Validity of using immunohistochemistry to predict treatment outcome in patients with non-small cell lung cancer not otherwise specified

Primary tumor type prediction based on US nationwide genomic profiling data in 13,522 patients

Timely and accurate primary tumor diagnosis is critical, and misdiagnoses and delays may cause undue health and economic burden. To predict primary tumor types based on genomics data from a de-identified US nationwide clinico-genomic database (CGDB), the XGBoost-based Clinico-Genomic Machine Learning Model (XC-GeM) was developed to predict 13 primary tumor types based on data from 12,060 patients in the CGDB, derived from routine clinical comprehensive genomic profiling (CGP) testing and chart-confirmed electronic health records (EHRs). The SHapley Additive exPlanations method was used to interpret model predictions. XC-GeM reached an outstanding area under the curve (AUC) of 0.965 and Matthew's correlation coefficient (MCC) of 0.742 in the holdout validation dataset. In the independent validation cohort of 955 patients, XC-GeM reached 0.954 AUC and 0.733 MCC and made correct predictions in 77% of non-small cell lung cancer (NSCLC), 86% of colorectal cancer, and 84% of breast cancer patients. Top predictors for the overall model (e.g. tumor mutational burden (TMB), gender, and KRAS alteration), and for specific tumor types (e.g., TMB and EGFR alteration for NSCLC) were supported by published studies. XC-GeM also achieved an excellent AUC of 0.880 and positive MCC of 0.540 in 507 patients with missing primary diagnosis. XC-GeM is the first algorithm to predict primary tumor type using US nationwide data from routine CGP testing and chart-confirmed EHRs, showing promising performance. It may enhance the accuracy and efficiency of cancer diagnoses, enabling more timely treatment choices and potentially leading to better outcomes.

Deciphering Lung Adenocarcinoma Heterogeneity: An Overview of Pathological and Clinical Features of Rare Subtypes

Lung cancer is one of the most frequently diagnosed cancers worldwide and the leading cause of cancer-related death. The 2021 World Health Organization (WHO) classification provided a detailed and updated categorization of lung adenocarcinomas with a special focus on rare histological types, including enteric, fetal and colloid types, as well as not otherwise specified adenocarcinoma, overall accounting for about 5-10% of all cases. However, rare entities are nowadays difficult to diagnose in most centers, and evidence of optimal therapeutic management for these patients is still lacking. In recent years, increasing knowledge about the mutational profile of lung cancer, in addition to the spreading diffusion of next-generation sequencing (NGS) in different centers, have been helpful in the identification of rare variants of lung cancer. Hence, the hope is that several new drugs will be available in the near future to treat these rare lung tumors, such as in targeted therapy and immunotherapy, which are often used in clinical practice for several malignancies. The aim of this review is to summarize the current knowledge about the molecular pathology and clinical management of the most common rare adenocarcinoma subtypes in order to provide a concise and updated report that can drive clinicians' choices in their routine practice.

Genetic Variants of CLEC4E and BIRC3 in Damage-Associated Molecular Patterns-Related Pathway Genes Predict Non-Small Cell Lung Cancer Survival

Accumulating evidence supports a role of various damage-associated molecular patterns (DAMPs) in progression of lung cancer, but roles of genetic variants of the DAMPs-related pathway genes in lung cancer survival remain unknown. We investigated associations of 18,588 single-nucleotide polymorphisms (SNPs) in 195 DAMPs-related pathway genes with non-small cell lung cancer (NSCLC) survival in a subset of genotyping data for 1,185 patients from the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial and validated the findings in another independent subset of genotyping data for 984 patients from Harvard Lung Cancer Susceptibility Study. We performed multivariate Cox proportional hazards regression analysis, followed by expression quantitative trait loci (eQTL) analysis, Kaplan-Meier survival analysis and bioinformatics functional prediction. We identified that two SNPs (i.e., CLEC4E rs10841847 G>A and BIRC3 rs11225211 G>A) were independently associated with NSCLC overall survival, with adjusted allelic hazards ratios of 0.89 (95% confidence interval=0.82-0.95 and P=0.001) and 0.82 (0.73-0.91 and P=0.0003), respectively; so were their combined predictive alleles from discovery and replication datasets (P_trend=0.0002 for overall survival). We also found that the CLEC4E rs10841847 A allele was associated with elevated mRNA expression levels in normal lymphoblastoid cells and whole blood cells, while the BIRC3 rs11225211 A allele was associated with increased mRNA expression levels in normal lung tissues. Collectively, these findings indicated that genetic variants of CLEC4E and BIRC3 in the DAMPs-related pathway genes were associated with NSCLC survival, likely by regulating the mRNA expression of the corresponding genes.

Diagnostic patterns of non-small-cell lung cancer at Princess Margaret Cancer Centre

Background

Accurate classification of lung cancer subtypes has become critical in tailoring lung cancer treatment. Our study aimed to evaluate changes in diagnostic testing and pathologic subtyping of advanced non-small-cell lung cancer (nsclc) over time at a major cancer centre.

Methods

In a review of patients diagnosed with advanced nsclc at Princess Margaret Cancer Centre between 2007-2009 and 2013-2015, diagnostic method, sample type and site, pathologic subtype, and use of immunohistochemistry (ihc) staining and molecular testing were abstracted.

Results

The review identified 238 patients in 2007-2009 and 283 patients in 2013-2015. Over time, the proportion of patients diagnosed with adenocarcinoma increased to 73.1% from 60.9%, and diagnoses of nsclc not otherwise specified (nos) decreased to 6.4% from 18.9%, p < 0.0001. Use of diagnostic bronchoscopy decreased (26.9% vs. 18.4%), and mediastinal sampling procedures, including endobronchial ultrasonography, increased (9.2% vs. 20.5%, p = 0.0001). Use of ihc increased over time to 76.3% from 41.6% (p < 0.0001). Larger surgical or core biopsy samples and those for which ihc was performed were more likely to undergo biomarker testing (both p < 0.01).

Conclusions

Customizing treatment based on pathologic subtype and molecular genotype has become key in treating patients with advanced lung cancer. Greater accuracy of pathology diagnosis is being achieved, including through the routine use of ihc.