Jim Zhongning Chen

Background Patients born with single ventricle anatomy typically undergo surgical palliation in three stages, culminating in the Fontan procedure. Assessment of flow across a Fontan fenestration by Doppler ultrasound theoretically allows for non-invasive estimation of the transpulmonary gradient (TPG). Our objective was to determine the relationship between Doppler-derived mean fenestration gradient (mFG) and direct catheter-based measurements of TPG in patients with fenestrated Fontans. Methods We performed a single-center retrospective cohort study of 59 patients with fenestrated Fontans completed between 2000 and 2022. The primary outcome was catheter-based measurement of TPG and the primary predictor was mFG from echo performed within 6 months of the catheterization. Linear regression and R² were used to determine the relationship between predictors and outcomes. Results Catheter-based measurements of TPG and mFG were weakly correlated (R² = 0.382, p < 0.001); the regression coefficient was 0.550, with a standard error of 0.09 for every increase in mFG (Cath TPG = 0.55 [mFG] + 1.92). mFG had a slightly better predictive relationship with cath-derived TPG in patients with systemic left ventricles with R² of 0.47, p < 0.004. Conclusion mFG accounts for approximately 38% of the variance in catheter-derived TPG. Although mFG is non-invasive and intuitive, mFG in Fontan patients should be interpreted with caution and direct measurement by cardiac catheterization should be considered.

Although Otto Warburg may be right about the role of glycolysis versus OXPHOS in cancer metabolism, it remains unclear whether an altered metabolism is causative or correlative and is the main driver or a mere passenger in the pathogenesis of cancer. Currently, most of our successful treatments are designed to eliminate non-cancer stem cells (non-CSCs) such as differentiated cancer cells. When the treatments also happen to control CSCs or the stem-ness niche, it is often unintended, unexpected, or undetected for lack of a pertinent theory about the origin of cancer that clarifies whether cancer is a metabolic, genetic, or stem cell disease. Perhaps cellular context matters. After all, metabolic activity may be different in different cell types and their respective microenvironments—whether it is in a normal progenitor stem cell vs. progeny differentiated cell and whether it is in a malignant CSC vs. non-CSC. In this perspective, we re-examine different types of cellular metabolism, e.g., glycolytic vs. mitochondrial, of glucose, glutamine, arginine, and fatty acids in CSCs and non-CSCs. We revisit the Warburg effect, an obesity epidemic, the aspartame story, and a ketogenic diet. We propose that a pertinent scientific theory about the origin of cancer and of cancer metabolism influences the direction of cancer research as well as the design of drug versus therapy development in cancer care.

Children treated for B-Acute Lymphoblastic Leukemia (ALL) have high rates of recurrence. In order to overcome such challenges, patients require consolidation therapy that ranges from additional chemotherapy to hematopoietic stem cell transplant. Even so, approximately 50% of children with first relapse survive long term, which is important to determine the appropriate consolidation therapies. Currently, the most relevant risk stratification includes age, minimal residual disease (MRD) status seen in bone marrow 28 days post-induction, and cell count at the time of diagnosis. As the body of literature grows, there is a greater need to incorporate molecular data to assist in risk stratification. In recent decades, microRNA (miRNA), which are non-coding RNA that regulate post-transcriptional modification, have been implicated in the pathogenesis of B-ALL. Due to the volume of variables, it is difficult to model such relationships using conventional methods. Thus, the objective was to generate a machine learning model to predict response to induction therapy by leveraging the Therapeutically Applicable Research to Generate Effect Treatment (TARGET) databases. Between January 1, 2004 and January 1, 2011, the TARGET database collected data on over 11,000 patients with ALL up to the age of 31 that included clinical data such as age, gender, cytogenetics, and event-free survival (EFS). An event was defined as induction failure, relapse, progression, second hematologic malignancies, and death. In the expansion phase 2 part of the study, 175 children with B-ALL had in-depth genomic sequencing. Because of the unpredictable downstream effects of dysregulation of miRNA, underlying assumptions of the Cox proportional hazard model may be violated. Machine learning models, such as the DeepSurv model, leverage deep neural networks to analyze survival data. The DeepSurv model has demonstrated superior performance in capturing linear and non-linear relationships between covariates and time-to-event analysis. In total, 171 features including age, MRD status, White Blood Cell (WBC) count at diagnosis, and 168 miRNA counts were included in the model. Random forest (RF) models were utilized to stratify the impact of features that are predictive. We created a 6773-parameter model using 171 features. A 10-fold cross-validation showed a concordance-index of 0.652 (CI: 0.582 - 0.752). Figure 1 shows the observed and predicted normalized EFS using DeepSurv. Notably, it is able to identify early relapse in the bottom L quadrant. Figure 2A shows the decision tree for the RF model that shows features and cut-offs as rules that cluster the EFS times. Figure 2B shows the features that were impactful for the RF decision rules. In this work, we aim to leverage machine learning algorithms and miRNA to identify children who are at high risk for early relapse and can help clinicians to strategize consolidation therapy. We showed a reasonable concordance score that gives us a signal for potential impact. Further research, utilizing larger sample sizes, should serve to externally validate this model. Appropriate clinical application of such models has the potential to improve survival and limit treatment toxicity through a truly personalized approach to the patient and the unique genetic profile of their malignancy. No relevant conflicts of interest to declare.

Biomarkers play a crucial role in the diagnosis, prognosis, and therapeutics of cancer. We use biomarkers to identify, image, monitor, and target cancer. In many respects, the discovery of pertinent biomarkers that distinguish fulminant from indolent neoplasms and sensitive from refractory malignancies would be a holy grail of cancer research and therapy. We propose that a stem cell versus genetic theory of cancer may not only enable us to track and trace the biological evolution of cancer but also empower us to attenuate its clinical course and optimize the clinical outcome of patients with cancer. Hence, a biomarker that identifies cancer stem cells (CSCs) and distinguishes them from non-CSCs may serve to elucidate inter-tumoral and intra-tumoral heterogeneity, elevate the values and utility of current prognostic and predictive tests, and enhance drug versus therapy development in cancer care. From this perspective, we focus on CSC biomarkers and discuss stemness or stem-like biomarkers in the context of a unified theory and a consideration of stem cell versus genetic origin. We review their role in primary and mixed tumors, in the elaboration of tumor subtypes, and in the imaging and monitoring of minimal residual diseases. We investigate how scientific theories influence the direction of scientific research and interpretation of experimental results, and how genomics and epigenomics affect the dynamics and trajectories of biomarkers in the conduct of cancer research and in the practice of cancer care.

A simple way to understand the immune system is to separate the self from non-self. If it is self, the immune system tolerates and spares. If it is non-self, the immune system attacks and destroys. Consequently, if cancer has a stem cell origin and is a stem cell disease, we have a serious problem and a major dilemma with immunotherapy. Because many refractory cancers are more self than non-self, immunotherapy may become an uphill battle and pyrrhic victory in cancer care. In this article, we elucidate cancer immunity. We demonstrate for whom, with what, as well as when and how to apply immunotherapy in cancer care. We illustrate that a stem cell theory of cancer affects our perspectives and narratives of cancer. Without a pertinent theory about cancer’s origin and nature, we may unwittingly perform misdirected cancer research and prescribe misguided cancer treatments. In the ongoing saga of immunotherapy, we are at a critical juncture. Because of the allure and promises of immunotherapy, we will be treating more patients not immediately threatened by their cancer. They may have more to lose than to gain, if we have a misconception and if we are on a wrong mission with immunotherapy. According to the stem cell theory of cancer, we should be careful with immunotherapy. When we do not know or realize that cancer originates from a stem cell and has stem-ness capabilities, we may cause more harm than good in some patients and fail to separate the truth from the myth about immunotherapy in cancer care.

Disclosures. No relevant conflicts of interest to declare.Figure 1

From this perspective, we wonder about the clinical implications of oncology recapturing ontogeny in the contexts of neoantigens, tumor biomarkers, and cancer targets. We ponder about the biological ramifications of finding remnants of mini-organs and residuals of tiny embryos in some tumors. We reminisce about classical experiments showing that the embryonic microenvironment possesses antitumorigenic properties. Ironically, a stem-ness niche—in the wrong place at the wrong time—is also an onco-niche. We marvel at the paradox of TGF-beta both as a tumor suppressor and a tumor promoter. We query about the dualism of EMT as a stem-ness trait engaged in both normal development and abnormal disease states, including various cancers. It is uncanny that during fetal development, proto-oncogenes wax, while tumor-suppressor genes wane. Similarly, during cancer development, proto-oncogenes awaken, while tumor-suppressor genes slumber. Importantly, targeting stem-like pathways has therapeutic implications because stem-ness may be the true driver, if not engine, of the malignant process. Furthermore, anti-stem-like activity elicits anti-cancer effects for a variety of cancers because stem-ness features may be a universal property of cancer. When a fetus survives and thrives despite immune surveillance and all the restraints of nature and the constraints of its niche, it is a perfect baby. Similarly, when a neoplasm survives and thrives in an otherwise healthy and immune-competent host, is it a perfect tumor? Therefore, a pertinent narrative of cancer depends on a proper perspective of cancer. If malignant cells are derived from stem cells, and both cells are intrinsically RB1 negative and TP53 null, do the absence of RB1 and loss of TP53 really matter in this whole narrative and an entirely different perspective of cancer?

Substantial milestones have been attained in the field of heart failure (HF) diagnostics and therapeutics in the past several years that have translated into decreased mortality but a paradoxical increase in HF-related hospitalizations. With increasing data digitalization and access, remote monitoring via wearables and implantables have the potential to transform ambulatory care workflow, with a particular focus on reducing HF hospitalizations. Additionally, artificial intelligence and machine learning (AI/ML) have been increasingly employed at multiple stages of healthcare due to their power in assimilating and integrating multidimensional multimodal data and the creation of accurate prediction models. With the ever-increasing troves of data, the implementation of AI/ML algorithms could help improve workflow and outcomes of HF patients, especially time series data collected via remote monitoring. In this review, we sought to describe the basics of AI/ML algorithms with a focus on time series forecasting and the current state of AI/ML within the context of wearable technology in HF, followed by a discussion of the present limitations, including data integration, privacy, and challenges specific to AI/ML application within healthcare.