Yang, Ping

Abstract Expression quantitative trait locus (eQTL) mapping is used to identify the functional link between a genomic variant and a gene’s expression. A significant eQTL association does not mean a causal relationship or mechanism, and further investigation is needed to understand how a single-nucleotide polymorphism (SNP) impacts gene expression. One of the most plausible explanations for eQTL is that a genomic variant affects transcription factor (TF) binding and thus impacts its regulation on target genes (TGs). However, the current eQTL model does not provide information on the TF and how its regulation is mediated by the SNP’s genotypes. Here, we propose a new method called differential co-expression QTL (ceQTL) among different alleles using Chow statistics to specifically detect eQTLs that are bound by a particular TF. We start with building a trio of TF, its TG, and related SNP, and then test the significant coefficient difference among different genotypes of the SNP. We applied this ceQTL model to simulated data and the lung tissue datasets from the genotype-tissue expression project. The simulated data results showed that the model was robust to detect true ceQTLs at variable sample sizes and different minor allele frequencies as measured by area under the curve. Our tool also performed a TF binding affinity analysis to add another layer of evidence for functional interpretation. In summary, ceQTL analysis provides a more interpretable and biological insight into the mechanism of eQTL and transcriptomic regulation, which would help us better understand how genomic variants affect phenotypes and diseases.

Abstract Motivation Intratumor heterogeneity arises from ongoing somatic evolution and complicates cancer diagnosis, prognosis, and treatment. Reconstructing evolutionary dynamics typically requires spatiotemporal samples, which are often unavailable in clinical settings. Computational approaches that can infer tumor evolutionary history from single-timepoint bulk sequencing data remain limited. Results We present estimating evolutionary events through single-timepoint sequencing (TEATIME), a novel computational framework that models tumors as mixtures of two competing cell populations: an ancestral clone with baseline fitness and a derived subclone with elevated fitness. Using cross-sectional bulk sequencing data, TEATIME estimates mutation rates, timing of subclone emergence, relative fitness, and number of generations of growth. To quantify intratumor fitness asymmetries, we introduce a novel metric—fitness diversity—which captures the imbalance between competing cell populations and serves as a measure of functional intratumor heterogeneity. Applying TEATIME to 33 tumor types from The Cancer Genome Atlas, we revealed divergent as well as convergent evolutionary patterns. Notably, we found that immune-hot microenvironments constraint subclonal expansion and limit fitness diversity. Moreover, we detected temporal dependencies in mutation acquisition, where early driver mutations in ancestral clones epistatically shape the fitness landscape, predisposing specific subclones to selective advantages. These findings underscore the importance of intratumor competition and tumor-microenvironment interactions in shaping evolutionary trajectories, driving intratumor heterogeneity. Lastly, we demonstrate that TEATIME-derived evolutionary parameters and fitness diversity offer novel prognostic insights across multiple cancer types. Availability and implementation R implementation of TEATIME is available on GitHub (https://github.com/liliulab/TEATIME) and Zenodo (https://zenodo.org/records/17422174).

PURPOSE Manual extraction of treatment outcomes from unstructured oncology clinical notes is a significant challenge for real-world evidence (RWE) generation. This study aimed to develop and evaluate a robust natural language processing (NLP) system to automatically extract cancer treatments and their associated RECIST-based response categories (complete response, partial response, stable disease, and progressive disease) from non–small cell lung cancer (NSCLC) clinical notes. METHODS This retrospective NLP development and validation study used a corpus of 250 NSCLC oncology notes from University of Pittsburgh Medical Center (UPMC) Hillman Cancer Center, annotated by physician experts. An end-to-end NLP pipeline was designed, integrating a rule-based module for entity extraction (treatments and responses) and a machine learning module using biomedical clinical bidirectional encoder representations from transformers for relation classification. The system's performance was evaluated on a held-out test set, with partial external validation for relation extraction on a Mayo Clinic data set. RESULTS The NLP system achieved high overall accuracy. On the UPMC test set (64 notes), the relation classification model attained an area under the receiver operating characteristic curve of 0.94 and an F1 score of 0.92 for linking treatments with documented responses. The rule-based entity extraction demonstrated a macro-averaged F1 score of 0.87 (precision 0.98, recall 0.81). Although precision was high for chemotherapy and most response types (1.00), recall for cancer surgery was 0.45. External validation at Mayo Clinic showed moderate relation extraction F1 scores (range: 0.51-0.64). CONCLUSION The proposed NLP system can reliably extract structured treatment and response information from unstructured NSCLC oncology notes with high accuracy. This automated approach can assist in abstracting critical cancer treatment outcomes from clinical narrative text, thereby streamlining real-world data analysis and supporting the generation of RWE in oncology.

PURPOSE The RECIST guidelines provide a standardized approach for evaluating the response of cancer to treatment, allowing for consistent comparison of treatment efficacy across different therapies and patients. However, collecting such information from electronic health records manually can be extremely labor-intensive and time-consuming because of the complexity and volume of clinical notes. The aim of this study is to apply natural language processing (NLP) techniques to automate this process, minimizing manual data collection efforts, and improving the consistency and reliability of the results. METHODS We proposed a complex, hybrid NLP system that automates the process of extracting, linking, and summarizing anticancer therapy and associated RECIST-like responses from narrative clinical text. The system consists of multiple machine learning–/deep learning–based and rule-based modules for diverse NLP tasks such as named entity recognition, assertion classification, relation extraction, and text normalization, to address different challenges associated with anticancer therapy and response information extraction. We then evaluated the system performances on two independent test sets from different institutions to demonstrate its effectiveness and generalizability. RESULTS The system used domain-specific language models, BioBERT and BioClinicalBERT, for high-performance therapy mentions identification and RECIST responses extraction and categorization. The best-performing model achieved a 0.66 score in linking therapy and RECIST response mentions, with end-to-end performance peaking at 0.74 after relation normalization, indicating substantial efficacy with room for improvement. CONCLUSION We developed, implemented, and tested an information extraction system from clinical notes for cancer treatment and efficacy assessment information. We expect this system will support future cancer research, particularly oncologic studies that focus on efficiently assessing the effectiveness and reliability of cancer therapeutics.

Abstract Background: Clinical, molecular, and genetic epidemiology studies displayed remarkable differences between ever- and never-smoking lung cancer. Methods: We conducted a stratified multi-population (European, East Asian, and African descent) association study on 44,823 ever-smokers and 20,074 never-smokers to identify novel variants that were missed in the non-stratified analysis. Functional analysis including expression quantitative trait loci (eQTL) colocalization and DNA damage assays, and annotation studies were conducted to evaluate the functional roles of the variants. We further evaluated the impact of smoking quantity on lung cancer risk for the variants associated with ever-smoking lung cancer. Results: Five novel independent loci, GABRA4, intergenic region 12q24.33, LRRC4C, LINC01088, and LCNL1 were identified with the association at two or three populations (P < 5 × 10−8). Further functional analysis provided multiple lines of evidence suggesting the variants affect lung cancer risk through excessive DNA damage (GABRA4) or cis-regulation of gene expression (LCNL1). The risk of variants from 12 independent regions, including the well-known CHRNA5, associated with ever-smoking lung cancer was evaluated for never-smokers, light-smokers (packyear ≤ 20), and moderate-to-heavy-smokers (packyear > 20). Different risk patterns were observed for the variants among the different groups by smoking behavior. Conclusions: We identified novel variants associated with lung cancer in only ever- or never-smoking groups that were missed by prior main-effect association studies. Impact: Our study highlights the genetic heterogeneity between ever- and never-smoking lung cancer and provides etiologic insights into the complicated genetic architecture of this deadly cancer.

AbstractBackgroundRecent therapeutic advances and screening technologies have improved survival among patients with lung cancer, who are now at high risk of developing second primary lung cancer (SPLC). Recently, an SPLC risk‐prediction model (called SPLC‐RAT) was developed and validated using data from population‐based epidemiological cohorts and clinical trials, but real‐world validation has been lacking. The predictive performance of SPLC‐RAT was evaluated in a hospital‐based cohort of lung cancer survivors.MethodsThe authors analyzed data from 8448 ever‐smoking patients diagnosed with initial primary lung cancer (IPLC) in 1997–2006 at Mayo Clinic, with each patient followed for SPLC through 2018. The predictive performance of SPLC‐RAT and further explored the potential of improving SPLC detection through risk model‐based surveillance using SPLC‐RAT versus existing clinical surveillance guidelines.ResultsOf 8448 IPLC patients, 483 (5.7%) developed SPLC over 26,470 person‐years. The application of SPLC‐RAT showed high discrimination area under the receiver operating characteristics curve: 0.81). When the cohort was stratified by a 10‐year risk threshold of ≥5.6% (i.e., 80th percentile from the SPLC‐RAT development cohort), the observed SPLC incidence was significantly elevated in the high‐risk versus low‐risk subgroup (13.1% vs. 1.1%, p < 1 × 10–6). The risk‐based surveillance through SPLC‐RAT (≥5.6% threshold) outperformed the National Comprehensive Cancer Network guidelines with higher sensitivity (86.4% vs. 79.4%) and specificity (38.9% vs. 30.4%) and required 20% fewer computed tomography follow‐ups needed to detect one SPLC (162 vs. 202).ConclusionIn a large, hospital‐based cohort, the authors validated the predictive performance of SPLC‐RAT in identifying high‐risk survivors of SPLC and showed its potential to improve SPLC detection through risk‐based surveillance.Plain Language Summary Lung cancer survivors have a high risk of developing second primary lung cancer (SPLC). However, no evidence‐based guidelines for SPLC surveillance are available for lung cancer survivors. Recently, an SPLC risk‐prediction model was developed and validated using data from population‐based epidemiological cohorts and clinical trials, but real‐world validation has been lacking. Using a large, real‐world cohort of lung cancer survivors, we showed the high predictive accuracy and risk‐stratification ability of the SPLC risk‐prediction model. Furthermore, we demonstrated the potential to enhance efficiency in detecting SPLC using risk model‐based surveillance strategies compared to the existing consensus‐based clinical guidelines, including the National Comprehensive Cancer Network.

AbstractBackgroundThere is a knowledge gap regarding lobar versus sublobar resection for atypical carcinoid (AC) of the lung. As such, the authors sought to understand and analyze the outcomes of sublobar resection versus lobectomy in this patient population.MethodsA retrospective analysis using the National Cancer Database was performed to compare overall survival (OS) between patients treated with lobectomy and patients treated with sublobar resection for AC of the lung between the years 2004 and 2016. Patient characteristics were compared with χ2 tests. The Kaplan–Meier method was used to estimate OS distributions, and the log‐rank test was used to compare distributions by treatment strategy. A multivariable Cox regression model was used to assess associations between the treatment strategy and OS. A propensity score matching method was also implemented to further eliminate treatment selection bias in the study sample.ResultsThe database identified 669 patients with T1–T4 and N0–N3 lung ACs that were surgically resected. Unadjusted Kaplan–Meier survival curves did not demonstrate an OS difference between lobectomy and sublobar resection (p = .094). After propensity score matching, curves demonstrated a numerical improvement in OS with lobectomy; however, it was not statistically significant (p = .5). In a subgroup analysis, lobectomy and node‐negative disease were associated with the best OS, whereas sublobar resection and node‐positive disease were associated with the worst OS (p < .0001). Nodal involvement was associated with worse survival, regardless of surgical treatment (p < .0001).ConclusionsIn patients with T1–T4 and N0–N3 ACs of the lung, lobectomy was not associated with an improvement in OS in comparison with sublobar resection.

Abstract The recent development of imaging and sequencing technologies enables systematic advances in the clinical study of lung cancer. Meanwhile, the human mind is limited in effectively handling and fully utilizing the accumulation of such enormous amounts of data. Machine learning-based approaches play a critical role in integrating and analyzing these large and complex datasets, which have extensively characterized lung cancer through the use of different perspectives from these accrued data. In this review, we provide an overview of machine learning-based approaches that strengthen the varying aspects of lung cancer diagnosis and therapy, including early detection, auxiliary diagnosis, prognosis prediction, and immunotherapy practice. Moreover, we highlight the challenges and opportunities for future applications of machine learning in lung cancer.

PURPOSEThe advancement of natural language processing (NLP) has promoted the use of detailed textual data in electronic health records (EHRs) to support cancer research and to facilitate patient care. In this review, we aim to assess EHR for cancer research and patient care by using the Minimal Common Oncology Data Elements (mCODE), which is a community-driven effort to define a minimal set of data elements for cancer research and practice. Specifically, we aim to assess the alignment of NLP-extracted data elements with mCODE and review existing NLP methodologies for extracting said data elements.METHODSPublished literature studies were searched to retrieve cancer-related NLP articles that were written in English and published between January 2010 and September 2020 from main literature databases. After the retrieval, articles with EHRs as the data source were manually identified. A charting form was developed for relevant study analysis and used to categorize data including four main topics: metadata, EHR data and targeted cancer types, NLP methodology, and oncology data elements and standards.RESULTSA total of 123 publications were selected finally and included in our analysis. We found that cancer research and patient care require some data elements beyond mCODE as expected. Transparency and reproductivity are not sufficient in NLP methods, and inconsistency in NLP evaluation exists.CONCLUSIONWe conducted a comprehensive review of cancer NLP for research and patient care using EHRs data. Issues and barriers for wide adoption of cancer NLP were identified and discussed.