Topology preserving stratification of tissue neoplasticity using Deep Neural Maps and microRNA signatures

Background Accurate cancer classification is essential for correct treatment selection and better prognostication. microRNAs (miRNAs) are small RNA molecules that negatively regulate gene expression, and their dyresgulation is a common disease mechanism in many cancers. Through a clearer understanding of miRNA dysregulation in cancer, improved mechanistic knowledge and better treatments can be sought. Results We present a topology-preserving deep learning framework to study miRNA dysregulation in cancer. Our study comprises miRNA expression profiles from 3685 cancer and non-cancer tissue samples and hierarchical annotations on organ and neoplasticity status. Using unsupervised learning, a two-dimensional topological map is trained to cluster similar tissue samples. Labelled samples are used after training to identify clustering accuracy in terms of tissue-of-origin and neoplasticity status. In addition, an approach using activation gradients is developed to determine the attention of the networks to miRNAs that drive the clustering. Using this deep learning framework, we classify the neoplasticity status of held-out test samples with an accuracy of 91.07%, the tissue-of-origin with 86.36%, and combined neoplasticity status and tissue-of-origin with an accuracy of 84.28%. The topological maps display the ability of miRNAs to recognize tissue types and neoplasticity status. Importantly, when our approach identifies samples that do not cluster well with their respective classes, activation gradients provide further insight in cancer subtypes or grades. Conclusions An unsupervised deep learning approach is developed for cancer classification and interpretation. This work provides an intuitive approach for understanding molecular properties of cancer and has significant potential for cancer classification and treatment selection.

Biomarkers for therapy selection in metastatic urothelial cancer

The treatment of metastatic urothelial cancer (mUC) has been transformed by recent progress in clinical trials and drug development. There are now three therapeutic classes with proven benefits in mUC: chemotherapy, immunotherapy, and targeted therapy. The optimal sequence and combination of these classes remain to be defined. Biomarker development is essential to guide treatment selection at each therapeutic juncture. Two biomarkers, programmed death-ligand 1 expression and fibroblast growth factor receptor alterations, have been incorporated into the mUC treatment paradigm thus far. This review discusses predictive biomarkers in development and their potential to influence mUC treatment selection moving forward.

Improving the causal treatment effect estimation with propensity scores by the bootstrap

When observational studies are used to establish the causal effects of treatments, the estimated effect is affected by treatment selection bias. The inverse propensity score weight (IPSW) is often used to deal with such bias. However, IPSW requires strong assumptions whose misspecifications and strategies to correct the misspecifications were rarely studied. We present a bootstrap bias correction of IPSW (BC-IPSW) to improve the performance of propensity score in dealing with treatment selection bias in the presence of failure to the ignorability and overlap assumptions. The approach was motivated by a real observational study to explore the potential of anticoagulant treatment for reducing mortality in patients with end-stage renal disease. The benefit of the treatment to enhance survival was demonstrated; the suggested BC-IPSW method indicated a statistically significant reduction in mortality for patients receiving the treatment. Using extensive simulations, we show that BC-IPSW substantially reduced the bias due to the misspecification of the ignorability and overlap assumptions. Further, we showed that IPSW is still useful to account for the lack of treatment randomization, but its advantages are stringently linked to the satisfaction of ignorability, indicating that the existence of relevant though unmeasured or unused covariates can worsen the selection bias.

Association of Frailty With Treatment Selection and Long‐Term Outcomes Among Patients With Chronic Limb‐Threatening Ischemia

Background The optimal treatment strategy for patients with chronic limb‐threatening ischemia (CLTI) is often unclear. Frailty has emerged as an important factor that can identify patients at greater risk of poor outcomes and guide treatment selection, but few studies have explored its utility among the CLTI population. We examine the association of a health record‐based frailty measure with treatment choice and long‐term outcomes among patients hospitalized with CLTI. Methods and Results We included patients aged >65 years hospitalized with CLTI in the Medicare Provider Analysis and Review data set between October 1, 2009 and September 30, 2015. The primary exposure was frailty, defined by the Claims‐based Frailty Indicator. Baseline frailty status and revascularization choice were examined using logistic regression. Cox proportional hazards regression was used to determine the association between frailty and death or amputation, stratifying by treatment strategy. Of 85 060 patients, 35 484 (42%) were classified as frail. Frail patients had lower likelihood of revascularization (adjusted odds ratio [OR], 0.78; 95% CI, 0.75‒0.82). Among those revascularized, frailty was associated with lower likelihood of surgical versus endovascular treatment (adjusted OR, 0.76; CI, 0.72‒0.81). Frail patients experienced increased risk of amputation or death, regardless of revascularization status (revascularized: adjusted hazard ratio [HR], 1.34; CI, 1.30‒1.38; non‐revascularized: adjusted HR, 1.22; CI, 1.17‒1.27). Among those revascularized, frailty was independently associated with amputation or death irrespective of revascularization strategy (surgical: adjusted HR, 1.36; CI, 1.31‒1.42; endovascular: aHR, 1.29; CI, 1.243‒1.35). Conclusions Among patients hospitalized with CLTI, frailty is an important independent predictor of revascularization strategy and longitudinal adverse outcomes.