Precision Medicine In Cancer Treatment Research Articles

A Rationally Designed Azobenzene Photoswitch for DNA G-Quadruplex Regulation in Live Cells.

G-quadruplex (G4) DNA structures are increasingly acknowledged as promising targets in cancer research, and the development of G4-specific stabilizing compounds may lay a fundamental foundation in precision medicine for cancer treatment. Here, we propose a light-responsive G4-binder for precise modulation of drug activation, providing dynamic and spatiotemporal control over G4-associated biological processes contributing to cancer cell death. We developed a specialized fluorinated azobenzene (AB) switch equipped with a quinoline unit and a positively charged carboxamide side chain, Q-Azo4F-C, designed for targeted binding to G4 structures within cells. Biophysical studies, combined with molecular dynamics simulations, provide insights into the unique coordination modes of the photoswitchable ligand in its trans and cis configurations when interacting with G4s. The observed variations in complexation processes between the two isomeric states in different cancer cell lines manifest in more than 25-fold reversible cytotoxic activity. Immunostaining conducted with the structure-specific G4 antibody (BG4), establishes a direct correlation between cytotoxicity and the varying extent of G4 induction regulated by the two isoforms. Finally, we demonstrate the photo-driven reversible regulation of G4 structures in lung cancer cells by Q-Azo4F-C. Our findings highlight the potential of light-responsive G4-binders in advancing precision cancer therapy through dynamic control of G4-mediated pathways.

A Rationally Designed Azobenzene Photoswitch for DNA G‐Quadruplex Regulation in Live Cells

AbstractG‐quadruplex (G4) DNA structures are increasingly acknowledged as promising targets in cancer research, and the development of G4‐specific stabilizing compounds may lay a fundamental foundation in precision medicine for cancer treatment. Here, we propose a light‐responsive G4‐binder for precise modulation of drug activation, providing dynamic and spatiotemporal control over G4‐associated biological processes contributing to cancer cell death. We developed a specialized fluorinated azobenzene (AB) switch equipped with a quinoline unit and a positively charged carboxamide side chain, Q‐Azo4F‐C, designed for targeted binding to G4 structures within cells. Biophysical studies, combined with molecular dynamics simulations, provide insights into the unique coordination modes of the photoswitchable ligand in its trans and cis configurations when interacting with G4s. The observed variations in complexation processes between the two isomeric states in different cancer cell lines manifest in more than 25‐fold reversible cytotoxic activity. Immunostaining conducted with the structure‐specific G4 antibody (BG4), establishes a direct correlation between cytotoxicity and the varying extent of G4 induction regulated by the two isoforms. Finally, we demonstrate the photo‐driven reversible regulation of G4 structures in lung cancer cells by Q‐Azo4F‐C. Our findings highlight the potential of light‐responsive G4‐binders in advancing precision cancer therapy through dynamic control of G4‐mediated pathways.

Thyroid Hormone Receptors as Tumor Suppressors in Cancer.

Accumulated research has revealed the multifaceted roles of thyroid hormone receptors (TRs) as potent tumor suppressors across various cancer types. This review explores the intricate mechanisms underlying TR-mediated tumor suppression, drawing insights from preclinical mouse models and cancer biology. This review examines the tumor-suppressive functions of TRs, particularly TRβ, in various cancers using preclinical models, revealing their ability to inhibit tumor initiation, progression, and metastasis. Molecular mechanisms underlying TR-mediated tumor suppression are discussed, including interactions with oncogenic signaling pathways like PI3K-AKT, JAK-STAT, and transforming growth factor β. Additionally, this paper examines TRs' effect on cancer stem cell activity and differentiation, showcasing their modulation of key cellular processes associated with tumor progression and therapeutic resistance. Insights from preclinical studies underscore the therapeutic potential of targeting TRs to impede cancer stemness and promote cancer cell differentiation, paving the way for precision medicine in cancer treatment and emphasizing the potential of TR-targeted therapies as promising approaches for treating cancers and improving patient outcomes.

Modified plant‐derived exosomes: Precision medicine in cancer treatment

AbstractPlant‐derived exosomes (PDEs) are extracellular vesicles (EVs) occurring naturally,which have propitious applications in the development of cost‐effective and fruitful cancer therapy with minimum aftereffects and ramifications. Recent advancements in research based on PDEs demonstrate their extraordinary advantages in cancer therapy. The components of PDEs exhibit accomplished cancer prevention activity and having insignificant or negligible toxicity. The conventional methods to deliver drugs to the target have various problems, several of which can be solved by using PDEs for drug delivery. The main constituents of PDEs are proteins, lipids, DNA and RNA. PDEs are believed to revolutionize cancer therapy due to their magnificent attributes, but only a few clinical trials on PDEs are in progress. The mechanisms and regulations by which PDEs execute anticancer properties are yet not completely understood. Hence, research are conducted worldwide to understand the mechanisms of action of cancer antagonist PDEs more comprehensively and perspicuously. Modified PDEs have prospect in evolution of precision medicine which can bring a new dimension in the treatment of cancer.

Drug screening on digital microfluidics for cancer precision medicine

Drug screening based on in-vitro primary tumor cell culture has demonstrated potential in personalized cancer diagnosis. However, the limited number of tumor cells, especially from patients with early stage cancer, has hindered the widespread application of this technique. Hence, we developed a digital microfluidic system for drug screening using primary tumor cells and established a working protocol for precision medicine. Smart control logic was developed to increase the throughput of the system and decrease its footprint to parallelly screen three drugs on a 4 × 4 cm2 chip in a device measuring 23 × 16 × 3.5 cm3. We validated this method in an MDA-MB-231 breast cancer xenograft mouse model and liver cancer specimens from patients, demonstrating tumor suppression in mice/patients treated with drugs that were screened to be effective on individual primary tumor cells. Mice treated with drugs screened on-chip as ineffective exhibited similar results to those in the control groups. The effective drug identified through on-chip screening demonstrated consistency with the absence of mutations in their related genes determined via exome sequencing of individual tumors, further validating this protocol. Therefore, this technique and system may promote advances in precision medicine for cancer treatment and, eventually, for any disease.

Nanotechnology‐Empowered Combination Cancer Immunotherapies: Mechanisms, Synergies, and Perspectives

This review is aiming to systematically elucidate the unique role of nanotechnology in optimizing therapeutic modalities for combinatorial cancer immunotherapy, which enables the synergistic integration of multiple treatment strategies. In particular, nanotechnology has enabled the synergistic combination of immunotherapy with physical therapies, chemotherapy, metal therapy, and nucleic acid therapy. In each combination regimen, nanocarriers play multifaceted roles by achieving targeted codelivery of different therapeutics and optimizing each individual treatment modality. This offers new paradigms to guide precision medicine in cancer treatment. Immunotherapy alone is unlikely to achieve personalized precision medicine for cancer, and new treatment modalities are needed in the future. To overcome technical bottlenecks and realize precise regulation of the tumor microenvironment for personalized cancer treatment, it is crucial to develop novel nanosystems with integrated sensing, targeting, and therapeutic functionalities.

Gene Therapy Advancements for Precision Cancer Treatment: Challenges and Opportunities in Sub-Saharan Africa – Nigeria Perspective

Recent breakthroughs in gene therapy have ushered in a new era in precision cancer treatment, offering a transformative approach to combating malignancy and high mortality rates. The development of cutting-edge techniques in gene therapy for cancer treatment has led to a more precise strategy in cancer treatment by focusing on the genetic architecture of the cancer cells and enhancing the immune response to these malignant cells. This article looks at four (4) basic approaches: gene replacement techniques, gene editing techniques, RNA-based techniques and oncolytic viruses; these strategies have revolutionised oncology and cancer treatment, especially in the 21st century, with many of their principles stemming from the 20th century. For example, using the CRISPR-Cas9 technology has revolutionised disease treatment - cancer since its development by Emmanuelle Charpentier and Jennifer Doudnagene by improving genetic expression and correcting mutation editing. Aside from these techniques, there are many emerging gene therapy approaches for cancer treatment, with many in their final clinical trial stage. These emerging techniques involve the utilisation of neoantigens/cancer vaccines, epigenetic modulation and combination therapy with immune checkpoint inhibitors. Although this novel approach offers better prospects than conventional cancer treatment, they still face challenges, such as ethical issues, regulation and enhancement of vectors. Sub-Saharan Africa - Nigeria stands a chance to benefit from these novel strategies for cancer through commitment, building infrastructures and private-public partnerships. The roles played by biomedical scientists and researchers are pivotal to offering better prospects for precision medicine in cancer treatment.

Targeted Therapies in Cancer Treatment: Unveiling the Latest Breakthroughs and Promising Approaches

This review article delves into the realm of cancer treatment, specifically focusing on targeted therapies. It aims to present the most recent breakthroughs and promising approaches in this rapidly evolving field. Targeted therapies have emerged as a revolutionary approach in cancer treatment, aiming to selectively and precisely attack cancer cells while sparing normal tissues. This article explores various targeted therapy strategies, including monoclonal antibodies, small molecule inhibitors, immunotherapies, and gene therapies. In recent years, there have been significant advancements in understanding the molecular and genetic basis of cancer, which has led to the identification of novel therapeutic targets. The article sheds light on these newly discovered targets and highlights their potential in designing more effective and personalized treatment regimens for cancer patients. Furthermore, the review addresses the challenges and limitations associated with targeted therapies, such as resistance mechanisms and the heterogeneity of tumors. Strategies to overcome these obstacles are discussed, including combination therapies and the development of next-generation targeted agents. The role of precision medicine in cancer treatment is also explored, emphasizing the importance of biomarker-guided therapy selection to optimize treatment outcomes. Additionally, the review touches upon the integration of targeted therapies with conventional treatments, such as chemotherapy and radiation therapy, to enhance overall treatment efficacy. Finally, the article examines ongoing clinical trials and preclinical studies that are investigating cutting-edge targeted therapies, showcasing the potential impact of these approaches in transforming cancer care. In conclusion, targeted therapies in cancer treatment represent a rapidly expanding field with remarkable breakthroughs and promising avenues. Understanding the latest advancements and challenges in this domain is essential to harness the full potential of targeted therapies and ultimately improve patient outcomes in the battle against cancer.

Importance of Patient Health Insurance Coverage and Out-of-Pocket Costs for Genomic Testing in Oncologists' Treatment Decisions.

Use of genomic testing, especially multimarker panels, is increasing in the United States. Not all tests and related treatments are covered by health insurance, which can result in substantial patient out-of-pocket (OOP) costs. Little is known about oncologists' treatment decisions with respect to patient insurance coverage and OOP costs for genomic testing. We identified 1,049 oncologists who used multimarker tumor panels from the 2017 National Survey of Precision Medicine in Cancer Treatment. Separate multivariable ordinal logistic regressions examined associations of oncologist-, practice-, and area-level characteristics and oncologists' ratings of importance (very, somewhat, or a little/not important) of insurance coverage and OOP costs for genomic testing in treatment decisions, adjusting for oncologist years of experience, sex, race and ethnicity, specialty, use of next-generation sequencing (NGS) tests, region, tumor boards, patient insurance mix, and area-level socioeconomic characteristics. Among oncologists, 47.3%, 32.7%, and 20.0% reported that patient insurance coverage for genomic testing was very, somewhat, or a little/not important, respectively, in treatment decisions. In addition, 56.9%, 28.0%, and 15.2% reported that OOP costs for testing were very, somewhat, or a little/not important, respectively. In adjusted analyses, oncologists who used NGS tests were more likely to report patient insurance and OOP costs as important (odds ratio [OR], 2.00 [95% CI, 1.16 to 3.45] and OR, 2.12 [95% CI, 1.22 to 3.68], respectively) in treatment decisions compared with oncologists who did not use these tests, as were oncologists who treated solid tumors, rather than only hematological cancers. More years of experience and higher percentages of Medicaid or self-paid/uninsured patients in the practice were associated with reporting insurance coverage (OR, 1.43 [95% CI, 1.09 to 1.89]) and OOP costs (OR, 1.51 [95% CI, 1.13 to 2.01]) as important. Oncologists in practices with molecular tumor boards for genomic tests were less likely to report coverage (OR, 0.63 [95% CI, 0.47 to 0.85]) and OOP costs (OR, 0.72 [95% CI, 0.53 to 0.97]) as important than their counterparts in practices without these tumor boards. Most oncologists rate patient health insurance and OOP costs for genomic tests as important considerations in subsequent treatment recommendations. Modifiable factors associated with these ratings can inform interventions to support patient-physician decision making about care.

Theranostic digital twins: Concept, framework and roadmap towards personalized radiopharmaceutical therapies.

Radiopharmaceutical therapy (RPT) is a rapidly developing field of nuclear medicine, with several RPTs already well established in the treatment of several different types of cancers. However, the current approaches to RPTs often follow a somewhat inflexible "one size fits all" paradigm, where patients are administered the same amount of radioactivity per cycle regardless of their individual characteristics and features. This approach fails to consider inter-patient variations in radiopharmacokinetics, radiation biology, and immunological factors, which can significantly impact treatment outcomes. To address this limitation, we propose the development of theranostic digital twins (TDTs) to personalize RPTs based on actual patient data. Our proposed roadmap outlines the steps needed to create and refine TDTs that can optimize radiation dose to tumors while minimizing toxicity to organs at risk. The TDT models incorporate physiologically-based radiopharmacokinetic (PBRPK) models, which are additionally linked to a radiobiological optimizer and an immunological modulator, taking into account factors that influence RPT response. By using TDT models, we envisage the ability to perform virtual clinical trials, selecting therapies towards improved treatment outcomes while minimizing risks associated with secondary effects. This framework could empower practitioners to ultimately develop tailored RPT solutions for subgroups and individual patients, thus improving the precision, accuracy, and efficacy of treatments while minimizing risks to patients. By incorporating TDT models into RPTs, we can pave the way for a new era of precision medicine in cancer treatment.

Current Clinical Application of the CAR-T Cell Therapy

In recent years, the U.S. Food and Drug Administration (FDA) has approved Chimeric Antigen Receptor (CAR)-T cell therapy as a novel approach to treating hematological diseases, including multiple myeloma, large B-cell lymphoma, and B-cell acute lymphoblastic leukemia. Using the patient's genetically altered T-cells, this novel strategy precisely targets antigens linked to cancer. Even with the encouraging outcomes of using bi-specific chimeric antigen receptors to stop tumor antigen evasion, difficulties still arise, especially when dealing with solid tumors. There are several challenges because of the intricate interactions among many elements in the tumor microenvironment, such as immunosuppression, hypoxia, decreased T-cell infiltration, elevated reactive oxygen species, and the erratic nature of tumor-associated antigens. This study examines current efforts to find reliable tumor-associated antigens in an attempt to address issues impeding the efficacy of CAR-T treatment. The fundamental objective is to create CAR-T cells that are economical while being adapted to the distinct characteristics of the tumor microenvironment. By means of a comprehensive analysis of the course of the therapy, the study explores its potential therapeutic uses for a range of tumor types, including solid and hematological malignancies. This work not only sheds light on the enormous obstacles to obtaining precision medicine in cancer treatment, but it also advances our knowledge of the promise that the specific therapy has. The presented results open the door for future developments in the field and provide a nuanced viewpoint on the revolutionary role that the treatment has played in the contemporary cancer scene.

Emerging roles of Aurora-A kinase in cancer therapy resistance.

Aurora kinase A (Aurora-A), a serine/threonine kinase, plays a pivotal role in various cellular processes, including mitotic entry, centrosome maturation and spindle formation. Overexpression or gene-amplification/mutation of Aurora-A kinase occurs in different types of cancer, including lung cancer, colorectal cancer, and breast cancer. Alteration of Aurora-A impacts multiple cancer hallmarks, especially, immortalization, energy metabolism, immune escape and cell death resistance which are involved in cancer progression and resistance. This review highlights the most recent advances in the oncogenic roles and related multiple cancer hallmarks of Aurora-A kinase-driving cancer therapy resistance, including chemoresistance (taxanes, cisplatin, cyclophosphamide), targeted therapy resistance (osimertinib, imatinib, sorafenib, etc.), endocrine therapy resistance (tamoxifen, fulvestrant) and radioresistance. Specifically, the mechanisms of Aurora-A kinase promote acquired resistance through modulating DNA damage repair, feedback activation bypass pathways, resistance to apoptosis, necroptosis and autophagy, metastasis, and stemness. Noticeably, our review also summarizes the promising synthetic lethality strategy for Aurora-A inhibitors in RB1, ARID1A and MYC gene mutation tumors, and potential synergistic strategy for mTOR, PAK1, MDM2, MEK inhibitors or PD-L1 antibodies combined with targeting Aurora-A kinase. In addition, we discuss the design and development of the novel class of Aurora-A inhibitors in precision medicine for cancer treatment.

MicroRNA-708 emerges as a potential candidate to target undruggable NRAS.

RAS, the most frequently mutated oncogene that drives tumorigenesis by promoting cell proliferation, survival, and motility, has been perceived as undruggable for the past three decades. However, intense research in the past has mainly focused on KRAS mutations, and targeted therapy for NRAS mutations remains an unmet medical need. NRAS mutation is frequently observed in several cancer types, including melanoma (15-20%), leukemia (10%), and occasionally other cancer types. Here, we report using miRNA-708, which targets the distinct 3' untranslated region (3'UTR) of NRAS, to develop miRNA-based precision medicine to treat NRAS mutation-driven cancers. We first confirmed that NRAS is a direct target of miRNA-708. Overexpression of miRNA-708 successfully reduced NRAS protein levels in melanoma, leukemia, and lung cancer cell lines with NRAS mutations, resulting in suppressed cell proliferation, anchorage-independent growth, and promotion of reactive oxygen species-induced apoptosis. Consistent with the functional data, the activities of NRAS-downstream effectors, the PI3K-AKT-mTOR or RAF-MEK-ERK signaling pathway, were impaired in miR-708 overexpressing cells. On the other hand, cell proliferation was not disturbed by miRNA-708 in cell lines carrying wild-type NRAS. Collectively, our data unveil the therapeutic potential of using miRNA-708 in NRAS mutation-driven cancers through direct depletion of constitutively active NRAS and thus inhibition of its downstream effectors to decelerate cancer progression. Harnessing the beneficial effects of miR-708 may therefore offer a potential avenue for small RNA-mediated precision medicine in cancer treatment.

Reconstructing Clonal Evolution-A Systematic Evaluation of Current Bioinformatics Approaches.

The accurate reconstruction of clonal evolution, including the identification of newly developing, highly aggressive subclones, is essential for the application of precision medicine in cancer treatment. Reconstruction, aiming for correct variant clustering and clonal evolution tree reconstruction, is commonly performed by tedious manual work. While there is a plethora of tools to automatically generate reconstruction, their reliability, especially reasons for unreliability, are not systematically assessed. We developed clevRsim-an approach to simulate clonal evolution data, including single-nucleotide variants as well as (overlapping) copy number variants. From this, we generated 88 data sets and performed a systematic evaluation of the tools for the reconstruction of clonal evolution. The results indicate a major negative influence of a high number of clones on both clustering and tree reconstruction. Low coverage as well as an extreme number of time points usually leads to poor clustering results. An underlying branched independent evolution hampers correct tree reconstruction. A further major decline in performance could be observed for large deletions and duplications overlapping single-nucleotide variants. In summary, to explore the full potential of reconstructing clonal evolution, improved algorithms that can properly handle the identified limitations are greatly needed.

Mitochondria‐Targeting Phthalocyanines and Porphyrins for Enhanced Photodynamic Tumor Therapy

AbstractPhotodynamic therapy (PDT) is a promising non‐invasive treatment modality for tumors. However, the lack of attack targets in tumor cells greatly affects the antitumor efficiency of PDT. Mitochondria, as one of the most pivotal subcellular organelles, generated most of the energy for tumor cells and play an important role in tumor cell proliferation. PDT targeting mitochondria provides a practicable scheme for accurate and efficient antitumor treatment. Hence, the strategies of mitochondria‐based PDT using phthalocyanines or porphyrins as photosensitizers was summarized, including covalent modification of photosensitizers, delivery of drug carriers and cellular respiration in mitochondria. This review will provide guidance in the development of precision medicine for cancer treatment.

Artificial intelligence assists precision medicine in cancer treatment.

Cancer is a major medical problem worldwide. Due to its high heterogeneity, the use of the same drugs or surgical methods in patients with the same tumor may have different curative effects, leading to the need for more accurate treatment methods for tumors and personalized treatments for patients. The precise treatment of tumors is essential, which renders obtaining an in-depth understanding of the changes that tumors undergo urgent, including changes in their genes, proteins and cancer cell phenotypes, in order to develop targeted treatment strategies for patients. Artificial intelligence (AI) based on big data can extract the hidden patterns, important information, and corresponding knowledge behind the enormous amount of data. For example, the ML and deep learning of subsets of AI can be used to mine the deep-level information in genomics, transcriptomics, proteomics, radiomics, digital pathological images, and other data, which can make clinicians synthetically and comprehensively understand tumors. In addition, AI can find new biomarkers from data to assist tumor screening, detection, diagnosis, treatment and prognosis prediction, so as to providing the best treatment for individual patients and improving their clinical outcomes.

From multi-omics data to the cancer druggable gene discovery: a novel machine learning-based approach

The development of targeted drugs allows precision medicine in cancer treatment and optimal targeted therapies. Accurate identification of cancer druggable genes helps strengthen the understanding of targeted cancer therapy and promotes precise cancer treatment. However, rare cancer-druggable genes have been found due to the multi-omics data's diversity and complexity. This study proposes deep forest for cancer druggable genes discovery (DF-CAGE), a novel machine learning-based method for cancer-druggable gene discovery. DF-CAGE integrated the somatic mutations, copy number variants, DNA methylation and RNA-Seq data across ˜10 000 TCGA profiles to identify the landscape of the cancer-druggable genes. We found that DF-CAGE discovers the commonalities of currently known cancer-druggable genes from the perspective of multi-omics data and achieved excellent performance on OncoKB, Target and Drugbank data sets. Among the ˜20 000 protein-coding genes, DF-CAGE pinpointed 465 potential cancer-druggable genes. We found that the candidate cancer druggable genes (CDG) are clinically meaningful and divided the CDG into known, reliable and potential gene sets. Finally, we analyzed the omics data's contribution to identifying druggable genes. We found that DF-CAGE reports druggable genes mainly based on the copy number variations (CNVs) data, the gene rearrangements and the mutation rates in the population. These findings may enlighten the future study and development of new drugs.

Multi-omics analysis: Paving the path toward achieving precision medicine in cancer treatment and immuno-oncology.

The acceleration of large-scale sequencing and the progress in high-throughput computational analyses, defined as omics, was a hallmark for the comprehension of the biological processes in human health and diseases. In cancerology, the omics approach, initiated by genomics and transcriptomics studies, has revealed an incredible complexity with unsuspected molecular diversity within a same tumor type as well as spatial and temporal heterogeneity of tumors. The integration of multiple biological layers of omics studies brought oncology to a new paradigm, from tumor site classification to pan-cancer molecular classification, offering new therapeutic opportunities for precision medicine. In this review, we will provide a comprehensive overview of the latest innovations for multi-omics integration in oncology and summarize the largest multi-omics dataset available for adult and pediatric cancers. We will present multi-omics techniques for characterizing cancer biology and show how multi-omics data can be combined with clinical data for the identification of prognostic and treatment-specific biomarkers, opening the way to personalized therapy. To conclude, we will detail the newest strategies for dissecting the tumor immune environment and host–tumor interaction. We will explore the advances in immunomics and microbiomics for biomarker identification to guide therapeutic decision in immuno-oncology.

Modulation of tumor environment in colorectal cancer – could gut microbiota be a key player?

The treatment paradigm of neoplastic diseases has dramatically shifted with the introduction of immune checkpoint inhibitors (ICI). They induce a durable response in a wide variety of solid tumors, but this response depends on the infiltration of lymphocytes capable of recognizing and killing tumor cells. The primary predictor of intrinsic immune resistance to ICIs is the absence of lymphocytes in the tumor, the so-called “cold tumors”. Colorectal cancer (CRC) remains one of the most common and challenging cancer, but it is not traditionally considered a highly immunogenic tumor. In fact, immunotherapy showed a remarkable antitumoral activity only on a small subset of CRC patients – the ones with microsatellite instability-high/deficient DNA mismatch repair (MSI-H/dMMR). Most CRCs display a molecular microsatellite stability/proficient DNA mismatch repair (MSS/pMMR) profile, so strategies to improve tumor immunogenicity are crucial. Therefore, ongoing studies investigate new approaches to convert “cold” to “hot” tumors in MSS/pMMR CRCs. In addition, it has been described that gut microbiota influences tumor development and the host immune response. Hence, the microbiota may modulate the immune response, becoming a promising biomarker to identify patients who will benefit from ICIs. Future data will help to better understand microbiota mechanisms and their role in ICI efficacy. Precision medicine in cancer treatment could involve modulation of the microbiota through different strategies to improve tumor immunogenicity. In this review, we aim to present the potential relationship between gut microbiota and the modulation of the immune system and the hypothetical implications in CRC treatment, namely ICIs.

Oncologist consideration of patient health insurance coverage and out-of-pocket costs for genomic testing in treatment decision.

6600 Background: Use of genomic testing, especially multi-marker tumor panels, is increasing in the United States. Not all tests and related treatments are covered by health insurance, which can result in substantial patient out-of-pocket (OOP) costs. Although most patients are concerned about OOP costs, little is known about oncologists’ treatment decisions with respect to patient health insurance coverage and OOP costs for genomic testing. Methods: We identified 1,049 oncologists who reported using multi-marker tumor panels from the 2017 National Survey of Precision Medicine in Cancer Treatment. Separate multivariable ordinal logistic regression analyses were used to assess the associations of oncologist, practice, and patient characteristics and the oncologist ratings of the importance of health insurance coverage and OOP cost for genomic testing as part of treatment decisions. Results: Among oncologists, 47.3%, 32.7% and 20.0% reported that patient insurance coverage for genomic testing was very important, somewhat important, and a little/not important, respectively, in treatment decisions. 56.9%, 28.0%, and 15.2% reported patient OOP costs for genomic testing were very, somewhat, or a little/not important in treatment decisions, respectively. In adjusted ordinal logistic regression analyses, oncologists who used next-generation gene sequencing tests were more likely to report patient health insurance and OOP costs for testing as important (odds ratio (OR) = 2.0; 95% confidence interval (CI): 1.2, 3.5) and (OR = 2.1; 95%CI: 1.2, 3.7), respectively) in treatment decisions. Oncologists with more years of experience, who treated solid tumors (rather than only hematological cancers), worked in practices without molecular tumor boards for genomic tests, and with higher percentages of patients insured by Medicaid or self-paid/uninsured also reported insurance coverage or OOP costs for testing were important in treatment decisions (all p < 0.05). Conclusions: Physician, practice, and patient characteristics were associated with oncologists’ ratings of the importance of patient health insurance and OOP costs in treatment decisions. Identifying factors that influence physicians’ priorities in treatment decisions may inform the development and targeting of interventions to support patient and physician discussions about oncology care.