Detection of Genomically Aberrant Cells within Circulating Tumor Microemboli (CTMs) Isolated from Early-Stage Breast Cancer Patients

Simple Summary Distant metastases derive from the shedding and dissemination of single cancer cells (CTCs) or circulating tumor emboli (CTMs) into circulation. Previous studies on CTMs were mainly run in patients with metastatic disease; however, we observed that CTMs are more frequently detected in patients with early-stage breast cancer. Here, we collected single CTMs and their relative primary tumor tissue samples in early-stage patients. By studying genomic aberrations, present in tumors cells and absent in normal cells, we predicted the tumor fraction thanks to a statistical model developed from a calibration curve with breast cancer cell lines. The tumor fraction ranged from 8% to 48% and CTMs contained specific and shared alterations with respect to tissue. Thus, CTMs may derive from different regions of the primary tumor or from occult micrometastases. Moreover, CTM-private mutations may inform us about specific metastasis-associated functions of involved genes that should be further explored in follow-up and mechanistic studies. Abstract Circulating tumor microemboli (CTMs) are clusters of cancer cells detached from solid tumors, whose study can reveal mechanisms underlying metastatization. As they frequently comprise unknown fractions of leukocytes, the analysis of copy number alterations (CNAs) is challenging. To address this, we titrated known numbers of leukocytes into cancer cells (MDA-MB-453 and MDA-MB-36, displaying high and low DNA content, respectively) generating tumor fractions from 0–100%. After low-pass sequencing, ichorCNA was identified as the best algorithm to build a linear mixed regression model for tumor fraction (TF) prediction. We then isolated 53 CTMs from blood samples of six early-stage breast cancer patients and predicted the TF of all clusters. We found that all clusters harbor cancer cells between 8 and 48%. Furthermore, by comparing the identified CNAs of CTMs with their matched primary tumors, we noted that only 31–71% of aberrations were shared. Surprisingly, CTM-private alterations were abundant (30–63%), whereas primary tumor-private alterations were rare (4–12%). This either indicates that CTMs are disseminated from further progressed regions of the primary tumor or stem from cancer cells already colonizing distant sites. In both cases, CTM-private mutations may inform us about specific metastasis-associated functions of involved genes that should be explored in follow-up and mechanistic studies.

[1]  G. Pruneri,et al.  Dissemination of Circulating Tumor Cell Clusters Occurs Early in Non‑metastatic Breast Cancer Patients , 2021 .

[2]  R. Piñeiro,et al.  Analysis of a Real-World Cohort of Metastatic Breast Cancer Patients Shows Circulating Tumor Cell Clusters (CTC-clusters) as Predictors of Patient Outcomes , 2020, Cancers.

[3]  N. Aceto Bring along your friends: Homotypic and heterotypic circulating tumor cell clustering to accelerate metastasis , 2020, Biomedical journal.

[4]  Crispin J. Miller,et al.  Profiling of Circulating Free DNA Using Targeted and Genome-wide Sequencing in Patients with SCLC , 2020, Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer.

[5]  M. Daidone,et al.  A novel circulating tumor cell subpopulation for treatment monitoring and molecular characterization in biliary tract cancer , 2019, International journal of cancer.

[6]  Ahmedin Jemal,et al.  Breast cancer statistics, 2019 , 2019, CA: a cancer journal for clinicians.

[7]  G. Hortobagyi,et al.  Circulating Tumor Cell Clusters in Patients with Metastatic Breast Cancer: a SWOG S0500 Translational Medicine Study , 2019, Clinical Cancer Research.

[8]  T. Oliver,et al.  Partners in Crime: Neutrophil-CTC Collusion in Metastasis. , 2019, Trends in immunology.

[9]  P. Hofman,et al.  Never Travel Alone: The Crosstalk of Circulating Tumor Cells and the Blood Microenvironment , 2019, Cells.

[10]  P. Jia,et al.  PMN-MDSCs Enhance CTC Metastatic Properties through Reciprocal Interactions via ROS/Notch/Nodal Signaling , 2019, International journal of molecular sciences.

[11]  N. Beerenwinkel,et al.  Neutrophils escort circulating tumour cells to enable cell cycle progression , 2019, Nature.

[12]  S. Haferkamp,et al.  Microfluidic enrichment, isolation and characterization of disseminated melanoma cells from lymph node samples , 2019, International journal of cancer.

[13]  J. C. Love,et al.  Tumor fraction in cell-free DNA as a biomarker in prostate cancer. , 2018, JCI insight.

[14]  Pingzhao Hu,et al.  Association Analysis of Somatic Copy Number Alteration Burden With Breast Cancer Survival , 2018, Front. Genet..

[15]  N. Loman,et al.  Longitudinal enumeration and cluster evaluation of circulating tumor cells improve prognostication for patients with newly diagnosed metastatic breast cancer in a prospective observational trial , 2018, Breast Cancer Research.

[16]  Nikhil Wagle,et al.  Association of Cell-Free DNA Tumor Fraction and Somatic Copy Number Alterations With Survival in Metastatic Triple-Negative Breast Cancer. , 2018, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[17]  Nikhil Wagle,et al.  Scalable whole-exome sequencing of cell-free DNA reveals high concordance with metastatic tumors , 2017, Nature Communications.

[18]  Ryan E. Mills,et al.  Rapid, ultra low coverage copy number profiling of cell-free DNA as a precision oncology screening strategy , 2017, Oncotarget.

[19]  M. Daidone,et al.  Detection of Circulating Tumour Cells in Urothelial Cancers and Clinical Correlations: Comparison of Two Methods , 2017, Disease markers.

[20]  Rainer Spang,et al.  Early dissemination seeds metastasis in breast cancer , 2016, Nature.

[21]  P. Bendahl,et al.  Prognostic impact of circulating tumor cell apoptosis and clusters in serial blood samples from patients with metastatic breast cancer in a prospective observational cohort , 2016, BMC Cancer.

[22]  H. Morreau,et al.  Digital Sorting of Pure Cell Populations Enables Unambiguous Genetic Analysis of Heterogeneous Formalin-Fixed Paraffin-Embedded Tumors by Next Generation Sequencing , 2016, Scientific Reports.

[23]  Yupei Zhao,et al.  Myeloid-derived suppressor cells (MDSC) facilitate distant metastasis of malignancies by shielding circulating tumor cells (CTC) from immune surveillance. , 2016, Medical hypotheses.

[24]  Hushan Yang,et al.  Prospective assessment of the prognostic value of circulating tumor cells and their clusters in patients with advanced-stage breast cancer , 2015, Breast Cancer Research and Treatment.

[25]  Ana Conesa,et al.  Qualimap 2: advanced multi-sample quality control for high-throughput sequencing data , 2015, Bioinform..

[26]  M. Daidone,et al.  Did Circulating Tumor Cells Tell us all they Could? The Missed Circulating Tumor Cell Message in Breast Cancer , 2015, The International journal of biological markers.

[27]  I. Fidler,et al.  The challenge of targeting metastasis , 2015, Cancer and Metastasis Reviews.

[28]  P. Neven,et al.  Presymptomatic Identification of Cancers in Pregnant Women During Noninvasive Prenatal Testing. , 2015, JAMA oncology.

[29]  J. Garber,et al.  Noninvasive Prenatal Testing and Incidental Detection of Occult Maternal Malignancies. , 2015, JAMA.

[30]  Dafydd G. Thomas,et al.  Significance of Circulating Tumor Cells in Metastatic Triple-Negative Breast Cancer Patients within a Randomized, Phase II Trial: TBCRC 019 , 2015, Clinical Cancer Research.

[31]  Nicolò Manaresi,et al.  Molecular profiling of single circulating tumor cells with diagnostic intention , 2014, EMBO molecular medicine.

[32]  Sridhar Ramaswamy,et al.  Circulating Tumor Cell Clusters Are Oligoclonal Precursors of Breast Cancer Metastasis , 2014, Cell.

[33]  John M S Bartlett,et al.  Recommendations for human epidermal growth factor receptor 2 testing in breast cancer: American Society of Clinical Oncology/College of American Pathologists clinical practice guideline update. , 2014, Archives of pathology & laboratory medicine.

[34]  Chris Sander,et al.  Emerging landscape of oncogenic signatures across human cancers , 2013, Nature Genetics.

[35]  Qingguo Wang,et al.  Computational tools for copy number variation (CNV) detection using next-generation sequencing data: features and perspectives , 2013, BMC Bioinformatics.

[36]  Yangho Chen,et al.  Supplementary Methods , 2012, Acta Neuropsychiatrica.

[37]  Jack Cuzick,et al.  Assessment of Ki67 in breast cancer: recommendations from the International Ki67 in Breast Cancer working group. , 2011, Journal of the National Cancer Institute.

[38]  Klaus Pantel,et al.  Circulating tumour cells in cancer patients: challenges and perspectives. , 2010, Trends in molecular medicine.

[39]  Anthony Rhodes,et al.  American Society of Clinical Oncology/College of American Pathologists guideline recommendations for immunohistochemical testing of estrogen and progesterone receptors in breast cancer. , 2010, Archives of pathology & laboratory medicine.

[40]  E. Perez,et al.  Breast cancer and aneusomy 17: implications for carcinogenesis and therapeutic response. , 2009, The Lancet. Oncology.

[41]  R. Eils,et al.  Systemic spread is an early step in breast cancer. , 2008, Cancer cell.

[42]  C. Klein Single cell amplification methods for the study of cancer and cellular ageing , 2005, Mechanisms of Ageing and Development.

[43]  Joe W. Gray,et al.  Genome Amplification of Chromosome 20 in Breast Cancer , 2003, Breast Cancer Research and Treatment.

[44]  M. Speicher,et al.  Comparative genomic hybridization, loss of heterozygosity, and DNA sequence analysis of single cells. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[45]  P. Meltzer,et al.  Independent amplification and frequent co-amplification of three nonsyntenic regions on the long arm of chromosome 20 in human breast cancer. , 1996, Cancer research.

[46]  H. Iyer,et al.  Regression Analysis-Concepts and Applications , 1995 .

[47]  L. Lin,et al.  A concordance correlation coefficient to evaluate reproducibility. , 1989, Biometrics.

[48]  D. Bates,et al.  Newton-Raphson and EM Algorithms for Linear Mixed-Effects Models for Repeated-Measures Data , 1988 .

[49]  D. Altman,et al.  STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT , 1986, The Lancet.

[50]  M. Olivé,et al.  Long-term human breast carcinoma cell lines of metastatic origin: Preliminary characterization , 1978, In Vitro.

[51]  Data Mining And Knowledge Discovery Handbook , 2022 .

[52]  R. Piñeiro,et al.  Relevance of CTC Clusters in Breast Cancer Metastasis. , 2020, Advances in experimental medicine and biology.

[53]  Hushan Yang,et al.  Longitudinally collected CTCs and CTC-clusters and clinical outcomes of metastatic breast cancer , 2016, Breast Cancer Research and Treatment.

[54]  Diana Miglioretti,et al.  Cell-free DNA Analysis for Noninvasive Examination of Trisomy. , 2015, The New England journal of medicine.