Comprehensive identification and characterization of somatic copy number alterations in triple‑negative breast cancer

Li, Zaibing; Zhang, Xiao; Hou, Chenxin; Zhou, Yuqing; Chen, Junli; Cai, Haoyang; Ye, Yifeng; Liu, Jinping; Huang, Ning

doi:10.3892/ijo.2019.4950

Cited by 13 publications

(13 citation statements)

References 69 publications

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“…The molecular characterization of recovered CTCs showed a prevalence of deletions on amplification and included chromosomes 5, 8, and 17 (Figure 5, panel A), which have already been described in the literature as frequently lost in primary TNBCs. 21 Moreover, the 10q and 21q were the most frequently altered chromosomal arms (Figure 5, panel B). A network analysis of physical interaction among BC-related genes from these altered regions identified a module of 28 nodes involved in therapeutically exploitable pathways including mismatch repair, PI3K/Akt, erbB, Raf, platinumresistence signaling, and regulation of immune response (Supplementary Figure S4, available at https://doi.org/10.…”

Section: Ctc Analysis At the Time Of Disease Progressionmentioning

confidence: 96%

Blood-based genomics of triple-negative breast cancer progression in patients treated with neoadjuvant chemotherapy

et al. 2021

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Background: As neoadjuvant chemotherapy (NAC) is increasingly used in triple-negative breast cancer (TNBC), we investigated the value of circulating tumor DNA (ctDNA) for patient monitoring prior, during, and after NAC, and circulating tumor cells (CTCs) for disease characterization at clinical progression. Materials and methods: Forty-two TNBC patients undergoing NAC were prospectively enrolled. Primary tumor mutations identified by targeted-gene sequencing were validated and tracked in 168 plasma samples longitudinally collected at multiple time-points by droplet digital polymerase chain reaction. At progression, plasma DNA underwent direct targeted-gene assay, and CTCs were collected and analyzed for copy number alterations (CNAs) by low-pass whole genome sequencing. Results: ctDNA detection after NAC was associated with increased risk of relapse, with 2-year event-free survival estimates being 44.4% [95% confidence interval (CI) 21.4%-92.3%] versus 77.4% (95% CI 57.8%-100%). ctDNA prognostic value remained worthy even after adjusting for age, residual disease, systemic inflammatory indices, and Ki-67 [hazard ratio (HR) 1.91; 95% CI 0.51-7.08]. During follow-up, ctDNA was undetectable in non-recurrent cases with the unique exception of one showing a temporary peak over eight samples. Conversely, ctDNA was detected in 8/11 recurrent cases, and predated the clinical diagnosis up to 13 months. Notably, recurrent cases without ctDNA developed locoregional, contralateral, and bone-only disease. At clinical progression, CTCs presented chromosome 10 and 21q CNAs whose network analysis showed connected modules including HER/PI3K/Ras/JAK signaling and immune response. Conclusion: ctDNA is not only associated with but is also predictive of prognosis in TNBC patients receiving NAC, and represents an exploitable tool, either alone or with CTCs, for personalized TNBC management.

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Section: Ctc Analysis At the Time Of Disease Progressionmentioning

confidence: 96%

Blood-based genomics of triple-negative breast cancer progression in patients treated with neoadjuvant chemotherapy

et al. 2021

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“…At present, breast cancer diagnosis and treatment follows an integrative approach whereby both clinical and histopathological features such as age at diagnosis, tumour size, lymph node metastasis and histological grade are utilised alongside tissue derived biomarkers [ 17 ]. However, it is widely accepted that breast cancer is largely dominated by chromosomal rearrangements [ 5 , 18 , 19 ], and a growing body of evidence suggests that the incorporation of the genomic landscape of the tumour into treatment decisions is extremely beneficial to the patient [ 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

Survival outcomes are associated with genomic instability in luminal breast cancers

2021

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Breast cancer is the leading cause of cancer related death among women. Breast cancers are generally diagnosed and treated based on clinical and histopathological features, along with subtype classification determined by the Prosigna Breast Cancer Prognostic Gene Signature Assay (also known as PAM50). Currently the copy number alteration (CNA) landscape of the tumour is not considered. We set out to examine the role of genomic instability (GI) in breast cancer survival since CNAs reflect GI and correlate with survival in other cancers. We focused on the 70% of breast cancers classified as luminal and carried out a comprehensive survival and association analysis using Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) data to determine whether CNA Score Quartiles derived from absolute CNA counts are associated with survival. Analysis revealed that patients diagnosed with luminal A breast cancer have a CNA landscape associated with disease specific survival, suggesting that CNA Score can provide a statistically robust prognostic factor. Furthermore, stratification of patients into subtypes based on gene expression has shown that luminal A and B cases overlap, and it is in this region we largely observe luminal A cases with reduced survival outlook. Therefore, luminal A breast cancer patients with quantitatively elevated CNA counts may benefit from more aggressive therapy. This demonstrates how individual genomic landscapes can facilitate personalisation of therapeutic interventions to optimise survival outcomes.

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“…We applied SCONCE to a published dataset, consisting of 34 diploid cells (as determined by cell sorting), and 4 tumor subpopulations (24,24,4, and 8 cells, respectively) from one triple negative breast cancer patient (33), a cancer type with prevalent CNAs (34).…”

Section: Real Data Preprocessingmentioning

confidence: 99%

SCONCE: A method for profiling Copy Number Alterations in Cancer Evolution using Single Cell Whole Genome Sequencing

Hui

Nielsen

2021

Preprint

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Copy number alterations are a significant driver in cancer growth and development, but remain poorly characterized on the single cell level. Although genome evolution in cancer cells is Markovian through evolutionary time, copy number alterations are not Markovian along the genome. However, existing methods call copy number profiles with Hidden Markov Models or change point detection algorithms based on changes in observed read depth, corrected by genome content, and do not account for the stochastic evolutionary process. We present a theoretical framework to use tumor evolutionary history to accurately call copy number alterations in a principled manner. In order to model the tumor evolutionary process and account for technical noise from low coverage single cell whole genome sequencing data, we developed SCONCE, a method based on a Hidden Markov Model to analyze read depth data from tumor cells using matched normal cells as negative controls. Using a combination of public datasets and simulations, we show SCONCE accurately decodes copy number profiles, with broader implications for understanding tumor evolution.

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Comprehensive identification and characterization of somatic copy number alterations in triple‑negative breast cancer

Cited by 13 publications

References 69 publications

Blood-based genomics of triple-negative breast cancer progression in patients treated with neoadjuvant chemotherapy

Blood-based genomics of triple-negative breast cancer progression in patients treated with neoadjuvant chemotherapy

Survival outcomes are associated with genomic instability in luminal breast cancers

SCONCE: A method for profiling Copy Number Alterations in Cancer Evolution using Single Cell Whole Genome Sequencing

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