A Large-Scale Proteomics Resource of Circulating Extracellular Vesicles for Biomarker Discovery in Pancreatic Cancer

Pancreatic cancer has the worst prognosis of all common tumors. Earlier cancer diagnosis could increase survival rates and better assessment of metastatic disease could improve patient care. As such, there is an urgent need to develop biomarkers to diagnose this deadly malignancy earlier. Analyzing circulating extracellular vesicles (cEVs) using 'liquid biopsies' offers an attractive approach to diagnose and monitor disease status. However, it is important to differentiate EV-associated proteins enriched in patients with pancreatic ductal adenocarcinoma (PDAC) from those with benign pancreatic diseases such as chronic pancreatitis and intraductal papillary mucinous neoplasm (IPMN). To meet this need, we combined the novel EVtrap method for highly efficient isolation of EVs from plasma and conducted proteomics analysis of samples from 124 individuals, including patients with PDAC, benign pancreatic diseases and controls. On average, 912 EV proteins were identified per 100μL of plasma. EVs containing high levels of PDCD6IP, SERPINA12 and RUVBL2 were associated with PDAC compared to the benign diseases in both discovery and validation cohorts. EVs with PSMB4, RUVBL2 and ANKAR were associated with metastasis, and those with CRP, RALB and CD55 correlated with poor clinical prognosis. Finally, we validated a 7-EV protein PDAC signature against a background of benign pancreatic diseases that yielded an 89% prediction accuracy for the diagnosis of PDAC. To our knowledge, our study represents the largest proteomics profiling of circulating EVs ever conducted in pancreatic cancer and provides a valuable open-source atlas to the scientific community with a comprehensive catalogue of novel cEVs that may assist in the development of biomarkers and improve the outcomes of patients with PDAC.

Large scale proteomics of circulating extracellular vesicles to reveal novel biomarkers for pancreatic cancer

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Background: Robust biomarkers are urgently needed to assist in diagnosing pancreatic cancer. Earlier cancer diagnosis could increase survival rates by an estimated 5-fold and more reliable and real-time assessment of treatment effects in patients with cancer could improve quality of life and reduce healthcare costs. Isolation of circulating extracellular vesicles (cEVs) as ‘liquid biopsies’ offers an advantageous approach to diagnose and monitor disease status. Methods: We conducted a comprehensive proteomics study of cEVs from plasma samples to identify EV proteins that may be used as biomarkers for the diagnosis and prognosis of pancreatic cancer. Patients with pancreatic ductal adenocarcinoma (PDAC) of various tumor stages, chronic pancreatitis, intraductal papillary mucinous neoplasm (IPMN), and age-matched controls were enrolled. EVs were isolated directly from plasma samples using the affinity-based EVTrap method then subject to quantitation by liquid chromatography-tandem mass spectrometry. Results: A total of 124 patients (93 with PDAC, 12 with chronic pancreatitis, 8 with IPMN and 11 controls) were included in the discovery cohort. The isolation of EVs with EVtrap allowed the identification on average of 912 EV proteins per 100µL of sample. Principal component analysis of the cEV proteome showed clear separation between PDAC and benign pancreatic diseases. Individuals with IPMN were more closely related to controls, whereas chronic pancreatitis cases were more related to PDAC. At the functional level, we noted that cytokeratin, protein folding chaperons, and actin dynamics regulators were among protein clusters more highly altered in the cEV of patients with PDAC. We further identified new cEV markers associated with metastatic disease, such as PSMB4, RUVBL2, and ANKAR, as well as other EV proteins with strong correlation to prognosis, such as CRP, RALB, and CD55. Finally, we validated a 7-protein PDACEV signature in a validation cohort of 36 separate patients (24 with PDAC, 6 with chronic pancreatitis and 6 with IPMN) which yielded an 89% prediction accuracy for the diagnosis of PDAC. Conclusions: This study provides a valuable resource to the scientific community with a comprehensive catalog of novel proteins on circulating EVs that may assist in the development of novel biomarkers and improve the outcomes of patients with pancreatic cancer.

Organoid Sensitivity Correlates with Therapeutic Response in Patients with Pancreatic Cancer

PURPOSE

Pancreatic ductal adenocarcinoma (PDAC) remains a significant health issue. For most patients there are no options for targeted therapy and existing treatments are limited by toxicity. The HOPE trial (Harnessing Organoids for PErsonalized Therapy) was a pilot feasibility trial aiming to prospectively generate patient derived organoids (PDOs) from patients with PDAC and test their drug sensitivity and correlation with clinical outcomes.

EXPERIMENTAL DESIGN

PDOs were established from a heterogeneous population of patients with PDAC including both basal and classical PDAC subtypes.

RESULTS

A method for classifying PDOs as sensitive or resistant to chemotherapy regimens was developed to predict the clinical outcome of study subjects. Drug sensitivity testing on PDOs correlated with clinical responses to treatment in individual patients.

CONCLUSION

These data support the investigation of PDOs to guide treatment in prospective interventional trials in PDAC.

Patient-derived tumor xenograft and organoid models established from resected pancreatic, duodenal and biliary cancers

Patient-derived xenograft (PDX) and their xenograft-derived organoid (XDO) models that recapitulate the genotypic and phenotypic landscape of patient cancers could help to advance research and lead to improved clinical management. PDX models were established from 276 pancreato-duodenal and biliary cancer resections. Initial, passage 0 (P0) engraftment rates were 59% (118/199) for pancreatic, 86% (25/29) for duodenal, and 35% (17/48) for biliary ductal tumors. Pancreatic ductal adenocarcinoma (PDAC), had a P0 engraftment rate of 62% (105/169). KRAS mutant and wild-type PDAC models were molecularly profiled, and XDO models were generated to perform initial drug response evaluations. Subsets of PDAC PDX models showed global copy number variants and gene expression profiles that were retained with serial passaging, and they showed a spectrum of somatic mutations represented in patient tumors. PDAC XDO models were established, with a success rate of 71% (10/14). Pathway activation of KRAS-MAPK in PDXs was independent of KRAS mutational status. Four wild-type KRAS models were characterized by one with EGFR (L747-P753 del), two with BRAF alterations (N486_P490del or V600E), and one with triple negative KRAS/EGFR/BRAF. Model OCIP256, characterized by BRAF (N486-P490 del), had activated phospho-ERK. A combination treatment of a pan-RAF inhibitor (LY3009120) and a MEK inhibitor (trametinib) effectively suppressed phospho-ERK and inhibited growth of OCIP256 XDO and PDX models. PDAC/duodenal adenocarcinoma have high success rates forming PDX/organoid and retaining their phenotypic and genotypic features. These models may be effective tools to evaluate novel drug combination therapies.

Abstract CT119: Organoid sensitivity in pancreatic cancer correlates with clinical response to treatment and reveals utility for reducing toxicity: Preliminary results from the HOPE trial

The HOPE trial (Harnessing Organoids for PErsonalized Therapy) was a pilot study to test the feasibility of generating patient derived organoids (PDOs) from patients with pancreatic cancer under real world conditions, test drug sensitivity against these PDOs, and correlate these findings with clinical outcomes. Biopsies were obtained primarily during routine clinical care from surgical specimens, ascites, fine needle biopsies (FNB) of primary tumors, and IR guided core biopsies of liver and lymph node metastases. PDOs were grown in WNT free media according to our previously published methods. PDO drug sensitivity testing was performed on a panel of drugs, AUC calculated, and sensitivity ranked. Patients were followed clinically and assessed for disease control. At data cutoff (January 2020), we enrolled a total of 76 subjects representing all stages of disease. Drug testing was performed successfully on PDOs generated from 12 of these subjects (16%). Factors contributing to success obtaining sufficient cells for PDO generation included modality, body part, and tumor cellularity. H&E and IHC corresponded in matched PDOs and donor tumors, as did DNA alterations. Transcriptomes of PDOs were classified as both ‘basal' and ‘classical' subtypes. When AUC values were annotated with clinical data, the Jenks break of 1.69 segregated matched PDO/AUC values into disease control and progressive disease. We estimated that a PDO AUC value <1.66 yields a > 99% probability of disease control from a regimen that contains this drug, whereas if all drugs in a regimen had an AUC > 2.75 there is a > 80% probability of accurately predicting resistance. To illustrate the potential of PDO testing to tailor treatment for an individual patient, we described a case of a subject with stage IV PDAC with a KRAS mutation and ERBB2 amplification. The subject had disease control with FOLFIRINOX, which was held for toxicity. The PDO showed resistance to oxaliplatin and the patient subsequently had an extended period of disease control with regimens which did not include oxaliplatin, highlighting the potential of PDO drug sensitivity testing to exclude ineffective treatments from combination chemotherapy and limit toxicity. In conclusion, we have shown the feasibility of collecting material via real-world clinical practice sufficient to develop PDOs suitable for rapidly screening multiple drugs, and have shown a high degree of correlation between clinical outcomes in patients with PDAC and matched PDO drug sensitivity. We determined preliminary criteria based on the AUC of individual drugs in PDOs to predict drug sensitivity in subjects. These results highlight the potential of PDOs to personalize therapy and allow for the exclusion of ineffective drugs from combination regimens thereby reducing toxicity. We anticipate this approach will be used in future trials to prospectively inform treatment selection for patients with PDAC.

Citation Format: Joseph Elan Grossman, Ling Huang, Lakshmi Muthuswamy, Sofia Perea, Dipikaa Akshinthala, Raul Gonzalez, Leo Tsai, Jonah Cohen, Mandeep Sawhney, Douglas Pleskow, Tyler M. Berzin, Bruno Bockorny, Andrea Bullock, Benjamin Schlechter, Mary Linton B. Peters, Catherine Conahan, Supraja Narasimhan, Christine Lim, Roger Davis, Robert Besaw, Martin Smith, Tara Kent, Mark Callery, Senthil K. Muthuswamy, Manuel Hidalgo. Organoid sensitivity in pancreatic cancer correlates with clinical response to treatment and reveals utility for reducing toxicity: Preliminary results from the HOPE trial [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr CT119.

Molecular heterogeneity of non-small cell lung carcinoma patient-derived xenografts closely reflect their primary tumors

Availability of lung cancer models that closely mimic human tumors remains a significant gap in cancer research, as tumor cell lines and mouse models may not recapitulate the spectrum of lung cancer heterogeneity seen in patients. We aimed to establish a patient-derived tumor xenograft (PDX) resource from surgically resected non-small cell lung cancer (NSCLC). Fresh tumor tissue from surgical resection was implanted and grown in the subcutaneous pocket of non-obese severe combined immune deficient (NOD SCID) gamma mice. Subsequent passages were in NOD SCID mice. A subset of matched patient and PDX tumors and non-neoplastic lung tissues were profiled by whole exome sequencing, single nucleotide polymorphism (SNP) and methylation arrays, and phosphotyrosine (pY)-proteome by mass spectrometry. The data were compared to published NSCLC datasets of NSCLC primary and cell lines. 127 stable PDXs were established from 441 lung carcinomas representing all major histological subtypes: 52 adenocarcinomas, 62 squamous cell carcinomas, one adeno-squamous carcinoma, five sarcomatoid carcinomas, five large cell neuroendocrine carcinomas, and two small cell lung cancers. Somatic mutations, gene copy number and expression profiles, and pY-proteome landscape of 36 PDXs showed greater similarity with patient tumors than with established cell lines. Novel somatic mutations on cancer associated genes were identified but only in PDXs, likely due to selective clonal growth in the PDXs that allows detection of these low allelic frequency mutations. The results provide the strongest evidence yet that PDXs established from lung cancers closely mimic the characteristics of patient primary tumors.

RB1 deficiency in triple-negative breast cancer induces mitochondrial protein translation

Triple-negative breast cancer (TNBC) includes basal-like and claudin-low subtypes for which no specific treatment is currently available. Although the retinoblastoma tumor-suppressor gene (RB1) is frequently lost together with TP53 in TNBC, it is not directly targetable. There is thus great interest in identifying vulnerabilities downstream of RB1 that can be therapeutically exploited. Here, we determined that combined inactivation of murine Rb and p53 in diverse mammary epithelial cells induced claudin-low-like TNBC with Met, Birc2/3-Mmp13-Yap1, and Pvt1-Myc amplifications. Gene set enrichment analysis revealed that Rb/p53-deficient tumors showed elevated expression of the mitochondrial protein translation (MPT) gene pathway relative to tumors harboring p53 deletion alone. Accordingly, bioinformatic, functional, and biochemical analyses showed that RB1-E2F complexes bind to MPT gene promoters to regulate transcription and control MPT. Additionally, a screen of US Food and Drug Administration-approved (FDA-approved) drugs identified the MPT antagonist tigecycline (TIG) as a potent inhibitor of Rb/p53-deficient tumor cell proliferation. TIG preferentially suppressed RB1-deficient TNBC cell proliferation, targeted both the bulk and cancer stem cell fraction, and strongly attenuated xenograft growth. It also cooperated with sulfasalazine, an FDA-approved inhibitor of cystine xCT antiporter, in culture and xenograft assays. Our results suggest that RB1 deficiency promotes cancer cell proliferation in part by enhancing mitochondrial function and identify TIG as a clinically approved drug for RB1-deficient TNBC.

Abstract B31: Establishment and molecular characterization of patient-derived tumor xenografts from resected tumors or ascites fluids of patients with pancreatic/ampullary/bile duct carcinomas

Pancreatic adenocarcinoma (PDAC) is the 4th most common cause of cancer deaths in North America, for both men and women with a 5-year survival rate of less than 5%. The poor prognosis rate is attributed to late presentation of the disease and the lack of effective treatment options. Large-scale genome sequencing efforts on PDAC tumors show evidence of high mutational burden and revealed a number of mutated genes affecting multiple oncogenic pathways. While there are significant endeavors in developing specific targeted agents against “driver” mutations, tumor diversity within and across patient population remains a key factor affecting therapeutic efficacy. In this context, the availability of large cohorts of genomically characterized patient-derived xenograft (PDX) tumor models may help to accelerate the development of novel therapies against this lethal cancer.

PDX models provide a renewable resource to maintain a patient's tumor ex vivo for pre-clinical or co-clinical studies. As part of The International Cancer Genome Consortium (ICGC), our laboratory has established 93 PDX models in non-obese diabetic and severe combined immune-deficient (NOD-SCID) mice from Whipple resection specimens. These tumors represent a heterogeneous group of neoplasms arising from the head, body and tail of pancreas, bile duct and Ampulla of Vater. All implantations including in the subcutaneous pocket at the flank or at the orthotopic pancreas site, were performed using 4-8 weeks old NOD-SCID mice. Successful growth and serial transplant to multiple mouse generations were observed in in 74 PDX models of the 93 implanted PDAC specimens, achieving an 80% engraftment rate, one of the highest reported in any type of cancer. Histology fidelity was preserved in the PDX models compared to corresponding patient tumors. Failed implants were due to specimens characterized by borderline malignancy and absence of tumor cells.

Whole exome sequencing and copy number aberration profiling was completed for 61 PDXs and blood from the matched patients. Cancer-specific single nucleotide variation (SNV) load varied widely from 38 to 305 in PDXs. The most recurrent activating mutation was observed in KRAS with 77% of PDX models showing alterations at codon G12 (65%), G13 (8%) and Q61 (4%); in addition, 26% PDXs had a copy number gain in KRAS. Molecular comparisons of the 21 PDX models and their matched patient tumors showed that alternate allele frequency of KRAS mutation from exome sequencing of primary tumor is a strong indicator of the tumor cellularity; a higher tumor cellularity results in a larger overlap of cancer specific alterations between xenografts and corresponding patient tumors.

We have demonstrated a successful establishment of PDX models that represent genomic architecture of major subclonal populations of patient PDAC primary tumors.

Citation Format: Nikolina Radulovich, Emin Ibrahimov, Carson Holt, Vibha Raghavan, Tracy Zhao, Rob Denroch, Nhu-An Pham, Steve Gallinger, Melania Pintilie, Lincoln Stein, John McPherson, Lakshmi Muthuswamy, Ming Sound Tsao. Establishment and molecular characterization of patient-derived tumor xenografts from resected tumors or ascites fluids of patients with pancreatic/ampullary/bile duct carcinomas. [abstract]. In: Proceedings of the AACR Special Conference: Patient-Derived Cancer Models: Present and Future Applications from Basic Science to the Clinic; Feb 11-14, 2016; New Orleans, LA. Philadelphia (PA): AACR; Clin Cancer Res 2016;22(16_Suppl):Abstract nr B31.

Abstract A2-49: Integrated Omic analysis of lung cancer reveals metabolism proteome signatures with prognostic impact

Cancer results from processes prone to selective pressure and dysregulation acting along the sequence-to-phenotype continuum DNA→RNA→Protein→Disease. An Omics Array integrating DNA gene copy number, mRNA transcriptome, and quantified proteome was assembled into a genetic map representing non-small cell lung carcinoma (NSCLC). Data were collected from patient-matched normal lung, primary tumors, and patient tumor-derived xenograft (PDX) tumors. Dysregulated proteins not previously implicated as cancer drivers were found encoded throughout the genome including but not limited to regions of recurrent DNA amplification/deletion in NSCLC. Unsupervised clustering revealed signatures comprising metabolism proteins particularly highly recapitulated between matched primary and PDX tumors, and which distinguished between the major NSCLC histological subtypes adenocarcinoma (ADC) and squamous cell carcinoma (SCC). Interrogation of The Cancer Genome Atlas (TCGA) revealed sizeable cohorts of NSCLC patients with DNA alterations in genes encoding the metabolism proteome signatures, and accompanied by differences in survival. Similar to the proteome signatures from which they were extrapolated, the gene mutation signatures with prognostic impact discriminated between the lung ADC and SCC subtypes. Serine hydroxymethyltransferase 2 (SHMT2), a key enzyme in serine/glycine and folate-dependent one-carbon metabolism, is upregulated in the proteomes of NSCLC primary and PDX tumours, and is implicated as a driver of recurrent chromosome 12q14.1 amplification in NSCLC. SHMT2, along with other enzymes implicated as anti-folate targets, is also part of a metabolism signature associated with poor outcome in lung ADC. The interrogation of cancer genomes and proteomes for alterations that are related products of selective pressures driving the cancer phenotype may be a general approach to uncover and group together cryptic, polygenic cancer drivers, which might represent new anti-cancer therapeutic targets.

Note: This abstract was not presented at the conference.

Citation Format: Michael F. Moran, Thomas Kislinger, Lei Li, Vladimir Ignatchenko, Yuhong Wei, Christine To, Paul Taylor, Jiefei Tong, Nhu An Pham, Melania Pintilie, Lakshmi Muthuswamy, Frances A. Shepherd, Ming Sound Tsao. Integrated Omic analysis of lung cancer reveals metabolism proteome signatures with prognostic impact. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A2-49.

Abstract SY33-04: Integrated omic analysis of lung cancer reveals metabolism-proteome signatures with prognostic impact

Cancer results from processes prone to selective pressure and dysregulation acting along the sequence-to- phenotype continuum DNA→RNA→Protein→Disease. An Omics Array integrating DNA gene copy number, mRNA transcriptome, and quantified proteome was assembled into a genetic map representing non-small cell lung carcinoma (NSCLC). Data were collected from patient-matched normal lung, primary tumors, and patient tumor-derived xenograft (PDX) tumors. Dysregulated proteins not previously implicated as cancer drivers were found encoded throughout the genome including but not limited to regions of recurrent DNA amplification/deletion in NSCLC. Unsupervised clustering revealed signatures comprising metabolism proteins particularly highly recapitulated between matched primary and PDX tumors, and which distinguished between the major NSCLC histological subtypes adenocarcinoma (ADC) and squamous cell carcinoma (SCC). Interrogation of The Cancer Genome Atlas (TCGA) revealed sizeable cohorts of NSCLC patients with DNA alterations in genes encoding the metabolism proteome signatures, and accompanied by differences in survival. Similar to the proteome signatures from which they were extrapolated, the gene mutation signatures with prognostic impact discriminated between the lung ADC and SCC subtypes. Serine hydroxymethyltransferase 2 (SHMT2), a key enzyme in serine/glycine and folate- dependent one-carbon metabolism, is upregulated in the proteomes of NSCLC primary and PDX tumours, and is implicated as a driver of recurrent chromosome 12q14.1 amplification in NSCLC. SHMT2, along with other enzymes implicated as anti-folate targets, is also part of a metabolism proteome signature associated with poor outcome in lung ADC. The interrogation of cancer genomes and proteomes for alterations that are related products of selective pressures driving the cancer phenotype may be a general approach to uncover and group together cryptic, polygenic cancer drivers, which might represent new anti-cancer therapeutic targets.

Citation Format: Michael F. Moran, Lei Li, Yuhong Wei, Paul Taylor, Christine To, Jiefei Tong, Vladimir Ignatchenko, Melania Pintilie, Nhu-An Pham, Wen Zhang, Lakshmi Muthuswamy, Frances A. Shepherd, Thomas Kislinger, Ming S. Tsao. Integrated omic analysis of lung cancer reveals metabolism-proteome signatures with prognostic impact. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr SY33-04. doi:10.1158/1538-7445.AM2015-SY33-04

Genetic and epigenetic profiling of CLL disease progression reveals limited somatic evolution and suggests a relationship to memory-cell development

We examined genetic and epigenetic changes that occur during disease progression from indolent to aggressive forms of chronic lymphocytic leukemia (CLL) using serial samples from 27 patients. Analysis of DNA mutations grouped the leukemia cases into three categories: evolving (26%), expanding (26%) and static (47%). Thus, approximately three-quarters of the CLL cases had little to no genetic subclonal evolution. However, we identified significant recurrent DNA methylation changes during progression at 4752 CpGs enriched for regions near Polycomb 2 repressive complex (PRC2) targets. Progression-associated CpGs near the PRC2 targets undergo methylation changes in the same direction during disease progression as during normal development from naive to memory B cells. Our study shows that CLL progression does not typically occur via subclonal evolution, but that certain CpG sites undergo recurrent methylation changes. Our results suggest CLL progression may involve developmental processes shared in common with the generation of normal memory B cells.

Abstract 2003: A molecular portrait of potentially curable prostate cancer

Intermediate risk prostate cancer (CaP) with Gleason score (GS) of 7 show up to 100x variability in genetic instability. As CaP is multifocal and likely multiclonal, there is a need to characterize heterogeneity for patient stratification, which would increase the ability to act on genomic information by adding adjuvant therapies to offset systemic occult metastases that currently limit cure in ∼30% of patients. Individual genetic portraits could be used to improve cure on combined clinical-molecular staging criteria.

We undertook a pilot study to assess the genetic heterogeneity of potentially curable GS=7 CaP. We selected 10 men with GS=7 CaP; 5 treated with external beam radiotherapy (frozen pre-treatment biopsies) and 5 treated with radical prostatectomy (RadP, frozen tumour). Additionally, DNA from 18 distinct formalin-fixed, paraffin-embedded (FFPE) foci from the 5 RadP were analysed. Each of these 28 foci were subjected to whole-genome sequencing (WGS) and OncoScan SNP arrays to yield comprehensive genetic profiles. mRNA expression was evaluated on frozen RadP by microarray. Germline DNA from whole-blood was also analysed.

Following independent pathology reviews and manual macro-dissection of tumour areas of ≥70% cellularity, WGS (≥50x tumour, ≥30x germline) was performed on as little as 50 ng genomic DNA, and OncoScan arrays were performed using as little as 30ng DNA using either amplified or innate genomic DNA. Regions of CaP in FFPE RadP were recorded using a tissue map to identify independent malignant foci, and ERG immunostaining was performed to assist in the identification. In cases where ERG-positive and -negative foci were adjacent, ERG staining was repeated on an un-stained slide to confirm separate foci based on 3D multi-section analyses. ERG fusion status was also assessed in frozen samples by aCGH or IHC.

Validation of SNVs via SNP array and deep-resequencing showed ∼99% accuracy. Tumour cellularity was estimated using Qpure and was >60% for all samples. Phylogenetic techniques were used to demonstrate clear multi-clonality in two tumours. Across all tumours, ∼50% of SNVs were specific to an individual tumour-region. Phylogenies were confirmed with both SNVs and CNAs, but CNAs generally exhibited greater concordance amongst different regions of the same tumour. Some previously observed recurrent mutations were previously identified as recurrent in CaP (e.g. SPOP), and the overall mutation rate for intermediate-risk CaP was only somewhat below that reported for castrate-resistant disease (11,230 somatic SNVs per tumour).

Our studies support the concept that a complete characterization of inter- and intra-CaP heterogeneity is possible in fresh and archival tissues; the latter is important for correlations to clinical outcome. These approaches can then be streamlined for high-throughput analyses within personalized medicine laboratories leading to “point of care” molecular tests and individualization of therapy.

Citation Format: Michael E. Fraser, Richard de Borja, Dominique Trudel, Nicholas J. Harding, Pablo H. Hennings-Yeomans, Alice Meng, Emilie R. Lalonde, Andrew Brown, Natalie S. Fox, Taryne Chong, Amin Zia, Michelle Sam, Jianxin Wang, Michelle A. Chan-Seng-Yue, Jeremy Johns, Lee Timms, Nicholas Buchner, Ada Wong, Fouad Yousif, Rob Denroche, Gaetano Zafarana, Maud HW Starmans, Hanbert Chen, Shaylan Govind, Francis Nguyen, Melania Pintilie, Neil Fleshner, Stanislav Volik, Lakshmi Muthuswamy, Colin C. Collins, Thomas J. Hudson, Lincoln D. Stein, Timothy Beck, John D. McPherson, Theodorus van der Kwast, Paul C. Boutros, Rob G. Bristow. A molecular portrait of potentially curable prostate cancer. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 2003. doi:10.1158/1538-7445.AM2013-2003

Abstract 5069: Genomic profiles of primary non-small cell lung cancer (NSCLC) xenograft tumors identify distinct gene signatures associated with histological subtypes

Xenografts established directly from patient tumors mirror closely the histology of the primary tumors. Therefore, primary tumor xenografts (PTXG) may serve as important preclinical models to evaluate novel anti-cancer drugs. We previously reported that the ability of resected tumors to engraft in NOD-scid mice is a strong predictor of relapse after surgery and poorer prognosis in NSCLC patients, and thus may represent biologically more aggressive cancers (Clin Cancer Res 2011;17:134-41). Genomic characterization of PTXG would help identify genetic aberrations that drive malignant oncogenic pathways in NSCLC. We characterized the somatic copy number alterations (CNA) of 36 PTGX, consisting of 15 adenocarcinoma (ADC), 18 squamous cell carcinoma (SCC), 2 large cell neuroendocrine carcinoma (LCNEC) and 1 large cell carcinoma (LC), along with 34 patient normal samples as controls using Illumina Omni-1 Quad SNP arrays. The gene expression profiles of the 36 PTGX were analyzed using Illumina Omni-1 Quad HT-12 v4 arrays. Histology-specific recurrent regions of CNA observed in PTGX are concordant with the published and publicly available primary NSCLC CNAs. We identified 1053 genes with somatic copy number gains and 932 genes with somatic copy number losses that distinguish between SCC and ADC. From integrative analysis of mRNA expression and somatic CNAs, we identified 325 genes specific to ADC and 2232 specific to SCC that are well correlated. Gene candidates that are deregulated in ADC include WRN, STK35, SIX1; and genes that are over-expressed in SCC include SOX2, RNF13, WNK1, PIK3CA, TFRC, TP63, PAK2 suggesting there is differential deregulation of signaling pathways between these two subtypes of lung cancer. We have identified candidate gene signatures that distinguish between ADC and SCC from PTXG, suggesting these xenograft models can provide a valuable resource to study cancer biology and preclinical drug target evaluation in vivo.

Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 5069. doi:1538-7445.AM2012-5069

Abstract 5127: Characterization of lung cancers by integrated genomic and proteomic analysis

Non-small cell lung carcinoma (NSCLC) represents 80% of lung cancers, the deadliest cancer worldwide. The genomic profiling of DNA and mRNA, and characterization of proteomes have begun to address the objective to stratify and treat tumors according to their molecular features. However, these data sets have largely been used independently and typically have not been integrated. Hence most cancers including NSCLC continue to be classified largely based on histology. Our aim for this study was to integrate a set of comprehensive functional genomics data sets in order to stratify a set of NSCLC primary tumors and establish that primary tumor xenografts mirror closely the primary tumors, and hence may serve as validated pre-clinical models. Our preliminary analyses indicated that engraftment is prognostic of poor clinical outcome (John et al., 2011, Clin Cancer Res, 17:134-41), and that the major NSCLC subtypes adenocarcinoma and squamous cell carcinoma are readily resolved according to their distinctive proteomes (Wei et al., 2011, J Proteome Res 10:161-74). Herein we characterized a collection of 12 each primary tumor (T), primary tumor xenograft (X), and patient-matched normal lung (N) by using mass spectrometry for proteome analysis, Illumina 1M Omni-Quad for somatic copy number alterations (SCNAs), and Illumina Omni-1 Quad HT-12 v4 for mRNA expression. Unsupervised hierarchical clustering of protein abundances and SCNAs independently revealed that primary tumor and xenografts are highly correlated with each other. This correlation was significantly enhanced in the proteome data when a small number of highly abundant blood-associated proteins were systematically identified and subtracted. We identified tumor-specific dysregulated proteins and SCNAs in T and X using N as a reference. Two thirds of T and X matched pairs could be identified based on Pearson Correlation Coefficients of the dysregulated proteins. This clearly demonstrates that the xenografts accurately recapitulated tumor proteomes. Proteins upregulated in tumors were expressed to a significant extent from regions of SCNA gain, and we found a high degree of concordance between mRNA expression levels and SCNAs. Some primary tumors had very highly correlated proteomic profiles, suggesting they may be effectively stratified according to their proteome signatures. In conclusion, our integrated analysis has validated the primary xenograft model, provided an initial systems level perspective on the central dogma in cancer, and reinforces the proteome as a distinctive molecular feature for lung tumor stratification.

Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 5127. doi:1538-7445.AM2012-5127

Abstract 3184: Whole genome sequencing of low-input fresh frozen prostate cancer biopsies

Prostate cancer is the most commonly diagnosed malignancy among men in the United States. Due to an aging population, prostate cancer incidence has been increasing, with an estimated 200,000 men being diagnosed in 2010 and more than 32,000 deaths resulting from this disease. Better predictors of patient prognosis and treatment outcome are required to individualize prostate cancer treatment. High-throughput genomic sequence-based approaches offer a unique opportunity to identify biomarkers of disease-progression, thereby enabling more individualized therapy. The Canadian Prostate Cancer Genome Network (CPC-GENE) is an outcomes-based initiative that will sequence 500 specimens from 350 prostate cancer patients over a 5-year time span. Previously, whole genome sequencing efforts from biopsy specimens have been hindered by insufficient quantities of extracted DNA required as input for sequencing library construction. As a proof of concept to demonstrate the ability to sequence low input amounts of DNA from prostate biopsies, whole genome sequencing has been initiated for 50 prostate tumor biopsy samples along with their matched blood-derived reference sample. An on-bead sample preparation protocol was optimized using decreasing quantities of input DNA and used to construct sequencing libraries from as low as 100ng of DNA derived from macrodissected fresh frozen prostate biopsies (>70% cellularity). Sequencing is performed on the Illumina HiSeq 2000 platform to generate coverage depths of 50x for tumor samples and 30x for reference samples. Following alignment using NovoAlign and variant-calling using GATK, we compared our results to genotyping-array results generated using the Affymetrix OncoScan platform. Single-nucleotide variants detected using arrays were validated >99% of the time by sequence data, confirming that the use of a low-input library did not hinder mutation detection. Sequencing does not exhibit significant genome-wide coverage biases, and CNV calls were compared between the genotyping arrays and the next-generation sequencing data. Outcomes from the sequencing and analysis of the initial 50 sample sets will similarly be applied over a 5-year period to characterize an additional 450 prostate specimens. The ability to whole genome sequence specimens where minimal amounts of extracted DNA exist presents new opportunities to sequence many samples previously deemed unusable, while also providing encouraging prospects for whole genome sequencing applications for future studies using biopsy specimens.

Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 3184. doi:1538-7445.AM2012-3184

Abstract 1853: Identification and characterization of genes in regions of recurrent genomic gain as putative therapeutic targets in pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is the fourth leading cause of cancer-related mortality in the United States. PDAC presents with the worst prognosis of all solid tumors and the 5-year survival rate for patients with advanced PDAC is only 2%. Current chemotherapies fail to attenuate the aggressiveness of this disease and as such, novel therapeutic strategies are needed. The purpose of our study is to identify putative therapeutic targets in PDAC and characterize the role of a target gene in tumor progression. Towards this aim, we sought to identify genes which are recurrently gained or amplified in pancreatic tumors. If increased copy of specific genes confers neoplastic properties, selective targeting of such genes may have therapeutic implications. We obtained publically available copy number alteration data on 60 PDAC genomes characterized in four independent PDAC studies. Using bioinformatics and computational approaches, we identified 20 genomic loci that are gained in at least one sample in three of the four PDAC datasets. Our integrated analysis of the genes mapping to these 20 loci results in a catalogue of 710 protein-coding genes and 46 miRNA genes, which are candidate targets for further analysis. In order to delineate appropriate biological models for functional validation of these genes in PDAC, we obtained SNP array-based copy number data using the Illumina OmniExpress platform and gene expression analysis from Illumina HT-12 BeadChip arrays from 30 human PDAC cell lines. We identified genes from our candidate gene list in which copy number alteration and gene expression are correlated as computed by a Spearman rank correlation coefficient, α. This gene set was enriched for genes with high correlation between copy number and expression in comparison to simulated gene sets (p = 0.007). Our data suggest that Epithelial cell-transforming sequence 2 oncogene (ECT2) on 3q26.3 is a strong candidate for functional validation. This gene encodes a Rho-specific guanine exchange factor involved in various cellular processes including regulation of G1-to-S phase transition in cell-cycle progression. To validate our in silico findings, we have designed experiments to investigate the role of ECT2 amplification in PDAC, using the 30 human PDAC cell lines we have genetically analyzed. This will be accomplished through differential inhibition of the ECT2 protein, using RNAi and small molcules, in cell lines in which ECT2 is amplified, compared to appropriate control lines. In conclusion, we have identified a set of candidate target genes in PDAC and are currently validating the role of one of these targets, ECT2, in PDAC tumor progression.

Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 1853. doi:1538-7445.AM2012-1853

High-resolution ROMA CGH and FISH analysis of aneuploid and diploid breast tumors

Combining representational oligonucleotide microarray analysis (ROMA) of tumor DNA with fluorescence in situ hybridization (FISH) of individual tumor cells provides the opportunity to detect and validate a wide range of amplifications, deletions, and rearrangements directly in frozen tumor samples. We have used these combined techniques to examine 101 aneuploid and diploid breast tumors for which long-term follow-up and detailed clinical information were available. We have determined that ROMA provides accurate and sensitive detection of duplications, amplifications, and deletions and yields defined boundaries for these events with a resolution of <50 kbp in most cases. We find that diploid tumors exhibit fewer rearrangements on average than aneuploids, but rearrangements occur at the same locations in both types. Diploid tumors reflect at least three consistent patterns of rearrangement. The reproducibility and frequency of these events, especially in very early stage tumors, provide insight into the earliest chromosomal events in breast cancer. We have also identified correlations between certain sets of rearrangement events and clinically relevant parameters such as long-term survival. These correlations may enable novel prognostic indicators for breast and other cancers as more samples are analyzed.