Combining enhanced sampling and deep learning dimensionality reduction for the study of the heat shock protein B8 and its pathological mutant K141E

The beauty and complexity of the small heat shock proteins: a report on the proceedings of the fourth workshop on small heat shock proteins

The Fourth Cell Stress Society International workshop on small heat shock proteins (sHSPs), a follow-up to successful workshops held in 2014, 2016 and 2018, took place as a virtual meeting on the 17-18 November 2022. The meeting was designed to provide an opportunity for those working on sHSPs to reconnect and discuss their latest work. The diversity of research in the sHSP field is reflected in the breadth of topics covered in the talks presented at this meeting. Here we summarise the presentations at this meeting and provide some perspectives on exciting future topics to be addressed in the field.

A deep learning-based model (DeepMPM) to help predict survival in patients with malignant pleural mesothelioma

Background

Malignant pleural mesothelioma (MPM) is a rare disease with limited treatment and poor prognosis, and a precise and reliable means to predicting MPM remains lacking for clinical use.

Methods

In the population-based cohort study, we collected clinical characteristics from the Surveillance, Epidemiology, and End Results (SEER) database. According to the time of diagnosis, the SEER data were divided into 2 cohorts: the training cohort (from 2010 to 2016) and the test cohort (from 2017 to 2019). The training cohort was used to train a deep learning-based predictive model derived from DeepSurv theory, which was validated by both the training and the test cohorts. All clinical characteristics were included and analyzed using Cox proportional risk regression or Kaplan-Meier curve to determine the risk factors and protective factors of MPM.

Results

The survival model included 3,130 cases (2,208 in the training cohort and 922 in the test cohort). As for model's performance, the area under the receiver operating characteristics curve (AUC) was 0.7037 [95% confidence interval (CI): 0.7030-0.7045] in the training cohort and 0.7076 (95% CI: 0.7067-0.7086) in the test cohort. Older age; male sex, sarcomatoid mesothelioma; and T4, N2, and M1 stage tended to be the risk factors for survival. Meanwhile, epithelioid mesothelioma, surgery, radiotherapy, and chemotherapy tended to be the protective factors. The median overall survival (OS) of patients who underwent surgery combined with radiotherapy was the longest, followed by those who underwent a combination of surgery, radiotherapy, and chemotherapy.

Conclusions

Our deep learning-based model precisely could predict the survival of patients with MPM; moreover, multimode combination therapy might provide more meaningful survival benefits.