Unraveling the clonal hierarchy of somatic genomic aberrations

A war on many fronts: cross disciplinary approaches for novel cancer treatment strategies

Cancer is a disease characterized by uncontrolled cellular growth where cancer cells take advantage of surrounding cellular populations to obtain resources and promote invasion. Carcinomas are the most common type of cancer accounting for almost 90% of cancer cases. One of the major subtypes of carcinomas are adenocarcinomas, which originate from glandular cells that line certain internal organs. Cancers such as breast, prostate, lung, pancreas, colon, esophageal, kidney are often adenocarcinomas. Current treatment strategies include surgery, chemotherapy, radiation, targeted therapy, and more recently immunotherapy. However, patients with adenocarcinomas often develop resistance or recur after the first line of treatment. Understanding how networks of tumor cells interact with each other and the tumor microenvironment is crucial to avoid recurrence, resistance, and high-dose therapy toxicities. In this review, we explore how mathematical modeling tools from different disciplines can aid in the development of effective and personalized cancer treatment strategies. Here, we describe how concepts from the disciplines of ecology and evolution, economics, and control engineering have been applied to mathematically model cancer dynamics and enhance treatment strategies.

The evolution of metastatic upper tract urothelial carcinoma through genomic-transcriptomic and single-cell protein markers analysis

The molecular characteristics of metastatic upper tract urothelial carcinoma (UTUC) are not well understood, and there is a lack of knowledge regarding the genomic and transcriptomic differences between primary and metastatic UTUC. To address these gaps, we integrate whole-exome sequencing, RNA sequencing, and Imaging Mass Cytometry using lanthanide metal-conjugated antibodies of 44 tumor samples from 28 patients with high-grade primary and metastatic UTUC. We perform a spatially-resolved single-cell analysis of cancer, immune, and stromal cells to understand the evolution of primary to metastatic UTUC. We discover that actionable genomic alterations are frequently discordant between primary and metastatic UTUC tumors in the same patient. In contrast, molecular subtype membership and immune depletion signature are stable across primary and matched metastatic UTUC. Molecular and immune subtypes are consistent between bulk RNA-sequencing and mass cytometry of protein markers from 340,798 single cells. Molecular subtypes at the single-cell level are highly conserved between primary and metastatic UTUC tumors within the same patient.

Molecular Mechanisms of Prostate Cancer Development in the Precision Medicine Era: A Comprehensive Review

The progression of prostate cancer (PCa) relies on the activation of the androgen receptor (AR) by androgens. Despite efforts to block this pathway through androgen deprivation therapy, resistance can occur through several mechanisms, including the abnormal activation of AR, resulting in castration-resistant PCa following the introduction of treatment. Mutations, amplifications, and splicing variants in AR-related genes have garnered attention in this regard. Furthermore, recent large-scale next-generation sequencing analysis has revealed the critical roles of AR and AR-related genes, as well as the DNA repair, PI3K, and cell cycle pathways, in the onset and progression of PCa. Moreover, research on epigenomics and microRNA has increasingly become popular; however, it has not translated into the development of effective therapeutic strategies. Additionally, treatments targeting homologous recombination repair mutations and the PI3K/Akt pathway have been developed and are increasingly accessible, and multiple clinical trials have investigated the efficacy of immune checkpoint inhibitors. In this comprehensive review, we outline the status of PCa research in genomics and briefly explore potential future developments in the field of epigenetic modifications and microRNAs.

MIMESIS: minimal DNA-methylation signatures to quantify and classify tumor signals in tissue and cell-free DNA samples

DNA-methylation alterations are common in cancer and display unique characteristics that make them ideal markers for tumor quantification and classification. Here we present MIMESIS, a computational framework exploiting minimal DNA-methylation signatures composed by a few dozen informative DNA-methylation sites to quantify and classify tumor signals in tissue and cell-free DNA samples. Extensive analyses of multiple independent and heterogenous datasets including >7200 samples demonstrate the capability of MIMESIS to provide precise estimations of tumor content and to enable accurate classification of tumor type and molecular subtype. To assess our framework for clinical applications, we designed a MIMESIS-informed assay incorporating the minimal signatures for breast cancer. Using both artificial samples and clinical serial cell-free DNA samples from patients with metastatic breast cancer, we show that our approach provides accurate estimations of tumor content, sensitive detection of tumor signal and the ability to capture clinically relevant molecular subtype in patients' circulation. This study provides evidence that our extremely parsimonious approach can be used to develop cost-effective and highly scalable DNA-methylation assays that could support and facilitate the implementation of precision oncology in clinical practice.

Fast, accurate, and racially unbiased pan-cancer tumor-only variant calling with tabular machine learning

Accurately identifying somatic mutations is essential for precision oncology and crucial for calculating tumor-mutational burden (TMB), an important predictor of response to immunotherapy. For tumor-only variant calling (i.e., when the cancer biopsy but not the patient's normal tissue sample is sequenced), accurately distinguishing somatic mutations from germline variants is a challenging problem that, when unaddressed, results in unreliable, biased, and inflated TMB estimates. Here, we apply machine learning to the task of somatic vs germline classification in tumor-only solid tumor samples using TabNet, XGBoost, and LightGBM, three machine-learning models for tabular data. We constructed a training set for supervised classification using features derived exclusively from tumor-only variant calling and drawing somatic and germline truth labels from an independent pipeline using the patient-matched normal samples. All three trained models achieved state-of-the-art performance on two holdout test datasets: a TCGA dataset including sarcoma, breast adenocarcinoma, and endometrial carcinoma samples (AUC > 94%), and a metastatic melanoma dataset (AUC > 85%). Concordance between matched-normal and tumor-only TMB improves from R2 = 0.006 to 0.71-0.76 with the addition of a machine-learning classifier, with LightGBM performing best. Notably, these machine-learning models generalize across cancer subtypes and capture kits with a call rate of 100%. We reproduce the recent finding that tumor-only TMB estimates for Black patients are extremely inflated relative to that of white patients due to the racial biases of germline databases. We show that our approach with XGBoost and LightGBM eliminates this significant racial bias in tumor-only variant calling.

Genomic profiling and network-level understanding uncover the potential genes and the pathways in hepatocellular carcinoma

Data integration with phenotypes such as gene expression, pathways or function, and protein-protein interactions data has proven to be a highly promising technique for improving human complex diseases, particularly cancer patient outcome prediction. Hepatocellular carcinoma is one of the most prevalent cancers, and the most common cause is chronic HBV and HCV infection, which is linked to the majority of cases, and HBV and HCV play a role in multistep carcinogenesis progression. We examined the list of known hepatocellular carcinoma biomarkers with the publicly available expression profile dataset of hepatocellular carcinoma infected with HCV from day 1 to day 10 in this study. The study covers an overexpression pattern for the selected biomarkers in clinical hepatocellular carcinoma patients, a combined investigation of these biomarkers with the gathered temporal dataset, temporal expression profiling changes, and temporal pathway enrichment following HCV infection. Following a temporal analysis, it was discovered that the early stages of HCV infection tend to be more harmful in terms of expression shifting patterns, and that there is no significant change after that, followed by a set of genes that are consistently altered. PI3K, cAMP, TGF, TNF, Rap1, NF-kB, Apoptosis, Longevity regulating pathway, signaling pathways regulating pluripotency of stem cells, Cytokine-cytokine receptor interaction, p53 signaling, Wnt signaling, Toll-like receptor signaling, and Hippo signaling pathways are just a few of the most commonly enriched pathways. The majority of these pathways are well-known for their roles in the immune system, infection and inflammation, and human illnesses like cancer. We also find that ADCY8, MYC, PTK2, CTNNB1, TP53, RB1, PRKCA, TCF7L2, PAK1, ITPR2, CYP3A4, UGT1A6, GCK, and FGFR2/3 appear to be among the prominent genes based on the networks of genes and pathways based on the copy number alterations, mutations, and structural variants study.

The evolving landscape of prostate cancer somatic mutations

BACKGROUND

The landscape of somatic mutations in prostate cancer (PCa) has quickly evolved over the past years.

RESULTS

This evolution was in part due to the improved quality and lower cost of genomic sequencing platforms available to an ever-larger group of clinicians and researchers. The result of these efforts is a better understanding of early and late mutations that are enriched or nearly exclusive to treated PCa. There are, however, some important limitations to the current knowledge. The expanding variety of next-generation sequencing (NGS) assays either capture a wide spectrum of mutations but at low coverage or are focused panels that cover a select number of genes, most often cancer-related, at a deep coverage. Both of these approaches have their advantages, but ultimately miss low-frequency mutations or fail to cover the spectrum of potential mutations. Additionally, some alterations, such as the common ETS gene fusions, require a mixture of DNA and RNA analysis to capture the true frequency. Finally, almost all studies rely on bulk PCa tumor samples, which fail to consider tumor heterogeneity. Given all these caveats, the true picture of the somatic landscape of PCa continues to develop.

SUMMARY

In this review, the focus will be on how the landscape of mutations evolves during disease progression considering therapy. It will focus on a select group of early and late mutations and utilize SPOP mutations to illustrate recurrent alterations that may have clinical implications.

Blood-based liquid biopsies for prostate cancer: clinical opportunities and challenges

Liquid biopsy has been established as a powerful, minimally invasive, tool to detect clinically actionable aberrations across numerous cancer types in real-time. With the development of new therapeutic agents in prostate cancer (PC) including DNA repair targeted therapies, this is especially attractive. However, there is unclarity on how best to screen for PC, improve risk stratification and ultimately how to treat advanced disease. Therefore, there is an urgent need to develop better biomarkers to help guide oncologists' decisions in these settings. Circulating tumour cells (CTCs), exosomes and cell-free DNA/RNA (cfDNA/cfRNA) analysis, including epigenetic features such as methylation, have all shown potential in prognostication, treatment response assessment and detection of emerging mechanisms of resistance. However, there are still challenges to overcome prior to implementing liquid biopsies in routine clinical practice such as preanalytical considerations including blood collection and storage, the cost of CTC isolation and enrichment, low-circulating tumour content as a limitation for genomic analysis and how to better interpret the sequencing data generated. In this review, we describe an overview of the up-to-date clinical opportunities in the management of PC through blood-based liquid biopsies and the next steps for its implementation in personalised treatment guidance.

Allele-informed copy number evaluation of plasma DNA samples from metastatic prostate cancer patients: the PCF_SELECT consortium assay

Sequencing of cell-free DNA (cfDNA) in cancer patients' plasma offers a minimally-invasive solution to detect tumor cell genomic alterations to aid real-time clinical decision-making. The reliability of copy number detection decreases at lower cfDNA tumor fractions, limiting utility at earlier stages of the disease. To test a novel strategy for detection of allelic imbalance, we developed a prostate cancer bespoke assay, PCF_SELECT, that includes an innovative sequencing panel covering ∼25 000 high minor allele frequency SNPs and tailored analytical solutions to enable allele-informed evaluation. First, we assessed it on plasma samples from 50 advanced prostate cancer patients. We then confirmed improved detection of genomic alterations in samples with <10% tumor fractions when compared against an independent assay. Finally, we applied PCF_SELECT to serial plasma samples intensively collected from three patients previously characterized as harboring alterations involving DNA repair genes and consequently offered PARP inhibition. We identified more extensive pan-genome allelic imbalance than previously recognized in prostate cancer. We confirmed high sensitivity detection of BRCA2 allelic imbalance with decreasing tumor fractions resultant from treatment and identified complex ATM genomic states that may be incongruent with protein losses. Overall, we present a framework for sensitive detection of allele-specific copy number changes in cfDNA.

Molecular Evaluation of Low-grade Low-stage Endometrial Cancer With and Without Recurrence

Low-grade, low-stage endometrioid carcinomas (LGLS EC) demonstrate 5-yr survival rates up to 95%. However, a small subset of these tumors recur, and little is known about prognostic markers or established mutation profiles associated with recurrence. The goal of the current study was to identify the molecular profiles of the primary carcinomas and the genomic differences between primary tumors and subsequent recurrences. Four cases of LGLS EC with recurrence and 8 cases without recurrence were evaluated via whole-exome sequencing. Three of the 4 recurrent tumors were evaluated via Oncomine Comprehensive Assay. The resulting molecular profiles of the primary and recurrent tumors were compared. Two of the 3 recurrent cases showed additional mutations in the recurrence. One recurrent tumor included an additional TP53 mutation and the other recurrent tumor showed POLE and DDR2 kinase gene mutation. The POLE mutation occurred outside the exonuclease domain. PIK3CA mutations were detected in 4 of 4 primary LGLS EC with recurrence and in 3 of 8 disease-free cases. LGLS EC with recurrence showed higher MSIsensor scores compared with LGLS without recurrence. The level of copy number gains in LGLS EC with recurrence was larger than LGLS EC without recurrence. This pilot study showed 1 of 3 recurrent cases gained a mutation associated with genetic instability (TP53) and 1 of them also acquired a mutation in the DDR2 kinase, a potential therapeutic target. We also noted a higher level of copy number gains, MSIsensor scores and PIK3CA mutations in the primary tumors that later recurred.

Baseline Plasma Tumor DNA (ctDNA) Correlates with PSA Kinetics in Metastatic Castration-Resistant Prostate Cancer (mCRPC) Treated with Abiraterone or Enzalutamide

Background: Baseline high circulating tumor DNA (ctDNA) fraction in plasma and androgen receptor (AR) copy number (CN) gain identify mCRPC patients with worse outcomes. This study aimed to assess if ctDNA associates with PSA kinetics. Methods: In this prospective biomarker study, we evaluate ctDNA fraction and AR CN from plasma samples. We divided patients into high and low ctDNA level and in AR gain and AR normal. Results: 220 baseline samples were collected from mCRPC treated with abiraterone (n = 140) or enzalutamide (n = 80). A lower rate of PSA decline ≥ 50% was observed in patients with high ctDNA (p = 0.017) and AR gain (p = 0.0003). Combining ctDNA fraction and AR CN, we found a different median PSA progression-free survival (PFS) among four groups: (1) low ctDNA/AR normal, (2) high ctDNA/AR normal, (3) low ctDNA/AR gain, and (4) high ctDNA/AR gain (11.4 vs. 5.0 vs. 4.8 vs. 3.7 months, p < 0.0001). In a multivariable analysis, high ctDNA, AR gain, PSA DT, PSA DT velocity remained independent predictors of PSA PFS. Conclusions: Elevated ctDNA levels and AR gain are negatively and independently correlated with PSA kinetics in mCRPC men treated with abiraterone or enzalutamide.

The genomic landscape of metastatic clear cell renal cell carcinoma after systemic therapy

Primary clear cell renal cell carcinoma (ccRCC) has been previously characterized, but the genomic landscape of metastatic ccRCC is largely unexplored. Here, we performed whole exome sequencing (WES) in 68 samples from 44 patients with ccRCC, including 52 samples from a metastatic site. SETD2, PBRM1, APC and VHL were the most frequently mutated genes in the metastatic ccRCC cohort. RBM10 and FBXW7 were also among the 10 most frequently mutated genes in metastatic tissues. Recurrent somatic copy number variations (CNV) were observed at the previously identified regions 3p25, 9p21 and 14q25, but also at 6p21 (CDKN1A) and 13q14 (RB1). No statistically significant differences were found between samples from therapy‐naïve and pretreated patients. Clonal evolution analyses with multiple samples from 13 patients suggested that early appearance of CNVs at 3p25, 9p21 and 14q25 may be associated with rapid clinical progression. Overall, the genomic landscapes of primary and metastatic ccRCC seem to share frequent CNVs at 3p25, 9p21 and 14q25. Future work will clarify the implication of RBM10 and FBXW7 mutations and 6p21 and 13q14 CNVs in metastatic ccRCC.

Past, Current, and Future Strategies to Target ERG Fusion-Positive Prostate Cancer

Simple Summary

In addition to its role in development and in the vascular and hematopoietic systems, ERG plays a central role in prostate cancer. Approximately 40–50% of prostate cancer cases are characterized by ERG gene fusions, which lead to ERG overexpression. Importantly, inhibition of ERG activity in prostate cancer cells decreases their viability. Therefore, inhibiting ERG might represent an important step to improve treatment efficacy for patients with ERG-positive prostate tumors. Here, we summarize the attempts made over the past years to repress ERG activity, the current use of ERG fusion detection and the strategies that might be utilized in the future to treat ERG fusion-positive tumors.

Abstract

The ETS family member ERG is a transcription factor with physiological roles during development and in the vascular and hematopoietic systems. ERG oncogenic activity characterizes several malignancies, including Ewing’s sarcoma, leukemia and prostate cancer (PCa). In PCa, ERG rearrangements with androgen-regulated genes—mostly TMPRSS2—characterize a large subset of patients across disease progression and result in androgen receptor (AR)-mediated overexpression of ERG in the prostate cells. Importantly, PCa cells overexpressing ERG are dependent on ERG activity for survival, further highlighting its therapeutic potential. Here, we review the current understanding of the role of ERG and its partners in PCa. We discuss the strategies developed in recent years to inhibit ERG activity, the current therapeutic utility of ERG fusion detection in PCa patients, and the possible future approaches to target ERG fusion-positive tumors.

A Functional Precision Oncology Approach to Identify Treatment Strategies for Myxofibrosarcoma Patients

In this era of precision medicine, numerous workflows for the targeting of high-recurrent mutations in common tumor types have been developed, leaving patients with rare diseases with few options. Here, we implement a functional precision oncology approach utilizing comprehensive genomic profiling in combination with high-throughput drug screening, to identify tumor-specific drug sensitivities for patients with rare tumor types such as myxofibrosarcoma. From a patient with a high-grade myxofibrosarcoma, who was enrolled in the Englander Institute for Precision Medicine (EIPM) program, we established patient-derived 3D sarco-spheres and xenograft models for functional testing. In the absence of a large cohort of clinically similar cases, high-throughput drug screening was performed on the patient-derived cells, and compared with two other myxofibrosarcoma lines and a benign fibroblast line to functionally identify tumor-specific drug sensitivities. The addition of functional drug sensitivity testing to complement genomic profiling identified multiple therapeutic options that were further validated in patient derived xenograft models. Genomic analyses detected the frequently known codeletion of the tumor suppressors CDKN2A/B together with the methylthioadenosine phosphorylase (MTAP) and a TP53 E286fs*50 mutation. High-throughput drug screening demonstrated tumor-specific sensitivity to compounds targeting the cell cycle. Based on genomic analysis and high-throughput drug screening, we show that targeting the cell cycle in these tumors is a powerful approach. IMPLICATIONS: This study demonstrates the potential of functional testing to aid clinical decision making for patients with rare or molecularly complex malignancies when combined with comprehensive genomic profiling.

The emerging roles of NGS in clinical oncology and personalized medicine

Next-generation sequencing (NGS) has been increasingly popular in genomics studies over the last decade, as new sequencing technology has been created and improved. Recently, NGS started to be used in clinical oncology to improve cancer therapy through diverse modalities ranging from finding novel and rare cancer mutations, discovering cancer mutation carriers to reaching specific therapeutic approaches known as personalized medicine (PM). PM has the potential to minimize medical expenses by shifting the current traditional medical approach of treating cancer and other diseases to an individualized preventive and predictive approach. Currently, NGS can speed up in the early diagnosis of diseases and discover pharmacogenetic markers that help in personalizing therapies. Despite the tremendous growth in our understanding of genetics, NGS holds the added advantage of providing more comprehensive picture of cancer landscape and uncovering cancer development pathways. In this review, we provided a complete overview of potential NGS applications in scientific and clinical oncology, with a particular emphasis on pharmacogenomics in the direction of precision medicine treatment options.

Cell-Free DNA-Methylation-Based Methods and Applications in Oncology

Liquid biopsy based on cell-free DNA (cfDNA) enables non-invasive dynamic assessment of disease status in patients with cancer, both in the early and advanced settings. The analysis of DNA-methylation (DNAm) from cfDNA samples holds great promise due to the intrinsic characteristics of DNAm being more prevalent, pervasive, and cell- and tumor-type specific than genomics, for which established cfDNA assays already exist. Herein, we report on recent advances on experimental strategies for the analysis of DNAm in cfDNA samples. We describe the main steps of DNAm-based analysis workflows, including pre-analytics of cfDNA samples, DNA treatment, assays for DNAm evaluation, and methods for data analysis. We report on protocols, biomolecular techniques, and computational strategies enabling DNAm evaluation in the context of cfDNA analysis, along with practical considerations on input sample requirements and costs. We provide an overview on existing studies exploiting cell-free DNAm biomarkers for the detection and monitoring of cancer in early and advanced settings, for the evaluation of drug resistance, and for the identification of the cell-of-origin of tumors. Finally, we report on DNAm-based tests approved for clinical use and summarize their performance in the context of liquid biopsy.

Common germline-somatic variant interactions in advanced urothelial cancer

The prevalence and biological consequences of deleterious germline variants in urothelial cancer (UC) are not fully characterized. We performed whole-exome sequencing (WES) of germline DNA and 157 primary and metastatic tumors from 80 UC patients. We developed a computational framework for identifying putative deleterious germline variants (pDGVs) from WES data. Here, we show that UC patients harbor a high prevalence of pDGVs that truncate tumor suppressor proteins. Deepening somatic loss of heterozygosity in serial tumor samples is observed, suggesting a critical role for these pDGVs in tumor progression. Significant intra-patient heterogeneity in germline-somatic variant interactions results in divergent biological pathway alterations between primary and metastatic tumors. Our results characterize the spectrum of germline variants in UC and highlight their roles in shaping the natural history of the disease. These findings could have broad clinical implications for cancer patients.

Integration of whole-exome and anchored PCR-based next generation sequencing significantly increases detection of actionable alterations in precision oncology

BACKGROUND

Frequency of clinically relevant mutations in solid tumors by targeted and whole-exome sequencing is ∼30%. Transcriptome analysis complements detection of actionable gene fusions in advanced cancer patients. Goal of this study was to determine the added value of anchored multiplex PCR (AMP)-based next-generation sequencing (NGS) assay to identify further potential drug targets, when coupled with whole-exome sequencing (WES).

METHODS

Selected series of fifty-six samples from 55 patients enrolled in our precision medicine study were interrogated by WES and AMP-based NGS. RNA-seq was performed in 19 cases. Clinically relevant and actionable alterations detected by three methods were integrated and analyzed.

RESULTS

AMP-based NGS detected 48 fusions in 31 samples (55.4%); 31.25% (15/48) were classified as targetable based on published literature. WES revealed 29 samples (51.8%) harbored targetable alterations. TMB-high and MSI-high status were observed in 12.7% and 1.8% of cases. RNA-seq from 19 samples identified 8 targetable fusions (42.1%), also captured by AMP-based NGS. When number of actionable fusions detected by AMP-based NGS were added to WES targetable alterations, 66.1% of samples had potential drug targets. When both WES and RNA-seq were analyzed, 57.8% of samples had targetable alterations.

CONCLUSIONS

This study highlights importance of an integrative genomic approach for precision oncology, including use of different NGS platforms with complementary features. Integrating RNA data (whole transcriptome or AMP-based NGS) significantly enhances detection of potential targets in cancer patients. In absence of fresh frozen tissue, AMP-based NGS is a robust method to detect actionable fusions using low-input RNA from archival tissue.

Role of specialized composition of SWI/SNF complexes in prostate cancer lineage plasticity

Advanced prostate cancer initially responds to hormonal treatment, but ultimately becomes resistant and requires more potent therapies. One mechanism of resistance observed in around 10–20% of these patients is lineage plasticity, which manifests in a partial or complete small cell or neuroendocrine prostate cancer (NEPC) phenotype. Here, we investigate the role of the mammalian SWI/SNF (mSWI/SNF) chromatin remodeling complex in NEPC. Using large patient datasets, patient-derived organoids and cancer cell lines, we identify mSWI/SNF subunits that are deregulated in NEPC and demonstrate that SMARCA4 (BRG1) overexpression is associated with aggressive disease. We also show that SWI/SNF complexes interact with different lineage-specific factors in NEPC compared to prostate adenocarcinoma. These data point to a role for mSWI/SNF complexes in therapy-related lineage plasticity, which may also be relevant for other solid tumors.

The differentiation of prostate adenocarcinoma to neuroendocrine prostate cancer (CRPC-NE) is a mechanism of resistance to androgen deprivation therapy. Here the authors show that SWI/SNF chromatin-remodeling complex is deregulated in CRPC-NE and that the complex interacts with different lineage specific factors throughout prostate cancer transdifferentiation.

Systematic Assessment of Tumor Purity and Its Clinical Implications

PURPOSE

The tumor microenvironment is complex, comprising heterogeneous cellular populations. As molecular profiles are frequently generated using bulk tissue sections, they represent an admixture of multiple cell types (including immune, stromal, and cancer cells) interacting with each other. Therefore, these molecular profiles are confounded by signals emanating from many cell types. Accurate assessment of residual cancer cell fraction is crucial for parameterization and interpretation of genomic analyses, as well as for accurately interpreting the clinical properties of the tumor.

MATERIALS AND METHODS

To benchmark cancer cell fraction estimation methods, 10 estimators were applied to a clinical cohort of 333 patients with prostate cancer. These methods include gold-standard multiobserver pathology estimates, as well as estimates inferred from genome, epigenome, and transcriptome data. In addition, two methods based on genomic and transcriptomic profiles were used to quantify tumor purity in 4,497 tumors across 12 cancer types. Bulk mRNA and microRNA profiles were subject to in silico deconvolution to estimate cancer cell-specific mRNA and microRNA profiles.

RESULTS

We present a systematic comparison of 10 tumor purity estimation methods on a cohort of 333 prostate tumors. We quantify variation among purity estimation methods and demonstrate how this influences interpretation of clinico-genomic analyses. Our data show poor concordance between pathologic and molecular purity estimates, necessitating caution when interpreting molecular results. Limited concordance between DNA- and mRNA-derived purity estimates remained a general pan-cancer phenomenon when tested in an additional 4,497 tumors spanning 12 cancer types.

CONCLUSION

The choice of tumor purity estimation method may have a profound impact on the interpretation of genomic assays. Taken together, these data highlight the need for improved assessment of tumor purity and quantitation of its influences on the molecular hallmarks of cancers.

PESM: A novel approach of tumor purity estimation based on sample specific methylation sites

Background: Tumor purity is of great significance for the study of tumor genotyping and the prediction of recurrence, which is significantly affected by tumor heterogeneity. Tumor heterogeneity is the basis of drug resistance in various cancer treatments, and DNA methylation plays a core role in the generation of tumor heterogeneity. Almost all types of cancer cells are associated with abnormal DNA methylation in certain regions of the genome. The selection of tumor-related differential methylation sites, which can be used as an indicator of tumor purity, has important implications for purity assessment. At present, the selection of information sites mostly focuses on inter-tumor heterogeneity and ignores the heterogeneity of tumor growth space that is sample specificity. Results: Considering the specificity of tumor samples and the information gain of individual tumor sample relative to the normal samples, we present an approach, PESM, to evaluate the tumor purity through the specificity difference methylation sites of tumor samples. Applied to more than 200 tumor samples of Prostate adenocarcinoma (PRAD) and Kidney renal clear cell carcinoma (KIRC), it shows that the tumor purity estimated by PESM is highly consistent with other existing methods. In addition, PESM performs better than the method that uses the integrated signal of methylation sites to estimate purity. Therefore, different information sites selection methods have an important impact on the estimation of tumor purity, and the selection of sample specific information sites has a certain significance for accurate identification of tumor purity of samples.

TPES: tumor purity estimation from SNVs

MOTIVATION

Tumor purity (TP) is the proportion of cancer cells in a tumor sample. TP impacts on the accurate assessment of molecular and genomics features as assayed with NGS approaches. State-of-the-art tools mainly rely on somatic copy-number alterations (SCNA) to quantify TP and therefore fail when a tumor genome is nearly euploid, i.e. 'non-aberrant' in terms of identifiable SCNAs.

RESULTS

We introduce a computational method, tumor purity estimation from single-nucleotide variants (SNVs), which derives TP from the allelic fraction distribution of SNVs. On more than 7800 whole-exome sequencing data of TCGA tumor samples, it showed high concordance with a range of TP tools (Spearman's correlation between 0.68 and 0.82; >9 SNVs) and rescued TP estimates of 1, 194 samples (15%) pan-cancer.

AVAILABILITY AND IMPLEMENTATION

TPES is available as an R package on CRAN and at https://bitbucket.org/l0ka/tpes.git.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Ploidy- and Purity-Adjusted Allele-Specific DNA Analysis Using CLONETv2

High‐throughput DNA sequencing technology provides base‐level and statistically rich information about the genomic content of a sample. In the contexts of cancer research and precision oncology, thousands of genomes from paired tumor and matched normal samples are profiled and processed to determine somatic copy‐number changes and single‐nucleotide variations. Higher‐order informative analyses, in the form of allele‐specific copy‐number assessments or subclonality quantification, require reliable estimates of tumor DNA ploidy and tumor cellularity. CLONETv2 provides a complete set of functions to process matched normal and tumor pairs using patient‐specific genotype data, is independent of low‐level tools (e.g., aligner, segmentation algorithm, mutation caller) and offers high‐level functions to compute allele‐specific copy number from segmented data and to identify subclonal population in the input sample. CLONETv2 is applicable to whole‐genome, whole‐exome and targeted sequencing data generated either from tissue or from liquid biopsy samples. © 2019 The Authors.

ABEMUS: platform-specific and data-informed detection of somatic SNVs in cfDNA

MOTIVATION

The use of liquid biopsies for cancer patients enables the non-invasive tracking of treatment response and tumor dynamics through single or serial blood drawn tests. Next-generation sequencing assays allow for the simultaneous interrogation of extended sets of somatic single-nucleotide variants (SNVs) in circulating cell-free DNA (cfDNA), a mixture of DNA molecules originating both from normal and tumor tissue cells. However, low circulating tumor DNA (ctDNA) fractions together with sequencing background noise and potential tumor heterogeneity challenge the ability to confidently call SNVs.

RESULTS

We present a computational methodology, called Adaptive Base Error Model in Ultra-deep Sequencing data (ABEMUS), which combines platform-specific genetic knowledge and empirical signal to readily detect and quantify somatic SNVs in cfDNA. We tested the capability of our method to analyze data generated using different platforms with distinct sequencing error properties and we compared ABEMUS performances with other popular SNV callers on both synthetic and real cancer patients sequencing data. Results show that ABEMUS performs better in most of the tested conditions proving its reliability in calling low variant allele frequencies somatic SNVs in low ctDNA levels plasma samples.

AVAILABILITY AND IMPLEMENTATION

ABEMUS is cross-platform and can be installed as R package. The source code is maintained on Github at http://github.com/cibiobcg/abemus, and it is also available at CRAN official R repository.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Genomic heterogeneity in bladder cancer: challenges and possible solutions to improve outcomes

Histological and molecular analyses of urothelial carcinoma often reveal intratumoural and intertumoural heterogeneity at the genomic, transcriptional and cellular levels. Despite the clonal initiation of the tumour, progression and metastasis often arise from subclones that can develop naturally or during therapy, resulting in molecular alterations with a heterogeneous distribution. Variant histologies in tumour tissues that have developed distinct morphological characteristics divergent from urothelial carcinoma are extreme examples of tumour heterogeneity. Ultimately, heterogeneity contributes to drug resistance and relapse after therapy, resulting in poor survival outcomes. Mutation profile differences between patients with muscle-invasive and metastatic urothelial cancer (interpatient heterogeneity) probably contribute to variability in response to chemotherapy and immunotherapy as first-line treatments. Heterogeneity can occur on multiple levels and averaging or normalizing these alterations is crucial for clinical trial and drug design to enable appropriate therapeutic targeting. Identification of the extent of heterogeneity might shape the choice of monotherapy or additional combination treatments to target different drivers and genetic events. Identification of the lethal tumour cell clones is required to improve survival of patients with urothelial carcinoma.

Cellular origins and genetic landscape of cutaneous gamma delta T cell lymphomas

Primary cutaneous γδ T cell lymphomas (PCGDTLs) represent a heterogeneous group of uncommon but aggressive cancers. Herein, we perform genome-wide DNA, RNA, and T cell receptor (TCR) sequencing on 29 cutaneous γδ lymphomas. We find that PCGDTLs are not uniformly derived from Vδ2 cells. Instead, the cell-of-origin depends on the tissue compartment from which the lymphomas are derived. Lymphomas arising from the outer layer of skin are derived from Vδ1 cells, the predominant γδ cell in the epidermis and dermis. In contrast, panniculitic lymphomas arise from Vδ2 cells, the predominant γδ T cell in the fat. We also show that TCR chain usage is non-random, suggesting common antigens for Vδ1 and Vδ2 lymphomas respectively. In addition, Vδ1 and Vδ2 PCGDTLs harbor similar genomic landscapes with potentially targetable oncogenic mutations in the JAK/STAT, MAPK, MYC, and chromatin modification pathways. Collectively, these findings suggest a paradigm for classifying, staging, and treating these diseases.

Genome-wide plasma DNA methylation features of metastatic prostate cancer

Tumor DNA circulates in the plasma of cancer patients admixed with DNA from noncancerous cells. The genomic landscape of plasma DNA has been characterized in metastatic castration-resistant prostate cancer (mCRPC) but the plasma methylome has not been extensively explored. Here, we performed next-generation sequencing (NGS) on plasma DNA with and without bisulfite treatment from mCRPC patients receiving either abiraterone or enzalutamide in the pre- or post-chemotherapy setting. Principal component analysis on the mCRPC plasma methylome indicated that the main contributor to methylation variance (principal component one, or PC1) was strongly correlated with genomically determined tumor fraction (r = -0.96; P < 10-8) and characterized by hypermethylation of targets of the polycomb repressor complex 2 components. Further deconvolution of the PC1 top-correlated segments revealed that these segments are comprised of methylation patterns specific to either prostate cancer or prostate normal epithelium. To extract information specific to an individual's cancer, we then focused on an orthogonal methylation signature, which revealed enrichment for androgen receptor binding sequences and hypomethylation of these segments associated with AR copy number gain. Individuals harboring this methylation pattern had a more aggressive clinical course. Plasma methylome analysis can accurately quantitate tumor fraction and identify distinct biologically relevant mCRPC phenotypes.

Proteomic and genomic signatures of repeat instability in cancer and adjacent normal tissues

Repetitive sequences are hotspots of evolution at multiple levels. However, due to difficulties involved in their assembly and analysis, the role of repeats in tumor evolution is poorly understood. We developed a rigorous motif-based methodology to quantify variations in the repeat content, beyond microsatellites, in proteomes and genomes directly from proteomic and genomic raw data. This method was applied to a wide range of tumors and normal tissues. We identify high similarity between repeat instability patterns in tumors and their patient-matched adjacent normal tissues. Nonetheless, tumor-specific signatures both in protein expression and in the genome strongly correlate with cancer progression and robustly predict the tumorigenic state. In a patient, the hierarchy of genomic repeat instability signatures accurately reconstructs tumor evolution, with primary tumors differentiated from metastases. We observe an inverse relationship between repeat instability and point mutation load within and across patients independent of other somatic aberrations. Thus, repeat instability is a distinct, transient, and compensatory adaptive mechanism in tumor evolution and a potential signal for early detection.

Cancer-Specific Thresholds Adjust for Whole Exome Sequencing-based Tumor Mutational Burden Distribution

Purpose

To understand the clinical context of tumor mutational burden (TMB) when comparing a pan-cancer threshold and a cancer-specific threshold.

Materials and Methods

Using whole exome sequencing (WES) data from primary tumors in The Cancer Genome Atlas (TCGA) (n=3,534) and advanced/metastatic tumors from Weill Cornell Medicine (WCM Advanced) (n=696), TMB status was determined using a pan-cancer and cancer-specific threshold. Survival curves, number of samples classified as TMB high, and predicted neoantigens were used to evaluate the differences between thresholds.

Results

The distribution of TMB varied dramatically between cancer types. A cancer-specific threshold was able to adjust for the different TMB distributions, while the pan-cancer threshold was often too stringent. The dynamic nature of the cancer-specific threshold resulted in more tumors being classified as TMB high compared to the static pan-cancer threshold. Additionally, no significant difference in survival outcomes was found with the cancer-specific threshold compared to the pan-cancer one. Further, the cancer-specific threshold maintains higher predicted neoantigen load for the TMB high samples compared to the TMB low samples, even when the threshold is lower than the pan-cancer threshold.

Conclusion

TMB is relative to the context of cancer type, metastatic state, and disease stage. Compared to a pan-cancer threshold, a cancer-specific threshold classifies more patients as TMB high while maintaining clinical outcomes that were not significantly different. Furthermore, the cancer-specific threshold identifies patients with a high number of predicted neoantigens. Due to the potential impact in cancer patient care, TMB status should be determined in a cancer-specific manner.

Landscape of transcriptomic interactions between breast cancer and its microenvironment

Solid tumours comprise mixtures of tumour cells (TCs) and tumour-adjacent cells (TACs), and the intricate interconnections between these diverse populations shape the tumour’s microenvironment. Despite this complexity, clinical genomic profiling is typically performed from bulk samples, without distinguishing TCs from TACs. To better understand TC–TAC interactions, we computationally distinguish their transcriptomes in 1780 primary breast tumours. We show that TC and TAC mRNA abundances are divergently associated with clinical phenotypes, including tumour subtypes and patient survival. These differences reflect distinct responses of TCs and TACs to specific somatic driver mutations, particularly TP53. These data further elucidate how the molecular interplay between breast tumours and their microenvironment drives aggressive tumour phenotypes.

The transcriptomic profile of tumour-adjacent cells provides important information about tumour context but its clinical utility is unclear. Here, in breast cancer, Fox et al. show that the mRNA abundances of tumour and tumour-adjacent cells hold prognostic information.

Tumor purity quantification by clonal DNA methylation signatures

Motivation

Controlling for tumor purity in molecular analyses is essential to allow for reliable genomic aberration calls, for inter-sample comparison and to monitor heterogeneity of cancer cell populations. In genome wide screening studies, the assessment of tumor purity is typically performed by means of computational methods that exploit somatic copy number aberrations.

Results

We present a strategy, called Purity Assessment from clonal MEthylation Sites (PAMES), which uses the methylation level of a few dozen, highly clonal, tumor type specific CpG sites to estimate the purity of tumor samples, without the need of a matched benign control. We trained and validated our method in more than 6000 samples from different datasets. Purity estimates by PAMES were highly concordant with other state-of-the-art tools and its evaluation in a cancer cell line dataset highlights its reliability to accurately estimate tumor admixtures. We extended the capability of PAMES to the analysis of CpG islands instead of the more platform-specific CpG sites and demonstrated its accuracy in a set of advanced tumors profiled by high throughput DNA methylation sequencing. These analyses show that PAMES is a valuable tool to assess the purity of tumor samples in the settings of clinical research and diagnostics.

Availability and implementation

https://github.com/cgplab/PAMES.

Contact

matteo.benelli@uslcentro.toscana.it or f.demichelis@unitn.it.

Supplementary information

Supplementary data are available at Bioinformatics online.

Treatment resistance in urothelial carcinoma: an evolutionary perspective

The emergence of treatment-resistant clones is a critical barrier to cure in patients with urothelial carcinoma. Setting the stage for the evolution of resistance, urothelial carcinoma is characterized by extensive mutational heterogeneity, which is detectable even in patients with early stage disease. Chemotherapy and immunotherapy both act as selective pressures that shape the evolutionary trajectory of urothelial carcinoma throughout the course of the disease. A detailed understanding of the dynamics of evolutionary drivers is required for the rational development of curative therapies. Herein, we describe the molecular basis of the clonal evolution of urothelial carcinomas and the use of genomic approaches to predict treatment responses. We discuss various mechanisms of resistance to chemotherapy with a focus on the mutagenic effects of the DNA dC->dU-editing enzymes APOBEC3 family of proteins. We also review the evolutionary mechanisms underlying resistance to immunotherapy, such as the loss of clonal tumour neoantigens. By dissecting treatment resistance through an evolutionary lens, the field will advance towards true precision medicine for urothelial carcinoma.

The Genomics of Prostate Cancer: A Historic Perspective

The genomics of prostate cancer (PCA) has been difficult to study compared with some other cancer types for a multitude of reasons, despite significant efforts since the early 1980s. Overcoming some of these obstacles has paved the way for greater insight into the genomics of PCA. The advent of high-throughput technologies coming from the initial use of microsatellite and oligonucleotide probes gave rise to techniques like comparative genomic hybridization (CGH). With the introduction of massively parallel genomic sequencing, referred to as next-generation sequencing (NGS), a deeper understanding of cancer genomics in general has occurred. Along with these technologic advances, there has been the development of computational biology and statistical approaches to address novel large data sets characterized by single base resolution. This review will provide a historic perspective of PCA genomics with an emphasis on the cardinal mutations and alterations observed to be consistently seen in PCA for both hormone-naïve localized PCA and castration-resistant prostate cancer (CRPC). There will be a focus on alterations that have the greatest potential to play a role in disease progression and therapy management.

Allele-Specific Expression Analysis in Cancer Using Next-Generation Sequencing Data

Allele-specific expression arises when transcriptional activity at the different alleles of a gene differs considerably. Although extensive research has been carried out to detect and characterize this phenomenon, the landscape of allele-specific expression in cancer is still poorly understood. In this chapter, we describe a fast and reliable analysis pipeline to study allele-specific expression in cancer using next-generation sequencing data. The pipeline provides a gene-level analysis approach that exploits paired germline DNA and tumor RNA sequencing data and benefits from parallel computation resources when available.

A Phase II Trial of the Aurora Kinase A Inhibitor Alisertib for Patients with Castration-resistant and Neuroendocrine Prostate Cancer: Efficacy and Biomarkers

PURPOSE

Neuroendocrine prostate cancer (NEPC) is an aggressive variant of prostate cancer that may develop de novo or as a mechanism of treatment resistance. N-myc is capable of driving NEPC progression. Alisertib inhibits the interaction between N-myc and its stabilizing factor Aurora-A, inhibiting N-myc signaling, and suppressing tumor growth.

PATIENTS AND METHODS

Sixty men were treated with alisertib 50 mg twice daily for 7 days every 21 days. Eligibility included metastatic prostate cancer and at least one: small-cell neuroendocrine morphology; ≥50% neuroendocrine marker expression; new liver metastases without PSA progression; or elevated serum neuroendocrine markers. The primary endpoint was 6-month radiographic progression-free survival (rPFS). Pretreatment biopsies were evaluated by whole exome and RNA-seq and patient-derived organoids were developed.

RESULTS

Median PSA was 1.13 ng/mL (0.01-514.2), number of prior therapies was 3, and 68% had visceral metastases. Genomic alterations involved RB1 (55%), TP53 (46%), PTEN (29%), BRCA2 (29%), and AR (27%), and there was a range of androgen receptor signaling and NEPC marker expression. Six-month rPFS was 13.4% and median overall survival was 9.5 months (7.3-13). Exceptional responders were identified, including complete resolution of liver metastases and prolonged stable disease, with tumors suggestive of N-myc and Aurora-A overactivity. Patient organoids exhibited concordant responses to alisertib and allowed for the dynamic testing of Aurora-N-myc complex disruption.

CONCLUSIONS

Although the study did not meet its primary endpoint, a subset of patients with advanced prostate cancer and molecular features supporting Aurora-A and N-myc activation achieved significant clinical benefit from single-agent alisertib.

The Genetic Landscape and Clonal Evolution of Breast Cancer Resistance to Palbociclib plus Fulvestrant in the PALOMA-3 Trial

CDK4/6 inhibition with endocrine therapy is now a standard of care for advanced estrogen receptor-positive breast cancer. Mechanisms of CDK4/6 inhibitor resistance have been described preclinically, with limited evidence from clinical samples. We conducted paired baseline and end-of-treatment circulating tumor DNA sequencing from 195 patients in the PALOMA-3 randomized phase III trial of palbociclib plus fulvestrant versus placebo plus fulvestrant. We show that clonal evolution occurs frequently during treatment, reflecting substantial subclonal complexity in breast cancer that has progressed after prior endocrine therapy. RB1 mutations emerged only in the palbociclib plus fulvestrant arm and in a minority of patients (6/127, 4.7%, P = 0.041). New driver mutations emerged in PIK3CA (P = 0.00069) and ESR1 after treatment in both arms, in particular ESR1 Y537S (P = 0.0037). Evolution of driver gene mutations was uncommon in patients progressing early on palbociclib plus fulvestrant but common in patients progressing later on treatment. These findings inform future treatment strategies to address resistance to palbociclib plus fulvestrant.Significance: Acquired mutations from fulvestrant are a major driver of resistance to fulvestrant and palbociclib combination therapy. ESR1 Y537S mutation promotes resistance to fulvestrant. Clonal evolution results in frequent acquisition of driver mutations in patients progressing late on therapy, which suggests that early and late progression have distinct mechanisms of resistance. Cancer Discov; 8(11); 1390-403. ©2018 AACR. See related commentary by Schiff and Jeselsohn, p. 1352 This article is highlighted in the In This Issue feature, p. 1333.

RB1 Heterogeneity in Advanced Metastatic Castration-Resistant Prostate Cancer

PURPOSE

Metastatic castration-resistant prostate cancer (mCRPC) is a lethal but clinically heterogeneous disease, with patients having variable benefit from endocrine and cytotoxic treatments. Intrapatient genomic heterogeneity could be a contributing factor to this clinical heterogeneity. Here, we used whole-genome sequencing (WGS) to investigate genomic heterogeneity in 21 previously treated CRPC metastases from 10 patients to investigate intrapatient molecular heterogeneity (IPMH).Experimental Design: WGS was performed on topographically separate metastases from patients with advanced metastatic prostate cancer. IPMH of the RB1 gene was identified and further evaluated by FISH and IHC assays.

RESULTS

WGS identified limited IPMH for putative driver events. However, heterogeneous genomic aberrations of RB1 were detected. We confirmed the presence of these RB1 somatic copy-number aberrations, initially identified by WGS, with FISH, and identified novel structural variants involving RB1 in 6 samples from 3 of these 10 patients (30%; 3/10). WGS uncovered a novel deleterious RB1 structural lesion constituted of an intragenic tandem duplication involving multiple exons and associating with protein loss. Using RB1 IHC in a large series of mCRPC biopsies, we identified heterogeneous expression in approximately 28% of mCRPCs.

CONCLUSIONS

mCRPCs have a high prevalence of RB1 genomic aberrations, with structural variants, including rearrangements, being common. Intrapatient genomic and expression heterogeneity favors RB1 aberrations as late, subclonal events that increase in prevalence due to treatment-selective pressures.

Core Biopsies from Prostate Cancer Patients in Active Surveillance Protocols Harbor PTEN and MYC Alterations

BACKGROUND

Genomic characterization of prostate cancer (PCa) biopsies may improve criteria for the selection of patients suitable for active surveillance (AS).

OBJECTIVE

To identify somatic genomic aberrations associated with adverse outcome as AS protocol exclusion indicators.

DESIGN, SETTING AND PARTICIPANTS

Whole-exome sequencing profiles were generated for Gleason score (GS)=3+3 biopsies obtained from 54 PCa patients enrolled in two AS protocols. Patients were selected as representative of a nonindolent population, consisting of 27 patients who dropped out from AS due to upgrading (ie, finding of GS>3+3 at a follow-up biopsy) within 2 yr, and a potentially indolent population, consisting of 27 patients in AS for ≥4 yr without any evidence of reclassification.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

The genomic alteration landscape of core biopsies was analyzed using an integrated computational pipeline and correlated with patient reclassification due to upgrading.

RESULTS AND LIMITATIONS

Of all the GS=3+3 biopsies of the study cohort, 34% showed clear evidence of somatic copy number aberrations along the genome. Of these, 39% came from the potentially indolent and 61% from the nonindolent population. Single-nucleotide variants demonstrated low allelic fractions and included a common F133C mutation in the SPOP gene. The minimally altered genomic landscape of the study cohort presented a distinct set of monoallelic deletions, including on 8p, 13q, 16q, and 21q, and rare amplifications of 8q, which were observed in both AS patient populations. Concerning lesions typically associated with adverse outcome, PTEN deletions and MYC amplification, though observed in a small number of cases, were detected exclusively or preferentially, respectively, in nonindolent patients. Such molecular findings were confirmed by immunohistochemistry on the same tissue blocks. The small sample size and the retrospective nature of the analysis represent the main study limitations.

CONCLUSIONS

Genomic features enriched in aggressive tumors can be detected in GS=3+3 core biopsies of AS patients.

PATIENT SUMMARY

PTEN and MYC alterations at the time of diagnosis would deserve investigation in larger cohorts of AS patients to assess their potential as biomarkers for a more precise/earlier identification of patients at risk of reclassification.

Tumor copy number alteration burden is a pan-cancer prognostic factor associated with recurrence and death

The level of copy number alteration (CNA), termed CNA burden, in the tumor genome is associated with recurrence of primary prostate cancer. Whether CNA burden is associated with prostate cancer survival or outcomes in other cancers is unknown. We analyzed the CNA landscape of conservatively treated prostate cancer in a biopsy and transurethral resection cohort, reflecting an increasingly common treatment approach. We find that CNA burden is prognostic for cancer-specific death, independent of standard clinical prognosticators. More broadly, we find CNA burden is significantly associated with disease-free and overall survival in primary breast, endometrial, renal clear cell, thyroid, and colorectal cancer in TCGA cohorts. To assess clinical applicability, we validated these findings in an independent pan-cancer cohort of patients whose tumors were sequenced using a clinically-certified next generation sequencing assay (MSK-IMPACT), where prognostic value varied based on cancer type. This prognostic association was affected by incorporating tumor purity in some cohorts. Overall, CNA burden of primary and metastatic tumors is a prognostic factor, potentially modulated by sample purity and measurable by current clinical sequencing.

Detecting repeated cancer evolution from multi-region tumor sequencing data

Recurrent successions of genomic changes, both within and between patients, reflect repeated evolutionary processes that are valuable for the anticipation of cancer progression. Multi-region sequencing allows the temporal order of some genomic changes in a tumor to be inferred, but the robust identification of repeated evolution across patients remains a challenge. We developed a machine-learning method based on transfer learning that allowed us to overcome the stochastic effects of cancer evolution and noise in data and identified hidden evolutionary patterns in cancer cohorts. When applied to multi-region sequencing datasets from lung, breast, renal, and colorectal cancer (768 samples from 178 patients), our method detected repeated evolutionary trajectories in subgroups of patients, which were reproduced in single-sample cohorts (n = 2,935). Our method provides a means of classifying patients on the basis of how their tumor evolved, with implications for the anticipation of disease progression.

Patient derived organoids to model rare prostate cancer phenotypes

A major hurdle in the study of rare tumors is a lack of existing preclinical models. Neuroendocrine prostate cancer is an uncommon and aggressive histologic variant of prostate cancer that may arise de novo or as a mechanism of treatment resistance in patients with pre-existing castration-resistant prostate cancer. There are few available models to study neuroendocrine prostate cancer. Here, we report the generation and characterization of tumor organoids derived from needle biopsies of metastatic lesions from four patients. We demonstrate genomic, transcriptomic, and epigenomic concordance between organoids and their corresponding patient tumors. We utilize these organoids to understand the biologic role of the epigenetic modifier EZH2 in driving molecular programs associated with neuroendocrine prostate cancer progression. High-throughput organoid drug screening nominated single agents and drug combinations suggesting repurposing opportunities. This proof of principle study represents a strategy for the study of rare cancer phenotypes.

ClonEvol: clonal ordering and visualization in cancer sequencing

Background

Reconstruction of clonal evolution is critical for understanding tumor progression and implementing personalized therapies. This is often done by clustering somatic variants based on their cellular prevalence estimated via bulk tumor sequencing of multiple samples. The clusters, consisting of the clonal marker variants, are then ordered based on their estimated cellular prevalence to reconstruct clonal evolution trees, a process referred to as 'clonal ordering'. However, cellular prevalence estimate is confounded by statistical variability and errors in sequencing/data analysis, and therefore inhibits accurate reconstruction of the clonal evolution. This problem is further complicated by intra- and inter-tumor heterogeneity. Furthermore, the field lacks a comprehensive visualization tool to facilitate the interpretation of complex clonal relationships. To address these challenges we developed ClonEvol, a unified software tool for clonal ordering, visualization, and interpretation.

Materials and methods

ClonEvol uses a bootstrap resampling technique to estimate the cellular fraction of the clones and probabilistically models the clonal ordering constraints to account for statistical variability. The bootstrapping allows identification of the sample founding- and sub-clones, thus enabling interpretation of clonal seeding. ClonEvol automates the generation of multiple widely used visualizations for reconstructing and interpreting clonal evolution.

Results

ClonEvol outperformed three of the state of the art tools (LICHeE, Canopy and PhyloWGS) for clonal evolution inference, showing more robust error tolerance and producing more accurate trees in a simulation. Building upon multiple recent publications that utilized ClonEvol to study metastasis and drug resistance in solid cancers, here we show that ClonEvol rediscovered relapsed subclones in two published acute myeloid leukemia patients. Furthermore, we demonstrated that through noninvasive monitoring ClonEvol recapitulated the emerging subclones throughout metastatic progression observed in the tumors of a published breast cancer patient.

Conclusions

ClonEvol has broad applicability for longitudinal monitoring of clonal populations in tumor biopsies, or noninvasively, to guide precision medicine.

Availability

ClonEvol is written in R and is available at https://github.com/ChrisMaherLab/ClonEvol.

Universal informative CpG sites for inferring tumor purity from DNA methylation microarray data

Tumor purity is an intrinsic property of tumor samples and potentially has severe impact on many types of data analysis. We have previously developed a statistical method, InfiniumPurify, which could infer purity of a tumor sample given its tumor type (available in TCGA) or a set of informative CpG (iDMC) sites. However, in many clinical practices, researchers may focus on a specific type of tumor samples that is not included in TCGA, and samples which are too few to identify reliable iDMCs. This greatly restricts the application of InfiniumPurify in cancer research. In this paper, we proposed an updated version of InfiniumPurify (termed as uiInfiniumPurify) through identifying a universal set of iDMCs (uiDMCs) and redesigning the algorithm to determine hyper- and hypo-methylation status of each uiDMC. Through the application, we estimated tumor purities of 8830 tumor samples from TCGA. Result shows that our estimates are highly consistent with those by other available methods. Consequently, the updated uiInfiniumPurify, can be applied to a single sample (or a few samples) of interest whose tumor type is not included in TCGA. This characteristic will greatly broaden the application of uiInfiniumPurify in cancer research.

Polyclonal origin of parathyroid tumors is common and is associated with multiple gland disease in primary hyperparathyroidism

BACKGROUND

Parathyroid tumors are mostly considered monoclonal neoplasms, the rationale for focused parathyroidectomy in primary hyperparathyroidism. We reported that flow sorting parathyroid tumor cells and methylation-sensitive polymerase chain reaction (me-PCR) of polymorphic human androgen receptor gene and phosphoglycerate kinase gene alleles in deoxyribonucleic acid reveals that ≤35% of parathyroid tumors are polyclonal. We sought to confirm these findings and assess for clinical relevance.

METHODS

Parathyroid tumors from 286 female primary hyperparathyroidism patients were analyzed for clonal status. Tumor clonal status was compared with clinical variables and operative findings. Statistical analysis was performed and significance was established at P < .05.

RESULTS

In the study, 176 (62%) patients were informative for human androgen receptor gene and/or phosphoglycerate kinase gene. Assignment of clonal status was made in 119 (68%) tumors, of which 64 (54%) were monoclonal and 55 (46%) were polyclonal. Comparison of tumor clonal status to clinical variables in patients with complete operative data (N = 82) showed that while clinical features were the same between tumor types, patients with polyclonal tumors more often had multiple gland disease (risk ratio 4.066, confidence interval, 1.016-16.26; P = .039) potentially missed at unilateral neck exploration.

CONCLUSION

This work confirms that primary hyperparathyroidism is often the result of polyclonal tumors and that parathyroid tumor clonal status may be associated with multiple gland disease.

The Genomics of Prostate Cancer: emerging understanding with technologic advances

With the advent of next-generation sequencing technologies and large whole-exome and genome studies in prostate and other cancers, our understanding of the landscape of genomic alterations has dramatically been refined. In additional to well-known alterations in genomic regions involving 8p, 8q, 10q23, common ETS translocations and androgen receptor amplifications, newer technology have uncovered recurrent mutations in SPOP, FOXA1, MED12, IDH and complex large scale genomic alterations (eg, chromoplexy). This review surveys the enhanced landscape of genomic alterations in clinically localized and advanced prostate cancer.

Bone biopsy protocol for advanced prostate cancer in the era of precision medicine

BACKGROUND

Metastatic biopsies are increasingly being performed in patients with advanced prostate cancer to search for actionable targets and/or to identify emerging resistance mechanisms. Due to a predominance of bone metastases and their sclerotic nature, obtaining sufficient tissue for clinical and genomic studies is challenging.

METHODS

Patients with prostate cancer bone metastases were enrolled between February 2013 and March 2017 on an institutional review board-approved protocol for prospective image-guided bone biopsy. Bone biopsies and blood clots were collected fresh. Compact bone was subjected to formalin with a decalcifying agent for diagnosis; bone marrow and blood clots were frozen in optimum cutting temperature formulation for next-generation sequencing. Frozen slides were cut from optimum cutting temperature cryomolds and evaluated for tumor histology and purity. Tissue was macrodissected for DNA and RNA extraction, and whole-exome sequencing and RNA sequencing were performed.

RESULTS

Seventy bone biopsies from 64 patients were performed. Diagnostic material confirming prostate cancer was successful in 60 of 70 cases (85.7%). The median DNA/RNA yield was 25.5 ng/μL and 16.2 ng/μL, respectively. Whole-exome sequencing was performed successfully in 49 of 60 cases (81.7%), with additional RNA sequencing performed in 20 of 60 cases (33.3%). Recurrent alterations were as expected, including those involving the AR, PTEN, TP53, BRCA2, and SPOP genes.

CONCLUSIONS

This prostate cancer bone biopsy protocol ensures a valuable source for high-quality DNA and RNA for tumor sequencing and may be used to detect actionable alterations and resistance mechanisms in patients with bone metastases. Cancer 2018;124:1008-15. © 2017 American Cancer Society.

Next-Generation Rapid Autopsies Enable Tumor Evolution Tracking and Generation of Preclinical Models

PURPOSE

Patients with cancer who graciously consent for autopsy represent an invaluable resource for the study of cancer biology. To advance the study of tumor evolution, metastases, and resistance to treatment, we developed a next-generation rapid autopsy program integrated within a broader precision medicine clinical trial that interrogates pre- and postmortem tissue samples for patients of all ages and cancer types.

MATERIALS AND METHODS

One hundred twenty-three (22%) of 554 patients who consented to the clinical trial also consented for rapid autopsy. This report comprises the first 15 autopsies, including patients with metastatic carcinoma (n = 10), melanoma (n = 1), and glioma (n = 4). Whole-exome sequencing (WES) was performed on frozen autopsy tumor samples from multiple anatomic sites and on non-neoplastic tissue. RNA sequencing (RNA-Seq) was performed on a subset of frozen samples. Tissue was also used for the development of preclinical models, including tumor organoids and patient-derived xenografts.

RESULTS

Three hundred forty-six frozen samples were procured in total. WES was performed on 113 samples and RNA-Seq on 72 samples. Successful cell strain, tumor organoid, and/or patient-derived xenograft development was achieved in four samples, including an inoperable pediatric glioma. WES data were used to assess clonal evolution and molecular heterogeneity of tumors in individual patients. Mutational profiles of primary tumors and metastases yielded candidate mediators of metastatic spread and organotropism including CUL9 and PIGM in metastatic ependymoma and ANKRD52 in metastatic melanoma to the lung. RNA-Seq data identified novel gene fusion candidates.

CONCLUSION

A next-generation sequencing-based autopsy program in conjunction with a pre-mortem precision medicine pipeline for diverse tumors affords a valuable window into clonal evolution, metastasis, and alterations underlying treatment. Moreover, such an autopsy program yields robust preclinical models of disease.

Natural and Chemotherapy-Induced Clonal Evolution of Tumors

Evolution and natural selection of tumoral clones in the process of transformation and the following carcinogenesis can be called natural clonal evolution. Its main driving factors are internal: genetic instability initiated by driver mutations and microenvironment, which enables selective pressure while forming the environment for cell transformation and their survival. We present our overview of contemporary research dealing with mechanisms of carcinogenesis in different localizations from precancerous pathologies to metastasis and relapse. It shows that natural clonal evolution establishes intratumoral heterogeneity and enables tumor progression. Tumors of monoclonal origin are of low-level intratumoral heterogeneity in the initial stages, and this increases with the size of the tumor. Tumors of polyclonal origin are of extremely high-level intratumoral heterogeneity in the initial stages and become more homogeneous when larger due to clonal expansion. In cases of chemotherapy-induced clonal evolution of a tumor, chemotherapy becomes the leading factor in treatment. The latest research shows that the impact of chemotherapy can radically increase the speed of clonal evolution and lead to new malignant and resistant clones that cause tumor metastasis. Another option of chemotherapy-induced clonal evolution is formation of a new dominant clone from a clone that was minor in the initial tumor and obtained free space due to elimination of sensitive clones by chemotherapy. As a result, in ~20% of cases, chemotherapy can stimulate metastasis and relapse of tumors due to clonal evolution. The conclusion of the overview formulates approaches to tumor treatment based on clonal evolution: in particular, precision therapy, prediction of metastasis stimulation in patients treated with chemotherapy, methods of genetic evaluation of chemotherapy efficiency and clonal-oriented treatment, and approaches to manipulating the clonal evolution of tumors are presented.

Personalized In Vitro and In Vivo Cancer Models to Guide Precision Medicine

Precision medicine is an approach that takes into account the influence of individuals' genes, environment, and lifestyle exposures to tailor interventions. Here, we describe the development of a robust precision cancer care platform that integrates whole-exome sequencing with a living biobank that enables high-throughput drug screens on patient-derived tumor organoids. To date, 56 tumor-derived organoid cultures and 19 patient-derived xenograft (PDX) models have been established from the 769 patients enrolled in an Institutional Review Board-approved clinical trial. Because genomics alone was insufficient to identify therapeutic options for the majority of patients with advanced disease, we used high-throughput drug screening to discover effective treatment strategies. Analysis of tumor-derived cells from four cases, two uterine malignancies and two colon cancers, identified effective drugs and drug combinations that were subsequently validated using 3-D cultures and PDX models. This platform thereby promotes the discovery of novel therapeutic approaches that can be assessed in clinical trials and provides personalized therapeutic options for individual patients where standard clinical options have been exhausted.Significance: Integration of genomic data with drug screening from personalized in vitro and in vivo cancer models guides precision cancer care and fuels next-generation research. Cancer Discov; 7(5); 462-77. ©2017 AACR.See related commentary by Picco and Garnett, p. 456This article is highlighted in the In This Issue feature, p. 443.