Editing of FAK mRNA by the enzyme ADAR in lung adenocarcinoma cells promotes migration and invasion.

Cohen’s kappa is often used to quantify the agreement between two pathologists. Nevertheless, a high prevalence of the feature of interest can lead to seemingly paradoxical results, such as low Cohen’s kappa values despite high “observed agreement.” Here, we investigate Cohen’s kappa using data from histologic subtyping assessment of lung adenocarcinomas and introduce alternative measures that can overcome this “kappa paradox.” A total of 50 frozen sections from stage I lung adenocarcinomas less than or equal to 3 cm in size were independently reviewed by two pathologists to determine the absence or presence of five histologic patterns (lepidic, papillary, acinar, micropapillary, solid). For each pattern, observed agreement (proportion of cases with concordant “absent” or “present” ratings) and Cohen’s kappa were calculated, along with Gwet’s AC1. The prevalence of any amount of the histologic patterns ranged from 42% (solid) to 97% (acinar). On the basis of Cohen’s kappa, there was substantial agreement for four of the five patterns (lepidic, 0.65; papillary, 0.67; micropapillary, 0.64; solid, 0.61). Acinar had the lowest Cohen’s kappa (0.43, moderate agreement), despite having the highest observed agreement (88%). In contrast, Gwet’s AC1 values were close to or higher than Cohen’s kappa across patterns (lepidic, 0.64; papillary, 0.69; micropapillary, 0.71; solid, 0.73; acinar, 0.85). The proportion of positive versus negative agreement was 93% versus 50% for acinar. Given the dependence of Cohen’s kappa on feature prevalence, interrater agreement studies should include complementary indices such as Gwet’s AC1 and proportions of specific agreement, especially in settings with a high prevalence of the feature of interest.

Currently, no available pathological or molecular measures of tumor angiogenesis predict response to antiangiogenic therapies used in clinical practice. Recognizing that tumor endothelial cells (EC) and EC activation and survival signaling are the direct targets of these therapies, we sought to develop an automated platform for quantifying activity of critical signaling pathways and other biological events in EC of patient tumors by histopathology. Computer image analysis of EC in highly heterogeneous human tumors by a statistical classifier trained using examples selected by human experts performed poorly due to subjectivity and selection bias. We hypothesized that the analysis can be optimized by a more active process to aid experts in identifying informative training examples. To test this hypothesis, we incorporated a novel active learning (AL) algorithm into FARSIGHT image analysis software that aids the expert by seeking out informative examples for the operator to label. The resulting FARSIGHT-AL system identified EC with specificity and sensitivity consistently greater than 0.9 and outperformed traditional supervised classification algorithms. The system modeled individual operator preferences and generated reproducible results. Using the results of EC classification, we also quantified proliferation (Ki67) and activity in important signal transduction pathways (MAP kinase, STAT3) in immunostained human clear cell renal cell carcinoma and other tumors. FARSIGHT-AL enables characterization of EC in conventionally preserved human tumors in a more automated process suitable for testing and validating in clinical trials. The results of our study support a unique opportunity for quantifying angiogenesis in a manner that can now be tested for its ability to identify novel predictive and response biomarkers.

The new grading system for lung adenocarcinoma (ADC) proposed by the International Association for the Study of Lung Cancer (IASLC) defines prognostic subgroups based on histologic patterns observed on surgical specimens. This study seeks to provide novel insights into the IASLC grading system, with a particular focus on recurrence-specific survival (RSS) and lung cancer-specific survival (LCSS) among patients with stage I ADC. Under the IASLC grading system, tumors were classified as grade 1 (lepidic predominant with <20% high-grade patterns [micropapillary, solid, complex glandular]), grade 2 (acinar or papillary predominant with <20% high-grade patterns), or grade 3 (≥20% high-grade patterns). Kaplan-Meier survival estimates, pathologic features, and genomic profiles were investigated for patients whose disease was reclassified to a higher grade under the IASLC grading system on the basis of the hypothesis that they would strongly resemble patients with predominant high-grade tumors. Overall, 423 of 1443 patients (29%) with grade 1 or 2 tumors by the predominant pattern-based grading system had their tumors upgraded to grade 3 by the IASLC grading system. The RSS curves for patients with upgraded tumors were significantly different from those for patients with grade 1 or 2 tumors (log-rank p<0.001) but not from those for patients with predominant high-grade patterns (p=0.3). Patients with upgraded tumors had a similar incidence of visceral pleural invasion and spread of tumor through air spaces as patients with predominant high-grade patterns. In multivariable models, the IASLC grading system remained significantly associated with RSS and LCSS after adjustment for aggressive pathologic features such as visceral pleural invasion and spread of tumor through air spaces. The IASLC grading system outperforms the predominant pattern-based grading system and appropriately reclassifies tumors into higher grades with worse prognosis, even after other pathologic features of aggressiveness are considered.

About 50% of patients with early-stage, surgically resected lung cancer will develop distant metastasis. There remains an unmet need to identify patients likely to develop recurrence and to design innovative therapies to decrease this risk. Two primary isoforms of BRMS1, v1 and v2, are present in humans. Using next-generation sequencing of BRMS1 on matched human noncancerous lung tissue and non–small cell lung cancer (NSCLC) specimens, we identified single-nucleotide polymorphism (SNP) rs1052566 that results in an A273V mutation of BRMS1v2. This SNP is homozygous ( BRMS1v2 ^A273V/A273V ) in 8% of the population and correlates with aggressive biology in lung adenocarcinoma (LUAD). Mechanistically, we show that BRMS1v2 A273V abolishes the metastasis suppressor function of BRMS1v2 and promotes robust cell invasion and metastases by activation of c-fos–mediated gene-specific transcriptional regulation. BRMS1v2 A273V increases cell invasion in vitro and increases metastases in both tail-vein injection xenografts and LUAD patient-derived organoid (PDO) intracardiac injection metastasis in vivo models. Moreover, we show that BRMS1v2 A273V fails to interact with nuclear Src, thereby activating intratumoral c-fos in vitro. Higher c-fos results in up-regulation of CEACAM6 , which drives metastases in vitro and in vivo. Using both xenograft and PDO metastasis models, we repurposed T5224 for treatment, a c-fos pharmacologic inhibitor investigated in clinical trials for arthritis, and observed suppression of metastases in BRMS1v2 ^A273V/A273V LUAD in mice. Collectively, we elucidate the mechanism of BRMS1v2 ^A273V/A273V -induced metastases and offer a putative therapeutic strategy for patients with LUAD who have this germline alteration.