BACKGROUND: The authors set out to assess the correlation between smoking‐related second primary tumor (SPT) development and cigarette smoking habits after diagnosis and definitive treatment in patients with early‐stage head and neck squamous cell carcinoma who were enrolled in a placebo‐controlled randomized chemoprevention trial of 13‐cis‐retinoic acid.METHODS: Longitudinal data collected for 10 years after the index diagnosis are presented for 1190 patients. Cox proportional hazards regression models were used to examine the effects of changes in smoking behavior on smoking‐related SPT development.RESULTS: One‐third of all patients who quit smoking within 12 months before randomization experienced recurrence, compared with 6.9% and 10.4% of all never‐smokers and former smokers, respectively. Approximately 16% of all current smokers stopped smoking, and nearly 22% of current smokers developed SPTs, compared with 14.5%, 13.2%, and 8.8% of all recent smokers, former smokers, and never‐smokers, respectively. The probability of developing a smoking‐related SPT was highest among patients who were current smokers at randomization. These patients, regardless of whether they ceased smoking during follow‐up, were nearly three times more likely than patients who had never smoked to develop a smoking‐related SPT. In contrast, former smokers and recent quitters who continued to abstain from smoking during follow‐up were approximately 1.5 times more likely to develop an SPT compared with patients who had never smoked.CONCLUSIONS: Patients who continue to smoke after the successful treatment of their index head and neck malignancies have a substantially higher risk of developing smoking‐related SPTs. Cancer 2004. © 2004 American Cancer Society.

Digital Twin (DT) technology has emerged as a transformative force in healthcare, offering unprecedented opportunities for personalized medicine, treatment optimization, and disease prevention. This comprehensive review examines the current state of DTs in healthcare, analyzing their implementation across different physiological levels—from cellular to whole-body systems. We systematically review the latest developments, methodologies, and applications while identifying challenges and opportunities. Our analysis encompasses technical frameworks for cardiovascular, neurological, respiratory, metabolic, hepatic, oncological, and cellular DTs, highlighting significant achievements such as population-scale cardiac modeling (3,461 patient cohort), reduced atrial fibrillation recurrence rates through patient-specific cardiac models, improved brain tumor radiotherapy planning, advanced liver regeneration modeling with real-time simulation capabilities, and enhanced glucose management in diabetes. We detail the methodological foundations supporting different DT implementations, including data acquisition strategies, physics-based modeling approaches, statistical learning algorithms, neural network-based control systems, and emerging artificial intelligence techniques. While discussing implementation challenges related to data quality, computational constraints, and validation requirements, we provide a forward-looking perspective on future opportunities for enhanced personalization, expanded application areas, and integration with emerging technologies. This review offers a multidimensional assessment of healthcare DTs and outlines future directions for their development and integration. This review demonstrates that while healthcare DTs have achieved remarkable clinical successes—from reducing cardiac arrhythmia recurrence rates by over 13% to enabling 97% accuracy in neurodegenerative disease prediction, and achieving sub-millisecond liver response predictions with high accuracy—their clinical translation requires addressing challenges such as data integration, computational scalability, digital equity, and validation frameworks.

Introduction: Translation initiation is activated in cancer through increase in eukaryotic initiation factor 4E (eIF4E), eIF4G, phosphorylated eIF4E-binding protein (p4E-BP1) and phosphorylated ribosomal protein S6 (pS6), and decreased programmed cell death protein 4 (pdcd4), a translational inhibitor. Further, translation elongation is deregulated though alterations in eukaryotic elongation factor 2 (eEF2) and eEF2 kinase (eEF2K). We sought to determine the association of these translational aberrations with clinical-pathologic factors and survival outcomes in hormone receptor-positive breast cancer. Methods: Primary tumors were collected from 190 patients with Stage I to III hormone receptor-positive breast cancer. Expression of eIF4E, eIF4G, 4E-BP1, p4E-BP1 T37/46, p4E-BP1 S65, p4E-BP1 T70, S6, pS6 S235/236, pS6 S240/244, pdcd4, eEF2 and eEF2K was assessed by reverse phase protein arrays. Univariable and multivariable analyses for recurrence-free survival (RFS) and overall survival (OS) were performed. Results: High eEF2, S6, pS6 S240/244, p4E-BP1 T70, and low pdcd4 were significantly associated with node positivity. Median follow-up for living patients was 96 months. High p4E-BP1 T36/47, p4E-BP1 S65, p4E-BP1 T70 and 4E-BP1 were associated with worse RFS. High p4E-BP1 T70 and pS6 S235/236, and low pdcd4, were associated with worse OS. In multivariable analysis, in addition to positive nodes, p4E-BP1 S65 remained a significant predictor of RFS (HR = 1.62, 95% CI = 1.13-2.31; P = 0.008). In addition to age, pS6 S235/236 (HR = 1.73, 95% CI = 1.03-2.90, P = 0.039), eEF2K (HR = 2.19, 95% CI = 1.35-3.56, P = 0.002) and pdcd4 (HR = 0.42, 95% CI = 0.25-0.70, P = 0.001) were associated with OS. Conclusions: Increased pS6, p4E-BP1, eEF2K and decreased pdcd4 are associated with poor prognosis in hormone receptor-positive breast cancer, suggesting their role as prognostic markers and therapeutic targets.

Background: In microbiome data analysis, unsupervised clustering is often used to identify naturally occurring clusters, which can then be assessed for associations with characteristics of interest. In this work, we systematically compared beta diversity and clustering methods commonly used in microbiome analyses. We applied these to four published datasets where highly distinct microbiome profiles could be seen between sample groups, as well a clinical dataset with less clear separation between groups. Results: Although no single method outperformed the others consistently, we did identify the key scenarios where certain methods can underperform. Specifically, the Bray Curtis (BC) metric resulted in poor clustering in a dataset where high-abundance OTUs were relatively rare. In contrast, the unweighted UniFrac (UU) metric clustered poorly on dataset with a high prevalence of low-abundance OTUs. To explore these hypotheses about BC and UU, we systematically modified the properties of the poorly performing datasets and found that this approach resulted in improved BC and UU performance. Based on these observations, we rationally combined BC and UU to generate a novel metric. We tested its performance while varying the relative contributions of each metric and also compared it with another combined metric, the generalized UniFrac distance. The proposed metric showed high performance across all datasets. Conclusions: Our systematic evaluation of clustering performance in these five datasets demonstrates that there is no existing clustering method that universally performs best across all datasets. We propose a combined metric of BC and UU that capitalizes on the complementary strengths of the two metrics.

Despite advances in early diagnosis and treatment of cancer patients, metastasis remains the major cause of mortality. TP53 is one of the most frequently mutated genes in human cancer, and these alterations can occur during the early stages of oncogenesis or as later events as tumors progress to more aggressive forms. Previous studies have suggested that p53 plays a role in cellular pathways that govern metastasis. To investigate how p53 deficiency contributes to late-stage tumor growth and metastasis, we developed paired isogenic patient-derived xenograft (PDX) models of triple-negative breast cancer (TNBC) differing only in p53 status for longitudinal analysis. Patient-derived isogenic human tumor lines differing only in p53 status were implanted into mouse mammary glands. Tumor growth and metastasis were monitored with bioluminescence imaging, and circulating tumor cells (CTCs) were quantified by flow cytometry. RNA-Seq was performed on p53-deficient and p53 wild-type tumors, and functional validation of a lead candidate gene was performed in vivo. Isogenic p53 wild-type and p53-deficient tumors metastasized out of mammary glands and colonized distant sites with similar frequency. However, p53-deficient tumors metastasized earlier than p53 wild-type tumors and grew faster in both primary and metastatic sites as a result of increased proliferation and decreased apoptosis. In addition, greater numbers of CTCs were detected in the blood of mice engrafted with p53-deficient tumors. However, when normalized to tumor mass, the number of CTCs isolated from mice bearing parental and p53-deficient tumors was not significantly different. Gene expression profiling followed by functional validation identified B cell translocation gene 2 (BTG2), a downstream effector of p53, as a negative regulator of tumor growth both at primary and metastatic sites. BTG2 expression status correlated with survival of TNBC patients. Using paired isogenic PDX-derived metastatic TNBC cells, loss of p53 promoted tumor growth and consequently increased tumor cell shedding into the blood, thus enhancing metastasis. Loss of BTG2 expression in p53-deficient tumors contributed to this metastatic potential by enhancing tumor growth in primary and metastatic sites. Furthermore, clinical data support conclusions generated from PDX models and indicate that BTG2 expression is a candidate prognostic biomarker for TNBC.