Copy-number intratumor heterogeneity increases the risk of relapse in chemotherapy-naive stage II colon cancer

Machine Learning Study of SNPs in Noncoding Regions to Predict Non-small Cell Lung Cancer Susceptibility

Non-small cell lung cancer (NSCLC) is the most common pathological subtype of lung cancer. Both environmental and genetic factors have been reported to impact the lung cancer susceptibility. We conducted a genome-wide association study (GWAS) of 287 NSCLC patients and 467 healthy controls in a Chinese population using the Illumina Genome-Wide Asian Screening Array Chip on 712,095 SNPs (single nucleotide polymorphisms). Using logistic regression modeling, GWAS identified 17 new noncoding region SNP loci associated with the NSCLC risk, and the top three (rs80040741, rs9568547, rs6010259) were under a stringent p-value (<3.02e-6). Notably, rs80040741 and rs6010259 were annotated from the intron regions of MUC3A and MLC1, respectively. Together with another five SNPs previously reported in Chinese NSCLC patients and another four covariates (e.g., smoking status, age, low dose CT screening, sex), a predictive model by machine learning methods can separate the NSCLC from healthy controls with an accuracy of 86%. This is the first time to apply machine learning method in predicting the NSCLC susceptibility using both genetic and clinical characteristics. Our findings will provide a promising method in NSCLC early diagnosis and improve our understanding of applying machine learning methods in precision medicine.

A novel 9-gene signature for the prediction of postoperative recurrence in stage II/III colorectal cancer

Background: Individualized recurrence risk prediction in patients with stage II/III colorectal cancer (CRC) is crucial for making postoperative treatment decisions. However, there is still a lack of effective approaches for identifying patients with stage II and III CRC at a high risk of recurrence. In this study, we aimed to establish a credible gene model for improving the risk assessment of patients with stage II/III CRC. Methods: Recurrence-free survival (RFS)-related genes were screened using Univariate Cox regression analysis in GSE17538, GSE39582, and GSE161158 cohorts. Common prognostic genes were identified by Venn diagram and subsequently subjected to least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate Cox regression analysis for signature construction. Kaplan-Meier (K-M), calibration, and receiver operating characteristic (ROC) curves were used to assess the predictive accuracy and superiority of our risk model. Single-sample gene set enrichment analysis (ssGSEA) was employed to investigate the relationship between the infiltrative abundances of immune cells and risk scores. Genes significantly associated with the risk scores were identified to explore the biological implications of the 9-gene signature. Results: Survival analysis identified 347 RFS-related genes. Using these genes, a 9-gene signature was constructed, which was composed of MRPL41, FGD3, RBM38, SPINK1, DKK1, GAL3ST4, INHBB, CTB-113P19.1, and FAM214B. K-M curves verified the survival differences between the low- and high-risk groups classified by the 9-gene signature. The area under the curve (AUC) values of this signature were close to or no less than the previously reported prognostic signatures and clinical factors, suggesting that this model could provide improved RFS prediction. The ssGSEA algorithm estimated that eight immune cells, including regulatory T cells, were aberrantly infiltrated in the high-risk group. Furthermore, the signature was associated with multiple oncogenic pathways, including cell adhesion and angiogenesis. Conclusion: A novel RFS prediction model for patients with stage II/III CRC was constructed using multicohort validation. The proposed signature may help clinicians better manage patients with stage II/III CRC.

Tumour break load is a biologically relevant feature of genomic instability with prognostic value in colorectal cancer

BACKGROUND

Clinically implemented prognostic biomarkers are lacking for the 80% of colorectal cancers (CRCs) that exhibit chromosomal instability (CIN). CIN is characterised by chromosome segregation errors and double-strand break repair defects that lead to somatic copy number aberrations (SCNAs) and chromosomal rearrangement-associated structural variants (SVs), respectively. We hypothesise that the number of SVs is a distinct feature of genomic instability and defined a new measure to quantify SVs: the tumour break load (TBL). The present study aimed to characterise the biological impact and clinical relevance of TBL in CRC.

METHODS

Disease-free survival and SCNA data were obtained from The Cancer Genome Atlas and two independent CRC studies. TBL was defined as the sum of SCNA-associated SVs. RNA gene expression data of microsatellite stable (MSS) CRC samples were used to train an RNA-based TBL classifier. Dichotomised DNA-based TBL data were used for survival analysis.

RESULTS

TBL shows large variation in CRC with poor correlation to tumour mutational burden and fraction of genome altered. TBL impact on tumour biology was illustrated by the high accuracy of classifying cancers in TBL-high and TBL-low (area under the receiver operating characteristic curve [AUC]: 0.88; p < 0.01). High TBL was associated with disease recurrence in 85 stages II-III MSS CRCs from The Cancer Genome Atlas (hazard ratio [HR]: 6.1; p = 0.007) and in two independent validation series of 57 untreated stages II-III (HR: 4.1; p = 0.012) and 74 untreated stage II MSS CRCs (HR: 2.4; p = 0.01).

CONCLUSION

TBL is a prognostic biomarker in patients with non-metastatic MSS CRC with great potential to be implemented in routine molecular diagnostics.

Genetic and microenvironmental intra-tumor heterogeneity impacts colorectal cancer evolution and metastatic development

Colorectal cancer (CRC) is a highly diverse disease, where different genomic instability pathways shape genetic clonal diversity and tumor microenvironment. Although intra-tumor heterogeneity has been characterized in primary tumors, its origin and consequences in CRC outcome is not fully understood. Therefore, we assessed intra- and inter-tumor heterogeneity of a prospective cohort of 136 CRC samples. We demonstrate that CRC diversity is forged by asynchronous forms of molecular alterations, where mutational and chromosomal instability collectively boost CRC genetic and microenvironment intra-tumor heterogeneity. We were able to depict predictor signatures of cancer-related genes that can foresee heterogeneity levels across the different tumor consensus molecular subtypes (CMS) and primary tumor location. Finally, we show that high genetic and microenvironment heterogeneity are associated with lower metastatic potential, whereas late-emerging copy number variations favor metastasis development and polyclonal seeding. This study provides an exhaustive portrait of the interplay between genetic and microenvironment intra-tumor heterogeneity across CMS subtypes, depicting molecular events with predictive value of CRC progression and metastasis development.

Metabolic pathway-based molecular subtyping of colon cancer reveals clinical immunotherapy potential and prognosis

PURPOSE

Colon cancer presents challenges to clinical diagnosis and management due to its high heterogeneity. For more efficient and convenient diagnosis and treatment of colon cancer, we are committed to characterizing the molecular features of colon cancer by pioneering a classification system based on metabolic pathways.

METHODS

Based on the 113 metabolic pathways and genes collected in the previous stage, we scored and filtered the metabolic pathways of each sample in the training set by ssGSEA, and obtained 16 metabolic pathways related to colon cancer recurrence. In consistent clustering of training set samples with recurrence-related metabolic pathway scores, we identified two robust molecular subtypes of colon cancer (MC1 and MC2). Furthermore, we performed multi-angle analysis on the survival differences of subtypes, metabolic characteristics, clinical characteristics, functional enrichment, immune infiltration, differences with other subtypes, stemness indices, TIDE prediction, and drug sensitivity, and finally constructed colon cancer prognostic model.

RESULTS

The results showed that the MC1 subtype had a poor prognosis based on higher immune activity and immune checkpoint gene expression. The MC2 subtype is associated with high metabolic activity and low expression of immune checkpoint genes and a better prognosis. The MC2 subtype was more responsive to PD-L1 immunotherapy than the MC1 subclass. However, we did not observe significant differences in tumor mutational burden between the two.

CONCLUSION

Two molecular subtypes of colon cancer based on metabolic pathways have distinct immune signatures. Constructing prognostic models based on subtype differential genes provides valuable reference for personalized therapy targeting unique tumor metabolic signatures.

Copy-number heterogeneity as high-risk feature of stage II colon cancer

Overall, the prognosis of patients suffering from stage II colon cancer is relatively favorable. However, a proportion of patients develop a recurrence following surgery. Clinical and histopathological properties that identify high‐risk patients are of limited value and better biomarkers are urgently required. In a recent issue of The Journal of Pathology, Lahoz et al proposed that copy‐number‐based biomarkers could be employed for patient stratification. The authors studied copy‐number alterations (CNAs) at the genomic scale by measuring the total CNA load (the aberrant genome fraction), and at a smaller scale by identifying common arm‐ or cytoband‐level alterations. Both the overall CNA load and specific chromosomal regions were associated with an increased risk of recurrence. Most interestingly, it was demonstrated that copy‐number intratumor heterogeneity, as defined by subclonal CNAs, is associated with poor disease outcome. This study demonstrates that structural genomic aberrations are promising biomarkers for patient stratification in early colon cancer. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.