Precision oncology: origins, optimism, and potential

Cancer drug development: The missing links

IMPACT STATEMENT

The success rate for cancer drugs which enter into phase 1 clinical trials is utterly less. Why the vast majority of drugs fail is not understood but suggests that pre-clinical studies are not adequate for human diseases. In 1975, as per the Tufts Center for the Study of Drug Development, pharmaceutical industries expended 100 million dollars for research and development of the average FDA approved drug. By 2005, this figure had more than quadrupled, to $1.3 billion. In order to recover their high and risky investment cost, pharmaceutical companies charge more for their products. However, there exists no correlation between drug development cost and actual sale of the drug. This high drug development cost could be due to the reason that all patients might not respond to the drug. Hence, a given drug has to be tested in large number of patients to show drug benefits and obtain significant results.

Review: Precision medicine and driver mutations: Computational methods, functional assays and conformational principles for interpreting cancer drivers

At the root of the so-called precision medicine or precision oncology, which is our focus here, is the hypothesis that cancer treatment would be considerably better if therapies were guided by a tumor’s genomic alterations. This hypothesis has sparked major initiatives focusing on whole-genome and/or exome sequencing, creation of large databases, and developing tools for their statistical analyses—all aspiring to identify actionable alterations, and thus molecular targets, in a patient. At the center of the massive amount of collected sequence data is their interpretations that largely rest on statistical analysis and phenotypic observations. Statistics is vital, because it guides identification of cancer-driving alterations. However, statistics of mutations do not identify a change in protein conformation; therefore, it may not define sufficiently accurate actionable mutations, neglecting those that are rare. Among the many thematic overviews of precision oncology, this review innovates by further comprehensively including precision pharmacology, and within this framework, articulating its protein structural landscape and consequences to cellular signaling pathways. It provides the underlying physicochemical basis, thereby also opening the door to a broader community.

Chasing the FOXO3: Insights into Its New Mitochondrial Lair in Colorectal Cancer Landscape

Colorectal cancer (CRC) poses a formidable challenge in terms of molecular heterogeneity, as it involves a variety of cancer-related pathways and molecular changes unique to an individual’s tumor. On the other hand, recent advances in DNA sequencing technologies provide an unprecedented capacity to comprehensively identify the genetic alterations resulting in tumorigenesis, raising the hope that new therapeutic approaches based on molecularly targeted drugs may prevent the occurrence of chemoresistance. Regulation of the transcription factor FOXO3a in response to extracellular cues plays a fundamental role in cellular homeostasis, being part of the molecular machinery that drives cells towards survival or death. Indeed, FOXO3a is controlled by a range of external stimuli, which not only influence its transcriptional activity, but also affect its subcellular localization. These regulation mechanisms are mediated by cancer-related signaling pathways that eventually drive changes in FOXO3a post-translational modifications (e.g., phosphorylation). Recent results showed that FOXO3a is imported into the mitochondria in tumor cells and tissues subjected to metabolic stress and cancer therapeutics, where it induces expression of the mitochondrial genome to support mitochondrial metabolism and cell survival. The current review discusses the potential clinical relevance of multidrug therapies that drive cancer cell fate by regulating critical pathways converging on FOXO3a.

A simple high-throughput approach identifies actionable drug sensitivities in patient-derived tumor organoids

Tumor organoids maintain cell-cell interactions, heterogeneity, microenvironment, and drug response of the sample they originate from. Thus, there is increasing interest in developing tumor organoid models for drug development and personalized medicine applications. Although organoids are in principle amenable to high-throughput screenings, progress has been hampered by technical constraints and extensive manipulations required by current methods. Here we introduce a miniaturized method that uses a simplified geometry by seeding cells around the rim of the wells (mini-rings). This allows high-throughput screenings in a format compatible with automation as shown using four patient-derived tumor organoids established from two ovarian and one peritoneal high-grade serous carcinomas and one carcinosarcoma of the ovary. Using our automated screening platform, we identified personalized responses by measuring viability, number, and size of organoids after exposure to 240 kinase inhibitors. Results are available within a week from surgery, a timeline compatible with therapeutic decision-making.

Assessing the Value of Next-Generation Sequencing Tests in a Dynamic Environment

Next-generation sequencing (NGS)-based technology has lowered the cost of cancer testing for genomic alterations and is now commercially available from a growing number of diagnostic laboratories. However, laboratories vary in the methodologies underlying their tests, the types and numbers of genomic alterations covered by the test, and the clinical annotation of the sequencing findings. Determining the value of NGS tests is dependent on whether it is used to support clinical trials or as a part of routine clinical care at a time when both the investigational drug pipeline and the list of U.S. Food and Drug Administration-approved or Compendium-listed therapeutics is in a high state of flux. Reimbursement policy for NGS testing by the Centers for Medicare & Medicaid is evolving as the value of NGS testing becomes more clearly defined for specific clinical situations. Patient care and clinical decisions-making are dependent on the oncologist's knowledge of when NGS testing has value. Here, we review principles and practice for NGS testing in this dynamic confluence of technology, cancer biology, and health care policy.

Machine learning predicts individual cancer patient responses to therapeutic drugs with high accuracy

Precision or personalized cancer medicine is a clinical approach that strives to customize therapies based upon the genomic profiles of individual patient tumors. Machine learning (ML) is a computational method particularly suited to the establishment of predictive models of drug response based on genomic profiles of targeted cells. We report here on the application of our previously established open-source support vector machine (SVM)-based algorithm to predict the responses of 175 individual cancer patients to a variety of standard-of-care chemotherapeutic drugs from the gene-expression profiles (RNA-seq or microarray) of individual patient tumors. The models were found to predict patient responses with >80% accuracy. The high PPV of our algorithms across multiple drugs suggests a potential clinical utility of our approach, particularly with respect to the identification of promising second-line treatments for patients failing standard-of-care first-line therapies.

Comparison of Treatment Recommendations by Molecular Tumor Boards Worldwide

Purpose

Precision oncology holds the promise of improving patient outcome. It is based on the idea that the testing of genomic biomarkers can lead to the recommendation of a treatment option tailored to the specific patient. To derive treatment recommendations from molecular profiles, interdisciplinary molecular tumor boards (MTBs) have been established recently in many academic institutions. The recommendation process in MTBs, however, has not been well defined, which limits applicability to larger clinical trials and patient populations.

Methods

We created four fictional patients on the basis of recent real cases with genomic information on mutations, fusions, copy numbers, and gene expression. We identified 29 tumor boards from nine countries worldwide and asked them to provide treatment recommendations for the sample patients. In addition, a questionnaire regarding the setup and methods used by MTBs was circulated.

Results

Five MTBs from four countries provided treatment recommendations and answered the questionnaire. For one patient, three tumor board treatment recommendations were identical, and two tumor boards had identical treatment strategies for the other three patients. There was heterogeneity in the interpretation of tumor and germline aberrations as well as in standards of prioritization.

Conclusion

Differences in the interpretation and recommendation process contribute to heterogeneity in MTB recommendations. Additional comparative analyses of recommendations could help improve rational decision making and lead to standardization.

Validating Comprehensive Next-Generation Sequencing Results for Precision Oncology: The NCT/DKTK Molecularly Aided Stratification for Tumor Eradication Research Experience

Purpose

Rapidly evolving genomics technologies, in particular comprehensive next-generation sequencing (NGS), have led to exponential growth in the understanding of cancer biology, shifting oncology toward personalized treatment strategies. However, comprehensive NGS approaches, such as whole-exome sequencing, have limitations that are related to the technology itself as well as to the input source. Hence, clinical implementation of comprehensive NGS in a quality-controlled diagnostic workflow requires both the standardization of sequencing procedures and continuous validation of sequencing results by orthogonal methods in an ongoing program to enable the determination of key test parameters and continuous improvement of NGS and bioinformatics pipelines.

Patients and Methods

We present validation data on 220 patients who were enrolled between 2013 and 2016 in a multi-institutional, genomics-guided precision oncology program (Molecularly Aided Stratification for Tumor Eradication Research) of the National Center for Tumor Diseases Heidelberg and the German Cancer Consortium.

Results

More than 90% of clinically actionable genomic alterations identified by combined whole-exome sequencing and transcriptome sequencing were successfully validated, with varying frequencies of discordant results across different types of alterations (fusions, 3.7%; single-nucleotide variants, 2.6%; amplifications, 1.1%; overexpression, 0.9%; deletions, 0.6%). The implementation of new computational methods for NGS data analysis led to a substantial improvement of gene fusion calling over time.

Conclusion

Collectively, these data demonstrate the value of a rigorous validation program that partners with comprehensive NGS to successfully implement and continuously improve cancer precision medicine in a clinical setting.

Precision oncology: separating the wheat from the chaff

Precision oncology based on next-generation sequencing (NGS) test is growing in daily clinical practice. However, the real impact of this strategy in patients' outcome on a large scale remains uncertain. In this review, we summarise existing literature on this topic, limitations for broad NGS implementation, bottlenecks in genomic variant interpretation and the role of molecular tumour boards.

The role of molecular enrichment on future therapies in hepatocellular carcinoma

Hepatocellular carcinomas (HCCs) are characterised by considerable phenotypic and molecular heterogeneity. Treating HCC and designing clinical trials are particularly challenging because co-existing liver disease, present in most patients, limits aggressive therapeutic options. Positive results in recent phase III clinical trials have confirmed the high value of anti-angiogenic therapies for HCC in both first (sorafenib and lenvatinib) and second line (regorafenib and cabozantinib) treatment modalities. However, failure of several large randomised controlled clinical trials over the last 10 years underlines the necessity for innovative treatment strategies and implementation of translational findings to overcome the unmet clinical need. Furthermore, the promising results from novel immunotherapies are likely to complement the landscape of active compounds for HCC and will require a completely different approach to patients, as well as the development of prognostic/predictive biomarkers. Given our increasing understanding of the most abundant molecular alterations in HCC, effective enrichment of patients based on clinical and molecular biomarkers, as well as adaptive clinical trials, are now feasible and should be implemented. Herein, we aim to review important aspects of precision medicine approaches in HCC that might contribute to improving the molecular subclassification of patients in a clinical trial setting and pave the way for novel therapeutic strategies.

Discrete dynamic network modeling of oncogenic signaling: Mechanistic insights for personalized treatment of cancer

Targeted drugs disrupting proteins that are dysregulated in cancer have emerged as promising treatments because of their specificity to cancer cell aberrations and thus their improved side effect profile. However, their success remains limited, largely due to existing or emergent therapy resistance. We suggest that this is due to limited understanding of the entire relevant cellular landscape. A class of mathematical models called discrete dynamic network models can be used to understand the integrated effect of an individual tumor's aberrations. We review the recent literature on discrete dynamic models of cancer and highlight their predicted therapeutic strategies. We believe dynamic network modeling can be used to drive treatment decision-making in a personalized manner to direct improved treatments in cancer.

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Cancer is rooted in incorrect cellular decisions caused by genetic alterations.

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Dynamic models of signaling networks can map the relevant repertoire of alterations.

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Discrete dynamic network models can predict therapeutic interventions.

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Progress in personalized medicine needs integration of multiple data and model types.

Prediction of overall survival for metastatic pancreatic cancer: Development and validation of a prognostic nomogram with data from open clinical trial and real-world study

It is necessary to develop prognostic tools of metastatic pancreatic cancer (MPC) for optimizing therapeutic strategies. Thus, we tried to develop and validate a prognostic nomogram of MPC. Data from 3 clinical trials (NCT00844649, NCT01124786, and NCT00574275) and 133 Chinese MPC patients were used for analysis. The former 2 trials were taken as the training cohort while NCT00574275 was used as the validation cohort. In addition, 133 MPC patients treated in China were taken as the testing cohort. Cox regression model was used to investigate prognostic factors in the training cohort. With these factors, we established a nomogram and verified it by Harrell's concordance index (C‐index) and calibration plots. Furthermore, the nomogram was externally validated in the validation cohort and testing cohort. In the training cohort (n = 445), performance status, liver metastasis, Carbohydrate antigen 19‐9 (CA19‐9) log‐value, absolute neutrophil count (ANC), and albumin were independent prognostic factors for overall survival (OS). A nomogram was established with these factors to predict OS and survival probabilities. The nomogram showed an acceptable discrimination ability (C‐index: .683) and good calibration, and was further externally validated in the validation cohort (n = 273, C‐index: .699) and testing cohort (n = 133, C‐index: .653).The nomogram total points (NTP) had the potential to stratify patients into 3‐risk groups with median OS of 11.7, 7.0 and 3.7 months (P < .001), respectively. In conclusion, the prognostic nomogram with NTP can predict OS for patients with MPC with considerable accuracy.

Machine Learning Detects Pan-cancer Ras Pathway Activation in The Cancer Genome Atlas

Precision oncology uses genomic evidence to match patients with treatment but often fails to identify all patients who may respond. The transcriptome of these "hidden responders" may reveal responsive molecular states. We describe and evaluate a machine-learning approach to classify aberrant pathway activity in tumors, which may aid in hidden responder identification. The algorithm integrates RNA-seq, copy number, and mutations from 33 different cancer types across The Cancer Genome Atlas (TCGA) PanCanAtlas project to predict aberrant molecular states in tumors. Applied to the Ras pathway, the method detects Ras activation across cancer types and identifies phenocopying variants. The model, trained on human tumors, can predict response to MEK inhibitors in wild-type Ras cell lines. We also present data that suggest that multiple hits in the Ras pathway confer increased Ras activity. The transcriptome is underused in precision oncology and, combined with machine learning, can aid in the identification of hidden responders.

Cardiovascular Precision Medicine in the Genomics Era

Precision medicine strives to delineate disease using multiple data sources-from genomics to digital health metrics-in order to be more precise and accurate in our diagnoses, definitions, and treatments of disease subtypes. By defining disease at a deeper level, we can treat patients based on an understanding of the molecular underpinnings of their presentations, rather than grouping patients into broad categories with one-size-fits-all treatments. In this review, the authors examine how precision medicine, specifically that surrounding genetic testing and genetic therapeutics, has begun to make strides in both common and rare cardiovascular diseases in the clinic and the laboratory, and how these advances are beginning to enable us to more effectively define risk, diagnose disease, and deliver therapeutics for each individual patient.

Poly-ligand profiling differentiates trastuzumab-treated breast cancer patients according to their outcomes

Assessing the phenotypic diversity underlying tumour progression requires the identification of variations in the respective molecular interaction networks. Here we report proof-of-concept for a platform called poly-ligand profiling (PLP) that surveys these system states and distinguishes breast cancer patients who did or did not derive benefit from trastuzumab. We perform tissue-SELEX on breast cancer specimens to enrich single-stranded DNA (ssDNA) libraries that preferentially interact with molecular components associated with the two clinical phenotypes. Testing of independent sample sets verifies the ability of PLP to classify trastuzumab-treated patients according to their clinical outcomes with ROC-AUC of 0.78. Standard HER2 testing of the same patients gives a ROC-AUC of 0.47. Kaplan–Meier analysis reveals a median increase in benefit from trastuzumab-containing treatments of 300 days for PLP-positive compared to PLP-negative patients. If prospectively validated, PLP may increase success rates in precision oncology and clinical trials, thus improving both patient care and drug development.

Patients’ selection is particularly important in cancer treatment. Here the authors present a proof-of-principle methodology that could be potentially important in assisting therapeutic decisions in the treatment of breast cancer patients.

Radiogenomics and IR

Radiogenomics involves the integration of mineable data from imaging phenotypes with genomic and clinical data to establish predictive models using machine learning. As a noninvasive surrogate for a tumor's in vivo genetic profile, radiogenomics may potentially provide data for patient treatment stratification. Radiogenomics may also supersede the shortcomings associated with genomic research, such as the limited availability of high-quality tissue and restricted sampling of tumoral subpopulations. Interventional radiologists are well suited to circumvent these obstacles through advancements in image-guided tissue biopsies and intraprocedural imaging. Comprehensive understanding of the radiogenomic process is crucial for interventional radiologists to contribute to this evolving field.

Current Molecular Targeted Therapies for Bone and Soft Tissue Sarcomas

Systemic treatment options for bone and soft tissue sarcomas remained unchanged until the 2000s. These cancers presented challenges in new drug development partly because of their rarity and heterogeneity. Many new molecular targeting drugs have been tried in the 2010s, and some were approved for bone and soft tissue sarcoma. As one of the first molecular targeted drugs approved for solid malignant tumors, imatinib's approval as a treatment for gastrointestinal stromal tumors (GISTs) has been a great achievement. Following imatinib, other tyrosine kinase inhibitors (TKIs) have been approved for GISTs such as sunitinib and regorafenib, and pazopanib was approved for non-GIST soft tissue sarcomas. Olaratumab, the monoclonal antibody that targets platelet-derived growth factor receptor (PDGFR)-α, was shown to extend the overall survival of soft tissue sarcoma patients and was approved in 2016 in the U.S. as a breakthrough therapy. For bone tumors, new drugs are limited to denosumab, a receptor activator of nuclear factor κB ligand (RANKL) inhibitor, for treating giant cell tumors of bone. In this review, we explain and summarize the current molecular targeting therapies approved and in development for bone and soft tissue sarcomas.

Precision oncology in the age of integrative genomics

Precision oncology applies genomic and other molecular analyses of tumor biopsies to improve the diagnosis and treatment of cancers. In addition to identifying therapeutic options, precision oncology tracks the response of a tumor to an intervention at the molecular level and detects drug resistance and the mechanisms by which it occurs. Integrative genomics can include sequencing specific panels of genes, exomes, or the entire triad of the patient's germline, tumor exome, and tumor transcriptome. Although the capabilities of sequencing technologies continue to improve, widespread adoption of genomics-driven precision oncology in the clinic has been held back by logistical, regulatory, financial, and ethical considerations. Nevertheless, integrative clinical sequencing programs applied at the point of care have the potential to improve the clinical management of cancer patients.

Evidence-Based Precision Oncology with the Cancer Targetome

A core tenet of precision oncology is the rational choice of drugs to interact with patient-specific biological targets of interest, but it is currently difficult for researchers to obtain consistent and well-supported target information for pharmaceutical drugs. We review current drug-target interaction resources and critically assess how supporting evidence is handled. We introduce the concept of a unified Cancer Targetome to aggregate drug-target interactions in an evidence-based framework. We discuss current unmet needs and the implications for evidence-based clinical omics. The focus of this review is precision oncology but the discussion is highly relevant to targeted therapies in any area.

Precision Medicine from a Public Health Perspective

Over the past decade, precision medicine (PM) approaches have received significant investment to create new therapies, learn more about disease processes, and potentially prevent diseases before they arise. However, in many ways, PM investments may come at the expense of existing public health measures that could have a greater impact on population health. As we tackle burgeoning public health concerns, such as obesity, and chronic diseases, such as cancer, it is not clear whether PM is aligned with public health or in conflict with its goals. We summarize the areas of promise demonstrated by PM, discuss the limitations of each of these areas from a population health perspective, and discuss how we can approach PM in a manner that is congruent with the core aims of public health.

Precision Oncology beyond Targeted Therapy: Combining Omics Data with Machine Learning Matches the Majority of Cancer Cells to Effective Therapeutics

Precision oncology involves identifying drugs that will effectively treat a tumor and then prescribing an optimal clinical treatment regimen. However, most first-line chemotherapy drugs do not have biomarkers to guide their application. For molecularly targeted drugs, using the genomic status of a drug target as a therapeutic indicator has limitations. In this study, machine learning methods (e.g., deep learning) were used to identify informative features from genome-scale omics data and to train classifiers for predicting the effectiveness of drugs in cancer cell lines. The methodology introduced here can accurately predict the efficacy of drugs, regardless of whether they are molecularly targeted or nonspecific chemotherapy drugs. This approach, on a per-drug basis, can identify sensitive cancer cells with an average sensitivity of 0.82 and specificity of 0.82; on a per-cell line basis, it can identify effective drugs with an average sensitivity of 0.80 and specificity of 0.82. This report describes a data-driven precision medicine approach that is not only generalizable but also optimizes therapeutic efficacy. The framework detailed herein, when successfully translated to clinical environments, could significantly broaden the scope of precision oncology beyond targeted therapies, benefiting an expanded proportion of cancer patients. Mol Cancer Res; 16(2); 269-78. ©2017 AACR.

Precision oncology using a clinician-directed, tailored approach to molecular profiling

AIM

Precision oncology involves molecularly matching patients to targeted agents usually in early drug development (EDD) programs. Molecular profiling (MP) identifies actionable targets. Comprehensive commercial MP platforms are costly and in resource limited environments, a more practical approach to MP is necessary to support EDD and precision oncology. We adopted a clinician-directed, tailored approach to MP to enrol patients onto molecularly targeted trials. We report the feasibility of this approach.

METHODS

All patients referred to the Royal Melbourne Hospital (RMH) EDD between September 2013 and September 2015 were identified in a prospective database. Key captured data included clinicopathological data, MP platform ordered (if any), molecular targets identified and subsequent enrolment onto clinical trials. EDD-clinician decisions to order MP and the platform utilized was guided by patient consultation, tumor type, trial availability and requirement for molecular information.

RESULTS

We identified 377 patients referred to RMH EDD. A total of 216 (57%) had MP ordered. The remainder had known actionable targets (19%), or were inappropriate for clinical trials (24%). In those undergoing MP, 187 genetic aberrations were found in 113 patients with 98 considered actionable targets in 86 patients. Ninety-eight (25%) patients were enrolled onto a clinical trial, including 40 (11%) receiving molecularly matched treatments. Median progression-free survival was improved in patients enrolled onto molecularly matched trials compared to those on unmatched trials (3.6 months vs 1.9 months, HR 0.58 [0.38-0.89], P  =  0.013).

CONCLUSION

A clinician-directed, tailored approach to the use of MP is feasible, resulting in 11% of patients enrolled onto molecularly matched trials.

The potential of liquid biopsies for the early detection of cancer

Precision medicine refers to the choosing of targeted therapies based on genetic data. Due to the increasing availability of data from large-scale tumor genome sequencing projects, genome-driven oncology may have enormous potential to change the clinical management of patients with cancer. To this end, components of tumors, which are shed into the circulation, i.e., circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), or extracellular vesicles, are increasingly being used for monitoring tumor genomes. A growing number of publications have documented that these “liquid biopsies” are informative regarding response to given therapies, are capable of detecting relapse with lead time compared to standard measures, and reveal mechanisms of resistance. However, the majority of published studies relate to advanced tumor stages and the use of liquid biopsies for detection of very early malignant disease stages is less well documented. In early disease stages, strategies for analysis are in principle relatively similar to advanced stages. However, at these early stages, several factors pose particular difficulties and challenges, including the lower frequency and volume of aberrations, potentially confounding phenomena such as clonal expansions of non-tumorous tissues or the accumulation of cancer-associated mutations with age, and the incomplete insight into driver alterations. Here we discuss biology, technical complexities and clinical significance for early cancer detection and their impact on precision oncology.

Functional precision cancer medicine-moving beyond pure genomics

The essential job of precision medicine is to match the right drugs to the right patients. In cancer, precision medicine has been nearly synonymous with genomics. However, sobering recent studies have generally shown that most patients with cancer who receive genomic testing do not benefit from a genomic precision medicine strategy. Although some call the entire project of precision cancer medicine into question, I suggest instead that the tools employed must be broadened. Instead of relying exclusively on big data measurements of initial conditions, we should also acquire highly actionable functional information by perturbing-for example, with cancer therapies-viable primary tumor cells from patients with cancer.

A system for detecting high impact-low frequency mutations in primary tumors and metastases

Tumor complexity and intratumor heterogeneity contribute to subclonal diversity. Despite advances in next-generation sequencing (NGS) and bioinformatics, detecting rare mutations in primary tumors and metastases contributing to subclonal diversity is a challenge for precision genomics. Here, in order to identify rare mutations, we adapted a recently described epithelial reprograming assay for short-term propagation of epithelial cells from primary and metastatic tumors. Using this approach, we expanded minor clones and obtained epithelial cell-specific DNA/RNA for quantitative NGS analysis. Comparative Ampliseq Comprehensive Cancer Panel sequence analyses were performed on DNA from unprocessed breast tumor and tumor cells propagated from the same tumor. We identified previously uncharacterized mutations present only in the cultured tumor cells, a subset of which has been reported in brain metastatic but not primary breast tumors. In addition, whole-genome sequencing identified mutations enriched in liver metastases of various cancers, including Notch pathway mutations/chromosomal inversions in 5/5 liver metastases, irrespective of cancer types. Mutations/rearrangements in FHIT, involved in purine metabolism, were detected in 4/5 liver metastases, and the same four liver metastases shared mutations in 32 genes, including mutations of different HLA-DR family members affecting OX40 signaling pathway, which could impact the immune response to metastatic cells. Pathway analyses of all mutated genes in liver metastases showed aberrant tumor necrosis factor and transforming growth factor signaling in metastatic cells. Epigenetic regulators including KMT2C/MLL3 and ARID1B, which are mutated in >50% of hepatocellular carcinomas, were also mutated in liver metastases. Thus, irrespective of cancer types, organ-specific metastases may share common genomic aberrations. Since recent studies show independent evolution of primary tumors and metastases and in most cases mutation burden is higher in metastases than primary tumors, the method described here may allow early detection of subclonal somatic alterations associated with metastatic progression and potentially identify therapeutically actionable, metastasis-specific genomic aberrations.

Natural diversity facilitates the discovery of conserved chemotherapeutic response mechanisms

Organismal fitness depends on adaptation to complex niches where chemical compounds and pathogens are omnipresent. These stresses can lead to the fixation of alleles in both xenobiotic responses and proliferative signaling pathways that promote survival in these niches. However, both xenobiotic responses and proliferative pathways vary within and among species. For example, genetic differences can accumulate within populations because xenobiotic exposures are not constant and selection is variable. Additionally, neutral genetic variation can accumulate in conserved proliferative pathway genes because these systems are robust to genetic perturbations given their essential roles in normal cell-fate specification. For these reasons, sensitizing mutations or chemical perturbations can disrupt pathways and reveal cryptic variation. With this fundamental view of how organisms respond to cytotoxic compounds and cryptic variation in conserved signaling pathways, it is not surprising that human patients have highly variable responses to chemotherapeutic compounds. These different responses result in the low FDA-approval rates for chemotherapeutics and underscore the need for new approaches to understand these diseases and therapeutic interventions. Model organisms, especially the classic invertebrate systems of Caenorhabditis elegans and Drosophila melanogaster, can be used to combine studies of natural variation across populations with responses to both xenobiotic compounds and chemotherapeutics targeted to conserved proliferative signaling pathways.

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

Genomics-driven cancer therapeutics has gained prominence in personalized cancer treatment. However, its utility in indications lacking biomarker-driven treatment strategies remains limited. Here we present a "phenotype-driven precision-oncology" approach, based on the notion that biological response to perturbations, chemical or genetic, in ex vivo patient-individualized models can serve as predictive biomarkers for therapeutic response in the clinic. We generated a library of "screenable" patient-derived primary cultures (PDCs) for head and neck squamous cell carcinomas that reproducibly predicted treatment response in matched patient-derived-xenograft models. Importantly, PDCs could guide clinical practice and predict tumour progression in two n = 1 co-clinical trials. Comprehensive "-omics" interrogation of PDCs derived from one of these models revealed YAP1 as a putative biomarker for treatment response and survival in ~24% of oral squamous cell carcinoma. We envision that scaling of the proposed PDC approach could uncover biomarkers for therapeutic stratification and guide real-time therapeutic decisions in the future.Treatment response in patient-derived models may serve as a biomarker for response in the clinic. Here, the authors use paired patient-derived mouse xenografts and patient-derived primary culture models from head and neck squamous cell carcinomas, including metastasis, as models for high-throughput screening of anti-cancer drugs.

Addressing the challenges of applying precision oncology

Precision oncology is described as the matching of the most accurate and effective treatments with the individual cancer patient. Identification of important gene mutations, such as BRCA1/2 that drive carcinogenesis, helped pave the way for precision diagnosis in cancer. Oncoproteins and their signaling pathways have been extensively studied, leading to the development of target-based precision therapies against several types of cancers. Although many challenges exist that could hinder the success of precision oncology, cutting-edge tools for precision diagnosis and precision therapy will assist in overcoming many of these difficulties. Based on the continued rapid progression of genomic analysis, drug development, and clinical trial design, precision oncology will ultimately become the standard of care in cancer therapeutics.

Breakthroughs in modern cancer therapy and elusive cardiotoxicity: Critical research-practice gaps, challenges, and insights

To date, five cancer treatment modalities have been defined. The three traditional modalities of cancer treatment are surgery, radiotherapy, and conventional chemotherapy, and the two modern modalities include molecularly targeted therapy (the fourth modality) and immunotherapy (the fifth modality). The cardiotoxicity associated with conventional chemotherapy and radiotherapy is well known. Similar adverse cardiac events are resurging with the fourth modality. Aside from the conventional and newer targeted agents, even the most newly developed, immune‐based therapeutic modalities of anticancer treatment (the fifth modality), e.g., immune checkpoint inhibitors and chimeric antigen receptor (CAR) T‐cell therapy, have unfortunately led to potentially lethal cardiotoxicity in patients. Cardiac complications represent unresolved and potentially life‐threatening conditions in cancer survivors, while effective clinical management remains quite challenging. As a consequence, morbidity and mortality related to cardiac complications now threaten to offset some favorable benefits of modern cancer treatments in cancer‐related survival, regardless of the oncologic prognosis. This review focuses on identifying critical research‐practice gaps, addressing real‐world challenges and pinpointing real‐time insights in general terms under the context of clinical cardiotoxicity induced by the fourth and fifth modalities of cancer treatment. The information ranges from basic science to clinical management in the field of cardio‐oncology and crosses the interface between oncology and onco‐pharmacology. The complexity of the ongoing clinical problem is addressed at different levels. A better understanding of these research‐practice gaps may advance research initiatives on the development of mechanism‐based diagnoses and treatments for the effective clinical management of cardiotoxicity.

Sex-Divergent Clinical Outcomes and Precision Medicine: An Important New Role for Institutional Review Boards and Research Ethics Committees

The efforts toward individualized medicine have constantly increased in an attempt to improve treatment options. These efforts have led to the development of small molecules which target specific molecular pathways involved in cancer progression. We have reviewed preclinical studies of sunitinib that incorporate sex as a covariate to explore possible sex-based differences in pharmacokinetics and drug–drug interactions (DDI) to attempt a relationship with published clinical outputs. We observed that covariate sex is lacking in most clinical outcome reports and suggest a series of ethic-based proposals to improve research activities and identify relevant different sex outcomes. We propose a deeper integration of preclinical, clinical, and translational research addressing statistical and clinical significance jointly; to embed specific sex-divergent endpoints to evaluate possible gender differences objectively during all stages of research; to pay greater attention to sex-divergent outcomes in polypharmacy scenarios, DDI and bioequivalence studies; the clear reporting of preclinical and clinical findings regarding sex-divergent outcomes; as well as to encourage the active role of scientists and the pharmaceutical industry to foster a new scientific culture through their research programs, practice, and participation in editorial boards and Institutional Ethics Review Boards (IRBs) and Research Ethics Committees (RECs). We establish the IRB/REC as the centerpiece for the implementation of these proposals. We suggest the expansion of its competence to follow up clinical trials to ensure that sex differences are addressed and recognized; to engage in data monitoring committees to improve clinical research cooperation and ethically address those potential clinical outcome differences between male and female patients to analyze their social and clinical implications in research and healthcare policies.

Biliary and pancreatic complications of molecular targeted therapies in cancer imaging

The purpose of this review is to familiarize radiologists with the different imaging manifestations of biliary and pancreatic toxicity of molecular targeted therapies. The advent of molecular targeted therapies for cancer treatment has prompted radiologists to be familiar with these new molecules, their patterns of response, and their class-specific toxicities. While liver and bowel toxicities have been extensively reported in literature, less is known about the pathogenesis and imaging of toxicity involving the pancreatobiliary system. Biliary and pancreatic toxicity of molecular targeted therapies present with variable manifestations and varying degrees of severity, from asymptomatic liver function tests elevation to acute pancreatitis or cholecystitis. Management of these conditions depends on the clinical scenario and the severity of the findings. In this article, we will (1) present the various classes of molecular targeted therapies most commonly associated with biliary and pancreatic toxicity; (2) illustrate imaging findings of drug-associated biliary and pancreatic injuries and their possible differential diagnosis; and (3) provide a guide for management of these conditions.

Patient monitoring through liquid biopsies using circulating tumor DNA

Tumors release components such as circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), and tumor-derived extracellular vesicles into the circulation. Multiple studies have demonstrated that molecular information about tumors and metastases can be extracted from these factors, which are therefore frequently referred to as "liquid biopsies." Liquid biopsies allow the longitudinal monitoring of tumor genomes non-invasively and may hence ensure that patients receive appropriate treatments that target the molecular features of their disease. Accordingly, the number of studies employing liquid biopsy based assays has been skyrocketing in the last few years. Here, we focus on three important issues, which are of high relevance for monitoring tumor genomes. First, we analyze the relation between the allele frequency of somatic tumor-specific mutations and the tumor fraction within plasma DNA. Second, we ask how well current tumor evolution models correlate with findings in longitudinal liquid biopsy studies. And, finally, as sensitivity is one of the key challenges of mutation detection, we address the challenge of detecting mutations occurring at very low allele frequencies in plasma DNA.

Precision Radiology: Predicting longevity using feature engineering and deep learning methods in a radiomics framework

Precision medicine approaches rely on obtaining precise knowledge of the true state of health of an individual patient, which results from a combination of their genetic risks and environmental exposures. This approach is currently limited by the lack of effective and efficient non-invasive medical tests to define the full range of phenotypic variation associated with individual health. Such knowledge is critical for improved early intervention, for better treatment decisions, and for ameliorating the steadily worsening epidemic of chronic disease. We present proof-of-concept experiments to demonstrate how routinely acquired cross-sectional CT imaging may be used to predict patient longevity as a proxy for overall individual health and disease status using computer image analysis techniques. Despite the limitations of a modest dataset and the use of off-the-shelf machine learning methods, our results are comparable to previous 'manual' clinical methods for longevity prediction. This work demonstrates that radiomics techniques can be used to extract biomarkers relevant to one of the most widely used outcomes in epidemiological and clinical research - mortality, and that deep learning with convolutional neural networks can be usefully applied to radiomics research. Computer image analysis applied to routinely collected medical images offers substantial potential to enhance precision medicine initiatives.

Emerging concepts in liquid biopsies

Characterizing and monitoring tumor genomes with blood samples could achieve significant improvements in precision medicine. As tumors shed parts of themselves into the circulation, analyses of circulating tumor cells, circulating tumor DNA, and tumor-derived exosomes, often referred to as "liquid biopsies", may enable tumor genome characterization by minimally invasive means. Indeed, multiple studies have described how molecular information about parent tumors can be extracted from these components. Here, we briefly summarize current technologies and then elaborate on emerging novel concepts that may further propel the field. We address normal and detectable mutation levels in the context of our current knowledge regarding the gradual accumulation of mutations during aging and in light of technological limitations. Finally, we discuss whether liquid biopsies are ready to be used in routine clinical practice.

Clinical Trials in the Genomic Era

Personalization of therapy to target specific molecular pathways has been placed in the forefront of cancer research. Initial reports from clinical trials designed to select patients for appropriate treatment on the basis of tumor characteristics not only have generated considerable excitement but also have identified several challenges. These challenges include the overcoming of regulatory and logistic difficulties, identification of the best selection biomarkers and diagnostic platforms that can be applied in the clinical setting, definition of relevant outcomes in small preselected patient populations, and the design of methods that facilitate rapid enrollment and interpretation of clinical trials by aggregating data across histologically diverse malignancies with common genetic alterations. Furthermore, because our knowledge of the functional consequences of many genetic alterations lags, investigators and sponsors struggle with choosing between ideal clinical trial designs and more practical ones. These challenges are amplified when more than one biomarker is used to select patients for a combination of targeted agents. This review summarizes the current status and challenges of clinical trials in the genomic era and proposes ways to address these challenges.

Precision medicine in pediatric oncology: Lessons learned and next steps

The maturation of genomic technologies has enabled new discoveries in disease pathogenesis as well as new approaches to patient care. In pediatric oncology, patients may now receive individualized genomic analysis to identify molecular aberrations of relevance for diagnosis and/or treatment. In this context, several recent clinical studies have begun to explore the feasibility and utility of genomics-driven precision medicine. Here, we review the major developments in this field, discuss current limitations, and explore aspects of the clinical implementation of precision medicine, which lack consensus. Lastly, we discuss ongoing scientific efforts in this arena, which may yield future clinical applications.

Advances in the pharmacotherapy of chronic heart failure with preserved ejection fraction: an ideal opportunity for precision medicine

INTRODUCTION

Heart failure with preserved ejection fraction (HFpEF), which comprises approximately 50% of all heart failure patients, is a challenging and complex clinical syndrome that is often thought to lack effective treatments. Areas covered: Despite the common mantra that HFpEF has no effective treatments, closer inspection of HFpEF clinical trials reveals that several of the drugs tested are associated with benefits in exercise capacity and quality of life, and reduction in heart failure hospitalization. Here we review major randomized controlled trials in HFpEF, focusing on renin-angiotensin-aldosterone system antagonists, organic nitrates, digoxin, beta-blockers, and phosphodiesterase-5 inhibitors. In addition, we review several classes of drugs currently in development for HFpEF such as neprilysin inhibitors, inorganic nitrates (nitrites), and soluble guanylate cyclase stimulators. Expert opinion: HFpEF should not be viewed as lacking effective treatments. While there have been no breakthrough clinical trials showing a reduction in mortality, several existing medications are likely to benefit specific subgroups of HFpEF patients. HFpEF is now well known to be a heterogeneous syndrome; thus, the clinical management of HFpEF patients and future HFpEF clinical trials will both likely require a nuanced, phenotype-specific approach instead of a one-size-fits-all tactic. Drug development for HFpEF therefore represents an exciting opportunity for personalized medicine.

Recent development on synthetic biological devices treating bladder cancer

Synthetic biology is an emerging field focusing on engineering genetic devices and biomolecular systems for a variety of applications from basic biology to biotechnology and medicine. Thanks to the tremendous advances in genomics and the chemical synthesis of DNA in the past decade, scientists are now able to engineer genetic devices and circuits for cancer research and intervention, which offer promising therapeutic strategies for cancer treatment. In this article, we provide a systemic review on recent development achieved by the synthetic biologists, oncologists and clinicians of one National "973" Plan. We expand the synthetic biology toolkits involving DNA, RNA and protein bio-parts to explore various issues in cancer research, such as elucidation of mechanisms and pathways, creation of new diagnostic tools and invention of novel therapeutic approaches. We claimed that the Chinese synthetic biologists are promoting the basic research productions of tumor synthetic biology into the clinic.

Next-Generation Sequencing and Result Interpretation in Clinical Oncology: Challenges of Personalized Cancer Therapy

The tools of next-generation sequencing (NGS) technology, such as targeted sequencing of candidate cancer genes and whole-exome and -genome sequencing, coupled with encouraging clinical results based on the use of targeted therapeutics and biomarker-guided clinical trials, are fueling further technological advancements of NGS technology. However, NGS data interpretation is associated with challenges that must be overcome to promote the techniques' effective integration into clinical oncology practice. Specifically, sequencing of a patient's tumor often yields 30-65 somatic variants, but most of these variants are "passenger" mutations that are phenotypically neutral and thus not targetable. Therefore, NGS data must be interpreted by multidisciplinary decision-support teams to determine mutation actionability and identify potential "drivers," so that the treating physician can prioritize what clinical decisions can be pursued in order to provide cancer therapy that is personalized to the patient and his or her unique genome.

The future of clinical cancer genomics

The current and future applications of genomics to the practice of preventive oncology are being impacted by a number of challenges. These include rapid advances in genomic science and technology that allow massively parallel sequencing of both tumors and the germline, a diminishing of intellectual property restrictions on diagnostic genetic applications, rapid expansion of access to the internet which includes mobile access to both genomic data and tools to communicate and interpret genetic data in a medical context, the expansion of for-profit diagnostic companies seeking to monetize genetic information, and a simultaneous effort to depict medical professionals as barriers to rather than facilitators of understanding one's genome. Addressing each of these issues will be required to bring "personalized" germline genomics to cancer prevention and care. A profound future challenge will be whether clinical cancer genomics will be "de-medicalized" by commercial interests and their advocates, or whether the future course of this field can be modulated in a responsible way that protects the public health while implementing powerful new medical tools for cancer prevention and early detection.

Molecular mechanisms underlying resistance to androgen deprivation therapy in prostate cancer

Prostate cancer (PCa) is the most widely diagnosed male cancer in the Western World and while low- and intermediate-risk PCa patients have a variety of treatment options, metastatic patients are limited to androgen deprivation therapy (ADT). This treatment paradigm has been in place for 75 years due to the unique role of androgens in promoting growth of prostatic epithelial cells via the transcription factor androgen receptor (AR) and downstream signaling pathways. Within 2 to 3 years of ADT, disease recurs-at which time, patients are considered to have castration-recurrent PCa (CR-PCa). A universal mechanism by which PCa becomes resistant to ADT has yet to be discovered. In this review article, we discuss underlying molecular mechanisms by which PCa evades ADT. Several major resistance pathways center on androgen signaling, including intratumoral and adrenal androgen production, AR-overexpression and amplification, expression of AR mutants, and constitutively-active AR splice variants. Other ADT resistance mechanisms, including activation of glucocorticoid receptor and impairment of DNA repair pathways are also discussed. New therapies have been approved for treatment of CR-PCa, but increase median survival by only 2-8 months. We discuss possible mechanisms of resistance to these new ADT agents. Finally, the practicality of the application of "precision oncology" to this continuing challenge of therapy resistance in metastatic or CR-PCa is examined. Empirical validation and clinical-based evidence are definitely needed to prove the superiority of "precision" treatment in providing a more targeted approach and curative therapies over the existing practices that are based on biological "cause-and-effect" relationship.

Transglutaminase-2: evolution from pedestrian protein to a promising therapeutic target

The ability of cancer cells to metastasize represents the most devastating feature of cancer. Currently, there are no specific biomarkers or therapeutic targets that can be used to predict the risk or to treat metastatic cancer. Many recent reports have demonstrated elevated expression of transglutaminase 2 (TG2) in multiple drug-resistant and metastatic cancer cells. TG2 is a multifunctional protein mostly known for catalyzing Ca²⁺-dependent -acyl transferase reaction to form protein crosslinks. Besides this transamidase activity, many Ca²⁺-independent and non-enzymatic activities of TG2 have been identified. Both, the enzymatic and non-enzymatic activities of TG2 have been implicated in diverse pathophysiological processes such as wound healing, cell growth, cell survival, extracellular matrix modification, apoptosis, and autophagy. Tumors have been frequently referred to as 'wounds that never heal'. Based on the observation that TG2 plays an important role in wound healing and inflammation is known to facilitate cancer growth and progression, we discuss the evidence that TG2 can reprogram inflammatory signaling networks that play fundamental roles in cancer progression. TG2-regulated signaling bestows on cancer cells the ability to proliferate, to resist cell death, to invade, to reprogram glucose metabolism and to metastasize, the attributes that are considered important hallmarks of cancer. Therefore, inhibiting TG2 may offer a novel therapeutic approach for managing and treatment of metastatic cancer. Strategies to inhibit TG2-regulated pathways will also be discussed.

Pathologists and liquid biopsies: to be or not to be?

Recently, the advent of therapies targeting genomic alterations has improved the care of patients with certain types of cancer. While molecular targets were initially detected in nucleic acid samples extracted from tumor tissue, detection of nucleic acids in circulating blood has allowed the development of what has become known as liquid biopsies, which provide a complementary and alternative sample source allowing identification of genomic alterations that might be addressed by targeted therapy. Consequently, liquid biopsies might rapidly revolutionize oncology practice in allowing administration of more effective treatments. Liquid biopsies also provide an approach towards short-term monitoring of metastatic cancer patients to evaluate efficacy of treatment and/or early detection of secondary mutations responsible for resistance to treatment. In this context, pathologists, who have already been required in recent years to take interest in the domain of molecular pathology of cancer, now face new challenges. The attitude of pathologists to and level of involvement in the practice of liquid biopsies, including mastering the methods employed in molecular analysis of blood samples, need close attention. Regardless of the level of involvement of pathologists in this new field, it is mandatory that oncologists, biologists, geneticists, and pathologists work together to coordinate the pre-analytical, analytical, and post-analytical phases of molecular assessment of tissue and liquid samples of individual cancer patients. The challenges include (1) implementation of effective and efficient procedures for reception and analysis of liquid and tissue samples for histopathological and molecular evaluation and (2) assuring short turn-around times to facilitate rapid optimization of individual patient treatment. In this paper, we will review the following: (1) recent data concerning the concept of liquid biopsies in oncology and its development for patient care, (2) advantages and limitations of molecular analyses performed on blood samples compared to those performed on tissue samples, and (3) short-term challenges facing pathologists in dealing with liquid biopsies of cancer patients and new strategies to early detect metastatic tumor cell clones.

An arsenal of magnetic nanoparticles; perspectives in the treatment of cancer

Nanomedicine is an emerging field, which constitutes a new direction in the treatment of cancer. Magnetic nanoparticles (MNPs) can circumvent vascular tissue to concentrate at the site of the tumor. Under the influence of an external, alternating magnetic field, MNPs generate high temperatures within the tumor and ablate malignant cells while inflicting minimal damage to healthy host tissue. Due to their theranostic properties, they constitute a promising candidate for the treatment of cancer. A critical review of the type, size and therapeutic effect of different MNPs is presented, following an appraisal of the literature in the last 5 years. This is a multibillion dollar industry, with a few studies moving to clinical trials within the next 5 years.

Precision medicine in acute myeloid leukemia: Hope, hype or both?

Precision medicine is interchangeably used with personalized medicine, genomic medicine and individualized medicine. Collectively, these terms refer to at least 5 distinct concepts in the context of AML. 1st, using molecular or omics data (e.g. genomics, epigenomics, transcriptomics, proteomics) to delineate or define subtypes of AML. 2nd, using these data to select the best therapy for someone with an AML subtype, such as a person with a FLT3-mutation. 3rd, using these data to monitor therapy-response such as measurable residual disease [MRD]-testing. 4th, using results of MRD-testing to select from amongst therapy-options such as additional chemotherapy or a haematopoietic cell transplant. And 5th, using these data to identify persons with hereditary forms of AML with potential therapy and surveillance implications. Here, we review these 5 conceptions and delineate where precision medicine is likely to afford greatest hope and where instead our rhetoric may constitute hype.