Heterogeneous expression of mismatch repair proteins and interpretation of immunohistochemical results in colorectal cancer and endometrial cancer

A Comprehensive Study of Heterogeneous Mismatch Repair Expression in Solid Tumors Reveals Different Immunohistochemical Patterns and Distinct Genetic Mechanisms

OBJECTIVE

Immunohistochemistry is routinely performed to detect mismatch repair deficiency in solid tumors. Heterogeneous MMR expression (MMR-het) has been reported occasionally but not systemically studied.

METHODS

In this study, we depicted MMR-het patterns of 40 tumors of different anatomical sites and analyzed MMR genetic alterations and tumor mutational burdens (TMB) through comprehensive genomic profiling.

RESULTS

The MMR-het patterns were classified into 4 subgroups: "single-loss" (3 cases), "MLH1/PMS2 double-loss" (16 cases), "MSH2/MSH6 double-loss" (8 cases), and "triple/tetra-loss" (13 cases). Seventeen MMR-het cases exhibited histological heterogeneity, in which MMR protein loss was generally confined to either poorly differentiated or well-differentiated tumor areas. All "single-loss" tumors had MMR somatic mutations and coexisting POLE exonuclease domain mutations. "MLH1/PMS2 double-loss" tumors unexceptionally harbored MLH1 hypermethylation without MMR germline mutations. In the "MSH2/MSH6 double-loss" subgroup, 4 cases had MSH2/MSH6 germline mutations, while another 4 cases had multiple MSH2/MSH6 somatic mutations. Additional POLE exonuclease domain mutations were identified in 2 cases. Tumors in the "triple/tetra-loss" subgroup generally had MLH1 abnormalities (8 MLH1 hypermethylation, 4 MLH1 germline mutation, 1 MLH1 double somatic mutations), and coexistent somatic mutations on MSH2/MSH6 . Thirty-one cases (83.8%) were TMB-H, and all POLE -mutated cases exhibited ultra-high TMB (111.4 to 524.2 mut/Mb).

CONCLUSION

Our findings highlighted the importance of accurately interpreting heterogeneous MMR protein staining patterns for developing a more efficient personalized genetic investigation strategy.

Response score-based protein structure analysis for cancer prediction aided by the Internet of Things

Medical diagnosis through prediction and analysis is par excellence in integrating modern technologies such as the Internet of Things (IoT). With the aid of such technologies, clinical assessments are eased with protracted computing. Specifically, cancer research through structure prediction and analysis is improved through human and machine interventions sustaining precision improvements. This article, therefore, introduces a Protein Structure Prediction Technique based on Three-Dimensional Sequence. This sequence is modeled using amino acids and their folds observed during the pre-initial cancer stages. The observed sequences and the inflammatory response score of the structure are used to predict the impact of cancer. In this process, ensemble learning is used to identify sequence and folding responses to improve inflammations. This score is correlated with the clinical data for structures and their folds independently for determining the structure changes. Such changes through different sequences are handled using repeated ensemble learning for matching and unmatching response scores. The introduced idea integrated with deep ensemble learning and IoT combination, notably employing stacking method for enhanced cancer prediction precision and interdisciplinary collaboration. The proposed technique improves prediction precision, data correlation, and change detection by 11.83%, 8.48%, and 13.23%, respectively. This technique reduces correlation time and complexity by 10.43% and 12.33%, respectively.