Lack of genomic heterogeneity at high-resolution aCGH between primary breast cancers and their paired lymph node metastases

Short somatic alterations at the site of copy number variation in breast cancer

Copy number variation (CNV) is a polymorphism in the human genome involving DNA fragments larger than 1 kb. Copy number variation sites provide hotspots of somatic alterations in cancers. Herein, we examined somatic alterations at sites of CNV in DNA from 20 invasive breast cancers using a Comparative Genomic Hybridization array specifically designed to detect the genome-wide CNV status of approximately 412 000 sites. Somatic copy number alterations (CNAs) were detected in 39.9% of the CNV probes examined. The most frequently altered regions were gains of 1q21-22 (90%), 8q21-24 (85%), 1q44 (85%), and 3q11 (85%) or losses of 16q22-24 (80%). Gene ontology analyses of genes within the CNA fragments revealed that cascades related to transcription and RNA metabolism correlated significantly with human epidermal growth factor receptor 2 positivity and menopausal status. Thirteen of 20 tumors showed CNAs in more than 35% of sites examined and a high prevalence of CNAs correlated significantly with estrogen receptor (ER) negativity, higher nuclear grade (NG), and higher Ki-67 labeling index. Finally, when CNA fragments were categorized according to their size, CNAs smaller than 10 kb correlated significantly with ER positivity and lower NG, whereas CNAs exceeding 10 Mb correlated with higher NG, ER negativity, and a higher Ki-67 labeling index. Most of these findings were confirmed or supported by quantitative PCR of representative DNA fragments in 72 additional breast cancers. These results suggest that most CNAs are caused by gain or loss of large chromosomal fragments and correlate with NG and several malignant features, whereas solitary CNAs of less than 10 kb could be involved in ER-positive breast carcinogenesis.

Comparative genomic analysis of primary tumors and metastases in breast cancer

Personalized medicine uses genomic information for selecting therapy in patients with metastatic cancer. An issue is the optimal tissue source (primary tumor or metastasis) for testing. We compared the DNA copy number and mutational profiles of primary breast cancers and paired metastases from 23 patients using whole-genome array-comparative genomic hybridization and next-generation sequencing of 365 “cancer-associated” genes. Primary tumors and metastases harbored copy number alterations (CNAs) and mutations common in breast cancer and showed concordant profiles. The global concordance regarding CNAs was shown by clustering and correlation matrix, which showed that each metastasis correlated more strongly with its paired tumor than with other samples. Genes with recurrent amplifications in breast cancer showed 100% (ERBB2, FGFR1), 96% (CCND1), and 88% (MYC) concordance for the amplified/non-amplified status. Among all samples, 499 mutations were identified, including 39 recurrent (AKT1, ERBB2, PIK3CA, TP53) and 460 non-recurrent variants. The tumors/metastases concordance of variants was 75%, higher for recurrent (92%) than for non-recurrent (73%) variants. Further mutational discordance came from very different variant allele frequencies for some variants. We showed that the chosen targeted therapy in two clinical trials of personalized medicine would be concordant in all but one patient (96%) when based on the molecular profiling of tumor and paired metastasis. Our results suggest that the genotyping of primary tumor may be acceptable to guide systemic treatment if the metastatic sample is not obtainable. However, given the rare but potentially relevant divergences for some actionable driver genes, the profiling of metastatic sample is recommended.

Array comparative genomic hybridization of 18 pancreatic ductal adenocarcinomas and their autologous metastases

BACKGROUND

Mortality rates of pancreatic cancer remain high, which is mainly due to advanced disease and metastasis. We hypothesized that genomic copy number alterations are enriched in metastatic cells compared to autologous primary tumors, which may inform on cancer-related pathways possibly serving as potential targets for specific therapies. We investigated 18 pancreatic ductal adenocarcinomas, including 39 lymph node and 5 distant metastases after surgical resection. Analysis was performed with array-based comparative genomic hybridization (aCGH).

RESULTS

Metastases acquire a higher frequency of copy number alterations with the highest in distant metastasis (median = 42, lymph node metastases: median = 23, primary tumors: median = 17). In lymph node metastases, gains were prevalent on chromosome bands 8q11.23-q24.3, 12q14.1, 17p12.1, 21q22.12, and losses on 3p21.31, 4p14, 8p23.3-p11.21,17p12-11.2. Genes on amplified regions are involved in cancer-related pathways such as WNT-signaling, also involved in metastasis.

CONCLUSIONS

Pancreatic cancers show a high degree of intratumor heterogeneity, which could lead to resistance of chemotherapy and worse outcome. ACGH analysis reveals regions preferentially gained or lost in synchronous metastases encoding for genes involved in cancer-related pathways, which could lead to novel therapeutic opportunities.

How to study and overcome tumor heterogeneity with circulating biomarkers: The breast cancer case

Breast cancer ranks first among female cancer-related deaths in Western countries. As the primary tumor can often be controlled by surgical resection, the survival of women with breast cancer is closely linked to the incidence of distant metastases. Molecular screening by next generation sequencing highlighted the spatial and temporal heterogeneity of solid tumors as well as the clonal evolution of cancer cells during progression and under treatment pressure. Such findings question whether an optimal assessment of disease progression and a screening for druggable mutations should be based on molecular features of primary or recurrent/metastatic lesions and therefore represent a crucial element for failure or success of personalized medicine. In fact, new targeted therapies may induce only short-term benefit annulled by the emergence of resistant clones with new driver mutations which would need to be rapidly and reliably identified. Serial tissue sampling is therefore essential but, unfortunately, also represents a problem since biopsies from solid lesions, which are invasive and potentially painful and risky, cannot be easily repeatedly sampled, are inaccessible or may not fully reflect tumor heterogeneity. The need to early detect and strike this "moving target" is now directing the scientific community toward liquid biopsy-based biomarkers, which include circulating tumor cells (CTC) and cell-free circulating tumor DNA (ctDNA), can be repeatedly assessed through non-invasive and easy-to-perform procedures and may act as reliable read-outs of functional and molecular features of recurrent/metastatic lesions. In this review we summarize the outcome of CTCs and ctDNA in breast cancer, with special reference on their role on unveiling and overcoming tumor heterogeneity, on their potential relevance for tumor surveillance and monitoring, and for the selection of therapeutic options. Finally, we propose integration between blood-based molecular and clinical approaches for monitoring disease progression according to the specific pattern of recurrence of the most aggressive breast cancer molecular subtypes.

FOXA1 overexpression mediates endocrine resistance by altering the ER transcriptome and IL-8 expression in ER-positive breast cancer

Forkhead box protein A1 (FOXA1) is a pioneer factor of estrogen receptor α (ER)-chromatin binding and function, yet its aberration in endocrine-resistant (Endo-R) breast cancer is unknown. Here, we report preclinical evidence for a role of FOXA1 in Endo-R breast cancer as well as evidence for its clinical significance. FOXA1 is gene-amplified and/or overexpressed in Endo-R derivatives of several breast cancer cell line models. Induced FOXA1 triggers oncogenic gene signatures and proteomic profiles highly associated with endocrine resistance. Integrated omics data reveal IL8 as one of the most perturbed genes regulated by FOXA1 and ER transcriptional reprogramming in Endo-R cells. IL-8 knockdown inhibits tamoxifen-resistant cell growth and invasion and partially attenuates the effect of overexpressed FOXA1. Our study highlights a role of FOXA1 via IL-8 signaling as a potential therapeutic target in FOXA1-overexpressing ER-positive tumors.

Cross-Clustering: A Partial Clustering Algorithm with Automatic Estimation of the Number of Clusters

Four of the most common limitations of the many available clustering methods are: i) the lack of a proper strategy to deal with outliers; ii) the need for a good a priori estimate of the number of clusters to obtain reasonable results; iii) the lack of a method able to detect when partitioning of a specific data set is not appropriate; and iv) the dependence of the result on the initialization. Here we propose Cross-clustering (CC), a partial clustering algorithm that overcomes these four limitations by combining the principles of two well established hierarchical clustering algorithms: Ward’s minimum variance and Complete-linkage. We validated CC by comparing it with a number of existing clustering methods, including Ward’s and Complete-linkage. We show on both simulated and real datasets, that CC performs better than the other methods in terms of: the identification of the correct number of clusters, the identification of outliers, and the determination of real cluster memberships. We used CC to cluster samples in order to identify disease subtypes, and on gene profiles, in order to determine groups of genes with the same behavior. Results obtained on a non-biological dataset show that the method is general enough to be successfully used in such diverse applications. The algorithm has been implemented in the statistical language R and is freely available from the CRAN contributed packages repository.

Clinical implications of molecular heterogeneity in triple negative breast cancer

Triple negative breast cancer (TNBC) is a molecularly heterogeneous disease lacking recurrent targetable alterations and thus therapeutic advances have been challenging. The absence of ER, PR and HER2 amplifications, leaves combination chemotherapy as the standard of care treatment option in the adjuvant, neoadjuvant and metastatic settings. Recently, multiple studies have shed some light on the heterogeneity of TNBC and identified distinct transcriptional subtypes with unique biologies. Herein we review the molecular heterogeneity and the impact on previous and future clinical trials.

Robust BRCA1-like classification of copy number profiles of samples repeated across different datasets and platforms

Breast cancers with BRCA1 germline mutation have a characteristic DNA copy number (CN) pattern. We developed a test that assigns CN profiles to be 'BRCA1-like' or 'non-BRCA1-like', which refers to resembling a BRCA1-mutated tumor or resembling a tumor without a BRCA1 mutation, respectively. Approximately one third of the BRCA1-like breast cancers have a BRCA1 mutation, one third has hypermethylation of the BRCA1 promoter and one third has an unknown reason for being BRCA1-like. This classification is indicative of patients' response to high dose alkylating and platinum containing chemotherapy regimens, which targets the inability of BRCA1 deficient cells to repair DNA double strand breaks. We investigated whether this classification can be reliably obtained with next generation sequencing and copy number platforms other than the bacterial artificial chromosome (BAC) array Comparative Genomic Hybridization (aCGH) on which it was originally developed. We investigated samples from 230 breast cancer patients for which a CN profile had been generated on two to five platforms, comprising low coverage CN sequencing, CN extraction from targeted sequencing panels (CopywriteR), Affymetrix SNP6.0, 135K/720K oligonucleotide aCGH, Affymetrix Oncoscan FFPE (MIP) technology, 3K BAC and 32K BAC aCGH. Pairwise comparison of genomic position-mapped profiles from the original aCGH platform and other platforms revealed concordance. For most cases, biological differences between samples exceeded the differences between platforms within one sample. We observed the same classification across different platforms in over 80% of the patients and kappa values of at least 0.36. Differential classification could be attributed to CN profiles that were not strongly associated to one class. In conclusion, we have shown that the genomic regions that define our BRCA1-like classifier are robustly measured by different CN profiling technologies, providing the possibility to retro- and prospectively investigate BRCA1-like classification across a wide range of CN platforms.