How to incorporate new agents into precise medicine for cholangiocarcinoma?

Cholangiocarcinoma, a rare and aggressive form of cancer originating from the bile ducts in the liver, poses a significant challenge for treatment. However, the emergence of precision medicine has brought newfound hope for more effective therapies. Several precision medicine approaches have demonstrated promise in the treatment of cholangiocarcinoma. One such approach is targeted therapy, which involves utilizing drugs that specifically target the genetic mutations or alterations present in the tumor cells. In the case of cholangiocarcinoma, mutations in the IDH1 and IDH2 genes are frequently observed. Immunotherapy is another precision medicine approach being explored for the treatment of cholangiocarcinoma. Immune checkpoint inhibitors like pembrolizumab and nivolumab can be used to bolster the body's immune response against cancer cells. While the response to immunotherapy can vary among individuals, studies have shown promising results, particularly in patients with high levels of tumor-infiltrating lymphocytes or microsatellite instability. Moreover, molecular profiling of cholangiocarcinoma tumors can play a crucial role in identifying potential targets for precision medicine. Through advanced next-generation sequencing techniques, specific gene alterations or dysregulations in pathways can be identified, potentially guiding treatment decisions. This personalized approach enables tailored treatment plans based on the unique genetic characteristics of each patient's tumor. In conclusion, the advent of precision medicine has opened up new avenues for the treatment of cholangiocarcinoma. Targeted therapy and immunotherapy have exhibited promising results, and further molecular profiling is expected to uncover additional therapeutic options. Such advancements represent a significant step forward in the quest to enhance outcomes for individuals affected by cholangiocarcinoma.

The Application of Precision Medicine in Structural Heart Diseases: A Step towards the Future.

The personalized applications of 3D printing in interventional cardiology and cardiac surgery represent a transformative paradigm in the management of structural heart diseases. This review underscores the pivotal role of 3D printing in enhancing procedural precision, from preoperative planning to procedural simulation, particularly in valvular heart diseases, such as aortic stenosis and mitral regurgitation. The ability to create patient-specific models contributes significantly to predicting and preventing complications like paravalvular leakage, ensuring optimal device selection, and improving outcomes. Additionally, 3D printing extends its impact beyond valvular diseases to tricuspid regurgitation and non-valvular structural heart conditions. The comprehensive synthesis of the existing literature presented here emphasizes the promising trajectory of individualized approaches facilitated by 3D printing, promising a future where tailored interventions based on precise anatomical considerations become standard practice in cardiovascular care.

Development of a precision tumor bone metastasis model by a magnetic micro-living-motor system.

An ideal bone metastasis animal model is critical and fundamental for mechanistic research and following development of new drug and treatment. Caudal artery (CA) injection allows bone metastasis in the hindlimb, while in-depth targeted and quantitative studies of bone metastasis require a new model to overcome its limitations. Here, we developed a targeted, quantitative, and highly consistent method for the modeling of bone metastasis with cell-based magnetic micro-living-motor (MLM) system created by effectively combining Fe3O4-PDA-Au with biosafety. The MLM system can achieve efficient migration, target site colonization and control tumorigenesis in bone precisely with the application of a magnetic field. In vivo, day 3 post cell injection, tumor bone metastasis signals were observed locally in the injected femur among 82.76% mice of the MLM group as compared to the 56.82% in the CA group, and the signal intensity was 45.1 and 95.9 times stronger than that in the left and right lower limbs of the CA group, respectively. Post-injection day 28, metastasis in vital organs was reduced by approximately 90% in the MLM group compared to the CA group. Our innovative use of the MLM system in the field of tumor modeling opens a new avenue for exploring the mechanisms of tumor bone metastasis, recurrence and drug resistance.

Application of deep learning radiomics in oral squamous cell carcinoma-Extracting more information from medical images using advanced feature analysis.

OBJECTIVE: To conduct a systematic review with meta-analyses to assess the recent scientific literature addressing the application of deep learning radiomics in oral squamous cell carcinoma (OSCC). MATERIALS AND METHODS: Electronic and manual literature retrieval was performed using PubMed, Web of Science, EMbase, Ovid-MEDLINE, and IEEE databases from 2012 to 2023. The ROBINS-I tool was used for quality evaluation; random-effects model was used; and results were reported according to the PRISMA statement. RESULTS: A total of 26 studies involving 64,731 medical images were included in quantitative synthesis. The meta-analysis showed that, the pooled sensitivity and specificity were 0.88 (95 %CI: 0.87∼0.88) and 0.80 (95 %CI: 0.80∼0.81), respectively. Deeks' asymmetry test revealed there existed slight publication bias (P = 0.03). CONCLUSIONS: The advances in the application of radiomics combined with learning algorithm in OSCC were reviewed, including diagnosis and differential diagnosis of OSCC, efficacy assessment and prognosis prediction. The demerits of deep learning radiomics at the current stage and its future development direction aimed at medical imaging diagnosis were also summarized and analyzed at the end of the article.

Medical students' knowledge on cancer predisposition syndromes and attitude toward eHealth.

PURPOSE: Individuals with cancer predisposition syndromes (CPS) inherit elevated cancer risks. Medical supply gaps for people at risk of CPS cause insufficient outreach and miss potential benefits of individualized care strategies. Increased awareness of CPS and progress in the eHealth sector are untapped sources of health care improvement for affected individuals. METHODS AND RESULTS: This study addressed German-speaking medical students with an online questionnaire in respect to their knowledge of CPS, their medical education, and perspectives. The study population (n = 404) reported interest in and knowledge of CPS, supported by a satisfactory and sustainable education for their prospective patient care. The next generation of doctors would implement eHealth to improve medical services. Skepticism about digitization was claimed by students. They were especially concerned about deterioration in the physician-patient relationship, data abuse, dependence on technology, and incorrect diagnoses. CONCLUSION: Due to increasing diagnosing of CPS and deeper knowledge, this topic is essential for the curriculum in medical schools. In particular, care providers need know-how on identifying patients at risk for a CPS, certain diagnostic and therapeutic steps, surveillance and prophylactic strategies to improve patients' outcomes. Education in medical school as well as implemented eHealth seems to have potential to meet this demand in an upcoming era of personalized medicine. What does this study add to the clinical work. Medical teaching on cancer predisposition syndromes should be expanded to improve knowledge and individualized and personalized healthcare.

CRISPR/Cas9: a powerful tool in colorectal cancer research.

Colorectal cancer (CRC) is one of the most common malignant cancers worldwide and seriously threatens human health. The clustered regulatory interspaced short palindromic repeat/CRISPR-associate nuclease 9 (CRISPR/Cas9) system is an adaptive immune system of bacteria or archaea. Since its introduction, research into various aspects of treatment approaches for CRC has been accelerated, including investigation of the oncogenes, tumor suppressor genes (TSGs), drug resistance genes, target genes, mouse model construction, and especially in genome-wide library screening. Furthermore, the CRISPR/Cas9 system can be utilized for gene therapy for CRC, specifically involving in the molecular targeted drug delivery or targeted knockout in vivo. In this review, we elucidate the mechanism of the CRISPR/Cas9 system and its comprehensive applications in CRC. Additionally, we discussed the issue of off-target effects associated with CRISPR/Cas9, which serves to restrict its practical application. Future research on CRC should in-depth and systematically utilize the CRISPR/Cas9 system thereby achieving clinical practice.

Development and validation of a novel DNA damage repair-related long non-coding RNA signature in predicting prognosis, immunity, and drug sensitivity in uterine corpus endometrial carcinoma.

Background: DNA damage response (DDR) confer resistance to chemoradiotherapy in cancer cells. However, the role of DDR-related lncRNAs (DRLs) in uterine corpus endometrial carcinoma (UCEC) is poorly understood. In this study, we aimed to identify a DRL-related prognostic signature that could guide the clinical treatment of UCEC. Methods: We extracted transcriptome and clinical data of patients with UCEC from The Cancer Genome Atlas (TCGA) database and identified DRLs using Spearman correlation analysis. Univariate and multivariate Cox analyses were used to determine candidate prognostic DRLs. The samples were randomly divided into training and test cohorts in a 1:1 ratio. A DRL-related risk signature was constructed from the training cohort data using the least absolute shrinkage and selection operator (LASSO) algorithm, and validated using the test and entire cohorts. Subsequently, a prognostic nomogram was developed using a multivariate Cox regression analysis. The functional annotation, immune microenvironment, tumor mutation burden (TMB), immune checkpoint blockade (ICB) efficacy, and drug sensitivity were also comprehensively analyzed between different risk groups. Finally, the function of AC019069.1 was validated in vitro. Results: A novel risk signature was developed based on nine DRLs. The risk score efficiently predicted the prognosis of patients with UCEC. Based on the median risk score, two subgroups were identified. The DDR-related pathways were upregulated in the high-risk group. Additionally, high-risk patients have low immune activity, poor response to ICB, and weak sensitivity to chemotherapeutic agents, possibly because of the proficient DDR system. Finally, we demonstrated AC019069.1 could promote cell proliferation, decrease apoptosis and maintain genome stability of UCEC cells. Conclusions: The developed DRL-related signature can predict the prognosis, immune microenvironment, immunotherapy, and chemoradiotherapy responsiveness of UCEC. Our study also revealed the potential value of DDR-targeted therapy in treating high-risk patients with UCEC.

Microfluidic design in single-cell sequencing and application to cancer precision medicine.

Single-cell sequencing (SCS) is a crucial tool to reveal the genetic and functional heterogeneity of tumors, providing unique insights into the clonal evolution, microenvironment, drug resistance, and metastatic progression of cancers. Microfluidics is a critical component of many SCS technologies and workflows, conferring advantages in throughput, economy, and automation. Here, we review the current landscape of microfluidic architectures and sequencing techniques for single-cell omics analysis and highlight how these have enabled recent applications in oncology research. We also discuss the challenges and the promise of microfluidics-based single-cell analysis in the future of precision oncology.

Functional analysis of a novel de novo SCN2A variant in a patient with seizures refractory to oxcarbazepine.

Objective: We admitted a female patient with infantile onset epilepsy (<3-month-old). The use of oxcarbazepine exacerbated epileptic seizures in the patient. In the present study, we aimed to identify the genetic basis of the infantile onset epilepsy in the patient, and determine the correlations among genotype, phenotype, and clinical drug response. Methods: We described the clinical characteristics of an infant with refractory epilepsy. Whole exome sequencing (WES) was used to screen for the pathogenic variant. Whole-cell patch-clamp was performed to determine functional outcomes of the variant. Results: WES identified a novel de novo SCN2A variant (c.468 G > C, p.K156N) in the patient. In comparison with wildtype, electrophysiology revealed that SCN2A-K156N variant in transfected cells demonstrated reduced sodium current density, delayed activation and accelerated inactivation process of Na+ channel, all of which suggested a loss-of-function (LOF) of Nav1.2 channel. Conclusion: We showed the importance of functional analysis for a SCN2A variant with unknown significance to determine pathogenicity, drug reactions, and genotype-phenotype correlations. For patients suffering from early infantile epilepsies, the use of oxcarbazepine in some SCN2A-related epilepsies requires vigilance to assess the possibility of epilepsy worsening.

Molecular cluster mining of high-grade serous ovarian cancer via multi-omics data analysis aids precise medicine.

PURPOSE: HGSOC is a kind of gynecological cancer with high mortality and strong heterogeneity. The study used multi-omics and multiple algorithms to identify novel molecular subtypes, which can help patients obtain more personalized treatments. METHODS: Firstly, the consensus clustering result was obtained using a consensus ensemble of ten classical clustering algorithms, based on mRNA, lncRNA, DNA methylation, and mutation data. The difference in signaling pathways was evaluated using the single-sample gene set enrichment analysis (ssGSEA). Meanwhile, the relationship between genetic alteration, response to immunotherapy, drug sensitivity, prognosis, and subtypes was further analyzed. Finally, the reliability of the new subtype was verified in three external datasets. RESULTS: Three molecular subtypes were identified. Immune desert subtype (CS1) had little enrichment in the immune microenvironment and metabolic pathways. Immune/non-stromal subtype (CS2) was enriched in the immune microenvironment and metabolism of polyamines. Immune/stromal subtype (CS3) not only enriched anti-tumor immune microenvironment characteristics but also enriched pro-tumor stroma characteristics, glycosaminoglycan metabolism, and sphingolipid metabolism. The CS2 had the best overall survival and the highest response rate to immunotherapy. The CS3 had the worst prognosis and the lowest response rate to immunotherapy but was more sensitive to PARP and VEGFR molecular-targeted therapy. The similar differences among three subtypes were successfully validated in three external cohorts. CONCLUSION: We used ten clustering algorithms to comprehensively analyze four types of omics data, identified three biologically significant subtypes of HGSOC patients, and provided personalized treatment recommendations for each subtype. Our findings provided novel views into the HGSOC subtypes and could provide potential clinical treatment strategies.

Progress of research on tumor organoids: A bibliometric analysis of relevant publications from 2011 to 2021.

Background: Research on tumor organoids has developed rapidly over the past 20 years, but a systematic analysis of current research trends is lacking. Researchers in the field need relevant references and knowledge of current research hot spots. Bibliometric analysis and visualization is a systematic method of acquiring an in-depth understanding of the status of research on tumor organoids. Methods: CiteSpace, VOSviewer and the Bibliometric Online Analysis Platform from the Web of Science Core Collection were used to analyze and predict publishing trends and research hot spots worldwide in the field of tumor organoids. Results: A total of 3,666 publications on tumor organoids were retrieved, and 2,939 eligible articles were included in the final analysis. The number of publications has grown significantly, with the United States of America as the leading country for research on tumor organoids. Among journals, Cancers published the largest number of articles. Harvard Medical School published the highest number of articles among all institutions. The Chinese Academy of Sciences was ranked highest among all contributing institutions on the importance of their publications. A trend in multi-disciplinary collaboration was observed in studies on tumor organoids. Keywords indicated that the current research largely concentrated on optimizing the construction of organoid models to use for medication development and screening in the clinical setting, and to provide patients with individualized treatment for gastric cancer and colorectal cancer, which are newly emerging research hotspots. Gastric and colorectal cancers were the top two tumors that have received increasing attention and have become the focal points of recent studies. Conclusion: This study analyzed 2,939 publications covering the topic of tumor organoids. Although optimizing the construction of organoid models has always been a hot topic in this field, the application of tumor organoids to the development of medications and screenings will foster individualized treatment for patients, which is another emerging hot spot in this field of research.

Osteosarcoma transcriptome data exploration reveals STC2 as a novel risk indicator in disease progression.

BACKGROUND: Osteosarcoma has been the most common primary bone malignant tumor in children and adolescents. Despite the considerable improvement in the understanding of genetic events attributing to the rapid development of molecular pathology, the current information is still lacking, partly due to the comprehensive and highly heterogeneous nature of osteosarcoma. The study is to identify more potential responsible genes during the development of osteosarcoma, thus identifying promising gene indicators and aiding more precise interpretation of the disease. METHODS: Firstly, from GEO database, osteosarcoma transcriptome microarrays were used to screen the differential expression genes (DEGS) in cancer comparing to normal bone samples, followed by GO/KEGG interpretation, risk score assessment and survival analysis of the genes, for the purpose of selecting a credible key gene. Further, the basic physicochemical properties, predicted cellular location, gene expression in human cancers, the association with clinical pathological features and potential signaling pathways involved in the key gene's regulation on osteosarcoma development were in succession explored. RESULTS: Based on the selected GEO osteosarcoma expression profiles, we identified the differential expression genes in osteosarcoma versus normal bone samples, and the genes were classified into four groups based on the difference level, further genes interpretation indicated that the high differently level (> 8 fold) genes were mainly located extracellular and related to matrix structural constituent regulation. Meanwhile, module function analysis of the 67 high differential level (> 8 fold) DEGS revealed a 22-gene containing extracellular matrix regulation associated hub gene cluster. Further survival analysis of the 22 genes revealed that STC2 was an independent prognosis indicator in osteosarcoma. Moreover, after validating the differential expression of STC2 in cancer vs. normal tissues using local hospital osteosarcoma samples by IHC and qRT-PCR experiment, the gene's physicochemical property revealed STC2 as a cellular stable and hydrophilic protein, and the gene's association with osteosarcoma clinical pathological parameters, expression in pan-cancers and the probable biological functions and signaling pathways it involved were explored. CONCLUSION: Using multiple bioinformatic analysis and local hospital samples validation, we revealed the gain of expression of STC2 in osteosarcoma, which associated statistical significantly with patients survival, and the gene's clinical features and potential biological functions were also explored. Although the results shall provide inspiring insights into further understanding of the disease, further experiments and detailed rigorous clinical trials are needed to reveal its potential drug-target role in clinical medical use.

The investigation of the complex population-drug-drug interaction between ritonavir-boosted lopinavir and chloroquine or ivermectin using physiologically-based pharmacokinetic modeling.

OBJECTIVES: Therapy failure caused by complex population-drug-drug (PDDI) interactions including CYP3A4 can be predicted using mechanistic physiologically-based pharmacokinetic (PBPK) modeling. A synergy between ritonavir-boosted lopinavir (LPVr), ivermectin, and chloroquine was suggested to improve COVID-19 treatment. This work aimed to study the PDDI of the two CYP3A4 substrates (ivermectin and chloroquine) with LPVr in mild-to-moderate COVID-19 adults, geriatrics, and pregnancy populations. METHODS: The PDDI of LPVr with ivermectin or chloroquine was investigated. Pearson's correlations between plasma, saliva, and lung interstitial fluid (ISF) levels were evaluated. Target site (lung epithelial lining fluid [ELF]) levels of ivermectin and chloroquine were estimated. RESULTS: Upon LPVr coadministration, while the chloroquine plasma levels were reduced by 30, 40, and 20%, the ivermectin plasma levels were increased by a minimum of 425, 234, and 453% in adults, geriatrics, and pregnancy populations, respectively. The established correlation equations can be useful in therapeutic drug monitoring (TDM) and dosing regimen optimization. CONCLUSIONS: Neither chloroquine nor ivermectin reached therapeutic ELF levels in the presence of LPVr despite reaching toxic ivermectin plasma levels. PBPK modeling, guided with TDM in saliva, can be advantageous to evaluate the probability of reaching therapeutic ELF levels in the presence of PDDI, especially in home-treated patients.

Exploration and perspective of paradigm and technical system of precise and intelligent minimally invasive surgery for gastric cancer / 中华消化外科杂志

As precision and intelligent medicine takeing hold, the concept of minimally invasive surgery for gastric cancer has evolved from simply pursuing the effect of surgical results to being more patient-centered, safer, less trauma, more efficient, and more in line with the spirit of the evidence-based decision-making comprehensive treatment model. The authors review the develop-ment and technical innovation of precise and intelligent surgery in recent years, and proposes the expanding direction of new paradigm of precise and intelligent minimally invasive surgery for gastric cancer based on the work and achievements of team work. It is believed that in the future, advances in artificial intelligence, fluorescence imaging, surgical navigation, optical biopsy and other technolo-gies will bring new vitality to minimally invasive surgery for gastric cancer.

Therapeutic strategies for EGFR-mutated non-small cell lung cancer patients with osimertinib resistance.

Epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) are the preferential options for advanced non-small cell lung cancer (NSCLC) patients harboring EGFR mutations. Osimertinib is a potent irreversible third-generation EGFR-TKI targeting EGFR mutations but has little effect on wild-type EGFR. In view of its remarkable efficacy and manageable safety, osimertinib was recommended as the standard first-line treatment for advanced or metastatic NSCLC patients with EGFR mutations. However, as the other EGFR-TKIs, osimertinib will inevitably develop acquired resistance, which limits its efficacy on the treatment of EGFR-mutated NSCLC patients. The etiology of triggering osimertinib resistance is complex including EGFR-dependent and EGFR-independent pathways, and different therapeutic strategies for the NSCLC patients with osimertinib resistance have been developed. Herein, we comprehensively summarized the resistance mechanisms of osimertinib and discuss in detail the potential therapeutic strategies for EGFR-mutated NSCLC patients suffering osimertinib resistance for the sake of the improvement of survival and further achievement of precise medicine.

Individual dynamic prediction and prognostic analysis for long-term allograft survival after kidney transplantation.

BACKGROUND: Predicting allograft survival is vital for efficient transplant success. With dynamic changes in patient conditions, clinical indicators may change longitudinally, and doctors' judgments may be highly variable. It is necessary to establish a dynamic model to precisely predict the individual risk/survival of new allografts. METHODS: The follow-up data of 407 patients were obtained from a renal allograft failure study. We introduced a landmarking-based dynamic Cox model that incorporated baseline values (age at transplantation, sex, weight) and longitudinal changes (glomerular filtration rate, proteinuria, hematocrit). Model performance was evaluated using Harrell's C-index and the Brier score. RESULTS: Six predictors were included in our analysis. The Kaplan-Meier estimates of survival at baseline showed an overall 5-year survival rate of 87.2%. The dynamic Cox model showed the individual survival prediction with more accuracy at different time points (for the 5-year survival prediction, the C-index = 0.789 and Brier score = 0.065 for the average of all time points) than the static Cox model at baseline (C-index = 0.558, Brier score = 0.095). Longitudinal covariate prognostic analysis (with time-varying effects) was performed. CONCLUSIONS: The dynamic Cox model can utilize clinical follow-up data, including longitudinal patient information. Dynamic prediction and prognostic analysis can be used to provide evidence and a reference to better guide clinical decision-making for applying early treatment to patients at high risk.

Endometrial cancer: Part I. Basic concept.

Endometrial cancer (EC) has become one of rapidly increasing women's cancers, contributing to the most common cancer of the female genital tract in high- and middle-incomed countries, including Taiwan. In general, EC is believed its favorable outcome; however, high-grade endometrial cancers have a tendency to recur and also have a high risk to be presented as an advanced stage or accompanied with metastatic lesions, which result in a biggest therapeutic challenge. The standard therapy includes complete staging surgery (sentinel node sampling)/optimal debulking surgery, and subsequent adjuvant therapy, by either radiotherapy locally or systemic therapy as chemotherapy or targeted therapy, or in combination or in subsequential strategy is made based on the risk stratification using clinicopathological prognostic factors. All efforts are made to minimize the risk of recurrence and possible therapeutic failure. In this part I, we would like to overview the general background knowledge (basic concept) about the cancer of uterine corpus, and discuss the recent transformation to patients-tailored therapy based on modern molecular technology as the optimal strategy to balance the therapeutic efficacy and treatment-related toxicity. Optimally, it is possible to reach the best benefits.

Using Patient-Derived Xenograft (PDX) Models as a 'Black Box' to Identify More Applicable Patients for ADP-Ribose Polymerase Inhibitor (PARPi) Treatment in Ovarian Cancer: Searching for Novel Molecular and Clinical Biomarkers and Performing a Prospective Preclinical Trial.

(1) The accuracy of patient-derived xenografts (PDXs) in predicting ADP-ribose polymerase inhibitor (PARPi) efficacy in ovarian cancer was tested, novel biomarkers were investigated, and whether PARPis could replace platinum-based chemotherapy as a first-line therapy was explored. (2) PDXs were reconstructed for 40 patients with ovarian cancer, and niraparib, olaparib and paclitaxel, and carboplatin (TC) sensitivity tests were conducted. Whole exon sequencing and homologous recombination deficiency (HRD) scores were performed, and patient clinical information was collected. The molecular biomarkers were identified by reverse-transcription quantitative PCR and immunoblotting. (3) Niraparib and olaparib sensitivity were tested in 26 patients and showed high consistency. Approximately half of BRCA wild-type, HRD-negative, and platinum-resistant patients may benefit from PARPis. AKT1 enrichment indicated PARPi resistance; high KRAS expression indicated PARPi sensitivity. CA125 below 10 U/mL during chemotherapy has a sensitivity and specificity similar to platinum sensitivity in predicting PARPi efficacy. Niraparib and TC sensitivity tests were performed on 23 patients, and TC showed a better response in this preclinical trial. (4) PDX can indicate individualized PARPi efficacy. Decreased CA125 levels and KRAS and ATK1 expression levels may be novel biomarkers. The preclinical evidence does not support the implementation of PARPis as the first-line treatment in an unselected population.

Role of copper ionophore-induced death in immune microenvironment and clinical prognosis of ccRCC: An integrated analysis.

Background: Clear cell renal cell carcinoma (ccRCC) is a malignancy with a high incidence rate and poor prognosis worldwide. Copper ionophore-induced death (CID) plays an important role in cancer progression. Methods: One training and three validation datasets were acquired from TCGA, GEO and ArrayExpress. K-means clustering was conducted to identify the CID subtypes. The ESTIMATE and CIBERSORT algorithms were employed to illustrate the immune microenvironment of ccRCC. LASSO Cox regression was applied to construct the CID feature-based prognostic model. The immunotherapy cohort was acquired from the literature to explore the potential risk scores for predicting immunotherapy responsiveness. Results: Two CID-related cancer subtypes of ccRCC were identified that performed different immune microenvironment characteristics and prognosis. Based on the identified subtypes, we analyzed the biological heterogeneity and constructed a gene prognostic model. The prognostic model performed well in one training dataset, three validation datasets, and different clinical pathological groups. The prognostic model has a good potential for predicting cancer immune features and immunotherapy responsiveness. Conclusion: CID plays an important role in the tumor microenvironment progression of ccRCC. The robust gene prognostic model developed can help predict cancer prognosis, immune features, and immunotherapy responsiveness.

Towards a precision immune checkpoint blockade immunotherapy in patients with colorectal cancer: Strategies and perspectives.

To date, immune checkpoint blockade (ICB) immunotherapy has become one of promise strategies in the management of patients with unresectable or metastatic colorectal cancer (CRC). However, clinical observations showed that not all the patients responded equally to ICBs, certain group of CRC patients with microsatellite-instability-low (MSI-L) phenotype was not sensitive to ICB immunotherapy. In addition, some primary responders might lose their sensitivity and become resistant to ICBs overtime. To obtain a better response rate and therapeutic efficacy, considerable attempts have been made toward to a precision medicine algorithm. Studies showed that multiple strategies based on the patient's individual condition might improve the response and therapeutic efficacy to ICBs. Therefore, we focused on and discussed precision strategies and perspectives e.g., how to early define candidates who will benefit from ICB immunotherapy prior treatment, overcome the primary and acquired resistance and improve the therapeutic response to ICBs in CRC patients with different microsatellite-instability statuses within the context of precision medicine algorithm in this review.