Search

Das Pankreaskarzinom ist eine sehr aggressive Krebserkrankung mit einer auserst schlechten Prognose. Das Uberleben der Patienten in samtlichen Tumorstadien ist jedoch sehr unterschiedlich. Standardtherapien, die sich uberwiegend an TNM-Klassifikation, Allgemeinzustand und Komorbiditat orientieren, sind in ihrer Effektivitat sehr variabel. Neue Technologien mit Multi-omics-Tumorcharakterisierungen haben in den letzten Jahren die molekulare Heterogenitat des Pankreaskarzinoms aufgezeigt. In der klinischen Routine wird das Pankreaskarzinom allerdings meist als uniforme Erkrankung betrachtet ohne Beachtung der individuellen Tumorbiologie. Aktuelle klinische Studien haben gezeigt, dass molekulare Analysen pharmakologische Zielstrukturen und prognose- bzw. therapierelevante Subtypen identifizieren konnen. Bessere Methoden zur Prognosepradiktion und Stratifizierung anhand klinischer und molekularer Parameter konnten dazu beitragen, effektivere, personalisierte multimodale Therapiekonzepte zu erstellen und werden einheitliche Standardtherapien ablosen.

Összefoglaló. A molekuláris diagnosztikai módszerek folyamatos fejlődésének köszönhetően egyre több onkogén genetikai eltérést azonosítanak. A neurotrofikus tropomiozin receptor-tirozin-kináz (NTRK-) génfúziók fontos precíziós onkológiai célpontok, melyek mindhárom NTRK-génben előfordulhatnak, onkogén-hajtóerőként viselkednek. A génfúziók különböző molekuláris diagnosztikai módszerekkel azonosíthatók, melyek közül a legpontosabb, legköltségesebb és legidőigényesebb meghatározást az újgenerációs szekvenálási technika jelenti. A tropomiozin receptor-tirozin-kináz (TRK-) fúziós fehérjék szelektív gátlása személyre szabott onkológiai kezelési lehetőséget jelent a tumor típusától, lokalizációjától és a beteg életkorától függetlenül. Az első generációs TRK-gátlók gyors, hatékony és tartós daganatellenes hatást biztosítanak kimutatott NTRK-fúzió-pozitív daganatok esetén, alacsony mellékhatásprofil mellett. Az első generációs TRK-gátlók mellett jelentkező ’on target’ rezisztenciát a második generációs TRK-gátlók oldják fel. Szekvenciális tirozin-kináz-inhibitor-kezeléssel tartós betegségmentes túlélés érhető el. Orv Hetil. 2021; 162(34): 1362–1369. Summary. Due to the continuous development of molecular diagnostic methods, more and more oncogenic genetic abnormalities are being identified. Neurotrophic tropomyosin receptor tyrosine kinase (NTRK) gene fusions are important precision oncology targets that can occur in all three NTRK genes and act as oncogenic drivers. Gene fusions can be identified by a variety of molecular diagnostic technologies, of which next-generation sequencing is the most accurate, costly and time-consuming determination. Selective inhibition of tropomyosin receptor tyrosine kinase (TRK) fusion proteins represents a personalized oncology treatment option regardless of tumour type, localization and patient age. First-generation TRK inhibitors provide rapid, efffective and long-lasting antitumor activity in NTRK fusion-positive tumors with a low side-effect profile. On target resistance to first-generation TRK inhibitors is resolved by second-generation TRK inhibitors. Durable disease-free survival can be achieved with sequential tyrosine kinase inhibitor therapies. Orv Hetil. 2021; 162(34): 1362–1369.

The American Joint Committee of Cancer (AJCC) tumor-node-metastasis (TNM) classification, with its periodical updates and modifications, has represented and still represents the basis of cancer staging. The historical, long-standing limitations of anatomic-based TNM staging have been recently "threatened" by the impressive amount of data derived from molecular analyses, which have led to an unprecedented level of understanding of cancer genomics. In fact, current era of personalized oncology has witnessed important efforts towards the integration between clinical, anatomical and molecular features; however, despite the promises, personalized oncology faces many obstacles, due to the complex relationship between tumor biomarkers, previously unknown cancer subtypes and clinical and anatomical characteristics. With regard to urothelial carcinoma (UC), the characterization of tumors in large cohorts of patients has provided important information concerning genetic alterations, revealing the presence of biologically relevant subtypes of UC. In the current review, we will provide an overview regarding this recent "translation" from the anatomic-based TNM to a novel horizon, aiming at further "tailoring" personalized oncology, especially focusing on recently published data about the molecular landscape of UC with its therapeutic and prognostic implications.

The current limitations of cancer diagnosis and molecular profiling based on invasive tissue biopsies or clinical imaging have led to the development of the Liquid Biopsy field (LB)

Simple Summary Clinical trials in pediatric oncology and personalized medicine are challenging due to the rarity of the disease, the low prevalence, and the ever-improving treatment outcomes. Many of the methods designed for small numbers and approaches used in classical population studies are not suitable for personalized pediatric oncology. There has been a change of perspective on the whole issue of rare diseases and personalized medicine. For example, a shift from a population to an individual perspective, generalizing from the individual to the population, using repeated measures and a within-subject design instead of parallel groups, exploring the variability instead of suppressing it, etc. N-of-1 should be understood as a whole range of approaches that fit the new inferential, evidential and analytical paradigms of modern medicine. Abstract Pediatric oncology is a critical area where the more efficient development of new treatments is urgently needed. The speed of approval of new drugs is still limited by regulatory requirements and a lack of innovative designs appropriate for trials in children. Childhood cancers meet the criteria of rare diseases. Personalized medicine brings it even closer to the horizon of individual cases. Thus, not all the traditional research tools, such as large-scale RCTs, are always suitable or even applicable, mainly due to limited sample sizes. Small samples and traditional versus subject-specific evidence are both distinctive issues in personalized pediatric oncology. Modern analytical approaches and adaptations of the paradigms of evidence are warranted. We have reviewed innovative trial designs and analytical methods developed for small populations, together with individualized approaches, given their applicability to pediatric oncology. We discuss traditional population-based and individualized perspectives of inferences and evidence, and explain the possibilities of using various methods in pediatric personalized oncology. We find that specific derivatives of the original N-of-1 trial design adapted for pediatric personalized oncology may represent an optimal analytical tool for this area of medicine. We conclude that no particular N-of-1 strategy can provide a solution. Rather, a whole range of approaches is needed to satisfy the new inferential and analytical paradigms of modern medicine. We reveal a new view of cancer as continuum model and discuss the “evidence puzzle”.

Cholangiocarcinoma (CC) is an aggressive malignancy with an inferior prognosis due to limited systemic treatment options. As preclinical models such as CC cell lines are extremely rare, this manuscript reports a protocol of cholangiocarcinoma patient-derived organoid culture as well as a protocol for the transition of 3D organoid lines to 2D cell lines. Tissue samples of non-cancer bile duct and cholangiocarcinoma were obtained during surgical resection. Organoid lines were generated following a standardized protocol. 2D cell lines were generated from established organoid lines following a novel protocol. Subcutaneous and orthotopic patient-derived xenografts were generated from CC organoid lines, histologically examined, and treated using standard CC protocols. Therapeutic responses of organoids and 2D cell lines were examined using standard CC agents. Next-generation exome and RNA sequencing was performed on primary tumors and CC organoid lines. Patient-derived organoids closely recapitulated the original features of the primary tumors on multiple levels. Treatment experiments demonstrated that patient-derived organoids of cholangiocarcinoma and organoid-derived xenografts can be used for the evaluation of novel treatments and may therefore be used in personalized oncology approaches. In summary, this study establishes cholangiocarcinoma organoids and organoid-derived cell lines, thus expanding translational research resources of cholangiocarcinoma.

Prostate cancer is one of the most common cancers for men in the United States. Nurse practitioners need to understand oncology care and potential applications of genetic and genomics for men with prostate cancer. Genetic testing options are available to assist in decision making regarding screening for prostate cancer and the treatment of prostate cancer. Translating and integrating genomic test results can assist health care providers, patients, and their families to determine optimal and personalized oncology care in prostate cancer.

The German Cancer Consortium (‘Deutsches Konsortium für Translationale Krebsforschung’, DKTK) is a long-term cancer consortium, bringing together the German Cancer Research Center (DKFZ), Germany's largest life science research center, and the leading University Medical Center-based Comprehensive Cancer Centers (CCCs) at seven sites across Germany. DKTK was founded in 2012 following international peer review and has positioned itself since then as the leading network for translational cancer research in Germany. DKTK is long term funded by the German Ministry of Research and Education and the federal states of each DKTK partner site. DKTK acts at the interface between basic and clinical cancer research, one major focus being to generate suitable multisite cooperation structures and provide the basis for including higher numbers of patients and facilitate effective collaborative forward and reverse translational cancer research. The consortium addresses areas of high scientific and medical relevance and develops critical infrastructures, for example, for omics technologies, clinical and research big data exchange and analysis, imaging, and clinical grade drug manufacturing. Moreover, DKTK provides a very attractive environment for interdisciplinary and interinstitutional training and career development for clinician and medical scientists. CA extern

Given the lack of a gold standard, the clinical usefulness of Comprehensive Genomic Profiling (CGP) has not been established. This systematic review aimed to evaluate evidence about the clinical benefit of CGP for patients with Non-small cell lung carcinoma (NSCLC). All controlled studies that evaluated the ability of CGP to detect actionable targets (ATs) reported increases in the number of samples with ATs. The frequency of ATs detected in uncontrolled case series ranged from 0.7 % for RET mutations to 45 % for EGFR mutations. The studies that evaluated therapies targeted to EGFR, ALK, ROS-1, MET, and RET mutations documented significant improvement in clinical outcomes. This review suggests that CGP tests may be clinically helpful for treating patients with NSCLC. Although current evidence is associated with a high risk of bias, the significant impact of NSCLC on individuals and society may justify the routine use of CGP testing for this disease.

Optimizing patient care based on precision oncology will inevitably become the standard of care. If we accept the principle that every persons’ cancer is different then the most effective therapies will have to be designed for the individual patient and for their tumors genetic profile. Access to tumor mutational profiling is now widely available but continues to be limited by cost and actionable information. For example, novel combinations of approved drugs are rarely considered. These considerations lead us to hypothesize that artificially induced Lung Adenocarcinoma (LUAD) derived lung organoids could provide a novel, alternate approach for LUAD disease modeling and large-scale targeted drug screening.In this project, we used data from a commercially available tumor mutation profile to generate and then validate the artificially induced LUAD-derived lung organoid simulations (aiLUNG-LUAD) to model LUAD and identify several drug combinations that effectively reverse the tumors’ genotypic and phenotypic features when compared with placebo. These results complement previous LUAD-derived lung organoids research and provide a novel and widely applicable cancer drug-screening approach for precision/individualized oncology.

Cancer is a multifactorial disease with increasing incidence. There are more than 100 different cancer types, defined by location, cell of origin, and genomic alterations that influence oncogenesis and therapeutic response. This heterogeneity between tumors of different patients and also the heterogeneity within the same patient’s tumor pose an enormous challenge to cancer treatment. In this review, we explore tumor heterogeneity on the longitudinal and the latitudinal axis, reviewing current and future approaches to study this heterogeneity and their potential to support oncologists in tailoring a patient’s treatment regimen. We highlight how the ideal of precision oncology is reaching far beyond the knowledge of genetic variants to inform clinical practice and discuss the technologies and strategies already available to improve our understanding and management of heterogeneity in cancer treatment. We will focus on integrating multi-omics technologies with suitable in vitro models and their proficiency in mimicking endogenous tumor heterogeneity.

Liquid biopsies, microRNAs, adoptive cellular therapies such as CAR T-cell therapy, and immune checkpoint inhibitors, and systems biology are recent trends in precision oncology that form tangential, however still very significant, advances in personalized oncology. Liquid biopsy remains a powerful tool as a noninvasive, elegant way of determining prognosis of certain malignancies. The development of novel immunotherapies has led to the term “Precision Medicine 2.0” to characterize them. MicroRNAs are being developed with diagnostic, prognostic, and predictive value. Cancer vaccines are in the process of forming a central component of the oncologist’s armamentarium against cancer. Other innovations such as the use of recombinant poliovirus for the treatment of gliomas are examples of the cutting-edge nature of this field. Challenges still remain however, including cost and access, and other ethical concerns.

This chapter focuses on applications of personalized medicine to cancers involving various organs and systems of the human body. Molecular diagnostics, biomarkers and other technologies described in earlier chapters are used to personalize treatment. Many of these are still in clinical trials. Companion diagnostics, biomarker-guided monitoring of therapy, and management of metastases as well as drug resistance are described briefly for each type of tumor.

This chapter describes the general principles and basics of personalized therapy of cancer. The biggest challenge for optimal treatment outcomes in cancer patients is the complex nature of the disease due to cellular heterogeneity and dysfunction of numerous molecular networks as results of genetic as well as environmental disturbances. Apart from variations among patient, tumor heterogeneity contributes to differences in clinical behavior and responses to treatment of tumors of the same diagnostic type.

We report a case of an uveal melanoma patient with GNAQ p.Gly48Leu who responded to MEK inhibition. At the time of the molecular analysis, the pathogenicity of the mutation was unknown. A tridimensional structural analysis showed that G&alpha, q can adopt active and inactive conformations that lead to substantial changes, involving three important switch regions. Our molecular modelling study predicted that GNAQ p.Gly48Leu introduces new favorable interactions in its active conformation, whereas little or no impact is expected in its inactive form. This strongly suggests that GNAQ p.Gly48Leu is a possible tumor-activating driver mutation, consequently triggering the MEK pathway. In addition, we also found an FGFR4 p.Cys172Gly mutation, which was predicted by molecular modelling analysis to lead to a gain of function by impacting the Ig-like domain 2 folding, which is involved in FGF binding and increases the stability of the homodimer. Based on these analyses, the patient received the MEK inhibitor trametinib with a lasting clinical benefit. This work highlights the importance of molecular modelling for personalized oncology.

Background Machine learning has been utilized to predict cancer drug response from multi-omics data generated from sensitivities of cancer cell lines to different therapeutic compounds. Here, we build machine learning models using gene expression data from patients’ primary tumor tissues to predict whether a patient will respond positively or negatively to two chemotherapeutics: 5-Fluorouracil and Gemcitabine. Results We focused on 5-Fluorouracil and Gemcitabine because based on our exclusion criteria, they provide the largest numbers of patients within TCGA. Normalized gene expression data were clustered and used as the input features for the study. We used matching clinical trial data to ascertain the response of these patients via multiple classification methods. Multiple clustering and classification methods were compared for prediction accuracy of drug response. Clara and random forest were found to be the best clustering and classification methods, respectively. The results show our models predict with up to 86% accuracy; despite the study’s limitation of sample size. We also found the genes most informative for predicting drug response were enriched in well-known cancer signaling pathways and highlighted their potential significance in chemotherapy prognosis. Conclusions Primary tumor gene expression is a good predictor of cancer drug response. Investment in larger datasets containing both patient gene expression and drug response is needed to support future work of machine learning models. Ultimately, such predictive models may aid oncologists with making critical treatment decisions.

ImportanceThe terms “personalized oncology” and “precision oncology” have increased in usage and have generated considerable traction in terms of public attention and research funding. To our knowledge, no prior study has as thoroughly documented the use of the “precision oncology” terminology over the last decade.ObjectiveTo determine how the use of the terms “personalized oncology” and “precision oncology” have changed over time.DesignA retrospective literature analysis using two databases (PubMed and Scopus) over 10 years was performed. Manuscripts using either term “personalized oncology” or “precision oncology” were collected. Manuscripts published in 2011, 2013, 2015, 2017 and through 30 June 2019 were pulled for text analysis. Common reasons for exclusion were if the search term appeared in the institution name only, the search term appeared only in keyword or publication title, or the search term was used to justify the relevance or application of research with no clear definition.SettingManuscripts published and catalogued in PubMed or Scopus.ResultsIn our study, we analysed 399 unique manuscripts published over the last decade. Over time, the terminology has shifted from “personalized oncology” to “precision oncology”. Targeted therapy, molecular biomarker-guided tumour profiling and next generation sequencing (ie, “omics-guided tumor profiling”) are the three most common definitions of the term. While these definitions are somewhat overlapping in concept, over the decade we observed an increase in the number of distinct interpretations of “precision oncology”, ranging from structural biology to clinical practice.Conclusions and relevanceWe have observed that the phrase “precision oncology” is shifting, overlapping and expanding in definition. This all-encompassing approach to defining “precision oncology” ironically renders the term imprecise. Our analysis highlights the inherent challenges in defining novel movements in medicine.

Integrated cancer patients profiling using combinations of PDC-PDx-Omics technologies are receiving growing scientific interest as a strong approach for clinical decisional workflows leading to personalized treatment.

Cancers of unknown primary site (CUPs) represent a heterogeneous group of metastatic tumors, which accounts for 3-5% of malignancies. Due to the poor prognosis and limited local and systemic treatment options, there is an urgent need for improvement of molecularly driven treatment strategies. We investigated the molecular profile and clinical course of 70 patients enrolled in a prospective precision oncology registry trial conducted by the National Center for Tumor Diseases (NCT) Heidelberg/Dresden and the German Cancer Consortium (DKTK) that addresses younger adults with advanced-stage cancer across histologies as well as patients with rare tumors (NCT/DKTK MASTER). Molecular analyses included whole genome sequencing (WGS, n=29), whole exome sequencing (WES, n=41) and transcriptome analysis (n=55). All patients were diagnosed with CUP-syndrome, 61/70 (87.1%) of diagnoses fulfilled the ESMO Clinical Practice Guidelines. Progression free survival (PFS) of the first treatment based on MASTER (PFS2) was compared to the PFS of the last prior systemic treatment (PFS1) in each individual patient. Within the coding sequence, we identified 0 to 1386 nonsynonymous point mutations (SNVs, median=41) and 0 to 38 insertions/deletions (indels, median=3) per sample. Hypermutation (≥100 SNVs and indels) was observed in 14 samples. Mutations of TP53 and KRAS were significantly enriched. Analysis of copy-number changes (CNVs) was performed in 51 samples (27 WGS and 24 WES) and revealed complex CNV profiles in most cases. Gains and losses involved single arms or whole chromosomes. Gains in chromosome 8q, 1q and 7 and losses in chromosome 6q and 17p occurred in more than 40% of the patients. Fusions of EML4-ALK and FGFR2 were found in three and six cases, respectively. In one case, pathological reevaluation for NUT midline carcinoma was recommended based on a NUTM1-MXI1 fusion. In total, pathological reevaluation based on characteristic genetic events was recommended in five cases. Germline analysis of 70 cases revealed five pathogenic variants (ACMG Class 5) in CHEK2, BRCA1, CDKN2A, NBN and ERCC3. In addition, one likely pathogenic variant (ACMG Class 4) was found in FH. The molecular tumor board recommended targeted therapy in 56/70 (80.0%) patients which could be applied in 20/56 (35.7%) cases. The molecularly driven treatment approaches translated into a median PFS2/1 ratio of 2.25 (n=17). Median PFS1 was 89 days (range 31-304, n=17) compared to a median PFS2 of 180 days (range 50-805, n=17). For three patients in which PFS1 could not be determined median PFS2 was 305 days (range 182-336). We demonstrate that a comprehensive molecular analysis of CUPs provides clinically relevant information and additional, molecularly stratified treatment approaches in many cases. These targeted therapies can be highly beneficial even in heavily pretreated patients. Citation Format: Maximilian Werner, Lino Möhrmann, Małgorzata Oleś, Andreas Mock, Arne Jahn, Simon Kreutzfeldt, Sebastian Uhrig, Martina Fröhlich, Barbara Hutter, Daniela Richter, Gina Rüter, Ivan Jelas, Rainer Hamacher, Johanna Falkenhorst, Sebastian Wagner, Christian Brandts, Melanie Börries, Anna Illert, Klaus Metzeler, Benedikt Westphalen, Alexander Desuki, Thomas Kindler, Albrecht Stenzinger, Evelin Schröck, Benedikt Brors, Peter Horak, Christoph Heining, Stefan Fröhling, Hanno Glimm. Genomics based personalized oncology of cancer of unknown primary [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 820.

The targeted treatment of advanced non-small cell lung cancer (NSCLC) harboring genomic rearrangement of ALK is a paradigm for personalized oncology. More than 15 different ALK fusion partners have been discovered in NSCLC patients. Most of these ALK fusions responded well to the ALK inhibitor crizotinib. Crizotinib is an oral MET/ALK inhibitor used as first-line therapy in the treatment of advanced NSCLC harboring ALK rearrangement. An understanding of the mechanisms by which tumors harbor primary drug resistance or acquired resistance to targeted therapies is critical for predicting which patients will respond to a specific therapy and for the identification of additional targetable pathways to maximize clinical benefits. Cap methyltransferase 1(CMTR1) also known as hMTr1, which is translate a human cap1 2ʹ-o-ribose methyltransferase. Here, we report the newly found ALK fusion, CMTR1-ALK, in a patient who has no response to the ALK inhibitor crizotinib. The results remind us that detecting ALK status is important, but that determining the ALK fusion type and function may be more important for patient.

Background: We present the case of a 50-year-old female whose metastatic pancreatic neuroendocrine tumor (pNET) diagnosis was delayed by the COVID-19 pandemic. The patient was in critical condition at the time of diagnosis due to the extensive tumor burden and failing liver functions. The clinical dilemma was to choose between two registered first-line molecularly-targeted agents (MTAs), sunitinib or everolimus, or to use chemotherapy to quickly reduce tumor burden. Methods: Cell-free DNA (cfDNA) from liquid biopsy was analyzed by next generation sequencing (NGS) using a comprehensive 591-gene panel. Next, a computational method, digital drug-assignment (DDA) was deployed for rapid clinical decision support. Results: NGS analysis identified 38 genetic alterations. DDA identified 6 potential drivers, 24 targets, and 79 MTAs. Everolimus was chosen for first-line therapy based on supporting molecular evidence and the highest DDA ranking among therapies registered in this tumor type. The patient’s general condition and liver functions rapidly improved, and CT control revealed partial response in the lymph nodes and stable disease elsewhere. Conclusion: Deployment of precision oncology using liquid biopsy, comprehensive molecular profiling, and DDA make personalized first-line therapy of advanced pNET feasible in clinical settings.

Cancer is linked with mutated genes, and the study of tumor-associated genetic alterations is gradually used for diagnostic and treatment purposes. The solid tumors are now obtained from biopsy and/or surgical or biopsy specimens. Tumor cells release circulating free DNA into the blood, but the majority of circulating DNA are often not of cancerous origin, and the detection of cancer-associated alleles in the blood has long been unbearable to achieve. Modern science has overwhelmed these restrictions, making it possible to identify both genetic and epigenetic aberrations. A liquid biopsy (LB) or blood sample can provide the genetic landscape of all cancerous lesions (primary and metastases) as well as offering the opportunity to systematically track genomic evolution. This communication will explore how LB is associated with personalized oncology to predict response to treatments and the development of acquired resistance.

Clear cell renal cell carcinoma represents the most common type of kidney cancer. Precision medicine approach to ccRCC requires an accurate stratification of patients that can predict prognosis and guide therapeutic decision. Transcription factors are implicated in the initiation and progression of human carcinogenesis. However, no comprehensive analysis of transcription factor activity has been proposed so far to realize patient stratification. Here we propose a novel approach to determine the subtypes of ccRCC patients based on global transcription factor activity landscape. Using the TCGA cohort dataset, we identified different subtypes that have distinct upregulated biomarkers and altered biological pathways. More important, this subtype information can be used to predict the overall survival of ccRCC patients. Our results suggest that transcription factor activity can be harnessed to perform patient stratification.

Non-small cell lung carcinoma (NSCLC) is the major cause of cancer-associated mortality. Identification of rearrangements in anaplastic lymphoma kinase (ALK) gene is an effective instrument for more effective targeted therapy of NSCLC using ALK inhibitors dramatically raising progression-free survival in the ALK-mutated group of patients. However, the tumors frequently develop resistance to ALK inhibitors. We describe here a case of 48 y.o. male patient with ALK-positive NSCLC who was clinically managed for 6.5 years from the diagnosis. The tumor was surgically resected, but 8 months later multiple brain metastases were discovered. The patient started receiving platinum-based chemotherapy and then was enrolled in a clinical trial of second-generation ALK inhibitor ceritinib, which resulted in a 21 months stabilization. Following disease relapse, the patient was successfully managed for 33 months with different lines of chemo- and local ablative therapies. Chemotherapy regimens, including off-label combination of crizotinib + bevacizumab + docetaxel, were selected using the cancer transcriptome data-guided bioinformatical decision support system Oncobox. These therapies led to additional stabilization for 22 months. Survival of our patient after developing resistance to ALK inhibitor was longer for 16 months than previously reported average survival for such cases. This case shows that transcriptomic-guided sequential personalized prescription of targeted therapies can be effective in terms of survival and quality of life in ALK-mutated NSCLC.

Introduction: Therapeutic drug monitoring in oncology is used to prevent major toxicities of selected anticancer agents due to overexposure by individualizing the dose based on a pharmacokinetic target. Areas covered: Numerous studies relating a relation between pharmacokinetic variability and toxicity have been reported since the eighties but very few have been implemented in clinical practice due to a lack of validation and harmonization, logistical constraints and reluctance from oncologists. Following recent recommendations, this paper highlights the current-validated applications of pharmacokinetic monitoring in oncology focusing on the safety of anticancer therapies. Expert opinion: Paradoxically given the oldness of the agents, guidelines of dose adjustment have been recently available for intravenous busulfan, 5-fluorouracil, and high-dose methotrexate. Interestingly, besides the enhancement of tolerability, it applies to potential curative clinical situations. In an era of personalized oncology that integrates complex molecular factors in the treatment of cancer, education is needed for oncologists to show the benefits of this valuable (even old) resource for the safety of patients. Therapeutic drug monitoring for busulfan, 5-fluorouracil and methotrexate will still hold in the future unless more active agents are available in the concerned indications.

Precision medicine (PM) means the customization of healthcare with decisions and practices adjusted to the individual patient. It includes personalized diagnostics, patients' sub-classification, individual treatment selection and the monitoring of its effectiveness. Currently, in oncology, PM is based on the molecular and cellular features of a tumor, its microenvironment and the patient's genetics and lifestyle. Surprisingly, the available targeted therapies were found effective only in a subset of patients. An in-depth understanding of tumor biology is crucial to improve their effectiveness and develop new therapeutic targets. Completion of genetic information with proteomics and metabolomics can give broader knowledge about tumor biology which consequently provides novel biomarkers and indicates new therapeutic targets. Recently, metabolomics and proteomics have extensively been applied in the field of oncology. In the context of PM, human studies, with the use of mass spectrometry (MS) which allows the detection of thousands of molecules in a large number of samples, are the most valuable. Such studies, focused on cancer biomarkers discovery or patients' stratification, are presented in this review. Moreover, the technical aspects of MS-based clinical proteomics and metabolomics are described.

International audience; Purpose: Using artificial intelligence techniques, we compute optimal personalized protocols for temozolomide administration in a population of patients with variability. Methods: Our optimizations are based on a Pharmacokinetics/Pharmacodynamics (PK/PD) model with population variability for temozolomide, inspired by Faivre et al. [10] and Panetta et al. [25,26]. The patient pharmacokinetic parameters can only be partially observed at admission and are progressively learned by Bayesian inference during treatment. For every patient, we seek to minimize tumor size while avoiding severe toxicity, i.e. maintaining an acceptable toxicity level. The optimization algorithm we rely on borrows from the field of artificial intelligence. Results: Optimal personalized protocols (OPP) achieve a sizable decrease in tumor size at the population level but also patient-wise. The tumor size is on average 67.2 g lighter than with the standard maximum-tolerated dose protocol (MTD) after 336 days (12 MTD cycles). The corresponding 90% confidence interval for average tumor size reduction amounts to 58.6–82.7 g. When treated with OPP, less patients experience severe toxicity in comparison to MTD. Major findings: We quantify in-silico the benefits offered by personalized oncology in the case of temozolomide administration. To do so, we compute optimal personalized protocols for a population of heterogeneous patients using artificial intelligence techniques. At each treatment day, the protocol is updated by taking into account the feedback obtained from patient's reaction to the drug administration. Personalized protocols greatly differ from each other, and from the standard MTD protocol. Benefits of personalization are very sizable: tumor sizes are much smaller on average and also patient-wise, while severe toxicity is made less frequent.

Only a subset of cancer patients currently benefit from personalized treatment approaches. Detection of actionable drug targets in cell-free DNA, more comprehensive molecular profiling integrating transcriptional analyses, and personalized combination regimens all hold promise in expanding the benefits of personalized oncology to larger numbers of cancer patients.

By definition, single nucleotide polymorphisms (SNPs) are loci with alleles that differ at a single base level, with the rarer allele having a frequency of at least 1% in a random set of individuals within a given population (1).

Objective Measuring the success of molecularly guided therapies is a major challenge in precision oncology trials. A commonly used endpoint is an intra-patient progression-free survival (PFS) ratio, defined as the PFS interval associated with molecularly guided therapy (PFS2) divided by the PFS interval associated with the last prior systemic therapy (PFS1), above 1.3 or, in some studies, above 1.33 or 1.5. Methods To investigate if the concept of PFS ratios is in agreement with actual response evaluations by physicians, we conducted a survey among members of the MASTER (Molecularly Aided Stratification for Tumor Eradication Research) Programme of the German Cancer Consortium who were asked to classify the success of molecularly guided therapies in 194 patients enrolled in the MOSCATO 01 trial based on PFS1 and PFS2 times. Results A comparison of classification profiles revealed three distinct clusters of PFS benefit assessments. Only 29% of assessments were consistent with a PFS ratio threshold of 1.3, whereas the remaining 71% of participants applied a different classification scheme that did not rely on the relation between PFS times alone, but also took into account absolute PFS1 intervals. Based on these community-driven insights, we developed a modified PFS ratio that incorporates the influence of absolute PFS1 intervals on the judgement of clinical benefit by physicians. Application of the modified PFS ratio to outcome data from two recent precision oncology trials, MOSCATO 01 and WINTHER, revealed significantly improved concordance with physician-perceived clinical benefit and identified comparable proportions of patients who benefited from molecularly guided therapies. Conclusions The modified PFS ratio may represent a meaningful clinical endpoint that could aid in the design and interpretation of future precision oncology trials.

By observing the activity of anti-cancer agents directly in tumors, there is potential to greatly expand our understanding of drug response and develop more personalized cancer treatments. Implantable microdevices (IMD) have been recently developed to deliver microdoses of chemotherapeutic agents locally into confined regions of live tumors, the tissue can be subsequently removed and analyzed to evaluate drug response. This method has the potential to rapidly screen multiple drugs, but requires surgical tissue removal and only evaluates drug response at a single timepoint when the tissue is excised. Here, we describe a “lab-in-a-tumor” implantable microdevice (LIT-IMD) platform to image cell-death drug response within a live tumor, without requiring surgical resection or tissue processing. The LIT-IMD is inserted into a live tumor and delivers multiple drug microdoses into spatially discrete locations. In parallel, it locally delivers microdose levels of a fluorescent cell-death assay, which diffuses into drug-exposed tissues and accumulates at sites of cell death. An integrated miniaturized fluorescence imaging probe images each region to evaluate drug-induced cell death. We demonstrate ability to evaluate multi-drug response over 8 h using murine tumor models and show correlation with gold-standard conventional fluorescence microscopy and histopathology. This is the first demonstration of a fully integrated platform for evaluating multiple chemotherapy responses in situ. This approach could enable a more complete understanding of drug activity in live tumors, and could expand the utility of drug-response measurements to a wide range of settings where surgery is not feasible.

Early breast cancer diagnosis is typically followed by surgical resection to remove the tumor, after which adjuvant therapy is administered for up to five years, or until relapse and presentation of metastatic disease. Archived tissue from the primary tumor is the initial source of material for NGS applications, in particular the identification of genomic driver mutations. In some cases, core needle biopsies may be collected at relapse or during later lines of therapy, but this occurs in a minority of patients and typically samples only one metastatic site. NGS on DNA from fresh biopsies is useful for real time assessment of genomic drivers, or to identify resistance mechanisms that may arise on prior therapies. In contrast to tissue, blood collection for NGS of ctDNA is more straightforward than obtaining fresh biopsies and has many conceptual uses over the natural history of breast cancer. Pending development of sensitive and specific methodologies, ctDNA could be used for screening and early detection. Analysis after surgery may be useful in detecting residual tumor DNA and identifying patients at high risk of relapse. Finally, in the metastatic setting ctDNA could be used to identify genomic drivers, dissect heterogeneity of disease, and in longitudinal monitoring to detect resistance mutations. This article is protected by copyright. All rights reserved

The widespread dissemination of the idea of personalized oncology has spread faster than the underlying science. The authors argue that the principles of clinical investigation need to be applied to address the many unanswered questions.

Personalized oncology aims to tailor therapy by targeting the unique genetic characteristics of a patient's tumor, whereas cancer immunotherapy focuses on activating the patient's immune system to control the tumor. The fusion of these ostensibly separate strategies has created a new dimension for personalized cancer immunotherapy. This entails the development of next-generation cancer vaccines that target neoantigens as well as the use of mutational signatures as predictive biomarkers for clinical response. The optimal use of immunotherapeutic agents will hinge on a robust understanding of the mutational profile of a cancer's genome that significantly dictates antitumor immunity and immunotherapeutic response.Significance: Cancer immunotherapy has provided substantial clinical benefit in a significant number of patients with advanced disease. However, the need for more precise immunotherapies and predictive biomarkers remains pressing. Recent progress in these areas has been promising and has created a framework for precision immune-oncology. Cancer Discov; 6(7); 703–13. ©2016 AACR.

e14051 Background: Adoption of next-generation sequencing (NGS) technology in routine clinical practice has enabled the detection of genetic aberrations such as single nucleotide variants, copy number alterations, and gene fusions. Pathway and network analyses (PNA) are key components for evaluation of NGS data in a clinical setting to explain findings involving thousands of altered genes and proteins with a smaller and more interpretable set of altered processes. Though PNA have been applied to identify driver genes and pathways in cohort-based analyses, its application in precision oncology remains unexplored. We investigate the potential utility of the Watson for Genomics (WfG) pathway analyses tool in interpreting complex and multiple genomic alterations in individual cancers. Methods: DNA and RNA isolated from 70 patient tumors across 30 different cancer types were processed with Illumina’s TST170 NGS platform. WfG’s feature of pathway analyses was used to identify gene variants, signaling pathways, networks, and the drugs targeting these alterations based on evidence in the clinical literature and FDA drug databases. Results: Analyses defined 5 different pathway/network models: 1) downstream therapeutic targets, 2) synthetic lethality, 3) combinatorial downstream targets + synthetic lethality, 4) two or more pathways converging to downstream targets, and 5) complex profile analyses. The five PNA models are illustrated by the following unique cases. 1) A thyroid cancer case with HRAS variant and activated RAF1 downstream pathway showed MAPK1/3 were suggestive of relevant targets. 2) An acute myeloid leukemia case with BRCA1, BRCA2 and PTEN variants, targeting a common synthetic lethal partner PARP1 was ideal for therapy. 3) A penile carcinoma case with BRAF, CDKN2A and TP53 variants, targeting the BRAF downstream pathway in combination with either CDKN2A or TP53 were the likely choice for therapy. 4) A glioma case with activated PI3K and MEK downstream pathway, targeting a common downstream marker would block both pathways. 5) A breast carcinoma case with a complex pathogenic variant profile provided relevant clinical information and levels of evidence for multiple drug targets. Conclusions: We discovered that the integrated WfG pathway analyses tool is ideal for visualization of the variants with levels of evidence from clinical literature and FDA drug databases that can help inform treatment options and provides a holistic understanding of a specific tumor profile allowing the treating clinician to select personalized targeted therapy.

Lung cancer is traditionally classified as small cell carcinoma (SCLC) and non-small cell carcinoma (NSCLC). Although advances in SCLC have been limited, NSCLC has become a paradigm for personalized oncology as practically all treatment decisions depend on biomarkers selecting patients for enhanced response to targeted therapies by tyrosine kinase or immune checkpoint inhibitors. As such, targeted therapies are usually more effective and less toxic than conventional combined chemotherapies as they target specific vulnerabilities of lung cancers. As a consequence survival of patients with advanced lung cancers is increasing for the first time in history.

Cancer genomics research aims to advance personalized oncology by finding and targeting specific genetic alterations associated with cancers. In genome-driven oncology, treatments are selected for individual patients on the basis of the findings of tumour genome sequencing. This personalized approach has prolonged the survival of subsets of patients with cancer. However, many patients do not respond to the predicted therapies based on the genomic profiles of their tumours. Furthermore, studies pairing genomic and proteomic analyses of samples from the same tumours have shown that the proteome contains novel information that cannot be discerned through genomic analysis alone. This observation has led to the concept of proteogenomics, in which both types of data are leveraged for a more complete view of tumour biology that might enable patients to be more successfully matched to effective treatments than they would using genomics alone. In this Perspective, we discuss the added value of proteogenomics over the current genome-driven approach to the clinical characterization of cancers and summarize current efforts to incorporate targeted proteomic measurements based on selected/multiple reaction monitoring (SRM/MRM) mass spectrometry into the clinical laboratory to facilitate clinical proteogenomics.

The identification of new molecular targets and biomarkers associated with high risk of recurrence and response to therapy represents one of the main clinical challenges in the management of advanced disease in endometrial cancer. In this sense, the field of liquid biopsy has emerged as a great revolution in oncology and is considered “the way” to reach personalised medicine. In this review, we discuss the promising but already relatively limited advances of liquid biopsy in endometrial cancer compared to other types of tumours like breast, colorectal or prostate cancer. We present recent data analysing circulating tumour material in minimally-invasive blood samples, but also in alternative forms of liquid biopsy like uterine aspirates. Proteomic and genomic studies focused on liquid-based uterine samples are resulting not only in optimal diagnostic tools but also in reliable approaches to address tumour heterogeneity. Likewise, circulating tumour cells (CTCs) and circulating tumour DNA (ctDNA) represent an opportunity for the correct stratification of patients, for the assessment of early recurrent disease or for the real-time monitoring of therapy responses. Appropriately designed studies and implementation in clinical trials will determine the value of liquid biopsy for precision oncology in endometrial cancer.

Progress in cancer treatment made by the beginning of the 21st century has shifted the paradigm from one-size-fits-all to tailor-made treatment. The popular vision, to study solid tumors through the relatively noninvasive sampling of blood, is one of the most thrilling and rapidly advancing fields in global cancer diagnostics. From this perspective, immune-cell analysis in cancer could play a pivotal role in oncology practice. This approach is driven both by rapid technological developments, including the analysis of circulating myeloid-derived suppressor cells (cMDSCs), and by the increasing application of (immune) therapies, the success or failure of which may depend on effective and timely measurements of relevant biomarkers. Although the implementation of these powerful noninvasive diagnostic capabilities in guiding precision cancer treatment is poised to change the ways in which we select and monitor cancer therapy, challenges remain. Here, we discuss the challenges associated with the analysis and clinical aspects of cMDSCs and assess whether the problems in implementing tumor-evolution monitoring as a global tool in personalized oncology can be overcome.

As Personalized Medicine tailored the field of precision oncology, many challenges have been arising to fulfill the dream of a full personalized health integrated system in cancer therapy. Personalized oncology has been addressed through the past decades in multiple disease and various stages using high throughput technology. This review gives hand on recent advances of personalized oncology in several cancer disease models including leukemia, melanoma, breast cancer, lung cancer, colorectal cancer, and prostate cancer. Moreover, the review enumerates technology-based assessment of personalized biomarkers, including chip micro-array, organ on chip, and next generation sequencing. Meanwhile addressing challenges faced in implementing true personalized health cancer in oncology setting, this review focuses on bridging the gap between omics data analytics and epidemiology to overcome the true challenge of direct application.

Many cancer types still cause mortality because of late and/or wrong diagnosis. Cancer is a heterogeneous disease of which prognosis, prediction, and treatment cannot be easily achieved. This is why standardized cancer treatment may not be effective, even in members of the same family. Therefore, the necessity of applying personalized cancer therapies has emerged. Molecular diagnostics aim to get patient specific information by genomics-based, proteomics-based molecular profiling assays and systems biology approaches. Cancer theranostics aim to decrease the latencies in treatment by combining diagnosis and therapy. The main objectives of cancer theranostics include identification of novel biomarkers for molecular diagnosis of different cancer types; developing molecular imaging probes and new techniques for early detection of cancer; and utilizing nanotechnology and molecular imaging for both imaging and treatment. With the appropriate applications in personalized oncology, more accurate diagnosis and therapies will be achieved in the right time, to the right cancer patient. To succeed in this approach, the enhancement of theranostic biomarkers is crucial. Advances in molecular biology techniques, such as genome sequencing and omics technologies, provide high-throughput data on the underlying molecular pathways of different cancer types. In accordance with the developments in these tools, more reliable and novel molecular biomarkers can be designated which may be more effective in cancer diagnosis and treatment.

Advances in tumor biology and human genetics that have identified driver events and critical dependencies, together with the development of drugs for specific targets, hold promise for an era of personalized oncology treatment. The efficacy of targeted therapies in patient populations selected for treatment on the basis of the molecular features of their tumors is shifting the current practice, and molecular profiling of patients to support clinical decision-making is becoming part of the everyday clinical practice in oncology. Actionable biomarkers are those that allow matching patients to specific therapies. Currently, there is a strong need to have accurate and reliable means of identifying these biomarkers. With the evolution of biotechnology and the advent of next-generation sequencing, platforms for molecular analysis have quickly evolved, allowing a high degree of multiplexing that aims to transform the use of targeted therapies in oncology. This article aims to summarize the different biomarker applications and discuss the current challenges along with the major technologies presently used in clinical practice.

Owing to recent advances in genomic technologies, personalized oncology is poised to fundamentally alter cancer therapy. In this paradigm, the mutational and transcriptional profiles of tumors are assessed, and personalized treatments are designed based on the specific molecular abnormalities relevant to each patient's cancer. To date, such approaches have yielded impressive clinical responses in some patients. However, a major limitation of this strategy has also been revealed: the vast majority of tumor mutations are not targetable by current pharmacological approaches. Immunotherapy offers a promising alternative to exploit tumor mutations as targets for clinical intervention. Mutated proteins can give rise to novel antigens (called neoantigens) that are recognized with high specificity by patient T cells. Indeed, neoantigen-specific T cells have been shown to underlie clinical responses to many standard treatments and immunotherapeutic interventions. Moreover, studies in mouse models targeting neoantigens, and early results from clinical trials, have established proof of concept for personalized immunotherapies targeting next-generation sequencing identified neoantigens. Here, we review basic immunological principles related to T-cell recognition of neoantigens, and we examine recent studies that use genomic data to design personalized immunotherapies. We discuss the opportunities and challenges that lie ahead on the road to improving patient outcomes by incorporating immunotherapy into the paradigm of personalized oncology.

ABSTRACT Molecular imaging techniques allow an individualization and optimization of therapy on a patient basis noninvasively. The availability of new hybrid scanners, like PET-computed tomography and PET-MRI allow the combined assessment of changes in morphology and function and are a unique tool for personalized cancer treatment. In particular, it is crucial to identify nonresponders as soon as possible for therapy guidance. The choice of the appropriate therapy and optimal treatment duration can help to avoid side effects and save costs. Furthermore, the development of new specific tracers will enable a more accurate assessment of a therapeutic result. Numerous peptides targeting receptor-active tumors are in development with a high potential in a large spectrum of tumors for theranostic approaches.

Pancreatic adenocarcinoma remains the fourth leading cause of cancer mortality in the U.S. Despite advances in surgical technique, radiotherapy technologies, and chemotherapeutics, the 5-year survival rate remains approximately 20% for the 15% of patients who are eligible for surgical resection. The majority of this group suffers metastatic recurrence. However, despite advances in therapies for patients with advanced pancreatic cancer, only surgery has consistently proven to improve long-term survival. Various combinations of chemotherapy, biologic-targeted therapy, and radiotherapy have been evaluated in different settings to improve outcomes. In this context, a neoadjuvant (preoperative) treatment strategy offers numerous potential benefits: (1) ensuring delivery of early, systemic therapy, (2) improving selection of patients for surgical therapy with truly localized disease, (3) potential downstaging of the neoplasm facilitating a negative margin resection in patients with locally advanced disease, and (4) providing a superior clinical trial mechanism capable of rapid assessment of the efficacy of novel therapeutics. This article reviews the recent trends in the management of pancreatic adenocarcinoma, with a particular emphasis on a multidisciplinary neoadjuvant approach to treatment.

site. In these early days of personalized oncology we learned how much context matters. Whereas vemurafenib works fine in melanoma patients with BRAF V600E mutations, no efficacy is seen colorectal cancers with the same mutation. In vitro screening approaches for synthetic lethality can be successfully used to identify drug combinations. However, as subsets of patients become smaller and smaller this will require an increased collaborative effort to succeed. In addition, an earlier collaboration between industry and licensing bodies leads to an accelerated assessment and development of innovative drugs.

This manuscript reports on a patient with a metastatic gastrointestinal stromal tumor (GIST) refractory to standard first-line treatment, who underwent a gallium-68 scan based on pre-clinical data of somatostatin receptor (SSTR) expression in such tumors. The gallium-68 DOTATATE scan determined significant somatostatin receptor avidity as hypothesized, suggesting that this imaging modality may be used as an option for diagnostic and follow-up purposes in GIST patients. In addition, peptide receptor-mediated radiotherapy (177Lu-PPRT) via SSTR may provide a novel treatment strategy in carefully selected SSTR-avid GIST patients with thyrosine kinase inhibitor (TKI)-resistant tumors such as this case, and this warrants further investigation in novel clinical trial concepts.