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1
Garfinkel, L. "Cancer Clusters." CA: A Cancer Journal for Clinicians 37, no. 1 (January 1, 1987): 20–25. http://dx.doi.org/10.3322/canjclin.37.1.20.
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Novak, Kristine. "Cancer clusters." Nature Reviews Cancer 3, no. 12 (December 2003): 888. http://dx.doi.org/10.1038/nrc1246.
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Man, Kingson, Alice Lim, Zahra Eftekhari, and Chi Wah Wong. "Clustering whole-exome sequences of breast cancer and associations with staging and molecular subtype." Journal of Clinical Oncology 41, no. 16_suppl (June 1, 2023): e12563-e12563. http://dx.doi.org/10.1200/jco.2023.41.16_suppl.e12563.
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e12563 Background: Large-scale genetic sequencing of breast cancer has enabled modern approaches to precision medicine, with the discovery of a handful of variants now known to be associated with breast cancer. However, it is critical to identify additional gene variants in breast cancer that are associated with clinically relevant features of cancers, such as staging and molecular subtype. Methods: We took an unsupervised machine learning approach that clustered the somatic whole exome sequences (WES) of 1533 breast cancers. We performed k-modes clustering on the binarized mutational state of the top 250 most frequently mutated genes. Following two rounds of clustering, 11 distinct “barcodes” for each genetic cluster’s mutation profile became apparent. We systematically tested each genetically defined cluster for associations with molecular subtypes of breast cancer. We performed non-parametric significance testing by randomly permuting cluster assignments to generate an empirical null distribution of the effect of clustering on proportions of the clinical factor of interest. Results: As an example of our set of results, two clusters showed roughly three-fold enrichment of triple-negative breast cancer (TNBC) patients, compared to the whole-group proportion. We calculated SHAP values to provide model explainability and identify the genes that placed a cancer into a particular cluster; TP53 and TTN were the strongest drivers in relation to TNBC. Genetic clusters were also found to associate with T-, N-, and M-stages. Conclusions: Our approach, which uses unsupervised machine learning on WES to create genetic groups of cancers, considers the joint mutational state – present or absent – of multiple genes for their clinical relevance. This reveals many additional variants that may have been previously overlooked or of uncertain significance.
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King, Jennifer, Kimberly Lind, Kristin Morrill, and Cynthia Thomson. "ASSOCIATION OF PSYCHOSOCIAL FACTORS WITH MORTALITY IN OLDER FEMALE SURVIVORS OF CANCER." Innovation in Aging 6, Supplement_1 (November 1, 2022): 601. http://dx.doi.org/10.1093/geroni/igac059.2245.
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Abstract Understanding factors associated with survival after a cancer diagnosis among older adults is critical as the population ages and cancer survivorship increases. The purpose of this study was to: (1) identify clusters of postmenopausal cancer survivor characteristics by demographic and lifestyle factors; 2) describe the characteristics of each cluster; and 3) evaluate the association of cluster assignment with survival. Participants from the Women’s Health Initiative (WHI) who reported either a prevalent cancer diagnosis at baseline (n=14294) or were diagnosed with a first primary incident cancer within the first 10 years of WHI (n=12934) were included. Latent class analysis was used to identify survivor clusters using psychosocial variables. Clusters were characterized using descriptive statistics. We tested for differences in cluster characteristics using ANOVA and Chi-square tests as appropriate. Cox proportional hazards regression was used to evaluate the association between cluster and mortality. Prevalent (n=7) and incident (n=9) cancer survivors clusters were identified. Among both (prevalent and incident) sets of clusters, age at WHI baseline, age at menopause, race, ethnicity, income, education, body mass index, diet quality, smoking, alcohol consumption and exercise all differed by cluster (p<.0001 for all). The most racially and ethnically diverse cluster had higher mortality rates compared to the largest most homogenous cluster; hazard ratio (95%CI) 1.30 (1.15, 1.48) and 1.33 (1.16, 1.53), respectively. Understanding how clusters of risk factors influence cancer survival in postmenopausal women will inform future interventions to improve outcomes and reduce health disparities for cancer survivors.
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Thun, M. J., and T. Sinks. "Understanding Cancer Clusters." CA: A Cancer Journal for Clinicians 54, no. 5 (September 1, 2004): 273–80. http://dx.doi.org/10.3322/canjclin.54.5.273.
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ENTERLINE, PHILIP E. "Evaluating Cancer Clusters." American Industrial Hygiene Association Journal 46, no. 3 (March 1985): B—10—B—13. http://dx.doi.org/10.1080/15298668591394608.
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Aktas, Aynur. "Cancer symptom clusters." Current Opinion in Supportive and Palliative Care 7, no. 1 (March 2013): 38–44. http://dx.doi.org/10.1097/spc.0b013e32835def5b.
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He, Yongshan, Yuanyuan Chen, Xuan Dai, and Shiyong Huang. "Dysregulation of Circadian Clock Genes Associated with Tumor Immunity and Prognosis in Patients with Colon Cancer." Computational and Mathematical Methods in Medicine 2022 (July 16, 2022): 1–19. http://dx.doi.org/10.1155/2022/4957996.
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Early research shows that disrupting the circadian rhythm increases the risk of various cancers. However, the roles of circadian clock genes in colorectal cancer, which is becoming more common and lethal in China, remained to be unclear. In conclusion, the present study has demonstrated that multiple CCGs were dysregulated and frequently mutated in CRC samples by analyzing the TCGA database. The higher expression levels of REV1, ADCYAP1, CSNK1D, NR1D1, CSNK1E, and CRY2 had a strong link with shorter DFS time in CRC patients, demonstrating that CCGs had an important regulatory role in CRC development. Moreover, 513 CRC tumor samples were divided into 3 categories, namely, cluster1 ( n = 428 ), cluster2 ( n = 83 ), and cluster 3 ( n = 109 ), based on the expression levels of the CCGs. Clinical significance analysis showed that the overall survival and disease-free survival of cluster 2 and cluster 3 were significantly shorter than those of cluster 1. The stemness scores in cluster 1 and cluster 2 were significantly higher than those of cluster 3 CRC samples. Clinically, we found that the C3 subtype had significantly higher percentage of T3/T4, N1/N2, and grades III and IV than groups C1 or C2. In addition, we reported that different CRC clusters had significantly different tumor-infiltrating immune cell signatures. Finally, pancancer analysis showed that higher expression of CSNK1D was correlated with shorter DFS time in multiple cancer types, such as COAD and LIHC, and was dysregulated in various cancers. In conclusion, we effectively developed a CCG-related predictive model and opened up new avenues for research into immune regulatory mechanisms and the development of immunotherapy for CRC.
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Rizzi, Maria, and Matteo D'Aloia. "COMPUTER AIDED SYSTEM FOR BREAST CANCER DIAGNOSIS." Biomedical Engineering: Applications, Basis and Communications 26, no. 03 (March 17, 2014): 1450033. http://dx.doi.org/10.4015/s1016237214500331.
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Computer aided detection and Diagnosis systems are becoming very useful and helpful in supporting physicians for early detection and control of some diseases such as neoplastic pathologies. In this paper, a computer aided system for breast cancer diagnosis in mammographic images is presented. In particular, the method looks for microcalcification cluster occurrence and makes the diagnosis of the detected abnormality. The procedure first detects microcalcifications having a cluster pattern and then classifies the abnormalities as benign or malignant clusters. The method formulates the differentiation between malignant and benign microcalcification clusters as a supervised learning problem implementing an artificial neural network classifier. As input to the classifier, the procedure uses image features automatically extracted from the detected clusters. The seven features used are related both to the distribution of microcalcifications within cluster and to the uniformity of their shape. The performance of the implemented system is evaluated taking into account the accuracy of classifying clusters. The obtained results make this method able to operate as a "second opinion" helping radiologists during the routine clinical practice. Moreover, the implemented method has a general validity and can be used to detect and to classify microcalcification clusters independently from the acquisition equipment adopted during the mammographic screening.
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Al Qadire, Mohammad, Omar Shamieh, Sameer Abdullah, and Faisal Albadainah. "Symptom Clusters’ Content, Stability and Correlation with the Quality of Life in a Heterogeneous Group of Cancer Patients: A Large-Scale Longitudinal Study." Clinical Nursing Research 29, no. 8 (June 11, 2020): 561–70. http://dx.doi.org/10.1177/1054773820933449.
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Cancer-related symptoms can negatively affect the quality of life, hinder or delay treatment, and increase suffering. This study aimed to explore symptom clusters among Jordanian cancer patients. A longitudinal survey design was used. The sample consisted of 1280 cancer patients treated in three selected hospitals. Two-thirds of the participants were female (63.5%) with a mean age of 52.7 SD 13.8 years and 40.3% had breast cancer. Five clusters were identified, the first was the psychological cluster of eight symptoms; the second was the treatment side-effects cluster consisting of ten symptoms; the third was the nausea and vomiting cluster comprising four symptoms; the fourth was the pain cluster comprising four symptoms; and last was the fatigue cluster, with three symptoms. Cancer patients through the journey of cancer treatment have several symptoms that tend to occur in five clusters which are negatively correlated with their quality of life.
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Labonté, Laura, Yuhua Li, Christina L. Addison, Marjorie Brand, Hedyeh Javidnia, Martin Corsten, Kevin Burns, and David S. Allan. "Distinct profile of vascular progenitor attachment to extracellular matrix proteins in cancer patients." Clinical & Investigative Medicine 35, no. 2 (April 1, 2012): 86. http://dx.doi.org/10.25011/cim.v35i2.16292.
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Background: Vascular progenitor cells (VPCs) facilitate angiogenesis and initiate vascular repair by homing in on sites of damage and adhering to extracellular matrix (ECM) proteins. VPCs also contribute to tumor angiogenesis and induce angiogenic switching in sites of metastatic cancer. In this study, the binding of attaching cells in VPC clusters that form in vitro on specific ECM proteins was investigated. Methods: VPC cluster assays were performed in vitro on ECM proteins enriched in cancer cells and in remodelling tissue. Profiles of VPC clusters from patients with cancer were compared to healthy controls. The role of VEGF and integrin-specific binding of angiogenic attaching cells was addressed. Results: VPC clusters from cancer patients were markedly increased on fibronectin relative to other ECM proteins tested, in contrast to VPC clusters from control subjects, which formed preferentially on laminin. Specific integrin-mediated binding of attaching cells in VPC clusters was matrix protein-dependent. Furthermore, cancer patients had elevated plasma VEGF levels compared to healthy controls and VEGF facilitated preferential VPC cluster formation on fibronectin. Incubating cells from healthy controls with VEGF induced a switch from the ‘healthy’ VPC binding profile to the profile observed in cancer patients with a marked increase in VPC cluster formation on fibronectin. Conclusion: The ECM proteins laminin and fibronectin support VPC cluster formation via specific integrins on attaching cells and can facilitate patterns of VPC cluster formation that are distinct in cancer patients. Larger studies, however, are needed to gain insight on how tumor angiogenesis may differ from normal repair processes.
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Wrammert, Kathryn C., Gwendolynn Harrell, Michael O'Neill, Anjali Grandhige, Danielle Moulia, and Aynur Aktas. "Symptom clustering among patients visiting a supportive oncology clinic." Journal of Clinical Oncology 32, no. 31_suppl (November 1, 2014): 188. http://dx.doi.org/10.1200/jco.2014.32.31_suppl.188.
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188 Background: Multiple symptoms are common and often severe in patients with cancer. Identification of symptoms which cluster may serve to elucidate the pathophysiology of the disease and aid in symptom management. Our aim was to define symptom clusters occurring among cancer outpatients receiving chemotherapy. Methods: New and returning patients referred to a supportive oncology clinic (SOC) from our health system’s oncologists from November 2011 through May 2014 completed the Condensed Memorial Symptom Assessment Scale plus a sexual dysfunction structured assessment. Data were collected prospectively from 323 consecutive initial visits. Patients rated from 0-4 how bothersome 15 cancer symptoms were; symptoms were then graded as present (1+) or absent (0). Hierarchical cluster analysis with average linkage was used to identify symptom clusters. The absolute value of the correlation between symptoms was used as the measure of similarity between pairs of symptoms. A correlation of ≥0.6 was used to define the final clusters. A symptom cluster was defined as two or more symptoms that predictably occur together. Results: Three clusters were identified: 1. Psychological (worrying, feeling sad, feeling nervous); 2. Treatment-related (lack of energy, feeling drowsy, difficulty concentrating, dry mouth, constipation); 3. Gastrointestinal (weight loss, lack of appetite, nausea). Pain, difficulty sleeping, shortness of breath, and loss of interest did not cluster with any symptom. Gastrointestinal symptoms are important within the clusters. The prevalence of worrying, feeling sad, and feeling nervous did not cluster with lack of energy or difficulty in sleeping, nor pain with worrying or feeling sad. Conclusions: Three symptom clusters were identified as showing high absolute correlation: a psychological cluster, treatment-related cluster, and gastrointestinal cluster. Identifying symptom clusters may promote our understanding of the pathophysiology of cancer, help prioritize effective pharmacotherapies, and identify drugs likely to help more than one symptom.
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Kim, Minhae, Kyunghee Kim, Changwon Lim, and Ji-Su Kim. "Symptom Clusters and Quality of Life According to the Survivorship Stage in Ovarian Cancer Survivors." Western Journal of Nursing Research 40, no. 9 (April 11, 2017): 1278–300. http://dx.doi.org/10.1177/0193945917701688.
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This cross-sectional study evaluated a convenience sample comprising 182 ovarian cancer survivors to identify symptom clusters according to the cancer survivorship stage and to determine their effects on quality of life using the European Organization for Research and Treatment of Cancer Quality of Life–C30 and –OV28 questionnaires. Factor and multiple regression analyses were performed to identify symptom clusters according to the cancer survivorship stage and the symptom clusters that affected the quality of life in each cancer survivorship stage, respectively. Participants in the acute, extended, and permanent survival stages accounted for 33%, 36.3%, and 30.7% of subjects, respectively. Overall, the most common symptom cluster was fatigue-diarrhea, and the symptom clusters affecting the quality of life differed according to the cancer survivorship stage. Thus, to improve the quality of life of ovarian cancer survivors, the main symptom clusters of each cancer survivorship stage must be identified, and management strategies for the related symptoms must be designed.
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Zainuddin, Andi Alfian, Amran Rahim, Muh Firdaus Kasim, Sri Ramadany Karim, Rina Masadah, and Syahrul Rauf. "Geospatial Analysis of Cervical Cancer Distribution in South Sulawesi Province." Open Access Macedonian Journal of Medical Sciences 10, B (September 10, 2022): 2296–301. http://dx.doi.org/10.3889/oamjms.2022.10417.
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Background: Cervical cancer, which is classified as a non-communicable disease, is a health problem that is of global concern at this time.1 Indonesia ranks second in the highest number of cervical cancer cases in the world with 32,469 cases per year. 1 For this reason, optimization efforts are carried out to prevent the increase in the prevalence of cervical cancer patients in the Province of South Sulawesi.
Objective: The purpose of this study was to make a geospatial analysis of the distribution of cervical cancer patients.
Methods: Geospatial analysis using Global Moran's I and Local Moran's I.
Result: The results of the geospatial analysis of the prevalence of cervical cancer in South Sulawesi Province show that in 2016 there were two spatial hotspot clusters (H-H), one coldspot spatial cluster (L-L), two spatial outlier clusters (H-L), and one spatial outlier cluster (L-H). In 2019, there were only two spatial hotspot clusters. Geospatial analysis of the prevalence of cervical cancer shows an increase in efforts to prevent cervical cancer from 2016 to 2019. However, there are still spatial hotspot clusters in 2019, especially in rural areas..
Conclusion: The efforts to prevent cervical cancer need to be optimized, especially in rural areas, in the future.
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Sammouda, Rachid, and Ali El-Zaart. "An Optimized Approach for Prostate Image Segmentation Using K-Means Clustering Algorithm with Elbow Method." Computational Intelligence and Neuroscience 2021 (November 15, 2021): 1–13. http://dx.doi.org/10.1155/2021/4553832.
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Prostate cancer disease is one of the common types that cause men’s prostate damage all over the world. Prostate-specific membrane antigen (PSMA) expressed by type-II is an extremely attractive style for imaging-based diagnosis of prostate cancer. Clinically, photodynamic therapy (PDT) is used as noninvasive therapy in treatment of several cancers and some other diseases. This paper aims to segment or cluster and analyze pixels of histological and near-infrared (NIR) prostate cancer images acquired by PSMA-targeting PDT low weight molecular agents. Such agents can provide image guidance to resection of the prostate tumors and permit for the subsequent PDT in order to remove remaining or noneradicable cancer cells. The color prostate image segmentation is accomplished using an optimized image segmentation approach. The optimized approach combines the k-means clustering algorithm with elbow method that can give better clustering of pixels through automatically determining the best number of clusters. Clusters’ statistics and ratio results of pixels in the segmented images show the applicability of the proposed approach for giving the optimum number of clusters for prostate cancer analysis and diagnosis.
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Hao, Jianling, Liyan Gu, Peng Liu, Lingjuan Zhang, Honglian Xu, Qun Qiu, and Wei Zhang. "Symptom clusters in patients with colorectal cancer after colostomy: a longitudinal study in Shanghai." Journal of International Medical Research 49, no. 12 (December 2021): 030006052110631. http://dx.doi.org/10.1177/03000605211063105.
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Objective Research is lacking regarding the experiences of patients after colostomy, which is needed so as to take necessary specific actions. In this study, we aimed to describe the trajectory of symptom clusters experienced by patients after colostomy over time. Methods This was a longitudinal observational study using data from 149 patients with colorectal cancer after colostomy. We investigated symptoms and symptom clusters at 2 weeks, 1 month, 3 months, 6 months, and 1 year after colostomy. Results Four main symptom clusters were identified, including a psychological symptom cluster, digestive and urinary symptom cluster, lack of energy symptom cluster, and pain symptom cluster in patients after colostomy in the first year after surgery. We further explored the symptom trajectory. Conclusions We explored symptom clusters and the trajectory of symptom resolution in patients after colostomy during the first year after surgery. Four stages were proposed to describe the different statuses of symptom clusters experienced by patients. Our findings may provide insight into how to improve symptom management and postoperative quality of life for patients after colostomy.
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Reduzzi, Carolina, Serena Di Cosimo, Lorenzo Gerratana, Rosita Motta, Antonia Martinetti, Andrea Vingiani, Paolo D’Amico, et al. "Circulating Tumor Cell Clusters Are Frequently Detected in Women with Early-Stage Breast Cancer." Cancers 13, no. 10 (May 13, 2021): 2356. http://dx.doi.org/10.3390/cancers13102356.
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The clinical relevance of circulating tumor cell clusters (CTC-clusters) in breast cancer (BC) has been mostly studied using the CellSearch®, a marker-dependent method detecting only epithelial-enriched clusters. However, due to epithelial-to-mesenchymal transition, resorting to marker-independent approaches can improve CTC-cluster detection. Blood samples collected from healthy donors and spiked-in with tumor mammospheres, or from BC patients, were processed for CTC-cluster detection with 3 technologies: CellSearch®, CellSieve™ filters, and ScreenCell® filters. In spiked-in samples, the 3 technologies showed similar recovery capability, whereas, in 19 clinical samples processed in parallel with CellSearch® and CellSieve™ filters, filtration allowed us to detect more CTC-clusters than CellSearch® (median number = 7 versus 1, p = 0.0038). Next, samples from 37 early BC (EBC) and 23 metastatic BC (MBC) patients were processed using ScreenCell® filters for attaining both unbiased enrichment and marker-independent identification (based on cytomorphological criteria). At baseline, CTC-clusters were detected in 70% of EBC cases and in 20% of MBC patients (median number = 2, range 0–20, versus 0, range 0–15, p = 0.0015). Marker-independent approaches for CTC-cluster assessment improve detection and show that CTC-clusters are more frequent in EBC than in MBC patients, a novel finding suggesting that dissemination of CTC-clusters is an early event in BC natural history.
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Li, Li-Jie, Wei-Min Chang, and Michael Hsiao. "Aberrant Expression of microRNA Clusters in Head and Neck Cancer Development and Progression: Current and Future Translational Impacts." Pharmaceuticals 14, no. 3 (February 27, 2021): 194. http://dx.doi.org/10.3390/ph14030194.
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MicroRNAs are small non-coding RNAs known to negative regulate endogenous genes. Some microRNAs have high sequence conservation and localize as clusters in the genome. Their coordination is regulated by simple genetic and epigenetic events mechanism. In cells, single microRNAs can regulate multiple genes and microRNA clusters contain multiple microRNAs. MicroRNAs can be differentially expressed and act as oncogenic or tumor suppressor microRNAs, which are based on the roles of microRNA-regulated genes. It is vital to understand their effects, regulation, and various biological functions under both normal and disease conditions. Head and neck squamous cell carcinomas are some of the leading causes of cancer-related deaths worldwide and are regulated by many factors, including the dysregulation of microRNAs and their clusters. In disease stages, microRNA clusters can potentially control every field of oncogenic function, including growth, proliferation, apoptosis, migration, and intercellular commutation. Furthermore, microRNA clusters are regulated by genetic mutations or translocations, transcription factors, and epigenetic modifications. Additionally, microRNA clusters harbor the potential to act therapeutically against cancer in the future. Here, we review recent advances in microRNA cluster research, especially relative to head and neck cancers, and discuss their regulation and biological functions under pathological conditions as well as translational applications.
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Amin, Raid, and James J. Burns. "Clusters of Adolescent and Young Adult Thyroid Cancer in Florida Counties." BioMed Research International 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/832573.
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Background. Thyroid cancer is a common cancer in adolescents and young adults ranking 4th in frequency. Thyroid cancer has captured the interest of epidemiologists because of its strong association to environmental factors. The goal of this study is to identify thyroid cancer clusters in Florida for the period 2000–2008. This will guide further discovery of potential risk factors within areas of the cluster compared to areas not in cluster.Methods. Thyroid cancer cases for ages 15–39 were obtained from the Florida Cancer Data System. Next, using the purely spatial Poisson analysis function in SaTScan, the geographic distribution of thyroid cancer cases by county was assessed for clusters. The reference population was obtained from the Census Bureau 2010, which enabled controlling for population age, sex, and race.Results. Two statistically significant clusters of thyroid cancer clusters were found in Florida: one in southern Florida (SF) (relative risk of 1.26;Pvalue of <0.001) and the other in northwestern Florida (NWF) (relative risk of 1.71;Pvalue of 0.012). These clusters persisted after controlling for demographics including sex, age, race.Conclusion. In summary, we found evidence of thyroid cancer clustering in South Florida and North West Florida for adolescents and young adult.
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Martinelli, F., C. Quinten, C. Coens, H. Flechtner, C. Gotay, T. Mendoza, D. Osoba, B. Reeve, X. Wang, and A. Bottomley. "Relationships among health-related quality of life indicators in cancer patients: A pooled study of baseline EORTC QLQ-C30 data from 6,739 patients." Journal of Clinical Oncology 27, no. 15_suppl (May 20, 2009): 9612. http://dx.doi.org/10.1200/jco.2009.27.15_suppl.9612.
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9612 Background: Cancer patients frequently experience multiple and co-occuring problems due to their illness and therapies. Clusters are defined as groups of two or more Health-Related Quality of Life (HRQoL) indicators that occur concurrently and may or may not have a common related cause. The objective of this meta-analysis was to identify how HRQoL indicators cluster among cancer patients. Methods: Retrospective pooling of 29 European Organisation for Research and Treatment of Cancer (EORTC) randomized clinical trials, among 10 cancer sites, yielded baseline EORTC QLQ-C30 HRQoL data for a total of 6739 patients. A cluster analysis was performed to identify clusters among the 15 HRQoL scales, via Ward's method. Cronbach's alpha coefficient (α) was used to measure internal consistency. Dendrograms of the HRQoL indicators were plotted for the overall data and for each cancer site. Results: Three main clusters emerged from the pooled dataset: a physical function-related cluster, consisting of physical and role functioning, fatigue and pain (α = 0.83); a psychological function-related cluster, consisting of emotional and cognitive functioning and insomnia (α = 0.64); and a gastrointestinal cluster, consisting of nausea and vomiting and appetite loss (α = 0.68). The same clusters were found in patients with metastatic and non-metastatic disease. The gastrointestinal cluster was reproduced in all 10 cancer sites. We found that pain was not correlated with the other variables of the physical function cluster for patients with brain, colorectal or pancreatic cancer. For the psychological component cluster, cognitive functioning was not correlated with the other variables of the cluster for breast or pancreatic cancer patients, while insomnia was found not to be correlated with the other variables of the cluster for prostate cancer patients. Conclusions: This study shows that relationships among HRQoL indicators exist and that three major constructs can be found: a physical, a psychological and a gastrointestinal component. Understanding these relationships may aid diagnostic criteria, and assessment, management, and prioritization of symptom care. No significant financial relationships to disclose.
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Bai, Jinbing, Deborah Watkins Bruner, Veronika Fedirko, Jonathan J. Beitler, Chao Zhou, Jianlei Gu, Hongyu Zhao, et al. "Gut Microbiome Associated with the Psychoneurological Symptom Cluster in Patients with Head and Neck Cancers." Cancers 12, no. 9 (September 6, 2020): 2531. http://dx.doi.org/10.3390/cancers12092531.
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Cancer patients experience a cluster of co-occurring psychoneurological symptoms (PNS) related to cancer treatments. The gut microbiome may affect severity of the PNS via neural, immune, and endocrine signaling pathways. However, the link between the gut microbiome and PNS has not been well investigated in cancer patients, including those with head and neck cancers (HNCs). This pilot study enrolled 13 patients with HNCs, who reported PNS using the Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (CTCAEs). Stool specimens were collected to analyze patients’ gut microbiome. All data were collected pre- and post-radiation therapy (RT). Associations between the bacterial abundances and the PNS clusters were analyzed using the linear discriminant analysis effect size; functional pathway analyses of 16S rRNA V3-V4 bacterial communities were conducted using Tax4fun. The high PNS cluster had a greater decrease in microbial evenness than the low PNS cluster from pre- to post-RT. The high and low PNS clusters showed significant differences using weighted UniFrac distance. Those individuals with the high PNS cluster were more likely to have higher abundances in phylum Bacteroidetes, order Bacteroidales, class Bacteroidia, and four genera (Ruminiclostridium9, Tyzzerella, Eubacterium_fissicatena, and DTU089), while the low PNS cluster had higher abundances in family Acidaminococcaceae and three genera (Lactococcus, Phascolarctobacterium, and Desulfovibrio). Both glycan metabolism (Lipopolysaccharide biosynthesis) and vitamin metabolism (folate biosynthesis and lipoic acid metabolism) were significantly different between the high and low PNS clusters pre- and post-RT. Our preliminary data suggest that the diversity and abundance of the gut microbiome play a potential role in developing PNS among cancer patients.
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Han, Claire J., Kerryn Reding, Bruce A. Cooper, Steven M. Paul, Yvette Conley, Marilyn J. Hammer, Fay Wright, Frances Cartwright, Jon Levine, and Christine Miaskowski. "Stability of symptom clusters in patients with gastrointestinal cancers receiving chemotherapy." Journal of Clinical Oncology 37, no. 27_suppl (September 20, 2019): 193. http://dx.doi.org/10.1200/jco.2019.37.27_suppl.193.
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193 Background: Patients with gastrointestinal (GI) cancers who undergo chemotherapy (CTX) experience on average of thirteen symptoms. These co-occurring symptoms often cluster together and can influence various patient outcomes including quality of life (QOL). However, little evidence is available on how these symptoms change during a cycle of chemotherapy (CTX). An evaluation of how these symptom cluster together and how these symptom clusters change over time may provide useful information to guide symptom management strategies tailored to multiple symptoms. Objectives: The purpose of this study was to identify and compare symptom clusters using three symptom dimensions (i.e., occurrence, severity, and distress) at different time points during CTX (i.e., prior to CTX [T1], one week after CTX administration [T2], and two weeks after CTX administration [T3]) in patients with GI cancers. Methods: A modified version of the Memorial Symptom Assessment Scale was used to assess the occurrence, severity, and distress of 38 symptoms. Exploratory factor analyses were used to create the symptom clusters. Results: Five distinct symptom clusters were identified across the three symptom dimensions and the three assessments (i.e., psychological, CTX-related, weight change, GI, and epithelial). Psychological, CTX-related and weight change clusters were relatively stable for all three symptom dimensions as well as across time. GI cluster was identified only at T1, while epithelial cluster was identified at T2 and T3 for all three symptom dimensions. Conclusions: The number and types of symptom clusters appear to be relatively stable over time and across the symptom dimensions. Timely management of symptom clusters should be continued over the course of CTX including the recovery phases. Further studies are needed to explore the mechanisms of symptom clusters in patients with GI cancers undergoing CTX.
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Wang, Zhanwei, Dionyssios Katsaros, Junlong Wang, Nicholetta Biglio, Brenda Y. Hernandez, Peiwen Fei, Lingeng Lu, Harvey Risch, and Herbert Yu. "Abstract 3020: Machine learning-based cluster analysis of immune cell subtypes and breast cancer survival." Cancer Research 83, no. 7_Supplement (April 4, 2023): 3020. http://dx.doi.org/10.1158/1538-7445.am2023-3020.
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Abstract Background: Host immunity involves various immune cells working in concert to achieve balanced immune response. Host immunity interacts with tumorigenic process impacting disease outcome. Clusters of different immune cells may indicate specific host-tumor interplay. Identifying the clusters may reveal unique host immunity in response to tumor growth. Methods: CIBERSORT was used to estimate relative abundances of 22 immune cell types in 3 datasets, METABRIC, TCGA, and our study. The cell type data in METABRIC were analyzed for cluster using unsupervised hierarchical clustering (UHC). The UHC results were employed to train machine learning models, random forest (RF), deep neural network (DNN), stepAIC, and elastic net. Kaplan-Meier and Cox regression survival analyses were performed to assess cell clusters in association with relapse-free and overall survival. Differentially expressed genes (DEGs) by immune cell clusters were interrogated with IPA for molecular signatures. Results: UHC analysis identified two distinct immune cell clusters, clusters A (83.2%) and B (16.8%). Memory B cells, plasma cells, CD8 positive T cells, resting memory CD4 T cells, activated NK cells, monocytes, M1 macrophages, and resting mast cells were more abundant in clusters A than B, whereas regulatory T cells and M0 and M2 macrophages were more in clusters B than A. Patients in cluster A had favorable survival compared to those in cluster B. Similar survival associations were also observed in TCGA and our study when using a RF model trained with the UHC results. The survival associations were independent from clinicopathological variables. IPA analysis showed that pathogen-induced cytokine storm signaling pathway, phagosome formation, and T cell receptor signaling were related to the cell type clusters. Conclusions: Our finding suggests that different immune cell clusters may indicate distinct immune responses to tumor growth, suggesting their potential for disease management. Citation Format: Zhanwei Wang, Dionyssios Katsaros, Junlong Wang, Nicholetta Biglio, Brenda Y. Hernandez, Peiwen Fei, Lingeng Lu, Harvey Risch, Herbert Yu. Machine learning-based cluster analysis of immune cell subtypes and breast cancer survival [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3020.
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Peng, Sen, Lora L. Hebert, Jennifer M. Eschbacher, and Suwon Kim. "Single-Cell RNA Sequencing of a Postmenopausal Normal Breast Tissue Identifies Multiple Cell Types That Contribute to Breast Cancer." Cancers 12, no. 12 (December 4, 2020): 3639. http://dx.doi.org/10.3390/cancers12123639.
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The human breast is composed of diverse cell types. Studies have delineated mammary epithelial cells, but the other cell types in the breast have scarcely been characterized. In order to gain insight into the cellular composition of the tissue, we performed droplet-mediated RNA sequencing of 3193 single cells isolated from a postmenopausal breast tissue without enriching for epithelial cells. Unbiased clustering analysis identified 10 distinct cell clusters, seven of which were nonepithelial devoid of cytokeratin expression. The remaining three cell clusters expressed cytokeratins (CKs), representing breast epithelial cells; Cluster 2 and Cluster 7 cells expressed luminal and basal CKs, respectively, whereas Cluster 9 cells expressed both luminal and basal CKs, as well as other CKs of unknown specificity. To assess which cell type(s) potentially contributes to breast cancer, we used the differential gene expression signature of each cell cluster to derive gene set variation analysis (GSVA) scores and classified breast tumors in The Cancer Gene Atlas (TGGA) dataset (n = 1100) by assigning the highest GSVA scoring cell cluster number for each tumor. The results showed that five clusters (Clusters 2, 3, 7, 8, and 9) could categorize >85% of breast tumors collectively. Notably, Cluster 2 (luminal epithelial) and Cluster 3 (fibroblast) tumors were equally prevalent in the luminal breast cancer subtypes, whereas Cluster 7 (basal epithelial) and Cluster 9 (other epithelial) tumors were present primarily in the triple-negative breast cancer (TNBC) subtype. Cluster 8 (immune) tumors were present in all subtypes, indicating that immune cells may contribute to breast cancer regardless of the subtypes. Cluster 9 tumors were significantly associated with poor patient survival in TNBC, suggesting that this epithelial cell type may give rise to an aggressive TNBC subset.
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Costa, Clotilde, Laura Muinelo-Romay, Victor Cebey-López, Thais Pereira-Veiga, Inés Martínez-Pena, Manuel Abreu, Alicia Abalo, et al. "Analysis of a Real-World Cohort of Metastatic Breast Cancer Patients Shows Circulating Tumor Cell Clusters (CTC-clusters) as Predictors of Patient Outcomes." Cancers 12, no. 5 (April 29, 2020): 1111. http://dx.doi.org/10.3390/cancers12051111.
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Circulating tumor cell (CTC) enumeration has emerged as a powerful biomarker for the assessment of prognosis and the response to treatment in metastatic breast cancer (MBC). Moreover, clinical evidences show that CTC-cluster counts add prognostic information to CTC enumeration, however, their significance is not well understood, and more clinical evidences are needed. We aim to evaluate the prognostic value of longitudinally collected single CTCs and CTC-clusters in a heterogeneous real-world cohort of 54 MBC patients. Blood samples were longitudinally collected at baseline and follow up. CTC and CTC-cluster enumeration was performed using the CellSearch® system. Associations with progression-free survival (PFS) and overall survival (OS) were evaluated using Cox proportional hazards modelling. Elevated CTC counts and CTC-clusters at baseline were significantly associated with a shorter survival time. In joint analysis, patients with high CTC counts and CTC-cluster at baseline were at a higher risk of progression and death, and longitudinal analysis showed that patients with CTC-clusters had significantly shorter survival compared to patients without clusters. Moreover, patients with CTC-cluster of a larger size were at a higher risk of death. A longitudinal analysis of a real-world cohort of MBC patients indicates that CTC-clusters analysis provides additional prognostic value to single CTC enumeration, and that CTC-cluster size correlates with patient outcome.
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Slavik, Catherine E., and Niko Yiannakoulias. "Investigating reports of cancer clusters in Canada: a qualitative study of public health communication practices and investigation procedures." Health Promotion and Chronic Disease Prevention in Canada 42, no. 11/12 (November 2022): 490–502. http://dx.doi.org/10.24095/hpcdp.42.11/12.04.
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Introduction Public health officials provide an important public service responding to community concerns around cancer and often receive requests to investigate patterns of cancer incidence and communicate findings with citizens. In this study, we identified procedures Canadian public health officials followed when investigating reports of cancer clusters, and explored the challenges officials faced conducting risk communication with communities. Methods Thirteen interviews were administered by telephone with 15 officials across Canadian jurisdictions and analyzed using thematic analysis. A content analysis of procedural documents received from five provinces was also undertaken. Results A third of provinces/territories in this study did not use any consistent guidelines to investigate reports of cancer clusters, a third used their own guidelines and a third used guidelines from other countries. Each Canadian jurisdiction identified a different agency or individual responsible for investigating cluster inquiries. Officials in most interviews considered public education to be the primary objective of risk communication during an investigation. Officials in only 4 of 13 interviews cited an overall positive response from the public after investigating reports of a cancer cluster. Conclusion Differences in practices used to investigate suspected cancer clusters by public health officials were revealed in this work. Establishing pan-Canadian cancer cluster guidelines could improve procedural consistency across jurisdictions and offer enhanced opportunities to compare cluster responses for evaluation. A reporting system to track reported clusters may improve information sharing between federal, provincial/territorial and local investigators. During formal investigations, face-to-face participatory communication approaches should be explored to improve citizen engagement and manage community concerns.
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Law, Ernest H., Maria J. Auil, Patricia A. Spears, Kiersten Berg, and Randall Winnette. "Voice Analysis of Cancer Experiences Among Patients With Breast Cancer: VOICE-BC." Journal of Patient Experience 8 (January 2021): 237437352110480. http://dx.doi.org/10.1177/23743735211048058.
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Patient experience literature in early-stage breast cancer (eBC) is limited. This study used a mixed-methods approach to examine patient conversations from public online forums to identify and evaluate eBC-related themes. Among 60,000 eBC-related posts published September 2014–2019, text from a random subset of 15,000 posts was extracted and grouped into linguistically similar, mutually exclusive clusters using an advanced natural language processing (NLP) algorithm. Clusters were characterized using four quantitative metrics: betweenness centrality (linguistic similarity to other areas of the cluster network), sentiment (general attitude toward a topic), recency (average date of posts), and volume (total number of posts). This analysis represented 3906 unique users (67% and 33% obtained from cancer–specific and general health/nonhealth forums, respectively). Of the 27 clusters identified, most important were “discussing recurrence & progression,” “understanding diagnosis & prognosis,” and “understanding cancer, biomarkers, and treatments.” Several major themes related to recurrence risk, diagnosis, monitoring, and treatment were identified. Additional emphasis on communicating the disease recurrence risk and shared decision-making could strengthen patient-clinician partnerships.
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Simons, Colinda C. J. M., Nadine S. M. Offermans, Monika Stoll, Piet A. van den Brandt, and Matty P. Weijenberg. "Empirical Investigation of Genomic Clusters Associated With Height and the Risk of Postmenopausal Breast and Colorectal Cancer in the Netherlands Cohort Study." American Journal of Epidemiology 191, no. 3 (November 2, 2021): 413–29. http://dx.doi.org/10.1093/aje/kwab259.
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Abstract We empirically investigated genomic clusters associated with both height and postmenopausal breast cancer (BC) or colorectal cancer (CRC) (or both) in the Netherlands Cohort Study to unravel shared underlying mechanisms between height and these cancers. The Netherlands Cohort Study (1986–2006) includes 120,852 participants (case-cohort study: nsubcohort = 5,000; 20.3 years of follow-up). Variants in clusters on chromosomes 2, 4, 5, 6 (2 clusters), 10, and 20 were genotyped using toenail DNA. Cluster-specific genetic risk scores were modeled in relation to height and postmenopausal BC and CRC risk using age-adjusted linear regression and multivariable-adjusted Cox regression, respectively. Only the chromosome 10 cluster risk score was associated with all 3 phenotypes in the same sex (women); that is, it was associated with increased height (βcontinuous = 0.34, P = 0.014), increased risk of hormone-receptor–positive BC (for estrogen-receptor–positive BC, hazard ratio (HRcontinuous score) = 1.10 (95% confidence interval (CI): 1.02, 1.20); for progesterone-receptor–positive BC, HRcontinuous score = 1.15 (95% CI: 1.04, 1.26)), and increased risk of distal colon (HRcontinuous score = 1.13, 95% CI: 1.01, 1.27) and rectal (HRcontinuous score = 1.14, 95% CI: 0.99, 1.30) cancer. The chromosome 10 cluster variants were all annotated to the zinc finger MIZ-type containing 1 gene (ZMIZ1), which is involved in androgen receptor activity. This suggests that hormone-related growth mechanisms could influence both height and postmenopausal BC and CRC.
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Sinks, Thomas. "Screenings and clusters: a cancer cluster in a chemical plant." Occupational and Environmental Medicine 71, no. 1 (November 21, 2013): 2–3. http://dx.doi.org/10.1136/oemed-2013-101789.
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Pidíkova, Paulína, Richard Reis, and Iveta Herichova. "miRNA Clusters with Down-Regulated Expression in Human Colorectal Cancer and Their Regulation." International Journal of Molecular Sciences 21, no. 13 (June 29, 2020): 4633. http://dx.doi.org/10.3390/ijms21134633.
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Regulation of microRNA (miRNA) expression has been extensively studied with respect to colorectal cancer (CRC), since CRC is one of the leading causes of cancer mortality worldwide. Transcriptional control of miRNAs creating clusters can be, to some extent, estimated from cluster position on a chromosome. Levels of miRNAs are also controlled by miRNAs “sponging” by long non-coding RNAs (ncRNAs). Both types of miRNA regulation strongly influence their function. We focused on clusters of miRNAs found to be down-regulated in CRC, containing miR-1, let-7, miR-15, miR-16, miR-99, miR-100, miR-125, miR-133, miR-143, miR-145, miR-192, miR-194, miR-195, miR-206, miR-215, miR-302, miR-367 and miR-497 and analysed their genome position, regulation and functions. Only evidence provided with the use of CRC in vivo and/or in vitro models was taken into consideration. Comprehensive research revealed that down-regulated miRNA clusters in CRC are mostly located in a gene intron and, in a majority of cases, miRNA clusters possess cluster-specific transcriptional regulation. For all selected clusters, regulation mediated by long ncRNA was experimentally demonstrated in CRC, at least in one cluster member. Oncostatic functions were predominantly linked with the reviewed miRNAs, and their high expression was usually associated with better survival. These findings implicate the potential of down-regulated clusters in CRC to become promising multi-targets for therapeutic manipulation.
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Shin, Hyewon, William N. Dudley, Robin Bartlett, Yutaka Yasui, Deokumar Srivastava, Kirsten K. Ness, Kevin R. Krull, Leslie L. Robison, Melissa M. Hudson, and I.-Chan Huang. "Determinants of symptom clusters and associations with health outcomes in childhood cancer survivors: A report from the St. Jude Lifetime Cohort (SJLIFE)." Journal of Clinical Oncology 39, no. 15_suppl (May 20, 2021): 10046. http://dx.doi.org/10.1200/jco.2021.39.15_suppl.10046.
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10046 Background: Childhood cancer survivors experience concurrent symptoms, but associations with health outcomes are unknown. We characterize symptom clusters among adult survivors of childhood cancer in SJLIFE and tests associations with health-related quality of life (HRQL) and clinically assessed physical and neurocognitive performance. Methods: This cross-sectional study includes survivors diagnosed when <18 years of age, ≥10 years off-therapy, and ≥18 years of age at evaluation. Survivors rated 37 symptoms over 10 domains (cardiac, pulmonary, sensory, motor, nausea, pain, fatigue, memory, anxiety, depression), representing 3 broader symptom groups (physical, somatic, psychological). They also underwent a rating of HRQL (SF-36 PCS/MCS) and testing of physical performance (quantitative sensory, motor, endurance, mobility) and neurocognition (processing speed, executive function, attention, memory problems). Latent class analysis determined survivors with distinct symptom burden. Polytomous logistic regression identified risk factors of symptom clusters; multivariable regression tested associations of symptom clusters with health outcomes. Results: Among 3,085 survivors, mean [SD] age at evaluation was 31.9 [8.3] years, time from diagnosis was 28.1 [9.1] years, 49.7% were female, 37.1% were treated for leukemia and 33.0% for solid tumors. Four groups of survivors with distinct symptom burden were found: Cluster 1 (52%, low prevalence in all 3 symptom groups); Cluster 2 (16%, low in physical, moderate in somatic, high in psychological); Cluster 3 (18%; high in physical, moderate in somatic, low in physiological); and Cluster 4 (14%, high in all 3 symptom groups). Compared to the lowest symptom burden (Cluster 1), survivors with highest burden (Cluster 4) were significantly more likely to be female (OR 2.5; 95%CI 1.9, 3.4), have below a high school education (OR 7.7; 95%CI 4.5, 13.3), no insurance (OR 1.5; 95%CI 1.1, 2.3) and previous exposure to corticosteroids (OR 1.8; 95%CI 1.0, 3.0). High physical, moderate somatic and low psychological symptom burden (Cluster 3) was associated with below high school education (OR 2.7; 95%CI 1.4, 5.0), exposure to platinum agents (OR 2.2; 95%CI 1.4, 3.7) and brain radiation ≥30Gy (OR 4.0; 95%CI 2.3, 6.9) in contrast to Cluster 1. Survivors in Cluster 4 had the poorest PCS, MCS, physical and neurocognitive outcomes vs in Clusters 2 or 3, whereas those in Cluster 1 had the best outcomes (F-values for 4 clusters: 291.4 [PCS], 269.2 [MCS], 61.5 [physical], 36.9 [neurocognitive], p-values <0.001; effect sizes for Clusters 4 vs 1: 0.4-2.0 [4 outcomes]). Conclusions: Nearly 50% of survivors belong to symptom clusters with ≥1 moderate/high burden groups, associated with the socio-demographic and treatment exposures. Survivors in the highest symptom burden cluster had the poorest HRQL and functional outcomes.
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Li, Shuhui, Wei Meng, Ziyi Guo, Min Liu, Yanyun He, Yanli Li, and Zhongliang Ma. "The miR-183 Cluster: Biogenesis, Functions, and Cell Communication via Exosomes in Cancer." Cells 12, no. 9 (May 5, 2023): 1315. http://dx.doi.org/10.3390/cells12091315.
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Cancer is one of the leading causes of human death. MicroRNAs have been found to be closely associated with cancer. The miR-183 cluster, comprising miR-183, miR-96, and miR-182, is transcribed as a polycistronic miRNA cluster. Importantly, in most cases, these clusters promote cancer development through different pathways. Exosomes, as extracellular vesicles, play an important role in cellular communication and the regulation of the tissue microenvironment. Interestingly, the miR-183 cluster can be detected in exosomes and plays a functional regulatory role in tumor development. Here, the biogenesis and functions of the miR-183 cluster in highly prevalent cancers and their relationship with other non-coding RNAs are summarized. In addition, the miR-183 cluster in exosomes has also been discussed. Finally, we discuss the miR-183 cluster as a promising target for cancer therapy. This review is expected to provide a new direction for cancer treatment.
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Panchenko, I. S., V. V. Rodionov, O. V. Burmenskaya, V. V. Kometova, V. K. Bozhenko, M. G. Sharafutdinov, S. V. Panchenko, and L. V. Matveeva. "CLINICAL AND MORPHOLOGICAL FEATURES OF MOLECULAR-GENETIC CLUSTERS IN TRIPLE NEGATIVE BREAST CANCER." Oncology bulletin of the Volga region 13, no. 1 (2022): 8–17. http://dx.doi.org/10.32000/2078-1466-2022-1-8-17.
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Breast cancer is the most common malignant tumors in women in the world. One of most unfavorable biological breast subtype ― is the triple negative breast cancer (TNBC). It occurs in young age, and characterized high rate of locoregional rates and distant metastases. Currently, according to molecular genetic profiling, TNBC ― is a group of tumors with different prognosis, course and response to treatment. In our study we analyzed the molecular genetic profiling of 246 cases of triple negative breast cancer. 45 genes were included in our gene signature. Using K-means clustering method, it was possible to identify 4 tumor clusters with different clinical and morphological features. The most indicative were patients of clusters 2 and 3, since it was in these clusters that most of the proposed genes were overexpressed. Cluster 1 characterized by hypoexpression of most genes, while patients of 4 cluster characterized average values of most genes. Each of resulting clusters had «molecular genetic» portrait, which provided information about the predominance of certain signaling pathways in the tumor, the impact of which can be used as an additional option in the treatment of patients with TNBC. It was possible to identify clinical and morphological features that were statistically significantly different (p≤0,05) in the presented molecular genetic clusters: clinical stage, status of regional lymph nodes, histological subtype, degree of tumor differentiation, Ki67 level. In addition, when we comparing the immunohistochemical (IHC) subtype of the tumor with the molecular type of the tumor, it turned out that the most heterogeneous cluster was the cluster 4, in which only 64,1% of tumors were true triple negative. Thus, molecular genetic profiling of triple negative breast cancer, in our opinion, should be considered as a perspective diagnostic method for this disease, as it will help to choose a correct and personalized treatment for an individual patient.
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Vansant, Gordon, Adam Jendrisak, Ramsay Sutton, Sarah Orr, David Lu, Joseph Schonhoft, Yipeng Wang, and Ryan Dittamore. "Functional cell profiling (FCP) of ~100,000 CTCs from multiple cancer types identifies morphologically distinguishable CTC subtypes within and between cancer types." Journal of Clinical Oncology 37, no. 15_suppl (May 20, 2019): e14553-e14553. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.e14553.
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e14553 Background: Different cancers subtypes can often be effectively treated with similar Rx classes (i.e. platinum or taxane Rx). Yet, within a disease patient therapy benefit can be variable. The origins of precision medicine derive from pathologic sub-stratification to guide therapy (e.g. SCLC vs. NSCLC). Using the Epic Sciences platform, we performed FPC analysis of ~100,000 single CTCs from multiple indications and sought to utilize high resolution digital pathology and machine learning to index metastatic cancers for the purpose of improving our understanding of therapy response and precision medicine. Methods: 92,300 CTCs underwent FCP analysis (single cell digital pathology features of cellular and sub-cellular morphometrics) were collected from prostate (1641 pts, 70,747 CTCs), breast (268 pts, 8,718 CTCs), NSCLC ( 110 pts, 1884 CTCs), SCLC ( 141 pts, 8,872 CTCs) and bladder (65 pts, 2079 CTCs) cancer pts. After pre-processing the raw data, a training set was balanced by sampling the same number of CTCs from each indication. K-means clustering was applied on the training set and optimized number of clusters were determined by using the elbow approach. After generating the clusters on the training set, the cluster centers were extracted from k-means, and used to train a k-Nearest Neighbor (k-NN) classifier to predict the cluster assignment for the remaining CTCs (test set). Results: The optimized # of clusters was 9. The % and characteristics of CTCs in each indication are listed below. BCa CTCs were more enriched in cluster c1, which had higher CK expression, while SCLC and some of mCRPC shared the small cell features (c5). Conclusions: Heterogeneous CTC phenotypic subtypes were observed across multiple indications. Each indication harbored subtype heterogeneity and shared clusters with other disease subtypes. Patient cluster subtype analysis to prognosis and therapy benefit are on-going. Analysis of linking of CTC subtypes genotypes (by single cell sequencing) and to patient survival on multiple indications is ongoing.[Table: see text]
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Yang, Chao, Yu-Tian Wang, and Chun-Hou Zheng. "A Random Walk Based Cluster Ensemble Approach for Data Integration and Cancer Subtyping." Genes 10, no. 1 (January 18, 2019): 66. http://dx.doi.org/10.3390/genes10010066.
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Availability of diverse types of high-throughput data increases the opportunities for researchers to develop computational methods to provide a more comprehensive view for the mechanism and therapy of cancer. One fundamental goal for oncology is to divide patients into subtypes with clinical and biological significance. Cluster ensemble fits this task exactly. It can improve the performance and robustness of clustering results by combining multiple basic clustering results. However, many existing cluster ensemble methods use a co-association matrix to summarize the co-occurrence statistics of the instance-cluster, where the relationship in the integration is only encapsulated at a rough level. Moreover, the relationship among clusters is completely ignored. Finding these missing associations could greatly expand the ability of cluster ensemble methods for cancer subtyping. In this paper, we propose the RWCE (Random Walk based Cluster Ensemble) to consider similarity among clusters. We first obtained a refined similarity between clusters by using random walk and a scaled exponential similarity kernel. Then, after being modeled as a bipartite graph, a more informative instance-cluster association matrix filled with the aforementioned cluster similarity was fed into a spectral clustering algorithm to get the final clustering result. We applied our method on six cancer types from The Cancer Genome Atlas (TCGA) and breast cancer from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC). Experimental results show that our method is competitive against existing methods. Further case study demonstrates that our method has the potential to find subtypes with clinical and biological significance.
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Marsoni, Silvia, Federica Zanardi, Fabio Iannelli, Elisa Salviato, Francesco Ferrari, Paolo Luraghi, Luca Lazzari, et al. "Mutational signatures of early-onset colorectal cancer." Journal of Clinical Oncology 37, no. 15_suppl (May 20, 2019): e15113-e15113. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.e15113.
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e15113 Background: Despite a reduction of Colorectal Cancer (CRC) incidence in western countries in the past decades, Early-Onset CRCs (EO-CRC, patients diagnosed with CRC ≤ 40 years old) incidence has increased. Although frequently occurring in the context of familial syndromes, EO-CRCs are mainly sporadic cases and phenotipically enriched for distal localisation and advanced stage at diagnosis. Whether EO-sporadic CRCs pathogenesis differs from that of normal-onset (NO) CRC and how this might impact incidence rates is currently unknown. This had prompted us to ask if, at the genetic level, “traces” of peculiar pathogenic processes could be identified in EO-specific (or -enriched) genetic signatures (GS). Methods: The mutational signatures of 424 TCGA CRC patient samples (19 EO and 405 ≥ 50 years at diagnosis) were analyzed and the similarity between each mutational profile and COSMIC GS was calculated using Bioconductor R package Mutational Patterns (doi: 10.1186/s13073-018-0539-0). Unsupervised hierarchical clustering of the samples according to similarity to GS was performed in single cohort and pooled analysis. Association between age and individual GS was assessed through grouped and linear correlation analysis. Results: EO-CRC patients were grouped in three main clusters: Cluster1 exposing a major similarity with GS10 (associated with defects in polymerase proofreading activity), Cluster2 showing stronger similarities with GS6 (associated with mismatch repair deficiency), and GS1 (associated with 5-MeC deamination) and Cluster3 presenting similarity with multiple GSs, such as 3 (associated with homologous recombination deficiency) and 5 (pathogenesis unknown). Overall this clustering was maintained when EO-CRC samples were pooled with NO CRC. Grouped and correlation analysis revealed no significant association between age and individual GSs, including GS1 (associated with age). Conclusions: These preliminary analyses show that the relative contribution of known GS is similar in EO and NO cohorts of patients. Possible enrichment for EO-CRC in specific signature clusters will be analysed on a wider sample series.
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Hu, Wen-Yang, Dan-Ping Hu, Lishi Xie, Larisa Nonn, Ranli Lu, Michael Abern, Toshihiro Shioda, and Gail S. Prins. "Keratin Profiling by Single-Cell RNA-Sequencing Identifies Human Prostate Stem Cell Lineage Hierarchy and Cancer Stem-Like Cells." International Journal of Molecular Sciences 22, no. 15 (July 28, 2021): 8109. http://dx.doi.org/10.3390/ijms22158109.
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Single prostate stem cells can generate stem and progenitor cells to form prostaspheres in 3D culture. Using a prostasphere-based label retention assay, we recently identified keratin 13 (KRT13)-enriched prostate stem cells at single-cell resolution, distinguishing them from daughter progenitors. Herein, we characterized the epithelial cell lineage hierarchy in prostaspheres using single-cell RNA-seq analysis. Keratin profiling revealed three clusters of label-retaining prostate stem cells; cluster I represents quiescent stem cells (PSCA, CD36, SPINK1, and KRT13/23/80/78/4 enriched), while clusters II and III represent active stem and bipotent progenitor cells (KRT16/17/6 enriched). Gene set enrichment analysis revealed enrichment of stem and cancer-related pathways in cluster I. In non-label-retaining daughter progenitor cells, three clusters were identified; cluster IV represents basal progenitors (KRT5/14/6/16 enriched), while clusters V and VI represent early and late-stage luminal progenitors, respectively (KRT8/18/10 enriched). Furthermore, MetaCore analysis showed enrichment of the “cytoskeleton remodeling–keratin filaments” pathway in cancer stem-like cells from human prostate cancer specimens. Along with common keratins (KRT13/23/80/78/4) in normal stem cells, unique keratins (KRT10/19/6C/16) were enriched in cancer stem-like cells. Clarification of these keratin profiles in human prostate stem cell lineage hierarchy and cancer stem-like cells can facilitate the identification and therapeutic targeting of prostate cancer stem-like cells.
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Coomans, Marijke B., Linda Dirven, Neil K. Aaronson, Brigitta G. Baumert, Martin Van Den Bent, Andrew Bottomley, Alba A. Brandes, et al. "Symptom clusters in newly diagnosed glioma patients: which symptom clusters are independently associated with functioning and global health status?" Neuro-Oncology 21, no. 11 (June 5, 2019): 1447–57. http://dx.doi.org/10.1093/neuonc/noz118.
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Abstract Background Symptom management in glioma patients remains challenging, as patients suffer from various concurrently occurring symptoms. This study aimed to identify symptom clusters and examine the association between these symptom clusters and patients’ functioning. Methods Data of the CODAGLIO project was used, including individual patient data from previously published international randomized controlled trials (RCTs) in glioma patients. Symptom prevalence and level of functioning were assessed with European Organisation for Research and Treatment of Cancer (EORTC) quality of life QLQ-C30 and QLQ-BN20 self-report questionnaires. Associations between symptoms were examined with Spearman correlation coefficients and partial correlation networks. Hierarchical cluster analyses were performed to identify symptom clusters. Multivariable regression analyses were performed to determine independent associations between the symptom clusters and functioning, adjusted for possible confounders. Results Included in the analysis were 4307 newly diagnosed glioma patients from 11 RCTs who completed the EORTC questionnaires before randomization. Many patients (44%) suffered from 5–10 symptoms simultaneously. Four symptom clusters were identified: a motor cluster, a fatigue cluster, a pain cluster, and a gastrointestinal/seizures/bladder control cluster. Having symptoms in the motor cluster was associated with decreased (≥10 points difference) physical, role, and social functioning (betas ranged from −11.3 to −15.9, all P < 0.001), independent of other factors. Similarly, having symptoms in the fatigue cluster was found to negatively influence role functioning (beta of −12.3, P < 0.001), independent of other factors. Conclusions Two symptom clusters, the fatigue and motor cluster, were frequently affected in glioma patients and were found to independently have a negative association with certain aspects of patients’ functioning as measured with a self-report questionnaire.
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Yennu, Sriram, Janet L. Williams, Gary B. Chisholm, and Eduardo Bruera. "The effects of dexamethasone and placebo on symptom clusters in advanced cancer patients: A preliminary report." Journal of Clinical Oncology 33, no. 29_suppl (October 10, 2015): 187. http://dx.doi.org/10.1200/jco.2015.33.29_suppl.187.
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187 Background: Advanced cancer patients frequently experience debilitating symptoms that occur in clusters, but few pharmacological studies have targeted symptom clusters. Our objective was to examine the effects of dexamethasone on symptom clusters. Methods: Secondary analysis of a recent RCT of dexamethasone (DEX) vs placebo (PL) on cancer symptoms as assessed by FACIT-F-Fatigue; FAACT-Anorexia-Cachexia; BPI - Pain; HADS- Anxiety-Depression; ESAS: Sleep, Drowsiness, Dyspnea. Symptom clusters were identified based on baseline symptoms [ESAS] using principal component analysis. Cluster scores were computed by adding each scale divided by the maximum value for the scale: Fatigue- Anorexia-Depression = (Fatigue /52 + Anorexia/48+ HADS-Depression/21); Sleep-Anxiety-Drowsiness = (Sleep/10+HADS-Anxiety/21+Drowsiness /10); Pain-Dyspnea = (BPI/10 +Dyspnea /10). Higher number indicates better QOL. Correlations and change in the severity of symptom clusters were analyzed. Results: In 114 evaluable patients, 3 clusters accounted for 63% of the total variance at baseline: Fatigue-anorexia/cachexia-depression cluster (FAD); sleep-anxiety-drowsiness cluster (SAD) and Pain-Dyspnea cluster (PD). Median (IQR) improvement in the FAD cluster at Day 15 and Day 8 was significantly higher in the DEX than in the PL group [0.22 (-0.04, 0.45) vs. 0.06 (-.30, .20), P = 0.016)] and [0.15 (-0.84, 0.35) vs-0.095 (-0.35, 0.16), p = 0.017] respectively. There was no significant change observed in SAD and PD after DEX. Median (IQR) scores for FAD and PD of the DEX group at baseline, day 8, and day 15 were 1.42(1.1,1.7),1.71(1.3,2.1),1.78(1.4,2.2); [1.1(0.8,1.4); 1.38(.04,1.6); 1.43(1.3,1.7) respectively and significantly correlated over time at Day 8 (r = 0.76; p < 0.001) Day 15 (r = 0.55;p < 0.001) [FAD]; Day 8 (r = 0.36; p < 0.001) Day 15 (r = 0.45; p < 0.001) [PD]. Conclusions: FAD cluster showed improvement with dexamethasone and consistent correlation overtime, as compared to SAD and PD cluster. These findings suggest that fatigue-anorexia/cachexia- and depression share a common a common pathophysiologic basis. Further studies are needed to investigate this cluster and target anti-inflammatory therapies. Clinical trial information: NCT00489307.
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Yu, Qian, Xu Han, and Da-Li Tian. "Deficiency of Functional Iron-Sulfur Domains in ABCE1 Inhibits the Proliferation and Migration of Lung Adenocarcinomas By Regulating the Biogenesis of Beta-Actin In Vitro." Cellular Physiology and Biochemistry 44, no. 2 (2017): 554–66. http://dx.doi.org/10.1159/000485090.
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Background/Aims: ATP-binding cassette transporter E1 (ABCE1), a unique ABC superfamily member that bears two Fe-S clusters, is essential for metastatic progression in lung cancer. Fe-S clusters within ABCE1 are crucial for ribosome dissociation and translation reinitiation; however, whether these clusters promote tumor proliferation and migration is unclear. Methods: The interaction between ABCE1 and β-actin was confirmed using GST pull-down. The lung adenocarcinoma (LUAD) cell line A549 was transduced with lentiviral packaging vectors overexpressing either wild-type ABCE1 or ABCE1 with Fe-S cluster deletions (ΔABCE1). The role of Fe-S clusters in the viability and migration of cancer cells was evaluated using clonogenic, MTT, Transwell and wound healing assays. Cytoskeletal rearrangement was determined using immunofluorescent techniques. Results: Fe-S clusters were the key domains in ABCE1 involved in binding to β-actin. The proliferative and migratory capacity increased in cells overexpressing ABCE1. However, the absence of Fe-S clusters reversed these effects. A549 cells overexpressing ABCE1 exhibited irregular morphology and increased levels of cytoskeletal polymerization as indicated by the immunofluorescence images. In contrast, cells expressing the Fe-S cluster deletion mutant presented opposing effects. Conclusion: These results demonstrate the indispensable role of Fe-S clusters when ABCE1 participates in the proliferation and migration of LUADs by interacting with β-actin. The Fe-S clusters of ABCE1 may be potential targets for the prevention of lung cancer metastasis.
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Thottathyl, Hymavathi, and Kanadam Karteeka Pavan. "Differential Evolution Model for Identification of Most Influenced Gene in Brest Cancer Data." Ingénierie des systèmes d information 27, no. 3 (June 30, 2022): 487–93. http://dx.doi.org/10.18280/isi.270316.
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Microarray technology generates a large amount of data. Clustering is a popular technique for locating genes that are expressed in close proximity. It entails examining a fresh dataset to determine whether similar traits can be used to identify any hidden groupings. There are large-dimensional datasets accessible, such as those produced from gene expression investigations, RNA microarray studies, or RNA sequencing studies. As a consequence, cluster analysis and producing well-separated clusters become more challenging. Good cluster separation is desirable since it suggests that items are not being placed in the erroneous clusters. In this study, it was recommended that a Differential Evolution-based (DE) Model be used to interpret the analysis of Brest cancer gene expression. To begin, cluster the gene expression data to find the genes most likely to be impacted by the illness. The appropriate number of clusters must be found in order to locate the gene with the highest effect in the gene collection. We used a DE model on the Brest cancer datasets in this work. We identified the best number of clusters by using the most impacted gene in this dataset as a benchmark. We then experimented with different cluster sizes. We used the DE method to three distinct breast cancer datasets and compared it to the current K-Means and K-medoids models. The results of the experiments show that the proposed DE model outperforms existing models significantly.
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McDonald, Megan, Erin Salinas, Eric Devor, Andreea Newtson, Kristina Thiel, Michael Goodheart, David Bender, Brian Smith, Kimberly Leslie, and Jesus Gonzalez-Bosquet. "Molecular Characterization of Non-responders to Chemotherapy in Serous Ovarian Cancer." International Journal of Molecular Sciences 20, no. 5 (March 7, 2019): 1175. http://dx.doi.org/10.3390/ijms20051175.
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Nearly one-third of patients with high-grade serous ovarian cancer (HGSC) do not respond to initial treatment with platinum-based therapy. Genomic and clinical characterization of these patients may lead to potential alternative therapies. Here, the objective is to classify non-responders into subsets using clinical and molecular features. Using patients from The Cancer Genome Atlas (TCGA) dataset with platinum-resistant or platinum-refractory HGSC, we performed a genome-wide unsupervised cluster analysis that integrated clinical data, gene copy number variations, gene somatic mutations, and DNA promoter methylation. Pathway enrichment analysis was performed for each cluster to identify the targetable processes. Following the unsupervised cluster analysis, three distinct clusters of non-responders emerged. Cluster 1 had overrepresentation of the stage IV disease and suboptimal debulking, under-expression of miRNAs and mRNAs, hypomethylated DNA, “loss of function” TP53 mutations, and the overexpression of genes in the PDGFR pathway. Cluster 2 had low miRNA expression, generalized hypermethylation, MUC17 mutations, and significant activation of the HIF-1 signaling pathway. Cluster 3 had more optimally cytoreduced stage III patients, overexpression of miRNAs, mixed methylation patterns, and “gain of function” TP53 mutations. However, the survival for all clusters was similar. Integration of genomic and clinical data from patients that do not respond to chemotherapy has identified different subgroups or clusters. Pathway analysis further identified the potential alternative therapeutic targets for each cluster.
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CALDWELL, GLYN G. "TWENTY-TWO YEARS OF CANCER CLUSTER INVESTIGATIONS AT THE CENTERS FOR DISEASE CONTROL." American Journal of Epidemiology 132, supp1 (July 1, 1990): 43–47. http://dx.doi.org/10.1093/oxfordjournals.aje.a115787.
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Abstract Beginning in 1961, the Centers for Disease Control investigated 108 cancer clusters and reported the findings in Epidemic Aid Reports. The clusters studied were of leukemia (38%), leukemia and lymphoma (30%), leukemia and other cancer combinations (13%), and all other cancer or combinations (19%). These clusters occurred in 29 states and five foreign countries, with the largest numbers from Connecticut (11), California (eight), Illinois (eight), New York (eight), Georgia (seven), Pennsylvania (six), and Iowa (five). All other states reported less than five. Eight different data collection methods were used, often in combinations, and four types of laboratory methods on four different specimen types. Although 14 different categories of associations were reported, no dear cause was found for any cluster. Nonetheless, concern about clusters by the public and media, and the need to investigate them, warrants the development of a uniform approach for use by local health departments.
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Gilbertson-White, Stephanie, Sanvesh Srivastava, Yunyi Li, Elyse Laures, Seyedehtanaz Saeidzadeh, Chi Yeung, and Sena Chae. "Multimorbidity, cancer, and symptoms: Using electronic health record data to cluster patients in multimorbidity phenotypes." Journal of Clinical Oncology 37, no. 31_suppl (November 1, 2019): 130. http://dx.doi.org/10.1200/jco.2019.37.31_suppl.130.
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130 Background: Cancer-related symptoms are associated with decreased quality of life, increased health care utilization, and shorter life expectancy. There is limited understanding of how multiple chronic conditions (MCC) contribute to variability in symptoms experienced in the context of cancer. Data mining the EHR will allow us to use real clinical data to identify multimorbidity phenotypes based on the clinical similarity of patients. Purpose of this study is to identify distinct subgroups of patients based on the MCC and cancer diagnoses and describe differences across these subgroups. Methods: EHR data was extracted from adult patients (n=2977) newly diagnosed with cancer in 2017 at one academic medical center. The SEER cancer site/histology list was used to group cancer diagnosis. MCC present for >6 months on the problem list or ICD-10 billing data were used. K-Means and K-Modes clustering procedures, with K equaling 7, were used to cluster patients based on MCC. Results: The sample consisted of 58% women, 93% white, with mean age of 62.4 (16.1) years. The most frequent cancers were GI (17%), gynecological (14%), and pulmonary (10%). The most frequent MCC were hypertension (33%), anemia (24%), and metabolic diseases (21%). Seven clusters correspond to following primary cancer sites: GI, pulmonary, urinary, gynecological, breast, endocrine, and skin. The MCC rates varied significantly across different primary sites with hypertension being present in call clusters, but anemia was present only in GI and urinary system cancers clusters. Conclusions: K-Means and K-Modes clustering procedures, with K equaling 7, produced similar clusters of cancer primary sites and MCCs, indicating our findings are stable and replicable. Our data extraction methods and clustering techniques worked well and can be expanded upon. Our next step is to repeat the data extraction and clustering analysis with the full data from the data warehouse (>30,000 records). The identified multimorbidity phenotypes will be used as inclusion criteria for prospective research with patients to explore the relationships among MCCs and symptoms in the context of cancer.
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Kenzik, Kelly, Joshua Richman, Erin E. Kent, Maria Pisu, and Smita Bhatia. "Impact of precancer multimorbidity clusters on survival and functional outcomes after cancer in older patients." Journal of Clinical Oncology 34, no. 7_suppl (March 1, 2016): 291. http://dx.doi.org/10.1200/jco.2016.34.7_suppl.291.
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291 Background: While multimorbidity clustering is a significant problem in older adults, the impact of clusters present prior to cancer on post-diagnosis survival and function is unknown. We used SEER-Medicare Health Outcomes Survey data for 4583 cancer patients to address this research gap. Methods: Patients with prostate (1741), breast (BC: 1345), colorectal (CRC: 904) and lung (593) cancer with pre- and post-diagnosis survey data were included. Surveys assessed comorbidity and activities of daily living (ADLs). Previously defined multimorbidity clusters were cardiovascular disease (CVD), skeletal, metabolic, pulmonary + major depressive disorder (MDD), and gastrointestinal (GI) + MDD. Cox regression models estimated hazard ratios (HR) for death after cancer diagnosis. Among those without pre-cancer ADL impairment, modified Poisson regression models estimated relative risk (RR) for developing post-cancer functional impairment (ADL ≤ 4). Models controlled for age, race, education, poverty level, stage, and treatment (radiation, surgery). Results: Median age at cancer diagnosis was 74y (65-103). Post-diagnosis mortality: After 6y median follow-up, mortality was 30%; 5y survival was 74%.Prostate, BC and CRC patients with pre-diagnosis CVD clusters were at increased risk of death compared to those without CVD cluster (HR 1.9, 2.0, 1.7, respectively, p < 0.05). Compared to those without the cluster, prostate and BC patients with metabolic cluster were at increased risk (HR 1.7, 1.9, respectively, p < 0.05) and prostate cancer patients with pulmonary conditions + MDD or GI + MDD (HR 1.9, 2.1, respectively, p < 0.05) were at increased risk. Post-diagnosis functional impairment: Prevalence of moderate functional impairment at a median of 1y after cancer diagnosis was 31%. Prostate, lung, and CRC survivors with GI + MDD had a significant RR of developing impairment (RR 1.8, 1.8, and 1.7, p < 0.001). For BC patients, those with skeletal cluster had a 2.1 RR (p < 0.001). Conclusions: Specific multimorbidity clusters prior to cancer are associated with post-cancer mortality and ADL impairment and identify at-risk groups where interventions can be instituted to decrease morbidity and mortality.
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Gleason, J., D. Case, S. Rapp, E. Ip, M. Naughton, J. Butler, K. McMullen, V. Stieber, P. Saconn, and E. Shaw. "Symptom clusters in newly-diagnosed brain tumor patients." Journal of Clinical Oncology 24, no. 18_suppl (June 20, 2006): 8587. http://dx.doi.org/10.1200/jco.2006.24.18_suppl.8587.
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8587 Background: A symptom cluster is 2 or more co-occurring symptoms. Patients with brain tumors experience disease and treatment-related symptoms that impact their health-related quality of life (QOL). Identifying symptom clusters will facilitate treatment and improve QOL outcomes. Methods: 66 patients were enrolled in a phase III, placebo-controlled, double-blind, prospective randomized clinical trial assessing the effect of prophylactic d-methylphenidate (d-MPH) on QOL in newly diagnosed brain tumor patients receiving brain radiation therapy (RT). Inclusion criteria were: age ≥ 13 years, primary or metastatic brain tumor, partial or whole brain RT with a total dose of ≥ 2,500 cGy in ≥ 10 fractions, KPS ≥ 70, and life expectancy ≥ 3 months. Patients received d-MPH 5–15 mg BID (or placebo) starting week 1 of RT and continuing for 8 weeks post-RT. QOL data were collected at baseline, the end of RT, and 4, 8, and 12 weeks following RT using the Functional Assessment of Cancer Therapy-Brain (FACT-Br) and the Center for Epidemiologic Studies Depression Scale (CES-D). Symptom data were analyzed using exploratory factor analysis, multi-dimensional scaling (MDS), and cluster analysis. Results: The study failed to show a treatment effect for d-MPH (Butler J et al, Int J Radiat Oncol Biol Physics 63 [Supp1]:80, 2005).Thus, both d-MPH and placebo patients were analyzed together. 58 and 48 patients were analyzed at baseline and the end of RT, respectively. Two symptom clusters were identified using exploratory factor analysis and supported by MDS and cluster analysis: an expressive language cluster including difficulty reading, writing, and finding the right words, and a mood cluster including feeling sad, anxious, and having depressed mood. Conclusions: Two symptom clusters were identified in patients undergoing brain RT: an expressive language cluster and a mood cluster. This suggests that interventions that target both cognitive function and mood should be utilized. Further research on symptom clusters in cancer patients is needed. This study was supported by NCI grant 1 U10 CA81851. No significant financial relationships to disclose.
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Aprile, G., M. Ramoni PhD, D. Keefe, and S. Sonis. "Network analyses to define chemotherapy toxicity clusters in patients with colorectal cancer (CRC)." Journal of Clinical Oncology 25, no. 18_suppl (June 20, 2007): 9045. http://dx.doi.org/10.1200/jco.2007.25.18_suppl.9045.
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9045 Background: CRC patients undergoing CT are likely to experience multiple concurrent toxicities. Rather than appearing singularly, the hypothesis that certain toxicities occur in clusters may suggest a common pathobiology. We used Markov networks (MN), a probabilistic graphical born at the confluence of statistics and artificial intelligence describing the dependency among set of variables, to identify clusters of CT-induced toxicities, to examine how clusters are connected to each other, and how single toxicities are related to a specific cluster. Methods: Using a standardized data collection tool, we retrospectively reviewed electronic medical charts of 300 consecutive CRC pts receiving FOLFOX, FOLFIRI or 5-FU to record baseline demographic and clinical information. Toxicities were recorded using NCI-CTC criteria during the first cycle of CT. Following the standard Bayesian approach, the MN clustering the CT-induced toxicities was learned from the data as the network with the highest posterior probability given the data. Results: The network, in which associations between toxicities are represented as links, identified five strongly-related symptom clusters: a constitutional cluster involving fatigue, anorexia, and weight loss; a gastrointestinal cluster where dehydration was the connector between diarrhea, constipation and bloating on a side and taste nausea and vomiting, taste alteration, fever and chills on the flipside; a dermatological cluster composed by dry skin, HFS, rash and itching and connected with hemorrhage/bleeding and wound complication toxicity. Furthermore, we noticed strong connections between cough, dyspnea and infection, with palpitation and pain and we detected another cluster where depression and anxiety where connected with cystitis. Conclusions: The application of network analyses to define CT-induced toxicity clusters is new. The technique was effective in defining the relationships between individual toxicities associated with cycle 1 therapy. The lack of randomness between the relationships defined by the network provides a strong suggestion that each cluster shares a common pathobiological basis, which may provide an opportunity for intervention. [Table: see text]
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de Rooij, Belle Hadewijch, Elyse R. Park, Giselle Katiria Perez, Julia Rabin, Katharine M. Quain, Don S. Dizon, Kathryn E. Post, et al. "Cluster analysis to demonstrate the need to individualize care for cancer survivors." Journal of Clinical Oncology 36, no. 7_suppl (March 1, 2018): 62. http://dx.doi.org/10.1200/jco.2018.36.7_suppl.62.
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62 Background: In efforts to inform clinical screening and development of survivorship care services, we sought to characterize patterns of healthcare needs among cancer survivors by 1) identifying and characterizing subgroups of survivors based on self reported health care needs, and 2) assessing socio-demographic, clinical and psychosocial factors associated with these subgroups. Methods: We conducted a cross-sectional self administered survey among patients presenting for routine follow-up care for early stage cancer (breast, gynecologic, neuro, gastro-intestinal, genito-urinary, thoracic, head and neck, melanoma, sarcoma and hematologic) at our academic medical center. Latent class cluster analysis was used to identify clusters of survivors based on survivorship care needs within 7 domains (side-effects, lifestyle/self-care, emotional coping, social support, sexual health, complementary services and practical support). Multiple logistic regression analyses were used to assess factors associated with these clusters. Results: Among 292 respondents, the highest unmet needs were related to information regarding side effects (53%), lifestyle/self-care (51%) and emotional coping (43%) domains. Our analysis identified 4 clusters of survivors: 1) low needs (N = 123, 42%), 2) mainly physical needs (N = 46, 16%), 3) mainly psychological needs (N = 57, 20%) and 4) both physical and psychological needs (N = 66, 23%). Compared to cluster 1, those in clusters 2, 3, and 4 were younger (P < 0.03); those in clusters 3 and 4 had higher levels of fear of recurrence, anxiety, depression and insomnia (P < 0.05); and those in clusters 2 and 4 reported higher levels of fatigue (P < 0.05). Conclusions: Unmet needs among cancer survivors are prevalent and must be addressed, however a substantial group of survivors report low or no healthcare needs. The wide variation in healthcare needs among cancer survivors suggests a need for screening all patients, followed by tailored interventions in clinical care delivery and research to improve outcomes.
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Yang, David S., Arash Saeedi, Aram Davtyan, Mohsen Fathi, Michael B. Sherman, Mohammad S. Safari, Alena Klindziuk, et al. "Mesoscopic protein-rich clusters host the nucleation of mutant p53 amyloid fibrils." Proceedings of the National Academy of Sciences 118, no. 10 (March 2, 2021): e2015618118. http://dx.doi.org/10.1073/pnas.2015618118.
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The protein p53 is a crucial tumor suppressor, often called “the guardian of the genome”; however, mutations transform p53 into a powerful cancer promoter. The oncogenic capacity of mutant p53 has been ascribed to enhanced propensity to fibrillize and recruit other cancer fighting proteins in the fibrils, yet the pathways of fibril nucleation and growth remain obscure. Here, we combine immunofluorescence three-dimensional confocal microscopy of human breast cancer cells with light scattering and transmission electron microscopy of solutions of the purified protein and molecular simulations to illuminate the mechanisms of phase transformations across multiple length scales, from cellular to molecular. We report that the p53 mutant R248Q (R, arginine; Q, glutamine) forms, both in cancer cells and in solutions, a condensate with unique properties, mesoscopic protein-rich clusters. The clusters dramatically diverge from other protein condensates. The cluster sizes are decoupled from the total cluster population volume and independent of the p53 concentration and the solution concentration at equilibrium with the clusters varies. We demonstrate that the clusters carry out a crucial biological function: they host and facilitate the nucleation of amyloid fibrils. We demonstrate that the p53 clusters are driven by structural destabilization of the core domain and not by interactions of its extensive unstructured region, in contradistinction to the dense liquids typical of disordered and partially disordered proteins. Two-step nucleation of mutant p53 amyloids suggests means to control fibrillization and the associated pathologies through modifying the cluster characteristics. Our findings exemplify interactions between distinct protein phases that activate complex physicochemical mechanisms operating in biological systems.
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Jairath, Neil K., Mark W. Farha, Sudharsan Srinivasan, Ruple Jairath, Michael D. Green, Robert T. Dess, William C. Jackson, et al. "Tumor Immune Microenvironment Clusters in Localized Prostate Adenocarcinoma: Prognostic Impact of Macrophage Enriched/Plasma Cell Non-Enriched Subtypes." Journal of Clinical Medicine 9, no. 6 (June 24, 2020): 1973. http://dx.doi.org/10.3390/jcm9061973.
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Background: Prostate cancer (PCa) is characterized by significant heterogeneity in its molecular, genomic, and immunologic characteristics. Methods: Whole transcriptome RNAseq data from The Cancer Genome Atlas of prostate adenocarcinomas (n = 492) was utilized. The immune microenvironment was characterized using the CIBERSORTX tool to identify immune cell type composition. Unsupervised hierarchical clustering was performed based on immune cell type content. Analyses of progression-free survival (PFS), distant metastases, and overall survival (OS) were performed using Kaplan–Meier estimates and Cox regression multivariable analyses. Results: Four immune clusters were identified, largely defined by plasma cell, CD4+ Memory Resting T Cells (CD4 MR), and M0 and M2 macrophage content (CD4 MRHighPlasma CellHighM0LowM2Mid, CD4 MRLowPlasma CellHighM0LowM2Low, CD4 MRHighPlasma CellLowM0HighM2Low, and CD4 MRHighPlasma CellLowM0LowM2High). The two macrophage-enriched/plasma cell non-enriched clusters (3 and 4) demonstrated worse PFS (HR 2.24, 95% CI 1.46–3.45, p = 0.0002) than the clusters 1 and 2. No metastatic events occurred in the plasma cell enriched, non-macrophage-enriched clusters. Comparing clusters 3 vs. 4, in patients treated by surgery alone, cluster 3 had zero progression events (p < 0.0001). However, cluster 3 patients had worse outcomes after post-operative radiotherapy (p = 0.018). Conclusion: Distinct tumor immune clusters with a macrophage-enriched, plasma cell non-enriched phenotype and reduced plasma cell enrichment independently characterize an aggressive phenotype in localized prostate cancer that may differentially respond to treatment.