A Molecular Genetic and Statistical Approach for the Diagnosis of Dual-Site Cancers

Brinkmann, Dirk; Ryan, Andy; Ayhan, Ayşe; McCluggage, W.; Feakins, Roger; Santibanez‐Koref, Mauro; Mein, Charles A.; Gayther, Simon A.; Jacobs, Ian

doi:10.1093/jnci/djh272

Cited by 46 publications

(33 citation statements)

References 37 publications

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“…For ovarian cancers, the overwhelming evidence from previous studies indicates that metastases develop from primary ovarian cancer by a linear clonal evolution model; but none of these studies analyzed multiple metastases nor multiple tissue regions within primary tumors and metastases as performed in this study. 5,6,[21][22][23] Maximum parsimony tree analysis of our data indicated that all of the metastatic lesions analyzed were clonally related to the primary tumor from the same patient. We did not see any evidence in this series of ovarian cancers in support of parallel evolution.…”

Section: Discussionmentioning

confidence: 76%

“…As with linkage analysis for tracking inherited transmission in families, the ability to distinguish between different allelic events at the same marker is a very powerful tool for studying clonality. 6,15 In conclusion, we have used genetic analysis of primary ovarian tumors and their associated metastatic lesions from several patients, to evaluate the mechanisms of clonal evolution and metastatic progression. Our data suggest that most, if not all metastases are clonally related to the primary tumors.…”

Section: Discussionmentioning

confidence: 99%

“…Tumor spread to more distant sites, including the contralateral ovary might also occur; bilateral ovarian cancers almost always represents a primary tumor and its metastasis rather than dual primary cancers. 5,6 Despite the widely accepted paradigm that tumor progression to metastasis occurs through linear clonal evolution, other mechanisms for this process have also been proposed. The parallel evolution model suggests that cells with metastatic potential separate from the primary tumor at an early stage in development and evolve independently from the primary tumor.…”

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confidence: 99%

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The clonal evolution of metastases from primary serous epithelial ovarian cancers

Khalique

Ayhan

Whittaker

et al. 2009

Intl Journal of Cancer

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Several models of evolution from primary cancers to metastases have been proposed; but the most widely accepted is the clonal evolution model proposed for colorectal cancer in which tumors develop by a process of linear clonal evolution driven by the accumulation of somatic genetic alterations. Various other models of cancer progression and metastasis have been proposed, including parallel evolution and the same gene model. The aim of this study was to investigate the evolution of metastases from primary cancer in 22 patients diagnosed with high-grade serous epithelial ovarian cancer. We established somatic genetic profiles based on the pattern of loss of heterozygosity, in several different regions of tumor tissue within the primary tumor and metastatic deposits from each case. Maximum parsimony tree analysis was used to examine the evolutionary relationship between the primary and metastatic samples for each patient. In addition, we investigated the extent of genetic heterogeneity within and between metastatic tumors compared with primary ovarian tumors. Our data suggest that most, if not all, metastases are clonally related to the primary tumors. However, the data oppose a single model of linear-clonal evolution whereby a late stage clone within the primary tumor acquires additional genetic changes that enable metastatic progression. Instead, the data support a model in which primary ovarian cancers have a common clonal origin, but become polyclonal, with different clones at both early and late stages of genetic divergence acquiring the ability to progress to metastasis. ' 2008 Wiley-Liss, Inc.Key words: ovarian cancer; genetic analysis; metastasis; clonal evolution; maximum parsimony; high-grade serous histology Genetic studies of colorectal cancer led Fearon and Vogelstein to propose that tumors develop by a process of clonal expansion driven by the accumulation of somatic genetic alterations, and this has become the most widely accepted model of tumor development.1-3 The final step in this process is progression to metastasis, when 1 or more clonal populations of cancer cells have acquired sufficient genetic and functional changes to enable secondary tumor formation.Epithelial cancers represent 90% of all malignant ovarian tumors; but the underlying genetic basis of ovarian tumor development remains poorly understood. This is partly because epithelial ovarian cancer is an extremely heterogeneous disease consisting of a wide spectrum of histological subtypes. There is mounting evidence that different molecular mechanisms are involved in the development of different ovarian tumor subtypes. Recent studies have also suggested that there is extensive genetic heterogeneity within ovarian tumors, which may hinder a better understanding of cancer development. Primary ovarian cancers tend to spread, at first, within the peritoneal cavity and the omentum, and are frequently associated with ascites, a fluid rich in growth factors and tumor cells disseminated from the primary cancer that fills the peritoneum. Tumor sp...

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Section: Discussionmentioning

confidence: 76%

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

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The clonal evolution of metastases from primary serous epithelial ovarian cancers

Khalique

Ayhan

Whittaker

et al. 2009

Intl Journal of Cancer

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“…The original histopathologic reports on which the patients' treatment regimens were based failed to provide a diagnosis in 26 (29%) cases. We have previously established the principle that genetic analysis could be used to classify patients with either DP or SPM ovarian/endometrial cancers (23). Here, we have shown that genetic analysis can be used to both confirm the histopathology diagnoses in patients with SPM or DP cancers and, perhaps more importantly, provide a diagnosis in >80% of cases in which pathology could not make a diagnosis.…”

Section: Discussionmentioning

confidence: 90%

Predicting Clinical Outcome in Patients Diagnosed with Synchronous Ovarian and Endometrial Cancer

Ramus

Elmasry

Luo

et al. 2008

Clinical Cancer Research

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Purpose: Patients with synchronous ovarian and endometrial cancers may represent cases of a single primary tumor with metastasis (SPM) or dual primary tumors (DP).The diagnosis given will influence the patient's treatment and prognosis. Currently, a diagnosis of SPM or DP is made using histologic criteria, which are frequently unable to make a definitive diagnosis. Experimental Design: In this study, we used genetic profiling to make a genetic diagnosis of SPM or DP in 90 patients with synchronous ovarian/endometrial cancers.We compared genetic diagnoses in these patients with the original histologic diagnoses and evaluated the clinical outcome in this series of patients based on their diagnoses. Results: Combining genetic and histologic approaches, we were able make a diagnosis in 88 of 90 cases, whereas histology alone was able to make a diagnosis in only 64 cases. Patients diagnosed with SPM had a significantly worse survival than patients with DP (P = 0.002). Patients in which both tumors were of endometrioid histology survived longer than patients of other histologic subtypes (P = 0.025), and patients diagnosed with SPM had a worse survival if the mode of spread was from ovary to endometrium rather than from endometrium to ovary (P = 0.019). Conclusions: Genetic analysis may represent a powerful tool for use in clinical practice for distinguishing between SPM and DP in patients with synchronous ovarian/endometrial cancer and predicting disease outcome. The data also suggest a hitherto uncharacterized level of heterogeneity in these cases, which, if accurately defined, could lead to improved treatment and survival.

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“…The loss parameter index refers to c = common origin, a = occurs in a during metastasis, and b = occurs in b during metastasis. This model was applied to a data set consisting of 62 patients diagnosed with concurrent endometrial and ovarian cancer (Brinkman et al, 2004).…”

Section: Models Attempting To Identify the Order Of Acquired Mutationmentioning

confidence: 99%

Cancer Genetics

Teare¹

2007

Handbook of Statistical Genetics

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Cancers result from an accumulation of inherited and somatic mutations yielding cells that have acquired the necessary characteristics for unregulated growth. The development of the tumour can be viewed as an evolutionary process, involving several classes of genes in tumour initiation and progression.This chapter provides an overview of the development of cancer genetic models. The earlier mathematical and statistical models based on population and cancer family observations, are followed by evolutionary models applied to molecular genetic sequence data.

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A Molecular Genetic and Statistical Approach for the Diagnosis of Dual-Site Cancers

Cited by 46 publications

References 37 publications

The clonal evolution of metastases from primary serous epithelial ovarian cancers

The clonal evolution of metastases from primary serous epithelial ovarian cancers

Predicting Clinical Outcome in Patients Diagnosed with Synchronous Ovarian and Endometrial Cancer

Cancer Genetics

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