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Abstract As a kind of carcinoma with increasing morbidity, poor prognosis, and high mortality, esophageal squamous cell carcinoma (ESCC) is challenging for clinical management. Chemotherapy has been the standard treatment for ESCC over decades, while its clinical outcomes remain unsatisfying. And the regimen that combine standard chemotherapy with targeted therapy also demonstrates little effect. However, the advent of immune checkpoint inhibitors (ICI) proved to be a game changer in cancer treatment. Recent clinical trials had sprung up to evaluate the combined effect of ICI and chemotherapy regarding first‐line ESCC treatment. What's more, researchers attempt to explore the possibility to implement ICI monotherapy regarding second‐line ESCC treatment. In conclusion, most of the first‐line trails present inspiring achievement, while ICI monotherapy indicates little improvement for ESCC treatment. To point out the heterogenicity that could be the potential reasons biasing the pooled results, the differences of PD‐L1 immunohistochemistry (IHC) assays, geographic regions, chemotherapy regimens, and sex disparity among these trails are discussed respectively. In addition, the adverse events occurred during the trails are summarized, which confirm the safety of immunotherapy and chemoimmunotherapy. The article comprehensively reviews the representative explorations of using chemoimmunotherapy strategies in ESCC, as well as the deficiencies among them. Moreover, we highlight some feasible approaches. It will be beneficial for conducting more precise clinical trials on chemoimmunotherapy for ESCC in the future, including the use of more appropriate PD‐L1 IHC assays, careful consideration of the heterogeneity of the enrolled population and the optimal combination of chemotherapy and ICI.

Abstract Accurate prognosis prediction is the key to achieving precision treatment and guiding the selection of adjuvant chemotherapy in high‐risk stage II/III colorectal cancer (CRC) patients. Here we developed a novel machine learning method, the random non‐negative matrix factorization (RNMF) algorithm, which outperformed traditional non‐negative matrix factorization in terms of computational speed, accuracy, and robustness in simulated data sets. Moreover, based on multidimensional data from CRC patients from The Cancer Genome Atlas database and DNA methylation data from those from Sun Yat‐sen University cancer center, we further demonstrated the excellent performance of a novel prognostic computational framework based on the RNMF (PCF_RNMF), which is capable of integrating multidimensional training while allowing survival prediction when single dimensional data for validation is provided. This novel algorithm has great potential to mitigate the challenge of integrating various types of data in public databases with clinically available single‐dimensional data to allow cost‐effective survival prediction for CRC patients in clinical practice.