Germline-focused analysis of tumour-detected variants in 49,264 cancer patients: ESMO Precision Medicine Working Group recommendations

Germline; Tumour-only sequencingLínia germinal; Seqüenciació només de tumorsLínea germinal; Secuenciación solo de tumorsBackground The European Society for Medical Oncology Precision Medicine Working Group (ESMO PMWG) was reconvened to update its 2018/19 recommendations on follow-up of putative germline variants detected on tumour-only sequencing, which were based on an analysis of 17 152 cancers. Methods We analysed an expanded dataset including 49 264 paired tumour-normal samples. We applied filters to tumour-detected variants based on variant allele frequency, predicted pathogenicity and population variant frequency. For 58 cancer-susceptibility genes, we then examined the proportion of filtered tumour-detected variants of true germline origin [germline conversion rate (GCR)]. We conducted subanalyses based on the age of cancer diagnosis, specific tumour types and ‘on-tumour’ status (established tumour-gene association). Results Analysis of 45 472 nonhypermutated solid malignancy tumour samples yielded 21 351 filtered tumour-detected variants of which 3515 were of true germline origin. 3.1% of true germline pathogenic variants were absent from the filtered tumour-detected variants. For genes such as BRCA1, BRCA2 and PALB2, the GCR in filtered tumour-detected variants was >80%; conversely for TP53, APC and STK11 this GCR was <2%. Conclusion Strategic germline-focused analysis can prioritise a subset of tumour-detected variants for which germline follow-up will produce the highest yield of most actionable true germline variants. We present updated recommendations around germline follow-up of tumour-only sequencing including (i) revision to 5% for the minimum per-gene GCR, (ii) inclusion of actionable intermediate penetrance genes ATM and CHEK2, (iii) definition of a set of seven ‘most actionable’ cancer-susceptibility genes (BRCA1, BRCA2, PALB2, MLH1, MSH2, MSH6 and RET) in which germline follow-up is recommended regardless of tumour type.This work was supported by the European Society for Medical Oncology (no grant number)

Ordered and deterministic cancer genome evolution after p53 loss

Although p53 inactivation promotes genomic instability1 and presents a route to malignancy for more than half of all human cancers2,3, the patterns through which heterogenous TP53 (encoding human p53) mutant genomes emerge and influence tumorigenesis remain poorly understood. Here, in a mouse model of pancreatic ductal adenocarcinoma that reports sporadic p53 loss of heterozygosity before cancer onset, we find that malignant properties enabled by p53 inactivation are acquired through a predictable pattern of genome evolution. Single-cell sequencing and in situ genotyping of cells from the point of p53 inactivation through progression to frank cancer reveal that this deterministic behaviour involves four sequential phases-Trp53 (encoding mouse p53) loss of heterozygosity, accumulation of deletions, genome doubling, and the emergence of gains and amplifications-each associated with specific histological stages across the premalignant and malignant spectrum. Despite rampant heterogeneity, the deletion events that follow p53 inactivation target functionally relevant pathways that can shape genomic evolution and remain fixed as homogenous events in diverse malignant populations. Thus, loss of p53-the 'guardian of the genome'-is not merely a gateway to genetic chaos but, rather, can enable deterministic patterns of genome evolution that may point to new strategies for the treatment of TP53-mutant tumours

Protein-altering germline mutations implicate novel genes related to lung cancer development

Few germline mutations are known to affect lung cancer risk. We performed analyses of rare variants from 39,146 individuals of European ancestry and investigated gene expression levels in 7,773 samples. We find a large-effect association with an ATM L2307F (rs56009889) mutation in adenocarcinoma for discovery (adjusted Odds Ratio = 8.82, P = 1.18 × 10−15) and replication (adjusted OR = 2.93, P = 2.22 × 10−3) that is more pronounced in females (adjusted OR = 6.81 and 3.19 and for discovery and replication). We observe an excess loss of heterozygosity in lung tumors among ATM L2307F allele carriers. L2307F is more frequent (4%) among Ashkenazi Jewish populations. We also observe an association in discovery (adjusted OR = 2.61, P = 7.98 × 10−22) and replication datasets (adjusted OR = 1.55, P = 0.06) with a loss-of-function mutation, Q4X (rs150665432) of an uncharacterized gene, KIAA0930. Our findings implicate germline genetic variants in ATM with lung cancer susceptibility and suggest KIAA0930 as a novel candidate gene for lung cancer risk

Robust stratification of breast cancer subtypes using differential patterns of transcript isoform expression

Breast cancer, the second leading cause of cancer death of women worldwide, is a heterogenous disease with multiple different subtypes. These subtypes carry important implications for prognosis and therapy. Interestingly, it is known that these different subtypes not only have different biological behaviors, but also have distinct gene expression profiles. However, it has not been rigorously explored whether particular transcriptional isoforms are also differentially expressed among breast cancer subtypes, or whether transcript isoforms from the same sets of genes can be used to differentiate subtypes. To address these questions, we analyzed the patterns of transcript isoform expression using a small set of RNA-sequencing data for eleven Estrogen Receptor positive (ER+) subtype and fourteen triple negative (TN) subtype tumors. We identified specific sets of isoforms that distinguish these tumor subtypes with higher fidelity than standard mRNA expression profiles. We found that alternate promoter usage, alternative splicing, and alternate 3’UTR usage are differentially regulated in breast cancer subtypes. Profiling of isoform expression in a second, independent cohort of 68 tumors confirmed that expression of splice isoforms differentiates breast cancer subtypes. Furthermore, analysis of RNAseq data from 594 cases from the TCGA cohort confirmed the ability of isoform usage to distinguish breast cancer subtypes. Also using our expression data, we identified several RNA processing factors that were differentially expressed between tumor subtypes and/or regulated by estrogen receptor, including YBX1, YBX2, MAGOH, MAGOHB, and PCBP2. RNAi knock-down of these RNA processing factors in MCF7 cells altered isoform expression. These results indicate that global dysregulation of splicing in breast cancer occurs in a subtype-specific and reproducible manner and is driven by specific differentially expressed RNA processing factors

Robust stratification of breast cancer subtypes using differential patterns of transcript isoform expression

<div><p>Breast cancer, the second leading cause of cancer death of women worldwide, is a heterogenous disease with multiple different subtypes. These subtypes carry important implications for prognosis and therapy. Interestingly, it is known that these different subtypes not only have different biological behaviors, but also have distinct gene expression profiles. However, it has not been rigorously explored whether particular transcriptional isoforms are also differentially expressed among breast cancer subtypes, or whether transcript isoforms from the same sets of genes can be used to differentiate subtypes. To address these questions, we analyzed the patterns of transcript isoform expression using a small set of RNA-sequencing data for eleven Estrogen Receptor positive (ER+) subtype and fourteen triple negative (TN) subtype tumors. We identified specific sets of isoforms that distinguish these tumor subtypes with higher fidelity than standard mRNA expression profiles. We found that alternate promoter usage, alternative splicing, and alternate 3’UTR usage are differentially regulated in breast cancer subtypes. Profiling of isoform expression in a second, independent cohort of 68 tumors confirmed that expression of splice isoforms differentiates breast cancer subtypes. Furthermore, analysis of RNAseq data from 594 cases from the TCGA cohort confirmed the ability of isoform usage to distinguish breast cancer subtypes. Also using our expression data, we identified several RNA processing factors that were differentially expressed between tumor subtypes and/or regulated by estrogen receptor, including YBX1, YBX2, MAGOH, MAGOHB, and PCBP2. RNAi knock-down of these RNA processing factors in MCF7 cells altered isoform expression. These results indicate that global dysregulation of splicing in breast cancer occurs in a subtype-specific and reproducible manner and is driven by specific differentially expressed RNA processing factors.</p></div

Differential isoform usage between breast cancer subtypes includes alternative splicing, alternative promoter usage, and alternative 3'UTR usage.

<p>A) Fraction of differentially expressed isoforms that differ in exon usage, 3’UTR usage, or 5’UTR usage, pairwise comparison. No one mechanism dominates. B) Schematic outlining different types of splicing, including exon skipping, intron inclusion, and alternative donor/acceptor sites. C) Pairwise comparison of exon splicing events shows fraction of each type occurring within differentially expressed isoforms. D) Plot of the number of exon skipping and intron retention events in each subtype. There is no significant difference between subtypes.</p

Isoform Overlaps between Knockdown and Tumor samples.

<p>Isoform Overlaps between Knockdown and Tumor samples.</p

Knockdown of RNA processing factors differentially expressed between subtypes alters expression of subtype-specific isoforms.

<p>A) Venn diagram showing the overlap for isoforms affected by splicing and isoforms differentially expressed between subtypes in our discovery cohort. In the case of both MAGOH and YBX1, the overlap is significant. (Fisher’s Exact Test). B). Top panel: Plot FPKM of multi-isoform genes from MAGOH knockdown (x-axis) and YBX1 knockdown (y-axis). Bottom panel: Plot FPKM of multi-isoform genes from MAGOH knockdown (x-axis) and MAGOHB knockdown (y-axis). In both cases, there is a high degree of overlap in isoform expression levels. C) Isoforms were classified into concordant or discordant based on direction of changes in the knock-down compared to expected direction of change from the breast cancer sequencing data. Many more isoforms were concordant than non-concordant, and there was no difference in fraction of concordant genes whether they were up regulated by knockdown (dark grey) or down regulated by knockdown (light gray) D) Pairwise differentially expressed isoforms (FDR <0.05) are mostly represented by isoforms that differ in exons, rather than TSS, indicating that knockdown of these RNA processing factors is affecting splicing.</p

Breast Cancer Isoform Abundance Differentiates Breast Cancer Subtypes in TCGA data.

<p>A) First two principal components derived from RefSeq gene RNAseq FPKM expression levels for multi-isoform genes only. TCGA samples are segregated by breast cancer subtype. B) P value plot for subtype-specific isoform expression in the TCGA data for 694 multi-isoform genes that were differentially expressed in the discovery cohort. 80% of these genes also show subtype-specific isoform expression in the TCGA cohort.</p