Integrated landscape of copy number variation and RNA expression associated with nodal metastasis in invasive ductal breast carcinoma

MetaCancer: A deep learning-based pan-cancer metastasis prediction model developed using multi-omics data

Predicting metastasis in the early stages means that clinicians have more time to adjust a treatment regimen to target the primary and metastasized cancer. In this regard, several computational approaches are being developed to identify metastasis early. However, most of the approaches focus on changes on one genomic level only, and they are not being developed from a pan-cancer perspective. Thus, we here present a deep learning (DL)-based model, MetaCancer, that differentiates pan-cancer metastasis status based on three heterogeneous data layers. In particular, we built the DL-based model using 400 patients' data that includes RNA sequencing (RNA-Seq), microRNA sequencing (microRNA-Seq), and DNA methylation data from The Cancer Genome Atlas (TCGA). We quantitatively assess the proposed convolutional variational autoencoder (CVAE) and alternative feature extraction methods. We further show that integrating mRNA, microRNA, and DNA methylation data as features improves our model's performance compared to when we used mRNA data only. In addition, we show that the mRNA-related features make a more significant contribution when attempting to distinguish the primary tumors from metastatic ones computationally. Lastly, we show that our DL model significantly outperformed a machine learning (ML) ensemble method based on various metrics.

Copy number variation in triple negative breast cancer samples associated with lymph node metastasis

Triple negative breast cancer (TNBC) is a highly metastatic and aggressive subtype of breast cancer and cases presenting with lymph node involvement have worse outcomes. This study aimed to determine the regions of copy number variation (CNV) associated with lymph node metastasis in TNBC patients. CNV analyses were performed in a study cohort of 23 invasive ductal carcinomas (IDCs), 12 lymph node metastases (LNmets), and 7 normal adjacent tissues (NATs); as well as in an independent cohort containing 70 TNBC IDCs and the same 7 NATs. CNV-associated genes were analyzed using GO-enrichment and Pathway analysis. The prognostic role for genes showing CNV-based changes in messenger RNA expression was determined using the Kaplan-Meier plotter database. For the IDCs, there were a number of variations that were common in both the study and independent cohorts in the amplified regions of 1q, 8q, 19 (p and q), 2p, 5p and the deleted regions in 8p followed by 5q, and 19p. The most frequently amplified regions in the LNmets of the study cohort were 4q28.3, 2p, 3q24, 1q21.2, 10p, 12p11.1, 8q, 20p11.22-20p11.21, 21q22.13, 6p22.1 and the most frequently deleted regions were in 1p36.23, 4q21.1 and 5q. A total of 686 (441 amplified and 245 deleted) genes were associated with LNmets. The LNmet-associated genes were highly enriched for “regulation of complement activation,” “regulation of protein activation cascade,” “regulation of humoral immune response,” “oxytocin signalling pathway,” and “TRAIL binding” pathways. Moreover, 6/686 LNmet-associated genes showed CNV-based changes in their mRNA expression of which, high expression of ASPM and KIF14 was significantly associated with worse relapse-free survival. This study has identified several CNV regions in TNBC that could play a major role in metastasis to the lymph node.

Lipidomics investigations into the tissue phospholipidomic landscape of invasive ductal carcinoma of the breast

The need of identifying alternative therapeutic targets for invasive ductal carcinoma (IDC) of the breast with high specificity and sensitivity for effective therapeutic intervention is crucial for lowering the risk of fatality. Lipidomics has emerged as a key area for the discovery of potential candidates owing to its several shared pathways between cancer cell proliferation and survival. In the current study, we performed comparative phospholipidomic analysis of IDC, benign and control tissue samples of the breast to identify the significant lipid alterations associated with malignant transformation. A total of 33 each age-matched tissue samples from malignant, benign and control were analyzed to identify the altered phospholipids by using liquid chromatography-multiple reaction monitoring mass spectrometry (LC-MRM/MS). A combination of univariate and multivariate statistical approaches was used to select the phospholipid species with the highest contribution in group segregation. Furthermore, these altered phospholipids were structurally confirmed by tandem mass spectrometry. A total of 244 phospholipids were detected consistently at quantifiable levels, out of which 32 were significantly altered in IDC of the breast. Moreover, in pairwise comparison of IDC against benign and control samples, 11 phospholipids were found to be significantly differentially expressed. Particularly, LPI 20:3, PE (22:1/22:2), LPE 20:0 and PC (20:4/22:4) were observed to be most significantly associated with IDC tissue samples. Apart from that, we also identified that long-chain unsaturated fatty acids were enriched in the IDC tissue samples as compared to benign and control samples, indicating its possible association with the invasive phenotype.

Identification of tissue phospholipid alternations associated with invasive ductal carcinoma of breast.

DNA Methylation and Gene Expression with Clinical Covariates Explain Variation in Aggressiveness and Survival of Pancreatic Cancer Patients

Pancreatic cancer (PC) is associated with a high mortality rate. We explored the interindividual variation of cancer outcomes, attributable to DNA methylation, gene expression, and clinical factors among PC patients. We aim to determine whether we could differentiate subjects with greater nodal involvement, higher cancer staging, and subsequent survival. We modeled every response variable as a function of a linear predictor involving the effects of clinical variables, methylation, and gene expression in a Bayesian framework. Our results highlight the overall importance of wide-spread alterations in methylation and gene expression patterns associated with survival, nodal metastasis, and staging.

The genomic landscape of metastasis in treatment-naïve breast cancer models

Metastasis remains the principle cause of mortality for breast cancer and presents a critical challenge because secondary lesions are often refractory to conventional treatments. While specific genetic alterations are tightly linked to primary tumor development and progression, the role of genetic alteration in the metastatic process is not well-understood. The theory of tumor evolution postulated by Peter Nowell in 1976 has yet to be proven in the context of metastasis. Therefore, in order to investigate how somatic evolution contributes to breast cancer metastasis, we performed exome, whole genome, and RNA sequencing of matched metastatic and primary tumors from pre-clinical mouse models of breast cancer. Here we show that in a treatment-naïve setting, recurrent single nucleotide variants and copy number variation, but not gene fusion events, play key metastasis-driving roles in breast cancer. For instance, we identified recurrent mutations in Kras, a known driver of colorectal and lung tumorigenesis that has not been previously implicated in breast cancer metastasis. However, in a set of in vivo proof-of-concept experiments we show that the Kras G12D mutation is sufficient to significantly promote metastasis using three syngeneic allograft models. The work herein confirms the existence of metastasis-driving mutations and presents a novel framework to identify actionable metastasis-targeted therapies.

The majority of breast cancer-associated deaths are due to metastatic disease, the process where cancerous cells leave the primary tumor and spread to a new location in the body, because metastatic tumors often become insensitive to the same therapies that were successful in treating the primary tumor. To date, this complex process has been attributed to dynamic changes in tumor cell gene expression regulated by epigenetic factors. Interestingly, while genomic alterations are accepted drivers of neoplastic transformation, it is unknown if such events contribute to metastatic spread. One reason for this is the limited availability of matched and treatment-naïve primary tumor and metastatic tumor samples from patients for comparative genomic testing. Here we use two pre-clinical mouse models of metastatic breast cancer to test if genomic alterations can drive metastatic capacity. We identified metastasis-specific events of single nucleotide variation and gene amplification in well-known oncogenic genes, as well as lesser known factors. We also show that expression of these mutant factors can drive metastasis of weakly and non-metastatic mouse mammary cancer cell lines when implanted in mice. Crucially, by observing and reporting this untested etiology of metastatic disease, specific genomic events can now be included in efforts to develop targets for metastasis-specific therapies.

Multi-omic signatures identify pan-cancer classes of tumors beyond tissue of origin

Despite recent advances in treatment, cancer continues to be one of the most lethal human maladies. One of the challenges of cancer treatment is the diversity among similar tumors that exhibit different clinical outcomes. Most of this variability comes from wide-spread molecular alterations that can be summarized by omic integration. Here, we have identified eight novel tumor groups (C1-8) via omic integration, characterized by unique cancer signatures and clinical characteristics. C3 had the best clinical outcomes, while C2 and C5 had poorest. C1, C7, and C8 were upregulated for cellular and mitochondrial translation, and relatively low proliferation. C6 and C4 were also downregulated for cellular and mitochondrial translation, and had high proliferation rates. C4 was represented by copy losses on chromosome 6, and had the highest number of metastatic samples. C8 was characterized by copy losses on chromosome 11, having also the lowest lymphocytic infiltration rate. C6 had the lowest natural killer infiltration rate and was represented by copy gains of genes in chromosome 11. C7 was represented by copy gains on chromosome 6, and had the highest upregulation in mitochondrial translation. We believe that, since molecularly alike tumors could respond similarly to treatment, our results could inform therapeutic action.