The properties that develop in many breast cancers (metastasis to other organs, resistance to drugs) may be due to cancer stem cells or other rare cells within the tumour. Hormone or chemotherapy may successfully kill the majority of tumour cells, but leave these rare cells intact to initiate new tumours within the breast or at other sites around the body.
At present we do not know how complex the mixture of different cell types within a single tumour may be.
Aims and Relevance
To approach this question we shall make use of a known property of breast cancer cells: a genomic instability that leads to a progressive scrambling of the chromosomes (genome) through breakage and re-joining as cells divide. These mutations are what give tumour cells new functional properties, such as drug resistance.
We will use the presence or absence of specific scrambling events as a sort of genetic fingerprint to determine:
- what cell types are present within advanced breast cancers
- the frequency of each cell type
- the relationship between the different cell types
We expect that these studies will validate our method as a way of distinguishing different cell types within tumours. This is the first step towards identifying the cells that must be destroyed in order to prevent the recurrence or spread of the disease.
Genomic analysis is usually performed on millions of cells at a time. This has been necessary in the past in order to extract enough DNA, proteins, and other molecules for analysis, but is problematic when the sample contains a mixture of different cell types.
Recent studies, such as cancer stem cell research, have highlighted the importance of identifying and characterizing rare cell types in mixed populations. We therefore need to develop new techniques that will allow us to analyse the genomes of single cells.
Single cell genomic analysis requires highly specialized equipment that can handle tiny volumes of liquid. This project aims to develop equipment and techniques to capture single cells, and to extract and analyse their DNA. The initial phase will use a variety of cell types, including human mammary cells, to help optimize the new techniques.
Recent related papers from the Aparicio Laboratory
- Shah SP, Roth A, Goya R, Oloumi A, Ha G, Zhao Y, Turashvili G, Ding J, Tse K, Haffari G, Bashashati A, Prentice L, Khattra J, Burleigh A, Yap D, Bernard V, McPherson A, Shumansky K, Crisan A, Giuliany R, Heravi-Moussavi A, Rosner J, Lai D, Birol I, Varhol R, Tam A, Dhalla N, Zeng T, Ma K, Chan S, Griffith M, Moradian A, Cheng G, Morin GB, Watson P, Gelmon K, Chia S, Chin SF, Curtis C, Rueda O, Pharoah PD, Damaraju S, Mackey J, Hoon K, Harkins T, Tadigotla V, Sigaroudinia M, Gascard P, Tlsty T, Costello JF, Meyer IM, Eaves CJ, Wasserman WW, Jones S, Huntsman D, Hirst M, Caldas C, Marra MA, Aparicio S. The clonal and mutational evolution spectrum of primary triple negative breast cancers. Nature 2012, doi:10.1038/nature10933
- Roth A, Morin R, Ding J, Crisan A, Ha G, Giuliany R, Bashashati A, Hirst M, Turashvili G, Oloumi A, Marra MA, Aparicio S, Shah SP. JointSNVMix : A Probabilistic Model For Accurate Detection Of Somatic Mutations In Normal/Tumour Paired Next Generation Sequencing Data. Bioinformatics 2012: 28(7):907-13
- White AK, VanInsberghe M, Petriv OI, Hamidi M, Sikorski D, Marra MA, Piret J, Aparicio S, Hansen CL. High-throughput microfluidic single-cell RT-qPCR. Proc Natl Acad Sci U S A. 2011: 108(34):13999-4004.
- Goya R, Sun MG, Morin RD, Leung G, Ha G, Wiegand KC, Senz J, Crisan A, Marra MA, Hirst M, Huntsman D, Murphy KP, Aparicio S, Shah SP. SNVMix: predicting single nucleotide variants from next generation sequencing of tumors. Bioinformatics 2010 26:730-6
- Shah SP, Morin RD, Khattra J, Prentice L, Pugh T, Burleigh A, Delaney A, Gelmon K, Guliany R, Senz J, Steidl C, Holt RA, Jones S, Sun M, Leung G, Moore R, Severson T, Taylor GA, Teschendorff AE, Tse K, Turashvili G, Varhol R, Warren RL, Watson P, Zhao Y, Caldas C, Huntsman D, Hirst M, Marra MA, Aparicio S. Mutational evolution in a lobular breast tumour profiled at single nucleotide resolution. Nature 2009: 461: 809-813