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. Author manuscript; available in PMC: 2020 Dec 15.
Published in final edited form as: Recent Results Cancer Res. 2015;200:81–94. doi: 10.1007/978-3-319-20291-4_4

Genetic Instability and Disease Prognostication

Timo Gemoll 1, Gert Auer 2, Thomas Ried 3, Jens K Habermann 4
PMCID: PMC7737009  NIHMSID: NIHMS1650741  PMID: 26376873

Abstract

Genetic instability is a striking feature of human cancers, with an impact on the genesis, progression and prognosis. The clinical importance of genomic instability and aneuploidy is underscored by its association with poor patient outcome in multiple cancer types, including breast and colon cancer. Interestingly, there is growing evidence that prognostic gene expression signatures simply reflect the degree of genomic instability. Additionally, also the proteome is affected by aneuploidy and has therefore become a powerful tool to screen for new targets for therapy, diagnosis and prognostication. In this context, the chapter presents the impact of genomic instability on disease prognostication occurring in human cancers.

Keywords: Genetic instability, Prognosis, Genomics, Proteomics, Cancer

1. Genomic Instability and Disease Prognostication in Colorectal Cancer

Colorectal cancer is globally the fourth leading cause of cancer mortality, with about 1.2 million new cases and 608,000 deaths worldwide per year (Jemal et al. 2011). The incidence of colorectal cancer is higher in developed countries although the disease is rarely diagnosed before the age of 40. Most patients with R0 resection of node-negative CRC are cured of their cancer by surgery, but an unacceptable number of patients experience relapse due to regional recurrence or to distant metastasis, or both. Today, Dukes classification of CRC and the Tumor-Node-Metastasis (TNM) classification system for solid tumors are the routine staging systems and the basis to evaluate patient prognosis in CRC. However, in terms of prognosis it has been shown that genomic instability plays an important role as do various demographical, pathological and molecular characteristics: next to age (Kearney et al. 1993; Cascinu et al. 1996), tumor stage (Sun 2006; Gerling et al. 2010), tumor location (Zarbo et al. 1997), histopathological grade (Schillaci et al. 1990), disease free survival and overall survival (Garrity et al. 2004; Chen et al. 2002), several studies have shown that patients with aneuploid tumors had a worse outcome compared to patients with euploid tumors (Witzig et al. 1991; Sinicrope et al. 2006; Bosari et al. 1992). Similarly, Gerling et al. (2010) presented survival data of CRC patients showing that advanced stage but diploid carcinomas had a similar prognosis as compared with early stage tumors, but the outlook for aneuploid carcinoma is typically unfavorable, indicating that aneuploidy in CRC more strongly impacts on prognosis than the tumor stage itself.

In one of the most comprehensive meta-analyses of 10,126 patients, Walther et al. (2008) demonstrated that genomic instability is associated with worse prognosis in CRC and that it could be used to stratify patient prognosis, in addition to pathological staging: CRC patients with aneuploid tumor cells—quantified by either flow cytometry (n = 9,526 patients) or image cytometry (n = 600 patients)—appeared to have a poorer survival irrespective of their ethnic background, anatomical location and treatment with 5-fluoroucacil (5-FU)-based adjuvant chemotherapy. In line with this, Guastadisegni et al. (2010) confirmed the association between MSI and favorable prognosis. Thirty-one eligible studies reporting survival for 12,782 patients characterized for MSI indicated that MSI has the potential to be used in the clinical setting as a prognostic and predictive marker. Being part of the meta-analysis, the study by Sinicrope et al. (2006) found that DNA ploidy was the strongest prognostic marker.

Interestingly, the pattern of chromosomal aneuploidy in sporadic (SCC) and ulcerative (UCC) colitis-associated colorectal carcinomas seems to be strinkingly conserved. Nevertheless, in a single cohort of 31 UCCs and 257 SCCs Gerling et al. (2010) associated the frequency of aneuploidy to clinical parameters and showed that UCCs have a higher frequency of aneuploidy compared to SCCs (100 % versus 74.6 %; p < 0.006). A logistic regression analysis assessed age, sex, UICC stage, T- and N-status, histologic tumor grading, underlying inflammation, and DNA ploidy status. Out of these features, only age and DNA ploidy status were significant contributing parameters, indicating both patients of higher age at diagnosis and patients with aneuploid malignancy have a poor survival prognosis. Additional logistic regression analysis comprising these two significant parameters only confirmed age [odds ratio (OR), 1.05; 95 % CI, 1.02–1.09; p = 0.003] and DNA ploidy (OR, 4.07; 95 % CI, 1.46–11.36; p = 0.007) to be independent prognostic parameters. Among those, DNA aneuploidy with an OR of 4.07 seemed to be the strongest independent prognostic marker for R0-resected colorectal cancer patients overall. The dominance of aneuploidy as an independent poor prognostic predictor in patients with SCC and UCC was further supported by the fact that patients with diploid tumors at advanced stages (UICC stage III/IV) did present a survival comparable to that of patients with aneuploid tumors at early stages. The latter finding might even suggest that the presence of aneuploid tumor cell populations may influence the patient’s prognosis more dominantly than tumor stage. This was in part supported by Laubert et al. (2013) who could demonstrate that aneuploidy and elevated CEA levels, apart from increasing T category, could predict metachronous metastases and thus assist individual risk assessment.

In this context, other authors report a comparable incidence of DNA aneuploidy in SCC. Interestingly, the high incidence of aneuploidy is not restricted to late-stage lesions but is found in more than 50 % of stage I CRC tumors. This was evaluated on the basis of single tumor samples and did not take into account that the intra-tumor heterogeneity could lead to an underestimation of the true occurrence of chromosomal aneuploidy and genomic instability, respectively (Flyger et al. 1999; Bondi et al. 2009).

The essential etiologic element of CRC is widely accepted to lie in genetic changes of epithelial cells in the colonic mucosa. Morphologic changes from normal mucosa and adenomatous polyps to cancer with accumulation of genetic aberrations are well documented (Fearon and Vogelstein 1990). However, individual colorectal adenomas and carcinomas have different propensities to progress to malignancy. In this context, genome, transcriptome and proteome analysis with respect to DNA ploidy data may yield aneuploidy-associated biomarkers that could assess the individual progression risk to malignancy. On the genome/transcriptome level, fluorescence in situ hybridization (FISH) with specific probe sets was used to screen a total of 47 samples [centromere probes for chromosomes 17 and 18 (CEP17 and CEP18), SMAD7 (SMAD family member 7; 18q21.1), EGFR (epidermal growth factor; 7p12), NCOA3 (nuclear receptor coactivator 3; 20q12), TP53 (Tumor protein 53; 17p13.1), MYC (v-myc avian myelocytomatosis viral oncogene homolog; 8q24.21), and RAB20 (member RAS oncogene family; 13q34)]. These samples reflected different stages during colorectal cancer development and included 18 adenomas of patients without synchronous or subsequent carcinoma, 23 adenomas of carcinoma patients, and 6 matched carcinomas (Habermann et al. 2011a). In summary, Habermann et al. concluded that genomic instability in colorectal adenomas is reflected by genomic amplification of the oncogenes EGFR, MYC, NCOA3, and RAB20. For NCOA3 it could be shown that a diploid signal count of that gene is associated with a longer adenoma recurrence-free observation time (p = 0.042).

On the proteome level, a comprehensive proteomic analysis of diploid and aneuploid colorectal cancer cell lines and clinical tissues was carried out (Gemoll et al. 2011). Two proteins, HDAC2 (histone deacetylase 2) and TXNL1 (thioredoxin-like 1), were not only significantly expressed in two-dimensional gel electrophoresis (2-DE) analysis and validated by Western blotting, but showed expression differences also in clinical samples, discerning aneuploid from diploid CRCs (Fig. 1). It seems that HDAC2 is overexpressed in colorectal cancer and associated with reduced survival (Ashktorab et al. 2009; Weichert et al. 2008). Furthermore, HDAC2 overexpression could be induced by a loss of the anaphase-promoting complex (APC), favoring the development of genomic instability. This is in line with the finding of HDAC2-overexpression in patients with aneuploid tumors by Gemoll et al. (2011). In contrast, TXNL1 is involved in the cellular response to sugar starvation stress and regulates the redox equilibrium in higher eukaryotes (Jimenez et al. 2006; Manandhar et al. 2009). TXNL1 binds to the transcription factor B-MYb and is overexpressed in diploid as compared to aneuploid carcinomas, thus potentially maintaining genomic stability (Gemoll et al. 2011). Interestingly, TXNL1 was also expressed at low levels in aneuploid endometrial malignancies (Gemoll et al. 2012).

Fig. 1.

Fig. 1

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a HDAC2 and TXNL1 immunohistochemical detection in colorectal cancer specimens based on a tissue microarray. Image examples are given at 800-fold magnification. b Tissue-microarray-based immunohistochemical evaluation of HDAC2 and TXNL1 comparing diploid versus aneuploid colorectal carcinoma specimens. Immunoreactivity was scored with “0” showing no positivity, “1” presenting up to 20 % immunopositive cells, “2” up to 50 %, and “3” above 50 % stained cells. Bar plots of the TMA analysis confirmed HDAC2 and TXNL1 as significantly (asterisk) differentially expressed proteins between diploid and aneuploid tumors. Figure adapted from Gemoll et al. (2011)

2. Genomic Instability and Disease Prognostication in Breast Cancer

Breast cancer is one of the major causes of morbidity and mortality in females all over the world (Jemal et al. 2011). Despite the fact that tremendous progress has been achieved in chemotherapy and radiation therapy, breast cancer is still one of the most frequent malignancies with poor prognosis. The effects of independent prognostic factors for survival of breast cancer patients, including estrogen receptor/progesterone receptor (ER/PR) status, HER2 gene amplification and/or overexpression, tumor size, lymph node status, histological grade, and age have been thoroughly recognized (Ferguson et al. 2013). Especially the tumor, node, and metastasis (TNM) system has been extensively used. However, breast cancer is a malignant disease with multiple driving factors involved, and it has been reported that molecular mechanisms may affect tumor growth and progression, thereby potentially limiting the prognostic value of the TNM system (Coradini and Daidone 2004; Song et al. 2013).

Aneuploidy is, in general, correlated to cell proliferation and poor differentiation but not disease stage (Silvestrini 2000). However, Fallenius et al. (1988) demonstrated that node positive non-aneuploid tumors exhibited a better survival than node negative but aneuploid tumors, indicating that ploidy in this study cohort was a stronger prognostic marker than node assessment.

In 2006, Kronenwett et al. (2006) introduced a new concept to measure a tumor cell population with high levels of clonal heterogeneity. The stemline-scatter-index (SSI) is computed with the sum of the proliferation index, the variance of the diploid G0/G1 peak, and the 5c exceeding rate (5cER). Primarily based on the ploidy classification by Auer et al. (1980), the SSI is able to divide cytometrically assessed diploid, tetraploid and aneuploid samples into genomically stable and unstable subtypes. A total of 890 invasive breast cancer patients with a mean follow-up of 8.9 years were evaluated by using this algorithm and showed a significantly better survival of genomically stable subtypes compared with the unstable subtype within each ploidy category (0.04 < p < 0.004).

To evaluate potential differences in gene expression patterns between genomically stable and unstable breast tumors, Habermann et al. (2009) examined 17 diploid genomic stable, 15 aneuploid genomic stable, and 16 aneuploid genomic unstable breast carcinomas. A 12-gene expression signature associated with genomic instability in breast cancer was defined and demonstrated a biological and prognostic value across multiple different cancer entities (Habermann et al. 2009; Mettu et al. 2010). For breast cancer, genomic unstable carcinomas in patient cohorts from Sorlie et al. (2003), van de Vijver et al. (2002), Sotiriou et al. (2003) were associated with distinct shorter relapse-free survival and metastasis-free survival (p < 0.04; Fig. 2). All three studies were not analyzed regarding genomic stability/instability of the samples, initially. However, it was shown that the 12-gene signature is independent of clinicopathological factors such as lymph node status, the NIH criteria, the St. Gallen criteria, and grading used for breast cancer prognostication. In addition, gene sets of the MammaPrint® (van de Vijver et al. 2002; van’t Veer et al. 2002) and Oncotype DX® (Paik et al. 2004) tests—two clinically used breast cancer prognostic gene expression signatures—were used to predict genomic instability: 84 % (MammaPrint®) and 91 % (Oncotype DX®) of all cases were correctly classified. Along this line, Swanton et al. (2009) corroborated the importance of genomic instability by showing a link between aneuploidy-associated gene expression and poor response to taxane, a microtubule-stabilizing (MTS) agent. A pre-therapeutic assessment of genomic instability could therefore even optimize treatment stratification.

Fig. 2.

Fig. 2

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Applying the 12-gene genomic instability signature for prediction of disease-free and overall survival in independent datasets using Kaplan-Meier analyses. The curves in red reflect carcinoma patients harboring the genomically stable signature, the curves in green represent patients with the one implying genomic instability. For all three examples, statistically significant association of genomic instability with shorter disease-free and overall survival was observed. Figure adapted from Habermann et al. (2011a, b)

In 2007, Yildrim-Assaf et al. (2007) published another example for histogram reclassification: Based on thresholds in the categories of 5cER (>10 aneuploid cells) and 9cER (>1 aneuploid cell), patients with node negative and positive breast cancers can be stratified into a high-risk subgroup with unfavorable prognosis. In total, 370 breast cancer patients showed histology grade, lymph node status, and the above-mentioned binary DNA histogram classification to be the three strongest relapse predictors in a Cox multivariate analysis. The significance of rare-event nuclei (5cER and 9cER) was emphasized by the work of Sidoni et al. (2001) who examined fresh scrape smears from 599 breast carcinomas. According to their results, single cell aneuploidy is a marker for genetic instability with an increased risk of tumor recurrences despite otherwise favorable prognostic parameters. The data seem in accordance with the concept of progressive genetic evolutionary changes in solid tumors (Bartek et al. 1990) and with the unfavorable prognostic significance of DNA hypertetraploidy component as demonstrated in both image (Siitonen et al. 1993) and flow cytometry studies (Pinto et al. 1997).

3. Genomic Instability and Disease Prognostication in Other Cancers

The relationship between genomic instability and cancer prognosis has been explored across a range of cancer types. Next to breast and colorectal cancer (see above), several retrospective studies, summarized in Table 1, consistently associated genomic instability with poor prognosis and demonstrated that it provided additional prognostic information beyond conventional clinical parameters (McGranahan et al. 2012).

Table 1.

Summary of flow and image cytometry studies relating to genomic instability in various cancer types

Cancer type Method of
measuring
genomic
instability		 Number
of
patients		 Outcome
Shorter survival/poor
prognosis of
aneuploid/genomic
instable tumors		 Reference
Colorectal cancer Flow cytometry 694 Higher survival rate of diploid tumors Witzig et al. (1991)
Flow cytometry 528 Higher survival rate of diploid tumors Sinicrope et al. (2006)
Image cytometry 213 Higher survival rate of diploid tumors without metastasis (Dukes’stage A & B) Bosari et al. (1992)
Image cytometry 288 Higher survival rate of diploid tumors; Aneuploidy strongest prognostic marker for CRC Gerling et al. (2010)
Flow and image cytometry 10,126 Higher survival rate of diploid tumors Walther et al. (2008)
Image cytometry 217 Higher survival rate of diploid tumors Laubert et al. (2013)
Genotyping of MSI markers 12,782 Higher survival rate of MSI tumors Guastadisegni et al. (2010)
Flow cytometry 163 Higher survival rate of diploid tumors Flyger et al. (1999)
Image cytometry 219 Higher survival rate of euploid tumors Bondi et al. (2009)
Image cytometry 47 Diploid signal count of NCOA3 is associated with a longer adenoma recurrence-free surveillance Habermann et al. (2011a, b)
Image cytometry 78 Higher survival rate of diploid tumors; HDAC2 & TXNL1 marker for genomic stability Gemoll et al. (2011)
Breast cancer Image cytometry 227 Higher survival rate of diploid tumors Fallenius et al. (1988)
Image cytometry 890 Higher survival rate of genomically stable subtypes Kronenwett et al. (2006)
Image cytometry 112 Higher survival rate of diploid tumors Auer et al. (1980)
Image cytometry 48 12-gene signature predict degree of genomic instability and disease prognostication Habermann et al. (2009), Mettu et al. (2010)
Image cytometry 370 Lower survival rate of highly aneuploid tumors Yildrim-Assaf et al. (2007)
Image cytometry 599 Single cell aneuploidy as marker for genomic instability and biologic aggressiveness Sidoni et al. (2001)
Image cytometry 134 Lower survival rate of tumors with cancer cells with >5c DNA content Siitonen et al. (1993)
Flow cytometry 860 Hypertetraploidy as marker for biologic aggressiveness Pinto et al. (1997)
Endometrial cancer Flow cytometry 256 and 203 Higher survival rate of diploid tumors Britton et al. (1989, 1990)
Flow cytometry 76 Higher survival rate of diploid tumors Ikeda et al. (1993)
Image cytometry 358 Higher survival rate of diploid tumors Lundgren et al. (2002)
Flow cytometry 174 Higher survival rate of diploid tumors Susini et al. (2007)
Flow cytometry 363 Higher survival rate of diploid tumors Wik et al. (2009)
Ovarian cancer Flow cytometry 682 Higher survival rate of diploid tumors Akeson et al. (2009)
Image cytometry 284 Higher survival rate of diploid tumors Kristensen et al. (2003)
Image cytometry 47 Higher survival rate of diploid tumors Kildal et al. (2004)
Large B-cell lymphoma H&E staining 54 Lower survival rate of patients with chromosomal instability Bakhoum et al. (2011)
Oral squamous cancer FISH 77 Lower survival rate of patients with chromosomal instability Sato et al. (2010)
Synovial sarcoma CGH 22 Lower survival rate of patients with specific chromosomal instability Nakagawa et al. (2006)
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In endometrial cancer, genomic instability has been quantified by either image cytometry or flow cytometry (Evans and Podratz 1996). Next to traditional phenotypic variables, including stage, histologic grade and subtype, Britton et al. (1989, 1990) showed prognostic significance in univariate analysis of 256 and 203 endometrial carcinomas. A more detailed assessment revealed DNA ploidy as an independent prognostic factor by Ikeda et al. (1993). In 2002, Lundgren et al. (2002) published a study of relapse free survival following surgical treatment in 358 consecutive patients and found that DNA diploidy predicted disease free-survival. Likewise, prospective and multivariate studies successfully indicated the status of genomic instability as an independent prognostic variable (Susini et al. 2007; Wik et al. 2009). In this context, it seems that the grade of genomic instability correlates with a recurrent pattern of chromosomal imbalances and dominates specific gene and protein expression changes, irrespective of the histopathological subtypes in endometrial cancers. In order to identify the impact of chromosomal aberrations on protein expression, Gemoll et al. mapped genomic imbalances with associated gene and protein expression changes of endometrial cancer patients (Gemoll et al. 2012; Habermann et al. 2011b): Next to recurrent genomic imbalances of the chromosome arms 1q, 3q, 8q, 4q, and 15q, two proteins, AKR7A2 (aflatoxin B1 aldehyde reductase member 2) and ANXA2 (Annexin A2), showed translational alterations in consistence with transcriptional changes. While AKR7A2 is involved in the detoxification of aldehydes and ketones, there is evidence that ANXA2 facilitates the reorganization of the extracellular matrix in physiological and pathological processes such as tumor invasion (Mai et al. 2000).

Furthermore, in a multivariate analysis of 682 and 284 ovarian cancers, genomic instability was found to be associated with a worse prognosis. Here, Akeson et al. (2009) determined age, stage, presence of residual tumor, histological subtype, CA125, and DNA ploidy status as univariate predictors of survival time. Along the same line, Kristensen et al. (2003) showed the predictive power of genomic instability in multivariate analysis with a hazard ration of 10.3. These findings are supported by the study of Kildal et al. (2004) that found clinical stage to be the strongest prognostic feature, followed by the extent of residual tumor, and DNA ploidy status.

Furthermore, studies in synovial and oral squamous cell carcinomas as well as diffuse B-cell lymphoma, have suggested that genomic instability is associated with poor prognosis (Mettu et al. 2010; Bakhoum et al. 2011; Sato et al. 2010; Nakagawa et al. 2006).

4. Conclusion

Genomic instability is a defining feature of human cancers. It has an impact on the expression levels of resident genes but in addition also on associated protein expression. Such aneuploidy-associated protein expression patterns could reveal novel diagnostic and therapeutic targets. The evidence for the selective contribution of genomic instability on prognosis is supported by several studies in which patients with aneuploid tumors had a worse outcome compared to patients with euploid tumors. Overall, the assessment of nuclear aneuploidy by image or flow cytometry could become a routine practice to assist in predicting individual cancer risk and in disease prognostication in solid tumors.

Contributor Information

Timo Gemoll, Section for Translational Surgical Oncology and Biobanking, Department of Surgery, University of Lübeck & University Medical Center Schleswig-Holstein, Lübeck, Germany.

Gert Auer, Karolinska Biomic Center, Karolinska Institutet, Stockholm, Sweden.

Thomas Ried, Genetics Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, USA.

Jens K. Habermann, Section for Translational Surgical Oncology and Biobanking, Department of Surgery, University of Lübeck & University Medical Center Schleswig-Holstein, Lübeck, Germany

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