bims-tumhet Biomed News
on Tumor Heterogeneity
Issue of 2021‒05‒23
eight papers selected by
Sergio Marchini
Humanitas Research

  1. BMC Genomics. 2021 May 17. 22(1): 354
      BACKGROUND: Copy number variations (CNVs) are a major type of structural genomic variants that underlie genetic architecture and phenotypic variation of complex traits, not only in humans, but also in livestock animals. We identified CNVs along the chicken genome and analyzed their association with performance traits. Genome-wide CNVs were inferred from Affymetrix® high density SNP-chip data for a broiler population. CNVs were concatenated into segments and association analyses were performed with linear mixed models considering a genomic relationship matrix, for birth weight, body weight at 21, 35, 41 and 42 days, feed intake from 35 to 41 days, feed conversion ratio from 35 to 41 days and, body weight gain from 35 to 41 days of age.RESULTS: We identified 23,214 autosomal CNVs, merged into 5042 distinct CNV regions (CNVRs), covering 12.84% of the chicken autosomal genome. One significant CNV segment was associated with BWG on GGA3 (q-value = 0.00443); one significant CNV segment was associated with BW35 (q-value = 0.00571), BW41 (q-value = 0.00180) and BW42 (q-value = 0.00130) on GGA3, and one significant CNV segment was associated with BW on GGA5 (q-value = 0.00432). All significant CNV segments were verified by qPCR, and a validation rate of 92.59% was observed. These CNV segments are located nearby genes, such as KCNJ11, MyoD1 and SOX6, known to underlie growth and development. Moreover, gene-set analyses revealed terms linked with muscle physiology, cellular processes regulation and potassium channels.
    CONCLUSIONS: Overall, this CNV-based GWAS study unravels potential candidate genes that may regulate performance traits in chickens. Our findings provide a foundation for future functional studies on the role of specific genes in regulating performance in chickens.
    Keywords:  CNVs; GWAS; Performance; QTLs; qPCR
  2. Genome Med. 2021 May 20. 13(1): 89
      BACKGROUND: Circulating tumor DNA (ctDNA) offers minimally invasive means to repeatedly interrogate tumor genomes, providing opportunities to monitor clonal dynamics induced by metastasis and therapeutic selective pressures. In metastatic cancers, ctDNA profiling allows for simultaneous analysis of both local and distant sites of recurrence. Despite the promise of ctDNA sampling, its utility in real-time genetic monitoring remains largely unexplored.METHODS: In this exploratory analysis, we characterize high-frequency ctDNA sample series collected over narrow time frames from seven patients with metastatic triple-negative breast cancer, each undergoing treatment with Cabozantinib, a multi-tyrosine kinase inhibitor (NCT01738438, ). Applying orthogonal whole exome sequencing, ultra-low pass whole genome sequencing, and 396-gene targeted panel sequencing, we analyzed 42 plasma-derived ctDNA libraries, representing 4-8 samples per patient with 6-42 days between samples. Integrating tumor fraction, copy number, and somatic variant information, we model tumor clonal dynamics, predict neoantigens, and evaluate consistency of genomic information from orthogonal assays.
    RESULTS: We measured considerable variation in ctDNA tumor faction in each patient, often conflicting with RECIST imaging response metrics. In orthogonal sequencing, we found high concordance between targeted panel and whole exome sequencing in both variant detection and variant allele frequency estimation (specificity = 95.5%, VAF correlation, r = 0.949), Copy number remained generally stable, despite resolution limitations posed by low tumor fraction. Through modeling, we inferred and tracked distinct clonal populations specific to each patient and built phylogenetic trees revealing alterations in hallmark breast cancer drivers, including TP53, PIK3CA, CDK4, and PTEN. Our modeling revealed varied responses to therapy, with some individuals displaying stable clonal profiles, while others showed signs of substantial expansion or reduction in prevalence, with characteristic alterations of varied literature annotation in relation to the study drug. Finally, we predicted and tracked neoantigen-producing alterations across time, exposing translationally relevant detection patterns.
    CONCLUSIONS: Despite technical challenges arising from low tumor content, metastatic ctDNA monitoring can aid our understanding of response and progression, while minimizing patient risk and discomfort. In this study, we demonstrate the potential for high-frequency monitoring of evolving genomic features, providing an important step toward scalable, translational genomics for clinical decision making.
    Keywords:  Circulating tumor DNA; Liquid biopsy; Neoantigens; Serial sequencing; Targeted panel sequencing; Tumor evolution; Ultra-low pass whole genome sequencing; ctDNA
  3. Ann Oncol. 2021 May 13. pii: S0923-7534(21)01554-4. [Epub ahead of print]
  4. Gastroenterology. 2021 May 13. pii: S0016-5085(21)02977-2. [Epub ahead of print]
      BACKGROUND & AIMS: Next generation sequencing (NGS) was recently approved by the FDA to detect microsatellite instability (MSI) arising from defective mismatch repair (dMMR) in patients with metastatic colorectal cancer (mCRC) prior to treatment with immune checkpoint inhibitors (ICI). In this study, we aimed to evaluate and improve the performance of NGS to identify MSI in CRC, especially dMMR mCRC treated with ICI.METHODS: CRC samples used in this post-hoc study were reassessed centrally for MSI and dMMR status using the reference methods of pentaplex PCR and immunohistochemistry (IHC). Whole exome (WES) was used to evaluate MSISensor, the FDA-approved and NGS-based method for assessment of MSI. This was performed in (i) a prospective, multicenter cohort (C1) of 102 mCRC patients (25 dMMR/MSI, 24 treated with ICI) from clinical trials NCT02840604 and NCT033501260, (ii) an independent retrospective, multicenter cohort of 113 patients (C2, 25 mCRC, 88 non-mCRC, all dMMR/MSI untreated with ICI), (iii) and a publicly available series of 118 CRC patients from the TCGA (C3, 51 dMMR/MSI). A new NGS-based algorithm, namely MSICare, was developed. Its performance for assessment of MSI was compared to MSISensor in C1, C2 and C3 at the exome-level or after downsampling sequencing data to the MSK-ImpactTM gene panel. MSICare was validated in an additional retrospective, multicenter cohort (C4) of 152 new CRC patients (137 dMMR/MSI) enriched in MSH6 and PMS2 deficient tumors (35 dMSH6, 9 dPMS2) following targeted sequencing of samples with an optimized set of microsatellite markers (MSIDIAG).
    RESULTS: At the exome-level, MSISensor was highly specific but failed to diagnose MSI in 16% of MSI/dMMR mCRC from C1 (4/25; sensitivity 84%, 95%CI: 63.9%-95.5%), 32% of mCRC (8/25; sensitivity 68%, 95%CI: 46.5%-85.1%) and 9.1% of nmCRC from C2 (8/88; sensitivity 90.9%, 95%CI: 82.9%-96%), and 9.8% of CRC from C3 (5/51; sensitivity 90.2%, 95%CI: 78.6%-96.7%). Misdiagnosis included 4 mCRCs treated with ICI of which 3 showed an overall response rate without progression at this date. At the exome-level, reevaluation of the MSI genomic signal using MSICare detected 100% of cases with true MSI status amongst C1 and C2. Further validation of MSICare was obtained in CRC tumors from C3, with 96.1% concordance for MSI status. Whereas misdiagnosis with MSISensor even increased when analyzing downsampled WES data from C1 and C2 with microsatellite markers restricted to the MSK-Impact gene panel (sensitivity 72.5%, 95%CI: 64.2-79.7%), particularly in MSH6 deficient setting, MSICare sensitivity and specificity remained optimal (100%). Similar results were obtained with MSICare following targeted NGS of tumors from C4 with the optimized microsatellite panel MSIDIAG (sensitivity 99.3%, 95%CI: 96%-100%; specificity 100%).
    CONCLUSIONS: In contrast to MSISensor, the new MSICare test we propose performs at least as efficiently as the reference method, MSI PCR, to detect MSI in CRC regardless of the defective MMR protein under both WES and targeted NGS conditions. We suggest MSICare may become rapidly a reference method for NGS-based testing of MSI in CRC, especially in mCRC where accurate MSI status is required before the prescription of ICI.
    Keywords:  Diagnostic test; Immunotherapy; Microsatellite instability (MSI); Mismatch repair deficiency (dMMR); Next-generation sequencing; Reference methods
  5. Front Cell Dev Biol. 2021 ;9 619330
      Carcinoma of unknown primary (CUP) is a type of metastatic cancer, the primary tumor site of which cannot be identified. CUP occupies approximately 5% of cancer incidences in the United States with usually unfavorable prognosis, making it a big threat to public health. Traditional methods to identify the tissue-of-origin (TOO) of CUP like immunohistochemistry can only deal with around 20% CUP patients. In recent years, more and more studies suggest that it is promising to solve the problem by integrating machine learning techniques with big biomedical data involving multiple types of biomarkers including epigenetic, genetic, and gene expression profiles, such as DNA methylation. Different biomarkers play different roles in cancer research; for example, genomic mutations in a patient's tumor could lead to specific anticancer drugs for treatment; DNA methylation and copy number variation could reveal tumor tissue of origin and molecular classification. However, there is no systematic comparison on which biomarker is better at identifying the cancer type and site of origin. In addition, it might also be possible to further improve the inference accuracy by integrating multiple types of biomarkers. In this study, we used primary tumor data rather than metastatic tumor data. Although the use of primary tumors may lead to some biases in our classification model, their tumor-of-origins are known. In addition, previous studies have suggested that the CUP prediction model built from primary tumors could efficiently predict TOO of metastatic cancers (Lal et al., 2013; Brachtel et al., 2016). We systematically compared the performances of three types of biomarkers including DNA methylation, gene expression profile, and somatic mutation as well as their combinations in inferring the TOO of CUP patients. First, we downloaded the gene expression profile, somatic mutation and DNA methylation data of 7,224 tumor samples across 21 common cancer types from the cancer genome atlas (TCGA) and generated seven different feature matrices through various combinations. Second, we performed feature selection by the Pearson correlation method. The selected features for each matrix were used to build up an XGBoost multi-label classification model to infer cancer TOO, an algorithm proven to be effective in a few previous studies. The performance of each biomarker and combination was compared by the 10-fold cross-validation process. Our results showed that the TOO tracing accuracy using gene expression profile was the highest, followed by DNA methylation, while somatic mutation performed the worst. Meanwhile, we found that simply combining multiple biomarkers does not have much effect in improving prediction accuracy.
    Keywords:  DNA methylation; gene expression; multi-classifier XGBoost; pearson correlation algorithm; somatic mutation; tumor tissue-of-origin
  6. Indian J Surg Oncol. 2021 Apr;12(Suppl 1): 103-110
      Large-scale molecular profiling and DNA sequencing has revolutionized cancer research. Precision medicine is a rapidly developing area in cancer care but it is not uniformly applied across different tumor types. Biomarker-based therapy is associated with improved outcomes, both in terms of progression-free survival and overall survival. Comprehensive genomic profiling (CGP) uses next-generation sequencing to analyze the complete coding sequence of hundreds of genes from a small amount of tissue. Genes included in these assays are those associated with cancer development or have diagnostic, prognostic, familial, or therapeutic implications Genomic profiling is emerging as a clinically viable tool to personalize patient's treatment. This article discusses how the insights gained through CGP can impact treatment plan in common gynecological cancers.
    Keywords:  Genomic profiling; Gynecological cancers; Precision medicine; The Cancer Genome Atlas (TCGA)
  7. Nat Biotechnol. 2021 May 20.
      Cancer progression is driven by both somatic copy number aberrations (CNAs) and chromatin remodeling, yet little is known about the interplay between these two classes of events in shaping the clonal diversity of cancers. We present Alleloscope, a method for allele-specific copy number estimation that can be applied to single-cell DNA- and/or transposase-accessible chromatin-sequencing (scDNA-seq, ATAC-seq) data, enabling combined analysis of allele-specific copy number and chromatin accessibility. On scDNA-seq data from gastric, colorectal and breast cancer samples, with validation using matched linked-read sequencing, Alleloscope finds pervasive occurrence of highly complex, multiallelic CNAs, in which cells that carry varying allelic configurations adding to the same total copy number coevolve within a tumor. On scATAC-seq from two basal cell carcinoma samples and a gastric cancer cell line, Alleloscope detected multiallelic copy number events and copy-neutral loss-of-heterozygosity, enabling dissection of the contributions of chromosomal instability and chromatin remodeling to tumor evolution.
  8. BMC Bioinformatics. 2021 May 15. 22(1): 250
      BACKGROUND: A pair of genes is defined as synthetically lethal if defects on both cause the death of the cell but a defect in only one of the two is compatible with cell viability. Ideally, if A and B are two synthetic lethal genes, inhibiting B should kill cancer cells with a defect on A, and should have no effects on normal cells. Thus, synthetic lethality can be exploited for highly selective cancer therapies, which need to exploit differences between normal and cancer cells.RESULTS: In this paper, we present a new method for predicting synthetic lethal (SL) gene pairs. As neighbouring genes in the genome have highly correlated profiles of copy number variations (CNAs), our method clusters proximal genes with a similar CNA profile, then predicts mutually exclusive group pairs, and finally identifies the SL gene pairs within each group pairs. For mutual-exclusion testing we use a graph-based method which takes into account the mutation frequencies of different subjects and genes. We use two different methods for selecting the pair of SL genes; the first is based on the gene essentiality measured in various conditions by means of the "Gene Activity Ranking Profile" GARP score; the second leverages the annotations of gene to biological pathways.
    CONCLUSIONS: This method is unique among current SL prediction approaches, it reduces false-positive SL predictions compared to previous methods, and it allows establishing explicit collateral lethality relationship of gene pairs within mutually exclusive group pairs.
    Keywords:  Copy number alteration; DNA damage repair genes; Synthetic lethality