Precision Medicine In Cancer Treatment Research Articles

Cancer-related somatic mutations alter adenosine A1 receptor pharmacology-A focus on mutations in the loops and C-terminus.

G protein-coupled receptors (GPCRs) are known to be involved in tumor progression and metastasis. The adenosine A1 receptor (A1 AR) has been detected to be over-expressed in various cancer cell lines. However, the role of A1 AR in tumor development is not yet well characterized. A series of A1 AR mutations were identified in the Cancer Genome Atlas from cancer patient samples. In this study, we have investigated the pharmacology of mutations located outside of the 7-transmembrane domain by using a "single-GPCR-one-G protein" yeast system. Concentration-growth curves were obtained with the full agonist CPA for 12mutant receptors and compared to the wild-type hA1 AR. Most mutations located at the extracellular loops (EL) reduced the levels of constitutive activity of the receptor and agonist potency. For mutants at the intracellular loops (ILs) of the receptor, an increased constitutive activity was found for mutant receptor L211R5.69 , while a decreased constitutive activity and agonist response were found for mutant receptor L113F34.51 . Lastly, mutations identified on the C-terminus did not significantly influence the pharmacological function of the receptor. A selection of mutations was also investigated in a mammalian system. Overall, similar effects on receptor activation compared to the yeast system were found with mutations located at the EL, but some contradictory effects were observed for mutations located at the IL. Taken together, this study will enrich the insight of A1 AR structure and function, enlightening the consequences of these mutations in cancer. Ultimately, this may provide potential precision medicine in cancer treatment.

Distribution of Genomic Testing Resources by Oncology Practice and Rurality: A Nationally Representative Study.

Oncologists are increasingly using molecular profiling to inform personalized patient treatment decisions. Despite its promising utility, the integration of genomic testing into diverse clinical health care settings across geographic settings has been understudied. We used data from the National Survey of Precision Medicine in Cancer Treatment, a nationally representative sample of practicing US oncologists, to assess the availability of six genomic testing resources, including on-site pathology, contracts with outside laboratories, on-site genetic counselors, internal policies or protocols for using genomic and biomarker testing, electronic medical record alerts, and genomic or molecular tumor boards. We used multivariate logistic regression models to examine differences in the availability of each genomic testing resource by practice type and rurality while adjusting for payer mix and patient volume. A larger proportion of multispecialty group and academic practices had genomic testing resources available compared with solo and nonacademic practices. Electronic medical record alerts were the least available resource, whereas contracts with outside laboratories were the most available resource. Compared with urban practices, there were significantly fewer practices located in rural areas that had on-site pathology, on-site genetic counselors, protocols for genomic tests, and molecular tumor boards. Genomic testing resources varied by practice type and geography among a nationally representative sample of practicing oncologists. This variation has important implications for the development of interventions and policies to support the more equitable delivery of precision oncology to patients with cancer.

Oncologist-Reported Reasons for Not Ordering Multimarker Tumor Panels: Results From a Nationally Representative Survey.

We used data collected from a nationally representative sample (N = 1,281) of medical oncologists participating in the National Cancer Institute's National Survey of Precision Medicine in Cancer Treatment. In addition to testing not being seen as relevant (87%) and no evidence of test utility (77%), the most frequently reported reasons for not ordering a multimarker tumor panel test was difficulty in obtaining sufficient tissue (57%) and using individual gene tests (72%). These reasons were more likely to be reported by oncologists practicing in rural clinics and less likely to be reported by oncologists with an academic affiliation or with access to genetic services such as on-site genetic counselors and internal genetic testing policies. Modifiable, organizational factors were associated with ordering multimarker tumor panels. Receipt of genomics training and organizational policies related to the use of genomics were associated with lower reporting of barriers to ordering multimarker tumor panels, pointing to potential targets for future studies aimed at increasing appropriate multimarker tumor panel testing in cancer treatment management.

Impact of Endoscopic Ultrasound-Guided Tissue Acquisition on Decision-Making in Precision Medicine for Pancreatic Cancer: Beyond Diagnosis.

Precision medicine in cancer treatment refers to targeted therapy based on the evaluation of biomarkers. Although precision medicine for pancreatic cancer (PC) remains challenging, novel biomarker-based therapies, such as pembrolizumab, olaparib, and entrectinib, have been emerging. Most commonly, endoscopic ultrasound-guided tissue acquisition (EUS-TA) had been used for the diagnosis of PC until now. However, advances in EUS-TA devices and biomarker testing, especially next-generation sequencing, have opened up the possibility of sequencing of various genes even in limited amounts of tissue samples obtained by EUS-TA, and identifying potential genetic alterations as therapeutic targets. Precision medicine benefits only a small population of patients with PC, but biomarker-based therapy has shown promising results in patients who once had no treatment options. Now, the role of EUS-TA has extended beyond diagnosis into decision-making regarding the treatment of PC. In this review, we mainly discuss tissue sampling by EUS-TA for biomarker testing and the current status of precision medicine for PC.

Smart Stimuli-Responsive and Mitochondria Targeting Delivery in Cancer Therapy.

Dysfunction in the mitochondria (Mc) contributes to tumor progression. It is a major challenge to deliver therapeutic agents specifically to the Mc for precise treatment. Smart drug delivery systems are based on stimuli-responsiveness and active targeting. Here, we give a whole list of documented pathways to achieve smart stimuli-responsive (St-) and Mc-targeted DDSs (St-Mc-DDSs) by combining St and Mc targeting strategies. We present the formulations, targeting characteristics of St-Mc-DDSs and clarify their anti-cancer mechanisms as well as improvement in efficacy and safety. St-Mc-DDSs usually not only have Mc-targeting groups, molecules (lipophilic cations, peptides, and aptamers) or materials but also sense the surrounding environment and correspondingly respond to internal biostimulators such as pH, redox changes, enzyme and glucose, and/or externally applied triggers such as light, magnet, temperature and ultrasound. St-Mc-DDSs exquisitely control the action site, increase therapeutic efficacy and decrease side effects of the drug. We summarize the clinical research progress and propose suggestions for follow-up research. St-Mc-DDSs may be an innovative and sensitive precision medicine for cancer treatment.

Next-Generation Sequencing-Based Biomarkers in Breast Cancer.

For the realization of precision medicine in cancer treatment, discovery, and validation of clinically useful biomarker is the most important prerequisite. Biomarkers are needed and used for evaluation of cancer susceptibility, cancer screening (early detection), cancer subtyping, prediction of prognosis, decision of appropriate adjuvant therapy and duration of therapy, and for monitoring of recurrence. Biomarkers are also needed for decision of target therapy in metastatic cancer and monitoring of their response during follow-up. Now is the era of Next-Generation Sequencing (NGS). NGS technology can detect almost all kind of genomic changes that occur in cancer that is different from normal condition. The cost also is now reasonably low to use in routine clinical practice.This chapter will review four kinds of NGS-based biomarkers that are already being used in clinical practice although the routine use is controversial, and that are promising and under active investigation focusing on studies done in Seoul National University Hospital (SNUH).

Abstract PO-210: Distribution of genomic testing resources by oncology practice setting and rurality: A nationally representative analysis

Abstract Purpose: This study seeks to understand how the availability of genomic testing resources for cancer treatment vary by oncology practice setting and rurality. Background: Pervasive inequities in access to cancer care resources exist at the neighborhood and health system level. In particular, rurality and practice type have been linked to diminished access to care and poorer health outcomes. While these inequities persist, there have been many advancements in cancer care technology and oncologists are increasingly using genomic testing and next-generation sequencing technology to inform treatment decisions. Little is known about the availability of genomic testing resources in different oncology practice settings and at differing levels of rurality. Methods: This study used data from the National Survey of Precision Medicine in Cancer Treatment, the first nationally representative sample of oncologists (N=1,281) practicing in the United States. Oncologists were identified from the American Medical Association Masterfile for 2017 and were sampled by specialty, census region, metropolitan statistical area, sex, and age. The outcome, availability of genomic testing resources, was measured using one survey question asking oncologists if their primary practice has the following genomic testing resources: on-site pathology, contracts with outside labs, on-site genetic counselors, internal protocols for using genomic testing, electronic medical records (EMR) that alert providers when to order a genomic test, and molecular tumor boards. The predictor variables were rurality and practice type. Rurality was determined using Rural Urban Continuum Codes (RUCC) from 2013. Practice type was measured using the following six categories: solo practice academic, solo practice non-academic, single specialty group academic, single specialty group non-academic, multispecialty group academic, and multispecialty group non-academic. Descriptive statistics were presented, and chi-square tests were used to assess statistically significant difference. The weighted sampling design of the survey was accounted for using SAS statistical package version 9.4 (SAS, Cary NC). Results: Higher proportions of academic practices had genomic testing resources than non-academic practices. Higher proportions of multispecialty groups had genomic testing resources compared to single specialty group and solo practices. Compared to urban practices, lower proportions of rural practices had genomic testing resources for all except contracts with outside labs and EMR alerts. Conclusion: These data highlight the unequal distribution of genomic testing resources for cancer treatment by practice type and rurality. Future studies and interventions should strive to further assess factors affecting access to cancer care resources and explore health system level approaches to improve health equity. Citation Format: Brittany D. Gardner, Janeth Sanchez, Michelle Doose, Sallie J. Weaver, Shobha Srinivasan, Andrew Freedman, Janet S. de Moor. Distribution of genomic testing resources by oncology practice setting and rurality: A nationally representative analysis [abstract]. In: Proceedings of the AACR Virtual Conference: Thirteenth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2020 Oct 2-4. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(12 Suppl):Abstract nr PO-210.

Two novel models and a parthenogenetic algorithm for detecting common driver pathways from pan-cancer data

With the rapid development of high-throughput sequencing technologies, huge volumes of generated cancer genomics data make it into reality to understand the carcinogenic pathogenesis from the molecular level. It is believed that the study of commonalities among different cancers is one of the significant problems for understanding cancers, and will be beneficial for personalized therapy and precision medicine in cancer treatment. The ComMDP method is a useful one for solving this problem. However, when there is a substantially difference among the number of samples, the method of accumulating the absolute weight value of every cancer, employed by the ComMDP method, may give rise to missing some driver pathways. In this paper, two mathematical models CDP-V and CDP-H, replacing the absolute weight values with relative ones, are presented by using variance and harmonic mean, respectively. By devising a sort of short chromosome code and a greedy based recombination operator, a parthenogenetic algorithm is proposed for solving these two models. Extensive experiments were performed on both simulated and real cancer data. The experimental results show that given several types of cancer, the gene sets identified based on the presented models and algorithm not only mutate in a large proportion of samples of these cancers, but have close proportion of mutated samples in each cancer. In addition, some biologically meaningful gene sets, which are missed by the ComMDP one, are indeed detected. Hence the identified methods based on the presented models and algorithm may become useful complementary tools for identifying cancer pathways.

Abstract B04: Study on screening of new targets, specific recognition, and intervention of bladder cancer

Abstract Bladder cancer is one of the most common malignant tumors in the urinary system. According to NIH statistics, the 5-year survival rate of bladder cancer patients has not improved since the last three decades. Tumor immunotherapy, such as PD-L1, CAR-T, and TCR-T, brings dawn to these patients. However, there are numerous factors affecting the clinical applications and trials, such as genetic variation, drug resistance, off-target, and the low specificity of recognition and intervention of bladder cancer. We aimed to identify new targets for the specific recognition and intervention of bladder cancer and introduce single target or target combination into immune cells to develop new technologies and products for early diagnosis and clinical treatment of bladder cancer. By using large-scale sequencing technology, our team identified 21 new genes, including UTX, MLL-MLL3, and CHD6, which were highly mutated in the tumorigenesis of bladder cancer, as well as the highest-ever-found mutated gene TERT. Based on CRISPR-Cas9 system, we constructed the “AND” gate genetic circuits for bladder cancer, which integrated intracellular information from two promoters as inputs and activated the output signals only when both inputs were activated simultaneously. Furthermore, some tumor-suppressor genes can be designed as output to achieve specific inhibition or killing of bladder cancer cells. The “Artificial Nucleic Acid Aptamer” tool was used to engineer the CRISPR-Cas9 system under the control of specific signaling molecules to complete the quantitative regulation of downstream gene transcription, achieving the effect of connecting two originally unrelated signaling pathways. By combining multiple signal conductors, the oncogenic signaling could activate the downstream tumor suppressor pathway, initiating cell apoptosis and specific killing of tumor cells. Therefore, this modified system could provide insight into precision medicine in cancer treatment. The design of a series of synthetic RNA components into signal connectors could interconnect two different signals at the translation level to inhibit or enhance the expression of target genes. It could also connect the internal and external signals, reshape the gene regulation network, and redirect the oncogenic signaling to the cell apoptosis. Based on these results, our team developed CAR-T and TCR-T engineered cells for the clinical trial of bladder cancer. The effective rate of CAR-T therapy on solid and nonsolid tumors reached 60% and 87.5%, respectively. The effective rate of TCR-T therapy for NY-ESO-1 positive bladder cancer patients reached 50%, of which 2 cases were completely alleviated. In summary, our team completed a full-chain study from original discoveries to clinical applications, significantly improving the specific recognition and intervention of bladder cancer and making an important contribution to increasing 5-year survival rate and quality of life of bladder cancer patients. Note: This abstract was not presented at the conference. Citation Format: Zhiming Cai. Study on screening of new targets, specific recognition, and intervention of bladder cancer [abstract]. In: Proceedings of the AACR Special Conference on Bladder Cancer: Transforming the Field; 2019 May 18-21; Denver, CO. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(15_Suppl):Abstract nr B04.

Insights into DNA-drug interactions in the era of omics.

Despite the rise of sophisticated new targeting strategies in cancer chemotherapy, many classic DNA-binding drugs remain on the front line of the therapy against cancer. Based on examples primarily from the author's laboratory, this article reviews the capabilities of several DNA-binding drugs to alter gene expression. Research is ongoing about the molecular bases of the inhibition of gene expression and how alteration of the cellular transcriptome can commit cancer cells to die. The development of a variety of omic techniques allows us to gain insights into the effect of antitumor drugs. Genome-wide approaches provide unbiased genomic data that can facilitate a deeper understanding of the cellular response to DNA-binding drugs. Moreover, the results of large-scale genomic studies are gathered in publicly available databases that can be used in developing precision medicine in cancer treatment.

Oncologist Confidence in Genomic Testing and Implications for Using Multimarker Tumor Panel Tests in Practice.

PURPOSEThe evolution of precision oncology increasingly requires oncologists to incorporate genomic testing into practice. Yet, providers’ confidence with genomic testing is poorly documented. This article describes medical oncologists’ confidence with genomic testing and the association between genomic confidence and test use.METHODSWe used data from the 2017 National Survey of Precision Medicine in Cancer Treatment to characterize oncologists’ confidence with genomic testing. Genomic confidence was examined separately by type of test user: next-generation sequencing (NGS) only, gene expression (GE) only, both NGS and GE, or nonuser. Predictors of genomic confidence were examined with multinomial logistic regression. The association between genomic confidence and test use was examined with multivariable linear regression.RESULTSMore than 75% of genomic test users were either moderately or very confident about using results from multimarker tumor panel tests to guide patient care. Confidence with using multimarker tumor panel tests was highest among both NGS and GE test users, with 60.1% very confident in using test results, and lowest among NGS-only test users, with 38.2% very confident in using test results. Oncologists were most confident in using single-gene tests and least confident in using whole-genome or -exome sequencing to guide patient care. Genomic confidence was positively associated with self-reported test use. In adjusted models, training in genomics, larger patient volume, and treating patients with solid tumors predicted higher genomic confidence. Onsite pathology services and receipt of electronic medical record alerts for genomic testing predicted lower genomic confidence.CONCLUSIONOncologists’ confidence varies by testing platform, patient volume, genomic training, and practice infrastructure. Research is needed to identify modifiable factors that can be targeted to enhance provider confidence with genomic testing.

Information Sources Used by Oncologists to Understand Multi-marker Tumor Panel Tests for Cancer Patients.

Precision medicine using multi-marker tumor panel (MMTP) testing can help inform cancer treatment decisions. Oncologists' knowledge of these tests and their ability to find up-to-date information about their application in clinical care is essential. This study aimed to (1) describe information sources used by oncologists to learn about new genomic tests and (2) examine characteristics associated with the use of each information source. The National Cancer Institute's National Survey of Precision Medicine in Cancer Treatment surveyed a nationally representative sample of oncologists about MMTP testing. We examined the use of 11 information sources among oncologists that reported using MMTP tests (n = 1222). Bivariate analyses were used to examine whether information sources differed by oncologist- and practice-level characteristics and type of MMTP test. Most oncologists reported using peer-reviewed medical literature (88.8%), scientific conferences (87.9%), and medical professional societies (83.8%) to learn about MMTPs. In contrast, government websites, FDA inserts, and foundation resources were each used by < 36% of oncologists. The use of information sources differed by oncologist and practice characteristics. For example, a greater percentage of oncologists with an academic affiliation used peer-reviewed medical literature and scientific conferences, as compared to those without an academic affiliation (p = 0.006). As the number and type of MMTP tests increase, providing oncologists with current information about their appropriate application is essential. Further understanding of how oncologists use specific information sources may improve the dissemination and effective implementation of new MMTPs and help tailor educational interventions based on provider characteristics.

A melt-electrowritten filter for capture and culture of circulating colon cancer cells.

Metastasis is the major cause of death in cancer patients accounting for about 90% of the mortality. The detection and analysis of the hallmark of metastasis, circulating tumor cells (CTCs), have significant impact in cancer biology and clinical practice. However, the scarcity of CTCs in blood, particularly in that of colorectal cancer patients, is a serious bottleneck in the development of CTC-based precision medicine. Herein, the melt electrowriting (MEW) technology was used for reproductive fabrication of a biocompatible antibody-presenting polycaprolactone filter with tailored porous structure. It is demonstrated, for the first time, that such filter can be used not only to catch cancer cells spiked in whole blood but also to culture the cancer cells directly on site. Specifically, HT29 colon cancer cells can be captured with an efficiency of 85%, and when spiked into 4 mL of whole blood, 47% were captured on one Ø12mm filter. Furthermore, repeated capture and culture experiments have shown that as few as 20 HT29 colon cancer cells spiked into 4 mL of whole blood can be captured on the filter and within 2 weeks be expanded on site to become tumor bodies that are visible to the untrained eye. This filter allows for downstream analysis, such as flow cytometry, immunocytochemistry, Western blotting, and rt-qPCR. This technology represents a simple and cost-effective platform that potentially enables fast and efficient culture of rare CTCs from patients’ blood. This provides non-invasive alternatives for solid biopsy tumor materials for treatment screening, with great potential to realize precision medicine for cancer treatment.

Prediction and Recommendation of Precision Medicine for Cancer using Machine Learning Techniques

Cancer is one of the major causes of death by disease and treatment of cancer is one of the most crucial phases of oncology. Precision medicine for cancer treatment is an approach that uses the genetic profile of individual patients. Researchers have not yet discovered all the genetic changes that causes cancer to develop, grow and spread. The Neuro-Genetic model is proposed here for the prediction and recommendation of precision medicine. The proposed work attempts to recommend precision medicine to cancer patients based upon the past genomic data of patient’s survival. The work will employ machine learning (ML) approaches to provide recommendations for different gene expressions. This work can be used in caner hospitals, research institutions for providing personalized treatment to the patient using precision medicine. Precision medicine can even be used to treat other complex diseases like diabetes, dentistry, cardiovascular diseases etc. Precision medicine is the kind of treatment to be offered in the near future.

CRISPR-cas9: a powerful tool towards precision medicine in cancer treatment.

Cancer is a highly heterogeneous disease in term of molecular signature even though it is originated from the same tissue type. Cancer heterogeneity may occur during its development or treatment, which is the main cause resulting in drug resistance and recurrence. Precision medicine refers to matching the right medicine to the right patients based on their molecular signatures. Therefore, a thorough understanding of the mechanism of tumorigenesis and drug resistance is essential to precision medicine. CRISPR-cas9 system is a powerful tool for gene editing and CRISPR-based high-throughput screening has been widely applied especially in searching for tumor-driven or synergistic lethal genes aiming to overcome drug resistance. In this review, we describe the progress of CRISPR-cas9-based unbiased screening in precision medicine including identification of new drug targets, biomarkers and elucidation of mechanisms leading to drug resistance. The existing challenges as well as the future directions are also discussed.

Factors Associated With Oncologist Discussions of the Costs of Genomic Testing and Related Treatments.

Use of genomic testing is increasing in the United States. Testing can be expensive, and not all tests and related treatments are covered by health insurance. Little is known about how often oncologists discuss costs of testing and treatment or about the factors associated with those discussions. We identified 1220 oncologists who reported discussing genomic testing with their cancer patients from the 2017 National Survey of Precision Medicine in Cancer Treatment. Multivariable polytomous logistic regression analyses were used to assess associations between oncologist and practice characteristics and the frequency of cost discussions. All statistical tests were two-sided. Among oncologists who discussed genomic testing with patients, 50.0% reported often discussing the likely costs of testing and related treatments, 26.3% reported sometimes discussing costs, and 23.7% reported never or rarely discussing costs. In adjusted analyses, oncologists with training in genomic testing or working in practices with electronic medical record alerts for genomic tests were more likely to have cost discussions sometimes (odds ratio [OR] = 2.09, 95% confidence interval [CI] = 1.19 to 3.69) or often (OR = 2.22, 95% CI = 1.30 to 3.79), respectively, compared to rarely or never. Other factors statistically significantly associated with more frequent cost discussions included treating solid tumors (rather than only hematological cancers), using next-generation sequencing gene panel tests, having higher patient volume, and working in practices with higher percentages of patients insured by Medicaid, or self-paid or uninsured. Interventions targeting modifiable oncologist and practice factors, such as training in genomic testing and use of electronic medical record alerts, may help improve cost discussions about genomic testing and related treatments.

The Role Of Circulating Tumor DNA In Therapeutic Resistance.

The application of precision medicine in cancer treatment has partly succeeded in reducing the side effects of unnecessary chemotherapeutics and in improving the survival rate of patients. However, with the long-term use of therapy, the dynamically changing intratumoral and intertumoral heterogeneity eventually gives rise to therapeutic resistance. In recent years, a novel testing technology (termed liquid biopsy) using circulating tumor DNAs (ctDNAs) extracted from peripheral blood samples from patients with cancer has brought about new expectations to the medical community. Using ctDNAs, clinicians can trace the heterogeneity pattern to duly adjust individual therapy and prolong overall survival for patients with cancer. Technological advances in detecting and characterizing ctDNAs (eg, development of next-generation sequencing) have provided clinicians with a valuable tool for genotyping tumors individually and identifying genetic and epigenetic alterations of the entire tumor to capture mutations associated with therapeutic resistance.

Clustering analysis of microRNA and mRNA expression data from TCGA using maximum edge-weighted matching algorithms

BackgroundmicroRNA (miRNA) is a short RNA (~ 22 nt) that regulates gene expression at the posttranscriptional level. Aberration of miRNA expressions could affect their targeting mRNAs involved in cancer-related signaling pathways. We conduct clustering analysis of miRNA and mRNA using expression data from the Cancer Genome Atlas (TCGA). We combine the Hungarian algorithm and blossom algorithm in graph theory. Data analysis is done using programming language R and Python.MethodsWe first quantify edge-weights of the miRNA-mRNA pairs by combining their expression correlation coefficient in tumor (T_CC) and correlation coefficient in normal (N_CC). We thereby introduce a bipartite graph partition procedure to identify cluster candidates. Specifically, we propose six weight formulas to quantify the change of miRNA-mRNA expression T_CC relative to N_CC, and apply the traditional hierarchical clustering to subjectively evaluate the different weight formulas of miRNA-mRNA pairs. Among these six different weight formulas, we choose the optimal one, which we define as the integrated mean value weights, to represent the connections between miRNA and mRNAs. Then the Hungarian algorithm and the blossom algorithm are employed on the miRNA-mRNA bipartite graph to passively determine the clusters. The combination of Hungarian and the blossom algorithms is dubbed maximum weighted merger method (MWMM).ResultsMWMM identifies clusters of different sizes that meet the mathematical criterion that internal connections inside a cluster are relatively denser than external connections outside the cluster and biological criterion that the intra-cluster Gene Ontology (GO) term similarities are larger than the inter-cluster GO term similarities. MWMM is developed using breast invasive carcinoma (BRCA) as training data set, but can also applies to other cancer type data sets. MWMM shows advantage in GO term similarity in most cancer types, when compared to other algorithms.ConclusionsmiRNAs and mRNAs that are likely to be affected by common underlying causal factors in cancer can be clustered by MWMM approach and potentially be used as candidate biomarkers for different cancer types and provide clues for targets of precision medicine in cancer treatment.

Genomics-Guided Immunotherapy for Precision Medicine in Cancer.

Next-generation sequencing (NGS) data have been central to the development of targeted therapy and immunotherapy for precision oncology. In targeted therapy, drugs directly attack cancer, by altering the expression of critical cancer genes identified with cancer genome profiling. Immunotherapy drugs indirectly attack cancer, by inducing the immune system to attack and treat cancer. Harnessing genomic data for deployment and development of immunotherapy comprises the field of immunogenomics. The discovery of a link between cancer cells escaping immune destruction and cancer progression, led to extensive research into this mechanism and drug development. In the past few years, FDA has granted accelerated approval to several immunotherapy cancer treatment drugs, pembrolizumab, nivolumab, and atezolizumab, belonging to the class of checkpoint inhibitors. Utilization of pretreatment genomic cancer screening to identify patients most likely to respond to immunotherapy and to customize immunotherapy for a given patient, promises to improve cancer treatment outcomes. Recent advances in molecular profiling, high-throughput sequencing, and computational efficiency has made immunogenomics the major tenet of precision medicine in cancer treatment. This review provides a brief overview on the state of art of immunogenomics in precision cancer medicine.

Patient’s insurance status impact on biomarker testing in the United States market.

e18114 Background: Precision medicine in cancer treatment is driven by biomarker testing. If a patient is not biomarker tested, he or she may not receive access to targeted therapies that often offer greater efficacy. The purpose of this research study is to examine the insurance type of patients that are untested for biomarkers in the United States. Methods: BrandImpact is a longitudinal panel of 450 Oncologists who continuously report cancer treatment decisions from the point-of-care. The BrandImpact data set includes 60,465 patient visits reported in 2018. This study followed market research best practices. This research included visits by patients diagnosed with metastatic: NSCLC, melanoma and ovarian cancer who were either tested or untested for the following biomarkers: EGFR, ALK, ROS1, PD-L1, BRAF and BRCA. Results: The baseline measure for this analysis is that 18% of the patient visits reported that were not biomarker tested. The table at the end of document outlines the untested patient visits by insurance type for comparison purposes. Conclusions: The rate of biomarker testing for metastatic cancer patients treated by Oncologists differs by insurance type. Patients with Medicare Only insurance (25%) are untested at a statistically significantly higher rate than the baseline All category. It should be noted that patients without insurance or who were cash-paying had the highest rates of being non-biomarker tested. It is recommended that further research be conducted into the reasons behind the significantly higher rate of non-testing for Medicare Only patients. It seems counter intuitive that this patient population is treated less than optimally due to limited access to targeted therapies. [Table: see text]