Genomic and proteomic studies of prostate cancer evolution

Genomic and proteomic studies of prostate cancer evolution

Genomic and proteomic studies of prostate cancer evolution James Brooks, MD & Robert West, MD, PhD NIH MCL/EDRN joint meeting Washington DC March 8-9, 2018 Hypothesis That early prostate cancer arises from definable molecular alterations in precursor lesions and progresses as a result of acquired lesions that confer aggressive features in a subpopulation of cells in precursor lesions and/or early tumors That at each step, there are downstream molecular alterations that confer, in a

probabilistic sense, the ability for some lesions to grow and spread and in others an indolent phenotype (dead end lesions). As such, defining the earliest genomic events, the evolutionary pathways to invasive carcinoma, the final constellation of genomic alterations, and the resultant extent of genomic heterogeneity (the building blocks for evolution), should illuminate the key genomic features distinguishing good and bad outcome prostate cancer Aims Aim 1. Investigate the early genomic evolution of good and adverse outcome prostate cancer. Aim 1A. Delineate genomic and gene-expression changes early in the development of prostate cancer, by profiling FFPE tissues comprising low grade prostate intraepithelial neoplasia (PIN), high grade PIN, and invasive carcinoma in each of 100 patients with known favorable or adverse outcomes.

Aim 1B. Define the genomic evolution of early prostate cancer, by using the above genomic changes to reconstruct lineage relationships and phylogenetic trees in good and bad outcome prostate cancer. Aim 1C. Evaluate candidate prognostic genomic features in a separate cohort of 225 prostate cancer patients with known favorable and adverse outcomes. Aim 2. Define the genomic and glycoproteomic heterogeneity of good and adverse outcome prostate cancer. Aim 2A: Determine the heterogeneity of genomic and gene-expression changes in localized prostate cancer, by profiling 3-6 separate regions from 40 cancers, half that recurred after radical prostatectomy. Aim 2B. Define the protein expression and glycoproteomic alterations in localized prostate cancers and their intralesional heterogeneity from patients with and without subsequent recurrence. Aim 2C: Integrate genomic alterations with transcript, protein and glycoprotein changes in localized prostate cancers, and investigate correlations with clinical outcomes. Aims

Aim 1. Investigate the early genomic evolution of good and adverse outcome prostate cancer. Aim 1A. Delineate genomic and gene-expression changes early in the development of prostate cancer, by profiling FFPE tissues comprising low grade prostate intraepithelial neoplasia (PIN), high grade PIN, and invasive carcinoma in each of 100 patients with known favorable or adverse outcomes. Aim 1B. Define the genomic evolution of early prostate cancer, by using the above genomic changes to reconstruct lineage relationships and phylogenetic trees in good and bad outcome prostate cancer. Aim 1C. Evaluate candidate prognostic genomic features in a separate cohort of 225 prostate cancer patients with known favorable and adverse outcomes. Aim 2. Define the genomic and glycoproteomic heterogeneity of good and adverse outcome prostate cancer. Aim 2A: Determine the heterogeneity of genomic and gene-expression changes in localized prostate cancer, by profiling 3-6 separate regions from 40 cancers, half that recurred after radical prostatectomy. Aim 2B. Define the protein expression and glycoproteomic alterations in localized prostate cancers and their intralesional heterogeneity from patients with and without subsequent recurrence.

Aim 2C: Integrate genomic alterations with transcript, protein and glycoprotein changes in localized prostate cancers, and investigate correlations with clinical outcomes. Glycosylation Site Mapping of Tissue-Derived Glycoproteins Sharon Pitteri Sarah Totten Protein Extraction From Prostate Tissue BCA Assay Intact Glycopeptide Analysis Trypsin Digestion

SAX-ERLIC SPE LC-MS/MS Byonic Search Glycosylation Site Mapping of Tissue-Derived Glycoproteins Total Glycosylation Identification in Cancer and Matched Normal Prostate Tissue (N=10 Pairs) Unique Glycopeptide Overlap Cancer vs. Normal Prostate Tissue

Unique Glycoprotein 207 257 Unique Glycosylation Sites 319 Unique Glycopeptides 1,043 439 347

Glycosylation Site Mapping of Tissue-Derived Glycoproteins Integrin alpha-1 ...QTQVGIVQYGEN217VTHEFNLNK ...N74R ...N748ITVR ...SQNDKFN840VSLTVK ...N418TTFNVESTK

...DSCESNHN883ITCK ...YN460HTGQVIIYR Glycosylation Site Mapping of Tissue-Derived Glycoproteins Prostatic Acid Phosphatase (ACPP) Tryptic Peptides Tryptic Glycopeptides A.) Cancer Tissue MR

N94 N220 N333 N94 N220 N333 B.) Matched Normal Tissue

MR Statistically Significant Protein Level Changes p-value < 0.001 > 50% of the cases P4 P5 P6 P7 P10P11P12P14P15P16P20P21P22P23P1 P2 P3 P8 P9 P13P17P18P19P24P25 Prostate Cancer vs Matched Normal Tissue

Parag Mallick Radiology Canary Center -4 0 4 Standardized log2 ratio Gene Symbol

Statistically Significant Protein Level Changes Recurrent vs. Non-Recurrent NON-RECURRENT 4+3 4+3 4+3

4+4 4+3 3+4 4+3 3+4 4+3 3+4

RECURRENT 3+4 3+4 3+4 4+3 4+3 4+4 4+4

4+4 4+4 4+4 4+3 4+4 4+3 4+3

4+4 P4 P5 P6 P7 P10P11P12P14P15P16 P20 P21P22P23 P1 P2 P3 P8 P9 P13P17 P18 P19P24 P25 COMMD6 PAPLN MBD1 HCLS1 CRYZ NAP1L1 SMC1A MVP AEBP1

LZIC MIEN1 ZFR TARS TNRC6B PDCD6IP HOOK1 FDX1 GSTM2 PDHB SMARCC2 RPS28 ANXA11 HPRT1

ASRGL1 RBM14 RHOC NUDCD2 TFPI2 SGSH AKAP13 BUB3 TNFRSF14 NAA10 EIF3G DAZAP1 ANG RNASE1

CTSL 7_SEP PACSIN2 HNRNPK NUP153 SCAF11 HNRNPH1 TMPRSS2 CPD MRPS18A -2 0

2 Standardized log2 ratio p-value < 0.05 > 50% of the cases DNAseq Laser-microdissected FFPE sections ( 500 cells) Targeted DNAseq 165 recurrently mutated prostate cancer genes Introns from TMPRSS2-ERG rearrangements CNA/LOH markers every 1Mb

CNA calls by CNVkit Uses both on/off target reads ( 100Kb resolution) Jon Pollack Pathology Christian Kunder, MD, PhD Gleason grade 3 Intraductal carcinoma Laser capture microdissection

Okyaz Eminaga, MD Low grade Prostatic Intraepithelial Neoplasia DNAseq defines relationships among cancer lesions Case 1 DNAseq reveals intratumor heterogeneity Case 13 SMART-3SEQ

template switching SMART-3SEQ accuracy at low RNA input 100 ng 10 ng 1 ng 100 pg 10 pg

Laser capture microdissection Read counts of bulk vs single cell libraries Bulk vs single cell gene expression profiles t-SNE CD163 marker of macrophages CD163

Detection of HER amplification in single cells 225 samples from 14 patients 39 samples of LGPIN and HGPIN DNA copy number prediction

Sample lineage relationship for pt 35

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