifacts.

Conclusion: Since no significant somatic mutations in the fibrocystic breast tissue, and only doubtful shared mutations between benign and associated cancer tissue were detected, it remains unclear why women with fibrocystic breast disease have a statistically significant increased risk of breast cancer. Further analyses, maybe on the level of gene expression, could help to clarify the role of these benign alterations in the development of breast cancer and help to identify women at greater risk of developing subsequent invasive cancer.

Atypical ductal hyperplasia is a multipotent precursor of breast carcinoma

The current model for breast cancer progression proposes independent 'low grade (LG)-like' and 'high grade (HG)-like' pathways but lacks a known precursor to HG cancer. We applied low-coverage whole-genome sequencing to atypical ductal hyperplasia (ADH) with and without carcinoma to shed light on breast cancer progression. Fourteen out of twenty isolated ADH cases harboured at least one copy number alteration (CNA), but had fewer aberrations than LG or HG ductal carcinoma in situ (DCIS). ADH carried more HG-like CNA than LG DCIS (e.g. 8q gain). Correspondingly, 64% (7/11) of ADH cases with synchronous HG carcinoma were clonally related, similar to LG carcinoma (67%, 6/9). This study represents a significant shift in our understanding of breast cancer progression, with ADH as a common precursor lesion to the independent 'low grade-like' and 'high grade-like' pathways. These data suggest that ADH can be a precursor of HG breast cancer and that LG and HG carcinomas can evolve from a similar ancestor lesion. We propose that although LG DCIS may be committed to a LG molecular pathway, ADH may remain multipotent, progressing to either LG or HG carcinoma. This multipotent nature suggests that some ADH cases could be more clinically significant than LG DCIS, requiring biomarkers for personalising management. Copyright © 2019 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Parity Differently Affects the Breast Cancer Specific Survival from Ductal Carcinoma In Situ to Invasive Cancer: A Registry-Based Retrospective Study from Korea

Purpose:

Multiparity might increase general mortality for women, but has inconclusive in patients with breast cancer. Here, we aim to discover their effect in terms of the breast cancer development hypothesis: from ductal carcinoma in situ to invasive carcinoma.

Methods:

We included 37 947 patients from the web-based breast cancer registration program of the Korean Breast Cancer Society and analyzed survivals using multivariate Cox regression analysis and whether the associations of these factors displayed linear trends. They were divided into the following groups: (1) pure ductal carcinoma in situ (DCIS), (2) invasive ductal carcinoma (IDC) mixed with intraductal component (DCIS-IDC), and (3) node negative pure IDC.

Results:

The mean age was 48.9 ± 9.9 years including premenopausal women was 61.8%. Although patients with parities of 1-3 had better prognosis compared with patients with nulliparous women, high parity (⩾4) increased the hazard ratio (HR) of overall survival (OS) (DCIS: HR, 1.52; 95% confidence interval [CI] 0.62-3.78; IDC: HR, 1.43, 95% CI 0.89-2.31; and DCIS-IDC: HR, 1.44, 95% CI 0.45-4.59) during 84.2 (±10.7) months. For breast cancer specific survival (BCSS), the HR of the IDC group (P-value for trend = .04) increased along with increasing parity and was worse than nulliparous patients, and the HR of the DCIS-IDC group increased but was better than nulliparous patients (P-value for trend = .02). Compared with nulliparous patients, any age at first birth (AFB) decreased HR of OS in the DCIS and IDC groups (DCIS: P = .01; IDC: P = .04).

Conclusions:

Parity show dual effects on OS of women with all ductal typed breast cancer but show different effects on BCSS in Korea.

Leveraging the variable natural history of ductal carcinoma in situ (DCIS) to select optimal therapy

PURPOSE

Ductal carcinoma in situ (DCIS) is a non-obligate precursor to invasive ductal carcinoma. The authors sought to discuss the evidence suggesting that not all DCIS will progress to invasive disease if left untreated.

RESULTS

Four lines of evidence align to suggest that not all of this in-situ disease progresses to invasive cancer: its prevalence on screening mammography, studies of missed diagnoses, incidental findings in autopsy specimens, and large retrospective reviews of those treated with excision alone.

CONCLUSION

A clearer understanding of the variable history of DCIS coupled with advances in genomic profiling of the disease holds the promise of reducing widespread over-treatment of this non-invasive cancer. Additionally, identification of higher risk of recurrence subsets may select patients for whom more aggressive treatment may be appropriate.

Ductal Carcinoma in Situ Biomarkers in a Precision Medicine Era: Current and Future Molecular-Based Testing

Historically, ductal carcinoma in situ (DCIS) of the breast has been managed aggressively with surgery and radiotherapy because of a risk of progression to invasive ductal carcinoma. However, this treatment paradigm has been challenged by overtreatment concerns and evidence that suggests that DCIS can be stratified according to risk of recurrence or risk of progression to invasive disease. Traditional methods of risk stratification include histologic grade and hormone receptor status. Recent technological advancements have enabled an era of precision medicine, where DCIS can be molecularly analyzed by tools, such as next-generation DNA and RNA sequencing, to identify molecular biomarkers for risk stratification. These findings have led to the development of tools such as the Oncotype DX Breast DCIS Score, a gene expression-based assay with the potential to prevent overtreatment in low-risk disease.

Using LICHeE and BAMSE for Reconstructing Cancer Phylogenetic Trees

The reconstruction of cancer phylogeny trees and quantifying the evolution of the disease is a challenging task. LICHeE and BAMSE are two computational tools designed and implemented recently for this purpose. They both utilize estimated variant allele fraction of somatic mutations across multiple samples to infer the most likely cancer phylogenies. This unit provides extensive guidelines for installing and running both LICHeE and BAMSE. © 2018 by John Wiley & Sons, Inc.

Atypical ductal hyperplasia: update on diagnosis, management, and molecular landscape

BACKGROUND

Atypical ductal hyperplasia (ADH) is a common diagnosis in the mammographic era and a significant clinical problem with wide variation in diagnosis and treatment. After a diagnosis of ADH on biopsy a proportion are upgraded to carcinoma upon excision; however, the remainder of patients are overtreated. While ADH is considered a non-obligate precursor of invasive carcinoma, the molecular taxonomy remains unknown.

MAIN TEXT

Although a few studies have revealed some of the key genomic characteristics of ADH, a clear understanding of the molecular changes associated with breast cancer progression has been limited by inadequately powered studies and low resolution methodology. Complicating factors such as family history, and whether the ADH present in a biopsy is an isolated lesion or part of a greater neoplastic process beyond the limited biopsy material, make accurate interpretation of genomic features and their impact on progression to malignancy a challenging task. This article will review the definitions and variable management of the patients diagnosed with ADH as well as the current knowledge of the molecular landscape of ADH and its clonal relationship with ductal carcinoma in situ and invasive carcinoma.

CONCLUSIONS

Molecular data of ADH remain sparse. Large prospective cohorts of pure ADH with clinical follow-up need to be evaluated at DNA, RNA, and protein levels in order to develop biomarkers of progression to carcinoma to guide management decisions.

The origins of breast cancer associated with mammographic density: a testable biological hypothesis

Background

Our purpose is to develop a testable biological hypothesis to explain the known increased risk of breast cancer associated with extensive percent mammographic density (PMD), and to reconcile the apparent paradox that although PMD decreases with increasing age, breast cancer incidence increases.

Methods

We used the Moolgavkar model of carcinogenesis as a framework to examine the known biological properties of the breast tissue components associated with PMD that includes epithelium and stroma, in relation to the development of breast cancer. In this model, normal epithelial cells undergo a mutation to become intermediate cells, which, after further mutation, become malignant cells. A clone of such cells grows to become a tumor. The model also incorporates changes with age in the number of susceptible epithelial cells associated with menarche, parity, and menopause. We used measurements of the radiological properties of breast tissue in 4454 healthy subjects aged from 15 to 80+ years to estimate cumulative exposure to PMD (CBD) in the population, and we examined the association of CBD with the age-incidence curve of breast cancer in the population.

Results

Extensive PMD is associated with a greater number of breast epithelial cells, lobules, and fibroblasts, and greater amounts of collagen and extracellular matrix. The known biological properties of these tissue components may, singly or in combination, promote the acquisition of mutations by breast epithelial cells specified by the Moolgavkar model, and the subsequent growth of a clone of malignant cells to form a tumor. We also show that estimated CBD in the population from ages 15 to 80+ years is closely associated with the age-incidence curve of breast cancer in the population.

Conclusions

These findings are consistent with the hypothesis that the biological properties of the breast tissue components associated with PMD increase the probability of the transition of normal epithelium to malignant cells, and that the accumulation of mutations with CBD may influence the age-incidence curve of breast cancer. This hypothesis gives rise to several testable predictions.

Electronic supplementary material

The online version of this article (10.1186/s13058-018-0941-y) contains supplementary material, which is available to authorized users.

Somatic evolutionary timings of driver mutations

BACKGROUND

A unified analysis of DNA sequences from hundreds of tumors concluded that the driver mutations primarily occur in the earliest stages of cancer formation, with relatively few driver mutation events detected in the late-arising subclones. However, emerging evidence from the sequencing of multiple tumors and tumor regions per individual suggests that late-arising subclones with additional driver mutations are underestimated in single-sample analyses.

METHODS

To test whether driver mutations generally map to early tumor development, we examined multi-regional tumor sequencing data from 101 individuals reported in 11 published studies. Following previous studies, we annotated mutations as early-arising when all tumors/regions had those mutations (ubiquitous). We then inferred the fraction of mutations occurring early and compared it with late-arising mutations that were found in only single tumors/regions.

RESULTS

While a large fraction of driver mutations in tumors occurred relatively early in cancers, later driver mutations occurred at least as frequently as the early drivers in a substantial number of patients. This result was robust to many different approaches to annotate driver mutations. The relative frequency of early and late driver mutations varied among patients of the same cancer type and in different cancer types. We found that previous reports of the preponderance of early driver mutations were primarily informed by analysis of single tumor variant allele profiles, with which it is challenging to clearly distinguish between early and late drivers.

CONCLUSIONS

The origin and preponderance of new driver mutations are not limited to early stages of tumor evolution, with different tumors and regions showing distinct driver mutations and, consequently, distinct characteristics. Therefore, tumors with extensive intratumor heterogeneity appear to have many newly acquired drivers.

Multiclonal Invasion in Breast Tumors Identified by Topographic Single Cell Sequencing

Ductal carcinoma in situ (DCIS) is an early-stage breast cancer that infrequently progresses to invasive ductal carcinoma (IDC). Genomic evolution has been difficult to delineate during invasion due to intratumor heterogeneity and the low number of tumor cells in the ducts. To overcome these challenges, we developed Topographic Single Cell Sequencing (TSCS) to measure genomic copy number profiles of single tumor cells while preserving their spatial context in tissue sections. We applied TSCS to 1,293 single cells from 10 synchronous patients with both DCIS and IDC regions in addition to exome sequencing. Our data reveal a direct genomic lineage between in situ and invasive tumor subpopulations and further show that most mutations and copy number aberrations evolved within the ducts prior to invasion. These results support a multiclonal invasion model, in which one or more clones escape the ducts and migrate into the adjacent tissues to establish the invasive carcinomas.

Is DCIS Overrated?

Ductal carcinoma in situ (DCIS), the noninvasive form of breast cancer (BC), comprises just over 20% of breast cancer cases diagnosed each year in the USA. Most patients are treated with local excision of the disease followed by whole breast radiation therapy. Total mastectomy is not an uncommon approach, and total mastectomy with a contralateral risk-reducing mastectomy has been on the rise in the past decade. In estrogen receptor-positive disease, patients are often offered endocrine ablative therapy with a selective estrogen receptor modulator or an aromatase inhibitor as both treatment and prevention. Local regional treatment options have no impact upon ultimate overall survival. Long-term survival rates are higher in patients with DCIS than with any other form of the disease. Are these strikingly high success rates a testament to effective treatment strategies or is there a significant subset of DCIS that was unlikely to ever progress to invasive ductal carcinoma? DCIS was not seen in the US prior to the advent of screening mammography. When compared to other countries, the USA has the highest utilization of screening mammography and the incidence rate of DCIS. Other lines of evidence include autopsy series examining the breast tissue of women who died of other causes, missed-diagnosis series and current retrospective reviews of DCIS, all align in support of the concept of DCIS as indolent in the majority of cases [3-14]. The evidence suggests that both patient and physician misconceptions about DCIS have led to overdiagnosis and over-treatment of DCIS. Recently, a gene expression profiling tool (12 gene assay, Oncotype DCIS) has emerged that shows considerable promise in predicting class in DCIS patients.

Complexity and Algorithms for Finding a Perfect Phylogeny from Mixed Tumor Samples

Hajirasouliha and Raphael (WABI 2014) proposed a model for deconvoluting mixed tumor samples measured from a collection of high-throughput sequencing reads. This is related to understanding tumor evolution and critical cancer mutations. In short, their formulation asks to split each row of a binary matrix so that the resulting matrix corresponds to a perfect phylogeny and has the minimum number of rows among all matrices with this property. In this paper, we disprove several claims about this problem, including an NP-hardness proof of it. However, we show that the problem is indeed NP-hard, by providing a different proof. We also prove NP-completeness of a variant of this problem proposed in the same paper. On the positive side, we propose an efficient (though not necessarily optimal) heuristic algorithm based on coloring co-comparability graphs, and a polynomial time algorithm for solving the problem optimally on matrix instances in which no column is contained in both columns of a pair of conflicting columns. Implementations of these algorithms are freely available at https://github.com/alexandrutomescu/MixedPerfectPhylogeny.

Association of a novel point mutation in MSH2 gene with familial multiple primary cancers

Background

Multiple primary cancers (MPC) have been identified as two or more cancers without any subordinate relationship that occur either simultaneously or metachronously in the same or different organs of an individual. Lynch syndrome is an autosomal dominant genetic disorder that increases the risk of many types of cancers. Lynch syndrome patients who suffer more than two cancers can also be considered as MPC; patients of this kind provide unique resources to learn how genetic mutation causes MPC in different tissues.

Methods

We performed a whole genome sequencing on blood cells and two tumor samples of a Lynch syndrome patient who was diagnosed with five primary cancers. The mutational landscape of the tumors, including somatic point mutations and copy number alternations, was characterized. We also compared Lynch syndrome with sporadic cancers and proposed a model to illustrate the mutational process by which Lynch syndrome progresses to MPC.

Results

We revealed a novel pathologic mutation on the MSH2 gene (G504 splicing) that associates with Lynch syndrome. Systematical comparison of the mutation landscape revealed that multiple cancers in the proband were evolutionarily independent. Integrative analysis showed that truncating mutations of DNA mismatch repair (MMR) genes were significantly enriched in the patient. A mutation progress model that included germline mutations of MMR genes, double hits of MMR system, mutations in tissue-specific driver genes, and rapid accumulation of additional passenger mutations was proposed to illustrate how MPC occurs in Lynch syndrome patients.

Conclusion

Our findings demonstrate that both germline and somatic alterations are driving forces of carcinogenesis, which may resolve the carcinogenic theory of Lynch syndrome.

Electronic supplementary material

The online version of this article (10.1186/s13045-017-0523-y) contains supplementary material, which is available to authorized users.

Detection of nasopharyngeal carcinoma susceptibility with single nucleotide polymorphism analysis using next-generation sequencing technology

Nasopharyngeal carcinoma (NPC) is a head and neck cancer with high incidence in South China and East Asia. To provide a theoretical basis for NPC risk screening and early prevention, we conducted a meta-analysis of relevant literature on the association of single nucleotide polymorphisms (SNP)s with NPC susceptibility. Further, expression of 15 candidate SNPs identified in the meta-analysis was evaluated in a cohort of NPC patients and healthy volunteers using next-generation sequencing technology. Among the 15 SNPs detected in the meta-analysis, miR-146a (rs2910164, C>G), HCG9 (rs3869062, A>G), HCG9 (rs16896923, T>C), MMP2 (rs243865, C>T), GABBR1 (rs2076483, T>C), and TP53 (rs1042522, C>G) were associated with decreased susceptibility to NPC, while GSTM1 (+/DEL), IL-10 (rs1800896, A>G), MDM2 (rs2279744, T>G), MDS1-EVI1 (rs6774494, G>A), XPC (rs2228000, C>T), HLA-F (rs3129055, T>C), SPLUNC1 (rs2752903, T>C; and rs750064, A>G), and GABBR1 (rs29232, G>A) were associated with increased susceptibility to NPC. In our case-control study, an association with increased risk for NPC was found for the AG vs AA genotype in HCG9 (rs3869062, A>G). In addition, heterozygous deletion of the GSTM1 allele was associated with increased susceptibility to NPC, while an SNP in GABBR1 (rs29232, G>A) was associated with decreased risk, and might thus have a protective role on NPC carcinogenesis. This work provides the first comprehensive assessment of SNP expression and its relationship to NPC risk. It suggests the need for well-designed, larger confirmatory studies to validate its findings.

Single-cell DNA sequencing reveals a late-dissemination model in metastatic colorectal cancer

Metastasis is a complex biological process that has been difficult to delineate in human colorectal cancer (CRC) patients. A major obstacle in understanding metastatic lineages is the extensive intra-tumor heterogeneity at the primary and metastatic tumor sites. To address this problem, we developed a highly multiplexed single-cell DNA sequencing approach to trace the metastatic lineages of two CRC patients with matched liver metastases. Single-cell copy number or mutational profiling was performed, in addition to bulk exome and targeted deep-sequencing. In the first patient, we observed monoclonal seeding, in which a single clone evolved a large number of mutations prior to migrating to the liver to establish the metastatic tumor. In the second patient, we observed polyclonal seeding, in which two independent clones seeded the metastatic liver tumor after having diverged at different time points from the primary tumor lineage. The single-cell data also revealed an unexpected independent tumor lineage that did not metastasize, and early progenitor clones with the "first hit" mutation in APC that subsequently gave rise to both the primary and metastatic tumors. Collectively, these data reveal a late-dissemination model of metastasis in two CRC patients and provide an unprecedented view of metastasis at single-cell genomic resolution.

Genome-wide reconstruction of complex structural variants using read clouds

In read cloud approaches, microfluidic partitioning of long genomic DNA fragments and barcoding of shorter fragments derived from these fragments retains long-range information in short sequencing reads. This combination of short reads with long-range information represents a powerful alternative to single-molecule long-read sequencing. We develop Genome-wide Reconstruction of Complex Structural Variants (GROC-SVs) for SV detection and assembly from read cloud data and apply this method to Illumina-sequenced 10x Genomics sarcoma and breast cancer data sets. Compared with short-fragment sequencing, GROC-SVs substantially improves the specificity of breakpoint detection at comparable sensitivity. This approach also performs sequence assembly across multiple breakpoints simultaneously, enabling the reconstruction of events exhibiting remarkable complexity. We show that chromothriptic rearrangements occurred before copy number amplifications, and that rates of single-nucleotide variants and SVs are not correlated. Our results support the use of read cloud approaches to advance the characterization of large and complex structural variation.

Computational Methods for Characterizing Cancer Mutational Heterogeneity

Advances in DNA sequencing technologies have allowed the characterization of somatic mutations in a large number of cancer genomes at an unprecedented level of detail, revealing the extreme genetic heterogeneity of cancer at two different levels: inter-tumor, with different patients of the same cancer type presenting different collections of somatic mutations, and intra-tumor, with different clones coexisting within the same tumor. Both inter-tumor and intra-tumor heterogeneity have crucial implications for clinical practices. Here, we review computational methods that use somatic alterations measured through next-generation DNA sequencing technologies for characterizing tumor heterogeneity and its association with clinical variables. We first review computational methods for studying inter-tumor heterogeneity, focusing on methods that attempt to summarize cancer heterogeneity by discovering pathways that are commonly mutated across different patients of the same cancer type. We then review computational methods for characterizing intra-tumor heterogeneity using information from bulk sequencing data or from single cell sequencing data. Finally, we present some of the recent computational methodologies that have been proposed to identify and assess the association between inter- or intra-tumor heterogeneity with clinical variables.

phyC: Clustering cancer evolutionary trees

Multi-regional sequencing provides new opportunities to investigate genetic heterogeneity within or between common tumors from an evolutionary perspective. Several state-of-the-art methods have been proposed for reconstructing cancer evolutionary trees based on multi-regional sequencing data to develop models of cancer evolution. However, there have been few studies on comparisons of a set of cancer evolutionary trees. We propose a clustering method (phyC) for cancer evolutionary trees, in which sub-groups of the trees are identified based on topology and edge length attributes. For interpretation, we also propose a method for evaluating the sub-clonal diversity of trees in the clusters, which provides insight into the acceleration of sub-clonal expansion. Simulation showed that the proposed method can detect true clusters with sufficient accuracy. Application of the method to actual multi-regional sequencing data of clear cell renal carcinoma and non-small cell lung cancer allowed for the detection of clusters related to cancer type or phenotype. phyC is implemented with R(≥3.2.2) and is available from https://github.com/ymatts/phyC.

Elucidating the differences between cancer evolutionary patterns among patients is valuable in personalized medicine, since therapeutic response mostly depends on cancer evolution process. Recently, computational methods have been extensively studied to reconstruct a cancer evolutionary pattern within a patient, which is visualized as a so-called “cancer evolutionary tree” constructed from multi-regional sequencing data. However, there have been few studies on comparisons of a set of cancer evolutionary trees to better understand the relationship between a set of cancer evolutionary patterns and patient phenotypes. Given a set of tree objects for multiple patients, we propose an unsupervised learning approach to identify subgroups of patients through clustering the respective cancer evolutionary trees. Using this approach, we effectively identified the patterns of different evolutionary modes in a simulation analysis, and also successfully detected the phenotype-related and cancer type-related subgroups to characterize tree structures within subgroups using actual datasets. We believe that the value and impact of our work will grow as more and more datasets for the cancer evolution of patients become available.

The evolution of tumour phylogenetics: principles and practice

Rapid advances in high-throughput sequencing and a growing realization of the importance of evolutionary theory to cancer genomics have led to a proliferation of phylogenetic studies of tumour progression. These studies have yielded not only new insights but also a plethora of experimental approaches, sometimes reaching conflicting or poorly supported conclusions. Here, we consider this body of work in light of the key computational principles underpinning phylogenetic inference, with the goal of providing practical guidance on the design and analysis of scientifically rigorous tumour phylogeny studies. We survey the range of methods and tools available to the researcher, their key applications, and the various unsolved problems, closing with a perspective on the prospects and broader implications of this field.

Lateral gene transfer between prokaryotes and eukaryotes

Lateral gene transfer (LGT) is an all-encompassing term for the movement of DNA between diverse organisms. LGT is synonymous with horizontal gene transfer, and the terms are used interchangeably throughout the scientific literature. While LGT has been recognized within the bacteria domain of life for decades, inter-domain LGTs are being increasingly described. LGTs between bacteria and complex multicellular organisms are of interest because they challenge the long-held dogma that such transfers could only occur in closely-related, single-celled organisms. Scientists will continue to challenge our understanding of LGT as we sequence more, diverse organisms, as we sequence more endosymbiont-colonized arthropods, and as we continue to appreciate LGT events, both young and old.

Tumor evolution: Linear, branching, neutral or punctuated?

Intratumor heterogeneity has been widely reported in human cancers, but our knowledge of how this genetic diversity emerges over time remains limited. A central challenge in studying tumor evolution is the difficulty in collecting longitudinal samples from cancer patients. Consequently, most studies have inferred tumor evolution from single time-point samples, providing very indirect information. These data have led to several competing models of tumor evolution: linear, branching, neutral and punctuated. Each model makes different assumptions regarding the timing of mutations and selection of clones, and therefore has different implications for the diagnosis and therapeutic treatment of cancer patients. Furthermore, emerging evidence suggests that models may change during tumor progression or operate concurrently for different classes of mutations. Finally, we discuss data that supports the theory that most human tumors evolve from a single cell in the normal tissue. This article is part of a Special Issue entitled: Evolutionary principles - heterogeneity in cancer?, edited by Dr. Robert A. Gatenby.

Genome evolution in ductal carcinoma in situ: invasion of the clones

Ductal carcinoma in situ (DCIS) is the most frequently diagnosed early-stage breast cancer. Only a subset of patients progress to invasive ductal carcinoma (IDC), and this presents a formidable clinical challenge for determining which patients to treat aggressively and which patients to monitor without therapeutic intervention. Understanding the molecular and genomic basis of invasion has been difficult to study in DCIS cancers due to several technical obstacles, including low tumour cellularity, lack of fresh-frozen tissues, and intratumour heterogeneity. In this review, we discuss the role of intratumour heterogeneity in the progression of DCIS to IDC in the context of three evolutionary models: independent lineages, evolutionary bottlenecks, and multiclonal invasion. We examine the evidence in support of these models and their relevance to the diagnosis and treatment of patients with DCIS. We also discuss how emerging technologies, such as single-cell sequencing, STAR-FISH, and imaging mass spectrometry, are likely to provide new insights into the evolution of this enigmatic disease. Copyright © 2016 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Progress in the clinical detection of heterogeneity in breast cancer

Breast cancer is currently the most common form of cancer and the second‐leading cause of death from cancer in women. Though considerable progress has been made in the treatment of breast cancer, the heterogeneity of tumors (both inter‐ and intratumor) remains a considerable diagnostic and prognostic challenge. From clinical observation to genetic mutations, the history of understanding the heterogeneity of breast cancer is lengthy and detailed. Effectively detecting heterogeneity in breast cancer is important during treatment. Various methods of depicting this heterogeneity are now available and include genetic, pathologic, and imaging analysis. These methods allow characterization of the heterogeneity of breast cancer on a genetic level, providing greater insight during the process of establishing an effective therapeutic plan. This study reviews how the understanding of tumor heterogeneity in breast cancer evolved, and further summarizes recent advances in the detection and monitoring of this heterogeneity in patients with breast cancer.

Multiplexed imaging reveals heterogeneity of PI3K/MAPK network signaling in breast lesions of known PIK3CA genotype

PURPOSE

Activating genetic changes in the phosphatidylinositol-3-kinase (PI3K) signaling pathway are found in over half of invasive breast cancers (IBCs). Previously, we discovered numerous hotspot PIK3CA mutations in proliferative breast lesions. Here, we investigate the spatial nature of PI3K pathway signaling and its relationship with PI3K genotype in breast lesions.

METHODS

We identified PI3K phosphosignaling network signatures in columnar cell change (CCL), usual ductal hyperplasia (UDH), ductal carcinoma in situ (DCIS), and IBC in 26 lesions of known PIK3CA genotype from 10 human breast specimens using a hyperspectral-based multiplexed tissue imaging platform (MTIP) to simultaneously quantitate PI3K/MAPK pathway targets (pAKT473, pAKT308, pPRAS40, pS6, and pERK) in FFPE tissue, with single-cell resolution.

RESULTS

We found that breast lesional epithelia contained spatially heterogeneous patterns of PI3K pathway phosphoprotein signatures, even within microscopic areas of CCL, UDH, DCIS, and IBC. Most lesions contained 3-12 unique phosphoprotein signatures within the same microscopic field. The dominant phosphoprotein signature for each lesion was not well correlated with lesion genotype or lesion histology, yet samples from the same patient tended to group together. Further, 5 UDH/CCL lesions across different patients had a common phosphosignature at the epithelial-stromal interface (possible myoepithelial cells) that was distinct from both the adjacent lesional epithelium and distinct from adjacent stroma.

CONCLUSION

We present the first spatial mapping of PI3K phosphoprotein networks in proliferative breast lesions and demonstrate complex PI3K signaling heterogeneity that defies simple correlation between PIK3CA genotype and phosphosignal pattern.

Comparative genomic analysis reveals bilateral breast cancers are genetically independent

Bilateral breast cancer (BBC) poses a major challenge for oncologists because of the cryptic relationship between the two lesions. The purpose of this study was to determine the origin of the contralateral breast cancer (either dependent or independent of the index tumor). Here, we used ultra-deep whole-exome sequencing and array comparative genomic hybridization (aCGH) to study four paired samples of BBCs with different tumor subtypes and time intervals between the developments of each tumor. We used two paired primary breast tumors and corresponding metastatic liver lesions as the control. We tested the origin independent nature of BBC in three ways: mutational concordance, mutational signature clustering, and clonality analysis using copy number profiles. We found that the paired BBC samples had near-zero concordant mutation rates, which were much lower than those of the paired primary/metastasis samples. The results of a mutational signature analysis also suggested that BBCs are independent of one another. A clonality analysis using aCGH data further revealed that paired BBC samples was clonally independent, in contrast to clonal related origin found for paired primary/metastasis samples. Our preliminary findings show that BBCs in Han Chinese women are origin independent and thus should be treated separately.

Allele-Specific Quantification of Structural Variations in Cancer Genomes

Aneuploidy and structural variations (SVs) generate cancer genomes containing a mixture of rearranged genomic segments with extensive somatic copy number alterations. However, existing methods can identify either SVs or allele-specific copy number alterations, but not both simultaneously, which provides a limited view of cancer genome structure. Here we introduce Weaver, an algorithm for the quantification and analysis of allele-specific copy numbers of SVs. Weaver uses a Markov Random Field to estimate joint probabilities of allele-specific copy number of SVs and their inter-connectivity based on paired-end whole-genome sequencing data. Weaver also predicts the timing of SVs relative to chromosome amplifications. We demonstrate the accuracy of Weaver using simulations and findings from whole-genome Optical Mapping. We apply Weaver to generate allele-specific copy numbers of SVs for MCF-7 and HeLa cell lines, and identify recurrent SV patterns in 44 TCGA ovarian cancer whole-genome sequencing datasets. Our approach provides a more complete assessment of the complex genomic architectures inherent to many cancer genomes.