Cancer Informatics最新文献

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.

Background: With cancer as one of the leading causes of death worldwide, accurate primary tumor type prediction is critical in identifying genetic factors that can inhibit or slow tumor progression. There have been efforts to categorize primary tumor types with gene expression data using machine learning, and more recently with deep learning, in the last several years.

Methods: In this paper, we developed four 1-dimensional (1D) Convolutional Neural Network (CNN) models to classify RNA-seq count data as one of 17 highly represented primary tumor types or 32 primary tumor types regardless of imbalanced representation. Additionally, we adapted the models to take as input either all Ensembl genes (60,483) or protein coding genes only (19,758). Unlike previous work, we avoided selection bias by not filtering genes based on expression values. RNA-seq count data expressed as FPKM-UQ of 9,025 and 10,940 samples from The Cancer Genome Atlas (TCGA) were downloaded from the Genomic Data Commons (GDC) corresponding to 17 and 32 primary tumor types respectively for training and validating the models.

Results: All 4 1D-CNN models had an overall accuracy of 94.7% to 97.6% on the test dataset. Further evaluation indicates that the models with protein coding genes only as features performed with better accuracy compared to the models with all Ensembl genes for both 17 and 32 primary tumor types. For all models, the accuracy by primary tumor type was above 80% for most primary tumor types.

Conclusions: We packaged all 4 models as a Python-based deep learning classification tool called TULIP (TUmor CLassIfication Predictor) for performing quality control on primary tumor samples and characterizing cancer samples of unknown tumor type. Further optimization of the models is needed to improve the accuracy of certain primary tumor types.