Deciphering intratumor heterogeneity and temporal acquisition of driver events to refine precision medicine

Systematic data analysis pipeline for quantitative morphological cell phenotyping

Quantitative morphological phenotyping (QMP) is an image-based method used to capture morphological features at both the cellular and population level. Its interdisciplinary nature, spanning from data collection to result analysis and interpretation, can lead to uncertainties, particularly among those new to this actively growing field. High analytical specificity for a typical QMP is achieved through sophisticated approaches that can leverage subtle cellular morphological changes. Here, we outline a systematic workflow to refine the QMP methodology. For a practical review, we describe the main steps of a typical QMP; in each step, we discuss the available methods, their applications, advantages, and disadvantages, along with the R functions and packages for easy implementation. This review does not cover theoretical backgrounds, but provides several references for interested researchers. It aims to broaden the horizons for future phenome studies and demonstrate how to exploit years of endeavors to achieve more with less.

STARD7 could be an immunological and prognostic biomarker: from pan-cancer analysis to hepatocellular carcinoma validation

BACKGROUND

As the emergence of technologies such as sequencing and gene mapping, significant advancements have been made in understanding the landscape of tumors. However, the effective treatment of tumors continues to pose a tremendous challenge in clinical practice, which highlights the importance of predicting tumor markers and studying drug resistance mechanisms. The prognosis and differential expression of STARD7 in human pan-cancer were investigated by bioinformatic methods and experimental verification.

METHODS

The expression, diagnostic, and prognostic significance of the STARD7 gene were comprehensive analyzed using bioinformatics techniques. Furthermore, we validated our projected outcomes in liver cancer through experimental methodologies, including the use of qRT-PCR, CCK8 and transwell assays.

RESULTS

The STARD7 gene exhibits differential expression in 25 tumors, with high expression observed in 22 tumors. These distinct expression patterns within different tumor types are closely associated with poor prognosis and diagnosis. Furthermore, the STARD7 gene plays a role in regulating the tumor immune microenvironment. Methylation levels of STARD7 vary among 20 types of tumors and are correlated with survival outcomes. Furthermore, the experiment results demonstrated that STARD7 is highly expressed in hepatocellular carcinoma cells. Suppression of STARD7 significantly impedes the proliferation, migration, and invasion of HepG-2 and SMMC-7721 cells.

CONCLUSIONS

STARD7 has the potential to function as a crucial prognostic biomarker and exhibit correlation with tumor immunity in various types of human cancers. The implications of our findings extend to informing cancer immune-therapy and promoting the advancement of precision immune-oncology.

Low-pass whole genome sequencing of circulating tumor cells to evaluate chromosomal instability in triple-negative breast cancer

Chromosomal Instability (CIN) is a common and evolving feature in breast cancer. Large-scale Transitions (LSTs), defined as chromosomal breakages leading to gains or losses of at least 10 Mb, have recently emerged as a metric of CIN due to their standardized definition across platforms. Herein, we report the feasibility of using low-pass Whole Genome Sequencing to assess LSTs, copy number alterations (CNAs) and their relationship in individual circulating tumor cells (CTCs) of triple-negative breast cancer (TNBC) patients. Initial assessment of LSTs in breast cancer cell lines consistently showed wide-ranging values (median 22, range 4-33, mean 21), indicating heterogeneous CIN. Subsequent analysis of CTCs revealed LST values (median 3, range 0-18, mean 5), particularly low during treatment, suggesting temporal changes in CIN levels. CNAs averaged 30 (range 5-49), with loss being predominant. As expected, CTCs with higher LSTs values exhibited increased CNAs. A CNA-based classifier of individual patient-derived CTCs, developed using machine learning, identified genes associated with both DNA proliferation and repair, such as RB1, MYC, and EXO1, as significant predictors of CIN. The model demonstrated a high predictive accuracy with an Area Under the Curve (AUC) of 0.89. Overall, these findings suggest that sequencing CTCs holds the potential to facilitate CIN evaluation and provide insights into its dynamic nature over time, with potential implications for monitoring TNBC progression through iterative assessments.

Targeted therapies in advanced biliary malignancies: a clinical review

INTRODUCTION

Despite several therapeutic advancements, the proportion of patients with advanced biliary tract cancers (BTC) surviving 5 years from diagnosis remains dismal. The increasing recognition of targetable genetic alterations in BTCs has ushered in a new era in the treatment of these patients. Newer therapeutic agents targeting mutations such as isocitrate dehydrogenase (IDH), fibroblastic growth factor receptor (FGFR), human epidermal growth factor receptor (HER), and so on have established a new standard of care for treatment upon progression on frontline therapy in patients with disease harboring these mutations.

AREAS COVERED

The current review aims to concisely summarize progress with various targeted therapy options for BTC. We also briefly discuss future directions in clinical and translational research for the adoption of a personalized approach for the treatment of unresectable or advanced BTC.

EXPERT OPINION

Several new agents continue to emerge as feasible treatment options for patients with advanced BTC harboring targetable mutations. There is a growing need to identify mechanisms to conquer primary and acquired resistance to these agents. The identification of potential biomarkers that predict response to targeted therapy may be helpful in adopting a more tailored approach. All patients receiving treatment for advanced BTC should undergo tissue genomic profiling at diagnosis.

Navigating resistance to ALK inhibitors in the lorlatinib era: a comprehensive perspective on NSCLC

INTRODUCTION

The emergence of anaplastic lymphoma kinase (ALK) rearrangements in non-small cell lung cancer (NSCLC) has revolutionized targeted therapy. This dynamic landscape, featuring novel ALK inhibitors and combination therapies, necessitates a profound understanding of resistance mechanisms for effective treatment strategies. Recognizing two primary categories - on-target and off-target resistance - underscores the need for comprehensive assessment.

AREAS COVERED

This review delves into the intricacies of resistance to ALK inhibitors, exploring complexities in identification and management. Molecular testing, pivotal for early detection and accurate diagnosis, forms the foundation for patient stratification and resistance management. The literature search methodology involved comprehensive exploration of Pubmed and Embase. The multifaceted perspective encompasses new therapeutic horizons, ongoing clinical trials, and their clinical implications post the recent approval of lorlatinib.

EXPERT OPINION

Our expert opinion encapsulates the critical importance of understanding resistance mechanisms in the context of ALK inhibitors for shaping successful treatment approaches. With a focus on molecular testing and comprehensive assessment, this review contributes valuable insights to the evolving landscape of NSCLC therapy.

Imaging Genomics of Glioma Revisited: Analytic Methods to Understand Spatial and Temporal Heterogeneity

An improved understanding of the cellular and molecular biologic processes responsible for brain tumor development, growth, and resistance to therapy is fundamental to improving clinical outcomes. Imaging genomics is the study of the relationships between microscopic, genetic, and molecular biologic features and macroscopic imaging features. Imaging genomics is beginning to shift clinical paradigms for diagnosing and treating brain tumors. This article provides an overview of imaging genomics in gliomas, in which imaging data including hallmarks such as IDH-mutation, MGMT methylation, and EGFR-mutation status can provide critical insights into the pretreatment and posttreatment stages. This article will accomplish the following: 1) review the methods used in imaging genomics, including visual analysis, quantitative analysis, and radiomics analysis; 2) recommend suitable analytic methods for imaging genomics according to biologic characteristics; 3) discuss the clinical applicability of imaging genomics; and 4) introduce subregional tumor habitat analysis with the goal of guiding future radiogenetics research endeavors toward translation into critically needed clinical applications.

Mutational Signature Changes in Patients With Metastatic Squamous Cell Carcinoma of the Anal Canal

PURPOSE

We examined the concordance of genetic mutations between pretreatment tumor tissue and posttreatment circulating tumor DNA (ctDNA) in patients with metastatic squamous cell carcinoma of the anal canal (SCCA) and assessed the impact of therapy on this concordance.

METHODS

We analyzed next-generation sequencing reports from pretreatment tumor tissue and posttreatment ctDNA in 11 patients with metastatic SCCA treated at Vanderbilt University Medical Center between 2017 and 2021.

RESULTS

Among the mutations identified in posttreatment ctDNA, 34.5% were also found in pretreatment tumor tissue, while 47.6% of pretreatment tumor tissue mutations were found in posttreatment ctDNA. Four patients had preservation of potentially actionable mutations in both pretreatment tissue and posttreatment ctDNA, while 7 patients had newly identified mutations in posttreatment ctDNA that were not present in pretreatment tumor tissue.

CONCLUSION

Patients with SCCA demonstrate a high degree of temporal mutational heterogeneity. This supports the hypothesis that ctDNA can serve as a real-time tracking mechanism for solid tumors' molecular evolution in response to therapy. Our findings highlight the potential of ctDNA in identifying emerging actionable mutations, supplementing information from tissue-based genomic assessments. Further research, ideally with larger and multi-institutional cohorts, is needed to validate our findings in this relatively rare tumor type.

The reckoning of chromosomal instability: past, present, future

Quantitative measures of CIN are crucial to our understanding of its role in cancer. Technological advances have changed the way CIN is quantified, offering increased accuracy and insight. Here, we review measures of CIN through its rise as a field, discuss considerations for its measurement, and look forward to future quantification of CIN.

Genetic Intratumor Heterogeneity Remodels the Immune Microenvironment and Induces Immune Evasion in Brain Metastasis of Lung Cancer

INTRODUCTION

Brain metastasis, with the highest incidence in patients with lung cancer, significantly worsens prognosis and poses challenges to clinical management. To date, how brain metastasis evades immune elimination remains unknown.

METHODS

Whole-exome sequencing and RNA sequencing were performed on 30 matched brain metastasis, primary lung adenocarcinoma, and normal tissues. Data from The Cancer Genome Atlas primary lung adenocarcinoma cohort, including multiplex immunofluorescence, were used to support the findings of bioinformatics analysis.

RESULTS

Our study highlights the key role of intratumor heterogeneity of genomic alterations in the metastasis process, mainly caused by homologous recombination deficiency or other somatic copy number alteration-associated mutation mechanisms, leading to increased genomic instability and genomic complexity. We further proposed a selection model of brain metastatic evolution in which intratumor heterogeneity drives immune remodeling, leading to immune escape through different mechanisms under local immune pressure.

CONCLUSIONS

Our findings provide novel insights into the metastatic process and immune escape mechanisms of brain metastasis and pave the way for precise immunotherapeutic strategies for patients with lung cancer with brain metastasis.

Blood-based liquid biopsy in advanced prostate cancer

Prostate cancer is characterized by several genetic alterations which could impact prognosis and therapeutic decisions in the advanced disease. Tissue biopsy is still considered the gold standard approach for molecular characterization in prostate cancer, but it has several limitations, including the possibility of insufficient/inadequate tumor tissue to be analyzed. Blood-based liquid biopsy is a non-invasive method to investigate tumor cell derivatives in the bloodstream, being a valid alternative to tissue biopsy for molecular characterization but also for predictive and/or prognostic purposes. In this review, we analyze the most relevant evidence in this field, focusing on clinically relevant targets such as HRD genetic alterations and also focusing on the differences between tissue and liquid biopsy in light of the data from the latest clinical trials.

Coefficient of Variation in Metastatic Lymph Nodes Determined by 18F-FDG PET/CT in Patients with Advanced NSCLC: Combination with Coefficient of Variation in Primary Tumors

Purpose The aim of the present study was to test whether the coefficient of variation (CoV) of 18F-FDG PET/CT images of metastatic lymph nodes and primary tumors may predict clinical outcome in patients with advanced non-small cell lung cancer (NSCLC). Materials and Methods Fifty-eight NSCLC patients who had undergone 18F-FDG PET/CT at diagnosis were evaluated. SUVmax, SUVmean, CoV, MTV and TLG were determined in targeted lymph nodes and corresponding primary tumors along with Total MTV (MTVTOT) and Whole-Body TLG (TLGWB) of all malignant lesions. Univariate analysis was performed using Cox proportional hazards regression whereas the Kaplan-Meier method and log-rank tests were used for survival analysis. Results Fifty-eight metastatic lymph nodes were analyzed and average values of SUVmax, SUVmean, CoV, MTV and TLG were 11.89 ± 8.54, 4.85 ± 1.90, 0.37 ± 0.16, 46.16 ± 99.59 mL and 256.84 ± 548.27 g, respectively, whereas in primary tumors they were 11.92 ± 6.21, 5.47 ± 2.34, 0.36 ± 0.14, 48.03 ± 64.45 mL and 285.21 ± 397.95 g, respectively. At univariate analysis, overall survival (OS) was predicted by SUVmax (p = 0.0363), SUVmean (p = 0.0200) and CoV (p = 0.0139) of targeted lymph nodes as well as by CoV of primary tumors (p = 0.0173), MTVTOT (p = 0.0007), TLGWB (p = 0.0129) and stage (p = 0.0122). Using Kaplan-Meier analysis, OS was significantly better in patients with CoV of targeted lymph nodes ≤ 0.29 than those with CoV > 0.29 (p = 0.0147), meanwhile patients with CoV of primary tumors > 0.38 had a better prognosis compared to those with CoV ≤ 0.38 (p = 0.0137). Finally, we combined the CoV values of targeted lymph nodes and primary tumors in all possible arrangements and a statistically significant difference was found among the four survival curves (p = 0.0133). In particular, patients with CoV of targeted lymph nodes ≤ 0.29 and CoV of primary tumors > 0.38 had the best prognosis. Conclusions The CoV of targeted lymph nodes combined with the CoV of primary tumors can predict prognosis of NSCLC patients.

Preoperative CT radiomics of esophageal squamous cell carcinoma and lymph node to predict nodal disease with a high diagnostic capability

PURPOSE

To develop CT radiomics models of resectable esophageal squamous cell carcinoma (ESCC) and lymph node (LN) to preoperatively identify LN+.

MATERIALS AND METHODS

299 consecutive patients with ESCC were enrolled in the study, 140 of whom were LN+ and 159 were LN-. Of the 299 patients, 249 (from the same hospital) were randomly divided into a training cohort (n = 174) and a test cohort (n = 75). The remaining 50 patients, from a second hospital, were assigned to an external validation cohort. In the training cohort, preoperative contrast-enhanced CT radiomics features of ESCC and LN were extracted, then integrated with clinical features to develop three models: ESCC, LN and combined. The performance of these models was assessed using area under receiver operating characteristic curve (AUC), and F-1 score, which were validated in both the test cohort and external validation cohort.

RESULTS

An ESCC model was developed for the training cohort utilizing the 8 tumor radiomics features, and an LN model was constructed using 9 nodal radiomics features. A combined model was constructed using both ESCC and LN extracted features, in addition to cT stage and LN+ distribution. This combined model had the highest predictive ability among the three models in the training cohort (AUC = 0.948, F1-score = 0.878). The predictive ability was validated in both the test and external validation cohorts (AUC = 0.885 and 0.867, F1-score = 0.816 and 0.773, respectively).

CONCLUSION

To preoperatively determine LN+, the combined model is superior to models of ESCC and LN alone.

A novel liquid biopsy assay for detection of ERBB2 (HER2) amplification in circulating tumor cells (CTCs)

Purpose

Circulating tumor cell (CTC)-based ERBB2 (HER2) assay is a laboratory test developed by Epic Sciences using single-cell genomics to detect ERBB2 (HER2) amplification in CTCs found in the peripheral blood of metastatic breast cancer (MBC) patients.

Patients and methods

Peripheral blood was collected in Streck tubes and centrifugation was used to remove plasma and red blood cells. The remaining nucleated cells were deposited on glass slides, immunofluorescent-stained with proprietary antibodies, scanned by a high-definition digital scanner, and analyzed by a proprietary algorithm. In addition, single-cell genomics was performed on selected CTC. Analytical validation was performed using white blood cells from healthy donors and breast cancer cell lines with known levels of ERBB2 amplification. Clinical concordance was assessed on MBC patients whose blood was tested by the CTC ERBB2 (HER2) assay and those results are compared to results of matched metastatic tissue biopsy (immunohistochemistry [IHC] 3+ or IHC2+/in situ hybridization [ISH+]).

Results

Epic's ERBB2 (HER2) assay detected 2-fold ERBB2 amplification with 85% sensitivity and 94% specificity. In the clinical concordance study, among the 50% of the cases that had ERBB2 status results from CTCs found to be chromosomally-unstable, the CTC ERBB2 (HER2) assay showed sensitivity of 69% and specificity of 78% when compared to HER2 status by metastatic tissue biopsy.

Conclusions

The CTC ERBB2 (HER2) assay can consistently detect ERBB2 status in MBC cell lines and in the population of patients with MBC with detectable chromosomally unstable CTCs for whom tissue biopsy is not available or is infeasible.

Feasibility Study of Computed Tomographic Radiomics Model for the Prediction of Early and Intermediate Stage Hepatocellular Carcinoma Using BCLC Staging

BACKGROUND

Hepatocellular carcinoma (HCC) is a serious health concern because of its high morbidity and mortality. The prognosis of HCC largely depends on the disease stage at diagnosis. Computed tomography (CT) image textural analysis is an image analysis technique that has emerged in recent years.

OBJECTIVE

To probe the feasibility of a CT radiomic model for predicting early (stages 0, A) and intermediate (stage B) HCC using Barcelona Clinic Liver Cancer (BCLC) staging.

METHODS

A total of 190 patients with stages 0, A, or B HCC according to CT-enhanced arterial and portal vein phase images were retrospectively assessed. The lesions were delineated manually to construct a region of interest (ROI) consisting of the entire tumor mass. Consequently, the textural profiles of the ROIs were extracted by specific software. Least absolute shrinkage and selection operator dimensionality reduction was used to screen the textural profiles and obtain the area under the receiver operating characteristic curve values.

RESULTS

Within the test cohort, the area under the curve (AUC) values associated with arterial-phase images and BCLC stages 0, A, and B disease were 0.99, 0.98, and 0.99, respectively. The overall accuracy rate was 92.7%. The AUC values associated with portal vein phase images and BCLC stages 0, A, and B disease were 0.98, 0.95, and 0.99, respectively, with an overall accuracy of 90.9%.

CONCLUSION

The CT radiomic model can be used to predict the BCLC stage of early-stage and intermediate-stage HCC.

Applications of Innovation Technologies for Personalized Cancer Medicine: Stem Cells and Gene-Editing Tools

Personalized medicine is a new approach toward safer and even cheaper treatments with minimal side effects and toxicity. Planning a therapy based on individual properties causes an effective result in a patient's treatment, especially in a complex disease such as cancer. The benefits of personalized medicine include not only early diagnosis with high accuracy but also a more appropriate and effective therapeutic approach based on the unique clinical, genetic, and epigenetic features and biomarker profiles of a specific patient's disease. In order to achieve personalized cancer therapy, understanding cancer biology plays an important role. One of the crucial applications of personalized medicine that has gained consideration more recently due to its capability in developing disease therapy is related to the field of stem cells. We review various applications of pluripotent, somatic, and cancer stem cells in personalized medicine, including targeted cancer therapy, cancer modeling, diagnostics, and drug screening. CRISPR-Cas gene-editing technology is then discussed as a state-of-the-art biotechnological advance with substantial impacts on medical and therapeutic applications. As part of this section, the role of CRISPR-Cas genome editing in recent cancer studies is reviewed as a further example of personalized medicine application.

ConDoR: tumor phylogeny inference with a copy-number constrained mutation loss model

A tumor contains a diverse collection of somatic mutations that reflect its past evolutionary history and that range in scale from single nucleotide variants (SNVs) to large-scale copy-number aberrations (CNAs). However, no current single-cell DNA sequencing (scDNA-seq) technology produces accurate measurements of both SNVs and CNAs, complicating the inference of tumor phylogenies. We introduce a new evolutionary model, the constrained k-Dollo model, that uses SNVs as phylogenetic markers but constrains losses of SNVs according to clusters of cells. We derive an algorithm, ConDoR, that infers phylogenies from targeted scDNA-seq data using this model. We demonstrate the advantages of ConDoR on simulated and real scDNA-seq data.

Spatial mapping of tumor heterogeneity in whole-body PET-CT: a feasibility study

BACKGROUND

Tumor heterogeneity is recognized as a predictor of treatment response and patient outcome. Quantification of tumor heterogeneity across all scales may therefore provide critical insight that ultimately improves cancer management.

METHODS

An image registration-based framework for the study of tumor heterogeneity in whole-body images was evaluated on a dataset of 490 FDG-PET-CT images of lung cancer, lymphoma, and melanoma patients. Voxel-, lesion- and subject-level features were extracted from the subjects' segmented lesion masks and mapped to female and male template spaces for voxel-wise analysis. Resulting lesion feature maps of the three subsets of cancer patients were studied visually and quantitatively. Lesion volumes and lesion distances in subject spaces were compared with resulting properties in template space. The strength of the association between subject and template space for these properties was evaluated with Pearson's correlation coefficient.

RESULTS

Spatial heterogeneity in terms of lesion frequency distribution in the body, metabolic activity, and lesion volume was seen between the three subsets of cancer patients. Lesion feature maps showed anatomical locations with low versus high mean feature value among lesions sampled in space and also highlighted sites with high variation between lesions in each cancer subset. Spatial properties of the lesion masks in subject space correlated strongly with the same properties measured in template space (lesion volume, R = 0.986, p < 0.001; total metabolic volume, R = 0.988, p < 0.001; maximum within-patient lesion distance, R = 0.997, p < 0.001). Lesion volume and total metabolic volume increased on average from subject to template space (lesion volume, 3.1 ± 52 ml; total metabolic volume, 53.9 ± 229 ml). Pair-wise lesion distance decreased on average by 0.1 ± 1.6 cm and maximum within-patient lesion distance increased on average by 0.5 ± 2.1 cm from subject to template space.

CONCLUSIONS

Spatial tumor heterogeneity between subsets of interest in cancer cohorts can successfully be explored in whole-body PET-CT images within the proposed framework. Whole-body studies are, however, especially prone to suffer from regional variation in lesion frequency, and thus statistical power, due to the non-uniform distribution of lesions across a large field of view.

Single-Cell Evolutionary Analysis Reveals Drivers of Plasticity and Mediators of Chemoresistance in Small Cell Lung Cancer

Small cell lung cancer (SCLC) is often a heterogeneous tumor, where dynamic regulation of key transcription factors can drive multiple populations of phenotypically different cells which contribute differentially to tumor dynamics. This tumor is characterized by a very low 2-year survival rate, high rates of metastasis, and rapid acquisition of chemoresistance. The heterogeneous nature of this tumor makes it difficult to study and to treat, as it is not clear how or when this heterogeneity arises. Here we describe temporal, single-cell analysis of SCLC to investigate tumor initiation and chemoresistance in both SCLC xenografts and an autochthonous SCLC model. We identify an early population of tumor cells with high expression of AP-1 network genes that are critical for tumor growth. Furthermore, we have identified and validated the cancer testis antigens (CTA) PAGE5 and GAGE2A as mediators of chemoresistance in human SCLC. CTAs have been successfully targeted in other tumor types and may be a promising avenue for targeted therapy in SCLC.

IMPLICATIONS

Understanding the evolutionary dynamics of SCLC can shed light on key mechanisms such as cellular plasticity, heterogeneity, and chemoresistance.

Clinical trial designs for evaluating and exploiting cancer evolution

The evolution of drug-resistant cell subpopulations causes cancer treatment failure. Current preclinical evidence shows that it is possible to model herding of clonal evolution and collateral sensitivity where an initial treatment could favourably influence the response to a subsequent one. Novel therapy strategies exploiting this understanding are being considered, and clinical trial designs for steering cancer evolution are needed. Furthermore, preclinical evidence suggests that different subsets of drug-sensitive and resistant clones could compete between themselves for nutrients/blood supply, and clones that populate a tumour do so at the expense of other clones. Treatment paradigms based on this clinical application of exploiting cell-cell competition include intermittent dosing regimens or cycling different treatments before progression. This will require clinical trial designs different from the conventional practice of evaluating responses to individual therapy regimens. Next-generation sequencing to assess clonal dynamics longitudinally will improve current radiological assessment of clinical response/resistance and be incorporated into trials exploiting evolution. Furthermore, if understood, clonal evolution can be used to therapeutic advantage, improving patient outcomes based on a new generation of clinical trials.

Research progress of extracellular vesicles as biomarkers in immunotherapy for non-small cell lung cancer

Lung cancer is one of the most severe forms of malignancy and a leading cause of cancer-related death worldwide, of which non-small cell lung cancer (NSCLC) is the most primary type observed in the clinic. NSCLC is mainly treated with surgery, radiotherapy, and chemotherapy. Additionally, targeted therapy and immunotherapy have also shown promising results. Several immunotherapies, including immune checkpoint inhibitors, have been developed for clinical use and have benefited patients with NSCLC. However, immunotherapy faces several challenges like poor response and unknown effective population. It is essential to identify novel predictive markers to further advance precision immunotherapy for NSCLC. Extracellular vesicles (EVs) present an important research direction. In this review, we focus on the role of EVs as a biomarker in NSCLC immunotherapy considering various perspectives, including the definition and properties of EVs, their role as biomarkers in current NSCLC immunotherapy, and different EV components as biomarkers in NSCLC immunotherapy research. We describe the cross-talk between the role of EVs as biomarkers and novel technical approaches or research concepts in NSCLC immunotherapy, such as neoadjuvants, multi-omics analysis, and the tumour microenvironment. This review will provide a reference for future research to improve the benefits of immunotherapy for patients with NSCLC.

Yeast-based evolutionary modeling of androgen receptor mutations and natural selection

Cancer progression is associated with the evolutionary accumulation of genetic mutations that are biologically significant. Mutations of the androgen receptor (AR) are associated with the development of prostate cancer (PCa) by responding to non-androgenic hormones, and the lack of annotations in their responsiveness to hormone ligands remains a daunting challenge. Here, we have used a yeast reporter system to quickly evaluate the responsiveness of all fifty clinical AR mutations to a variety of steroidal ligands including dihydrotestosterone (DHT), 17β-estradiol (E2), progesterone (PROG), and cyproterone acetate (CPA). Based on an AR-driven reporter that synthesizes histidine, a basic amino acid required for yeast survival and propagation, the yeast reporter system enabling clonal selection was further empowered by combining with a random DNA mutagenesis library to simulate the natural evolution of AR gene under the selective pressures of steroidal ligands. In a time-frame of 1-2 weeks, 19 AR mutants were identified, in which 11 AR mutants were validated for activation by tested steroidal compounds. The high efficiency of our artificial evolution strategy was further evidenced by a sequential selection that enabled the discovery of multipoint AR mutations and evolution directions under the pressure of steroidal ligands. In summary, our designer yeast is a portable reporter module that can be readily adapted to streamline high-throughput AR-compound screening, used as a PCa clinical reference, and combined with additional bioassay systems to further extend its potential.

Reciprocal interplays between MicroRNAs and pluripotency transcription factors in dictating stemness features in human cancers

The interplay between microRNAs (miRNAs) and pluripotency transcription factors (TFs) orchestrates the acquisition of cancer stem cell (CSC) features during the course of malignant transformation, rendering them essential cancer cell dependencies and therapeutic vulnerabilities. In this review, we discuss emerging themes in tumor heterogeneity, including the clonal evolution and the CSC models and their implications in resistance to cancer therapies, and then provide thorough coverage on the roles played by key TFs in maintaining normal and malignant stem cell pluripotency and plasticity. In addition, we discuss the reciprocal interactions between miRNAs and MYC, OCT4, NANOG, SOX2, and KLF4 pluripotency TFs and their contributions to tumorigenesis. We provide our view on the potential to interfere with key miRNA-TF networks through the use of RNA-based therapeutics as single agents or in combination with other therapeutic strategies, to abrogate the CSC state and render tumor cells more responsive to standard and targeted therapies.

A new machine learning method for cancer mutation analysis

It is complicated to identify cancer-causing mutations. The recurrence of a mutation in patients remains one of the most reliable features of mutation driver status. However, some mutations are more likely to happen than others for various reasons. Different sequencing analysis has revealed that cancer driver genes operate across complex pathways and networks, with mutations often arising in a mutually exclusive pattern. Genes with low-frequency mutations are understudied as cancer-related genes, especially in the context of networks. Here we propose a machine learning method to study the functionality of mutually exclusive genes in the networks derived from mutation associations, gene-gene interactions, and graph clustering. These networks have indicated critical biological components in the essential pathways, especially those mutated at low frequency. Studying the network and not just the impact of a single gene significantly increases the statistical power of clinical analysis. The proposed method identified important driver genes with different frequencies. We studied the function and the associated pathways in which the candidate driver genes participate. By introducing lower-frequency genes, we recognized less studied cancer-related pathways. We also proposed a novel clustering method to specify driver modules. We evaluated each driver module with different criteria, including the terms of biological processes and the number of simultaneous mutations in each cancer. Materials and implementations are available at: https://github.com/MahnazHabibi/MutationAnalysis.

Multi-omics analysis: Paving the path toward achieving precision medicine in cancer treatment and immuno-oncology

The acceleration of large-scale sequencing and the progress in high-throughput computational analyses, defined as omics, was a hallmark for the comprehension of the biological processes in human health and diseases. In cancerology, the omics approach, initiated by genomics and transcriptomics studies, has revealed an incredible complexity with unsuspected molecular diversity within a same tumor type as well as spatial and temporal heterogeneity of tumors. The integration of multiple biological layers of omics studies brought oncology to a new paradigm, from tumor site classification to pan-cancer molecular classification, offering new therapeutic opportunities for precision medicine. In this review, we will provide a comprehensive overview of the latest innovations for multi-omics integration in oncology and summarize the largest multi-omics dataset available for adult and pediatric cancers. We will present multi-omics techniques for characterizing cancer biology and show how multi-omics data can be combined with clinical data for the identification of prognostic and treatment-specific biomarkers, opening the way to personalized therapy. To conclude, we will detail the newest strategies for dissecting the tumor immune environment and host–tumor interaction. We will explore the advances in immunomics and microbiomics for biomarker identification to guide therapeutic decision in immuno-oncology.

Roles of circulating tumor DNA in PD-1/PD-L1 immune checkpoint Inhibitors: Current evidence and future directions

Circulating tumor DNA (ctDNA) sequencing holds considerable promise for early diagnosis and detection of surveillance and minimal residual disease. Blood ctDNA monitors specific cancers by detecting the alterations found in cancer cells, such as apoptosis and necrosis. Due to the short half-life, ctDNA reflects the actual burden of other treatments on tumors. In addition, ctDNA might be preferable to monitor tumor development and treatment compared with invasive tissue biopsy. ctDNA-based liquid biopsy brings remarkable strength to targeted therapy and precision medicine. Notably, multiple ctDNA analysis platforms have been broadly applied in clinical immunotherapy. Through targeted sequencing, early variations in ctDNA could predict response to immune checkpoint inhibitor (ICI). Several studies have demonstrated a correlation between ctDNA kinetics and anti-PD1 antibodies. The need for further research and development remains, although this biomarker holds significant prospects for early cancer detection. This review focuses on describing the basis of ctDNA and its current utilities in oncology and immunotherapy, either for clinical management or early detection, highlighting its advantages and inherent limitations.

18F-FDG PET/CT radiomic analysis for classifying and predicting microvascular invasion in hepatocellular carcinoma and intrahepatic cholangiocarcinoma

Background

Microvascular invasion (MVI) is a critical risk factor for early recurrence of hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC). The aim of this study was to explore the contribution of ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) radiomic features for the preoperative prediction of HCC and ICC classification and MVI.

Methods

In this retrospective study, 127 (HCC: ICC =76:51) patients with suspected MVI accompanied by either HCC or ICC were included (In HCC group, MVI positive: negative =46:30 in ICC group, MVI positive: negative =31:20). Results-driven feature engineering workflow was used to select the most predictive feature combinations. The prediction model was based on supervised machine learning classifier. Ten-fold cross validation on training cohort and independent test cohort were constructed to ensure stability and generalization ability of models.

Results

For HCC and ICC classification, radiomics predictors composed of two PET and one CT feature achieved area under the curve (AUC) of 0.86 (accuracy, sensitivity, specificity was 0.82, 0.78, 0.88, respectively) on test cohort. For MVI prediction, in HCC group, our MVI prediction model achieved AUC of 0.88 (accuracy, sensitivity, specificity was 0.78, 0.88, 0.60 respectively) with three PET features associated with tumor stage on test cohort. In ICC group, the phenotype composed of two PET features and carbohydrate antigen 19-9 (CA19-9) achieved AUC of 0.90 (accuracy, sensitivity, specificity was 0.77, 0.75, 0.80, respectively).

Conclusions

¹⁸F-FDG PET/CT radiomic features integrating clinical factors have potential in HCC and ICC classification and MVI prediction, while PET features have dominant predictive power in model performance. The prediction model has value in providing a non-invasive biomarker for an earlier indication and comprehensive quantification of primary liver cancers.

Microfluidics for detection of exosomes and microRNAs in cancer: State of the art

Exosomes are small extracellular vesicles with sizes ranging from 30-150 nanometers that contain proteins, lipids, mRNAs, microRNAs, and double-stranded DNA derived from the cells of origin. Exosomes can be taken up by target cells, acting as a means of cell-to-cell communication. The discovery of these vesicles in body fluids and their participation in cell communication has led to major breakthroughs in diagnosis, prognosis, and treatment of several conditions (e.g., cancer). However, conventional isolation and evaluation of exosomes and their microRNA content suffers from high cost, lengthy processes, difficult standardization, low purity, and poor yield. The emergence of microfluidics devices with increased efficiency in sieving, trapping, and immunological separation of small volumes could provide improved detection and monitoring of exosomes involved in cancer. Microfluidics techniques hold promise for advances in development of diagnostic and prognostic devices. This review covers ongoing research on microfluidics devices for detection of microRNAs and exosomes as biomarkers and their translation to point-of-care and clinical applications.

Case Report: PTEN Mutation Induced by anti-PD-1 Therapy in Stage IV Lung Adenocarcinoma

Lung cancer is the most common solid tumor in the worldwide. Targeted therapy and immunotherapy are important treatment options in advanced non-small cell lung cancer (NSCLC). The association of PTEN mutation and tumor immunotherapy is less established for patients with NSCLC. We present the case of an Asian woman diagnosed with stage IV lung adenocarcinoma harboring an ERBB2 mutation. She received Nivolumab treatment when her disease progresses after previous chemotherapy and Afatinib treatment. However, the patient did not response to Nivolumab. PTEN mutation was detected by next-generation sequencing (NGS) after treatment with Nivolumab. PTEN, a secondary mutation, may be served as a biomarker of resistance to anti-PD-1 immunotherapy in lung adenocarcinoma. The relationship between PTEN mutation and immunotherapy is complex and needs further study.

Multi-staged gene expression profiling reveals potential genes and the critical pathways in kidney cancer

Cancer is among the highly complex disease and renal cell carcinoma is the sixth-leading cause of cancer death. In order to understand complex diseases such as cancer, diabetes and kidney diseases, high-throughput data are generated at large scale and it has helped in the research and diagnostic advancement. However, to unravel the meaningful information from such large datasets for comprehensive and minute understanding of cell phenotypes and disease pathophysiology remains a trivial challenge and also the molecular events leading to disease onset and progression are not well understood. With this goal, we have collected gene expression datasets from publicly available dataset which are for two different stages (I and II) for renal cell carcinoma and furthermore, the TCGA and cBioPortal database have been utilized for clinical relevance understanding. In this work, we have applied computational approach to unravel the differentially expressed genes, their networks for the enriched pathways. Based on our results, we conclude that among the most dominantly altered pathways for renal cell carcinoma, are PI3K-Akt, Foxo, endocytosis, MAPK, Tight junction, cytokine-cytokine receptor interaction pathways and the major source of alteration for these pathways are MAP3K13, CHAF1A, FDX1, ARHGAP26, ITGBL1, C10orf118, MTO1, LAMP2, STAMBP, DLC1, NSMAF, YY1, TPGS2, SCARB2, PRSS23, SYNJ1, CNPPD1, PPP2R5E. In terms of clinical significance, there are large number of differentially expressed genes which appears to be playing critical roles in survival.

Clinical Impact of High Throughput Sequencing on Liquid Biopsy in Advanced Solid Cancer

Background: Cancer therapies targeting actionable molecular alterations (AMA) have developed, but the clinical routine impact of high-throughput molecular profiling remains unclear. We present a monocentric experience of molecular profiling based on liquid biopsy in patients with cancer. Methods: Patients included had solid cancer and underwent cfDNA genomic profiling with FoudationOne Liquid CDx (F1LCDx) test, analyzing 324 genes. Primary endpoint was to describe patients with an AMA for whom clinical decisions were impacted by F1LCDx test results. Results: 191 patients were included, mostly with lung cancer (46%). An AMA was found in 52%. The most common molecular alterations were: TP53 (52%), KRAS (14%) and DNMT3 (11%). The most common AMA were: CHEK2 (10%), PIK3CA (9%), ATM (7%). There was no difference in progression-free survival (2.66 months vs. 3.81 months, p = 0.17), overall survival (5.3 months vs. 7.1 months, p = 0.64), or PFS2/PFS1 ratio ≥ 1.3 (20% vs. 24%, p = 0.72) between patients receiving a molecularly matched therapy (MMT) or a non-MMT, respectively. Patients with a MMT had an overall response rate of 19% and a disease control of 32%. Conclusions: Routine cfDNA molecular profiling is feasible and can lead to the access of targeted therapies. However, no notable benefit in patient’s outcomes was shown in this unselected pan-cancer study.

Intratumor genetic heterogeneity and clonal evolution to decode endometrial cancer progression

Analyzing different tumor regions by next generation sequencing allows the assessment of intratumor genetic heterogeneity (ITGH), a phenomenon that has been studied widely in some tumor types but has been less well explored in endometrial carcinoma (EC). In this study, we sought to characterize the spatial and temporal heterogeneity of 9 different ECs using whole-exome sequencing, and by performing targeted sequencing validation of the 42 primary tumor regions and 30 metastatic samples analyzed. In addition, copy number alterations of serous carcinomas were assessed by comparative genomic hybridization arrays. From the somatic mutations, identified by whole-exome sequencing, 532 were validated by targeted sequencing. Based on these data, the phylogenetic tree reconstructed for each case allowed us to establish the tumors’ evolution and correlate this to tumor progression, prognosis, and the presence of recurrent disease. Moreover, we studied the genetic landscape of an ambiguous EC and the molecular profile obtained was used to guide the selection of a potential personalized therapy for this patient, which was subsequently validated by preclinical testing in patient-derived xenograft models. Overall, our study reveals the impact of analyzing different tumor regions to decipher the ITGH in ECs, which could help make the best treatment decision.

Quantification of tumor heterogeneity: from data acquisition to metric generation

Tumors are unique and complex ecosystems, in which heterogeneous cell subpopulations with variable molecular profiles, aggressiveness, and proliferation potential coexist and interact. Understanding how heterogeneity influences tumor progression has important clinical implications for improving diagnosis, prognosis, and treatment response prediction. Several recent innovations in data acquisition methods and computational metrics have enabled the quantification of spatiotemporal heterogeneity across different scales of tumor organization. Here, we summarize the most promising efforts from a common experimental and computational perspective, discussing their advantages, shortcomings, and challenges. With personalized medicine entering a new era of unprecedented opportunities, our vision is that of future workflows integrating across modalities, scales, and dimensions to capture intricate aspects of the tumor ecosystem and to open new avenues for improved patient care.

Genomic Profiling of Blood-Derived Circulating Tumor DNA from Patients with Advanced Biliary Tract Cancer

Background: Biliary tract cancer is a highly lethal malignancy with poor clinical outcome. Accumulating evidence indicates targeted therapeutics may provide new hope for improving treatment response in BTC, hence better understanding the genomic profile is particularly important. Since tumor tissue may not be available for some patients, a complementary method is urgently needed. Circulating tumor DNA (ctDNA) provides a noninvasive means for detecting genomic alterations, and has been regarded as a promising tool to guide clinical therapies. Methods: Next-generation sequencing of 150 cancer-related genes was used to detect gene alterations in blood-derived ctDNA from 154 Chinese patients with BTC. Genomic alterations were analyzed and compared with an internal tissue genomic database and TCGA database. Results: 94.8% patients had at least one change detected in their ctDNA. The median maximum somatic allele frequency was 6.47% (ranging 0.1-34.8%). TP53 and KRAS were the most often mutated genes. The frequencies of single nucleotide variation in commonly mutated genes in ctDNA were similar to those detected in tissue samples, TP53 (35.1 vs. 40.4%) and KRAS (20.1 vs. 22.6%). Pathway analysis revealed that mutated genes were mapped to several key pathways including PI3K-Akt, p53, ErbB and Ras signaling pathway. In addition, patients harboring LRP1B, TP53, and ErbB family mutations presented significantly higher tumor mutation burden. Conclusions: These findings demonstrated that ctDNA testing by NGS was feasible in revealing genomic changes and could be a viable alternative to tissue biopsy in patients with metastatic BTC.

Progress of MRI Radiomics in Hepatocellular Carcinoma

Background

Hepatocellular carcinoma (HCC) is the sixth most common cancer in the world and the third leading cause of cancer-related death. Although the diagnostic scheme of HCC is currently undergoing refinement, the prognosis of HCC is still not satisfactory. In addition to certain factors, such as tumor size and number and vascular invasion displayed on traditional imaging, some histopathological features and gene expression parameters are also important for the prognosis of HCC patients. However, most parameters are based on postoperative pathological examinations, which cannot help with preoperative decision-making. As a new field, radiomics extracts high-throughput imaging data from different types of images to build models and predict clinical outcomes noninvasively before surgery, rendering it a powerful aid for making personalized treatment decisions preoperatively.

Objective

This study reviewed the workflow of radiomics and the research progress on magnetic resonance imaging (MRI) radiomics in the diagnosis and treatment of HCC.

Methods

A literature review was conducted by searching PubMed for search of relevant peer-reviewed articles published from May 2017 to June 2021.The search keywords included HCC, MRI, radiomics, deep learning, artificial intelligence, machine learning, neural network, texture analysis, diagnosis, histopathology, microvascular invasion, surgical resection, radiofrequency, recurrence, relapse, transarterial chemoembolization, targeted therapy, immunotherapy, therapeutic response, and prognosis.

Results

Radiomics features on MRI can be used as biomarkers to determine the differential diagnosis, histological grade, microvascular invasion status, gene expression status, local and systemic therapeutic responses, and prognosis of HCC patients.

Conclusion

Radiomics is a promising new imaging method. MRI radiomics has high application value in the diagnosis and treatment of HCC.

Intratumor heterogeneity: the hidden barrier to immunotherapy against MSI tumors from the perspective of IFN-γ signaling and tumor-infiltrating lymphocytes

In this era of precision medicine, with the help of biomarkers, immunotherapy has significantly improved prognosis of many patients with malignant tumor. Deficient mismatch repair (dMMR)/microsatellite instability (MSI) status is used as a biomarker in clinical practice to predict favorable response to immunotherapy and prognosis. MSI is an important characteristic which facilitates mutation and improves the likelihood of a favorable response to immunotherapy. However, many patients with dMMR/MSI still respond poorly to immunotherapies, which partly results from intratumor heterogeneity propelled by dMMR/MSI. In this review, we discuss how dMMR/MSI facilitates mutations in tumor cells and generates intratumor heterogeneity, especially through type II interferon (IFN-γ) signaling and tumor-infiltrating lymphocytes (TILs). We discuss the mechanism of immunotherapy from the perspective of dMMR/MSI, molecular pathways and TILs, and we discuss how intratumor heterogeneity hinders the therapeutic effect of immunotherapy. Finally, we summarize present techniques and strategies to look at the tumor as a whole to design personalized regimes and achieve favorable prognosis.

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.

Next Generation Sequencing in the Management of Leptomeningeal Metastases of Non-Small Cell Lung Cancer: A Case Report and Literature Review

BACKGROUND

Diagnosis of Leptomeningeal Metastases (LM) from Non-Small Cell Lung Cancer (NSCLC) is usually based on clinical symptoms, Cerebral-Spinal Fluid (CSF) cytology, and neuro-imaging. However, early diagnosis of LM in NSCLC is challenging due to the low sensitivity of these approaches. The Next-Generation Sequencing (NGS) using CSF could help improve the diagnosis of LM and guide its treatment options.

CASE PRESENTATION

We report a 39-year-old male NSCLC patient with negative molecular testing results in the lung cancer tissue sample. The patient developed symptoms of LM with the negative CSF cytology and MRI; however, the NGS analysis of CSF revealed an EGFR exon 19 del mutation. The patient attained 6 months of Progression-Free Survival (PFS) by treating with erlotinib and anlotinib before the neurological symptoms appeared again. EGFR Thr790Met was positive in the CSF but negative in his plasma. The patient was then treated with osimertinib therapy and the response was maintained for more than 1 year.

RESULTS & DISCUSSION

This case is the first study reporting the clinical benefit of using the combination of erlotinib and anlotinib for the treatment of LM with the EGFR 19 del, osimertinib with EGFR T790M mutation in CSF, but negative gene mutation in the blood or lung tumor biopsy specimens. Our results support that genetic analysis should be performed with CSF samples in all cases of suspected LM when the results of testing for EGFR/ALK/ROS1 mutation in blood samples or tumor biopsy specimens are negative, as these patients could benefit from treatment of TKIs in a poor prognostic setting.

CONCLUSION

In parallel to current patents, NGS could be applied as a novel strategy in the managing of NSCLC patients with LM.

Radiomics in Hepatocellular Carcinoma

BACKGROUND

Radiomics is an emerging field that extracts high-throughput imaging data from different types of images to build models and predict clinical outcomes ina non-invasive manner. This field currently is in the initial growth phase and lacks standardized evaluation criteria but remains a very promising tool for the future todevelop suitable biomarkers for diagnosis, prognosis, and treatment response assessments. The analysis of hepatocellular carcinoma by radiomics will contribute toearly diagnosis and treatment of tumors and improve survival and cure rates.

AIM

Herein, we aimed to provide an up-to-date overview of the principles of radiomics specifically regarding hepatocellular carcinoma (HCC) and discuss the current challenges and future advancements of radiomics for HCC.

Characterizing genetic intra-tumor heterogeneity across 2,658 human cancer genomes

Intra-tumor heterogeneity (ITH) is a mechanism of therapeutic resistance and therefore an important clinical challenge. However, the extent, origin, and drivers of ITH across cancer types are poorly understood. To address this, we extensively characterize ITH across whole-genome sequences of 2,658 cancer samples spanning 38 cancer types. Nearly all informative samples (95.1%) contain evidence of distinct subclonal expansions with frequent branching relationships between subclones. We observe positive selection of subclonal driver mutations across most cancer types and identify cancer type-specific subclonal patterns of driver gene mutations, fusions, structural variants, and copy number alterations as well as dynamic changes in mutational processes between subclonal expansions. Our results underline the importance of ITH and its drivers in tumor evolution and provide a pan-cancer resource of comprehensively annotated subclonal events from whole-genome sequencing data.

Circulating tumor DNA and their added value in molecular oncology

New methods for molecular diagnosis are now available in oncology thanks to the discovery of circulating tumor DNA molecules in the plasma of cancer patients. By utilizing blood samples, rather than traditional tissue sampling, clinical practice is on the verge of new discoveries from the analysis of cell-free DNA (cfDNA). The method, known as a "liquid biopsy", consists of analyzing therapeutic targets and drug-resistant conferring gene mutations in circulating tumor cells (CTC) and cell-free circulating tumor DNA (ctDNA). These are subsequently released from primary tumors and metastatic deposits into the peripheral blood. The advantages of the method can be observed in the diagnosis, but also in the choice of treatment for solid tumors (e.g. non-small cell lung carcinomas [NSCLC]). In order to interpret the results, an understanding of the biological characteristics of circulating tumor DNA is required. Currently there is no consensus as to how a liquid biopsy should be conducted. In this review, we will assess the pros of ctDNA as analytes in peripheral blood samples and its impact on clinical applications in solid tumors and hematological malignancies. We will also address practical issues facing clinical implementation, such as pre-analytical factors. Moreover, we will emphasize the open questions that remain when considering the current state of personalized medicine and targeted therapy.

When Tissue is an Issue the Liquid Biopsy is Nonissue: A Review

Precision medicine has impacted the field of medical oncology by introducing personalized therapies, improving all measurable outcomes. This field, in turn, has expanded to obtaining and analyzing a vast and ever-increasing amount of genomic information. One technique currently applied is the liquid biopsy, which consists of detecting and isolating DNA and exosomes in cancer patients. Newly developed techniques have made it possible to use the liquid biopsy in a wide range of settings. However, challenges regarding the validation of its clinical utility exist because of a lack of standardization across different techniques and tumor types, confounder genomic information, lack of appropriate clinical trial designs, and a non-measured, and therefore not estimated, economic impact on population health. Nowadays, liquid biopsy is not routinely used, but ongoing research is increasing its popularity, and a new era in oncology is developing. Therefore, it is essential to have an in-depth understanding of the liquid biopsy technique. In this review, we summarize the leading techniques and liquid biopsy applications in cancer.

Mutation and expression alterations of histone methylation-related NSD2, KDM2B and SETMAR genes in colon cancers

Epigenetic dysregulation is a hallmark of cancers, and examples of its cancer-associated expression and mutation alterations are rapidly growing. Histone methylation, a process by which methyl groups are transferred to amino acids of histone proteins, is crucial for the epigenetic gene regulation. NSD2 (nuclear receptor-binding SET domain protein 2) and SETMAR are epigenetic regulators for histone methylation. KDM2B, also known as FBXL10, is a histone demethylase that targets histone methylation processes. They are known to be altered in many cancers, but somatic frameshift mutation and expression of these genes remain undetermined in many other subsets of cancers, including high microsatellite instability (MSI-H) colon cancer (CC). In this study, we analyzed mononucleotide repeats in coding sequences of NSD2, KDM2B and SETMAR genes, and found frameshift mutations in 10 %, 2 % and 1 % of CCs with MSI-H, respectively. Of note, there was no frameshift mutation of these genes in microsatellite stable (MSS) CCs. In addition, we discovered that 2 and 2 of 16 CRCs (12.5 % and 12.5 %) harbored intratumoral heterogeneity (ITH) of the NSD2 and KDM2B frameshift mutations, respectively. In the immunohistochemistry for NSD2, intensity of NSD2 immunostaining in MSI-H CC is decreased compared to that in MSS. These results suggest that NSD2 might be altered at multiple levels (frameshift mutation, mutational ITH and expression) in MSI-H CCs, and could be related to MSI-H cancer pathogenesis.

Use of Gefitinib in EGFR-Amplified Refractory Solid Tumors: An Open-Label, Single-Arm, Single-Center Prospective Pilot Study

BACKGROUND

Treatment options for patients with chemotherapy-refractory solid tumors are limited.

OBJECTIVE

We conducted an open-label, single-arm, single-center phase II trial to evaluate the efficacy and safety of gefitinib in patients with chemotherapy-refractory solid tumors and EGFR amplification or sensitivity to an EGFR inhibitor identified through a drug-screening platform with patient-derived tumor cells (PDCs).

PATIENTS AND METHODS

EGFR amplification was detected by targeted sequencing. Sensitivity to an EGFR inhibitor was established in chemical screening using PDCs. Gefitinib (250 mg daily) was administered continuously in 28-day cycles until the occurrence of disease progression, unacceptable toxicity, or death due to any cause. The primary endpoint was the objective response rate (ORR).

RESULTS

In total, 15 patients were assigned to the present study. The most common tumor type was glioblastoma multiforme (n = 9, 60%), followed by gastric cancer (n = 3, 20%), anal squamous cancer, rectal cancer, and sarcoma (each n = 1, 6.7%). Among 13 evaluable patients, one patient had a partial response and five had stable disease, with an ORR of 7.7% and a disease control rate of 46.1%. The median progression-free survival was 2.1 months (95% confidence interval [CI] 0.77-3.43). The most common adverse events were diarrhea (26.7%) and skin rash (26.7%).

CONCLUSION

Gefitinib demonstrated modest anti-tumor activity and a manageable safety profile in chemotherapy-refractory solid tumors with EGFR amplification or sensitivity to an EGFR inhibitor identified through a drug-screening platform with PDCs. CLINICALTRIALS.

GOV IDENTIFIER

NCT02447419.

AI-Driver: an ensemble method for identifying driver mutations in personal cancer genomes

The current challenge in cancer research is to increase the resolution of driver prediction from gene-level to mutation-level, which is more closely aligned with the goal of precision cancer medicine. Improved methods to distinguish drivers from passengers are urgently needed to dig out driver mutations from increasing exome sequencing studies. Here, we developed an ensemble method, AI-Driver (AI-based driver classifier, https://github.com/hatchetProject/AI-Driver), to predict the driver status of somatic missense mutations based on 23 pathogenicity features. AI-Driver has the best overall performance compared with any individual tool and two cancer-specific driver predicting methods. We demonstrate the superior and stable performance of our model using four independent benchmarks. We provide pre-computed AI-Driver scores for all possible human missense variants (http://aidriver.maolab.org/) to identify driver mutations in the sea of somatic mutations discovered by personal cancer sequencing. We believe that AI-Driver together with pre-computed database will play vital important roles in the human cancer studies, such as identification of driver mutation in personal cancer genomes, discovery of targeting sites for cancer therapeutic treatments and prediction of tumor biomarkers for early diagnosis by liquid biopsy.

EGFR and BRAF mutations in inverted sinonasal papilloma - a more complex landscape?

Inverted (Schneiderian) sinonasal papilloma (ISP) is a neoplasm derived from mucosa of the sinonasal tract characterized by local aggressive growth, a tendency to recur and an association with sinonasal carcinoma. The etiology of ISP remains unclear. Recently, identical mutations in exons 19 and 20 of the oncogene EGFR were reported in ISP and ISP-associated sinonasal carcinoma. Nevertheless, it remains unclear whether recurring ISPs show identical EGFR mutations at different time points or whether these mutations are identical throughout the respective ISP sample. We used Sanger sequencing to test 60 formalin-fixed paraffin embedded ISP samples from 40 patients regarding mutations in exons 19 and 20 of EGFR—together with exon 15 of BRAF. Overall, 32 samples of 22 patients showed a mutation in EGFR exon 20, whereas 28 samples of 18 patients showed none. No mutation in EGFR exon 19 was found in any sample. Four samples of four patients showed a BRAF exon 15 mutation. Interestingly, samples of four patients exhibited genetic heterogeneity, enabling us to report this in ISP for the first time.

Extracellular Vesicles as Biomarkers in Cancer Immunotherapy

Simple Summary

Extracellular vesicles (EVs) are small particles found throughout the body. EVs are released by living cells and contain cargo representing the cell of origin. In recent years, EVs have gained attention in cancer research. Since the cargo found inside EVs can be traced back to the cell of origin, EVs shed from cancer cells, in particular, may be used to better describe and characterize a patient’s tumor. EVs have been found and isolated from a variety of bodily fluids, including blood, saliva, and amniotic fluid, and therefore offer a non-invasive way of also diagnosing and monitoring patients before, during, and after cancer immunotherapy. The aim of this review article was to summarize some of the recent work conducted in this field and the challenges we face moving forward in utilizing EVs for cancer diagnostic and therapeutic purposes in cancer immunotherapy in the clinical setting.

Abstract

Extracellular vesicles (EVs), including exosomes and microvesicles, are membrane-bound vesicles secreted by most cell types during both physiologic conditions as well in response to cellular stress. EVs play an important role in intercellular communication and are emerging as key players in tumor immunology. Tumor-derived EVs (TDEs) harbor a diverse array of tumor neoantigens and contain unique molecular signature that is reflective of tumor’s underlying genetic complexity. As such they offer a glimpse into the immune tumor microenvironment (TME) and have the potential to be a novel, minimally invasive biomarker for cancer immunotherapy. Immune checkpoint inhibitors (ICI), such as anti- programmed death-1(PD-1) and its ligand (PD-L1) antibodies, have revolutionized the treatment of a wide variety of solid tumors including head and neck squamous cell carcinoma, urothelial carcinoma, melanoma, non-small cell lung cancer, and others. Typically, an invasive tissue biopsy is required both for histologic diagnosis and next-generation sequencing efforts; the latter have become more widespread in daily clinical practice. There is an unmet need for noninvasive or minimally invasive (e.g., plasma-based) biomarkers both for diagnosis and treatment monitoring. Targeted analysis of EVs in biospecimens, such as plasma and saliva could serve this purpose by potentially obviating the need for tissue sample. In this review, we describe the current challenges of biomarkers in cancer immunotherapy as well as the mechanistic role of TDEs in modulating antitumor immune response.

Beyond tissue biopsy: a diagnostic framework to address tumor heterogeneity in lung cancer

PURPOSE OF REVIEW

The objective of this review is to discuss the strength and limitations of tissue and liquid biopsy and functional imaging to capture spatial and temporal tumor heterogeneity either alone or as part of a diagnostic framework in non-small cell lung cancer (NSCLC).

RECENT FINDINGS

NSCLC displays genetic and phenotypic heterogeneity - a detailed knowledge of which is crucial to personalize treatment. Tissue biopsy often lacks spatial and temporal resolution. Thus, NSCLC needs to be characterized by complementary diagnostic methods to resolve heterogeneity. Liquid biopsy offers detection of tumor biomarkers and for example, the classification and monitoring of EGFR mutations in NSCLC. It allows repeated sampling, and therefore, appears promising to address temporal aspects of tumor heterogeneity. Functional imaging methods and emerging image analytic tools, such as radiomics capture temporal and spatial heterogeneity. Further standardization of radiomics is required to allow introduction into clinical routine.

SUMMARY

To augment the potential of precision therapy, improved diagnostic characterization of tumors is pivotal. We suggest a comprehensive diagnostic framework combining tissue and liquid biopsy and functional imaging to address the known aspects of spatial and temporal tumor heterogeneity on the example of NSCLC. We envision how this framework might be implemented in clinical practice.

The diagnostic accuracy of digital PCR, ARMS and NGS for detecting KRAS mutation in cell-free DNA of patients with colorectal cancer: A protocol for systematic review and meta-analysis

INTRODUCTION

Cetuximab and panitumumab have been used clinically to treat metastatic colorectal cancer for more than 15 years. Before the treatment is given, it is required to determine the KRAS mutation status since it would lead to drug resistance. Tumor tissue sample is traditionally used for cancer genotyping. In recent years, liquid biopsy sample has been intensively investigated as a surrogate for tumor tissue sample due to its non-invasiveness and better presentation of tumor heterogeneity. The aim of this study is to systematically summarize the accuracy of KRAS mutation measurement in colorectal cancer using cell-free DNA in liquid biopsy samples, with tumor tissue sample as reference (gold standard).

METHODS AND ANALYSIS

We will search literatures in the following databases: Pubmed, Embase, and Cochrane Library. Systemic review and meta-analysis will be performed to summarize the accuracy of KRAS mutation measurement in colorectal cancer using liquid biopsy sample, and subgroup analysis will be performed on different testing platforms, and on metastatic and non-metastatic colorectal cancer.

TIMELINE

This study will start on June 1, 2020, and is expected to be finished by November 1, 2020.

ETHICS AND DISSEMINATION

Ethical approval will not be required since the data obtained and analyzed in this study will not be on individual patients. Study results will be disseminated as an official publication in a peer-reviewed journal.Registration: PROSPERO CRD42020176682.