NIPT-PG: empowering non-invasive prenatal testing to learn from population genomics through an incremental pan-genomic approach.

Non-invasive prenatal testing (NIPT) is a quite popular approach for detecting fetal genomic aneuploidies. However, due to the limitations on sequencing read length and coverage, NIPT suffers a bottleneck on further improving performance and conducting earlier detection. The errors mainly come from reference biases and population polymorphism. To break this bottleneck, we proposed NIPT-PG, which enables the NIPT algorithm to learn from population data. A pan-genome model is introduced to incorporate variant and polymorphic loci information from tested population. Subsequently, we proposed a sequence-to-graph alignment method, which considers the read mis-match rates during the mapping process, and an indexing method using hash indexing and adjacency lists to accelerate the read alignment process. Finally, by integrating multi-source aligned read and polymorphic sites across the pan-genome, NIPT-PG obtains a more accurate z-score, thereby improving the accuracy of chromosomal aneuploidy detection. We tested NIPT-PG on two simulated datasets and 745 real-world cell-free DNA sequencing data sets from pregnant women. Results demonstrate that NIPT-PG outperforms the standard z-score test. Furthermore, combining experimental and theoretical analyses, we demonstrate the probably approximately correct learnability of NIPT-PG. In summary, NIPT-PG provides a new perspective for fetal chromosomal aneuploidies detection. NIPT-PG may have broad applications in clinical testing, and its detection results can serve as a reference for false positive samples approaching the critical threshold.

An immuno-score signature of tumor immune microenvironment predicts clinical outcomes in locally advanced rectal cancer.

Background and purpose: Accumulating evidence indicates that neoadjuvant chemoradiotherapy(nCRT) success has an immune-associated constituent in locally advanced rectal cancer (LARC). The immune-associated configuration of the tumor microenvironment associated with responses to treatment was explored in LARC in this study. Material and methods: A novel analytic framework was developed based on within-sample relative expression orderings for identifying tumor immune-associated gene pairs and identified an immuno-score signature from bulk transcriptome profiling analysis of 200 LARC patients. And sequencing and microarray analysis of gene expression was conducted to investigate the association between the signature and response to nCRT, immunotherapy, and cell function of CD4 and CD8. The results were validated using 111 pretreated samples from publicly available datasets in multiple aspects and survival analyses. Results: The immuno-score signature of 18 immune-related gene pairs (referred to as IPS) was validated on bulk microarray and RNA-Seq data. According to the model's immune score, LARC patients were divided into high- and low-score groups. The patients with high-score were greater sensitivity to nCRT and immunotherapy, gaining a significantly improved prognosis. In addition, the immune-score gene pair signature was associated with type I anti-tumor T cell responses, positive regulators of T cell functions, and chromosomal instability while reflecting differences between CD8+ T cell subtypes. Conclusion: The immuno-score signature underlines a key role of tumor immune components in nCRT response, and predicts the prognosis of LARC patients as well.

A 41-Gene Pair Signature for Predicting the Pathological Response of Locally Advanced Rectal Cancer to Neoadjuvant Chemoradiation.

Background and Purpose: Pathological response status is a standard reference for the early evaluation of the effect of neoadjuvant chemoradiation (nCRT) on locally advanced rectal cancer (LARC) patients. Various patients respond differently to nCRT, but identifying the pathological response of LARC to nCRT remains a challenge. Therefore, we aimed to identify a signature that can predict the response of LARC to nCRT. Material and Methods: The gene expression profiles of 111 LARC patients receiving fluorouracil-based nCRT were used to obtain gene pairs with within-sample relative expression orderings related to pathological response. These reversal gene pairs were ranked according to the mean decrease Gini index provided by the random forest algorithm to obtain the signature. This signature was verified in two public cohorts of 46 and 42 samples, and a cohort of 33 samples measured at our laboratory. In addition, the signature was used to predict disease-free survival benefits in a series of colorectal cancer datasets. Results: A 41-gene pair signature (41-GPS) was identified in the training cohort with an accuracy of 84.68% and an area under the receiver operating characteristic curve (AUC) of 0.94. In the two public test cohorts, the accuracy was 93.37 and 73.81%, with AUCs of 0.97 and 0.86, respectively. In our dataset, the AUC was 0.80. The results of the survival analysis show that 41-GPS plays an effective role in identifying patients who will respond to nCRT and have a better prognosis. Conclusion: The signature consisting of 41 gene pairs can robustly predict the clinical pathological response of LARC patients to nCRT.