Genetic heterogeneity of Myc-induced mammary tumors reflecting diverse phenotypes including metastatic potential - PubMed (original) (raw)

Genetic heterogeneity of Myc-induced mammary tumors reflecting diverse phenotypes including metastatic potential

Eran R Andrechek et al. Proc Natl Acad Sci U S A. 2009.

Abstract

Human cancers result from a complex series of genetic alterations, resulting in heterogeneous disease states. Dissecting this heterogeneity is critical for understanding underlying mechanisms and providing opportunities for therapeutics matching the complexity. Mouse models of cancer have generally been used to reduce this complexity and focus on the role of single genes. Nevertheless, our analysis of tumors arising in the MMTV-Myc model of mammary carcinogenesis reveals substantial heterogeneity, seen in both histological and expression phenotypes. One contribution to this heterogeneity is the substantial frequency of activating Ras mutations. Additionally, we show that these Myc-induced mammary tumors exhibit even greater heterogeneity, revealed by distinct histological subtypes as well as distinct patterns of gene expression, than many other mouse models of tumorigenesis. Two of the major histological subtypes are characterized by differential patterns of cellular signaling pathways, including beta-catenin and Stat3 activities. We also demonstrate that one of the MMTV-Myc mammary tumor subgroups exhibits metastatic capacity and that the signature derived from the subgroup can predict metastatic potential of human breast cancer. Together, these data reveal that a combination of histological and genomic analyses can uncover substantial heterogeneity in mammary tumor formation and therefore highlight aspects of tumor phenotype not evident in the population as a whole.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.

Fig. 1.

Varied initiating oncogenic events causes differences in tumor histology. Tumor histology from the MMTV-Myc (WT) and MMTV-Myc T58A (T58A) lines was examined. The various dominant histological patterns were scored for the various tumor lines and are shown (A). These patterns included microacinar, papillary, squamous, EMT, solid, adenocarcinoma and tumors with a mixed population of various types of tumors (Mixed). The percentage of tumors for a given class and transgenic line is shown. Examples of the histological types scored in (A) are shown and include; microacinar tumors (B), papillary (C), squamous (K14 IHC) (D), adenocarcinoma (E), mixed (F), and EMT (G).

Fig. 2.

Fig. 2.

Unsupervised hierarchical clustering differentiates tumors based on histological pattern and initiating oncogenic events. Unsupervised hierarchical clustering reveals the diversity arising in the tumors from wild type MMTV-Myc, MMTV-Myc T58A, and MMTV-Neu tumors. The various histological subtypes and genotypes are shown in the legend key at the bottom and are represented in the heat map style legend for each data point at the top of the figure. Samples with an activating mutation in kras are denoted with an x. Various clusters that were examined further are shown on the right axis and are labeled A–G. The clusters that were define a histological subtype are labeled at the bottom in colored boxes.

Fig. 3.

Fig. 3.

Pathway probabilities cluster tumor samples. Probability of pathway activation for MMTV-Myc, MMTV-Myc T58A, and MMTV-Neu tumors was calculated for the genes and conditions listed on the right axis of the heat map. Probability (from 0 to 1) is represented by a color scale with the probability of being like control represented in blue and probability of being like the signaling pathway of interest being red. The probabilities were clustered for pathways and the samples were ordered in the same sequence shown for unsupervised clustering in Fig. 4. The histology, genotype, and kras mutation status are shown for each sample with the color codes being identified in the legend and kras mutations being illustrated with an × (A). The predicted probability of Ras pathway activation was graphed against Myc pathway activation with _r_2 = 0.5651 with P < 0.0001 (B). The probability of Ras pathway activation was also examined in tumors with wild type and mutant kras sequence. Using a Mann-Whitney test, the significance was illustrated with P < 0.0001 (C).

Fig. 4.

Fig. 4.

Metastatic signature predictions identify samples with pulmonary metastasis. The probability of a metastatic signature was calculated and is illustrated in a heat map with a low probability of metastasis shown in blue and a high probability shown in red (A). Histological sections from the lung were examined for 20 samples with the lowest probability and 20 samples with the highest probability of the metastatic signature. The histology of these pulmonary samples was closely examined and the percentage of samples with metastasis calculated (B). Representative histology from a sample lacking pulmonary metastasis (C) and with numerous small tumor nodules in the lung (D) are shown for the tumors with a corresponding * in panel A.

Fig. 5.

Fig. 5.

EMT mouse subgroup similarities in human breast cancer. Pathway probabilities for human breast cancer samples were calculated and clustered and are represented in a heat map (A). Signatures for the major mouse subgroups were generated, validated and then were applied to the human breast cancer dataset in the same order as the clustered samples (B), revealing that the EMT signature was enriched in samples that were negative for ER, PR and HER2. When we compared the EMT probability between triple negative human cancers and all other breast cancer samples, we found a significant elevation of EMT probability in the triple negative group (C). We then examined the probability of lung metastasis in the various mouse tumor subtypes (D) and found that it correlated with the EMT subgroup. In addition, we found a strong correlation between the probability of EMT and lung metastasis in human breast cancer (E).

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References

    1. Weir BA, et al. Characterizing the cancer genome in lung adenocarcinoma. Nature. 2007;450:893–898. - PMC - PubMed
    1. Wood LD, et al. The genomic landscapes of human breast and colorectal cancers. Science. 2007;318:1108–1113. - PubMed
    1. Sjoblom T, et al. The consensus coding sequences of human breast and colorectal cancers. Science. 2006;314:268–274. - PubMed
    1. Mullighan CG, et al. Genome-wide analysis of genetic alterations in acute lymphoblastic leukaemia. Nature. 2007;446:758–764. - PubMed
    1. Ding L, et al. Somatic mutations effect key pathways in lung adenocarcinoma. Nature. 2008;455:1069–1075. - PMC - PubMed

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