Epistatic interactions and drug response

A personalized pharmaco-epistatic network model of precision medicine

Precision medicine, the utilization of targeted treatments to address an individual's disease, relies on knowledge about the genetic cause of that individual's drug response. Here, we present a functional graph (FunGraph) theory to chart comprehensive pharmacogenetic architecture for each and every patient. FunGraph is the combination of functional mapping - a dynamic model for genetic mapping and evolutionary game theory guiding interactive strategies. It coalesces all pharmacogenetic factors into multilayer and multiplex networks that fully capture bidirectional, signed and weighted epistasis. It can visualize and interrogate how epistasis moves in the cell and how this movement leads to patient- and context-specific genetic architecture in response to organismic physiology. We discuss the future implementation of FunGraph to achieve precision medicine. Teaser: We present a functional graph (FunGraph) theory to draw a complete picture of pharmacogenetic architecture underlying interindividual variability in drug response. FunGraph can characterize how each gene acts and interacts with every other gene to mediate therapeutic response.

Interpretable Machine Learning Models to Predict the Resistance of Breast Cancer Patients to Doxorubicin from Their microRNA Profiles

Doxorubicin is a common treatment for breast cancer. However, not all patients respond to this drug, which sometimes causes life-threatening side effects. Accurately anticipating doxorubicin-resistant patients would therefore permit to spare them this risk while considering alternative treatments without delay. Stratifying patients based on molecular markers in their pretreatment tumors is a promising approach to advance toward this ambitious goal, but single-gene gene markers such as HER2 expression have not shown to be sufficiently predictive. The recent availability of matched doxorubicin-response and diverse molecular profiles across breast cancer patients permits now analysis at a much larger scale. 16 machine learning algorithms and 8 molecular profiles are systematically evaluated on the same cohort of patients. Only 2 of the 128 resulting models are substantially predictive, showing that they can be easily missed by a standard-scale analysis. The best model is classification and regression tree (CART) nonlinearly combining 4 selected miRNA isoforms to predict doxorubicin response (median Matthew correlation coefficient (MCC) and area under the curve (AUC) of 0.56 and 0.80, respectively). By contrast, HER2 expression is significantly less predictive (median MCC and AUC of 0.14 and 0.57, respectively). As the predictive accuracy of this CART model increases with larger training sets, its update with future data should result in even better accuracy.

MACHINE LEARNING FOR DETECTING EPISTASIS INTERACTIONS AND ITS RELEVANCE TO PERSONALIZED MEDICINE IN ALZHEIMER’S DISEASE: SYSTEMATIC REVIEW

Alzheimer’s disease (AD) is a progressive disease that attacks the brain’s neurons and causes problems in memory, thinking, and reasoning skills. Personalized Medicine (PM) needs a better and more accurate understanding of the relationship between human genetic data and complex diseases like AD. The goal of PM is to tailor the treatment of a case person to his individual properties. PM requires the prediction of a person’s disease from genetic data, and its success depends on the accurate detection of genetic biomarkers. Single Nucleotide polymorphisms (SNPs) are considered the most prevalent type of variation in the human genome. Epistasis has a biological relevance to complex diseases and has an important impact on PM. Detection of the most significant epistasis interactions associated with complex diseases is a big challenge. This paper reviews several machine learning techniques and algorithms to detect the most significant epistasis interactions in Alzheimer’s disease. We discuss many machine learning techniques that can be used for detecting SNPs’ combinations like Random Forests, Support Vector Machines, Multifactor Dimensionality Reduction, Neural Network, and Deep Learning. This review paper highlights the pros and cons of these techniques and explains how they can be applied in an efficient framework to apply knowledge discovery and data mining in AD disease.

High-throughput screening and genome-wide analyses of 44 anticancer drugs in the 1000 Genomes cell lines reveals an association of the NQO1 gene with the response of multiple anticancer drugs

Cancer patients exhibit a broad range of inter-individual variability in response and toxicity to widely used anticancer drugs, and genetic variation is a major contributor to this variability. To identify new genes that influence the response of 44 FDA-approved anticancer drug treatments widely used to treat various types of cancer, we conducted high-throughput screening and genome-wide association mapping using 680 lymphoblastoid cell lines from the 1000 Genomes Project. The drug treatments considered in this study represent nine drug classes widely used in the treatment of cancer in addition to the paclitaxel + epirubicin combination therapy commonly used for breast cancer patients. Our genome-wide association study (GWAS) found several significant and suggestive associations. We prioritized consistent associations for functional follow-up using gene-expression analyses. The NAD(P)H quinone dehydrogenase 1 (NQO1) gene was found to be associated with the dose-response of arsenic trioxide, erlotinib, trametinib, and a combination treatment of paclitaxel + epirubicin. NQO1 has previously been shown as a biomarker of epirubicin response, but our results reveal novel associations with these additional treatments. Baseline gene expression of NQO1 was positively correlated with response for 43 of the 44 treatments surveyed. By interrogating the functional mechanisms of this association, the results demonstrate differences in both baseline and drug-exposed induction.

In the burgeoning field of personalized medicine, genetic variation is recognized as a major contributor to patients’ differential responses to drugs. Lymphoblastoid cell lines (LCLs) are a consistent and convenient representation of cells used for in vitro research. Human genome sequencing with LCLs can identify new genes that influence individuals’ drug responses, including the dose-response relationship, which describes the relationship between physiological response and the amount of exposure to a substance. In this work, we conduct high-throughput screening and genome-wide association mapping using 680 LCLs from the 1000 Genomes Project to identify new genes that influence individual response to 44 widely used anticancer drugs. We found the NQO1 gene to be associated with the dose-response of several drugs, namely arsenic trioxide, erlotinib, trametinib, and the paclitaxel + epirubicin combination, and performed follow-up analyses to better understand its functional role in drug response. Our results indicate NQO1 expression is correlated with increased drug resistance and provide some evidence that SNP rs1800566 influences drug response by altering protein activity for these four treatments. With further research, NQO1 has potential use as a therapeutic target, for example, suppressing NQO1 expression to increase sensitivity to particular drugs.

The somatic molecular evolution of cancer: Mutation, selection, and epistasis

Cancer progression has been attributed to somatic changes in single-nucleotide variants, copy-number aberrations, loss of heterozygosity, chromosomal instability, epistatic interactions, and the tumor microenvironment. It is not entirely clear which of these changes are essential and which are ancillary to cancer. The dynamic nature of cancer evolution in a patient can be illuminated using several concepts and tools from classical evolutionary biology. Neutral mutation rates in cancer cells are calculable from genomic data such as synonymous mutations, and selective pressures are calculable from rates of fixation occurring beyond the expectation by neutral mutation and drift. However, these cancer effect sizes of mutations are complicated by epistatic interactions that can determine the likely sequence of gene mutations. In turn, longitudinal phylogenetic analyses of somatic cancer progression offer an opportunity to identify key moments in cancer evolution, relating the timing of driver mutations to corresponding landmarks in the clinical timeline. These analyses reveal temporal aspects of genetic and phenotypic change during tumorigenesis and across clinical timescales. Using a related framework, clonal deconvolution, physical locations of clones, and their phylogenetic relations can be used to infer tumor migration histories. Additionally, genetic interactions with the tumor microenvironment can be analyzed with longstanding approaches applied to organismal genotype-by-environment interactions. Fitness landscapes for cancer evolution relating to genotype, phenotype, and environment could enable more accurate, personalized therapeutic strategies. An understanding of the trajectories underlying the evolution of neoplasms, primary, and metastatic tumors promises fundamental advances toward accurate and personalized predictions of therapeutic response.

Widespread epistasis regulates glucose homeostasis and gene expression

The relative contributions of additive versus non-additive interactions in the regulation of complex traits remains controversial. This may be in part because large-scale epistasis has traditionally been difficult to detect in complex, multi-cellular organisms. We hypothesized that it would be easier to detect interactions using mouse chromosome substitution strains that simultaneously incorporate allelic variation in many genes on a controlled genetic background. Analyzing metabolic traits and gene expression levels in the offspring of a series of crosses between mouse chromosome substitution strains demonstrated that inter-chromosomal epistasis was a dominant feature of these complex traits. Epistasis typically accounted for a larger proportion of the heritable effects than those due solely to additive effects. These epistatic interactions typically resulted in trait values returning to the levels of the parental CSS host strain. Due to the large epistatic effects, analyses that did not account for interactions consistently underestimated the true effect sizes due to allelic variation or failed to detect the loci controlling trait variation. These studies demonstrate that epistatic interactions are a common feature of complex traits and thus identifying these interactions is key to understanding their genetic regulation.

Most complex traits and diseases are regulated by the combined influence of multiple genetic variants. However, it remains controversial whether these genetic variants independently influence complex traits, and therefore the impact of each variant could be simply added together (additivity), or whether the variants work together to influence trait variation, in which case the combined impact of multiple variants would differ from the summed impact of each individual variant (epistasis). In this study in mice, we discovered that the genetic regulation of blood sugar levels and gene expression in the liver were predominantly controlled by non-additive interactions, whereas body weight was predominantly controlled by additive interactions. Remarkably, the expression level of nearly 25% of all genes in the liver was controlled by non-additive interactions. The non-additive interactions typically acted to return trait values to the levels detected in control mice, thus contributing to a reduction in trait variation. We also demonstrated that not accounting for non-additive interactions significantly underestimated the phenotypic effect of a genetic variant on a particular genetic background, suggesting that many previously identified risk loci may have significantly larger effects on disease susceptibility in a subset of individuals. These studies highlight the importance of understanding interactions between genetic variants to better understand disease risk and personalize clinical care.

TP53 Mutational Spectrum in Endometrioid and Serous Endometrial Cancers

Endometrial carcinomas (ECs) are heterogeneous at the genetic level. Although TP53 mutations are highly recurrent in serous endometrial carcinomas (SECs), these are also present in a subset of endometrioid endometrial carcinomas (EECs). Here, we sought to define the frequency, pattern, distribution, and type of TP53 somatic mutations in ECs by performing a reanalysis of the publicly available data from The Cancer Genome Atlas (TCGA). A total of 228 EECs (n=186) and SECs (n=42) from the TCGA data set, for which an integrated genomic characterization was performed, were interrogated for the presence and type of TP53 mutations, and for mutations in genes frequently mutated in ECs. TP53 mutations were found in 15% of EECs and 88% of SECs, and in 91% of copy-number-high and 35% of polymerase (DNA directed), epsilon, catalytic subunit (POLE) integrative genomic subtypes. In addition to differences in prevalence, variations in the type and pattern of TP53 mutations were observed between histologic types and between integrative genomic subtypes. TP53 hotspot mutations were significantly more frequently found in SECs (46%) than in EECs (15%). TP53-mutant EECs significantly more frequently harbored a co-occurring PTEN mutation than TP53-mutant SECs. Finally, a subset of TP53-mutant ECs (22%) was found to harbor frameshift or nonsense mutations. Given that nonsense and frameshift TP53 mutations result in distinct p53 immunohistochemical results that require careful interpretation, and that EECs and SECs display different patterns, types, and distributions of TP53 mutations, the use of the TP53/p53 status alone for the differential diagnosis of EECs and SECs may not be sufficient.

Lack of association of CASC8 rs1447295 with colorectal cancer in Iranian population: A multicenter case-control study

Several studies reported the potential role of the rs1447295 polymorphism in susceptibility to cancer. This variant located in the cancer susceptibility candidate 8 (CASC8) is a long noncoding RNA (lnRNA) gene and does not code protein. LnRNA transcripts play a potential regulatory role in the expression of key genes involved in multiple cellular pathways, including cell cycle, pluripotency, and immune response. The aim of this study is to evaluate this association with colorectal cancer (CRC) in a large case-control study of the Iranian population. After extraction of genomic DNA by the standard protocols, the rs1447295 was genotyped in 2416 subjects (46% patients). Results of this case-control demonstrated no significant association between the rs1447295 polymorphism and risk of CRC or its characteristics under allele or alternative genotype models. In conclusion, it is unlikely that the rs1447295 polymorphism is a risk variant for the development of CRC in Iranian population.

Genetic analysis of uterine adenosarcomas and phyllodes tumors of the breast

Uterine adenosarcomas and breast phyllodes tumors (PTs) are morphologically similar, being composed of stromal projections in a leaf-like fashion lined by epithelial cells. Here, we investigated whether their histologic similarities would be mirrored at the genetic level. The previously reported repertoires of somatic genetic alterations found in 19 adenosarcomas and 22 PTs (six benign, six borderline, and 10 malignant) were compared. PTs significantly more frequently displayed mutations affecting MED12, the TERT gene promoter and bona fide cancer genes, whereas adenosarcomas harbored a higher rate of MDM2/CDK4 and TERT gene amplifications. Pathway analyses based on the genes affected by somatic genetic alterations in these tumors indicated that Wnt signaling likely plays a role in the biology of adenosarcomas and benign/borderline PTs. In conclusion, despite the differences at the gene level, PTs and adenosarcomas share remarkable morphologic similarities and enrichment for somatic genetic alterations affecting Wnt pathway-related genes.

Genetic Heterogeneity in Therapy-Naïve Synchronous Primary Breast Cancers and Their Metastases

Purpose: Paired primary breast cancers and metachronous metastases after adjuvant treatment are reported to differ in their clonal composition and genetic alterations, but it is unclear whether these differences stem from the selective pressures of the metastatic process, the systemic therapies, or both. We sought to define the repertoire of genetic alterations in breast cancer patients with de novo metastatic disease who had not received local or systemic therapy.Experimental Design: Up to two anatomically distinct core biopsies of primary breast cancers and synchronous distant metastases from nine patients who presented with metastatic disease were subjected to high-depth whole-exome sequencing. Mutations, copy number alterations and their cancer cell fractions, and mutation signatures were defined using state-of-the-art bioinformatics methods. All mutations identified were validated with orthogonal methods.Results: Genomic differences were observed between primary and metastatic deposits, with a median of 60% (range 6%-95%) of shared somatic mutations. Although mutations in known driver genes including TP53, PIK3CA, and GATA3 were preferentially clonal in both sites, primary breast cancers and their synchronous metastases displayed spatial intratumor heterogeneity. Likely pathogenic mutations affecting epithelial-to-mesenchymal transition-related genes, including SMAD4, TCF7L2, and TCF4 (ITF2), were found to be restricted to or enriched in the metastatic lesions. Mutational signatures of trunk mutations differed from those of mutations enriched in the primary tumor or the metastasis in six cases.Conclusions: Synchronous primary breast cancers and metastases differ in their repertoire of somatic genetic alterations even in the absence of systemic therapy. Mutational signature shifts might contribute to spatial intratumor genetic heterogeneity. Clin Cancer Res; 23(15); 4402-15. ©2017 AACR.

Rediscovering Secondary Tumors of the Prostate in the Molecular Era

Metastatic involvement of the prostate from noncontiguous solid tumors is a rare event occurring by means of vascular dissemination. The reported cases of biopsy and surgical samples with metastatic involvement have increased; however, a comprehensive understanding of secondary tumors of the prostate is currently missing. Metastases to the prostate carry a dismal prognosis and may pose serious diagnostic challenges to both clinicians and pathologists, with crucial therapeutic implications. Secondary tumors of the prostate spread more frequently from the digestive tract, the lung, and the kidney. The integration of clinicoradiologic data with appropriate pathologic and immunohistochemical analyses is essential for the identification and the characterization of secondary tumors of the prostate, whereas molecular analyses could provide additional and complementary information, enabling precise diagnosis and appropriate clinical management. Patients with solitary metastases could benefit from prostatic resection and adjuvant therapy, whereas in cases of disseminated diseases, symptom control may be obtained with palliative procedures. The purpose of this review was to assess the current state of knowledge of secondary tumors involving the prostate gland and to discuss short-term future perspectives, while providing a practical approach to these uncommon conditions for pathologists and oncologists.

Lack of PRKD2 and PRKD3 kinase domain somatic mutations in PRKD1 wild-type classic polymorphous low-grade adenocarcinomas of the salivary gland

AIMS

Polymorphous low-grade adenocarcinoma (PLGA) is the second most common intra-oral salivary gland malignancy. The vast majority of PLGAs harbour a PRKD1 E710D hot-spot somatic mutation or somatic rearrangements of PRKD1, PRKD2 or PRKD3. Given the kinase domain homology among PRKD1, PRKD2 and PRKD3, we sought to define whether PLGAs lacking PRKD1 somatic mutations or PRKD gene family rearrangements would be driven by somatic mutations affecting the kinase domains of PRKD2 or PRKD3.

METHODS AND RESULTS

DNA was extracted from eight microdissected PLGAs lacking PRKD1 somatic mutations or PRKD gene family rearrangements. Samples were thoroughly centrally reviewed, microdissected and subjected to Sanger sequencing of the kinase domains of the PRKD2 and PRKD3 genes. None of the PLGAs lacking PRKD1 somatic mutations or PRKD gene family rearrangements harboured somatic mutations in the kinase domains of the PRKD2 or PRKD3 genes.

CONCLUSION

PLGAs lacking PRKD1 somatic mutations or PRKD gene family rearrangements are unlikely to harbour somatic mutations in the kinase domains of PRKD2 or PRKD3. Further studies are warranted to define the driver genetic events in this subgroup of PLGAs.

Precision Medicine for Continuing Phenotype Expansion of Human Genetic Diseases

Determining the exact genetic causes for a patient and providing definite molecular diagnoses are core elements of precision medicine. Individualized patient care is often limited by our current knowledge of disease etiologies and commonly used phenotypic-based diagnostic approach. The broad and incompletely understood phenotypic spectrum of a disease and various underlying genetic heterogeneity also present extra challenges to our clinical practice. With the rapid adaptation of new sequence technology in clinical setting for diagnostic purpose, phenotypic expansions of disease spectrum are becoming increasingly common. Understanding the underlying molecular mechanisms will help us to integrate genomic information into the workup of individualized patient care and make better clinical decisions.

Intra-tumor genetic heterogeneity and alternative driver genetic alterations in breast cancers with heterogeneous HER2 gene amplification

Background

HER2 is overexpressed and amplified in approximately 15% of invasive breast cancers, and is the molecular target and predictive marker of response to anti-HER2 agents. In a subset of these cases, heterogeneous distribution of HER2 gene amplification can be found, which creates clinically challenging scenarios. Currently, breast cancers with HER2 amplification/overexpression in just over 10% of cancer cells are considered HER2-positive for clinical purposes; however, it is unclear as to whether the HER2-negative components of such tumors would be driven by distinct genetic alterations. Here we sought to characterize the pathologic and genetic features of the HER2-positive and HER2-negative components of breast cancers with heterogeneous HER2 gene amplification and to define the repertoire of potential driver genetic alterations in the HER2-negative components of these cases.

Results

We separately analyzed the HER2-negative and HER2-positive components of 12 HER2 heterogeneous breast cancers using gene copy number profiling and massively parallel sequencing, and identified potential driver genetic alterations restricted to the HER2-negative cells in each case. In vitro experiments provided functional evidence to suggest that BRF2 and DSN1 overexpression/amplification, and the HER2 I767M mutation may be alterations that compensate for the lack of HER2 amplification in the HER2-negative components of HER2 heterogeneous breast cancers.

Conclusions

Our results indicate that even driver genetic alterations, such as HER2 gene amplification, can be heterogeneously distributed within a cancer, and that the HER2-negative components are likely driven by genetic alterations not present in the HER2-positive components, including BRF2 and DSN1 amplification and HER2 somatic mutations.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-015-0657-6) contains supplementary material, which is available to authorized users.

Breast cancer genomics from microarrays to massively parallel sequencing: paradigms and new insights

Rapid advancements in massively parallel sequencing methods have enabled the analysis of breast cancer genomes at an unprecedented resolution, which have revealed the remarkable heterogeneity of the disease. As a result, we now accept that despite originating in the breast, estrogen receptor (ER)-positive and ER-negative breast cancers are completely different diseases at the molecular level. It has become apparent that there are very few highly recurrently mutated genes such as TP53, PIK3CA, and GATA3, that no two breast cancers display an identical repertoire of somatic genetic alterations at base-pair resolution and that there might not be a single highly recurrently mutated gene that defines each of the "intrinsic" subtypes of breast cancer (ie, basal-like, HER2-enriched, luminal A, and luminal B). Breast cancer heterogeneity, however, extends beyond the diversity between tumors. There is burgeoning evidence to demonstrate that at least some primary breast cancers are composed of multiple, genetically diverse clones at diagnosis and that metastatic lesions may differ in their repertoire of somatic genetic alterations when compared with their respective primary tumors. Several biological phenomena may shape the reported intratumor genetic heterogeneity observed in breast cancers, including the different mutational processes and multiple types of genomic instability. Harnessing the emerging concepts of the diversity of breast cancer genomes and the phenomenon of intratumor genetic heterogeneity will be essential for the development of optimal methods for diagnosis, disease monitoring, and the matching of patients to the drugs that would benefit them the most.

Benchmarking mutation effect prediction algorithms using functionally validated cancer-related missense mutations

BACKGROUND

Massively parallel sequencing studies have led to the identification of a large number of mutations present in a minority of cancers of a given site. Hence, methods to identify the likely pathogenic mutations that are worth exploring experimentally and clinically are required. We sought to compare the performance of 15 mutation effect prediction algorithms and their agreement. As a hypothesis-generating aim, we sought to define whether combinations of prediction algorithms would improve the functional effect predictions of specific mutations.

RESULTS

Literature and database mining of single nucleotide variants (SNVs) affecting 15 cancer genes was performed to identify mutations supported by functional evidence or hereditary disease association to be classified either as non-neutral (n = 849) or neutral (n = 140) with respect to their impact on protein function. These SNVs were employed to test the performance of 15 mutation effect prediction algorithms. The accuracy of the prediction algorithms varies considerably. Although all algorithms perform consistently well in terms of positive predictive value, their negative predictive value varies substantially. Cancer-specific mutation effect predictors display no-to-almost perfect agreement in their predictions of these SNVs, whereas the non-cancer-specific predictors showed no-to-moderate agreement. Combinations of predictors modestly improve accuracy and significantly improve negative predictive values.

CONCLUSIONS

The information provided by mutation effect predictors is not equivalent. No algorithm is able to predict sufficiently accurately SNVs that should be taken forward for experimental or clinical testing. Combining algorithms aggregates orthogonal information and may result in improvements in the negative predictive value of mutation effect predictions.

Breast cancer intra-tumor heterogeneity

In recent years it has become clear that cancer cells within a single tumor can display striking morphological, genetic and behavioral variability. Burgeoning genetic, epigenetic and phenomenological data support the existence of intra-tumor genetic heterogeneity in breast cancers; however, its basis is yet to be fully defined. Two of the most widely evoked concepts to explain the origin of heterogeneity within tumors are the cancer stem cell hypothesis and the clonal evolution model. Although the cancer stem cell model appeared to provide an explanation for the variability among the neoplastic cells within a given cancer, advances in massively parallel sequencing have provided several lines of evidence to suggest that intra-tumor genetic heterogeneity likely plays a fundamental role in the phenotypic heterogeneity observed in cancers. Many challenges remain, however, in the interpretation of the next generation sequencing results obtained so far. Here we review the models that explain tumor heterogeneity, the causes of intra-tumor genetic diversity and their impact on our understanding and management of breast cancer, methods to study intra-tumor heterogeneity and the assessment of intra-tumor genetic heterogeneity in the clinic.

Pathology in drug discovery and development

The rapid pace of drug discovery and drug development in oncology, immunology and ophthalmology brings new challenges; the efficient and effective development of new targeted drugs will require more detailed molecular classifications of histologically homogeneous diseases that show heterogeneous clinical outcomes. To this end, single companion diagnostics for specific drugs will be replaced by multiplex diagnostics for entire therapeutic areas, preserving tissue and enabling rapid molecular taxonomy. The field will move away from the development of new molecular entities as single agents, to which resistance is common. Instead, a detailed understanding of the pathological mechanisms of resistance, in patients and in preclinical models, will be key to the validation of scientifically rational and clinically effective drug combinations. To remain at the heart of disease diagnosis and appropriate management, pathologists must evolve into translational biologists and biomarker scientists. Herein, we provide examples of where this metamorphosis has already taken place, in lung cancer and melanoma, where the transformation has yet to begin, in the use of immunotherapies for ophthalmology and oncology, and where there is fertile soil for a revolution in treatment, in efforts to classify glioblastoma and personalize treatment. The challenges of disease heterogeneity, the regulatory environment and adequate tissue are ever present, but these too are being overcome in dedicated academic centres. In summary, the tools necessary to overcome the 'whens' and 'ifs' of the molecular revolution are in the hands of pathologists today; it is a matter of standardization, training and leadership to bring these into routine practice and translate science into patient benefit. This Annual Review Issue of the Journal of Pathology highlights the central role for pathology in modern drug discovery and development.

Establishing the origin of metastatic deposits in the setting of multiple primary malignancies: the role of massively parallel sequencing

In this proof-of-principle study, we sought to define whether targeted capture massively parallel sequencing can be employed to determine the origin of metastatic deposits in cases of synchronous primary malignancies and metastases in distinct anatomical sites. DNA samples extracted from synchronous tumor masses in the breast, adnexal, and pelvic-peritoneal regions from a 62-year-old BRCA1 germline mutation carrier were subjected to targeted massively parallel sequencing using a platform comprising 300 cancer genes known to harbor actionable mutations. In addition to BRCA1 germline mutations, all lesions harbored somatic loss of the BRCA1 wild-type allele and TP53 somatic mutations. The primary breast cancer displayed a TP53 frameshift (p.Q317fs) mutation, whereas and the adnexal lesion harbored a TP53 nonsense (p.R213*) mutation, consistent with a diagnosis of two independent primary tumors (i.e. breast and ovarian cancer). The adnexal tumor and all pelvic-peritoneal implants harbored identical TP53 (p.R213*) and NCOA2 (p.G952R) somatic mutations. Evidence of genetic heterogeneity within and between lesions was observed, both in terms of somatic mutations and copy number aberrations. The repertoires of somatic genetic aberrations found in the breast, ovarian, and pelvic-peritoneal lesions provided direct evidence in support of the distinct origin of the breast and ovarian cancers, and established that the pelvic-peritoneal implants were clonally related to the ovarian lesion. These observations were consistent with those obtained with immunohistochemical analyses employing markers to differentiate between carcinomas of the breast and ovary, including WT1 and PAX8. Our results on this case of a patient with BRCA1-mutant breast and ovarian cancer demonstrate that massively parallel sequencing may constitute a useful tool to define the relationship, clonality and intra-tumor genetic heterogeneity between primary tumor masses and their metastatic deposits in patients with multiple primary malignancies and synchronous metastases.