GIInger predicts homologous recombination deficiency and patient response to PARPi treatment from shallow genomic profiles.

Homologous recombination deficiency (HRD) is a predictive biomarker for poly(ADP-ribose) polymerase 1 inhibitor (PARPi) sensitivity. Routine HRD testing relies on identifying BRCA mutations, but additional HRD-positive patients can be identified by measuring genomic instability (GI), a consequence of HRD. However, the cost and complexity of available solutions hamper GI testing. We introduce a deep learning framework, GIInger, that identifies GI from HRD-induced scarring observed in low-pass whole-genome sequencing data. GIInger seamlessly integrates into standard BRCA testing workflows and yields reproducible results concordant with a reference method in a multisite study of 327 ovarian cancer samples. Applied to a BRCA wild-type enriched subgroup of 195 PAOLA-1 clinical trial patients, GIInger identified HRD-positive patients who experienced significantly extended progression-free survival when treated with PARPi. GIInger is, therefore, a cost-effective and easy-to-implement method for accurately stratifying patients with ovarian cancer for first-line PARPi treatment.

Lung metastases and subsequent malignant transformation of a fumarate hydratase -deficient uterine leiomyoma.

Uterine leiomyomas, or fibroids, are very common smooth muscle tumors. Their potential to metastasize or transform into leiomyosarcomas is extremely low. Here, we report a patient who underwent hysterectomy due to a large leiomyoma and who was diagnosed with pulmonary tumors seven and nine years later. Histopathological re-evaluation confirmed the cellular leiomyoma diagnosis for the uterine tumor, whereas the pulmonary tumors met the diagnostic criteria of a leiomyosarcoma. Whole-exome sequencing revealed very similar mutational profiles in all three tumors, including a somatic homozygous deletion in a rare, but well-established leiomyoma driver gene FH. Tumor evolution analysis confirmed the clonal origin of all three tumors. In addition to mutations shared by all three tumors, pulmonary tumors harbored additional alterations affecting e.g. the cancer-associated genes NRG1 and MYOCD. The second pulmonary leiomyosarcoma harbored additional changes, including a mutation in FGFR1. In global gene expression profiling, the uterine tumor showed similar expression patterns as other FH-deficient leiomyomas. Taken together, this comprehensive molecular data supports the occasional metastatic capability and malignant transformation of uterine leiomyomas. Further studies are required to confirm whether FH-deficient tumors and/or tumors with cellular histopathology have higher malignant potential than other uterine leiomyomas.

Molecular features encoded in the ctDNA reveal heterogeneity and predict outcome in high-risk aggressive B-cell lymphoma.

Inadequate molecular and clinical stratification of the patients with high-risk diffuse large B-cell lymphoma (DLBCL) is a clinical challenge hampering the establishment of personalized therapeutic options. We studied the translational significance of liquid biopsy in a uniformly treated trial cohort. Pretreatment circulating tumor DNA (ctDNA) revealed hidden clinical and biological heterogeneity, and high ctDNA burden determined increased risk of relapse and death independently of conventional risk factors. Genomic dissection of pretreatment ctDNA revealed translationally relevant phenotypic, molecular, and prognostic information that extended beyond diagnostic tissue biopsies. During therapy, chemorefractory lymphomas exhibited diverging ctDNA kinetics, whereas end-of-therapy negativity for minimal residual disease (MRD) characterized cured patients and resolved clinical enigmas, including false residual PET positivity. Furthermore, we discovered fragmentation disparities in the cell-free DNA that characterize lymphoma-derived ctDNA and, as a proof-of-concept for their clinical application, used machine learning to show that end-of-therapy fragmentation patterns predict outcome. Altogether, we have discovered novel molecular determinants in the liquid biopsy that can noninvasively guide treatment decisions.

PRISM: recovering cell-type-specific expression profiles from individual composite RNA-seq samples.

MOTIVATION: A major challenge in analyzing cancer patient transcriptomes is that the tumors are inherently heterogeneous and evolving. We analyzed 214 bulk RNA samples of a longitudinal, prospective ovarian cancer cohort and found that the sample composition changes systematically due to chemotherapy and between the anatomical sites, preventing direct comparison of treatment-naive and treated samples. RESULTS: To overcome this, we developed PRISM, a latent statistical framework to simultaneously extract the sample composition and cell-type-specific whole-transcriptome profiles adapted to each individual sample. Our results indicate that the PRISM-derived composition-free transcriptomic profiles and signatures derived from them predict the patient response better than the composite raw bulk data. We validated our findings in independent ovarian cancer and melanoma cohorts, and verified that PRISM accurately estimates the composition and cell-type-specific expression through whole-genome sequencing and RNA in situ hybridization experiments. AVAILABILITYAND IMPLEMENTATION: https://bitbucket.org/anthakki/prism. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Molecular background delineates outcome of double protein expressor diffuse large B-cell lymphoma.

Concomitant deregulation of MYC and BCL2 comprises clinically significant, yet poorly characterized biological high-risk feature in diffuse large B-cell lymphoma (DLBCL). To interrogate these lymphomas, we analyzed translocations and protein expression of BCL2, BCL6, and MYC; correlated the findings with comprehensive mutational, transcriptomic, and clinical data in 181 patients with primary DLBCL; and validated the key findings in independent data sets. Structural variations of BCL2 were subtype-specific and specifically increased BCL2 expression. Molecular dissection of MYC deregulation revealed associations with other lymphoma drivers, including loss of TP53, and distinctive gene expression profiles. Double protein expression (DPE) arose from heterogeneous molecular backgrounds that exhibited subtype-dependent patterns. In the germinal center B-cell (GCB) DLBCL, concurrent alterations of MYC and BCL2 loci gave rise to the majority of DPE DLBCLs, whereas among the activated B-cell (ABC) DLBCLs, concurrent alterations were infrequent. Clinically, DPE DLBCL defined a prognostic entity, which was independent of the International Prognostic Index (IPI) and cell of origin, and together with the loss of TP53 had a synergistic dismal impact on survival. In the DPE DLBCL, the loss of TP53 was associated with a chemorefractory disease, whereas among the other DLBCLs, no correlation with survival was seen. Importantly, BCL6 translocations identified non-GCB lymphomas with favorable BN2/C1-like survival independent of IPI and concurrent DPE status. Taken together, our findings define molecular characteristics of the DPE in DLBCL, and recognize clinically feasible predictors of outcome. Given the emerging taxonomical significance of BCL2, BCL6, MYC, and TP53, our findings provide further depth and validation to the genomic classification of DLBCL.

Distinct subtypes of diffuse large B-cell lymphoma defined by hypermutated genes.

Diffuse large B-cell lymphoma (DLBCL) is a biologically and clinically heterogeneous disease whose personalized clinical management requires robust molecular stratification. Here, we show that somatic hypermutation (SHM) patterns constitute a marker for DLBCL molecular classification. The activity of SHM mutational processes delineated the cell of origin (COO) in DLBCL. Expression of the herein identified 36 SHM target genes stratified DLBCL into four novel SHM subtypes. In a meta-analysis of patients with DLBCL treated with immunochemotherapy, the SHM subtypes were significantly associated with overall survival (1642 patients) and progression-free survival (795 patients). Multivariate analysis of survival indicated that the prognostic impact of the SHM subtypes is independent from the COO classification and the International Prognostic Index. Furthermore, the SHM subtypes had a distinct clinical outcome within each of the COO subtypes, and strikingly, even within unclassified DLBCL. The genetic landscape of the four SHM subtypes indicated unique associations with driver alterations and oncogenic signaling in DLBCL, which suggests a possibility for therapeutic exploitation. These findings provide a biologically driven classification system in DLBCL with potential clinical applications.

Impact of constitutional TET2 haploinsufficiency on molecular and clinical phenotype in humans.

Clonal hematopoiesis driven by somatic heterozygous TET2 loss is linked to malignant degeneration via consequent aberrant DNA methylation, and possibly to cardiovascular disease via increased cytokine and chemokine expression as reported in mice. Here, we discover a germline TET2 mutation in a lymphoma family. We observe neither unusual predisposition to atherosclerosis nor abnormal pro-inflammatory cytokine or chemokine expression. The latter finding is confirmed in cells from three additional unrelated TET2 germline mutation carriers. The TET2 defect elevates blood DNA methylation levels, especially at active enhancers and cell-type specific regulatory regions with binding sequences of master transcription factors involved in hematopoiesis. The regions display reduced methylation relative to all open chromatin regions in four DNMT3A germline mutation carriers, potentially due to TET2-mediated oxidation. Our findings provide insight into the interplay between epigenetic modulators and transcription factor activity in hematological neoplasia, but do not confirm the putative role of TET2 in atherosclerosis.

Genetic predisposition to uterine leiomyoma is determined by loci for genitourinary development and genome stability.

Uterine leiomyomas (ULs) are benign tumors that are a major burden to women's health. A genome-wide association study on 15,453 UL cases and 392,628 controls was performed, followed by replication of the genomic risk in six cohorts. Effects of the risk alleles were evaluated in view of molecular and clinical characteristics. 22 loci displayed a genome-wide significant association. The likely predisposition genes could be grouped to two biological processes. Genes involved in genome stability were represented by TERT, TERC, OBFC1 - highlighting the role of telomere maintenance - TP53 and ATM. Genes involved in genitourinary development, WNT4, WT1, SALL1, MED12, ESR1, GREB1, FOXO1, DMRT1 and uterine stem cell marker antigen CD44, formed another strong subgroup. The combined risk contributed by the 22 loci was associated with MED12 mutation-positive tumors. The findings link genes for uterine development and genetic stability to leiomyomagenesis, and in part explain the more frequent occurrence of UL in women of African origin.

Identifying differentially methylated sites in samples with varying tumor purity.

Motivation: DNA methylation aberrations are common in many cancer types. A major challenge hindering comparison of patient-derived samples is that they comprise of heterogeneous collection of cancer and microenvironment cells. We present a computational method that allows comparing cancer methylomes in two or more heterogeneous tumor samples featuring differing, unknown fraction of cancer cells. The method is unique in that it allows comparison also in the absence of normal cell control samples and without prior tumor purity estimates, as these are often unavailable or unreliable in clinical samples. Results: We use simulations and next-generation methylome, RNA and whole-genome sequencing data from two cancer types to demonstrate that the method is accurate and outperforms alternatives. The results show that our method adapts well to various cancer types and to a wide range of tumor content, and works robustly without a control or with controls derived from various sources. Availability and implementation: The method is freely available at https://bitbucket.org/anthakki/dmml. Supplementary information: Supplementary data are available at Bioinformatics online.

A Functional Homologous Recombination Assay Predicts Primary Chemotherapy Response and Long-Term Survival in Ovarian Cancer Patients.

Purpose: Homologous recombination deficiency (HRD) correlates with platinum sensitivity in patients with ovarian cancer, which clinically is the most useful predictor of sensitivity to PARPi. To date, there are no reliable diagnostic tools to anticipate response to platinum-based chemotherapy, thus we aimed to develop an ex vivo functional HRD detection test that could predict both platinum-sensitivity and patient eligibility to targeted drug treatments.Experimental Design: We obtained a functional HR score by quantifying homologous recombination (HR) repair after ionizing radiation-induced DNA damage in primary ovarian cancer samples (n = 32). Samples clustered in 3 categories: HR-deficient, HR-low, and HR-proficient. We analyzed the HR score association with platinum sensitivity and treatment response, platinum-free interval (PFI) and overall survival (OS), and compared it with other clinical parameters. In parallel, we performed DNA-sequencing of HR genes to assess if functional HRD can be predicted by currently offered genetic screening.Results: Low HR scores predicted primary platinum sensitivity with high statistical significance (P = 0.0103), associated with longer PFI (HR-deficient vs. HR-proficient: 531 vs. 53 days), and significantly correlated with improved OS (HR score <35 vs. ≥35, hazard ratio = 0.08, P = 0.0116). At the genomic level, we identified a few unclear mutations in HR genes and the mutational signature associated with HRD, but, overall, genetic screening failed to predict functional HRD.Conclusions: We developed an ex vivo assay that detects tumor functional HRD and an HR score able to predict platinum sensitivity, which holds the clinically relevant potential to become the routine companion diagnostic in the management of patients with ovarian cancer. Clin Cancer Res; 24(18); 4482-93. ©2018 AACR.

Evolutionary history of metastatic breast cancer reveals minimal seeding from axillary lymph nodes.

Metastatic breast cancers are still incurable. Characterizing the evolutionary landscape of these cancers, including the role of metastatic axillary lymph nodes (ALNs) in seeding distant organ metastasis, can provide a rational basis for effective treatments. Here, we have described the genomic analyses of the primary tumors and metastatic lesions from 99 samples obtained from 20 patients with breast cancer. Our evolutionary analyses revealed diverse spreading and seeding patterns that govern tumor progression. Although linear evolution to successive metastatic sites was common, parallel evolution from the primary tumor to multiple distant sites was also evident. Metastatic spreading was frequently coupled with polyclonal seeding, in which multiple metastatic subclones originated from the primary tumor and/or other distant metastases. Synchronous ALN metastasis, a well-established prognosticator of breast cancer, was not involved in seeding the distant metastasis, suggesting a hematogenous route for cancer dissemination. Clonal evolution coincided frequently with emerging driver alterations and evolving mutational processes, notably an increase in apolipoprotein B mRNA-editing enzyme, catalytic polypeptide-like-associated (APOBEC-associated) mutagenesis. Our data provide genomic evidence for a role of ALN metastasis in seeding distant organ metastasis and elucidate the evolving mutational landscape during cancer progression.

Exome sequencing of primary breast cancers with paired metastatic lesions reveals metastasis-enriched mutations in the A-kinase anchoring protein family (AKAPs).

BACKGROUND: Tumor heterogeneity in breast cancer tumors is today widely recognized. Most of the available knowledge in genetic variation however, relates to the primary tumor while metastatic lesions are much less studied. Many studies have revealed marked alterations of standard prognostic and predictive factors during tumor progression. Characterization of paired primary- and metastatic tissues should therefore be fundamental in order to understand mechanisms of tumor progression, clonal relationship to tumor evolution as well as the therapeutic aspects of systemic disease. METHODS: We performed full exome sequencing of primary breast cancers and their metastases in a cohort of ten patients and further confirmed our findings in an additional cohort of 20 patients with paired primary and metastatic tumors. Furthermore, we used gene expression from the metastatic lesions and a primary breast cancer data set to study the gene expression of the AKAP gene family. RESULTS: We report that somatic mutations in A-kinase anchoring proteins are enriched in metastatic lesions. The frequency of mutation in the AKAP gene family was 10% in the primary tumors and 40% in metastatic lesions. Several copy number variations, including deletions in regions containing AKAP genes were detected and showed consistent patterns in both investigated cohorts. In a second cohort containing 20 patients with paired primary and metastatic lesions, AKAP mutations showed an increasing variant allele frequency after multiple relapses. Furthermore, gene expression profiles from the metastatic lesions (n = 120) revealed differential expression patterns of AKAPs relative to the tumor PAM50 intrinsic subtype, which were most apparent in the basal-like subtype. This pattern was confirmed in primary tumors from TCGA (n = 522) and in a third independent cohort (n = 182). CONCLUSION: Several studies from primary cancers have reported individual AKAP genes to be associated with cancer risk and metastatic relapses as well as direct involvement in cellular invasion and migration processes. Our findings reveal an enrichment of mutations in AKAP genes in metastatic breast cancers and suggest the involvement of AKAPs in the metastatic process. In addition, we report an AKAP gene expression pattern that consistently follows the tumor intrinsic subtype, further suggesting AKAP family members as relevant players in breast cancer biology.

MicroRNAs regulate key cell survival pathways and mediate chemosensitivity during progression of diffuse large B-cell lymphoma.

Despite better therapeutic options and improved survival of diffuse large B-cell lymphoma (DLBCL), 30-40% of the patients experience relapse or have primary refractory disease with a dismal prognosis. To identify biological correlates for treatment resistance, we profiled microRNAs (miRNAs) of matched primary and relapsed DLBCL by next-generation sequencing. Altogether 492 miRNAs were expressed in the DLBCL samples. Thirteen miRNAs showed significant differential expression between primary and relapse specimen pairs. Integration of the differentially expressed miRNAs with matched mRNA expression profiles identified highly anti-correlated, putative targets, which were significantly enriched in cancer-associated pathways, including phosphatidylinositol (PI)), mitogen-activated protein kinase (MAPK), and B-cell receptor (BCR) signaling. Expression data suggested activation of these pathways during disease progression, and functional analyses validated that miR-370-3p, miR-381-3p, and miR-409-3p downregulate genes on the PI, MAPK, and BCR signaling pathways, and enhance chemosensitivity of DLBCL cells in vitro. High expression of selected target genes, that is, PIP5K1 and IMPA1, was found to be associated with poor survival in two independent cohorts of chemoimmunotherapy-treated patients (n = 92 and n = 233). Taken together, our results demonstrate that differentially expressed miRNAs contribute to disease progression by regulating key cell survival pathways and by mediating chemosensitivity, thus representing potential novel therapeutic targets.

Intra-tumor heterogeneity in breast cancer has limited impact on transcriptomic-based molecular profiling.

BACKGROUND: Transcriptomic profiling of breast tumors provides opportunity for subtyping and molecular-based patient stratification. In diagnostic applications the specimen profiled should be representative of the expression profile of the whole tumor and ideally capture properties of the most aggressive part of the tumor. However, breast cancers commonly exhibit intra-tumor heterogeneity at molecular, genomic and in phenotypic level, which can arise during tumor evolution. Currently it is not established to what extent a random sampling approach may influence molecular breast cancer diagnostics. METHODS: In this study we applied RNA-sequencing to quantify gene expression in 43 pieces (2-5 pieces per tumor) from 12 breast tumors (Cohort 1). We determined molecular subtype and transcriptomic grade for all tumor pieces and analysed to what extent pieces originating from the same tumors are concordant or discordant with each other. Additionally, we validated our finding in an independent cohort consisting of 19 pieces (2-6 pieces per tumor) from 6 breast tumors (Cohort 2) profiled using microarray technique. Exome sequencing was also performed on this cohort, to investigate the extent of intra-tumor genomic heterogeneity versus the intra-tumor molecular subtype classifications. RESULTS: Molecular subtyping was consistent in 11 out of 12 tumors and transcriptomic grade assignments were consistent in 11 out of 12 tumors as well. Molecular subtype predictions revealed consistent subtypes in four out of six patients in this cohort 2. Interestingly, we observed extensive intra-tumor genomic heterogeneity in these tumor pieces but not in their molecular subtype classifications. CONCLUSIONS: Our results suggest that macroscopic intra-tumoral transcriptomic heterogeneity is limited and unlikely to have an impact on molecular diagnostics for most patients.

Comparative analysis of methods for identifying somatic copy number alterations from deep sequencing data.

Somatic copy-number alterations (SCNAs) are an important type of structural variation affecting tumor pathogenesis. Accurate detection of genomic regions with SCNAs is crucial for cancer genomics as these regions contain likely drivers of cancer development. Deep sequencing technology provides single-nucleotide resolution genomic data and is considered one of the best measurement technologies to detect SCNAs. Although several algorithms have been developed to detect SCNAs from whole-genome and whole-exome sequencing data, their relative performance has not been studied. Here, we have compared ten SCNA detection algorithms in both simulated and primary tumor deep sequencing data. In addition, we have evaluated the applicability of exome sequencing data for SCNA detection. Our results show that (i) clear differences exist in sensitivity and specificity between the algorithms, (ii) SCNA detection algorithms are able to identify most of the complex chromosomal alterations and (iii) exome sequencing data are suitable for SCNA detection.