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Tsai and De Morais et al
Supplementary Figure S1: The ratio of CDKN2A/CCND1 reflects Rb functional and
genetic status and distinguishes small cell carcinoma from prostatic
adenocarcinoma in publicly available data from patient derived xenografts (PDX).
(A) Gene expression data from Vancouver Prostate Centre PDX (1) demonstrates that
high CDKN2A/CCND1 correlates with a previously described functional Rb loss gene
expression score. The highest scoring tumors on both scores are predominantly
neuroendocrine carcinomas (NE) with RB1 homozygous loss, while the
adenocarcinomas (AdCa) show generally lower CDKN2A/CCND1 scores. One
adenocarcinoma xenograft, #331, shows hemizygous RB1 loss with a non-frameshift
indel mutation in the second allele and has a higher CDKN2A/CCND1 score. Of note,
this adenocarcinoma reliably transdifferentiates to a neuroendocrine carcinoma (sample
#331R) when deprived of androgen (1). (B) Low CCND1 expression is present among
neuroendocrine xenografts with RB1 inactivation and in the transdifferentiation
adenocarcinoma #331. Of note, the neuroendocrine xenografts (#370 and #352),
derived from the same patient, have a known deletion of CDKN2A (2), leading to
unusually low CDKN2A message levels compared to all other samples. CDKN2A
expression is relatively high in the neuroendocrine/adenocarcinoma samples #331 and
#331R with RB1 inactivation. (C) Gene expression data from MD Anderson PDX (3)
demonstrates that high CDKN2A/CCND1 correlates with a previously described
functional Rb loss gene expression score. The highest scoring xenografts on both
scores are small cell (SCPC) and large cell (LCNEC) neuroendocrine carcinomas, while
the adenocarcinomas (AdCa) show generally lower CDKN2A/CCND1 scores. (D)
CCND1 and CDKN2A expression levels demonstrate higher CDKN2A and lower
CCND1 expression among small cell carcinoma xenografts in general compared to
adenocarcinoma xenografts.
Supplementary Figure S2: The ratio of CDKN2A/CCND1 is not as tightly correlated
with Rb functional and genetic status in publicly available data from castate
resistant prostate cancer (CRPC) metastases in rapid autopsy cohort. (A) Gene
expression data from the Michigan rapid autopsy cohort (4) demonstrates a weaker
correlation between Rb functional loss gene expression score and CDKN2A/CCND1
expression levels. Of note, multiple non-small cell CRPC samples with homozygous
RB1 loss have low Rb functional loss scores and CDKN2A/CCND1 ratios. The two
small cell carcinoma metastatic samples (SC, #22 and 24) score among the highest on
both metrics, but cluster with a number of non-small cell CRPC samples (#11, 32, 25).
(B) CCND1 and CDKN2A expression levels demonstrate higher CDKN2A and lower
CCND1 expression among small cell carcinoma cases (#22 and 24) compared to most
non-small cell CRPC cases (with the exception of #11, 32, 25, as described in A).
Supplementary Figure S3: Short-term and neo-adjuvant androgen deprivation
therapy (ADT) has not exhibited direct effects on CCND1, CDKN2A, or RB1 in past
gene expression studies of prostatic adenocarcinomas. (A) For in vitro LNCaP
cells undergoing short-term androgen deprivation alone or followed by
dihydrotestosterone (DHT) stimulation (5), expression levels of CCND1, CDKN2A, and
RB1 were relatively unchanged while Rb functional loss score decreased. Known
androgen-regulated genes KLK3 and TMPRSS2 are shown for comparison.
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Tsai and De Morais et al
Percentages listed (%) refer to the proportion of genes (out of 19764 sampled by the
microarray) with greater or equal change (in total sum absolute value) under the
experimental conditions. For example, 72% of genes change as much or more than
CCND1 under androgen modulation, with KLK3 changing the most. (B) For 7 patients
with locally advanced or metastatic prostate cancer from the GenTax study, CCND1,
CDKN2A, and RB1 were not significantly changed by ADT in RNA-seq data from
biopsies both before and 22 weeks after initiation of ADT (6). Rb loss scores decreased
slightly for each patient. Graphs are based on counts per million and FDR-adjusted pvalues computed with the edgeR package (6, 13).
Supplementary Figure S4: A high ratio of CDKN2A/CCND1 is associated with
shorter interval to development of metastasis among high risk surgically treated
men receiving adjuvant androgen deprivation therapy (ADT).
(A) CCND1 and
CDKN2A expression levels are inversely correlated in high risk adenocarcinoma
samples. (B) A high ratio of CDKN2A/CCND1 (top tertile of expression) is associated
with more rapid metastasis among men who were treated with adjuvant ADT, but not in
men who did not receive ADT. This association is largely driven by low CCND1 levels
(see Figure 4) as high CDKN2A is not associated with more rapid metastasis.
Supplementary Methods:
Gene expression and copy number analysis of public datasets:
Public microarray or RNA-seq datasets GSE19445, GSE32967, GSE35988, GSE41193
and GSE48403 were downloaded from Gene Expression Omnibus (GEO). Affymetrix
Human Genome Plus 2.0 Array data (GSE32967, MD Anderson PDX, Fig S1 and
GSE19445, LNCaP, Fig S3) was pre-processed by Robust Multi-array Average (RMA)
algorithm for background-correction, normalization, and summarization (7), using the
Bioconductor package affyPLM and with gene summarizations based on the ENTREZG
custom CDF from the Brainarray project. Agilent 44K Whole Human Genome
Microarray data (GSE35988, Michigan autopsy, Fig S2) was processed as described in
the Methods relying on the Bioconductor limma package (8), followed by batch
correction using the ComBat software package (9) to account for the array used (1x44
or 4x44). For this dataset, only samples with previously reported RB1 copy number
calls were included (10). Agilent 60K SurePrint G3 Human Gene Expression Microarray
data (GSE41193, Vancouver PDX, Fig S1) was processed similarly, except for skipping
the Loess normalization step, as this study was performed by single-color labeling.
Data for most biological replicate samples in this dataset were excluded. Agilent
SurePrint G3 Human CGH Microarray data (GSE41193, Vancouver PDX, Fig S1) was
normalized by population-based intensity-based Lowess using the software package
popLowess (11). Circular binary segmentation and evaluation of copy number calls was
then performed using the Bioconductor package CGHcall (12, 13). Mapped RNA-seq
count data (GSE48403) was analyzed by the edgeR package as described in (6) to
detect differential expression in a matched-pairs design through a generalized linear
model (14, 15).
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Supplementary References:
1. Lin D, Wyatt AW, Xue H, Wang Y, Dong X, Haegert A, et al. High fidelity patientderived xenografts for accelerating prostate cancer discovery and drug
development. Cancer Res 2014;74(4):1272-83.
2. Collins CC, Volik SV, Lapuk AV, Wang Y, Gout PW, Wu C, et al. Next generation
sequencing of prostate cancer from a patient identifies a deficiency of
methylthioadenosine phosphorylase, an exploitable tumor target. Mol Cancer
Ther 2012;11(3):775-83.
3. Tzelepi V, Zhang J, Lu JF, Kleb B, Wu G, Wan X, et al. Modeling a lethal
prostate cancer variant with small-cell carcinoma features. Clin Cancer Res
2012;18(3):666-77.
4. Grasso CS, Wu YM, Robinson DR, Cao X, Dhanasekaran SM, Khan AP, et al.
The mutational landscape of lethal castration-resistant prostate cancer. Nature
2012;487(7406):239-43.
5. Haffner MC, Aryee MJ, Toubaji A, Esopi DM, Albadine R, Gurel B, et al.
Androgen-induced TOP2B-mediated double-strand breaks and prostate cancer
gene rearrangements. Nat Genet 2010; 42(8):668-75.
6. Rajan P, Sudbery IM, Villasevil ME, Mui E, Fleming J, Davis M, et al. Nextgeneration sequencing of advanced prostate cancer treated with androgendeprivation therapy. Eur Urol. 2014; 66:32–39.
7. Izirray R, Bolstad B, Collin F, Cope L, Hobbs B, Speed T. Summaries of
Affymetrix GeneChip probe level data. Nucleic Acids Research 2003;31(4):e15.
8. Smyth GK, Speed T. Normalization of cDNA microarray data. Methods
2003;31(4):265-73.
9. Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression
data using empirical bayes methods. Biostatistics 2007;8(1):118-27
10. Sharma A, Yeow WS, Ertel A, Coleman I, Clegg N, Thangavel C, et al. The
retinoblastoma tumor suppressor controls androgen signaling and human
prostate cancer progression. J Clin Invest 2010;120(12):4478-92.
11. Staaf J, Jonsson G, Ringner M, Vallon-Christersson J. Normalization of arrayCGH data: Influence of copy number imbalances. BMC Genomics 2007;8:382.
12. Olshen AB, Venkatraman ES, Lucito R, Wigler M. Circular binary segmentation
for the analysis of array-based DNA copy number data. Biostatistics
2004;5(4):557-72.
13. van de Wiel MA, Kim KI, Vosse SJ, van Wieringen WN, Wilting SM, Ylstra B.
CGHcall: Calling aberrations for array CGH tumor profiles. Bioinformatics
2007;23(7):892-4.
14. Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for
differential expression analysis of digital gene expression data. Bioinformatics
2010; 26(1):139-40.
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15. McCarthy DJ, Chen Y, Smyth GK. Differential expression analysis of multifactor
RNA-Seq experiments with respect to biological variation. Nucleic Acids Res
2012; 40(10):4288-97.
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