Controlling the confounding effect of metabolic gene expression to identify actual metabolite targets in microsatellite instability cancers.

BACKGROUND: The metabolome is the best representation of cancer phenotypes. Gene expression can be considered a confounding covariate affecting metabolite levels. Data integration across metabolomics and genomics to establish the biological relevance of cancer metabolism is challenging. This study aimed to eliminate the confounding effect of metabolic gene expression to reflect actual metabolite levels in microsatellite instability (MSI) cancers. METHODS: In this study, we propose a new strategy using covariate-adjusted tensor classification in high dimensions (CATCH) models to integrate metabolite and metabolic gene expression data to classify MSI and microsatellite stability (MSS) cancers. We used datasets from the Cancer Cell Line Encyclopedia (CCLE) phase II project and treated metabolomic data as tensor predictors and data on gene expression of metabolic enzymes as confounding covariates. RESULTS: The CATCH model performed well, with high accuracy (0.82), sensitivity (0.66), specificity (0.88), precision (0.65), and F1 score (0.65). Seven metabolite features adjusted for metabolic gene expression, namely, 3-phosphoglycerate, 6-phosphogluconate, cholesterol ester, lysophosphatidylethanolamine (LPE), phosphatidylcholine, reduced glutathione, and sarcosine, were found in MSI cancers. Only one metabolite, Hippurate, was present in MSS cancers. The gene expression of phosphofructokinase 1 (PFKP), which is involved in the glycolytic pathway, was related to 3-phosphoglycerate. ALDH4A1 and GPT2 were associated with sarcosine. LPE was associated with the expression of CHPT1, which is involved in lipid metabolism. The glycolysis, nucleotide, glutamate, and lipid metabolic pathways were enriched in MSI cancers. CONCLUSIONS: We propose an effective CATCH model for predicting MSI cancer status. By controlling the confounding effect of metabolic gene expression, we identified cancer metabolic biomarkers and therapeutic targets. In addition, we provided the possible biology and genetics of MSI cancer metabolism.

The multinomial mixed-effect regression model for predicting PCOC phases in hospice patients.

PURPOSE: The Palliative Care Outcomes Collaboration (PCOC) aims to enhance patient outcomes systematically. However, identifying crucial items and accurately determining PCOC phases remain challenging. This study aims to identify essential PCOC data items and construct a prediction model to accurately classify PCOC phases in terminal patients. METHODS: A retrospective cohort study assessed PCOC data items across four PCOC phases: stable, unstable, deteriorating, and terminal. From July 2020 to March 2023, terminal patients were enrolled. A multinomial mixed-effect regression model was used for the analysis of multivariate PCOC repeated measurement data. RESULTS: The dataset comprised 1933 terminally ill patients from 4 different hospice service settings. A total of 13,219 phases of care were analyzed. There were significant differences in the symptom assessment scale, palliative care problem severity score, Australia-modified Karnofsky performance status, and resource utilization groups-activities of daily living among the four PCOC phases of care. Clinical needs, including pain and other symptoms, declined from unstable to terminal phases, while psychological/spiritual and functional status for bed mobility, eating, and transfers increased. A robust prediction model achieved areas under the curves (AUCs) of 0.94, 0.94, 0.920, and 0.96 for stable, unstable, deteriorating, and terminal phases, respectively. CONCLUSIONS: Critical PCOC items distinguishing between PCOC phases were identified, enabling the development of an accurate prediction model. This model enhances hospice care quality by facilitating timely interventions and adjustments based on patients' PCOC phases.

'A good ending but not the end': Exploring family preparations surrounding a relative's death and the Afterlife - A qualitative study.

BACKGROUND: Adequate death preparation positively influences families' experience before death and during bereavement. However, how to prepare families in non-Western cultures has received scant attention. AIM: To explore family caregivers' experiences in preparing for a relative's death in specialist palliative care in Taiwan. DESIGN: A qualitative study employing reflexive thematic analysis of data collected from semi-structured interviews was conducted. SETTING/PARTICIPANTS: Twenty-two family caregivers from seven hospitals participated. RESULTS: The overarching theme was 'getting everything right to have no regrets between the dead and the living'. We developed two themes to explain preparations for the time surrounding and after the death, including the deceased' afterlife: (1) 'having a good ending but not the end of the relationship', which addresses preparations for the death itself, the funeral, the afterlife and maintaining connections and (2) 'using religious beliefs and cultural norms to guide preparation', which explores perceptions of a good death, including refrain from strong emotions before and after the death. CONCLUSION: Funeral arrangements, enhancing the deceased's afterlife and maintaining connections to the deceased are crucial for families' experiences which can be impacted by actions they take as they prepare for the death. A culturally appropriate death is beneficial for the dying relative which includes preparing to not show strong emotions during and after the death. These insights inform the importance of the cultural context in death preparation in Taiwan and provide perspectives for palliative care beyond Western culture, potentially benefiting Chinese populations, predominantly East Asian and Buddhist societies.

Using machine learning to predict bacteremia in urgent care patients on the basis of triage data and laboratory results.

BACKGROUND: Despite advancements in antimicrobial therapies, bacteremia remains a life-threatening condition. Appropriate antimicrobials must be promptly administered to ensure patient survival. However, diagnosing bacteremia based on blood cultures is time-consuming and not something emergency department (ED) personnel are routinely trained to do. METHODS: This retrospective cohort study developed several machine learning (ML) models to predict bacteremia in adults initially presenting with fever or hypothermia, comprising logistic regression, random forest, extreme gradient boosting, support vector machine, k-nearest neighbor, multilayer perceptron, and ensemble models. Random oversampling and synthetic minority oversampling techniques were adopted to balance the dataset. The variables included demographic characteristics, comorbidities, immunocompromised status, clinical characteristics, subjective symptoms reported during ED triage, and laboratory data. The study outcome was an episode of bacteremia. RESULTS: Of the 5063 patients with initial fever or hypothermia from whom blood cultures were obtained, 128 (2.5 %) were diagnosed with bacteremia. We combined 36 selected variables and 10 symptoms subjectively reported by patients into features for analysis in our models. The ensemble model outperformed other models, with an area under the receiver operating characteristic curve (AUROC) of 0.930 and an F1-score of 0.735. The AUROC of all models was higher than 0.80. CONCLUSION: The ML models developed effectively predicted bacteremia among febrile or hypothermic patients in the ED, with all models demonstrating high AUROC values and rapid processing times. The findings suggest that ED clinicians can effectively utilize ML techniques to develop predictive models for addressing clinical challenges.

Dual role of sprouty2 as an inhibitor of RAS/ERK-driven proliferation and a promoter of cancer invasion in KRAS wild-type colorectal cancer.

Sprouty2 (SPRY2) is known to inhibit the RAS/MAPK/ERK pathway, and is a potential study target for cancer. The effect of SPRY2 in colorectal cancer (CRC) and whether it is influenced by KRAS mutation are not known. We manipulated SPRY2 gene expression and used an activating KRAS-mutant plasmid to determine its effect on CRC cell function in vitro and/or in vivo. We performed SPRY2 immunohistochemical staining in 143 CRC specimens and analyzed the staining results with various clinicopathological characteristics in relation to KRAS mutation status. SPRY2 knockdown in Caco-2 cells carrying the wild-type (WT) KRAS gene upregulated phosphorylated ERK (p-ERK) levels and increased cell proliferation in vitro, but inhibited cell invasion. However, SPRY2 knockdown in SW480 cells (activating KRAS mutant) or Caco-2 cells transfected with KRAS-mutant plasmid did not significantly alter p-ERK levels, cell proliferation, or invasion. The xenografts of SPRY2-knockdown Caco-2 cells were larger with less deep muscle invasion than those of control cells. The clinical cohort study revealed a positive association of SPRY2 protein expression with pT status, lymphovascular invasion, and perineural invasion in KRAS-WT CRCs. However, the associations were not observed in KRAS-mutant CRCs. Interestingly, high SPRY2 expression was related to shorter cancer-specific survival in both KRAS-WT and KRAS-mutant CRC patients. Our study demonstrated the dual role of SPRY2 as an inhibitor of RAS/ERK-driven proliferation and as a promoter of cancer invasion in KRAS-WT CRC. SPRY2 may promote the invasion and progression of KRAS-WT CRC, and might also enhance KRAS-mutant CRC progression through pathways other than invasion.

Precise uncertain significance prediction using latent space matrix factorization models: genomics variant and heterogeneous clinical data-driven approaches.

Several studies to date have proposed different types of interpreters for measuring the degree of pathogenicity of variants. However, in predicting the disease type and disease-gene associations, scholars face two essential challenges, namely the vast number of existing variants and the existence of variants which are recognized as variant of uncertain significance (VUS). To tackle these challenges, we propose algorithms to assign a significance to each gene rather than each variant, describing its degree of pathogenicity. Since the interpreters identified most of the variants as VUS, most of the gene scores were identified as uncertain significance. To predict the uncertain significance scores, we design two matrix factorization-based models: the common latent space model uses genomics variant data as well as heterogeneous clinical data, while the single-matrix factorization model can be used when heterogeneous clinical data are unavailable. We have managed to show that the models successfully predict the uncertain significance scores with low error and high accuracy. Moreover, to evaluate the effectiveness of our novel input features, we train five different multi-label classifiers including a feedforward neural network with the same feature set and show they all achieve high accuracy as the main impact of our approach comes from the features. Availability: The source code is freely available at https://github.com/sabdollahi/CoLaSpSMFM.

A novel deep learning-based algorithm combining histopathological features with tissue areas to predict colorectal cancer survival from whole-slide images.

BACKGROUND: Many methodologies for selecting histopathological images, such as sample image patches or segment histology from regions of interest (ROIs) or whole-slide images (WSIs), have been utilized to develop survival models. With gigapixel WSIs exhibiting diverse histological appearances, obtaining clinically prognostic and explainable features remains challenging. Therefore, we propose a novel deep learning-based algorithm combining tissue areas with histopathological features to predict cancer survival. METHODS: The Cancer Genome Atlas Colon Adenocarcinoma (TCGA-COAD) dataset was used in this investigation. A deep convolutional survival model (DeepConvSurv) extracted histopathological information from the image patches of nine different tissue types, including tumors, lymphocytes, stroma, and mucus. The tissue map of the WSIs was segmented using image processing techniques that involved localizing and quantifying the tissue region. Six survival models with the concordance index (C-index) were used as the evaluation metrics. RESULTS: We extracted 128 histopathological features from four histological types and five tissue area features from WSIs to predict colorectal cancer survival. Our method performed better in six distinct survival models than the Whole Slide Histopathological Images Survival Analysis framework (WSISA), which adaptively sampled patches using K-means from WSIs. The best performance using histopathological features was 0.679 using LASSO-Cox. Compared to histopathological features alone, tissue area features increased the C-index by 2.5%. Based on histopathological features and tissue area features, our approach achieved performance of 0.704 with RIDGE-Cox. CONCLUSIONS: A deep learning-based algorithm combining histopathological features with tissue area proved clinically relevant and effective for predicting cancer survival.

Lidocaine transdermal patches reduced pain intensity in neuropathic cancer patients already receiving opioid treatment.

BACKGROUND: Limited efficacy has been observed when using opioids to treat neuropathic pain. Lidocaine patches reduce neuropathic pain in postherpetic neuralgia, but their benefits for cancer-related neuropathic pain remain unclear. This study aimed to investigate a treatment for cancer-related neuropathic pain. METHODS: We conducted a prospective, open-label, single-arm study to assess the efficacy and safety of lidocaine transdermal patches in patients experiencing localized, superficial, neuropathic cancer pain. Terminal cancer patients already receiving opioid treatment participated in the 3-day study. The primary endpoint was pain intensity evaluated by the numerical rating scale (NRS). The secondary endpoints were the pain relief score and the quality of analgesic treatment. RESULTS: The results showed a significant difference in the median NRS over 3 days (Kruskal-Wallis test, p < 0.0001). The median NRS pain intensity from Day 1 to Day 3 was 4.0 with 95% C.I. (3.3, 5.0), 3.0 (2.5, 3.5), and 2.6 (2.0, 3.0), respectively. The difference between the median NRS pain intensities of any 2 days was significant (Wilcoxon signed-rank test, p < 0.0001). The generalized estimating equation (GEE) estimation model showed significant differences between the NRS pain intensities on any 2 days. There was no significant difference in the pain relief score or the quality of analgesic treatment. CONCLUSIONS: In this study, the 5% lidocaine transdermal patch reduced the NRS pain intensity in neuropathic cancer patients already receiving opioid treatment. Treatment of localized and superficial neuropathic pain caused by cancer was well tolerated and effective.

CHK2 activation contributes to the development of oxaliplatin resistance in colorectal cancer.

BACKGROUND: Colorectal cancer (CRC), the most common cancer type, causes high morbidity and mortality. Patients who develop drug resistance to oxaliplatin-based regimens have short overall survival. Thus, identifying molecules involved in the development of oxaliplatin resistance is critical for designing therapeutic strategies. METHODS: A proteomic screen was performed to reveal altered protein kinase phosphorylation in oxaliplatin-resistant (OR) CRC tumour spheroids. The function of CHK2 was characterised using several biochemical techniques and evident using in vitro cell and in vivo tumour models. RESULTS: We revealed that the level of phospho-CHK2(Thr68) was elevated in OR CRC cells and in ~30% of tumour samples from patients with OR CRC. We demonstrated that oxaliplatin activated several phosphatidylinositol 3-kinase-related kinases (PIKKs) and CHK2 downstream effectors and enhanced CHK2/PARP1 interaction to facilitate DNA repair. A phosphorylation mimicking CHK2 mutant, CHK2T68D, but not a kinase-dead CHK2 mutant, CHK2D347A, promoted DNA repair, the CHK2/PARP1 interaction, and cell growth in the presence of oxaliplatin. Finally, we showed that a CHK2 inhibitor, BML-277, reduced protein poly(ADP-ribosyl)ation (PARylation), FANCD2 monoubiquitination, homologous recombination and OR CRC cell growth in vitro and in vivo. CONCLUSION: Our findings suggest that CHK2 activity is critical for modulating oxaliplatin response and that CHK2 is a potential therapeutic target for OR CRC.

Impact of BRCA mutation on the survival and risk of contralateral breast cancer in Asian breast cancer patients.

PURPOSE: Breast cancer is increasing around the globe, including Asia. We aimed to examine the survival and risk of contralateral breast cancer (CBC) in Asian breast cancer patients with BRCA mutations. METHODS: A total of 128 breast cancer patients with germline BRCA mutations and 4,754 control breast cancer patients were enrolled. Data on clinical-pathologic characteristics, survival, and CBC were collected from the medical record. The rates of survival and CBC were estimated by Kaplan-Meier method. RESULTS: The mean age of onset in BRCA mutation carriers was significantly younger than control patients (BRCA vs. Non-BRCA: 43.9 vs. 53.2 years old). BRCA mutation carriers had a higher proportion of triple-negative breast cancer (TNBC) (52%) than control patients (12%, p < 0.001). The risk of CBC was significantly higher in BRCA mutation patients than in control cases (hazard ratio (HR) = 3.95, 95% CI 2.71-5.75); when stratified by genotype, the HRs (95%CI) were 4.84 (3.00-7.82) for BRCA1 and 3.13 (1.78-5.49) for BRCA2 carriers, respectively. Moreover, BRCA1 mutation patients with triple-negative breast cancer (TNBC) as their first breast cancer had the highest risk of CBC (HR = 5.55, 95% CI 3.29-9.34). However, we did not observe any differences in relapse-free survival and overall survival between mutation carriers and control patients. CONCLUSION: Our study suggest that BRCA patients had a significantly higher risk of developing CBC, particularly for BRCA1 mutation carriers with TNBC as the first breast cancer.