Personalized cancer therapy-leveraging a knowledge base for clinical decision-making

Advances in personalized medicine: translating genomic insights into targeted therapies for cancer treatment

Background

Personalized medicine has revolutionized cancer treatment by utilizing genomic insights to tailor therapies based on individual molecular profiles. This approach enhances therapeutic efficacy, minimizes adverse effects, and addresses tumor heterogeneity through precision-targeted interventions.

Methods

A scoping review was conducted through a comprehensive literature search in PubMed, using MeSH terms and keywords related to genomic profiling and targeted cancer therapies. Eligible studies included original research involving cancer patients who underwent genomic profiling and targeted therapies from January 1, 1950, to February 9, 2025.

Results

Advances in next-generation sequencing (NGS) and bioinformatics have accelerated the identification of clinically relevant mutations-such as epidermal growth factor receptor (EGFR) in non-small cell lung cancer (NSCLC) and BRAF V600E in melanoma-enabling the development of effective targeted therapies. Emerging technologies like clustered regularly interspaced short palindromic repeats (CRISPR) gene editing and artificial intelligence (AI) are further refining treatment selection by enabling more precise and adaptive therapeutic strategies. Despite these innovations, challenges persist regarding data interpretation, equitable access, costs, regulatory frameworks, and integration into routine clinical workflows.

Conclusions

Genomic profiling is central to the advancement of precision oncology. The convergence of genomics, gene editing, and AI is paving the way toward more personalized, efficient, and inclusive cancer care. Realizing the full potential of personalized medicine will require interdisciplinary collaboration, investment in infrastructure, and ethical oversight to ensure broad, equitable, and responsible implementation in clinical practice.

Epigenomics-guided precision oncology: Chromatin variants in prostate tumor evolution

Prostate cancer is a common malignancy that in 5%-30% leads to treatment-resistant and highly aggressive disease. Metastasis-potential and treatment-resistance is thought to rely on increased plasticity of the cancer cells-a mechanism whereby cancer cells alter their identity to adapt to changing environments or therapeutic pressures to create cellular heterogeneity. To understand the molecular basis of this plasticity, genomic studies have uncovered genetic variants to capture clonal heterogeneity of primary tumors and metastases. As cellular plasticity is largely driven by non-genetic events, complementary studies in cancer epigenomics are now being conducted to identify chromatin variants. These variants, defined as genomic loci in cancer cells that show changes in chromatin state due to the loss or gain of epigenomic marks, inclusive of histone post-translational modifications, DNA methylation and histone variants, are considered the fundamental units of epigenomic heterogeneity. In prostate cancer chromatin variants hold the promise of guiding the new era of precision oncology. In this review, we explore the role of epigenomic heterogeneity in prostate cancer, focusing on how chromatin variants contribute to tumor evolution and therapy resistance. We therefore discuss their impact on cellular plasticity and stochastic events, highlighting the value of single-cell sequencing and liquid biopsy epigenomic assays to uncover new therapeutic targets and biomarkers. Ultimately, this review aims to support a new era of precision oncology, utilizing insights from epigenomics to improve prostate cancer patient outcomes.

Extracellular vesicles in liquid biopsies: there is hope for oral squamous cell carcinoma

Current approaches to oral cancer diagnosis primarily involve physical examination, tissue biopsy, and advanced computer-aided imaging techniques. However, despite these advances, patient survival rates have not significantly improved. Hence, there is a critical need to develop minimally invasive tools with high sensitivity and specificity to improve patient survival and quality of life. Liquid biopsy is a non-invasive, real-time method for predicting cancer status and potentially serves as a biomarker source for treatment response. Liquid biopsy includes rich biologically relevant components, such as circulating tumor cells, circulating tumor DNA, and extracellular vesicles (EVs). EVs are particularly intriguing due to their relatively high abundance in most biofluids, with the potential to identify specific cargo derived from circulating tumor EVs. Moreover, normal cells in lymph nodes can uptake EVs, fostering a pre-metastatic microenvironment that facilitates lymph node metastases - a common occurrence in oral cancers. This review encompasses English language publications over the last twenty years, focusing on methods for isolating EVs from saliva, blood, and lymphatic fluids, as well as the collection methods employed. Seventeen cases met the inclusion criteria according to ISEV guidelines, including 10 saliva cases, 6 blood cases, and 1 lymphatic fluid case. This review also highlighted research gaps in oral squamous cell carcinoma (OSCC) EVs, including a lack of multi-omics studies and the exploration of potential EV markers for drug resistance, as well as a notable underutilization of microfluidic technologies to translate liquid biopsy EV findings into clinical applications.

Real-World Performance of Integrative Clinical Genomics in Pediatric Precision Oncology

Despite significant improvement in the survival of pediatric patients with cancer, treatment outcomes for high-risk, relapsed, and refractory cancers remain unsatisfactory. Moreover, prolonged survival is frequently associated with long-term adverse effects due to intensive multimodal treatments. Accelerating the progress of pediatric oncology requires both therapeutic advances and strategies to mitigate the long-term cytotoxic side effects, potentially through targeting specific molecular drivers of pediatric malignancies. In this report, we present the results of integrative genomic and transcriptomic profiling of 230 patients with malignant solid tumors (the "primary cohort") and 18 patients with recurrent or otherwise difficult-to-treat nonmalignant conditions (the "secondary cohort"). The integrative workflow for the primary cohort enabled the identification of clinically significant single nucleotide variants, small insertions/deletions, and fusion genes, which were found in 55% and 28% of patients, respectively. For 38% of patients, molecularly informed treatment recommendations were made. In the secondary cohort, known or potentially driving alteration was detected in 89% of cases, including a suspected novel causal gene for patients with inclusion body infantile digital fibromatosis. Furthermore, 47% of findings also brought therapeutic implications for subsequent management. Across both cohorts, changes or refinements to the original histopathological diagnoses were achieved in 4% of cases. Our study demonstrates the efficacy of integrating advanced genomic and transcriptomic analyses to identify therapeutic targets, refine diagnoses, and optimize treatment strategies for challenging pediatric and young adult malignancies and underscores the need for broad implementation of precision oncology in clinical settings.

Unveiling epigenetic regulatory elements associated with breast cancer development

Breast cancer is the most common cancer in women and the 2nd most common cancer worldwide, yearly impacting over 2 million females and causing 650 thousand deaths. It has been widely studied, but its epigenetic variation is not entirely unveiled. We aimed to identify epigenetic mechanisms impacting the expression of breast cancer related genes to detect new potential biomarkers and therapeutic targets. We considered The Cancer Genome Atlas database with over 800 samples and several omics datasets such as mRNA, miRNA, DNA methylation, which we used to select 2701 features that were statistically significant to differ between cancer and control samples using the Monte Carlo Feature Selection and Interdependency Discovery algorithm, from an initial total of 417,486. Their biological impact on cancerogenesis was confirmed using: statistical analysis, natural language processing, linear and machine learning models as well as: transcription factors identification, drugs and 3D chromatin structure analyses. Classification of cancer vs control samples on the selected features returned high classification weighted Accuracy from 0.91 to 0.98 depending on feature-type: mRNA, miRNA, DNA methylation, and classification algorithm. In general, cancer samples showed lower expression of differentially expressed genes and increased β-values of differentially methylated sites. We identified mRNAs whose expression is well explained by miRNA expression and differentially methylated sites β-values. We recognized differentially methylated sites possibly affecting NRF1 and MXI1 transcription factors binding, causing a disturbance in NKAPL and PITX1 expression, respectively. Our 3D models showed more loosely packed chromatin in cancer. This study successfully points out numerous possible regulatory dependencies.

Precision oncology platforms: practical strategies for genomic database utilization in cancer treatment

In recent years, the expansion of molecularly targeted cancer therapies has significantly advanced precision oncology. Parallel developments in next-generation sequencing (NGS) technologies have also improved precision oncology applications, making genomic analysis of tumors more affordable and accessible. Targeted NGS panels now enable the rapid identification of diverse actionable mutations, requiring clinicians to efficiently assess the predictive value of cancer biomarkers for specific treatments. The urgency for timely and accurate decision-making in oncology emphasizes the importance of reliable precision oncology software. Online clinical decision-making tools and associated cancer databases have been designed by consolidating genomic data into standardized, accessible formats. These new platforms are highly integrated and crucial for identifying actionable somatic genomic biomarkers essential for tumor survival, determining corresponding drug targets, and selecting appropriate treatments based on the mutational profile of each patient's tumor. To help oncologists and translational cancer researchers unfamiliar with these tools, we review the utility, accuracy, and comprehensiveness of several commonly used precision medicine software options currently available. Our analysis categorized selected genomic databases based on their primary content, utility, and how well they provide practical guidance for interpreting somatic biomarker data. We identified several comprehensive, mostly open-access platforms that are easy to use for genetic biomarker searches, each with unique features and limitations. Among the precision oncology tools we evaluated, we found MyCancerGenome and OncoKB to be the first choice, offering comprehensive, accurate up-to-date information on the clinical significance of somatic mutations. To illustrate the application of these precision oncology tools in clinical settings, we evaluated three case studies to see how use of the platforms could have influenced treatment planning. Most of the precision oncology software evaluated could be easily streamlined into clinical workflows to provide updated information on approved drugs and clinical trials related the actionable mutations detected. Some platforms were very intuitive and easy to use, while others, often developed in smaller academic settings, were more difficult to navigate and may not be updated consistently. Future enhancements, incorporating artificial intelligence algorithms, are likely to improve integration of the platforms with diverse big data sources, enabling more accurate predictions of potential therapeutic responses.

Spheroid Model of Mammary Tumor Cells: Epithelial-Mesenchymal Transition and Doxorubicin Response

Breast cancer is the most prevalent cancer among women worldwide. Therapeutic strategies to control tumors and metastasis are still challenging. Three-dimensional (3D) spheroid-type systems more accurately replicate the features of tumors in vivo, working as a better platform for performing therapeutic response analysis. This work aimed to characterize the epithelial-mesenchymal transition and doxorubicin (dox) response in a mammary tumor spheroid (MTS) model. We evaluated the doxorubicin treatment effect on MCF-7 spheroid diameter, cell viability, death, migration and proteins involved in the epithelial-mesenchymal transition (EMT) process. Spheroids were also produced from tumors formed from 4T1 and 67NR cell lines. MTSs mimicked avascular tumor characteristics, exhibited adherens junction proteins and independently produced their own extracellular matrix. Our spheroid model supports the 3D culturing of cells isolated from mice mammary tumors. Through the migration assay, we verified a reduction in E-cadherin expression and an increase in vimentin expression as the cells became more distant from spheroids. Dox promoted cytotoxicity in MTSs and inhibited cell migration and the EMT process. These results suggest, for the first time, that this model reproduces aspects of the EMT process and describes the potential of dox in inhibiting the metastatic process, which can be further explored.

New Breakthroughs in the Diagnosis of Leptomeningeal Carcinomatosis: A Review of Liquid Biopsies of Cerebrospinal Fluid

Leptomeningeal carcinomatosis represents a terminal stage and is a devastating complication of cancer. Despite its high incidence, current diagnostic methods fail to accurately detect this condition in a timely manner. This failure to diagnose leads to the refusal of treatment and the absence of clinical trials, hampering the development of new therapy strategies. The use of liquid biopsy is revolutionizing the field of diagnostic oncology. The dynamic and non-invasive detection of tumor markers has enormous potential in cancer diagnostics and treatment. Leptomeningeal carcinomatosis is a condition where invasive tissue biopsy is not part of the routine diagnostic analysis, making liquid biopsy an essential diagnostic tool. Several elements in cerebrospinal fluid (CSF) have been investigated as potential targets of liquid biopsy, including free circulating tumor cells, free circulating nucleic acids, proteins, exosomes, and even non-tumor cells as part of the dynamic tumor microenvironment. This review aims to summarize current breakthroughs in the research on liquid biopsy, including the latest breakthroughs in the identification of tumor cells and nucleic acids, and give an overview of future directions in the diagnosis of leptomeningeal carcinomatosis.

Actionability classification of variants of unknown significance correlates with functional effect

Genomically-informed therapy requires consideration of the functional impact of genomic alterations on protein expression and/or function. However, a substantial number of variants are of unknown significance (VUS). The MD Anderson Precision Oncology Decision Support (PODS) team developed an actionability classification scheme that categorizes VUS as either "Unknown" or "Potentially" actionable based on their location within functional domains and/or proximity to known oncogenic variants. We then compared PODS VUS actionability classification with results from a functional genomics platform consisting of mutant generation and cell viability assays. 106 (24%) of 438 VUS in 20 actionable genes were classified as oncogenic in functional assays. Variants categorized by PODS as Potentially actionable (N = 204) were more likely to be oncogenic than those categorized as Unknown (N = 230) (37% vs 13%, p = 4.08e-09). Our results demonstrate that rule-based actionability classification of VUS can identify patients more likely to have actionable variants for consideration with genomically-matched therapy.

Prognosis of lasso-like penalized Cox models with tumor profiling improves prediction over clinical data alone and benefits from bi-dimensional pre-screening

BACKGROUND

Prediction of patient survival from tumor molecular '-omics' data is a key step toward personalized medicine. Cox models performed on RNA profiling datasets are popular for clinical outcome predictions. But these models are applied in the context of "high dimension", as the number p of covariates (gene expressions) greatly exceeds the number n of patients and e of events. Thus, pre-screening together with penalization methods are widely used for dimensional reduction.

METHODS

In the present paper, (i) we benchmark the performance of the lasso penalization and three variants (i.e., ridge, elastic net, adaptive elastic net) on 16 cancers from TCGA after pre-screening, (ii) we propose a bi-dimensional pre-screening procedure based on both gene variability and p-values from single variable Cox models to predict survival, and (iii) we compare our results with iterative sure independence screening (ISIS).

RESULTS

First, we show that integration of mRNA-seq data with clinical data improves predictions over clinical data alone. Second, our bi-dimensional pre-screening procedure can only improve, in moderation, the C-index and/or the integrated Brier score, while excluding irrelevant genes for prediction. We demonstrate that the different penalization methods reached comparable prediction performances, with slight differences among datasets. Finally, we provide advice in the case of multi-omics data integration.

CONCLUSIONS

Tumor profiles convey more prognostic information than clinical variables such as stage for many cancer subtypes. Lasso and Ridge penalizations perform similarly than Elastic Net penalizations for Cox models in high-dimension. Pre-screening of the top 200 genes in term of single variable Cox model p-values is a practical way to reduce dimension, which may be particularly useful when integrating multi-omics.

OncoPubMiner: a platform for mining oncology publications

Updated and expert-quality knowledge bases are fundamental to biomedical research. A knowledge base established with human participation and subject to multiple inspections is needed to support clinical decision making, especially in the growing field of precision oncology. The number of original publications in this field has risen dramatically with the advances in technology and the evolution of in-depth research. Consequently, the issue of how to gather and mine these articles accurately and efficiently now requires close consideration. In this study, we present OncoPubMiner (https://oncopubminer.chosenmedinfo.com), a free and powerful system that combines text mining, data structure customisation, publication search with online reading and project-centred and team-based data collection to form a one-stop 'keyword in-knowledge out' oncology publication mining platform. The platform was constructed by integrating all open-access abstracts from PubMed and full-text articles from PubMed Central, and it is updated daily. OncoPubMiner makes obtaining precision oncology knowledge from scientific articles straightforward and will assist researchers in efficiently developing structured knowledge base systems and bring us closer to achieving precision oncology goals.

Effect of chain length on the cytotoxic activity of (alkyl-ω-ol)triphenyltin(IV) loaded into SBA-15 nanostructured silica and in vivo study of SBA-15~Cl|Ph3Sn(CH2)8OH

A series of nanostructured SBA-15-based materials functionalized with the tetraorganotin(IV) metallodrugs Ph₃Sn(CH₂)_nOH (n = 3, 4, 6, 8 and 11) are synthesized and structurally characterized by different techniques used in solid-state chemistry. The cytotoxicity of both the organotin(IV) compounds and the tin-functionalized SBA-15 materials are studied against different cancer cell lines observing that the materials have similar cytotoxic activity in comparison with the free organotin compounds in terms of mass. However, considering that the percentage of active metal compound loaded into material is low, the utilization of mesoporous silica as drug vehicle clearly improves the cytotoxic effectiveness of metal-based drugs against cancer cells. One of the most potent between all tested systems is material SBA-15~Cl|Ph₃Sn(CH₂)₈OH. Its cytotoxicity seems to come from additional mechanisms apart from apoptosis provoking cell reprogram in B16 melanoma into more mature and less aggressive phenotype. Moderated production of ROS/RNS is probably in the background of observed phenomenon. Obtained results are further confirmed in syngeneic mouse model of melanoma in C57BL6 mice. The in vivo results show that SBA-15 do not disturb tumor growth, while both Ph₃Sn(CH₂)₈OH and SBA-15~Cl|Ph₃Sn(CH₂)₈OH significantly decreases tumor volume with an enhancement of the antitumor potential of the tetraorganotin(IV) compound upon immobilization in SBA-15.

Progress on carbon dots and hydroxyapatite based biocompatible luminescent nanomaterials for cancer theranostics

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Biocompatible carbon dots (CDs) and nanohydroxyapatite (nHA) have attracted much attention for the development of optical imaging probes.

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This review discusses the development of CD and nHA based nanomaterials as multifunctional agents for cancer theranostics.

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The effect of synthesis strategies and doping on photoluminescent properties along with tuning of emission in biological window has been briefly reviewed.

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The cancer targeting strategies, biocompatibility and biodistribution of CDs and nHA based luminescent probes is discussed.

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A summary of current challenges and future perspectives is provided.

Despite the significant advancement in cancer diagnosis and therapy, a huge burden remains. Consequently, much research has been diverted on the development of multifunctional nanomaterials for improvement in conventional diagnosis and therapy. Luminescent nanomaterials offer a versatile platform for the development of such materials as their intrinsic photoluminescence (PL) property offers convergence of diagnosis as well as therapy at the same time. However, the clinical translation of nanomaterials faces various challenges, including biocompatibility and cost-effective scale up production. Thus, luminescent materials with facile synthesis approach along with intrinsic biocompatibility and anticancerous activity hold significant importance. As a result, carbon dots (CDs) and nanohydroxyapatite (nHA) have attracted much attention for the development of optical imaging probes. CDs are the newest members of the carbonaceous nanomaterials family that possess intrinsic luminescent and therapeutic properties, making them a promising candidate for cancer theranostic. Additionally, nHA is an excellent bioactive material due to its compositional similarity to the human bone matrix. The nHA crystal can efficiently host rare-earth elements to attain luminescent property, which can further be implemented for cancer theranostic applications. Herein, the development of CDs and nHA based nanomaterials as multifunctional agents for cancer has been briefly discussed. The emphasis has been given to different synthesis strategies leading to different morphologies and tunable PL spectra, followed by their diverse applications as biocompatible theranostic agents. Finally, the review has been summarized with the current challenges and future perspectives.

Liquid biopsy: a step closer to transform diagnosis, prognosis and future of cancer treatments

Over the past decade, invasive techniques for diagnosing and monitoring cancers are slowly being replaced by non-invasive methods such as liquid biopsy. Liquid biopsies have drastically revolutionized the field of clinical oncology, offering ease in tumor sampling, continuous monitoring by repeated sampling, devising personalized therapeutic regimens, and screening for therapeutic resistance. Liquid biopsies consist of isolating tumor-derived entities like circulating tumor cells, circulating tumor DNA, tumor extracellular vesicles, etc., present in the body fluids of patients with cancer, followed by an analysis of genomic and proteomic data contained within them. Methods for isolation and analysis of liquid biopsies have rapidly evolved over the past few years as described in the review, thus providing greater details about tumor characteristics such as tumor progression, tumor staging, heterogeneity, gene mutations, and clonal evolution, etc. Liquid biopsies from cancer patients have opened up newer avenues in detection and continuous monitoring, treatment based on precision medicine, and screening of markers for therapeutic resistance. Though the technology of liquid biopsies is still evolving, its non-invasive nature promises to open new eras in clinical oncology. The purpose of this review is to provide an overview of the current methodologies involved in liquid biopsies and their application in isolating tumor markers for detection, prognosis, and monitoring cancer treatment outcomes.

A platform for oncogenomic reporting and interpretation

Manual interpretation of variants remains rate limiting in precision oncology. The increasing scale and complexity of molecular data generated from comprehensive sequencing of cancer samples requires advanced interpretative platforms as precision oncology expands beyond individual patients to entire populations. To address this unmet need, we introduce a Platform for Oncogenomic Reporting and Interpretation (PORI), comprising an analytic framework that facilitates the interpretation and reporting of somatic variants in cancer. PORI integrates reporting and graph knowledge base tools combined with support for manual curation at the reporting stage. PORI represents an open-source platform alternative to commercial reporting solutions suitable for comprehensive genomic data sets in precision oncology. We demonstrate the utility of PORI by matching 9,961 pan-cancer genome atlas tumours to the graph knowledge base, calculating therapeutically informative alterations, and making available reports describing select individual samples.

The interpretation of somatic variants in cancer is challenging due to the scale and complexity of sequencing data. Here, the authors present PORI, an open-source framework for interpreting somatic variants in cancer using graph knowledge base tools, automated reporting, and manual curation.

Targeting Oncogenic Pathways in the Era of Personalized Oncology: A Systemic Analysis Reveals Highly Mutated Signaling Pathways in Cancer Patients and Potential Therapeutic Targets

Cancer is the second leading cause of death globally. One of the main hallmarks in cancer is the functional deregulation of crucial molecular pathways via driver genetic events that lead to abnormal gene expression, giving cells a selective growth advantage. Driver events are defined as mutations, fusions and copy number alterations that are causally implicated in oncogenesis. Molecular analysis on tissues that have originated from a wide range of anatomical areas has shown that mutations in different members of several pathways are implicated in different cancer types. In recent decades, significant efforts have been made to incorporate this knowledge into daily medical practice, providing substantial insight towards clinical diagnosis and personalized therapies. However, since there is still a strong need for more effective drug development, a deep understanding of the involved signaling mechanisms and the interconnections between these pathways is highly anticipated. Here, we perform a systemic analysis on cancer patients included in the Pan-Cancer Atlas project, with the aim to select the ten most highly mutated signaling pathways (p53, RTK-RAS, lipids metabolism, PI-3-Kinase/Akt, ubiquitination, b-catenin/Wnt, Notch, cell cycle, homology directed repair (HDR) and splicing) and to provide a detailed description of each pathway, along with the corresponding therapeutic applications currently being developed or applied. The ultimate scope is to review the current knowledge on highly mutated pathways and to address the attractive perspectives arising from ongoing experimental studies for the clinical implementation of personalized medicine.

Artificial intelligence in clinical research of cancers

Several factors, including advances in computational algorithms, the availability of high-performance computing hardware, and the assembly of large community-based databases, have led to the extensive application of Artificial Intelligence (AI) in the biomedical domain for nearly 20 years. AI algorithms have attained expert-level performance in cancer research. However, only a few AI-based applications have been approved for use in the real world. Whether AI will eventually be capable of replacing medical experts has been a hot topic. In this article, we first summarize the cancer research status using AI in the past two decades, including the consensus on the procedure of AI based on an ideal paradigm and current efforts of the expertise and domain knowledge. Next, the available data of AI process in the biomedical domain are surveyed. Then, we review the methods and applications of AI in cancer clinical research categorized by the data types including radiographic imaging, cancer genome, medical records, drug information and biomedical literatures. At last, we discuss challenges in moving AI from theoretical research to real-world cancer research applications and the perspectives toward the future realization of AI participating cancer treatment.

Knowledge bases and software support for variant interpretation in precision oncology

Precision oncology is a rapidly evolving interdisciplinary medical specialty. Comprehensive cancer panels are becoming increasingly available at pathology departments worldwide, creating the urgent need for scalable cancer variant annotation and molecularly informed treatment recommendations. A wealth of mainly academia-driven knowledge bases calls for software tools supporting the multi-step diagnostic process. We derive a comprehensive list of knowledge bases relevant for variant interpretation by a review of existing literature followed by a survey among medical experts from university hospitals in Germany. In addition, we review cancer variant interpretation tools, which integrate multiple knowledge bases. We categorize the knowledge bases along the diagnostic process in precision oncology and analyze programmatic access options as well as the integration of knowledge bases into software tools. The most commonly used knowledge bases provide good programmatic access options and have been integrated into a range of software tools. For the wider set of knowledge bases, access options vary across different parts of the diagnostic process. Programmatic access is limited for information regarding clinical classifications of variants and for therapy recommendations. The main issue for databases used for biological classification of pathogenic variants and pathway context information is the lack of standardized interfaces. There is no single cancer variant interpretation tool that integrates all identified knowledge bases. Specialized tools are available and need to be further developed for different steps in the diagnostic process.

Advances of Tumorigenesis, Diagnosis at Early Stage, and Cellular Immunotherapy in Gastrointestinal Malignancies

Globally, in 2018, 4.8 million new patients have a diagnosis of gastrointestinal (GI) cancers, while 3.4 million people died of such disorders. GI malignancies are tightly relevant to 26% of the world-wide cancer incidence and occupies 35% of all cancer-associated deaths. In this article, we principally investigated molecular and cellular mechanisms of tumorigenesis in five major GI cancers occurring at esophagus, stomach, liver, pancreas, and colorectal region that illustrate high morbidity in Eastern and Western countries. Moreover, through this investigation, we not only emphasize importance of the tumor microenvironment in development and treatment of malignant tumors but also identify significance of M2PK, miRNAs, ctDNAs, circRNAs, and CTCs in early detection of GI cancers, as well as systematically evaluate contribution of personalized precision medicine including cellular immunotherapy, new antigen and vaccine therapy, and oncolytic virotherapy in treatment of GI cancers.

Clinical cancer genomic profiling

Technological innovation and rapid reduction in sequencing costs have enabled the genomic profiling of hundreds of cancer-associated genes as a component of routine cancer care. Tumour genomic profiling can refine cancer subtype classification, identify which patients are most likely to benefit from systemic therapies and screen for germline variants that influence heritable cancer risk. Here, we discuss ongoing efforts to enhance the clinical utility of tumour genomic profiling by integrating tumour and germline analyses, characterizing allelic context and identifying mutational signatures that influence therapy response. We also discuss the potential clinical utility of more comprehensive whole-genome and whole-transcriptome sequencing and ultra-sensitive cell-free DNA profiling platforms, which allow for minimally invasive, serial analyses of tumour-derived DNA in blood.

A Curriculum for Genomic Education of Molecular Genetic Pathology Fellows: A Report of the Association for Molecular Pathology Training and Education Committee

Molecular genetic pathology (MGP) is a subspecialty of pathology and medical genetics and genomics. Genomic testing, which we define as that which generates large data sets and interrogates large segments of the genome in a single assay, is increasingly recognized as essential for optimal patient care through precision medicine. The most common genomic testing technologies in clinical laboratories are next-generation sequencing and microarray. It is essential to train in these methods and to consider the data generated in the context of the diagnosis, medical history, and other clinical findings of individual patients. Accordingly, updating the MGP fellowship curriculum to include genomics is timely, important, and challenging. At the completion of training, an MGP fellow should be capable of independently interpreting and signing out results of a wide range of genomic assays and, given the appropriate context and institutional support, of developing and validating new assays in compliance with applicable regulations. The Genomics Task Force of the MGP Program Directors, a working group of the Association for Molecular Pathology Training and Education Committee, has developed a genomics curriculum framework and recommendations specific to the MGP fellowship. These recommendations are presented for consideration and implementation by MGP fellowship programs with the understanding that MGP programs exist in a diversity of clinical practice environments with a spectrum of available resources.

Criteria-based curation of a therapy-focused compendium to support treatment recommendations in precision oncology

While several resources exist that interpret therapeutic significance of genomic alterations in cancer, many regional real-world issues limit access to drugs. There is a need for a pragmatic, evidence-based, context-adapted tool to guide clinical management based on molecular biomarkers. To this end, we have structured a compendium of approved and experimental therapies with associated biomarkers following a survey of drug regulatory databases, existing knowledge bases, and published literature. Each biomarker-disease-therapy triplet was categorised using a tiering system reflective of key therapeutic considerations: approved and reimbursed therapies with respect to a jurisdiction (Tier 1), evidence of efficacy or approval in another jurisdiction (Tier 2), evidence of antitumour activity (Tier 3), and plausible biological rationale (Tier 4). Two resistance categories were defined: lack of efficacy (Tier R1) or antitumor activity (Tier R2). Based on this framework, we curated a digital resource focused on drugs relevant in the Australian healthcare system (TOPOGRAPH: Therapy Oriented Precision Oncology Guidelines for Recommending Anticancer Pharmaceuticals). As of November 2020, TOPOGRAPH comprised 2810 biomarker-disease-therapy triplets in 989 expert-appraised entries, including 373 therapies, 199 biomarkers, and 106 cancer types. In the 345 therapies catalogued, 84 (24%) and 65 (19%) were designated Tiers 1 and 2, respectively, while 271 (79%) therapies were supported by preclinical studies, early clinical trials, retrospective studies, or case series (Tiers 3 and 4). A companion algorithm was also developed to support rational, context-appropriate treatment selection informed by molecular biomarkers. This framework can be readily adapted to build similar resources in other jurisdictions to support therapeutic decision-making.

CanDriS: posterior profiling of cancer-driving sites based on two-component evolutionary model

Current cancer genomics databases have accumulated millions of somatic mutations that remain to be further explored. Due to the over-excess mutations unrelated to cancer, the great challenge is to identify somatic mutations that are cancer-driven. Under the notion that carcinogenesis is a form of somatic-cell evolution, we developed a two-component mixture model: while the ground component corresponds to passenger mutations, the rapidly evolving component corresponds to driver mutations. Then, we implemented an empirical Bayesian procedure to calculate the posterior probability of a site being cancer-driven. Based on these, we developed a software CanDriS (Cancer Driver Sites) to profile the potential cancer-driving sites for thousands of tumor samples from the Cancer Genome Atlas and International Cancer Genome Consortium across tumor types and pan-cancer level. As a result, we identified that approximately 1% of the sites have posterior probabilities larger than 0.90 and listed potential cancer-wide and cancer-specific driver mutations. By comprehensively profiling all potential cancer-driving sites, CanDriS greatly enhances our ability to refine our knowledge of the genetic basis of cancer and might guide clinical medication in the upcoming era of precision medicine. The results were displayed in a database CandrisDB (http://biopharm.zju.edu.cn/candrisdb/).

Targeting cancer epigenetics with CRISPR-dCAS9: Principles and prospects

Cancer therapeutics is an ever-evolving field due to incessant demands for effective and precise treatment options. Over the last few decades, cancer treatment strategies have shifted somewhat from surgery to targeted precision medicine. CRISPR-dCas9 is an emerging version of precision cancer therapy that has been adapted from the prokaryotic CRISPR-Cas system. Once ligated to epigenetic effectors (EE), CRISPR-dCas9 can function as an epigenetic editing tool and CRISPR-dCas9-EE complexes could be exploited to alter cancerous epigenetic features associated with different cancer hallmarks. In this article, we discuss the rationale of epigenetic editing as a therapeutic strategy against cancer. We also outline how sgRNA-dCas9 was derived from the CRISPR-Cas system. In addition, the current status of sgRNA-dCas9 use (in vivo and in vitro) in cancer is updated with a molecular illustration of CRISPR-dCas9-mediated epigenetic and transcriptional modulation. As sgRNA-dCas9 is still at the developmental phase, challenges are inherent to its use. We evaluate major challenges in targeting cancer with sgRNA-dCas9 such as off-target effects, lack of sgRNA designing rubrics, target site selection dilemmas and deficient sgRNA-dCas9 delivery systems. Finally, we appraise the sgRNA-dCas9 as a prospective cancer therapeutic by summarizing ongoing improvements of sgRNA-dCas9 methodology.

HIF-Prolyl Hydroxylase Domain Proteins (PHDs) in Cancer-Potential Targets for Anti-Tumor Therapy?

Solid tumors are typically associated with unbridled proliferation of malignant cells, accompanied by an immature and dysfunctional tumor-associated vascular network. Consequent impairment in transport of nutrients and oxygen eventually leads to a hypoxic environment wherein cells must adapt to survive and overcome these stresses. Hypoxia inducible factors (HIFs) are central transcription factors in the hypoxia response and drive the expression of a vast number of survival genes in cancer cells and in cells in the tumor microenvironment. HIFs are tightly controlled by a class of oxygen sensors, the HIF-prolyl hydroxylase domain proteins (PHDs), which hydroxylate HIFs, thereby marking them for proteasomal degradation. Remarkable and intense research during the past decade has revealed that, contrary to expectations, PHDs are often overexpressed in many tumor types, and that inhibition of PHDs can lead to decreased tumor growth, impaired metastasis, and diminished tumor-associated immune-tolerance. Therefore, PHDs represent an attractive therapeutic target in cancer research. Multiple PHD inhibitors have been developed that were either recently accepted in China as erythropoiesis stimulating agents (ESA) or are currently in phase III trials. We review here the function of HIFs and PHDs in cancer and related therapeutic opportunities.

Photoacoustic imaging biomarkers for monitoring biophysical changes during nanobubble-mediated radiation treatment

The development of novel anticancer therapies warrants the parallel development of biomarkers that can quantify their effectiveness. Photoacoustic imaging has the potential to measure changes in tumor vasculature during treatment. Establishing the accuracy of imaging biomarkers requires direct comparisons with gold histological standards. In this work, we explore whether a new class of submicron, vascular disrupting, ultrasonically stimulated nanobubbles enhance radiation therapy. In vivo experiments were conducted on mice bearing prostate cancer tumors. Combined nanobubble plus radiation treatments were compared against conventional microbubbles and radiation alone (single 8 Gy fraction). Acoustic resolution photoacoustic imaging was used to monitor the effects of the treatments 2- and 24-hs post-administration. Histological examination provided metrics of tumor vascularity and tumoral cell death, both of which were compared to photoacoustic-derived biomarkers. Photoacoustic metrics of oxygen saturation reveal a 20 % decrease in oxygenation within 24 h post-treatment. The spectral slope metric could separate the response of the nanobubble treatments from the microbubble counterparts. This study shows that histopathological assessment correlated well with photoacoustic biomarkers of treatment response.

ASH2L drives proliferation and sensitivity to bleomycin and other genotoxins in Hodgkin's lymphoma and testicular cancer cells

It is of clinical importance to identify biomarkers predicting the efficacy of DNA damaging drugs (genotoxins) so that nonresponders are not unduly exposed to the deleterious effects of otherwise inefficient drugs. Here, we initially focused on the bleomycin genotoxin because of the limited information about the genes implicated in the sensitivity or resistance to this compound. Using a whole-genome CRISPR/Cas9 gene knockout approach, we identified ASH2L, a core component of the H3K4 methyl transferase complex, as a protein required for bleomycin sensitivity in L1236 Hodgkin lymphoma. Knocking down ASH2L in these cells and in the NT2D1 testicular cancer cell line rendered them resistant to bleomycin, etoposide, and cisplatin but did not affect their sensitivity toward ATM or ATR inhibitors. ASH2L knockdown decreased cell proliferation and facilitated DNA repair via homologous recombination and nonhomologous end-joining mechanisms. Data from the Tumor Cancer Genome Atlas indicate that patients with testicular cancer carrying alterations in the ASH2L gene are more likely to relapse than patients with unaltered ASH2L genes. The cell models we have used are derived from cancers currently treated either partially (Hodgkin’s lymphoma), or entirely (testicular cancer) with genotoxins. For such cancers, ASH2L levels could be used as a biomarker to predict the response to genotoxins. In situations where tumors are expressing low levels of ASH2L, which may allow them to resist genotoxic treatment, the use of ATR or ATM inhibitors may be more efficacious as our data indicate that ASH2L knockdown does not affect sensitivity to these inhibitors.

Mitotic Poisons in Research and Medicine

Cancer is one of the greatest challenges of the modern medicine. Although much effort has been made in the development of novel cancer therapeutics, it still remains one of the most common causes of human death in the world, mainly in low and middle-income countries. According to the World Health Organization (WHO), cancer treatment services are not available in more then 70% of low-income countries (90% of high-income countries have them available), and also approximately 70% of cancer deaths are reported in low-income countries. Various approaches on how to combat cancer diseases have since been described, targeting cell division being among them. The so-called mitotic poisons are one of the cornerstones in cancer therapies. The idea that cancer cells usually divide almost uncontrolled and far more rapidly than normal cells have led us to think about such compounds that would take advantage of this difference and target the division of such cells. Many groups of such compounds with different modes of action have been reported so far. In this review article, the main approaches on how to target cancer cell mitosis are described, involving microtubule inhibition, targeting aurora and polo-like kinases and kinesins inhibition. The main representatives of all groups of compounds are discussed and attention has also been paid to the presence and future of the clinical use of these compounds as well as their novel derivatives, reviewing the finished and ongoing clinical trials.

Comparison of three commercial decision support platforms for matching of next-generation sequencing results with therapies in patients with cancer

OBJECTIVE

Precision oncology depends on translating molecular data into therapy recommendations. However, with the growing complexity of next-generation sequencing-based tests, clinical interpretation of somatic genomic mutations has evolved into a formidable task. Here, we compared the performance of three commercial clinical decision support tools, that is, NAVIFY Mutation Profiler (NAVIFY; Roche), QIAGEN Clinical Insight (QCI) Interpret (QIAGEN) and CureMatch Bionov (CureMatch).

METHODS

In order to obtain the current status of the respective tumour genome, we analysed cell-free DNA from patients with metastatic breast, colorectal or non-small cell lung cancer. We evaluated somatic copy number alterations and in parallel applied a 77-gene panel (AVENIO ctDNA Expanded Panel). We then assessed the concordance of tier classification approaches between NAVIFY and QCI and compared the strategies to determine actionability among all three platforms. Finally, we quantified the alignment of treatment suggestions across all decision tools.

RESULTS

Each platform varied in its mode of variant classification and strategy for identifying druggable targets and clinical trials, which resulted in major discrepancies. Even the frequency of concordant actionable events for tier I-A or tier I-B classifications was only 4.3%, 9.5% and 28.4% when comparing NAVIFY with QCI, NAVIFY with CureMatch and CureMatch with QCI, respectively, and the obtained treatment recommendations differed drastically.

CONCLUSIONS

Treatment decisions based on molecular markers appear at present to be arbitrary and dependent on the chosen strategy. As a consequence, tumours with identical molecular profiles would be differently treated, which challenges the promising concepts of genome-informed medicine.

A new pentacyclic pyrylium fluorescent probe that responds to pH imbalance during apoptosis

Efficient fluorophores with easy synthetic routes and fast responses are of great importance in clinical diagnostics. Herein, we report a new, rigid pentacyclic pyrylium fluorophore, PS-OMe, synthesised in a single step by a modified Vilsmeier-Haack reaction. Insights into the reaction mechanism facilitated a new reaction protocol for the efficient synthesis of PS-OMe which upon demethylation resulted in a "turn-on" pH sensor, PS-OH. This new fluorescent probe has been successfully used to monitor intracellular acidification at physiological pH. From the fluorescence image analysis, we were able to quantify the intracellular dynamic pH change during apoptosis. This new pH probe is a potential chemical tool for screening, drug discovery and dose determination in cancer therapy.

Current trends in cancer immunotherapy: a literature-mining analysis

Cancer immunotherapy is a rapidly growing field that is completely transforming oncology care. Mining this knowledge base for biomedically important information is becoming increasingly challenging, due to the expanding number of scientific publications, and the dynamic evolution of this subject with time. In this study, we have employed a literature-mining approach that was used to analyze the cancer immunotherapy-related publications listed in PubMed and quantify emerging trends. A total of 93,033 publications published in 5055 journals have been retrieved, and 141 meaningful topics have been identified, which were further classified into eight distinct categories. Statistical analysis indicates a mean annual increase in the number of published papers of approximately 8% in the last 20 years. The research topics that exhibited the highest trends included "immune checkpoint inhibitors," "tumor microenvironment," "HPV vaccination," "CAR T-cells," and "gene mutations/tumor profiling." The top identified cancer types included "lung," "colorectal," and "breast cancer," and a shift in popularity from hematological to solid tumors was observed. As regards clinical research, a transition from early phase clinical trials to randomized control trials was recorded, indicating that the field is entering a more advanced phase of development. Overall, this mining approach provided an unbiased analysis of the cancer immunotherapy literature in a time-conserving and scale-efficient manner.

Role of Folic Acid in the Therapeutic Action of Nanostructured Porous Silica Functionalized with Organotin(IV) Compounds Against Different Cancer Cell Lines

The synthesis, characterization and cytotoxic activity against different cancer cell lines of various mesoporous silica-based materials containing folate targeting moieties and a cytotoxic fragment based on a triphenyltin(IV) derivative have been studied. Two different mesoporous nanostructured silica systems have been used: firstly, micronic silica particles of the MSU-2 type and, secondly, mesoporous silica nanoparticles (MSNs) of about 80 nm. Both series of materials have been characterized by different methods, such as powder X-ray diffraction, X-ray fluorescence, absorption spectroscopy and microscopy. In addition, these systems have been tested against four different cancer cell lines, namely, OVCAR-3, DLD-1, A2780 and A431, in order to observe if the size of the silica-based systems and the quantity of incorporated folic acid influence their cytotoxic action. The results show that the materials are more active when the quantity of folic acid is higher, especially in those cells that overexpress folate receptors such as OVCAR-3 and DLD-1. In addition, the study of the potential modulation of the soluble folate receptor alpha (FOLR1) by treatment with the synthesized materials has been carried out using OVCAR-3, DLD-1, A2780 and A431 tumour cell lines. The results show that a relatively high concentration of folic acid functionalization of the nanostructured silica together with the incorporation of the cytotoxic tin fragment leads to an increase in the quantity of the soluble FOLR1 secreted by the tumour cells. In addition, the studies reported here show that this increase of the soluble FOLR1 occurs presumably by cutting the glycosyl-phosphatidylinositol anchor of membrane FR-α and by the release of intracellular FR-α. This study validates the potential use of a combination of mesoporous silica materials co-functionalized with folate targeting molecules and an organotin(IV) drug as a strategy for the therapeutic treatment of several cancer cells overexpressing folate receptors.

Morphologic and Genomic Heterogeneity in the Evolution and Progression of Breast Cancer

: Breast cancer is a remarkably complex and diverse disease. Subtyping based on morphology, genomics, biomarkers and/or clinical parameters seeks to stratify optimal approaches for management, but it is clear that every breast cancer is fundamentally unique. Intra-tumour heterogeneity adds further complexity and impacts a patient's response to neoadjuvant or adjuvant therapy. Here, we review some established and more recent evidence related to the complex nature of breast cancer evolution. We describe morphologic and genomic diversity as it arises spontaneously during the early stages of tumour evolution, and also in the context of treatment where the changing subclonal architecture of a tumour is driven by the inherent adaptability of tumour cells to evolve and resist the selective pressures of therapy.

Comparison of Annotation Services for Next-Generation Sequencing in a Large-Scale Precision Oncology Program

PURPOSE

Next-generation sequencing (NGS) multigene panel testing has become widespread, including the Veterans Affairs (VA), through the VA National Precision Oncology Program (NPOP). The interpretation of genomic alterations remains a bottleneck for realizing precision medicine. We sought to examine the concordance for pathogenicity determination and clinical actionability of annotation services in NPOP.

METHODS

Unique gene variants were generated from NGS gene panel results using two sequencing services. For each unique gene variant, annotations were provided through N-of-One (NoO), IBM Watson for Genomics (WfG), and OncoKB. Annotations for pathogenicity (all three sources) and actionability (WfG and OncoKB) were examined for concordance. Cohen's kappa statistic was calculated to measure agreement between annotation services.

RESULTS

Among 1,227 NGS results obtained between 2015 and 2017, 1,388 unique variants were identified in 117 genes. The genes with the largest number of variants included TP53 (270), STK11 (92), and CDKN2A (81). The most common cancer type was lung adenocarcinoma (440), followed by colon adenocarcinoma (113). For pathogenic and likely pathogenic variants, there was 30% agreement between WfG and NoO (kappa, -0.26), 76% agreement between WfG and OncoKB (kappa, 0.22), and 42% agreement between NoO and OncoKB (kappa, -0.07). For level 1 drug actionability of gene variant-diagnosis combinations, there was moderate agreement between WfG and OncoKB (96.9%; kappa, 0.44), with 27 combinations identified as level 1 by both services, 58 by WfG alone, and 6 variants by OncoKB alone.

CONCLUSION

There is substantial variability in pathogenicity assessment of NGS variants in solid tumors by annotation services. In addition, there was only moderate agreement in level 1 therapeutic actionability recommendations between WfG and OncoKB. Improvement in the precision of NGS multigene panel annotation is needed.

The Role of Nicotinamide in Cancer Chemoprevention and Therapy

Nicotinamide (NAM) is a water-soluble form of Vitamin B3 (niacin) and a precursor of nicotinamide-adenine dinucleotide (NAD⁺) which regulates cellular energy metabolism. Except for its role in the production of adenosine triphosphate (ATP), NAD⁺ acts as a substrate for several enzymes including sirtuin 1 (SIRT1) and poly ADP-ribose polymerase 1 (PARP1). Notably, NAM is an inhibitor of both SIRT1 and PARP1. Accumulating evidence suggests that NAM plays a role in cancer prevention and therapy. Phase III clinical trials have confirmed its clinical efficacy for non-melanoma skin cancer chemoprevention or as an adjunct to radiotherapy against head and neck, laryngeal, and urinary bladder cancers. Evidence for other cancers has mostly been collected through preclinical research and, in its majority, is not yet evidence-based. NAM has potential as a safe, well-tolerated, and cost-effective agent to be used in cancer chemoprevention and therapy. However, more preclinical studies and clinical trials are needed to fully unravel its value.

A Review of Precision Oncology Knowledgebases for Determining the Clinical Actionability of Genetic Variants

The increased availability of tumor genetic testing and targeted cancer therapies contributes to the advancement of precision medicine in the field of oncology. Precision oncology knowledgebases provide a way of organizing clinically relevant genetic information in a way that is easily accessible for both oncologists and patients, facilitating the genetic-based clinical decision making. Many organizations and companies have built precision oncology knowledgebases, intended for multiple users. In general, these knowledgebases offer information on cancer-related genetic variants as well as their associated diagnostic, prognostic, and therapeutic implications, but they often differ in their information curations, designs, and user experiences. It is advisable that oncologists use multiple knowledgebases during their practice to have them complement each other. In the future, convergence toward common standards and formats is needed to ensure that the comprehensive knowledge across all sources can be unified to bring the oncology community closer to the achievement of the goal of precision oncology.

Prediction of Drug Efficacy in Colon Cancer Preclinical Models Using a Novel Ranking Method of Gene Expression

The presence of stromal cells in tumors is altering the significance of molecular profiling when using standard methods of gene expression quantification. We developed a novel normalization method to rank target gene expression in tumor samples by comparisons with reference samples representing the different cell types found in a tumor. The score for each target gene obtained after normalization, is aimed to be predictive of targeted therapies efficiency. We performed this qPCR analysis on human colorectal cancers to demonstrate the importance of reference samples to obtain accurate data and on a collection of patient-derived xenografted (PDX) colon tumors treated with Cetuximab (anti-EGFR) to demonstrate that the calculated EGFR score is predictive of Cetuximab efficacy. Interestingly, the score allowed to select an efficient treatment in a PDX model refractory to standard of care. This method is opening a novel way to predict targeted therapy efficiency which could be extended to several tumor types, and to unlimited target genes.

Epigenetic polypharmacology: A new frontier for epi-drug discovery

Recently, despite the great success achieved by the so-called "magic bullets" in the treatment of different diseases through a marked and specific interaction with the target of interest, the pharmacological research is moving toward the development of "molecular network active compounds," embracing the related polypharmacology approach. This strategy was born to overcome the main limitations of the single target therapy leading to a superior therapeutic effect, a decrease of adverse reactions, and a reduction of potential mechanism(s) of drug resistance caused by robustness and redundancy of biological pathways. It has become clear that multifactorial diseases such as cancer, neurological, and inflammatory disorders, may require more complex therapeutic approaches hitting a certain biological system as a whole. Concerning epigenetics, the goal of the multi-epi-target approach consists in the development of small molecules able to simultaneously and (often) reversibly bind different specific epi-targets. To date, two dual histone deacetylase/kinase inhibitors (CUDC-101 and CUDC-907) are in an advanced stage of clinical trials. In the last years, the growing interest in polypharmacology encouraged the publication of high-quality reviews on combination therapy and hybrid molecules. Hence, to update the state-of-the-art of these therapeutic approaches avoiding redundancy, herein we focused only on multiple medication therapies and multitargeting compounds exploiting epigenetic plus nonepigenetic drugs reported in the literature in 2018. In addition, all the multi-epi-target inhibitors known in literature so far, hitting two or more epigenetic targets, have been included.

The pivotal role of sampling recurrent tumors in the precision care of patients with tumors of the central nervous system

Effective management of brain and spine tumors relies on a multidisciplinary approach encompassing surgery, radiation, and systemic therapy. In the era of personalized oncology, the latter is complemented by various molecularly targeting agents. Precise identification of cellular targets for these drugs requires comprehensive profiling of the cancer genome coupled with an efficient analytic pipeline, leading to an informed decision on drug selection, prognosis, and confirmation of the original pathological diagnosis. Acquisition of optimal tumor tissue for such analysis is paramount and often presents logistical challenges in neurosurgery. Here, we describe the experience and results of the Personalized OncoGenomics (POG) program with a focus on tumors of the central nervous system (CNS). Patients with recurrent CNS tumors were consented and enrolled into the POG program prior to accrual of tumor and matched blood followed by whole-genome and transcriptome sequencing and processing through the POG bioinformatic pipeline. Sixteen patients were enrolled into POG. In each case, POG analyses identified genomic drivers including novel oncogenic fusions, aberrant pathways, and putative therapeutic targets. POG has highlighted that personalized oncology is truly a multidisciplinary field, one in which neurosurgeons must play a vital role if these programs are to succeed and benefit our patients.

Proteomic investigation of intra-tumor heterogeneity using network-based contextualization - A case study on prostate cancer

Cancer is a heterogeneous disease, confounding the identification of relevant markers and drug targets. Network-based analysis is robust against noise, potentially offering a promising approach towards biomarker identification. We describe here the application of two network-based methods, qPSP (Quantitative Proteomics Signature Profiling) and PFSNet (Paired Fuzzy SubNetworks), in an intra-tissue proteome data set of prostate tissue samples. Despite high basal variation, we find that traditional statistical analysis may exaggerate the extent of heterogeneity. We also report that network-based analysis outperforms protein-based feature selection with concomitantly higher cross-validation accuracy. Overall, network-based analysis provides emergent signal that boosts sensitivity while retaining good precision. It is a potential means of circumventing heterogeneity for stable biomarker discovery.

Comprehensive elaboration of database resources utilized in next-generation sequencing-based tumor somatic mutation detection

The rapid evolution of next-generation sequencing (NGS)-based tumor genomic profile detection and the emergence of molecularly targeted therapies have enabled precision oncology. In NGS-based analysis, various types of databases have been developed to perform different functions. However, many problems still exist when using these public databases. Therefore, it is important to better understand the characteristics and limitations of each database and have them complement each other to provide useful clinical evidence for NGS testing. In this review, we elaborate on the important role of databases and their concrete applications in NGS-based somatic mutation detection. We introduce the typically used databases for sequence alignment, variant filtration, and variant interpretation, and compare the differences between the databases with similar functions. Subsequently, we determine the limitations of each database and provide the corresponding solutions. Furthermore, we present an overview diagram to clearly illustrate the database used in the entire NGS-based somatic mutation detection pipeline.

Cancer Treatment in the Genomic Era

The complexity of human cancer underlies its devastating clinical consequences. Drugs designed to target the genetic alterations that drive cancer have improved the outcome for many patients, but not the majority of them. Here, we review the genomic landscape of cancer, how genomic data can provide much more than a sum of its parts, and the approaches developed to identify and validate genomic alterations with potential therapeutic value. We highlight notable successes and pitfalls in predicting the value of potential therapeutic targets and discuss the use of multi-omic data to better understand cancer dependencies and drug sensitivity. We discuss how integrated approaches to collecting, curating, and sharing these large data sets might improve the identification and prioritization of cancer vulnerabilities as well as patient stratification within clinical trials. Finally, we outline how future approaches might improve the efficiency and speed of translating genomic data into clinically effective therapies and how the use of unbiased genome-wide information can identify novel predictive biomarkers that can be either simple or complex.

Data ambiguity and clinical decision making: A qualitative case study of the use of predictive information technologies in a personalized cancer clinical trial

Personalized medicine aims to tailor the treatment to the specific characteristics of the individual patient. In the process, physicians engage with multiple sources of data and information to decide on a personalized treatment. This article draws on a qualitative case study of a clinical trial testing a method for matching treatments for advanced cancer patients. Specialists in the trial used data and information processed by a specifically developed drug-efficacy predictive algorithm and other information artifacts to make personalized clinical decisions. While using high-resolution data in the trial was expected to provide a more accurate basis for action, sociomaterial engagements of oncologists with data and its representation by artifacts paradoxically hindered personalized clinical decisions. I contend that the engagement between human discretion, ambiguous data, and malleable artifacts in this non-standardized trial produced moments of contradiction within entanglement. Sociomaterial approaches should acknowledge such conflicts in further analyses of medical practice transitions.

Single-cell protein profiling in microchambers with barcoded beads

Single-cell profiling provides insights into cellular behaviour that macroscale cell cultures and bulk measurements cannot reveal. In the context of personalized cancer treatment, the profiling of individual tumour cells may lead to higher success rates for therapies by rapidly selecting the most efficacious drugs. Currently, genomic analysis at the single-cell level is available through highly sensitive sequencing approaches. However, the identification and quantification of intracellular or secreted proteins or metabolites remains challenging. Here, we introduce a microfluidic method that facilitates capture, automated data acquisition and the multiplexed quantification of proteins from individual cells. The microfluidic platform comprises 1026 chambers with a volume of 152 pL each, in which single cells and barcoded beads are co-immobilized. We demonstrated multiplexed single-cell protein quantification with three different mammalian cell lines, including two model breast cancer cell lines. We established on-chip immunoassays for glyceraldehyde-3-phosphate dehydrogenase (GAPDH), galectin-3 (Gal-3) and galectin-3 binding protein (Gal-3bp) with detection limits as low as 7.0 × 10⁴, 2.3 × 10⁵ and 1.8 × 10³ molecules per cell, respectively. The three investigated cell types had high cytosolic levels of GAPDH and could be clearly differentiated by their expression levels of Gal-3 and Gal-3bp, which are important factors that contribute to cancer metastasis. Because it employed commercially available barcoded beads for this study, our platform could be easily used for the single-cell protein profiling of several hundred different targets. Moreover, this versatile method is applicable to the analysis of bacteria, yeast and mammalian cells and nanometre-sized lipid vesicles.