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Genomic Profiling Defines Subtypes of Prostate Cancer with the Potential for Therapeutic Stratification

Jamie R. Schoenborn, Pete Nelson and Min Fang
Jamie R. Schoenborn
Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
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Pete Nelson
Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
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Min Fang
Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
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DOI: 10.1158/1078-0432.CCR-12-3606 Published August 2013
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    Figure 1.

    Relationships of common genomic alterations identified in localized and advanced in prostate cancer. Approximately 50% of both primary and advanced prostate cancers harbor ERG gene fusion or other ETS family gene rearrangement. Primary prostate cancers without ERG rearrangement can be subclassified on the basis of SPINK1 overexpression (10), SPOP mutations, and select SCNAs, which are often mutually exclusive. The size of the pie chart pieces on the left represents approximate frequencies of each subgroup. Nearly all advanced-stage prostate cancers have amplification or mutation of AR or abnormalities of other AR pathway components. However, the genetic complexity associated with most advanced-stage prostate cancers precludes their classification into distinct subgroups based on genetic profiles. Among this complexity exist a number of commonly observed genetic aberrations, as shown. Metastatic tumors may display some or all of these aberrations. Therefore, the pie chart pieces on the right simply help define functional groups of various genes with mutations observed in advanced prostate cancer, without indication for frequencies or mutual exclusivity. See text for detail. Onc/TSG, oncogenes and tumor suppressor genes.

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    Figure 2.

    Considerations for targeted therapy based on key pathways perturbed in prostate cancer. Current standard of care involves active surveillance for low-risk localized prostate cancers; hormonal therapy, radical prostatectomy, or radiation therapy for higher-risk localized disease; and androgen pathway suppression for metastatic disease with chemotherapy and immunotherapy at the time of disease progression. This figure shows the potential for targeted therapy in molecularly defined subtypes of prostate cancer. Genomic alterations are classified on the basis of the class of molecular pathways affected (inner circle). Therapeutic agents (outer circle) targeting respective pathways are grouped with the genes (middle circle) commonly altered in these pathways, coordinated by color wherever possible. Selected agents in various phases of clinical trials are superscripted: aFDA-approved, bphase III clinical trials, cphase I/II clinical trials; preclinical development not marked. Although the antiandrogen therapy abiraterone, the microtubule inhibitor cabazitaxel, and the immunotherapy sipuleucel-T are already in clinical use, aberrations of NCOA2 and FOXA1 genes (white) are recent findings, the functional significance and therapeutic implications of which await further investigation. HDACi, histone deacetylase inhibitor.

Tables

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  • Table 1.

    Most common SCNAs in human prostate cancer

    Reported frequency in prostate cancera
    CytobandEventSize, MbGenes of interestPrimary cancerAdvanced cancerDTCs or CTCs
    Xp11.22-q13.1Gain18–67.8AR50% CRPC (15, 24, 25, 68)45% AdvDTC (6, 7, 69)
    1p12-q43Gain11745%–65% CRPC (24)50% (69)
    1q32.1-q32.3Gain12.50ELK4, PTPRC, ELF3, PTPN7, MDM4, RAB7L1, RASSF5, IL24, IL10, CAMK1G(5)(24, 70)45% AdvDTC
    3q26.1Gain43.80GMPS, PIK3CA, MLF1, SKIL, CCNL1, ECT213%–39% (5, 71)(24, 70)20% (7)
    6q14.3-15Loss13.67CYB5R4, NT5E, SNX14, SYNCRIP, HTR1E, CGA, GJB740% (5, 18, 64, 72, 73)55% (64, 70, 72, 25)25% (6, 7)
    7p22.3-q36.3Gain158.40(5, 18)25%–55% CRPC (68, 70, 72, 25)40% (6, 7)
    8p12-q24.3Gain97.64MYC, MAF, EYA1, MSC, TRPA1, KCNB220%–30% (5, 18, 64, 71–73, 74)64%–82% CRPC (15, 62, 68, 70, 72)50%–65% (6, 7, 69)
    8p23-p11Loss19.58NKX3-153%–67% (5, 18, 71–73, 74)67%–74% of CRPC (15, 68, 72)36%–90% of AdvDTCs; 20–23% LocDTCs (6, 7, 69)
    9q31.3Gain22.79PTPN3, AKAP2, DAPK1, SYK(5)30% (70, 72)25%–45% AdvDTC (6, 7, 69)
    10p13Loss1.12ITGA8, PTER, C1QL3, RSU118% (64, 70, 73)(25)(6)
    10q11.21Loss0.58RET, RasGEF1A, HNRNPF, ZNF239, ZNF485, ZNF32(64, 73)55% AdvDTC (6)
    10q22-q24Loss24.91CFLP1, KILLIN, PTEN, RNLS, LIPJ, LIPF, LIPK, LIPN, LIPM, ANKRD22, STAMBPL1, ACTA212%–25% (5, 18, 62, 64, 72–74)36%–80% (15, 62, 68, 70, 25)36% (69)
    11p13-p12Loss4.72(4, 6, 64, 70, 73)(25)45% AdvDTC (6)
    12p13Loss1.46BCL2L14, LRP6, MANSC1, LOH12CR1, DUSP16, CREBL2, GPR19, CDKN1B, ETV630% (5, 18)30%–50% (4, 64, 70, 72)
    13q12.3- q14.2Loss2.63HSPH1, B3GALTL, RXFP2, EEF1DP3, FRY, ZAR1L, BRCA2, N4BP2L1, CG030, PDS5B, KL, STARD13, EXOSC8, FAM48A, CSNK1A1L, POSTN, TRPC4, UFM1, FREM2, KBTBD6, KBTBD7, MTRF1, NAA16, OR7E37P, C13ORF15, SPERT, SIAH2, RB1, FOXO111%–40% (5, 18, 62, 64, 71–73, 74)35%–95% mets (15, 62, 64, 68, 70, 72, 25)21%–44% of LocDTC; 36%–55% AdvDTC (6, 69)
    15q25.1-q26.3Loss21.30(64)40% CRPC20–25% (7)
    16q11.2-q24.3Loss33.56WWOX33%–38% (5, 18, 64, 71, 73, 74)57%-82% (72)33% (7)
    17p13.1Loss4.28RPAIN, AIPL1, XAF, DLG4, PER1, TP5320%–30% (5, 18, 64)
    17p13.3-p11.2Loss19.5030% (64, 73)51%–61% CRPC
    17q21.31Loss0.15DHX8, ETV420% (18)(24)
    17q24.2-q25.3Loss8.9012%–41% CRPC (24, 70)
    18q22.3Loss0.29CBLN2, NETO120%–25% (5, 18, 71, 73)40% (24, 72)50% (7)
    21q22.3Loss0.25ERG, NCRNA00114, ETS2, PSMG1, BRWD1, HMGN1, WRB, LCA5L, SH3BGR, C21orf88, B3GALT5, IGSF5, PCP4, DSCAM, C21orf130, BACE2, PLAC4, FAM3B, MX2, MX1, TMPRSS233%–50% (5, 8, 18, 73, 74)33% (25)

    NOTE: SCNA regions are listed in chromosomal order. Well-characterized cancer genes are in bold. References are indicated for reported frequencies of SCNAs. In general, only SCNAs with a frequency >40% in at least one cancer category are listed. Size is based on reported results and indicates the broader region of overlap across studies. Actual size reported in individual samples may vary, especially for studies using recently developed technologies such as high-density single-nucleotide polymorphism comparative genomic hybridization (CGH) arrays and next-generation sequencing that permit a greater limit of resolution. In general, SCNAs are smaller in primary tumors than those observed in metastases and may only cover a portion of the region listed.

    • ↵aNumbers in parentheses correspond with studies cited as references in this article.

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Clinical Cancer Research: 19 (15)
August 2013
Volume 19, Issue 15
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Genomic Profiling Defines Subtypes of Prostate Cancer with the Potential for Therapeutic Stratification
Jamie R. Schoenborn, Pete Nelson and Min Fang
Clin Cancer Res August 1 2013 (19) (15) 4058-4066; DOI: 10.1158/1078-0432.CCR-12-3606

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Genomic Profiling Defines Subtypes of Prostate Cancer with the Potential for Therapeutic Stratification
Jamie R. Schoenborn, Pete Nelson and Min Fang
Clin Cancer Res August 1 2013 (19) (15) 4058-4066; DOI: 10.1158/1078-0432.CCR-12-3606
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