Landscapes of cellular phenotypic diversity in breast cancer xenografts and their impact on drug response.
Animals
Benzamides
/ pharmacology
Breast Neoplasms
/ drug therapy
Cell Line, Tumor
Drug Resistance, Neoplasm
/ drug effects
Female
Heterografts
/ drug effects
Humans
MCF-7 Cells
Mice, Inbred NOD
Mice, Knockout
Mice, SCID
Morpholines
/ pharmacology
Piperazines
/ pharmacology
Protein Kinase Inhibitors
/ pharmacology
Pyridines
/ pharmacology
Pyrimidines
/ pharmacology
Treatment Outcome
Xenograft Model Antitumor Assays
/ methods
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
31 03 2021
31 03 2021
Historique:
received:
28
08
2020
accepted:
26
02
2021
entrez:
1
4
2021
pubmed:
2
4
2021
medline:
20
4
2021
Statut:
epublish
Résumé
The heterogeneity of breast cancer plays a major role in drug response and resistance and has been extensively characterized at the genomic level. Here, a single-cell breast cancer mass cytometry (BCMC) panel is optimized to identify cell phenotypes and their oncogenic signalling states in a biobank of patient-derived tumour xenograft (PDTX) models representing the diversity of human breast cancer. The BCMC panel identifies 13 cellular phenotypes (11 human and 2 murine), associated with both breast cancer subtypes and specific genomic features. Pre-treatment cellular phenotypic composition is a determinant of response to anticancer therapies. Single-cell profiling also reveals drug-induced cellular phenotypic dynamics, unravelling previously unnoticed intra-tumour response diversity. The comprehensive view of the landscapes of cellular phenotypic heterogeneity in PDTXs uncovered by the BCMC panel, which is mirrored in primary human tumours, has profound implications for understanding and predicting therapy response and resistance.
Identifiants
pubmed: 33790302
doi: 10.1038/s41467-021-22303-z
pii: 10.1038/s41467-021-22303-z
pmc: PMC8012607
doi:
Substances chimiques
Benzamides
0
Morpholines
0
Piperazines
0
Protein Kinase Inhibitors
0
Pyridines
0
Pyrimidines
0
vistusertib
0BSC3P4H5X
palbociclib
G9ZF61LE7G
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1998Subventions
Organisme : Cancer Research UK
ID : C9545/A24042
Pays : United Kingdom
Organisme : Howard Hughes Medical Institute
Pays : United States
Organisme : Medical Research Council
ID : MR/M008975/1
Pays : United Kingdom
Organisme : NCI NIH HHS
ID : P30 CA008748
Pays : United States
Organisme : Cancer Research UK
ID : A16942
Pays : United Kingdom
Organisme : Cancer Research UK
ID : A29580
Pays : United Kingdom
Investigateurs
H R Ali
(HR)
M Al Sa'd
(M)
S Alon
(S)
S Aparicio
(S)
G Battistoni
(G)
S Balasubramanian
(S)
R Becker
(R)
B Bodenmiller
(B)
E S Boyden
(ES)
D Bressan
(D)
A Bruna
(A)
Marcel Burger
(M)
C Caldas
(C)
M Callari
(M)
I G Cannell
(IG)
H Casbolt
(H)
N Chornay
(N)
Y Cui
(Y)
A Dariush
(A)
K Dinh
(K)
A Emenari
(A)
Y Eyal-Lubling
(Y)
J Fan
(J)
A Fatemi
(A)
E Fisher
(E)
E A González-Solares
(EA)
C González-Fernández
(C)
D Goodwin
(D)
W Greenwood
(W)
F Grimaldi
(F)
G J Hannon
(GJ)
O Harris
(O)
S Harris
(S)
C Jauset
(C)
J A Joyce
(JA)
E D Karagiannis
(ED)
T Kovačević
(T)
L Kuett
(L)
R Kunes
(R)
Yoldaş A Küpcü
(YA)
D Lai
(D)
E Laks
(E)
H Lee
(H)
M Lee
(M)
G Lerda
(G)
Y Li
(Y)
A McPherson
(A)
N Millar
(N)
C M Mulvey
(CM)
F Nugent
(F)
C H O'Flanagan
(CH)
M Paez-Ribes
(M)
I Pearsall
(I)
F Qosaj
(F)
A J Roth
(AJ)
O M Rueda
(OM)
T Ruiz
(T)
K Sawicka
(K)
L A Sepúlveda
(LA)
S P Shah
(SP)
A Shea
(A)
A Sinha
(A)
A Smith
(A)
S Tavaré
(S)
S Tietscher
(S)
I Vázquez-García
(I)
S L Vogl
(SL)
N A Walton
(NA)
A T Wassie
(AT)
S S Watson
(SS)
J Weselak
(J)
S A Wild
(SA)
E Williams
(E)
J Windhager
(J)
T Whitmarsh
(T)
C Xia
(C)
P Zheng
(P)
X Zhuang
(X)
Références
Torre, L. A., Siegel, R. L., Ward, E. M. & Jemal, A. Global cancer incidence and mortality rates and trends–an update. Cancer Epidemiol. Biomark. Prev. 25, 16–27 (2016).
doi: 10.1158/1055-9965.EPI-15-0578
Gonzalez-Angulo, A. M., Morales-Vasquez, F. & Hortobagyi, G. N. Overview of resistance to systemic therapy in patients with breast cancer. Adv. Exp. Med. Biol. 608, 1–22 (2007).
pubmed: 17993229
doi: 10.1007/978-0-387-74039-3_1
Polyak, K. Heterogeneity in breast cancer. J. Clin. Invest. 121, 3786–3788 (2011).
pubmed: 21965334
pmcid: 3195489
doi: 10.1172/JCI60534
Cancer Genome Atlas Network Comprehensive molecular portraits of human breast tumours. Nature 490, 61–70 (2012).
doi: 10.1038/nature11412
Rueda, O. M. et al. Dynamics of breast-cancer relapse reveal late-recurring ER-positive genomic subgroups. Nature 567, 399–404 (2019).
pubmed: 30867590
pmcid: 6647838
doi: 10.1038/s41586-019-1007-8
Curtis, C. et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 486, 346–352 (2012).
pubmed: 22522925
pmcid: 3440846
doi: 10.1038/nature10983
Pereira, B. et al. The somatic mutation profiles of 2,433 breast cancers refines their genomic and transcriptomic landscapes. Nat. Commun. 7, 11479 (2016).
pubmed: 27161491
pmcid: 4866047
doi: 10.1038/ncomms11479
Lawrence, R. T. et al. The proteomic landscape of triple-negative breast cancer. Cell Rep. 11, 630–644 (2015).
pubmed: 25892236
pmcid: 4425736
doi: 10.1016/j.celrep.2015.03.050
Geiger, T., Madden, S. F., Gallagher, W. M., Cox, J. & Mann, M. Proteomic portrait of human breast cancer progression identifies novel prognostic markers. Cancer Res. 72, 2428–2439 (2012).
pubmed: 22414580
doi: 10.1158/0008-5472.CAN-11-3711
Yuan, Y. et al. Assessing the clinical utility of cancer genomic and proteomic data across tumor types. Nat. Biotechnol. 32, 644–652 (2014).
pubmed: 24952901
pmcid: 4102885
doi: 10.1038/nbt.2940
de Bruin, E. C. et al. Spatial and temporal diversity in genomic instability processes defines lung cancer evolution. Science 346, 251–256 (2014).
pubmed: 25301630
pmcid: 4636050
doi: 10.1126/science.1253462
Gerlinger, M. et al. Genomic architecture and evolution of clear cell renal cell carcinomas defined by multiregion sequencing. Nat. Genet. 46, 225–233 (2014).
pubmed: 24487277
pmcid: 4636053
doi: 10.1038/ng.2891
Johnson, B. E. et al. Mutational analysis reveals the origin and therapy-driven evolution of recurrent glioma. Science 343, 189–193 (2014).
doi: 10.1126/science.1239947
pubmed: 24336570
Gundem, G. et al. The evolutionary history of lethal metastatic prostate cancer. Nature 520, 353–357 (2015).
pubmed: 25830880
pmcid: 4413032
doi: 10.1038/nature14347
Hao, J. J. et al. Spatial intratumoral heterogeneity and temporal clonal evolution in esophageal squamous cell carcinoma. Nat. Genet. 48, 1500–1507 (2016).
pubmed: 27749841
pmcid: 5127772
doi: 10.1038/ng.3683
Shah, S. P. et al. Mutational evolution in a lobular breast tumour profiled at single nucleotide resolution. Nature 461, 809–813 (2009).
pubmed: 19812674
doi: 10.1038/nature08489
Navin, N. et al. Tumour evolution inferred by single-cell sequencing. Nature 472, 90–94 (2011).
pubmed: 21399628
pmcid: 4504184
doi: 10.1038/nature09807
Navin, N. et al. Inferring tumor progression from genomic heterogeneity. Genome Res. 20, 68–80 (2010).
pubmed: 19903760
pmcid: 2798832
doi: 10.1101/gr.099622.109
Nowell, P. C. The clonal evolution of tumor cell populations. Science 194, 23–28 (1976).
pubmed: 959840
doi: 10.1126/science.959840
Yates, L. R. et al. Subclonal diversification of primary breast cancer revealed by multiregion sequencing. Nat. Med. 21, 751–759 (2015).
pubmed: 26099045
pmcid: 4500826
doi: 10.1038/nm.3886
Shaffer, S. M. et al. Rare cell variability and drug-induced reprogramming as a mode of cancer drug resistance. Nature 546, 431–435 (2017).
pubmed: 28607484
pmcid: 5542814
doi: 10.1038/nature22794
Patten, D. K. et al. Enhancer mapping uncovers phenotypic heterogeneity and evolution in patients with luminal breast cancer. Nat. Med. 24, 1469–1480 (2018).
pubmed: 30038216
pmcid: 6130800
doi: 10.1038/s41591-018-0091-x
Bandura, D. R. et al. Mass cytometry: technique for real time single cell multitarget immunoassay based on inductively coupled plasma time-of-flight mass spectrometry. Anal. Chem. 81, 6813–6822 (2009).
pubmed: 19601617
doi: 10.1021/ac901049w
Knapp, D., Kannan, N., Pellacani, D. & Eaves, C. J. Mass cytometric analysis reveals viable activated caspase-3(+) luminal progenitors in the normal adult human mammary gland. Cell Rep. 21, 1116–1126 (2017).
pubmed: 29069592
doi: 10.1016/j.celrep.2017.09.096
Bjornson, Z. B., Nolan, G. P. & Fantl, W. J. Single-cell mass cytometry for analysis of immune system functional states. Curr. Opin. Immunol. 25, 484–494 (2013).
pubmed: 23999316
doi: 10.1016/j.coi.2013.07.004
Han, L. et al. Single-cell mass cytometry reveals intracellular survival/proliferative signaling in FLT3-ITD-mutated AML stem/progenitor cells. Cytom. A 87, 346–356 (2015).
doi: 10.1002/cyto.a.22628
Levine, J. H. et al. Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. Cell 162, 184–197 (2015).
pubmed: 26095251
pmcid: 4508757
doi: 10.1016/j.cell.2015.05.047
Wagner, J. et al. A single-cell atlas of the tumor and immune ecosystem of human breast cancer. Cell 177, 1330–1345.e1318 (2019).
pubmed: 30982598
pmcid: 6526772
doi: 10.1016/j.cell.2019.03.005
Ali, H. R. et al. Imaging mass cytometry and multiplatform genomics define the phenogenomic landscape of breast cancer. Nat. Cancer 1, 163–175 (2020).
doi: 10.1038/s43018-020-0026-6
pubmed: 35122013
Qin, X. et al. Cell-type-specific signaling networks in heterocellular organoids. Nat. Methods 17, 335–342 (2020).
pubmed: 32066960
pmcid: 7060080
doi: 10.1038/s41592-020-0737-8
Jackson, H. W. et al. The single-cell pathology landscape of breast cancer. Nature 578, 615–620 (2020).
pubmed: 31959985
doi: 10.1038/s41586-019-1876-x
Keren, L. et al. A structured tumor-immune microenvironment in triple negative breast cancer revealed by multiplexed ion beam imaging. Cell 174, 1373–1387 e1319 (2018).
pubmed: 30193111
pmcid: 6132072
doi: 10.1016/j.cell.2018.08.039
Bruna, A., Rueda, O. M. & Caldas, C. Modeling Breast Cancer Intertumor and Intratumor Heterogeneity Using Xenografts. Cold Spring Harb. Symp. Quant. Biol. 81, 227–230 (2016).
pubmed: 28381438
doi: 10.1101/sqb.2016.81.031112
Beshiri, M. L. et al. A PDX/organoid biobank of advanced prostate cancers captures genomic and phenotypic heterogeneity for disease modeling and therapeutic screening. Clin. Cancer Res 24, 4332–4345 (2018).
pubmed: 29748182
pmcid: 6125202
doi: 10.1158/1078-0432.CCR-18-0409
Prasetyanti, P. R. et al. Capturing colorectal cancer inter-tumor heterogeneity in patient-derived xenograft (PDX) models. Int J. Cancer 144, 366–371 (2019).
pubmed: 30151914
doi: 10.1002/ijc.31767
Gao, H. et al. High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response. Nat. Med 21, 1318–1325 (2015).
pubmed: 26479923
doi: 10.1038/nm.3954
Bruna, A. et al. A biobank of breast cancer explants with preserved intra-tumor heterogeneity to screen anticancer compounds. Cell 167, 260–274 e222 (2016).
pubmed: 27641504
pmcid: 5037319
doi: 10.1016/j.cell.2016.08.041
Keller, P. J. et al. Mapping the cellular and molecular heterogeneity of normal and malignant breast tissues and cultured cell lines. Breast Cancer Res. 12, R87 (2010).
pubmed: 20964822
pmcid: 3096980
doi: 10.1186/bcr2755
Stingl, J., Eaves, C. J., Zandieh, I. & Emerman, J. T. Characterization of bipotent mammary epithelial progenitor cells in normal adult human breast tissue. Breast Cancer Res. Treat. 67, 93–109 (2001).
pubmed: 11519870
doi: 10.1023/A:1010615124301
Raouf, A. et al. Transcriptome analysis of the normal human mammary cell commitment and differentiation process. Cell Stem Cell 3, 109–118 (2008).
pubmed: 18593563
doi: 10.1016/j.stem.2008.05.018
Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603–607 (2012).
pubmed: 22460905
pmcid: 3320027
doi: 10.1038/nature11003
Hartmann, F. J., Simonds, E. F. & Bendall, S. C. A universal live cell barcoding-platform for multiplexed human single cell analysis. Sci. Rep. 8, 10770 (2018).
pubmed: 30018331
pmcid: 6050312
doi: 10.1038/s41598-018-28791-2
Lawson, D. A. et al. Single-cell analysis reveals a stem-cell program in human metastatic breast cancer cells. Nature 526, 131–135 (2015).
pubmed: 26416748
pmcid: 4648562
doi: 10.1038/nature15260
Guichard, S. M. et al. AZD2014, an inhibitor of mTORC1 and mTORC2, is highly effective in ER+ breast cancer when administered using intermittent or continuous schedules. Mol. Cancer Ther. 14, 2508–2518 (2015).
pubmed: 26358751
doi: 10.1158/1535-7163.MCT-15-0365
Leung, E. Y., Askarian-Amiri, M., Finlay, G. J., Rewcastle, G. W. & Baguley, B. C. Potentiation of growth inhibitory responses of the mTOR inhibitor everolimus by dual mTORC1/2 inhibitors in cultured breast cancer cell lines. PLoS ONE 10, e0131400 (2015).
pubmed: 26148118
pmcid: 4492962
doi: 10.1371/journal.pone.0131400
Rodrik-Outmezguine, V. S. et al. Overcoming mTOR resistance mutations with a new-generation mTOR inhibitor. Nature 534, 272–276 (2016).
pubmed: 27279227
pmcid: 4902179
doi: 10.1038/nature17963
Tanguy, M. L. et al. Cdk4/6 inhibitors and overall survival: power of first-line trials in metastatic breast cancer. NPJ Breast Cancer 4, 14 (2018).
pubmed: 29951582
pmcid: 6018749
doi: 10.1038/s41523-018-0068-4
Fry, D. W. et al. Specific inhibition of cyclin-dependent kinase 4/6 by PD 0332991 and associated antitumor activity in human tumor xenografts. Mol. Cancer Ther. 3, 1427–1438 (2004).
pubmed: 15542782
doi: 10.1158/1535-7163.1427.3.11
Michaloglou, C. et al. Combined inhibition of mTOR and CDK4/6 is required for optimal blockade of E2F function and long-term growth inhibition in estrogen receptor-positive breast cancer. Mol. Cancer Ther. 17, 908–920 (2018).
pubmed: 29483206
pmcid: 6485624
doi: 10.1158/1535-7163.MCT-17-0537
DeRose, Y. S. et al. Tumor grafts derived from women with breast cancer authentically reflect tumor pathology, growth, metastasis and disease outcomes. Nat. Med. 17, 1514–1520 (2011).
pubmed: 22019887
pmcid: 3553601
doi: 10.1038/nm.2454
Krishnaswamy, S. et al. Systems biology. Conditional density-based analysis of T cell signaling in single-cell data. Science 346, 1250689 (2014).
pubmed: 25342659
pmcid: 4334155
doi: 10.1126/science.1250689
Rojo, F. et al. 4E-binding protein 1, a cell signaling hallmark in breast cancer that correlates with pathologic grade and prognosis. Clin. Cancer Res. 13, 81–89 (2007).
pubmed: 17200342
doi: 10.1158/1078-0432.CCR-06-1560
Indovina, P., Pentimalli, F., Casini, N., Vocca, I. & Giordano, A. RB1 dual role in proliferation and apoptosis: cell fate control and implications for cancer therapy. Oncotarget 6, 17873–17890 (2015).
pubmed: 26160835
pmcid: 4627222
doi: 10.18632/oncotarget.4286
Zhang, W. & Liu, H. T. MAPK signal pathways in the regulation of cell proliferation in mammalian cells. Cell Res. 12, 9–18 (2002).
pubmed: 11942415
doi: 10.1038/sj.cr.7290105
Dawson, S. J., Rueda, O. M., Aparicio, S. & Caldas, C. A new genome-driven integrated classification of breast cancer and its implications. EMBO J. 32, 617–628 (2013).
pubmed: 23395906
pmcid: 3590990
doi: 10.1038/emboj.2013.19
Mroz, E. A. & Rocco, J. W. MATH, a novel measure of intratumor genetic heterogeneity, is high in poor-outcome classes of head and neck squamous cell carcinoma. Oral. Oncol. 49, 211–215 (2013).
pubmed: 23079694
doi: 10.1016/j.oraloncology.2012.09.007
Wall, J. V. & Jenkins, C. R. Practical Statistics for Astronomers (2012).
Liberzon, A. et al. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst. 1, 417–425 (2015).
pubmed: 26771021
pmcid: 4707969
doi: 10.1016/j.cels.2015.12.004
Gupta, P. B. et al. Stochastic state transitions give rise to phenotypic equilibrium in populations of cancer cells. Cell 146, 633–644 (2011).
pubmed: 21854987
doi: 10.1016/j.cell.2011.07.026
Califano, A. & Alvarez, M. J. The recurrent architecture of tumour initiation, progression and drug sensitivity. Nat. Rev. Cancer 17, 116–130 (2017).
pubmed: 27977008
doi: 10.1038/nrc.2016.124
Gupta, P. B., Pastushenko, I., Skibinski, A., Blanpain, C. & Kuperwasser, C. Phenotypic plasticity: driver of cancer initiation, progression, and therapy resistance. Cell Stem Cell 24, 65–78 (2019).
pubmed: 30554963
doi: 10.1016/j.stem.2018.11.011
Ali, H. R. et al. Genome-driven integrated classification of breast cancer validated in over 7,500 samples. Genome Biol. 15, 431 (2014).
pubmed: 25164602
pmcid: 4166472
doi: 10.1186/s13059-014-0431-1
Xue, Y. et al. An approach to suppress the evolution of resistance in BRAF(V600E)-mutant cancer. Nat. Med. 23, 929–937 (2017).
pubmed: 28714990
pmcid: 5696266
doi: 10.1038/nm.4369
Bhang, H. E. et al. Studying clonal dynamics in response to cancer therapy using high-complexity barcoding. Nat. Med. 21, 440–448 (2015).
pubmed: 25849130
doi: 10.1038/nm.3841
Campbell, L. L. & Polyak, K. Breast tumor heterogeneity: cancer stem cells or clonal evolution? Cell Cycle 6, 2332–2338 (2007).
pubmed: 17786053
doi: 10.4161/cc.6.19.4914
Sachs, N. et al. A living biobank of breast cancer organoids captures disease heterogeneity. Cell 172, 373–386 e310 (2018).
doi: 10.1016/j.cell.2017.11.010
pubmed: 29224780
Ghandi, M. et al. Next-generation characterization of the Cancer Cell Line Encyclopedia. Nature 569, 503–508 (2019).
pubmed: 31068700
pmcid: 6697103
doi: 10.1038/s41586-019-1186-3
Behan, F. M. et al. Prioritization of cancer therapeutic targets using CRISPR-Cas9 screens. Nature 568, 511–516 (2019).
pubmed: 30971826
doi: 10.1038/s41586-019-1103-9
Marusyk, A., Janiszewska, M. & Polyak, K. Intratumor heterogeneity: the rosetta stone of therapy resistance. Cancer Cell 37, 471–484 (2020).
pubmed: 32289271
doi: 10.1016/j.ccell.2020.03.007
pmcid: 7181408
McCarthy, D. J., Chen, Y. & Smyth, G. K. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res. 40, 4288–4297 (2012).
pubmed: 22287627
pmcid: 3378882
doi: 10.1093/nar/gks042
Durinck, S., Spellman, P. T., Birney, E. & Huber, W. Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nat. Protoc. 4, 1184–1191 (2009).
pubmed: 19617889
pmcid: 3159387
doi: 10.1038/nprot.2009.97
Bodenmiller, B. et al. Multiplexed mass cytometry profiling of cellular states perturbed by small-molecule regulators. Nat. Biotechnol. 30, 858–867 (2012).
pubmed: 22902532
pmcid: 3627543
doi: 10.1038/nbt.2317
Berthois, Y., Katzenellenbogen, J. A. & Katzenellenbogen, B. S. Phenol red in tissue culture media is a weak estrogen: implications concerning the study of estrogen-responsive cells in culture. Proc. Natl Acad. Sci. USA 83, 2496–2500 (1986).
pubmed: 3458212
doi: 10.1073/pnas.83.8.2496
pmcid: 323325
Orlova, D. Y. et al. Earth Mover’s distance (EMD): a true metric for comparing biomarker expression levels in cell populations. PLoS ONE 11, e0151859 (2016).
pubmed: 27008164
pmcid: 4805242
doi: 10.1371/journal.pone.0151859
van Dijk, D. et al. Recovering gene Interactions from single-cell data using data diffusion. Cell 174, 716–729.e727 (2018).
pubmed: 29961576
pmcid: 6771278
doi: 10.1016/j.cell.2018.05.061
Lindholm, E. M. et al. Proteomic characterization of breast cancer xenografts identifies early and late bevacizumab-induced responses and predicts effective drug combinations. Clin. Cancer Res. 20, 404–412 (2014).
pubmed: 24192926
doi: 10.1158/1078-0432.CCR-13-1865
Friedman, J., Hastie, T. & Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33, 1–22 (2010).
pubmed: 20808728
pmcid: 2929880
doi: 10.18637/jss.v033.i01
Casper, J. et al. The UCSC Genome Browser database: 2018 update. Nucleic Acids Res. 46, D762–D769 (2018).
pubmed: 29106570
doi: 10.1093/nar/gkx1020
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
pubmed: 23104886
doi: 10.1093/bioinformatics/bts635
Callari, M. et al. Computational approach to discriminate human and mouse sequences in patient-derived tumour xenografts. BMC Genomics 19, 19 (2018).
pubmed: 29304755
pmcid: 5755132
doi: 10.1186/s12864-017-4414-y
Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).
doi: 10.1093/bioinformatics/btt656
pubmed: 24227677