Whole Exome Sequencing of Rapid Autopsy Tumors and Xenograft Models Reveals Possible Driver Mutations Underlying Tumor Progression

Establishment and Thorough Characterization of Xenograft (PDX) Models Derived from Patients with Pancreatic Cancer for Molecular Analyses and Chemosensitivity Testing

Patient-derived xenograft (PDX) tumor models are essential for identifying new biomarkers, signaling pathways and novel targets, to better define key factors of therapy response and resistance mechanisms. Therefore, this study aimed at establishing pancreas carcinoma (PC) PDX models with thorough molecular characterization, and the identification of signatures defining responsiveness toward drug treatment. In total, 45 PC-PDXs were generated from 120 patient tumor specimens and the identity of PDX and corresponding patient tumors was validated. The majority of engrafted PDX models represent ductal adenocarcinomas (PDAC). The PDX growth characteristics were assessed, with great variations in doubling times (4 to 32 days). The mutational analyses revealed an individual mutational profile of the PDXs, predominantly showing alterations in the genes encoding KRAS, TP53, FAT1, KMT2D, MUC4, RNF213, ATR, MUC16, GNAS, RANBP2 and CDKN2A. Sensitivity of PDX toward standard of care (SoC) drugs gemcitabine, 5-fluorouracil, oxaliplatin and abraxane, and combinations thereof, revealed PDX models with sensitivity and resistance toward these treatments. We performed correlation analyses of drug sensitivity of these PDX models and their molecular profile to identify signatures for response and resistance. This study strongly supports the importance and value of PDX models for improvement in therapies of PC.

Insights into the metastatic cascade through research autopsies

Metastasis is a complex process and the leading cause of cancer-related death globally. Recent studies have demonstrated that genomic sequencing data from paired primary and metastatic tumours can be used to trace the evolutionary origins of cells responsible for metastasis. This approach has yielded new insights into the genomic alterations that engender metastatic potential, and the mechanisms by which cancer spreads. Given that the reliability of these approaches is contingent upon how representative the samples are of primary and metastatic tumour heterogeneity, we review insights from studies that have reconstructed the evolution of metastasis within the context of their cohorts and designs. We discuss the role of research autopsies in achieving the comprehensive sampling necessary to advance the current understanding of metastasis.

Integrative Multi-OMICs Identifies Therapeutic Response Biomarkers and Confirms Fidelity of Clinically Annotated, Serially Passaged Patient-Derived Xenografts Established from Primary and Metastatic Pediatric and AYA Solid Tumors

Establishment of clinically annotated, molecularly characterized, patient-derived xenografts (PDXs) from treatment-naïve and pretreated patients provides a platform to test precision genomics-guided therapies. An integrated multi-OMICS pipeline was developed to identify cancer-associated pathways and evaluate stability of molecular signatures in a panel of pediatric and AYA PDXs following serial passaging in mice. Original solid tumor samples and their corresponding PDXs were evaluated by whole-genome sequencing, RNA-seq, immunoblotting, pathway enrichment analyses, and the drug−gene interaction database to identify as well as cross-validate actionable targets in patients with sarcomas or Wilms tumors. While some divergence between original tumor and the respective PDX was evident, majority of alterations were not functionally impactful, and oncogenic pathway activation was maintained following serial passaging. CDK4/6 and BETs were prioritized as biomarkers of therapeutic response in osteosarcoma PDXs with pertinent molecular signatures. Inhibition of CDK4/6 or BETs decreased osteosarcoma PDX growth (two-way ANOVA, p < 0.05) confirming mechanistic involvement in growth. Linking patient treatment history with molecular and efficacy data in PDX will provide a strong rationale for targeted therapy and improve our understanding of which therapy is most beneficial in patients at diagnosis and in those already exposed to therapy.

Challenges in genomic analysis of model systems and primary tumors of pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is characterized by aggressive tumor behavior and poor prognosis. Recent next-generation sequencing (NGS)-based genomic studies have provided novel treatment modes for pancreatic cancer via the identification of cancer driver variants and molecular subtypes in PDAC. Genome-wide approaches have been extended to model systems such as patient-derived xenografts (PDXs), organoids, and cell lines for pre-clinical purposes. However, the genomic characteristics vary in the model systems, which is mainly attributed to the clonal evolution of cancer cells during their construction and culture. Moreover, fundamental limitations such as low tumor cellularity and the complex tumor microenvironment of PDAC hinder the confirmation of genomic features in the primary tumor and model systems. The occurrence of these phenomena and their associated complexities may lead to false insights into the understanding of mechanisms and dynamics in tumor tissues of patients. In this review, we describe various model systems and discuss differences in the results based on genomics and transcriptomics between primary tumors and model systems. Finally, we introduce practical strategies to improve the accuracy of genomic analysis of primary tissues and model systems.

Cancer evolution: Darwin and beyond

Clinical and laboratory studies over recent decades have established branched evolution as a feature of cancer. However, while grounded in somatic selection, several lines of evidence suggest a Darwinian model alone is insufficient to fully explain cancer evolution. First, the role of macroevolutionary events in tumour initiation and progression contradicts Darwin's central thesis of gradualism. Whole-genome doubling, chromosomal chromoplexy and chromothripsis represent examples of single catastrophic events which can drive tumour evolution. Second, neutral evolution can play a role in some tumours, indicating that selection is not always driving evolution. Third, increasing appreciation of the role of the ageing soma has led to recent generalised theories of age-dependent carcinogenesis. Here, we review these concepts and others, which collectively argue for a model of cancer evolution which extends beyond Darwin. We also highlight clinical opportunities which can be grasped through targeting cancer vulnerabilities arising from non-Darwinian patterns of evolution.

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.

Rapid Autopsy Programs and Research Support: The Pre– and Post–COVID-19 Environments

Each rapid autopsy is a powerful opportunity to supply multiple researchers with many valuable tissue specimens at the same time. Since the beginning of the development of rapid autopsy, the overriding organizing principle for all rapid autopsy programs has been that the samples or organs must be removed and processed as rapidly as possible. To accomplish this, some rapid autopsy programs are focused on only 1 tumor type, whereas others accept patients demonstrating all tumor types and sometimes other diseases as well. Rapid autopsy programs are logistically complicated and labor-intensive structures; therefore, the key to their success is program flexibility and maintaining a multidisciplinary focus. The necessary collaborations in the complex relationships between clinicians and researchers can be broken down into a series of thought and action steps that must be understood, accepted, and practiced by all participants. A crucial part of the precase steps (prior to death) for a rapid autopsy is the study consenting process. It is extremely important that this individualized consent is obtained for postmortem specimens and that it is written in terms general enough to be used for patients with all types of diseases and for an appropriate range of future research uses. The advent of SARS-CoV-2/COVID-19 (severe acute respiratory syndrome coronavirus 2/coronavirus disease 2019) has presented new challenges and opportunities to the field of autopsy pathology. Guidelines and practice had to be created and adapted to protect physicians and staff while maximizing diagnostic yield. However, any autopsy performed on a patient dying of or with COVID-19 represents a unique opportunity to contribute to understanding the disease mechanisms and to improve death certification, thus assisting in both clinical care and the development of health public policy.

Rapid Autopsy Programs and Research Support: The Pre- and Post-COVID-19 Environments

Each rapid autopsy is a powerful opportunity to supply multiple researchers with many valuable tissue specimens at the same time. Since the beginning of the development of rapid autopsy, the overriding organizing principle for all RAPs has been that the samples or organs must be removed and processed as rapidly as possible. To accomplish this some rapid autopsy programs are focused just on one tumor type, while others accept patients demonstrating all tumor types and sometimes other diseases as well. RAPs are logistically complicated and labor-intensive structures, therefore, the key to their success is program flexibility and maintaining a multidisciplinary focus. The necessary collaborations in the complex relationships between clinicians and researchers can be broken down into a series of thought and action steps that must be understood, accepted, and practiced by all participants. A crucial part of the pre-case steps (prior to death) for a rapid autopsy is the study consenting process. It is extremely important that this individualized consent is obtained for postmortem specimens and that it is written in general enough terms to be used for patients with all types of diseases and for an appropriate range of future research uses. The advent of Sars-CoV-2/COVID-19 has presented new challenges and opportunities to the field of autopsy pathology. Guidelines and practice had to be created and adapted to protect physicians and staff while maximizing diagnostic yield. However, any autopsy performed on a patient dying of or with COVID-19 represents a unique opportunity to contribute to understanding of disease mechanisms and to improve death certification, thus assisting in both clinical care and the development of health public policy.

Reviving the Autopsy for Modern Cancer Evolution Research

Outstanding questions plaguing oncologists, centred around tumour evolution and heterogeneity, include the development of treatment resistance, immune evasion, and optimal drug targeting strategies. Such questions are difficult to study in limited cancer tissues collected during a patient's routine clinical care, and may be better investigated in the breadth of cancer tissues that may be permissible to collect during autopsies. We are starting to better understand key tumour evolution challenges based on advances facilitated by autopsy studies completed to date. This review article explores the great progress in understanding that cancer tissues collected at autopsy have already enabled, including the shared origin of metastatic cells, the importance of early whole-genome doubling events for amplifying genes needed for tumour survival, and the creation of a wealth of tissue resources powered to answer future questions, including patient-derived xenografts, cell lines, and a wide range of banked tissues. We also highlight the future role of these programmes in advancing our understanding of cancer evolution. The research autopsy provides a special opportunity for cancer patients to give the ultimate gift-to selflessly donate their tissues towards better cancer care.

iWhale: a computational pipeline based on Docker and SCons for detection and annotation of somatic variants in cancer WES data

Whole exome sequencing (WES) is a powerful approach for discovering sequence variants in cancer cells but its time effectiveness is limited by the complexity and issues of WES data analysis. Here we present iWhale, a customizable pipeline based on Docker and SCons, reliably detecting somatic variants by three complementary callers (MuTect2, Strelka2 and VarScan2). The results are combined to obtain a single variant call format file for each sample and variants are annotated by integrating a wide range of information extracted from several reference databases, ultimately allowing variant and gene prioritization according to different criteria. iWhale allows users to conduct a complex series of WES analyses with a powerful yet customizable and easy-to-use tool, running on most operating systems (macOs, GNU/Linux and Windows).

iWhale code is freely available at https://github.com/alexcoppe/iWhale and the docker image is downloadable from https://hub.docker.com/r/alexcoppe/iwhale.

Bioinformatics and computational approaches for analyzing patient-derived disease models in cancer research

Patient-derived organoids (PDO) and patient-derived xenografts (PDX) continue to emerge as important preclinical platforms for investigations into the molecular landscape of cancer. While the advantages and disadvantage of these models have been described in detail, this review focuses in particular on the bioinformatics and state-of-the art techniques that accompany preclinical model development. We discuss the strength and limitations of currently used technologies, particularly 'omics profiling and bioinformatics analyses, in addressing the 'efficacy' of preclinical models, both for tumour characterization as well as their use in identifying potential therapeutics. We select pancreatic ductal adenocarcinoma (PDAC) as a case study to highlight the state of the art of the field, and address new avenues for improved bioinformatics characterization of preclinical models.

Establishment of chemosensitivity tests in triple-negative and BRCA-mutated breast cancer patient-derived xenograft models

Purpose

A patient-derived xenograft (PDX) model is an in vivo animal model which provides biological and genomic profiles similar to a primary tumor. The characterization of factors that influence the establishment of PDX is crucial. Furthermore, PDX models can provide a platform for chemosensitivity tests to evaluate the effectiveness of a target agent before applying it in clinical trials.

Methods

We implanted 83 cases of breast cancer into NOD.Cg-Prkdcscid Il2rgtm1Sug/Jic mice, to develop PDX models. Clinicopathological factors of primary tumors were reviewed to identify the factors affecting engraftment success rates. After the establishment of PDX models, we performed olaparib and carboplatin chemosensitivity tests. We used PDX models from triple-negative breast cancer (TNBC) with neoadjuvant chemotherapy and/or germline BRCA1 mutations in chemosensitivity tests.

Results

The univariate analyses (p<0.05) showed factors which were significantly associated with successful engraftment of PDX models include poor histologic grade, presence of BRCA mutation, aggressive diseases, and death. Factors which were independently associated with successful engraftment of PDX models on multivariate analyses include poor histologic grade and aggressive diseases status. In chemosensitivity tests, a PDX model with the BRCA1 L1780P mutation showed partial response to olaparib and complete response to carboplatin.

Conclusions

Successful engraftment of PDX models was significantly associated with aggressive diseases. Patients who have aggressive diseases status, large tumors, and poor histologic grade are ideal candidates for developing successful PDX models. Chemosensitivity tests using the PDX models provide additional information about alternative treatment strategies for residual TNBC after neoadjuvant chemotherapy.

Neoantigens retention in patient derived xenograft models mediates autologous T cells activation in ovarian cancer

Ovarian cancer (OC) has an overall modest number of mutations that facilitate a functional immune infiltrate able to recognize tumor mutated antigens, or neoantigens. Although patient-derived xenografts (PDXs) can partially model the tumor mutational load and mimic response to chemotherapy, no study profiled a neoantigen-driven response in OC PDXs. Here we demonstrate that the genomic status of the primary tumor from an OC patient can be recapitulated in vivo in a PDX model, with the goal of defining autologous T cells activation by neoantigens using in silico, in vitro and in vivo approaches. By profiling the PDX mutanome we discovered three main clusters of mutations defining the expansion, retraction or conservation of tumor clones based on their variant allele frequencies (VAF). RNASeq analyses revealed a strong functional conservation between the primary tumor and PDXs, highlighted by the upregulation of antigen presenting pathways. We tested in vitro a set of 30 neoantigens for recognition by autologous T cells and identified a core of six neoantigens that define a potent T cell activation able to slow tumor growth in vivo. The pattern of recognition of these six neoantigens indicates the pre-existence of anti-tumor immunity in the patient. To evaluate the breadth of T cell activation, we performed single cell sequencing profiling the TCR repertoire upon stimulation with neoantigenic moieties and identified sequence motifs that define an oligoclonal and autologous T cell response. Overall, these results indicate that OC PDXs can be a valid tool to model OC response to immunotherapy.

Expanding the Role for Kidney Biopsies in Acute Kidney Injury

Acute kidney injury (AKI) is a highly heterogeneous, common, and potentially devastating condition associated with markedly increased hospital length of stay, cost, mortality, and morbidity. Expanding the role for kidney biopsies in AKI may offer fresh insights into disease heterogeneity, molecular mechanisms, and therapeutic targets. A number of challenges face investigators and clinicians considering research biopsies in AKI: ensuring patient safety, ensuring the ethical conduct of research studies, and maximizing the scientific yield of the kidney tissue obtained. The societal benefits of research that lead to novel strategies for preventing and treating AKI would be enormous. Rethinking our current approach to the role of kidney biopsy for AKI diagnosis and research may be a major step toward the promise of personalized medicine in nephrology.

Somatic mutation detection and classification through probabilistic integration of clonal population information

Somatic mutations are a primary contributor to malignancy in human cells. Accurate detection of mutations is needed to define the clonal composition of tumours whereby clones may have distinct phenotypic properties. Although analysis of mutations over multiple tumour samples from the same patient has the potential to enhance identification of clones, few analytic methods exploit the correlation structure across samples. We posited that incorporating clonal information into joint analysis over multiple samples would improve mutation detection, particularly those with low prevalence. In this paper, we develop a new procedure called MuClone, for detection of mutations across multiple tumour samples of a patient from whole genome or exome sequencing data. In addition to mutation detection, MuClone classifies mutations into biologically meaningful groups and allows us to study clonal dynamics. We show that, on lung and ovarian cancer datasets, MuClone improves somatic mutation detection sensitivity over competing approaches without compromising specificity.

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.

Rapid Research Autopsy: Piecing the Puzzle of Tumor Heterogeneity

Tumor heterogeneity decreases the effectiveness of anticancer therapies and is an important topic in translational cancer research, given its relevance in clinical oncology. Here, we discuss how rapid research autopsy of cancer patients can elucidate heterogeneity-associated processes including cancer evolution and acquired therapeutic resistance. In practice, rapid research autopsy is performed shortly after a patient's passing to procure multiple metastatic tumor samples for genomic studies through next-generation sequencing and development of patient-derived xenografts or organoids. Mechanistic insights gained from research autopsy studies of cancer patients can help identify new targets for therapeutic intervention. Finally, the success of research autopsy programs is bolstered by collaboration across different medical and scientific disciplines in addition to support from patients and families.

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.

Never let it go: Stopping key mechanisms underlying metastasis to fight pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is an extremely aggressive neoplasm, predicted to become the second leading cause of cancer-related deaths before 2030. This dismal trend is mainly due to lack of effective treatments against its metastatic behavior. Therefore, a better understanding of the key mechanisms underlying metastasis should provide new opportunities for therapeutic purposes. Genomic analyses revealed that aberrations that fuel PDAC tumorigenesis and progression, such as SMAD4 loss, are also implicated in metastasis. Recently, microRNAs have been shown to play a regulatory role in the metastatic behavior of many tumors, including PDAC. In particular, miR-10 and miR-21 have appeared as master regulators of the metastatic program, while members of the miR-200 family are involved in the epithelial-to-mesenchymal switch, favoring cell migration and invasiveness. Several studies have also found a close relationship between cancer stem cells (CSCs) and biological features of metastasis, and the CSC markers ALDH1, ABCG2 and c-Met are expressed at high levels in metastatic PDAC cells. Emerging evidence reveals that exosomes are involved in the modulation of the tumor microenvironment and can initiate PDAC pre-metastatic niche formation in the liver and lungs. In this review, we provide an overview of the role of all these pivotal factors in the metastatic behavior of PDAC, and discuss their potential exploitation in the clinic to improve current therapeutics and identify new drug targets.

Prevalent Accumulation of Non-Optimal Codons through Somatic Mutations in Human Cancers

Cancer is characterized by uncontrolled cell growth, and the cause of different cancers is generally attributed to checkpoint dysregulation of cell proliferation and apoptosis. Recent studies have shown that non-optimal codons were preferentially adopted by genes to generate cell cycle-dependent oscillations in protein levels. This raises the intriguing question of how dynamic changes of codon usage modulate the cancer genome to cope with a non-controlled proliferative cell cycle. In this study, we comprehensively analyzed the somatic mutations of codons in human cancers, and found that non-optimal codons tended to be accumulated through both synonymous and non-synonymous mutations compared with other types of genomic substitution. We further demonstrated that non-optimal codons were prevalently accumulated across different types of cancers, amino acids, and chromosomes, and genes with accumulation of non-optimal codons tended to be involved in protein interaction/signaling networks and encoded important enzymes in metabolic networks that played roles in cancer-related pathways. This study provides insights into the dynamics of codons in the cancer genome and demonstrates that accumulation of non-optimal codons may be an adaptive strategy for cancerous cells to win the competition with normal cells. This deeper interpretation of the patterns and the functional characterization of somatic mutations of codons will help to broaden the current understanding of the molecular basis of cancers.