What are the Most Important Statistical Ideas of the Past 50 Years?

Disruptions in antigen processing and presentation machinery on sarcoma

BACKGROUND

The antigen processing machinery (APM) plays a critical role in generating tumor-specific antigens that can be recognized and targeted by the immune system. Proper functioning of APM components is essential for presenting these antigens on the surface of tumor cells, enabling immune detection and destruction. In many cancers, defects in APM can lead to immune evasion, contributing to tumor progression and poor clinical outcomes. However, the status of the APM in sarcomas is not well characterized, limiting the development of effective immunotherapeutic strategies for these patients.

METHODS

We investigated 126 patients with 8 types of bone and soft tissue sarcoma operated between 2001-2021. Tissue microarrays mapped 11 specific areas in each case. The presence/absence of APM protein was determined through immunohistochemistry. Bayesian networks were used.

RESULTS

All investigated sarcomas had some defects in APM. The least damaged component was HLA Class I subunit β2-microglobulin and HLA Class II. The proteasome LMP10 subunit was defective in leiomyosarcoma (LMS), myxoid liposarcoma (MLPS), and dedifferentiated liposarcoma (DDLPS), while MHC I transporting unit TAP2 was altered in undifferentiated pleomorphic sarcoma (UPS), gastrointestinal stromal tumor (GIST), and chordoma (CH). Among different neoplastic areas, high-grade areas showed different patterns of expression compared to high lymphocytic infiltrate areas. Heterogeneity at the patient level was also observed. Loss of any APM component was prognostic of distant metastasis (DM) for LMS and DDLPS and of overall survival (OS) for LMS.

CONCLUSION

Sarcomas exhibit a high degree of defects in APM components, with differences among histotypes and tumoral areas. The most commonly altered APM components were HLA Class I subunit β2-microglobulin, HLA Class I subunit α (HC10), and MHC I transporting unit TAP2. The loss of APM components was prognostic of DM and OS and clinically relevant for LMS and DDLPS. This study explores sarcoma molecular mechanisms, enriching personalized therapeutic approaches.

Novel calibration design improves knowledge transfer across products for the characterization of pharmaceutical bioprocesses

Modern machine learning has the potential to fundamentally change the way bioprocesses are developed. In particular, horizontal knowledge transfer methods, which seek to exploit data from historical processes to facilitate process development for a new product, provide an opportunity to rethink current workflows. In this work, we first assess the potential of two knowledge transfer approaches, meta learning and one-hot encoding, in combination with Gaussian process (GP) models. We compare their performance with GPs trained only on data of the new process, that is, local models. Using simulated mammalian cell culture data, we observe that both knowledge transfer approaches exhibit test set errors that are approximately halved compared to those of the local models when two, four, or eight experiments of the new product are used for training. Subsequently, we address the question whether experiments for a new product could be designed more effectively by exploiting existing knowledge. In particular, we suggest to specifically design a few runs for the novel product to calibrate knowledge transfer models, a task that we coin calibration design. We propose a customized objective function to identify a set of calibration design runs, which exploits differences in the process evolution of historical products. In two simulated case studies, we observed that training with calibration designs yields similar test set errors compared to common design of experiments approaches. However, the former requires approximately four times fewer experiments. Overall, the results suggest that process development could be significantly streamlined when systematically carrying knowledge from one product to the next.

A Causal Approach to Functional Mediation Analysis with Application to a Smoking Cessation Intervention

The increase in the use of mobile and wearable devices now allows dense assessment of mediating processes over time. For example, a pharmacological intervention may have an effect on smoking cessation via reductions in momentary withdrawal symptoms. We define and identify the causal direct and indirect effects in terms of potential outcomes on the mean difference and odds ratio scales, and present a method for estimating and testing the indirect effect of a randomized treatment on a distal binary variable as mediated by the nonparametric trajectory of an intensively measured longitudinal variable (e.g., from ecological momentary assessment). Coverage of a bootstrap test for the indirect effect is demonstrated via simulation. An empirical example is presented based on estimating later smoking abstinence from patterns of craving during smoking cessation treatment. We provide an R package, funmediation, available on CRAN at https://cran.r-project.org/web/packages/funmediation/index.html, to conveniently apply this technique. We conclude by discussing possible extensions to multiple mediators and directions for future research.

Linking research of biomedical datasets

Biomedical data preprocessing and efficient computing can be as important as the statistical methods used to fit the data; data processing needs to consider application scenarios, data acquisition and individual rights and interests. We review common principles, knowledge and methods of integrated research according to the whole-pipeline processing mechanism diverse, coherent, sharing, auditable and ecological. First, neuromorphic and native algorithms integrate diverse datasets, providing linear scalability and high visualization. Second, the choice mechanism of different preprocessing, analysis and transaction methods from raw to neuromorphic was summarized on the node and coordinator platforms. Third, combination of node, network, cloud, edge, swarm and graph builds an ecosystem of cohort integrated research and clinical diagnosis and treatment. Looking forward, it is vital to simultaneously combine deep computing, mass data storage and massively parallel communication.

Causality in statistics and data science education

Statisticians and data scientists transform raw data into understanding and insight. Ideally, these insights empower people to act and make better decisions. However, data is often misleading especially when trying to draw conclusions about causality (for example, Simpson’s paradox). Therefore, developing causal thinking in undergraduate statistics and data science programs is important. However, there is very little guidance in the education literature about what topics and learning outcomes, specific to causality, are most important. In this paper, we propose a causality curriculum for undergraduate statistics and data science programs. Students should be able to think causally, which is defined as a broad pattern of thinking that enables individuals to appropriately assess claims of causality based upon statistical evidence. They should understand how the data generating process affects their conclusions and how to incorporate knowledge from subject matter experts in areas of application. Important topics in causality for the undergraduate curriculum include the potential outcomes framework and counterfactuals, measures of association versus causal effects, confounding, causal diagrams, and methods for estimating causal effects.

Ingredients for Responsible Machine Learning: A Commented Review of The Hitchhiker’s Guide to Responsible Machine Learning

In The hitchhiker’s guide to responsible machine learning, Biecek, Kozak, and Zawada (here BKZ) provide an illustrated and engaging step-by-step guide on how to perform a machine learning (ML) analysis such that the algorithms, the software, and the entire process is interpretable and transparent for both the data scientist and the end user. This review summarises BKZ’s book and elaborates on three elements key to ML analyses: inductive inference, causality, and interpretability.