Phenotype-driven precision oncology as a guide for clinical decisions one patient at a time

Preclinical in vitro models of HNSCC and their role in drug discovery - an emphasis on the cancer microenvironment and microbiota

INTRODUCTION

Head and neck squamous cell carcinoma (HNSCC) is the seventh most common cancer worldwide. Treatment options and patient outcomes have not improved significantly over the past decades, increasing the need for better preclinical models. Holistic approaches that include an intact and functional immune compartment along with the patient's individual tumor microbiome will help improve the predictive value of novel drug efficacy.

AREAS COVERED

In this review, we describe the challenges of modeling the complex and heterogeneous tumor landscape in HNSCC and the importance of sophisticated patient-specific 3D in vitro models to pave the way for clinical trials with novel immunomodulatory drugs. We also discuss the impact of the tumor microbiome and the potential implications for prospective drug screening and validation trials.

EXPERT OPINION

The repertoire of well-characterized preclinical 3D in vitro models continues to grow. With the increasing attention to the complex cellular, immunological, molecular, and spatio-temporal characteristics of tumors, well-designed proof-of-concept studies to test novel drug efficacy are on the verge of providing valuable, practice-changing insights for clinical trials. Bringing together expertise and improving collaboration between clinicians, academics, and regulatory agencies will facilitate the translation of preclinical findings into clinically meaningful outcomes.

Heterogeneity, Bayesian thinking, and phenotyping in critical care: A primer

PURPOSE

To familiarize clinicians with the emerging concepts in critical care research of Bayesian thinking and personalized medicine through phenotyping and explain their clinical relevance by highlighting how they address the issues of frequent negative trials and heterogeneity of treatment effect.

SUMMARY

The past decades have seen many negative (effect-neutral) critical care trials of promising interventions, culminating in calls to improve the field's research through adopting Bayesian thinking and increasing personalization of critical care medicine through phenotyping. Bayesian analyses add interpretive power for clinicians as they summarize treatment effects based on probabilities of benefit or harm, contrasting with conventional frequentist statistics that either affirm or reject a null hypothesis. Critical care trials are beginning to include prospective Bayesian analyses, and many trials have undergone reanalysis with Bayesian methods. Phenotyping seeks to identify treatable traits to target interventions to patients expected to derive benefit. Phenotyping and subphenotyping have gained prominence in the most syndromic and heterogenous critical care disease states, acute respiratory distress syndrome and sepsis. Grouping of patients has been informative across a spectrum of clinically observable physiological parameters, biomarkers, and genomic data. Bayesian thinking and phenotyping are emerging as elements of adaptive clinical trials and predictive enrichment, paving the way for a new era of high-quality evidence. These concepts share a common goal, sifting through the noise of heterogeneity in critical care to increase the value of existing and future research.

CONCLUSION

The future of critical care medicine will inevitably involve modification of statistical methods through Bayesian analyses and targeted therapeutics via phenotyping. Clinicians must be familiar with these systems that support recommendations to improve decision-making in the gray areas of critical care practice.

Therapeutic application of extracellular vesicular EGFR isoform D as a co-drug to target squamous cell cancers with tyrosine kinase inhibitors

Targeting wild-type epidermal growth factor receptor (EGFR) using tyrosine kinase inhibitors (TKIs) never achieved its purported success in cancers such as head and neck squamous cell carcinoma, which are largely EGFR-dependent. We had previously shown that exceptional responders to TKIs have a genetic aberration that results in overexpression of an EGFR splice variant, isoform D (IsoD). IsoD lacks an integral transmembrane and kinase domain and is secreted in extracellular vesicles (EVs) in TKI-sensitive patient-derived cultures. Remarkably, the exquisite sensitivity to TKIs could be transferred to TKI-resistant tumor cells, and IsoD protein in the EV is necessary and sufficient to transfer the phenotype in vitro and in vivo across multiple models and drugs. This drug response requires an intact endocytic mechanism, binding to full-length EGFR, and signaling through Src-phosphorylation within the endosomal compartment. We propose a therapeutic strategy using EVs containing EGFR IsoD as a co-drug to expand the use of TKI therapy to EGFR-driven cancers.

An AI-assisted integrated, scalable, single-cell phenomic-transcriptomic platform to elucidate intratumor heterogeneity against immune response

We present a novel framework combining single-cell phenotypic data with single-cell transcriptomic analysis to identify factors underpinning heterogeneity in antitumor immune response. We developed a pairwise, tumor-immune discretized interaction assay between natural killer (NK-92MI) cells and patient-derived head and neck squamous cell carcinoma (HNSCC) cell lines on a microfluidic cell-trapping platform. Furthermore we generated a deep-learning computer vision algorithm that is capable of automating the acquisition and analysis of a large, live-cell imaging data set (>1 million) of paired tumor-immune interactions spanning a time course of 24 h across multiple HNSCC lines (n = 10). Finally, we combined the response data measured by Kaplan-Meier survival analysis against NK-mediated killing with downstream single-cell transcriptomic analysis to interrogate molecular signatures associated with NK-effector response. As proof-of-concept for the proposed framework, we efficiently identified MHC class I-driven cytotoxic resistance as a key mechanism for immune evasion in nonresponders, while enhanced expression of cell adhesion molecules was found to be correlated with sensitivity against NK-mediated cytotoxicity. We conclude that this integrated, data-driven phenotypic approach holds tremendous promise in advancing the rapid identification of new mechanisms and therapeutic targets related to immune evasion and response.

Breakage fusion bridge cycles drive high oncogene copy number, but not intratumoral genetic heterogeneity or rapid cancer genome change

Oncogene amplification is a major driver of cancer pathogenesis. Breakage fusion bridge (BFB) cycles, like extrachromosomal DNA (ecDNA), can lead to high copy numbers of oncogenes, but their impact on intratumoral heterogeneity, treatment response, and patient survival are not well understood due to difficulty in detecting them by DNA sequencing. We describe a novel algorithm that detects and reconstructs BFB amplifications using optical genome maps (OGMs), called OM2BFB. OM2BFB showed high precision (>93%) and recall (92%) in detecting BFB amplifications in cancer cell lines, PDX models and primary tumors. OM-based comparisons demonstrated that short-read BFB detection using our AmpliconSuite (AS) toolkit also achieved high precision, albeit with reduced sensitivity. We detected 371 BFB events using whole genome sequences from 2,557 primary tumors and cancer lines. BFB amplifications were preferentially found in cervical, head and neck, lung, and esophageal cancers, but rarely in brain cancers. BFB amplified genes show lower variance of gene expression, with fewer options for regulatory rewiring relative to ecDNA amplified genes. BFB positive (BFB (+)) tumors showed reduced heterogeneity of amplicon structures, and delayed onset of resistance, relative to ecDNA(+) tumors. EcDNA and BFB amplifications represent contrasting mechanisms to increase the copy numbers of oncogene with markedly different characteristics that suggest different routes for intervention.

Predicting drug response from single-cell expression profiles of tumours

BACKGROUND

Intra-tumour heterogeneity (ITH) presents a significant obstacle in formulating effective treatment strategies in clinical practice. Single-cell RNA sequencing (scRNA-seq) has evolved as a powerful instrument for probing ITH at the transcriptional level, offering an unparalleled opportunity for therapeutic intervention.

RESULTS

Drug response prediction at the single-cell level is an emerging field of research that aims to improve the efficacy and precision of cancer treatments. Here, we introduce DREEP (Drug Response Estimation from single-cell Expression Profiles), a computational method that leverages publicly available pharmacogenomic screens from GDSC2, CTRP2, and PRISM and functional enrichment analysis to predict single-cell drug sensitivity from transcriptomic data. We validated DREEP extensively in vitro using several independent single-cell datasets with over 200 cancer cell lines and showed its accuracy and robustness. Additionally, we also applied DREEP to molecularly barcoded breast cancer cells and identified drugs that can selectively target specific cell populations.

CONCLUSIONS

DREEP provides an in silico framework to prioritize drugs from single-cell transcriptional profiles of tumours and thus helps in designing personalized treatment strategies and accelerating drug repurposing studies. DREEP is available at https://github.com/gambalab/DREEP .

Melt-Encoded-Tags for Expanded Optical Readout in Digital PCR (METEOR-dPCR) Enables Highly Multiplexed Quantitative Gene Panel Profiling

Digital PCR (dPCR) is an important tool for precise nucleic acid quantification in clinical setting, but the limited multiplexing capability restricts its applications for quantitative gene panel profiling. Here, this work describes melt-encoded-tags for expanded optical readout in digital PCR (METEOR-dPCR), a simple two-step assay that enables simultaneous quantification of a large panel of arbitrary genes in a dPCR platform. Target genes are quantitatively converted into DNA tags with unique melting temperatures through a ligation approach. These tags are then counted and distinguished by their melt-curve profiles on a dPCR platform. A multiplexing capacity of M^N, where M is the number of resolvable melting temperature and N is the number of fluorescence channel, can be achieved. This work validates METEOR-dPCR with simultaneous DNA copy number profiling of 60 targets using dPCR in cancer cells, and demonstrates its sensitivity for estimating tumor fraction in mixed tumor and normal DNA samples. The rapid, quantitative, and highly multiplexed METEOR-dPCR assay will have wide appeal for many clinical applications.

Single cell analysis in head and neck cancer reveals potential immune evasion mechanisms during early metastasis

Profiling tumors at single-cell resolution provides an opportunity to understand complexities underpinning lymph-node metastases in head and neck squamous-cell carcinoma. Single-cell RNAseq (scRNAseq) analysis of cancer-cell trajectories identifies a subpopulation of pre-metastatic cells, driven by actionable pathways including AXL and AURK. Blocking these two proteins blunts tumor invasion in patient-derived cultures. Furthermore, scRNAseq analyses of tumor-infiltrating CD8 + T-lymphocytes show two distinct trajectories to T-cell dysfunction, corroborated by their clonal architecture based on single-cell T-cell receptor sequencing. By determining key modulators of these trajectories, followed by validation using external datasets and functional experiments, we uncover a role for SOX4 in mediating T-cell exhaustion. Finally, interactome analyses between pre-metastatic tumor cells and CD8 + T-lymphocytes uncover a putative role for the Midkine pathway in immune-modulation and this is confirmed by scRNAseq of tumors from humanized mice. Aside from specific findings, this study demonstrates the importance of tumor heterogeneity analyses in identifying key vulnerabilities during early metastasis.

Ex Vivo Functional Assay for Evaluating Treatment Response in Tumor Tissue of Head and Neck Squamous Cell Carcinoma

BACKGROUND

Head and neck squamous cell carcinoma (HNSCC) displays a large heterogeneity in treatment response, and consequently in patient prognosis. Despite extensive efforts, no clinically validated model is available to predict tumor response. Here we describe a functional test for predicting tumor response to radiation and chemotherapy on the level of the individual patient.

METHODS

Resection material of 17 primary HNSCC patients was cultured ex vivo, irradiated or cisplatin-treated, after which the effect on tumor cell vitality was analyzed several days after treatment.

RESULTS

Ionizing radiation (IR) affected tumor cell growth and viability with a clear dose-response relationship, and marked heterogeneity between tumors was observed. After a single dose of 5Gy, proliferation in IR-sensitive tumors dropped below 30% of the untreated level, while IR-resistant tumors maintained at least 60% of proliferation. IR-sensitive tumors showed on average a twofold increase in apoptosis, as well as an increased number and size of DNA damage foci after treatment. No differences in the homologous recombination (HR) proficiency between IR-sensitive and -resistant tumors were detected. Cisplatin caused a decrease in proliferation, as well as induction of apoptosis, again with marked variation between the samples.

CONCLUSIONS

Our functional ex vivo assay discriminated between IR-sensitive and IR-resistant HNSCC tumors, and may also be suitable for predicting response to cisplatin. Its predictive value is currently under investigation in a prospective clinical study.

Evolution of precision oncology-guided treatment paradigms

Cancer treatment is gradually evolving from the classical use of nonspecific cytotoxic drugs targeting generic mechanisms of cell growth and proliferation. Instead, new "patient-specific treatment paradigms" that are based on an individual patient's tumor-specific molecular features are emerging, and these include "druggable" genomic alterations such as oncogenic driver mutations, downstream activities of cancer-signaling pathways, and the expression of specific genes involved in tumorigenesis and cancer progression. This evolving landscape of making evidence-based treatment decisions forms the foundation of precision oncology, which aims to deliver "the right drug, to the right patient and at the right time". The long-term vision for this approach is to maximize the treatment efficacy while minimizing exposure to ineffective therapy and reducing co-morbidity-related side effects. Successful clinical translation and implementation of this vision have the potential to revolutionize treatment paradigms from predominantly reactive, to more evidence-based, proactive and predictive care. In this article, we review the past and current approaches in precision oncology, and describe their remarkable power and limitations. We also speculate on the evolution of newly emerging methodologies of the future that can be used to address some of the key challenges associated with the existing paradigms. This article is categorized under: Cancer > Genetics/Genomics/Epigenetics Cancer > Molecular and Cellular Physiology Cancer > Computational Models.

Feasibility of high-throughput drug sensitivity screening (HDS)-guided treatment for children with refractory or relapsed acute myeloid leukemia

Relapsed/refractory (rel/ref) acute myeloid leukemia (AML) has a very high mortality rate. At present, hematopoietic stem cell transplantation (HSCT) is the most effective treatment for rel/ref AML. The remission of the primary disease before HSCT is crucial for the transplantation to be effective. Therefore, it is critical to choose a suitable type of chemotherapy before HSCT. Here, we recorded the outcomes of high-throughput drug sensitivity screening (HDS) in children with rel/ref AML. Thirty-seven pediatric rel/ref AML patients who received HDS from September 2017 until July 2021 were analyzed retrospectively. Most of the patients (24 patients, 64.9%) had adverse cytogenetics. Two patients had rel/ref AML with central nervous system leukemia. The complete remission (CR) rate was 67.6%. Eight patients developed IV grade bone marrow suppression. Twenty-three patients (62.2%) underwent HSCT. The 3-year overall survival (OS) and EFS rates were 45.9% and 43.2%, respectively. Infection in the myelosuppression stage was the main cause of death. The outcome of HDS was superior to the commonly reported rates. These results suggest that HDS may be a novel treatment option for pediatric patients with rel/ref AML, and it is a promising transitional regimen prior to HSCT.

An ex vivo platform to guide drug combination treatment in relapsed/refractory lymphoma

Although combination therapy is the standard of care for relapsed/refractory non-Hodgkin's lymphoma (RR-NHL), combination treatment chosen for an individual patient is empirical, and response rates remain poor in individuals with chemotherapy-resistant disease. Here, we evaluate an experimental-analytic method, quadratic phenotypic optimization platform (QPOP), for prediction of patient-specific drug combination efficacy from a limited quantity of biopsied tumor samples. In this prospective study, we enrolled 71 patients with RR-NHL (39 B cell NHL and 32 NK/T cell NHL) with a median of two prior lines of treatment, at two academic hospitals in Singapore from November 2017 to August 2021. Fresh biopsies underwent ex vivo testing using a panel of 12 drugs with known efficacy against NHL to identify effective single and combination treatments. Individualized QPOP reports were generated for 67 of 75 patient samples, with a median turnaround time of 6 days from sample collection to report generation. Doublet drug combinations containing copanlisib or romidepsin were most effective against B cell NHL and NK/T cell NHL samples, respectively. Off-label QPOP-guided therapy offered at physician discretion in the absence of standard options (n = 17) resulted in five complete responses. Among patients with more than two prior lines of therapy, the rates of progressive disease were lower with QPOP-guided treatments than with conventional chemotherapy. Overall, this study shows that the identification of patient-specific drug combinations through ex vivo analysis was achievable for RR-NHL in a clinically applicable time frame. These data provide the basis for a prospective clinical trial evaluating ex vivo-guided combination therapy in RR-NHL.

A comparative study of tumour-on-chip models with patient-derived xenografts for predicting chemotherapy efficacy in colorectal cancer patients

Inter-patient and intra-tumour heterogeneity (ITH) have prompted the need for a more personalised approach to cancer therapy. Although patient-derived xenograft (PDX) models can generate drug response specific to patients, they are not sustainable in terms of cost and time and have limited scalability. Tumour Organ-on-Chip (OoC) models are in vitro alternatives that can recapitulate some aspects of the 3D tumour microenvironment and can be scaled up for drug screening. While many tumour OoC systems have been developed to date, there have been limited validation studies to ascertain whether drug responses obtained from tumour OoCs are comparable to those predicted from patient-derived xenograft (PDX) models. In this study, we established a multiplexed tumour OoC device, that consists of an 8 × 4 array (32-plex) of culture chamber coupled to a concentration gradient generator. The device enabled perfusion culture of primary PDX-derived tumour spheroids to obtain dose-dependent response of 5 distinct standard-of-care (SOC) chemotherapeutic drugs for 3 colorectal cancer (CRC) patients. The in vitro efficacies of the chemotherapeutic drugs were rank-ordered for individual patients and compared to the in vivo efficacy obtained from matched PDX models. We show that quantitative correlation analysis between the drug efficacies predicted via the microfluidic perfusion culture is predictive of response in animal PDX models. This is a first study showing a comparative framework to quantitatively correlate the drug response predictions made by a microfluidic tumour organ-on-chip (OoC) model with that of PDX animal models.

Precision Medicine in Head and Neck Cancers: Genomic and Preclinical Approaches

Head and neck cancers (HNCs) represent the sixth most widespread malignancy worldwide. Surgery, radiotherapy, chemotherapeutic and immunotherapeutic drugs represent the main clinical approaches for HNC patients. Moreover, HNCs are characterised by an elevated mutational load; however, specific genetic mutations or biomarkers have not yet been found. In this scenario, personalised medicine is showing its efficacy. To study the reliability and the effects of personalised treatments, preclinical research can take advantage of next-generation sequencing and innovative technologies that have been developed to obtain genomic and multi-omic profiles to drive personalised treatments. The crosstalk between malignant and healthy components, as well as interactions with extracellular matrices, are important features which are responsible for treatment failure. Preclinical research has constantly implemented in vitro and in vivo models to mimic the natural tumour microenvironment. Among them, 3D systems have been developed to reproduce the tumour mass architecture, such as biomimetic scaffolds and organoids. In addition, in vivo models have been changed over the last decades to overcome problems such as animal management complexity and time-consuming experiments. In this review, we will explore the new approaches aimed to improve preclinical tools to study and apply precision medicine as a therapeutic option for patients affected by HNCs.

Application of High Throughput Technologies in the Development of Acute Myeloid Leukemia Therapy: Challenges and Progress

Acute myeloid leukemia (AML) is a complex hematological malignancy characterized by extensive heterogeneity in genetics, response to therapy and long-term outcomes, making it a prototype example of development for personalized medicine. Given the accessibility to hematologic malignancy patient samples and recent advances in high-throughput technologies, large amounts of biological data that are clinically relevant for diagnosis, risk stratification and targeted drug development have been generated. Recent studies highlight the potential of implementing genomic-based and phenotypic-based screens in clinics to improve survival in patients with refractory AML. In this review, we will discuss successful applications as well as challenges of most up-to-date high-throughput technologies, including artificial intelligence (AI) approaches, in the development of personalized medicine for AML, and recent clinical studies for evaluating the utility of integrating genomics-guided and drug sensitivity testing-guided treatment approaches for AML patients.

Functional Therapeutic Target Validation Using Pediatric Zebrafish Xenograft Models

Simple Summary

Despite the major progress of precision and personalized oncology, a significant therapeutic benefit is only achieved in cases with directly druggable genetic alterations. This highlights the need for additional methods that reliably predict each individual patient’s response in a clinically meaningful time, e.g., through ex vivo functional drug screen profiling. Moreover, patient-derived xenograft (PDX) models are more predictive than cell culture studies, as they reconstruct cell–cell and cell–extracellular matrix (ECM) interactions and consider the tumor microenvironment, drug metabolism and toxicities. Zebrafish PDXs (zPDX) are nowadays emerging as a fast model allowing for multiple drugs to be tested at the same time in a clinically relevant time window. Here, we show that functional drug response profiling of zPDX from primary material obtained through the INdividualized Therapy FOr Relapsed Malignancies in Childhood (INFORM) pediatric precision oncology pipeline provides additional key information for personalized precision oncology.

Abstract

The survival rate among children with relapsed tumors remains poor, due to tumor heterogeneity, lack of directly actionable tumor drivers and multidrug resistance. Novel personalized medicine approaches tailored to each tumor are urgently needed to improve cancer treatment. Current pediatric precision oncology platforms, such as the INFORM (INdividualized Therapy FOr Relapsed Malignancies in Childhood) study, reveal that molecular profiling of tumor tissue identifies targets associated with clinical benefit in a subgroup of patients only and should be complemented with functional drug testing. In such an approach, patient-derived tumor cells are exposed to a library of approved oncological drugs in a physiological setting, e.g., in the form of animal avatars injected with patient tumor cells. We used molecularly fully characterized tumor samples from the INFORM study to compare drug screen results of individual patient-derived cell models in functional assays: (i) patient-derived spheroid cultures within a few days after tumor dissociation; (ii) tumor cells reisolated from the corresponding mouse PDX; (iii) corresponding long-term organoid-like cultures and (iv) drug evaluation with the corresponding zebrafish PDX (zPDX) model. Each model had its advantage and complemented the others for drug hit and drug combination selection. Our results provide evidence that in vivo zPDX drug screening is a promising add-on to current functional drug screening in precision medicine platforms.

Desmoglein-3 induces YAP phosphorylation and inactivation during collective migration of oral carcinoma cells

Alterations of the Hippo-YAP pathway are potential targets for oral squamous cell carcinoma (OSCC) therapy, but heterogeneity in this pathway could be responsible for therapeutic resistance. We analysed the Hippo-YAP signatures in a cohort of characterised keratinocyte cell lines derived from the mouth floor and buccal mucosa from different stages of OSCC tumour progression and focused on the specific role of YAP on invasive and metastatic potential. We confirmed heterogeneity in the Hippo-YAP pathway in OSCC lines, including overexpression of YAP1, WWTR1 (often referred to as TAZ) and the major Hippo signalling components, as well as the variations in the genes encoding the intercellular anchoring junctional proteins, which could potentially regulate the Hippo pathway. Specifically, desmoglein-3 (DSG3) exhibits a unique and mutually exclusive regulation of YAP via YAP phosphorylation during the collective migration of OSCC cells. Mechanistically, such regulation is associated with inhibition of phosphorylation of epidermal growth factor receptor (EGFR) (S695/Y1086) and its downstream effectors heat shock protein beta-1 (Hsp27) (S78/S82) and transcription factor AP-1 (c-Jun) (S63), leading to YAP phosphorylation coupled with its cytoplasmic translocation and inactivation. Additionally, OSCC lines display distinct phenotypes of YAP dependency or a mixed YAP and TAZ dependency for cell migration, and present distinct patterns in YAP abundance and activity, with the latter being associated with YAP nuclear localisation. In conclusion, this study has provided evidence for a newly identified paradigm in the Hippo-YAP pathway and suggests a new regulation mechanism involved in the control of collective migration in OSCC cells.

Predicting heterogeneity in clone-specific therapeutic vulnerabilities using single-cell transcriptomic signatures

While understanding molecular heterogeneity across patients underpins precision oncology, there is increasing appreciation for taking intra-tumor heterogeneity into account. Based on large-scale analysis of cancer omics datasets, we highlight the importance of intra-tumor transcriptomic heterogeneity (ITTH) for predicting clinical outcomes. Leveraging single-cell RNA-seq (scRNA-seq) with a recommender system (CaDRReS-Sc), we show that heterogeneous gene-expression signatures can predict drug response with high accuracy (80%). Using patient-proximal cell lines, we established the validity of CaDRReS-Sc's monotherapy (Pearson r>0.6) and combinatorial predictions targeting clone-specific vulnerabilities (>10% improvement). Applying CaDRReS-Sc to rapidly expanding scRNA-seq compendiums can serve as in silico screen to accelerate drug-repurposing studies. Availability: https://github.com/CSB5/CaDRReS-Sc .

The Hippo pathway: a renewed insight in the craniofacial diseases and hard tissue remodeling

The Hippo pathway plays an important role in many pathophysiological processes, including cell proliferation and differentiation, cell death, cell migration and invasion. Because of its extensive functions, Hippo pathway is closely related to not only growth and development, but also many diseases, including inflammation and cancer. In this study, the role of Hippo pathway in craniofacial diseases and hard tissue remodeling was reviewed, in attempting to find new research directions.

Defining the Role of Immunotherapy in the Curative Treatment of Locoregionally Advanced Head and Neck Cancer: Promises, Challenges, and Opportunities

Recent advancements in the development of immunotherapies have raised the hope for patients with locally-advanced HNSCC (LA-HNSCC) to achieve improved oncologic outcomes without the heavy burden of treatment-related morbidity. While there are several ongoing late phase clinical trials that seek to determine whether immunotherapy can be effectively employed in the definitive setting, initial results from concurrent immuno-radiotherapy therapy trials have not shown strong evidence of benefit. Encouragingly, evidence from preclinical studies and early-phase neoadjuvant studies have begun to show potential pathways forward, with therapeutic combinations and sequences that intentionally spare tumor draining lymphatics in order to maximize the synergy between definitive local therapy and immunotherapy. The intent of this review is to summarize the scientific rationale and current clinical evidence for employing immunotherapy for LA-HNSCC as well as the ongoing efforts and challenges to determine how to optimally deliver and sequence immunotherapy alongside traditional therapeutics. In both the preclinical and clinical settings, we will discuss the application of immunotherapies to both surgical and radiotherapeutic management of HNSCC.

Prognostic Value of an m5C RNA Methylation Regulator-Related Signature for Clear Cell Renal Cell Carcinoma

Purpose

Clear cell renal cell carcinoma (ccRCC) is highly heterogeneous and is one of the most lethal types of cancer within the urinary system. Aberrant expression of 5-methylcytosine (m⁵C) RNA methylation regulators has been shown to result in occurrence and progression of tumors. However, the role of these regulators in ccRCC remains unclear.

Materials and Methods

We extracted RNA sequencing expression data with corresponding clinical information of patients with ccRCC from The Cancer Genome Atlas (TCGA) database. We then compared the expression profiles of m⁵C RNA methylation regulators between normal and ccRCC tissues, and determined different subtypes through consensus clustering analysis. In addition, we constructed a prognostic signature and evaluated it using a range of bioinformatics approaches. The expression of signature-related genes was subsequently verified in the clinical samples using qRT-PCR.

Results

We identified 12 differentially expressed m⁵C RNA methylation regulators between cancer and normal control samples. Two clusters of patients with ccRCC and diverse clinicopathological characteristics and prognoses were then determined through consensus clustering analysis. Functional annotations revealed that m⁵C RNA regulators were significantly correlated with the ccRCC progression. Moreover, we constructed a four-gene risk score signature (comprised of NOP2, NSUN4, NSUN6, and TET2) and divided the patients with ccRCC into high- and low-risk groups based on the median risk score. The risk score was associated with clinicopathological features and was an independent prognostic indicator of ccRCC. Our stratified analysis results suggest that the signature has high prognostic value. Based on qRT-PCR results, the NOP2 and NSUN4 mRNA expressions were higher and those of NSUN6 and TET2 were lower in ccRCC tissues than in normal tissues.

Conclusion

Our results demonstrate that m⁵C RNA methylation regulators may affect ccRCC progression and could be exploited for diagnostic and prognostic purposes.

The optimization of combinatorial drug therapies: Strategies and laboratorial platforms

Designing optimal combinatorial drug therapies is challenging, because the drug interactions depend not only on the drugs involved, but also on their doses. With recent advances, combinatorial drug therapy is closer than ever to clinical application. Herein, we summarize approaches and advances over the past decade for identifying and optimizing drug combination therapies, with innovations across research fields, covering physical laboratory platforms for combination screening to computational models and algorithms designed for synergism prediction and optimization. By comparing different types of approach, we detail a three-step workflow that could maximize the overall optimization efficiency, thus enabling the application of personalized optimization of combinatorial drug therapy.

Targeted Treatment of Head and Neck (Pre)Cancer: Preclinical Target Identification and Development of Novel Therapeutic Applications

Head and neck squamous cell carcinomas (HNSCC) develop in the mucosal lining of the upper-aerodigestive tract. In carcinogen-induced HNSCC, tumors emerge from premalignant mucosal changes characterized by tumor-associated genetic alterations, also coined as 'fields' that are occasionally visible as leukoplakia or erythroplakia lesions but are mostly invisible. Consequently, HNSCC is generally diagnosed de novo at more advanced stages in about 70% of new diagnosis. Despite intense multimodality treatment protocols, the overall 5-years survival rate is 50-60% for patients with advanced stage of disease and seems to have reached a plateau. Of notable concern is the lack of further improvement in prognosis despite advances in treatment. This can be attributed to the late clinical presentation, failure of advanced HNSCC to respond to treatment, the deficit of effective targeted therapies to eradicate tumors and precancerous changes, and the lack of suitable markers for screening and personalized therapy. The molecular landscape of head and neck cancer has been elucidated in great detail, but the absence of oncogenic mutations hampers the identification of druggable targets for therapy to improve outcome of HNSCC. Currently, functional genomic approaches are being explored to identify potential therapeutic targets. Identification and validation of essential genes for both HNSCC and oral premalignancies, accompanied with biomarkers for therapy response, are being investigated. Attentive diagnosis and targeted therapy of the preceding oral premalignant (preHNSCC) changes may prevent the development of tumors. As classic oncogene addiction through activating mutations is not a realistic concept for treatment of HNSCC, synthetic lethality and collateral lethality need to be exploited, next to immune therapies. In recent studies it was shown that cell cycle regulation and DNA damage response pathways become significantly altered in HNSCC causing replication stress, which is an avenue that deserves further exploitation as an HNSCC vulnerability for treatment. The focus of this review is to summarize the current literature on the preclinical identification of potential druggable targets for therapy of (pre)HNSCC, emerging from the variety of gene knockdown and knockout strategies, and the testing of targeted inhibitors. We will conclude with a future perspective on targeted therapy of HNSCC and premalignant changes.

Cancer Explant Models

Overcoming the challenges of understanding and treating cancer requires reliable patient-derived models of cancer (PDMCs). For decades, cancer research and therapeutic development relied primarily on cancer cell lines because of their prevalence, reproducibility, and simplicity to maintain. However, findings from research conducted in cell lines are rarely recapitulated in vivo and seldom directly translatable to patients. The tumor microenvironment (TME), tumor-stromal interactions, and associations with host immune cells produce profound changes in tumor phenotype and complexity not captured in traditional monolayer cell culture. In this chapter, we present various cancer explant models and discuss their applicability based on specific research aims. We discuss the appropriateness of these models for basic science questions, drug screening/development, and for personalized, precision medicine. We also consider logistical factors such as resource cost, technical difficulty, and accessibility. We finish this chapter with a practical guide intended to help the reader select the cancer explant model system(s) that best address their research aims.

Contribution of Genomics to the Surgical Management and Study of Oral Cancer

Background

Oral squamous cell carcinoma (OSCC) is the most frequent type of tumor arising from the oral cavity. Surgery is the cornerstone of the treatment of these cancers. Tumor biology has long been overlooked as an important contributor to the outcome of surgical procedures, but recent studies are challenging this concept. Molecular analyses of tumor DNA or RNA provide a rich source of information about the biology of OSCC.

Methods

We searched for relevant articles using PubMed. We examined in particular the prospect of applying molecular methods for minimally invasive exploration of OSCC biology.

Results

We examined five potential applications of genomics to the surgical management and study of OSCC: i) assessing oral potentially malignant lesions; ii) tumor staging prior to surgery; iii) predicting postoperative risk in locally advanced tumors; iv) measuring minimal residual disease and optimizing the longitudinal monitoring of OSCC; and v) predicting the efficacy of medical treatment.

Conclusions

Genomic information can be harnessed in order to identify new biomarkers that could improve the staging, choice of therapy and management of OSCC. The identification of new biomarkers is awaited for better personalization of the surgical treatment of OSCC.

High-Content Analysis-Based Sensitivity Prediction and Novel Therapeutics Screening for c-Met-Addicted Glioblastoma

Simple Summary

Real-time ex vivo drug testing tailors individual therapeutics based on predicted drug responses. Most technologies to date rely on conventional drug screening that provides low confidence data. Here, we present high-content analysis-based drug testing of glioblastoma patients to identify the right glioblastoma patients for a given drug. This generates multi-parameter biomarker and phenotype readouts providing a better reliability of the assay. Additionally, we showed a high-content drug repurposing screen and defined a new c-Met-inhibiting function of the CDK4/6 inhibitor Abemaciclib. Large-scale high throughput screening results demonstrate that Abemaciclib sensitivity in glioblastoma patients is highly correlated with the c-Met inhibitors sensitivity, further supporting the accuracy of the platform and important new clinical implications regarding multiple functions of Abemaciclib.

Abstract

(1) Background: Recent advances in precision oncology research rely on indicating specific genetic alterations associated with treatment sensitivity. Developing ex vivo systems to identify cancer patients who will respond to a specific drug remains important. (2) Methods: cells from 12 patients with glioblastoma were isolated, cultured, and subjected to high-content screening. Multi-parameter analyses assessed the c-Met level, cell viability, apoptosis, cell motility, and migration. A drug repurposing screen and large-scale drug sensitivity screening data across 59 cancer cell lines and patient-derived cells were obtained from 125 glioblastoma samples. (3) Results: High-content analysis of patient-derived cells provided robust and accurate drug responses to c-Met-targeted agents. Only the cells of one glioblastoma patient (PDC6) showed elevated c-Met level and high susceptibility to the c-Met inhibitors. Multi-parameter image analysis also reflected a decreased c-Met expression and reduced cell growth and motility by a c-Met-targeting antibody. In addition, a drug repurposing screen identified Abemaciclib as a distinct CDK4/6 inhibitor with a potent c-Met-inhibitory function. Consistent with this, we present large-scale drug sensitivity screening data showing that the Abemaciclib response correlates with the response to c-Met inhibitors. (4) Conclusions: Our study provides a new insight into high-content screening platforms supporting drug sensitivity prediction and novel therapeutics screening.

Patient-Derived Xenograft and Organoid Models for Precision Medicine Targeting of the Tumour Microenvironment in Head and Neck Cancer

Patient survival from head and neck squamous cell carcinoma (HNSCC), the seventh most common cause of cancer, has not markedly improved in recent years despite the approval of targeted therapies and immunotherapy agents. Precision medicine approaches that seek to individualise therapy through the use of predictive biomarkers and stratification strategies offer opportunities to improve therapeutic success in HNSCC. To enable precision medicine of HNSCC, an understanding of the microenvironment that influences tumour growth and response to therapy is required alongside research tools that recapitulate the features of human tumours. In this review, we highlight the importance of the tumour microenvironment in HNSCC, with a focus on tumour hypoxia, and discuss the fidelity of patient-derived xenograft and organoids for modelling human HNSCC and response to therapy. We describe the benefits of patient-derived models over alternative preclinical models and their limitations in clinical relevance and how these impact their utility in precision medicine in HNSCC for the discovery of new therapeutic agents, as well as predictive biomarkers to identify patients' most likely to respond to therapy.

Two high-yield complementary methods to sort cell populations by their 2D or 3D migration speed

The potential to migrate is one of the most fundamental functions for various epithelial, mesenchymal, and immune cells. Image analysis of motile cell populations, both primary and cultured, typically reveals an intercellular variability in migration speeds. However, cell migration chromatography, the sorting of large populations of cells based on their migratory characteristics, cannot be easily performed. The lack of such methods has hindered our understanding of the direct correlation between the capacity to migrate and other cellular properties. Here, we report two novel, easily implementable and readily scalable methods to sort millions of live migratory cancer and immune cells based on their spontaneous migration in two-dimensional and three-dimensional microenvironments, respectively. Correlative downstream transcriptomic, molecular and functional tests reveal marked differences between the fast and slow subpopulations in patient-derived cancer cells. We further employ our method to reveal that sorting the most migratory cytotoxic T lymphocytes yields a pool of cells with enhanced cytotoxicity against cancer cells. This phenotypic assay opens new avenues for the precise characterization of the mechanisms underlying hither to unexplained heterogeneities in migratory phenotypes within a cell population, and for the targeted enrichment of the most potent migratory leukocytes in immunotherapies.

Head and neck cancer patient-derived xenograft models - A systematic review

BACKGROUND

Patient-derived xenograft (PDX) involve the direct surgical transfer of fresh human tumor samples to immunodeficient mice. This systematic review aimed to identify publications of head and neck cancer PDX (HNC-PDX) models, describing the main methodological characteristics and outcomes.

METHODS

An electronic search was undertaken in four databases, including publications having used HNC-PDX. Data were analyzed descriptively.

RESULTS

63 articles were yielded. The nude mouse was one most commonly animal model used (38.8 %), and squamous cell carcinoma accounted for the majority of HNC-PDX (80.6 %). Tumors were mostly implanted in the flank (86.3 %), and the latency period ranged from 30 to 401 days. The successful rate ranged from 17 % to 100 %. Different drugs and pathways were identified.

CONCLUSION

HNC-PDX appears to significantly recapitulate the morphology of the original HNC and represents a valuable method in translational research for the assessment of the in vivo effect of novel therapies for HNC.

Phenotype-related drug sensitivity analysis of single CTCs for medicine evaluation

Due to the heterogeneous and variable drug sensitivity of tumor cells, real-time monitoring of a patient's drug response is desirable for implementing personalized and dynamic therapy. Although considerable efforts have been directed at drug screening in living cells, performing repeated drug sensitivity analysis using patient-derived primary tumor cells at the single-cell level remains challenging. Here, we present an efficient approach to assess phenotype-related drug sensitivity at the single-cell level using patient-derived circulating tumor cells (CTCs) based on a drug sensitivity microfluidic chip (DS-Chip). The DS-Chip consists of a drug gradient generator and parallel cell traps, achieving continuous single CTC capture, drug gradient distributions, drug stimulation, fluorescent probe labeling and three-color fluorescence imaging. Based on the established DS-Chip, we investigated the drug sensitivity of single cells by simultaneously monitoring epithelial–mesenchymal transition (EMT) biomarkers and apoptosis in living cells, and verified the correlation between EMT gradients and drug sensitivity. Using the new approach, we further tested the optimal drug response dose in individual CTCs isolated from 5 cancer patients through fluorescence analysis of EMT and apoptosis. The DS-Chip allows noninvasive and real-time measurements of the drug sensitivity of a patient's tumor cells during therapy. This developed approach has practical significance and can effectively guide drug selection and therapeutic evaluation for personalized medicine.

Due to the heterogeneous and variable drug sensitivity of tumor cells, real-time monitoring of a patient's drug response is desirable for implementing personalized and dynamic therapy.

Single-cell analysis of EphA clustering phenotypes to probe cancer cell heterogeneity

The Eph family of receptor tyrosine kinases is crucial for assembly and maintenance of healthy tissues. Dysfunction in Eph signaling is causally associated with cancer progression. In breast cancer cells, dysregulated Eph signaling has been linked to alterations in receptor clustering abilities. Here, we implemented a single-cell assay and a scoring scheme to systematically probe the spatial organization of activated EphA receptors in multiple carcinoma cells. We show that cancer cells retain EphA clustering phenotype over several generations, and the degree of clustering reported for migration potential both at population and single-cell levels. Finally, using patient-derived cancer lines, we probed the evolution of EphA signalling in cell populations that underwent metastatic transformation and acquisition of drug resistance. Taken together, our scalable approach provides a reliable scoring scheme for EphA clustering that is consistent over multiple carcinomas and can assay heterogeneity of cancer cell populations in a cost- and time-effective manner.

Ravasio et al. develop an assay to quantify the clustering of Eph receptor EphA2 upon ligand binding, based on the scoring of single cells. They discover that clustering phenotype predicts cell and population migration potential in cancer cells and reflects Eph associated gene expression profiles in cancer cell lines.

Preclinical models of head and neck squamous cell carcinoma for a basic understanding of cancer biology and its translation into efficient therapies

Comprehensive molecular characterization of head and neck squamous cell carcinoma (HNSCC) has led to the identification of distinct molecular subgroups with fundamental differences in biological properties and clinical behavior. Despite improvements in tumor classification and increased understanding about the signaling pathways involved in neoplastic transformation and disease progression, current standard-of-care treatment for HNSCC mostly remains to be based on a stage-dependent strategy whereby all patients at the same stage receive the same treatment. Preclinical models that closely resemble molecular HNSCC subgroups that can be exploited for dissecting the biological function of genetic variants and/or altered gene expression will be highly valuable for translating molecular findings into improved clinical care. In the present review, we merge and discuss existing and new information on established cell lines, primary two- and three-dimensional ex vivo tumor cultures from HNSCC patients, and animal models. We review their value in elucidating the basic biology of HNSCC, molecular mechanisms of treatment resistance and their potential for the development of novel molecularly stratified treatment.

Quantitative Image-Based Cell Viability (QuantICV) Assay for Microfluidic 3D Tissue Culture Applications

Microfluidic 3D tissue culture systems are attractive for in vitro drug testing applications due to the ability of these platforms to generate 3D tissue models and perform drug testing at a very small scale. However, the minute cell number and liquid volume impose significant technical challenges to perform quantitative cell viability measurements using conventional colorimetric or fluorometric assays, such as MTS or Alamar Blue. Similarly, live-dead staining approaches often utilize metabolic dyes that typically label the cytoplasm of live cells, which makes it difficult to segment and count individual cells in compact 3D tissue cultures. In this paper, we present a quantitative image-based cell viability (QuantICV) assay technique that circumvents current challenges of performing the quantitative cell viability assay in microfluidic 3D tissue cultures. A pair of cell-impermeant nuclear dyes (EthD-1 and DAPI) were used to sequentially label the nuclei of necrotic and total cell populations, respectively. Confocal microscopy and image processing algorithms were employed to visualize and quantify the cell nuclei in the 3D tissue volume. The QuantICV assay was validated and showed good concordance with the conventional bulk MTS assay in static 2D and 3D tumor cell cultures. Finally, the QuantICV assay was employed as an on-chip readout to determine the differential dose responses of parental and metastatic 3D oral squamous cell carcinoma (OSCC) to Gefitinib in a microfluidic 3D culture device. This proposed technique can be useful in microfluidic cell cultures as well as in a situation where conventional cell viability assays are not available.

Circulating extracellular vesicle-associated TGFβ3 modulates response to cytotoxic therapy in head and neck squamous cell carcinoma

Management of locally advanced head and neck squamous cell carcinoma (HNSCC) requires a multi-prong approach comprising surgery, radiation and/or chemotherapy, yet outcomes are limited. This is largely due to a paucity of biomarkers that can predict response to specific treatment modalities. Here, we evaluated TGFβ3 protein levels in extracellular vesicles (EVs) released by HNSCC cells as a predictor for response to chemoradiation therapy (CRT). To this end, specific EV-fractions were isolated from cell lines or HNSCC patient plasma, and TGFβ3 protein was quantified. In patients treated with CRT, TGFβ3 levels were found to be significantly higher in plasma EV-fractions or non-responders compared with responders. High levels of TGFβ3 levels in Annexin V-EVs were associated with the worst progression-free survival. In vitro experiments demonstrated that TGFβ3 silencing sensitized HNSCC cells to cytotoxic therapies, and this phenotype could be rescued by treatment with exogenous. In addition, specific EV-fractions shed by cisplatin-resistant cells were sufficient to transfer the resistant phenotype to sensitive cells through activation of TGFβ-signaling pathway. Therefore, our data show that TGFβ3 transmitted through EV plays a significant role in response to cytotoxic therapy, which can be exploited as a potential biomarker for CRT response in HNSCC patients treated with curative intent.

Enabling Technologies for Personalized and Precision Medicine

Individualizing patient treatment is a core objective of the medical field. Reaching this objective has been elusive owing to the complex set of factors contributing to both disease and health; many factors, from genes to proteins, remain unknown in their role in human physiology. Accurately diagnosing, monitoring, and treating disorders requires advances in biomarker discovery, the subsequent development of accurate signatures that correspond with dynamic disease states, as well as therapeutic interventions that can be continuously optimized and modulated for dose and drug selection. This work highlights key breakthroughs in the development of enabling technologies that further the goal of personalized and precision medicine, and remaining challenges that, when addressed, may forge unprecedented capabilities in realizing truly individualized patient care.

Zebrafish Avatars towards Personalized Medicine-A Comparative Review between Avatar Models

Cancer frequency and prevalence have been increasing in the past decades, with devastating impacts on patients and their families. Despite the great advances in targeted approaches, there is still a lack of methods to predict individual patient responses, and therefore treatments are tailored according to average response rates. “Omics” approaches are used for patient stratification and choice of therapeutic options towards a more precise medicine. These methods, however, do not consider all genetic and non-genetic dynamic interactions that occur upon drug treatment. Therefore, the need to directly challenge patient cells in a personalized manner remains. The present review addresses the state of the art of patient-derived in vitro and in vivo models, from organoids to mouse and zebrafish Avatars. The predictive power of each model based on the retrospective correlation with the patient clinical outcome will be considered. Finally, the review is focused on the emerging zebrafish Avatars and their unique characteristics allowing a fast analysis of local and systemic effects of drug treatments at the single-cell level. We also address the technical challenges that the field has yet to overcome.

Antibody therapies for multiple myeloma

Introduction: Multiple myeloma (MM) is characterized by the uncontrollable proliferation of plasma cells and the excessive production of a specific type of immunoglobulin. Immune system is deregulated in MM and, thus, immunotherapy is a promising therapeutic strategy.Areas covered: The first approach is to use monoclonal antibodies that recognize specific antigens on the surface of myeloma cells, such as CD38 and B-cell maturation antigen. Upon binding to their target, monoclonal antibodies activate the immune cells to destroy the malignant cell. Anti-CD38 molecules as part of highly effective combination regimens have been approved in both newly diagnosed and relapsed/refractory patients and have significantly changed the myeloma treatment landscape in the recent years. Another strategy is to use antibodies that bind both to a molecule on the surface of the myeloma cell and another molecule on the surface of a T-cell (bispecific antibodies). Consecutively, the T-cell comes close to and recognizes the myeloma cell. These have shown promising results in heavily pre-treated patients.Expert opinion: Antibody therapy has significantly enhanced the armamentarium against MM. Further research should focus on tailoring the combination regimens based on disease and patient characteristics in order to optimize the efficacy and safety.

Conditionally reprogrammed colorectal cancer cells combined with mouse avatars identify synergy between EGFR and MEK or CDK4/6 inhibitors

Preclinical models, including patient-derived xenograft (PDX) and organoid and primary cell culture, are essential for studies of cancer cell biology and facilitate translational research and individualization of therapy. We explored the optimum preclinical model by modifying the conventional conditional reprogramming (CR) system followed by screening effective targeted drug combinations against colorectal cancer (CRC). By modifying the ingredients of the culture medium used in a conventional CR system, a novel individualized CR system (termed i-CR) was established. Tumor samples from CRC patients were collected and PDX models were derived followed by high-throughput i-CR drug screening and validation of the effective targeted drug combinations. The i-CR system selectively expanded tumor cells rather than normal epithelial cells and facilitated high-throughput drug screening when combined with high-content imaging and quantitative analysis of cell proliferation. Using inhibitors targeting multiple signaling pathways identified by high-throughput i-CR drug screening, we discovered that inhibition of the EGFR and MEK or CDK4/6 pathways exerted a synergistic inhibitory effect against CRC, and we noted super-synergistic effects when EGFR, MEK, and CDK4/6 inhibitors were used simultaneously. These data were validated using paired PDX models, which showed marked inhibition of tumor growth. The novel i-CR system combined with PDX models will enable individualization of therapy and drug discovery, and strategies combining EGFR, MEK, and CDK4/6 inhibitors warrant clinical validation.

A review on multiplatform evaluations of semi-automatic open-source based image segmentation for cranio-maxillofacial surgery

BACKGROUND AND OBJECTIVES

Computer-assisted technologies, such as image-based segmentation, play an important role in the diagnosis and treatment support in cranio-maxillofacial surgery. However, although many segmentation software packages exist, their clinical in-house use is often challenging due to constrained technical, human or financial resources. Especially technological solutions or systematic evaluations of open-source based segmentation approaches are lacking. The aim of this contribution is to assess and review the segmentation quality and the potential clinical use of multiple commonly available and license-free segmentation methods on different medical platforms.

METHODS

In this contribution, the quality and accuracy of open-source segmentation methods was assessed on different platforms using patient-specific clinical CT-data and reviewed with the literature. The image-based segmentation algorithms GrowCut, Robust Statistics Segmenter, Region Growing 3D, Otsu & Picking, Canny Segmentation and Geodesic Segmenter were investigated in the mandible on the platforms 3D Slicer, MITK and MeVisLab. Comparisons were made between the segmentation algorithms and the ground truth segmentations of the same anatomy performed by two clinical experts (n = 20). Assessment parameters were the Dice Score Coefficient (DSC), the Hausdorff Distance (HD), and Pearsons correlation coefficient (r).

RESULTS

The segmentation accuracy was highest with the GrowCut (DSC 85.6%, HD 33.5 voxel) and the Canny (DSC 82.1%, HD 8.5 voxel) algorithm. Statistical differences between the assessment parameters were not significant (p < 0.05) and correlation coefficients were close to the value one (r > 0.94) for any of the comparison made between the segmentation methods and the ground truth schemes. Functionally stable and time-saving segmentations were observed.

CONCLUSION

High quality image-based semi-automatic segmentation was provided by the GrowCut and the Canny segmentation method. In the cranio-maxillofacial complex, these segmentation methods provide algorithmic alternatives for image-based segmentation in the clinical practice for e.g. surgical planning or visualization of treatment results and offer advantages through their open-source availability. This is the first systematic multi-platform comparison that evaluates multiple license-free, open-source segmentation methods based on clinical data for the improvement of algorithms and a potential clinical use in patient-individualized medicine. The results presented are reproducible by others and can be used for clinical and research purposes.

Temporal phenotyping by mining healthcare data to derive lines of therapy for cancer

Lines of therapy (LOT) derived from real-world healthcare data not only depict real-world cancer treatment sequences, but also help define patient phenotypes along the course of disease progression and therapeutic interventions. The sequence of prescribed anticancer therapies can be defined as temporal phenotyping resulting from changes in morphological (tumor staging), biochemical (biomarker testing), physiological (disease progression), and behavioral (physician prescribing and patient adherence) parameters. We introduce a novel methodology that is a two-part approach: 1) create an algorithm to derive patient-level LOT and 2) aggregate LOT information via clustering to derive temporal phenotypes, in conjunction with visualization techniques, within a large insurance claims dataset. We demonstrated the methodology using two examples: metastatic non-small cell lung cancer and metastatic melanoma. First, we generated a longitudinal patient cohort for each cancer type and applied a set of rules to derive patient-level LOT. Then the LOT algorithm outputs for each cancer type were visualized using Sankey plots and K-means clusters based on durations of LOT and of gaps in therapy between LOT. We found differential distribution of temporal phenotypes across clusters. Our approach to identify temporal patient phenotypes can increase the quality and utility of analyses conducted using claims datasets, with the potential for application to multiple oncology disease areas across diverse healthcare data sources. The understanding of LOT as defining patients' temporal phenotypes can contribute to continuous health learning of disease progression and its interaction with different treatment pathways; in addition, this understanding can provide new insights that can be applied by tailoring treatment sequences for the patient phenotypes who will benefit.

Estimation of the Percentage of US Patients With Cancer Who Benefit From Genome-Driven Oncology

Importance

To date, the benefit of genome-driven cancer therapy has not been quantified.

Objective

We sought to estimate the annual percentage of patients in the United States with advanced or metastatic cancer who could be eligible for and benefit from US Food and Drug Administration (FDA)-approved genome-driven therapy from 2006 to 2018.

Design, Setting, and Participants

Retrospective cross-sectional study using publically available data of (1) demographic characteristics of patients with advanced or metastatic cancer; (2) FDA data on cancer drugs approved from January 2006 through January 2018; (3) measures of response and duration of response from drug labels; and (4) published reports estimating the frequency of various genomic aberrations used to estimate what percentage of patients would have been eligible for and would have benefited from genome-driven therapy during the studied period.

Main Outcomes and Measures

Estimated percentage of US patients with cancer eligible for and benefiting from genome-targeted and genome-informed therapy by year, response rate of genome-informed indications, and duration of response.

Results

A total of 31 drugs with 38 FDA-approved indications met our inclusion criteria for genome-targeted or genome-informed therapy from January 1, 2006, through January 31, 2018. The estimated number of patients eligible for genome-targeted therapy in 2006 was 28 729 of a total 564 830 patients with metastatic cancer, or 5.09% (95% CI, 5.03%-5.14%). By 2018, this number had increased to 50 811 of 609 640, or 8.33% (95% CI, 8.26%-8.40%). For genome-informed therapy in 2006, the eligible number of patients was 59 301 of 564 830, or 10.50% (95% CI, 10.42%-10.58%). In 2018, genome-informed treatment could be offered to 94 157 of 609 640, or 15.44% (95% CI, 15.35%-15.53%) of patients with metastatic cancer. The percentage of patients with cancer estimated to benefit from genome-targeted therapy in 2006 was 0.70% (95% CI, 0.68%-0.72%), and in 2018, it had increased to 4.90% (95% CI, 4.85%-4.95%). For genome-informed treatment in 2006, the percentage estimated to benefit was 1.31% (95% CI, 1.28%-1.34%), and in 2018, it had increased to 6.62% (95% CI, 6.56%-6.68%). The median overall response rate for all genome-informed drugs through January 2018 was 54%, and the median duration of response was 29.5 months.

Conclusions and Relevance

Although the number of patients eligible for genome-driven treatment has increased over time, these drugs have helped a minority of patients with advanced cancer. To accelerate progress in precision oncology, novel trial designs of genomic therapies should be developed, and broad portfolios of drug development, including immunotherapeutic and cytotoxic approaches, should be pursued.

Hiding in Plain Sight: Epigenetic Plasticity in Drug-Induced Tumor Evolution

Cancer is a heterogeneous disease with key differences at the cellular and molecular levels. Acquisition of these differences during the course of tumor development manifests into functional and phenotypic heterogeneity leading to tumor diversity, also referred to as intra-tumor heterogeneity (ITH). Within a tumor, there are subpopulations of cells capable of tumor initiation and maintenance. These cells often exhibit resistance to standard-of-care anti-cancer drugs. However, the role of various subpopulations (clones) in drug resistance remains to be investigated. Moreover, the jury is still out about whether drug resistance is a result of clonal selection of preexisting cells, or the cells acquire resistance by dynamic re-wiring of their epigenome. Therefore, we investigated the drug-induced tumor evolution in patient-derived primary cells of head and neck squamous cell carcinoma. Our data demonstrated the role of a preexisting poised epigenetic state in drug-induced adaptive evolution of tumor cells. Importantly, the combination of chemotherapy and epigenetic inhibitors can prevent/delay drug-induced tumor evolution.

Precision Medicine Tools to Guide Therapy and Monitor Response to Treatment in a HER-2+ Gastric Cancer Patient: Case Report

Trastuzumab, has played a major role in improving treatment outcomes in HER-2 positive gastric cancer. However, once there is disease progression there is a paucity of evidence for second line therapy. Patient-derived xenografts (PDXs) in combination with liquid biopsies can help guide individual therapeutic decisions and have now started to be studied. In the present case we established a PDX model from a metastatic HER-2+ gastric cancer patient and after the first engraftment passage we performed a mouse clinical trial to test T-DM1 as an alternative therapy for the patient. The PDX tumor response served as a guide to administer T-DM1 therapy to the patient who responded to treatment before relapsing 6 months later. Throughout out the clinical follow up of the patient, ctDNA levels of HER-2 copy number and a PIK3CA mutation were monitored and we found their correlation with drug response and disease progression to outperform that of CEA levels. This study highlights the utility of applying precision medicine tools combining PDX models to guide therapy with circulating tumor DNA (ctDNA) to monitor treatment response and disease progression.

Self-aligning Tetris-Like (TILE) modular microfluidic platform for mimicking multi-organ interactions

Multi-organ perfusion systems offer the unique opportunity to mimic different physiological systemic interactions. However, existing multi-organ culture platforms have limited flexibility in specifying the culture conditions, device architectures, and fluidic connectivity simultaneously. Here, we report a modular microfluidic platform that addresses this limitation by enabling easy conversion of existing microfluidic devices into tissue and fluid control modules with self-aligning magnetic interconnects. This enables a 'stick-n-play' approach to assemble planar perfusion circuits that are amenable to both bioimaging-based and analytical measurements. A myriad of tissue culture and flow control TILE modules were successfully constructed with backward compatibility. Finally, we demonstrate applications in constructing recirculating multi-organ systems to emulate liver-mediated bioactivation of nutraceuticals and prodrugs to modulate their therapeutic efficacies in the context of atherosclerosis and cancer. This platform greatly facilitates the integration of existing organs-on-chip models to provide an intuitive and flexible way for users to configure different multi-organ perfusion systems.