Cancer Informatics最新文献

Background: Breast cancer (BC) has been reported as one of the most common cancers diagnosed in females throughout the world. Survival rate of BC patients is affected by metastasis. So, exploring its underlying mechanisms and identifying related biomarkers to monitor BC relapse/recurrence using new statistical methods is essential. This study investigated the high-dimensional gene-expression profiles of BC patients using penalized additive hazards regression models.

Methods: A publicly available dataset related to the time to metastasis in BC patients (GSE2034) was used. There was information of 22 283 genes expression profiles related to 286 BC patients. Penalized additive hazards regression models with different penalties, including LASSO, SCAD, SICA, MCP and Elastic net were used to identify metastasis related genes.

Results: Five regression models with penalties were applied in the additive hazards model and jointly found 9 genes including SNU13, CLINT1, MAPK9, ABCC5, NKX3-1, NCOR2, COL2A1, and ZNF219. According the median of the prognostic index calculated using the regression coefficients of the penalized additive hazards model, the patients were labeled as high/low risk groups. A significant difference was detected in the survival curves of the identified groups. The selected genes were examined using validation data and were significantly associated with the hazard of metastasis.

Conclusion: This study showed that MAPK9, NKX3-1, NCOR1, ABCC5, and CD44 are the potential recurrence and metastatic predictors in breast cancer and can be taken into account as candidates for further research in tumorigenesis, invasion, metastasis, and epithelial-mesenchymal transition of breast cancer.

Introduction: One of the most pressing goals for cancer immunotherapy at this time is the identification of actionable antigens.

Methods: This study relies on the following considerations and approaches to identify potential breast cancer antigens: (i) the significant role of the adaptive immune receptor, complementarity determining region-3 (CDR3) in antigen binding, and the existence cancer testis antigens (CTAs); (ii) chemical attractiveness; and (iii) informing the relevance of the integration of items (i) and (ii) with patient outcome and tumor gene expression data.

Results: We have assessed CTAs for associations with survival, based on their chemical complementarity with tumor resident T-cell receptor (TCR), CDR3s. Also, we have established gene expression correlations with the high TCR CDR3-CTA chemical complementarities, for Granzyme B, and other immune biomarkers.

Conclusions: Overall, for several independent TCR CDR3 breast cancer datasets, the CTA, ARMC3, stood out as a completely novel, candidate antigen based on multiple algorithms with highly consistent approaches. This conclusion was facilitated by use of the recently constructed Adaptive Match web tool.

Objective: The aim of this study was to evaluate the post-marketing safety, tolerability, immunogenicity and efficacy of Bevacizumab (manufactured by Hetero Biopharma) in a broader population of patients with solid tumors.

Patients and methods: This phase IV, prospective, multi-centric clinical study was carried out in Indian patients with solid malignancies (metastatic colorectal cancer, non-squamous non-small-cell lung cancer, metastatic renal cell carcinoma) treated with Bevacizumab between April 2018 and July 2019. This study included 203 patients from 16 tertiary care oncology centers across India for safety assessment, of which a subset of 115 patients who have consented were also evaluated for efficacy and immunogenicity. This study was prospectively registered in the Clinical Trial Registry of India (CTRI), and was commenced only after receiving approval from the competent authority (Central Drugs Standard Control Organization, CDSCO).

Results: Out of the 203 enrolled patients, 121 (59.6%) patients reported 338 adverse events (AEs) during this study. Of 338 reported AEs, 14 serious adverse events (SAEs) were reported by 13 patients including 6 fatal SAEs, assessed as unrelated to the study medication and 7 non-fatal SAEs, 5 assessed as related, and 3 unrelated to Bevacizumab. Most AEs reported in this study (33.9%) were general disorders and administration site conditions, followed by gastrointestinal disorders (29.1%). The most frequently reported AEs were diarrhea (11.3%), asthenia (10.3%), headache (8.9%), pain (7.4%), vomiting (7.9%), and neutropenia (5.9%). At the end of the study, 2 (1.75%) of 69 patients reported antibodies to Bevacizumab without affecting safety and efficacy. However, at the end of 12 months, no patient had reported antibodies to Bevacizumab. Complete response (CR), partial response (PR), stable disease (SD), and progressive disease (PD) were reported in 18.3%, 22.6%, 9.6%, and 8.7% of patients, respectively. The overall response rate (CR + PR) was reported in 40.9% of patients at the end of the study. Disease control rate (DCR), also known as the clinical benefit rate (CBR) was reported in 50.4% of patients.

Conclusions: Bevacizumab (Cizumab, Hetero Biopharma) was observed to be safe, well tolerated, lacking immunogenicity, and efficacious in the treatment of solid tumors. The findings of this phase IV study of Bevacizumab, primarily as a combination therapy regimen suggest its suitability and rationality for usage in multiple solid malignancies.

Clinical trial registry number: CTRI/2018/4/13371 [Registered on CTRI http://ctri.nic.in/Clinicaltrials/advsearch.php : 19/04/2018]; Trial Registered Prospectively.

Host immunogenetics play a critical role in the human immune response to melanoma, influencing both melanoma prevalence and immunotherapy outcomes. Beneficial outcomes that stimulate T cell response hinge on binding affinity and immunogenicity of human leukocyte antigen (HLA) with melanoma antigen epitopes. Here, we use an in silico approach to characterize binding affinity and immunogenicity of 69 HLA Class I human leukocyte antigen alleles to epitopes of 11 known melanoma antigens. The findings document a significant proportion of positively immunogenic epitope-allele combinations, with the highest proportions of positive immunogenicity found for the Q13072/BAGE1 melanoma antigen and alleles of the HLA B and C genes. The findings are discussed in terms of a personalized precision HLA-mediated adjunct to immune checkpoint blockade immunotherapy to maximize tumor elimination.

Genomic instability is considered a fundamental factor involved in any neoplastic disease. Consequently, the genetically unstable cells contribute to intratumoral genetic heterogeneity and phenotypic diversity of cancer. These genetic alterations can be detected by several diagnostic techniques of molecular biology and the detection of alteration in genomic integrity may serve as reliable genetic molecular markers for the early detection of cancer or cancer-related abnormal changes in the body cells. These genetic molecular markers can detect cancer earlier than any other method of cancer diagnosis, once a tumor is diagnosed, then replacement or therapeutic manipulation of these cancer-related abnormal genetic changes can be possible, which leads toward effective and target-specific cancer treatment and in many cases, personalized treatment of cancer could be performed without the adverse effects of chemotherapy and radiotherapy. In this review, we describe how these genetic molecular markers can be detected and the possible ways for the application of this gene diagnosis for gene therapy that can attack cancerous cells, directly or indirectly, which lead to overall improved management and quality of life for a cancer patient.

Aim: In this study our aim was to elucidate whether advanced cancer patients benefit from antibiotic treatment in the last days of life in addition to reviewing the relevant costs and effects.

Materials and methods: We reviewed medical records from 100 end-stage cancer patients and their antibiotic use during the hospitalization in Imam Khomeini hospital. Patient's medical records were analyzed retrospectively for cause and periodicity of infections, fever, increase in acute phase proteins, cultures, type and cost of antibiotic.

Results: Microorganisms were found in only 29 patients (29%) and the most microorganism among the patients was E. coli (6%). About 78% of the patients had clinical symptoms. The highest dose of antibiotics was related to Ceftriaxone (40.2%) and in the second place was Metronidazole (34.7%) and the lowest dose was related to Levofloxacin, Gentamycin and Colistin (1.4%). Fifty-one patients (71%) did not have any side effects due to antibiotics. The most common side effect of antibiotics among patients was skin rash (12.5%). The average estimated cost for antibiotic use was 7 935 540 Rials (24.4 dollars).

Conclusion: Prescription of antibiotics was not effective in symptom control in advanced cancer patients. The cost of using antibiotics during hospitalization is very high and also the risk of developing resistant pathogens during admission should be considered. Antibiotic side effects also occur in patients, causing more harm to the patient at the end of life. Therefore, the benefits of antibiotic advice in this time is less than its negative effects.

Background: Cancer development and progression involve a complex network of pathways among which certain pathways play a pivotal role in promoting tumor growth and survival. An important pathway in this context is the PI3K/AKT pathway, which regulates crucial cellular processes including proliferation, viability, and metabolic regulation. Dysregulation of this pathway has been strongly linked to the development of various types of cancers. Consequently, it is imperative to identify the key proteins within this pathway as potential targets for impeding cancer cell proliferation and survival.

Results: One of the key findings of this study was the identification of signaling proteins that dominate various forms of PI3K/Akt pathway. Furthermore, proteins play critical roles in cancer networks, acting as oncogenes that promote cancer development or as tumor suppressor genes that inhibit tumor growth. This study identified several genes, including KIT, ERBB2, PDGFRA, MET, FGFR2, and FGFR3, which are involved in various types of the PI3K/Akt pathways. Additionally, this study identified 55 proteins that are commonly found in various forms of PI3K/Akt, and these proteins play crucial roles in regulating various biological functions.

Conclusions: This study highlights the importance of identifying key proteins involved in the PI3K/AKT pathway. In this study, we identified several genes involved in different pathways that play essential roles in the activation, signaling, and regulation of the pathway. Understanding the proteins participating in the PI3K/AKT pathway is vital for the development of targeted therapies, not only for cancer but also for other related diseases. By elucidating their roles and functions, this study contributes to the advancement of knowledge in the field and paves the way for the development of effective treatments targeting this pathway.

Tumor mutational burden (TMB), a surrogate for tumor neoepitope burden, is used as a pan-tumor biomarker to identify patients who may benefit from anti-program cell death 1 (PD1) immunotherapy, but it is an imperfect biomarker. Multiple additional genomic characteristics are associated with anti-PD1 responses, but the combined predictive value of these features and the added informativeness of each respective feature remains unknown. We evaluated whether machine learning (ML) approaches using proposed determinants of anti-PD1 response derived from whole exome sequencing (WES) could improve prediction of anti-PD1 responders over TMB alone. Random forest classifiers were trained on publicly available anti-PD1 data (n = 104), and subsequently tested on an independent anti-PD1 cohort (n = 69). Both the training and test datasets included a range of cancer types such as non-small cell lung cancer (NSCLC), head and neck squamous cell carcinoma (HNSCC), melanoma, and smaller numbers of patients from other tumor types. Features used include summaries such as TMB and number of frameshift mutations, as well as more gene-level features such as counts of mutations associated with immune checkpoint response and resistance. Both ML algorithms demonstrated area under the receiver-operator curves (AUC) that exceeded TMB alone (AUC 0.63 "human-guided," 0.64 "cluster," and 0.58 TMB alone). Mutations within oncogenes disproportionately modulate anti-PD1 responses relative to their overall contribution to tumor neoepitope burden. The use of a ML algorithm evaluating multiple proposed genomic determinants of anti-PD1 responses modestly improves performance over TMB alone, highlighting the need to integrate other biomarkers to further improve model performance.

Background: We evaluated treatment concordance between the Colorado All Payer Claims Database (APCD) and the Colorado Central Cancer Registry (CCCR) to explore whether APCDs can augment registry data. We compare treatment concordance for breast cancer, an extensively studied site with an inpatient reporting source and select leukemias that are often diagnosed outpatient.

Methods: We analyzed concordance by cancer type and treatment, patient demographics, reporting source, and health insurance, calculating the sensitivity, specificity, positive predictive values (PPV) and Kappa statistics. We estimated an adjusted logistic regression model to assess whether the APCD statistically significantly reports additional cancer-directed treatments.

Results: Among women with breast cancer, 14% had chemotherapy treatments that were absent from the CCCR. Missing treatments were more common among women younger than age 50 (15%) and patients aged 75 and older (19%), rural residents (17%), and when the reporting source was outpatient (22%). Similar and more pronounced patterns for people with leukemia were observed. Concordance for oral treatments was lower for each cancer. Sensitivity and PPVs were high, with moderate Kappa statistics. The APCD was 5.3 percentage points less likely to identify additional treatments for breast cancer patients and 10 percentage points more likely to identify additional treatments when the reporting source was an outpatient facility.

Conclusion: A robust data infrastructure is needed to investigate research questions that require population-level analyses, particularly for questions seeking to reduce health inequity and comparisons across payers, including Medicare Advantage and fee-for-service. APCD data are a step toward creating an infrastructure for cancer, particularly for patients who reside in rural areas and/or receive care from outpatient centers.

The creation of patient-derived cancer organoids represents a key advance in preclinical modeling and has recently been applied to a variety of human solid tumor types. However, conventional methods used to assess in vivo tumor tissue treatment response are poorly suited for the evaluation of cancer organoids because they are time-intensive and involve tissue destruction. To address this issue, we established a suite of 3-dimensional patient-derived glioma organoids, treated them with chemoradiotherapy, stained organoids with non-toxic cell dyes, and imaged them using a rapid laser scanning confocal microscopy method termed "Apex Imaging." We then developed and tested a fragmentation algorithm to quantify heterogeneity in the topography of the organoids as a potential surrogate marker of viability. This algorithm, SSDquant, provides a 3-dimensional visual representation of the organoid surface and a numerical measurement of the sum-squared distance (SSD) from the derived mass center of the organoid. We tested whether SSD scores correlate with traditional immunohistochemistry-derived cell viability markers (cellularity and cleaved caspase 3 expression) and observed statistically significant associations between them using linear regression analysis. Our work describes a quantitative, non-invasive approach for the serial measurement of patient-derived cancer organoid viability, thus opening new avenues for the application of these models to studies of cancer biology and therapy.