Delineating the longitudinal tumor evolution using organoid models

Cancer is an evolutionary process fueled by genetic or epigenetic alterations in the genome.Understanding the evolutionary dynamics that are operative at different stages of tumor progression might inform effective strategies in early detection,diagnosis,and treatment of cancer.However,our understanding on the dynamics of tumor evolution through time is very limited since it is usually impossible to sample patient tumors repeatedly.The recent advances in in vitro 3D organoid culture technologies have opened new avenues for the development of more realistic human cancer models that mimic many in vivo biological characteristics in human tumors.Here,we review recent progresses and challenges in cancer genomic evolution studies and advantages of using tumor organoids to study cancer evolution.We propose to establish an experimental evolution model based on continuous passages of patient-derived organoids and longitudinal sampling to study clonal dynamics and evolutionary patterns over time.Development and integration of population genetic theories and computational models into time-course genomic data in tumor organoids will help to pinpoint the key cellular mechanisms underlying cancer evolutionary dynamics,thus providing novel insights on therapeutic strategies for highly dynamic and heterogeneous tumors.

Evolutionary model of brain tumor circulating cells: Cellular galaxy

BACKGROUND Although circulating tumor cells(CTCs)have been the focus of consideration for a decade,a categorized cell-based diagnostic strategy is unavailable.The personalized management and complementary/analytical-strategy of data require an alphabetic guide.Therefore,we aimed to determine the behavior of CTCs in tumor and blood in order to provide the hypothetical-based agenda in the brain neoplasms.Exploring the protein expression(PE)using a single cell-based method would clarify the heterogeneity and diversity in tumor and blood,which are key events in the evolution in brain tumors.In fact,heterogeneity,diversity,and evolution are required for cancer initiation and progression.AIM To explore CTCs in brain tumors and blood cells and to assay intensity of PE through personalized insight.METHODS The focal population included 14 patients with meningioma,and four patients with metastatic brain tumors(T).PE was assayed by immunofluorescence in tumors cells and CTCs in 18 patients with brain tumors.Ratio test was applied between the T cells and CTCs in tumor tissue and in vascular system.T/CTC ratio-based classification of PE in macrophage chemoattractant chemokine ligand 2(CCL2),vascular endothelial growth factor(VEGF),epidermal growth factor(EGF),CD133,cyclin E,neurofilament marker,cytokeratin 19,and leukocyte common antigen(CD45)were investigated.RESULTS Total analyzed cells ranged between 10794-92283 for tumor cells and between 117-2870 for CTCs.Characteristics of histopathologic and status of an ataxiatelangiectasia mutated polymorphism(D1853N)in 18 patients affected with brain tumors were also provided.The course of evolution and metastatic event relied on the elevated protein expression in CTCs,which could be considered as a prognostic value.Diverse protein expression of the migrated cells into the blood stream and the tumor was indicative of the occurrence of evolution.Besides,the harmonic co-expression between CCL2/EGF and CCL2/VEGF could facilitate the tumor progression including the metastatic event.Expression of these proteins in the migrated vasculature and into the buccal tissue offered a non-invasive followup detection in neoplastic disorders.PE-exploration of neurofilament marker/CD133/VEGF of the CTCs in meningioma and cytokeratin 19/CD45/cyclin E in the patients with metastatic brain tumor would clarify the tumor biology of the brain neoplastic disorders.CONCLUSION The alphabetical base of the evolutionary mechanisms relies on dual-,triple-,and multi-models with diverse intensity of expression.In fact,cross-talk between initiative and the complementary channels defines the evolutionary insight in cancer.A diverse-model of protein expression,including low,medium,and high intensity,is the key requirement for the completed model.The cluster of cells with diverse expression and remarkable co-expression between CCL2/EGF/VEGF and NM/CD133/VEGF in CTCs may be indicative of probable invasiveness of the tumor.Furthermore,the mode of cytokeratin-19+/CD45-can be traced in the metastatic patients.

Profiling of hepatocellular carcinoma neoantigens reveals immune microenvironment and clonal evolution related patterns

Objective: Neoantigens derived from tumor-specific genomic alterations have demonstrated great potential for immunotherapeutic interventions in cancers. However, the comprehensive profile of hepatocellular carcinoma(HCC) neoantigens and their complex interplay with immune microenvironment and tumor evolution have not been fully addressed.Methods: Here we integrated whole exome sequencing data, transcriptome sequencing data and clinical information of 72 primary HCC patients to characterize the HCC neoantigen profile, and systematically explored its interactions with tumor clonal evolution, driver mutations and immune microenvironments.Results: We observed that higher somatic mutation/neoantigen load was associated with better clinical outcomes and HCC patients could be further divided into two subgroups with distinct prognosis based on their neoantigen expression patterns. HCC subgroup with neoantigen expression probability high(NEP-H) showed more aggressive pathologic features including increased incidence of tumor thrombus(P=0.038), higher recurrence rate(P=0.029),more inclined to lack tumor capsule(P=0.026) and with more microsatellite instability sites(P=0.006). In addition,NEP-H subgroup was also characterized by higher chance to be involved in tumor clonal evolution [odds ratio(OR)=46.7, P<0.001]. Gene set enrichment analysis revealed that upregulation of MYC and its targets could suppress immune responses, leading to elevated neoantigen expression proportion in tumor cells. Furthermore, we discovered an immune escape mechanism that tumors could become more inconspicuous by evolving subclones with less immunogenicity. We observed that smaller clonal mutation clusters with higher immunogenicity in tumor were more likely to involve in clonal evolution. Based on identified neoantigen profiles, we also discovered series of neoantigenic hotspot genes, which could serve as potential actionable targets in future.Conclusions: Our results revealed the landscape of HCC neoantigens and discovered two clinically relevant subgroups with distinct neoantigen expression patterns, suggesting the neoantigen expression should be fully considered in future immunotherapeutic interventions.

Circulating tumor DNA in lung cancer: real-time monitoring of disease evolution and treatment response

Lung cancer is one of the leading causes of all cancer-related deaths. Circulating tumor DNA (ctDNA) is released from apoptotic and necrotic tumor cells. Several sensitive techniques have been invented and adapted to quantify ctDNA genomic alterations. Applications of ctDNA in lung cancer include early diagnosis and detection, prognosis prediction, detecting mutations and structural alterations, minimal residual disease, tumor mutational burden, and tumor evolution tracking. Compared to surgical biopsy and radiographic imaging, the advantages of ctDNA are that it is a non-invasive procedure, allows real-time monitoring, and has relatively high sensitivity and specificity. Given the massive research on non-small cell lung cancer, attention should be paid to small cell lung cancer.

Genomic evolution during locoregional recurrence in colorectal cancer determined by whole-exome sequencing: a retrospective observational study

Objective:The genomic landscapes of metastatic colorectal cancer(mCRC)have been extensively studied;however,the genetic mechanisms underlying the locoregional recurrence(LR)of CRC remain unclear.The objective of our study was to investigate genomic evolution during LR in CRC using high-throughput sequencing.Methods:Twenty-three CRC patients with matched primary and LR tissues were recruited from Nanfang Hospital and Zhejiang Cancer Hospital between January 2011 and December 2018.The last date of follow-up was March 2020.Tissue samples were analyzed by whole-exome sequencing and the genomic profiles were depicted by single nucleotide variation,mutational signature,copy number variation,clonal architecture,and other features.The evolutionary process was speculated with comparison of the genetic variations between primary and LR lesions.The disseminating clusters from primary to LR lesions were identified by variant allele frequency dynamics.Furthermore,the early-recurrent biomarker was explored by comparing the indel signature between early-and late-recurrent patients.The study was approved by the Institutional Review Board of Nanfang Hospital of Southern Medical University(approval No.2020010)on September 11,2020.Results:The results highlighted distinct origins of LR between patients with high microsatellite instability and microsatellite stability.LR lesions evolved independently in patients with high microsatellite instability,while LR lesions were highly clonally related to the primary lesions in patients with microsatellite stability.Late-acquired variations in LR lesions encompassed a wide range of driver genes involved in histone methylation,DNA replication,T cell activation,PDCD1 gain,and LMNA loss.Furthermore,clonal analysis of the disseminating cells identified a dominant polyclonal seeding pattern during LR.The indel signature ID4 was associated with significantly shorter disease-free survival in patients with relapsed CRC according to a public dataset.Conclusion:These findings pose a challenge for the development of new approaches targeting the interactions of multiple clones in the establishment of LR and in terms of optimizing the clinical management of susceptible patients.

Algorithmic approaches to clonal reconstruction in heterogeneous cell populations

Background:The reconstruction of clonal haplotypes and their evolutionary history in evolving populations is a common problem in both microbial evolutionary biology and cancer biology.The clonal theory of evolution provides a theoretical framework for modeling the evolution of clones.Results:In this paper,we review the theoretical framework and assumptions over which the clonal reconstruction problem is formulated.We formally define the problem and then discuss the complexity and solution space of the problem.Various methods have been proposed to find the phylogeny that best explains the observed data.We categorize these methods based on the type of input data that they use(space-resolved or time-resolved),and also based on their computational formulation as either combinatorial or probabilistic.It is crucial to understand the different types of input data because each provides essential but distinct information for drastically reducing the solution space of the clonal reconstruction problem.Complementary information provided by single cell sequencing or from whole genome sequencing of randomly isolated clones can also improve the accuracy of clonal reconstruction.We briefly review the existing algorithms and their relationships.Finally we summarize the tools that are developed for either directly solving the clonal reconstruction problem or a related computational problem.Conclusions:In this review,we discuss the various formulations of the problem of inferring the clonal evolutionary history from allele frequeny data,review existing algorithms and catergorize them according to their problem formulation and solution approaches.We note that most of the available clonal inference algorithms were developed for elucidating tumor evolution whereas clonal reconstruction for unicellular genomes are less addressed.We conclude the review by discussing more open problems such as the lack of benchmark datasets and comparison of performance between available tools.