Reliability and Variability of Ki-67 Digital Image Analysis Methods for Clinical Diagnostics in Breast Cancer.

Ki-67 is a nuclear protein associated with proliferation, and a strong potential biomarker in breast cancer, but is not routinely measured in current clinical management owing to a lack of standardization. Digital image analysis (DIA) is a promising technology that could allow high-throughput analysis and standardization. There is a dearth of data on the clinical reliability as well as intra- and interalgorithmic variability of different DIA methods. In this study, we scored and compared a set of breast cancer cases in which manually counted Ki-67 has already been demonstrated to have prognostic value (n = 278) to 5 DIA methods, namely Aperio ePathology (Lieca Biosystems), Definiens Tissue Studio (Definiens AG), Qupath, an unsupervised immunohistochemical color histogram algorithm, and a deep-learning pipeline piNET. The piNET system achieved high agreement (interclass correlation coefficient: 0.850) and correlation (R = 0.85) with the reference score. The Qupath algorithm exhibited a high degree of reproducibility among all rater instances (interclass correlation coefficient: 0.889). Although piNET performed well against absolute manual counts, none of the tested DIA methods classified common Ki-67 cutoffs with high agreement or reached the clinically relevant Cohen's κ of at least 0.8. The highest agreement achieved was a Cohen's κ statistic of 0.73 for cutoffs 20% and 25% by the piNET system. The main contributors to interalgorithmic variation and poor cutoff characterization included heterogeneous tumor biology, varying algorithm implementation, and setting assignments. It appears that image segmentation is the primary explanation for semiautomated intra-algorithmic variation, which involves significant manual intervention to correct. Automated pipelines, such as piNET, may be crucial in developing robust and reproducible unbiased DIA approaches to accurately quantify Ki-67 for clinical diagnosis in the future.

Sensitive tumour detection and classification using plasma cell-free DNA methylomes.

The use of liquid biopsies for cancer detection and management is rapidly gaining prominence1. Current methods for the detection of circulating tumour DNA involve sequencing somatic mutations using cell-free DNA, but the sensitivity of these methods may be low among patients with early-stage cancer given the limited number of recurrent mutations2-5. By contrast, large-scale epigenetic alterations-which are tissue- and cancer-type specific-are not similarly constrained6 and therefore potentially have greater ability to detect and classify cancers in patients with early-stage disease. Here we develop a sensitive, immunoprecipitation-based protocol to analyse the methylome of small quantities of circulating cell-free DNA, and demonstrate the ability to detect large-scale DNA methylation changes that are enriched for tumour-specific patterns. We also demonstrate robust performance in cancer detection and classification across an extensive collection of plasma samples from several tumour types. This work sets the stage to establish biomarkers for the minimally invasive detection, interception and classification of early-stage cancers based on plasma cell-free DNA methylation patterns.

A renewed model of pancreatic cancer evolution based on genomic rearrangement patterns.

Pancreatic cancer, a highly aggressive tumour type with uniformly poor prognosis, exemplifies the classically held view of stepwise cancer development. The current model of tumorigenesis, based on analyses of precursor lesions, termed pancreatic intraepithelial neoplasm (PanINs) lesions, makes two predictions: first, that pancreatic cancer develops through a particular sequence of genetic alterations (KRAS, followed by CDKN2A, then TP53 and SMAD4); and second, that the evolutionary trajectory of pancreatic cancer progression is gradual because each alteration is acquired independently. A shortcoming of this model is that clonally expanded precursor lesions do not always belong to the tumour lineage, indicating that the evolutionary trajectory of the tumour lineage and precursor lesions can be divergent. This prevailing model of tumorigenesis has contributed to the clinical notion that pancreatic cancer evolves slowly and presents at a late stage. However, the propensity for this disease to rapidly metastasize and the inability to improve patient outcomes, despite efforts aimed at early detection, suggest that pancreatic cancer progression is not gradual. Here, using newly developed informatics tools, we tracked changes in DNA copy number and their associated rearrangements in tumour-enriched genomes and found that pancreatic cancer tumorigenesis is neither gradual nor follows the accepted mutation order. Two-thirds of tumours harbour complex rearrangement patterns associated with mitotic errors, consistent with punctuated equilibrium as the principal evolutionary trajectory. In a subset of cases, the consequence of such errors is the simultaneous, rather than sequential, knockout of canonical preneoplastic genetic drivers that are likely to set-off invasive cancer growth. These findings challenge the current progression model of pancreatic cancer and provide insights into the mutational processes that give rise to these aggressive tumours.

Whole genomes define concordance of matched primary, xenograft, and organoid models of pancreas cancer.

Pancreatic ductal adenocarcinoma (PDAC) has the worst prognosis among solid malignancies and improved therapeutic strategies are needed to improve outcomes. Patient-derived xenografts (PDX) and patient-derived organoids (PDO) serve as promising tools to identify new drugs with therapeutic potential in PDAC. For these preclinical disease models to be effective, they should both recapitulate the molecular heterogeneity of PDAC and validate patient-specific therapeutic sensitivities. To date however, deep characterization of the molecular heterogeneity of PDAC PDX and PDO models and comparison with matched human tumour remains largely unaddressed at the whole genome level. We conducted a comprehensive assessment of the genetic landscape of 16 whole-genome pairs of tumours and matched PDX, from primary PDAC and liver metastasis, including a unique cohort of 5 'trios' of matched primary tumour, PDX, and PDO. We developed a pipeline to score concordance between PDAC models and their paired human tumours for genomic events, including mutations, structural variations, and copy number variations. Tumour-model comparisons of mutations displayed single-gene concordance across major PDAC driver genes, but relatively poor agreement across the greater mutational load. Genome-wide and chromosome-centric analysis of structural variation (SV) events highlights previously unrecognized concordance across chromosomes that demonstrate clustered SV events. We found that polyploidy presented a major challenge when assessing copy number changes; however, ploidy-corrected copy number states suggest good agreement between donor-model pairs. Collectively, our investigations highlight that while PDXs and PDOs may serve as tractable and transplantable systems for probing the molecular properties of PDAC, these models may best serve selective analyses across different levels of genomic complexity.

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.

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.

A pilot window-of-opportunity study of preoperative fluvastatin in localized prostate cancer.

BACKGROUND: Statins inhibit HMG-CoA reductase, the rate-limiting enzyme of the mevalonate pathway. Epidemiological and pre-clinical evidence support an association between statin use and delayed prostate cancer (PCa) progression. Here, we evaluated the effects of neoadjuvant fluvastatin treatment on markers of cell proliferation and apoptosis in men with localized PCa. METHODS: Thirty-three men were treated daily with 80 mg fluvastatin for 4-12 weeks in a single-arm window-of-opportunity study between diagnosis of localized PCa and radical prostatectomy (RP) (ClinicalTrials.gov: NCT01992042). Percent Ki67 and cleaved Caspase-3 (CC3)-positive cells in tumor tissues were evaluated in 23 patients by immunohistochemistry before and after treatment. Serum and intraprostatic fluvastatin concentrations were quantified by liquid chromatography-mass spectrometry. RESULTS: Baseline characteristics included a median prostate-specific antigen (PSA) level of 6.48 ng/mL (IQR: 4.21-10.33). The median duration of fluvastatin treatment was 49 days (range: 27-102). Median serum low-density lipoprotein levels decreased by 35% after treatment, indicating patient compliance. Median PSA decreased by 12%, but this was not statistically significant in our small cohort. The mean fluvastatin concentration measured in the serum was 0.2 µM (range: 0.0-1.1 µM), and in prostatic tissue was 8.5 nM (range: 0.0-77.0 nM). At these concentrations, fluvastatin induced PCa cell death in vitro in a dose- and time-dependent manner. In patients, fluvastatin treatment did not significantly alter intratumoral Ki67 positivity; however, a median 2.7-fold increase in CC3 positivity (95% CI: 1.9-5.0, p = 0.007) was observed in post-fluvastatin RP tissues compared with matched pre-treatment biopsy controls. In a subset analysis, this increase in CC3 was more pronounced in men on fluvastatin for >50 days. CONCLUSIONS: Fluvastatin prior to RP achieves measurable drug concentrations in prostatic tissue and is associated with promising effects on tumor cell apoptosis. These data warrant further investigation into the anti-neoplastic effects of statins in prostate tissue.

GATA6 Expression Distinguishes Classical and Basal-like Subtypes in Advanced Pancreatic Cancer.

PURPOSE: To determine the impact of basal-like and classical subtypes in advanced pancreatic ductal adenocarcinoma (PDAC) and to explore GATA6 expression as a surrogate biomarker. EXPERIMENTAL DESIGN: Within the COMPASS trial, patients proceeding to chemotherapy for advanced PDAC undergo tumor biopsy for RNA-sequencing (RNA-seq). Overall response rate (ORR) and overall survival (OS) were stratified by subtypes and according to chemotherapy received. Correlation of GATA6 with the subtypes using gene expression profiling, in situ hybridization (ISH) was explored. RESULTS: Between December 2015 and May 2019, 195 patients (95%) had enough tissue for RNA-seq; 39 (20%) were classified as basal-like and 156 (80%) as classical. RECIST response data were available for 157 patients; 29 basal-like and 128 classical where the ORR was 10% versus 33%, respectively (P = 0.02). In patients with basal-like tumors treated with modified FOLFIRINOX (n = 22), the progression rate was 60% compared with 15% in classical PDAC (P = 0.0002). Median OS in the intention-to-treat population (n = 195) was 9.3 months for classical versus 5.9 months for basal-like PDAC (HR, 0.47; 95% confidence interval, 0.32-0.69; P = 0.0001). GATA6 expression by RNA-seq highly correlated with the classifier (P < 0.001) and ISH predicted the subtypes with sensitivity of 89% and specificity of 83%. In a multivariate analysis, GATA6 expression was prognostic (P = 0.02). In exploratory analyses, basal-like tumors, could be identified by keratin 5, were more hypoxic and enriched for a T-cell-inflamed gene expression signature. CONCLUSIONS: The basal-like subtype is chemoresistant and can be distinguished from classical PDAC by GATA6 expression.See related commentary by Collisson, p. 4715.

A Four-Chemokine Signature Is Associated with a T-cell-Inflamed Phenotype in Primary and Metastatic Pancreatic Cancer.

PURPOSE: The molecular drivers of antitumor immunity in pancreatic ductal adenocarcinoma (PDAC) are poorly understood, posing a major obstacle for the identification of patients potentially amenable for immune-checkpoint blockade or other novel strategies. Here, we explore the association of chemokine expression with effector T-cell infiltration in PDAC. EXPERIMENTAL DESIGN: Discovery cohorts comprised 113 primary resected PDAC and 107 PDAC liver metastases. Validation cohorts comprised 182 PDAC from The Cancer Genome Atlas and 92 PDACs from the Australian International Cancer Genome Consortium. We explored associations between immune cell counts by immunohistochemistry, chemokine expression, and transcriptional hallmarks of antitumor immunity by RNA sequencing (RNA-seq), and mutational burden by whole-genome sequencing. RESULTS: Among all known human chemokines, a coregulated set of four (CCL4, CCL5, CXCL9, and CXCL10) was strongly associated with CD8+ T-cell infiltration (P < 0.001). Expression of this "4-chemokine signature" positively correlated with transcriptional metrics of T-cell activation (ZAP70, ITK, and IL2RB), cytolytic activity (GZMA and PRF1), and immunosuppression (PDL1, PD1, CTLA4, TIM3, TIGIT, LAG3, FASLG, and IDO1). Furthermore, the 4-chemokine signature marked tumors with increased T-cell activation scores (MHC I presentation, T-cell/APC costimulation) and elevated expression of innate immune sensing pathways involved in T-cell priming (STING and NLRP3 inflammasome pathways, BATF3-driven dendritic cells). Importantly, expression of this 4-chemokine signature was consistently indicative of a T-cell-inflamed phenotype across primary PDAC and PDAC liver metastases. CONCLUSIONS: A conserved 4-chemokine signature marks resectable and metastatic PDAC tumors with an active antitumor phenotype. This could have implications for the appropriate selection of PDAC patients in immunotherapy trials.

Transcription phenotypes of pancreatic cancer are driven by genomic events during tumor evolution.

Pancreatic adenocarcinoma presents as a spectrum of a highly aggressive disease in patients. The basis of this disease heterogeneity has proved difficult to resolve due to poor tumor cellularity and extensive genomic instability. To address this, a dataset of whole genomes and transcriptomes was generated from purified epithelium of primary and metastatic tumors. Transcriptome analysis demonstrated that molecular subtypes are a product of a gene expression continuum driven by a mixture of intratumoral subpopulations, which was confirmed by single-cell analysis. Integrated whole-genome analysis uncovered that molecular subtypes are linked to specific copy number aberrations in genes such as mutant KRAS and GATA6. By mapping tumor genetic histories, tetraploidization emerged as a key mutational process behind these events. Taken together, these data support the premise that the constellation of genomic aberrations in the tumor gives rise to the molecular subtype, and that disease heterogeneity is due to ongoing genomic instability during progression.

Author Correction: Transcription phenotypes of pancreatic cancer are driven by genomic events during tumor evolution.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Developing a pan-cancer research autopsy programme.

AIMS: Rapid procurement of a wide variety of metastatic and primary cancers and normal tissues after death through rapid autopsy opens largely unexplored avenues in cancer research. We describe a high-volume rapid research autopsy programme at a large academic medical centre. METHODS: Advanced-stage cancer patients, most commonly inpatients in palliative care facilities, were approached to participate in a cancer research autopsy programme with the goal of acquiring multidimensionally annotated tissue for cancer research. On death of an enrolled patient, a predetermined notification plan was enacted, with the medical oncologist/clinical research coordinator informing a team of pathologists, researchers and allied staff. Quality assurance metrics were measured. Thereafter, tissues were annotated in a tissue bioinformatics database and linked to electronic patient records. All banked tissues were reviewed for tumour integrity, including DNA and RNA quality. RESULTS: Over 100 rapid research autopsies from diverse cancer sites were performed, and specimens were procured and annotated with detailed clinical information, including treatment and response. Tissues were successfully enabling studies of tumour immunology, xenografts, genomics and proteomics. CONCLUSIONS: Large-scale rapid procurement and biobanking of cancer tissues from a rapid autopsy programme is feasible. Multidisciplinary integration between health and administrative staff from medical oncology, palliative care, pathology and biospecimen sciences is critical for the success of this challenging endeavour.

Integration of Genomic and Transcriptional Features in Pancreatic Cancer Reveals Increased Cell Cycle Progression in Metastases.

We integrated clinical, genomic, and transcriptomic data from 224 primaries and 95 metastases from 289 patients to characterize progression of pancreatic ductal adenocarcinoma (PDAC). Driver gene alterations and mutational and expression-based signatures were preserved, with truncations, inversions, and translocations most conserved. Cell cycle progression (CCP) increased with sequential inactivation of tumor suppressors, yet remained higher in metastases, perhaps driven by cell cycle regulatory gene variants. Half of the cases were hypoxic by expression markers, overlapping with molecular subtypes. Paired tumor heterogeneity showed cancer cell migration by Halstedian progression. Multiple PDACs arising synchronously and metachronously in the same pancreas were actually intra-parenchymal metastases, not independent primary tumors. Established clinical co-variates dominated survival analyses, although CCP and hypoxia may inform clinical practice.

Genomics-Driven Precision Medicine for Advanced Pancreatic Cancer: Early Results from the COMPASS Trial.

Purpose: To perform real-time whole genome sequencing (WGS) and RNA sequencing (RNASeq) of advanced pancreatic ductal adenocarcinoma (PDAC) to identify predictive mutational and transcriptional features for better treatment selection.Experimental Design: Patients with advanced PDAC were prospectively recruited prior to first-line combination chemotherapy. Fresh tumor tissue was acquired by image-guided percutaneous core biopsy for WGS and RNASeq. Laser capture microdissection was performed for all cases. Primary endpoint was feasibility to report WGS results prior to first disease assessment CT scan at 8 weeks. The main secondary endpoint was discovery of patient subsets with predictive mutational and transcriptional signatures.Results: Sixty-three patients underwent a tumor biopsy between December 2015 and June 2017. WGS and RNASeq were successful in 62 (98%) and 60 (95%), respectively. Genomic results were reported at a median of 35 days (range, 19-52 days) from biopsy, meeting the primary feasibility endpoint. Objective responses to first-line chemotherapy were significantly better in patients with the classical PDAC RNA subtype compared with those with the basal-like subtype (P = 0.004). The best progression-free survival was observed in those with classical subtype treated with m-FOLFIRINOX. GATA6 expression in tumor measured by RNA in situ hybridization was found to be a robust surrogate biomarker for differentiating classical and basal-like PDAC subtypes. Potentially actionable genetic alterations were found in 30% of patients.Conclusions: Prospective genomic profiling of advanced PDAC is feasible, and our early data indicate that chemotherapy response differs among patients with different genomic/transcriptomic subtypes. Clin Cancer Res; 24(6); 1344-54. ©2017 AACR.

LSD1-Mediated Epigenetic Reprogramming Drives CENPE Expression and Prostate Cancer Progression.

Androgen receptor (AR) signaling is a key driver of prostate cancer, and androgen-deprivation therapy (ADT) is a standard treatment for patients with advanced and metastatic disease. However, patients receiving ADT eventually develop incurable castration-resistant prostate cancer (CRPC). Here, we report that the chromatin modifier LSD1, an important regulator of AR transcriptional activity, undergoes epigenetic reprogramming in CRPC. LSD1 reprogramming in this setting activated a subset of cell-cycle genes, including CENPE, a centromere binding protein and mitotic kinesin. CENPE was regulated by the co-binding of LSD1 and AR to its promoter, which was associated with loss of RB1 in CRPC. Notably, genetic deletion or pharmacological inhibition of CENPE significantly decreases tumor growth. Our findings show how LSD1-mediated epigenetic reprogramming drives CRPC, and they offer a mechanistic rationale for its therapeutic targeting in this disease. Cancer Res; 77(20); 5479-90. ©2017 AACR.

Specimen quality evaluation in Canadian biobanks participating in the COEUR repository.

Human biological specimens are important for translational research programs such as the Canadian Ovarian Experimental Unified Resource (COEUR) funded by the Terry Fox Research Institute. Sample quality is an important consideration, as it directly impacts the quality of ensuing research. The aim of the present study was to determine the quality of tissues collected from different sites contributing to the COEUR cohort. Samples from high-grade serous ovarian tumors (fresh frozen and corresponding paraffin-embedded tissues) were provided by nine participating Canadian biobanks. All samples were shipped to a central site using a Standard Operating Protocol (SOP). DNA and RNA extraction was conducted by the quality control division of the Canadian Tumor Repository Network (CTRNet). DNA quality was determined by ß-globin gene PCR amplification, and RNA quality by the RNA integrity number (RIN), as measured by the Agilent BioAnalyzer. DNA of acceptable quality had at least three bands of ß-globin amplified from DNA (n=115/135), and a RIN number ≥7 was considered very good for RNA (n=80/135). Sample preparation and storage time had little effect on RNA or DNA quality. Protein expression was assessed on tissue microarray by immunohistochemistry with antibodies against p53, WT1, E-cadherin, CK-7, and Ki67 from formalin fixed-paraffin embedded (FFPE) tissues. As seen with a nonhierarchical clustering statistical method, there was no significant difference in immunostaining of paraffin tissues among specimens from different biobanks. Interestingly, patients with worse outcome were highly positive for p53 and weak for WT1. In conclusion, while there was no common SOP for retrospectively collected material across Canadian biobanks, these results indicate that specimens collected at these multiple sites are of comparable quality, and can serve as an adequate resource to create a national cohort for the validation of molecular biomarkers in ovarian cancer.