Single Signature Research Articles

Abstract LBA036: Whole-exome sequencing of germ cell tumors in childhood

Abstract The germ cell tumors (GCTs) are a rare group of neoplasms that affect about 3.5% of pediatric patients, that comprise both benign and malignant histologies. They are part of a heterogenous group clinically and pathologically complex, derived from totipotent primordial germ cells. Despite their heterogeneity, GCTs can occur in gonadal and extragonadal sites, presenting different histological subtypes. Pediatric cancers in general are known to have low mutational burdens or ‘quiet’ genomes relative to adult tumors, with pediatric malignant tumors having 1,000-fold fewer somatic mutations than several adult cancers. Molecular genetic analyses of pediatric GCT revealed distinct biological differences between childhood GCT compared to those of adults tumors, however, most of those studies have been performed in adult patients, and there is not much knowledge about molecular alterations in children. In contrast to many other solid cancers, previous studies show that GCTs have a relatively low mutational burden which comprise embryonic epigenetic state, copy number alterations (CNAs) and single nucleotide mutation or somatic insertion/deletion (SNV/indel). According to those studies, molecular alterations in the KIT, KRAS and NRAS genes and chromosome 12p amplification are frequently found. Therefore, the aim of this study is to perform a genomic profile by whole-exome sequencing (WES) of pediatric patients diagnosed with GCTs at the Barretos Children’s Cancer Hospital and compare them with adult GCT database. We reported the WES using Illumina paired-end sequencing strategy (>100 X-fold coverage) of sixteen cases and respective matched normal. Data analysis was performed as follows: Mutect, Pindel and Mutsig for SNVs/Indels; HMMcopy, Nexus Copy Number and Gistic for CNAs; and Signal (Cosmic v3) for mutational signatures. The somatic alterations found were compared with data available in the literature and database on adult GCTs. Our samples represent 10 ovarian GCTs, five testicular GCTs and one mediastinal tumor. Somatic mutations in cancer genomes are caused by multiple mutational processes, which may generate a characteristic mutational signature. Analyses of single base substitution signature (SBSS) showed SBSS39 in 11 samples (68,75%) and the SBSS22 in two ovary samples (12,5%). Copy number alterations were observed on chromosomes 4, 7, 8, 10, 12, 21, and 22, with gain of genes KRAS, CCND2, ETV6, KDM5A, MYC, and OLIG2, and loss of genes KIT, FBXW7, PDGFRA, and PTEN. In addition, mutations were observed in following genes MTOR (19%), KIT (12%), CTNNB1 (12%), ATM (12%), KRAS (6%) and PI3KCA (6%). Even though there have been limitations in pediatric CGT studies, our analyzes show that the somatic alterations found correspond to the alterations established in adult CGT and in recent pediatric CGT studies. Therefore, additional whole-exome sequencing studies are necessary to identify and confirm gene mutations associated to pediatric GCT. Citation Format: Janaina Mello Soares Galvão. Whole-exome sequencing of germ cell tumors in childhood [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2021 Oct 7-10. Philadelphia (PA): AACR; Mol Cancer Ther 2021;20(12 Suppl):Abstract nr LBA036.

β-Sheet Richness of the Circulating Tumor-Derived Extracellular Vesicles for Noninvasive Pancreatic Cancer Screening

Tumor-derived extracellular vesicles (EVs) are under intensive study for their potential as noninvasive diagnosis biomarkers. Most EV-based cancer diagnostic assays trace supernumerary of a single cancer-associated marker or marker signatures. These types of biomarker assays are either subtype-specific or vulnerable to be masked by high background signals. In this study, we introduce using the β-sheet richness (BR) of the tumor-derived EVs as an effective way to discriminate EVs originating from malignant and nonmalignant cells, where EV contents are evaluated as a collective attribute rather than single factors. Circular dichroism, Fourier transform infrared spectroscopy, fluorescence staining assays, and a de novo workflow combining proteomics, bioinformatics, and protein folding simulations were employed to validate the collective attribute at both cellular and EV levels. Based on the BR of the tumorous EVs, we integrated immunoprecipitation and fluorescence labeling targeting the circulating tumor-derived EVs in serum and developed the process into a clinical assay, named EvIPThT. The assay can distinguish patients with and without malignant disease in a pilot cohort, with weak correlations to prognosis biomarkers, suggesting the potential for a cancer screening panel with existing prognostic biomarkers to improve overall performance.

Targeted Quantification of Carbon Metabolites Identifies Metabolic Progression Markers and an Undiagnosed Case of SDH-Deficient Clear Cell Renal Cell Carcinoma in a German Cohort.

Renal cell carcinoma (RCC) is among the 10 most common cancer entities and can be categorised into distinct subtypes by differential expression of Krebs cycle genes. We investigated the predictive value of several targeted metabolites with regards to tumour stages and patient survival in an unselected cohort of 420 RCCs. Unsupervised hierarchical clustering of metabolite ratios identified two main clusters separated by α-ketoglutarate (α-KG) levels and sub-clusters with differential levels of the oncometabolite 2-hydroxyglutarate (2HG). Sub-clusters characterised by high 2HG were enriched in higher tumour stages, suggesting metabolite profiles might be suitable predictors of tumour stage or survival. Bootstrap forest models based on single metabolite signatures showed that lactate, 2HG, citrate, aspartate, asparagine, and glutamine better predicted the cancer-specific survival (CSS) of clear cell RCC patients, whereas succinate and α-ketoglutarate were better CSS predictors for papillary RCC patients. Additionally, this assay identifies rare cases of tumours with SDHx mutations, which are caused predominantly by germline mutations and which predispose to development of different neoplasms. Hence, analysis of selected metabolites should be further evaluated for potential utility in liquid biopsies, which can be obtained using less invasive methods and potentially facilitate disease monitoring for both patients and caregivers.

Examining the Prevalence of Homologous Recombination Repair Defects in ER+ Breast Cancers

Cancers with defects in the homologous recombination repair (HRD) pathway exhibit increased sensitivity to platinum agents and PARP inhibitors (PARPi). HRD can occur secondary to germline or somatic acquired genetic alterations in HR genes or BRCA1 promoter methylation, but a plurality of HRD cancers do not have known explanatory mechanisms. HRD is known to be most frequent in triple negative breast cancers. We sought to determine the frequency of HRD, defined by genomic signatures, in ER+/Her2- breast cancers, the largest segment of the overall breast cancer population.The Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) has made the whole-genomes of 2,658 cases available for analysis. We used the available breast cancer cases in this dataset as our study substrate and further categorized them as ER+/Her2-, TNBC, or unlabeled. Large-scale state transitions (LST) are well-described ultra-structural "genomic scars" that can be assessed and scored using computational techniques, and serve as an inference for HRD. LST has been associated with responsiveness to PARPi, consistent with its use as a surrogate for HRD. We dichotomized LST scores > = 15 as HRD. We further examined the prevalence of the single base substitution signature 3 (SBS3) and indel signature 6 (ID6) previously associated with BRCA1/2 cases by the PCAWG project as additional surrogates for HRD. The Mann-Whitney test was used to assess differences across groups.There were 73 breast cancer genomes contributed to PCAWG by the EU breast cancer cohort, all of which were labeled ER+/Her2-. There were 44 breast cancer genomes contributed by UK ("UK cohort") labeled TNBC. As expected, we observed statistically significant differences in mean LST scores and SBS3 between the ER+ and TNBC cohorts. Within the ER+/Her2- cohort, 11 of 73 cases (15%) had HRD as defined by high LST. The HRD status of these 11 cases is supported by elevated ID6 and SBS3 compared to the 62 cases with low LST.Our work suggests that HRD may be as prevalent as 15% in ER+Her2- breast cancer. Given the preponderance of ER+ breast cancer, women with ER+ breast cancer with HRD may outnumber the 40% of women with TNBC with HRD. In a future with rapid, clinical grade assays for HRD, many more breast cancer patients may be eligible for synthetically lethal combination therapies than previously anticipated.

Deep Ensemble Neural Network Approach for Federal Highway Administration Axle-Based Vehicle Classification Using Advanced Single Inductive Loops

The Federal Highway Administration (FHWA) vehicle classification scheme is designed to serve various transportation needs such as pavement design, emission estimation, and transportation planning. Many transportation agencies rely on Weigh-In-Motion and Automatic Vehicle Classification sites to collect these essential vehicle classification counts. However, the spatial coverage of these detection sites across the highway network is limited by high installation and maintenance costs. One cost-effective approach has been the use of single inductive loop sensors as an alternative to obtaining FHWA vehicle classification data. However, most data sets used to develop such models are skewed since many classes associated with larger truck configurations are less commonly observed in the roadway network. This makes it more difficult to accurately classify under-represented classes, even though many of these minority classes may have disproportionately adverse effects on pavement infrastructure and the environment. Therefore, previous models have been unable to adequately classify under-represented classes, and the overall performance of the models is often masked by excellent classification accuracy of majority classes, such as passenger vehicles and five-axle tractor-trailers. To resolve the challenge of imbalanced data sets in the FHWA vehicle classification, this paper constructed a bootstrap aggregating deep neural network model on a truck-focused data set using single inductive loop signatures. The proposed method significantly improved the model performance on several truck classes, especially minority classes such as Classes 7 and 11 which were overlooked in previous research. The model was tested on a distinct data set obtained from four spatially independent sites and achieved an accuracy of 0.87 and an average F1 score of 0.72.

Phylogeographic and SNPs Analyses of Bemisia tabaci B Mitotype Populations Reveal Only Two of Eight Haplotypes Are Invasive.

Simple SummaryThe whitefly Bemisia tabaci taxon consists of an undefined number of morphologically identical genetic variants of which only a few, including the B, harbor invasive haplotypes. These haplotypes have potential to upsurge and become important pests and plant virus vectors in irrigated agroecosystems worldwide. In the 1980s, unprecedented outbreaks associated with the B variant were reported worldwide, however, the precise origin(s) of the invasive haplotypes has not been determined. In this study, available B. tabaci mitochondrial gene sequences were examined for patterns of conserved single nucleotide changes (SNPs). The whitefly sequence records represented North Africa-Middle Eastern habitats, the proposed B variant center of origin, and distant locales recently invaded by haplotype(s) of the B variant. Unexpectedly, the analysis revealed eight SNPs groups (haplotypes) demonstrating that the genetic architecture of the B mitoype is more complex than previously recognized. Also, the distribution patterns of the eight B haplotypes were tightly linked to well-defined eco-geographic regions, suggesting the different groups have diversified by geographic isolation. Contrary to claims that collectively, the B variant is invasive, only two of the eight haplotypic groups have established in geographical locations outside of their zone of endemism.The Bemisia tabaci cryptic species contains 39 known mitotypes of which the B and Q are best recognized for having established outside their extant endemic range. In the 1980s, previously uncharacterized haplotype(s) of the B mitotype rapidly established in tropical and subtropical locales distant from their presumed center of origin, leading to displacement of several native mitotypes and extreme damage to crops and other vegetation particularly in irrigated agroecosystems. To trace the natural and evolutionary history of the invasive B haplotypes, a phylo-biogeographic study was undertaken. Patterns of single nucleotide polymorphisms (SNPs) and signatures potentially indicative of geographic isolation were investigated using a globally representative mitochondrial cytochrome oxidase I gene (mtCOI) sequence database. Eight haplotype groups within the North Africa-Middle East (NAFME) region were differentiated, NAFME 1–8. The NAFME 1–3 haplotypes were members of the same population that is associated with warm desert climate niches of the Arabian Peninsula and east coastal Africa-Ethiopia. The NAFME 4 and 5 haplotypes are endemic to warm and cold semi-arid niches delimited by the Irano-Turanian floristic region, itself harboring extensive biodiversity. Haplotypes 6 and 7 co-occurred in the Middle East along eastern Mediterranean Sea landmasses, while NAFME 8 was found to be endemic to Cyprus, Turkey, and desert micro-niches throughout Egypt and Israel. Contrary to claims that collectively, the B mitotype is invasive, NAFME 6 and 8 are the only haplotypes to have established in geographical locations outside of their zone of endemism.

The global 21-cm signal of a network of cosmic string wakes

ABSTRACT In previous works, we discussed the 21-cm signature of a single cosmic string wake. However, the 21-cm brightness temperature is influenced by a network of cosmic string wakes, and not one single wake. In this work, we consider the signal from a network of wakes laid down during the matter era. We also improve on the previous calculation of a single wake signature. Finally, we calculate the enhancement of the global 21-cm brightness temperature due to a network of wakes and discuss its affects of the signal measured in the Wouthuysen–Field absorption trough. We estimated that for string tensions between 10−8 and 10−7 there would be between a 10 per cent and a factor of 2 enhancement in the signal.

Identification of Immune Function-Related Subtypes in Cutaneous Melanoma.

Tumour immunotherapy combined with molecular typing is a new therapy to help select patients. However, molecular typing algorithms related to tumour immune function have not been thoroughly explored. We herein proposed a single sample immune signature network (SING) method to identify new immune function-related subtypes of cutaneous melanoma of the skin. A sample-specific network and tumour microenvironment were constructed based on the immune annotation of cutaneous melanoma samples. Then, the differences and heterogeneity of immune function among different subtypes were analysed and verified. A total of 327 cases of cutaneous melanoma were divided into normal and immune classes; the immune class had more immune enrichment characteristics. After further subdividing the 327 cases into three immune-related subtypes, the degree of immune enrichment in the “high immune subtype” was greater than that in other subtypes. Similar results were validated in both tumour samples and cell lines. Sample-specific networks and the tumour microenvironment based on immune annotation contribute to the mining of cutaneous melanoma immune function-related subtypes. Mutations in B2M and PTEN are considered potential therapeutic targets that can improve the immune response. Patients with a high immune subtype can generally obtain a better immune prognosis effect, and the prognosis may be improved when combined with TGF-β inhibitors.

NGS-based multi-factor prediction model of platinum sensitivity for epithelial ovarian cancer

<h3>Objectives:</h3> Platinum sensitivity is the most important predictor of prognosis and response to further treatment in ovarian cancer, while it is still roughly prejudged in line with the interval of progression free survival in clinical practice. Realizing individualized precision medicine require refining predictive model that could enable selection of patients who would benefit from platinum-based or non-platinum containing therapy. The study aims to identify and integrate novel biomarkers for prediction of platinum sensitivity in ovarian cancer. <h3>Methods:</h3> 60 patients with epithelial ovarian cancer receiving standard initial treatment were recruited from 2016 to 2019. According to interval of progress free survival (PFS), 44 patients (PFS>6 months) and 16 patients (PFS≤6 months) were defined as platinum-sensitive and platinum-resistant recurrence, respectively. We performed next generation sequencing (NGS) analysis of the entire genomic features of ovarian tumor tissue samples obtained from debulking surgery. Multiple genomic variation were compared between platinum sensitive and resistant patients, including single-nucleotide variant (SNV), single base or indel signature, focal copy number variation (CNV), mutational clonality, whole-genome duplication (WGD), and co-variation in DNA damage repair (DDR) pathways. Collectively, a multi-factor-based prediction model was trained and validate with Lasso regression. <h3>Results:</h3> Analysis of single base signature revealed that the contribution of SBS10a in platinum-sensitive patients was significantly higher than that of platinum-resistant patients (P<0.05). Consistently, patients with SBS10a signature exhibited higher platinum sensitivity (P<0.01) and longer PFS than SBS10a negative patients (15.8m vs 10.1m, P<0.05). Focal CNVs involved in DDR pathways which believed to play a pivotal role in platinum response were emphatically investigated. The results demonstrated the incidence of FAM175A LOH in platinum-sensitive patients was significantly higher than that of platinum-resistant patients (P<0.01), meanwhile the median PFS of patients with FAM175A LOH was significantly longer than patients with wild-type FAM175A (16.5m vs 9.2m, P<0.05). Moreover, patients with WGD showed higher proportion of platinum resistance (P<0.01) and shorter median PFS than patients with normal chromosomal ploidy (9.1m vs 16.47m, p<0.05). Unlucky, there was no marked difference in the distribution of overall SNV or DDR related SNV with top frequency between platinum sensitive and resistant cases. Patients with clonal DDR mutations or co-variation in DDR pathways exhibited higher proportion of platinum response and longer PFS than patients without these variations, but the differences were not statistically significant. By integrating of SBS10a, FAM175A LOH and WGD which showed significant differences in platinum sensitivity in univariate analyses, a lasso regression model was employed to train and validate with all patients. A promising performance with the AUC value 0.864 was observed in ROC curve, suggesting the predictive performance of this model was favorable. <h3>Conclusions:</h3> Our study successfully established a prediction model containing three novel biomarkers FAM175A LOH, SBS10a mutation and WGD through in-depth mining of NGS, which has an extremely potential of predictive value for platinum sensitivity in epithelial ovarian cancer. However, it requires larger cohort for further validation.

Collective Signature Protocols for Signing Groups based on Problem of Finding Roots Modulo Large Prime Number

Generally, digital signature algorithms are based on a single difficult computational problem like prime factorization problem, discrete logarithm problem, elliptic curve problem. There are also many other algorithms which are based on the hybrid combination of prime factorization problem and discrete logarithm problem. Both are true for different types of digital signatures like single digital signature, group digital signature, collective digital signature etc. In this paper we propose collective signature protocols for signing groups based on difficulty of problem of finding roots modulo large prime number. The proposed collective signatures protocols have significant merits one of which is connected with possibility of their practical using on the base of the existing public key infrastructures.

COLLECTIVE SIGNATURE PROTOCOLS FOR SIGNING GROUPS BASED ON PROBLEM OF FINDING ROOTS MODULO LARGE PRIME NUMBER

Generally, digital signature algorithms are based on a single difficult computational problem like prime factorization problem, discrete logarithm problem, elliptic curve problem. There are also many other algorithms which are based on the hybrid combination of prime factorization problem and discrete logarithm problem. Both are true for different types of digital signatures like single digital signature, group digital signature, collective digital signature etc. In this paper we propose collective signature protocols for signing groups based on difficulty of problem of finding roots modulo large prime number. The proposed collective signatures protocols have significant merits one of which is connected with possibility of their practical using on the base of the existing public key infrastructures.

A novel method for developing post-quantum cryptoschemes and a practical signature algorithm

Purpose The practical purpose of this research is to propose a candidate for post-quantum signature standard that is free of significant drawback of the finalists of the NIST world competition, which consists in the large size of the signature and the public key. The practical purpose is to propose a fundamentally new method for development of algebraic digital signature algorithms. Design/methodology/approach The proposed method is distinguished by the use of two different finite commutative associative algebras as a single algebraic support of the digital signature scheme and setting two different verification equation for a single signature. A single public key is computed as the first and the second public keys, elements of which are computed exponentiating two different generators of cyclic groups in each of the algebras. Findings Additionally, a scalar multiplication by a private integer is performed as final step of calculation of every element of the public key. The same powers and the same scalar values are used to compute the first and the second public keys by the same mathematic formulas. Due to such design, the said generators are kept in secret, providing resistance to quantum attacks. Two new finite commutative associative algebras, multiplicative group of which possesses four-dimensional cyclicity, have been proposed as a suitable algebraic support. Originality/value The introduced method is novel and includes new techniques for designing algebraic signature schemes that resist quantum attacks. On its base, a new practical post-quantum signature scheme with relatively small size of signature and public key is developed.

MicroRNA-based signatures impacting clinical course and biology of ovarian cancer: a miRNOmics study

BackgroundIn Western countries, ovarian cancer (OC) still represents the leading cause of gynecological cancer-related deaths, despite the remarkable gains in therapeutical options. Novel biomarkers of early diagnosis, prognosis definition and prediction of treatment outcomes are of pivotal importance. Prior studies have shown the potentials of micro-ribonucleic acids (miRNAs) as biomarkers for OC and other cancers.MethodsWe focused on the prognostic and/or predictive potential of miRNAs in OC by conducting a comprehensive array profiling of miRNA expression levels in ovarian tissue samples from 17 non-neoplastic controls, and 60 tumor samples from OC patients treated at the Regina Elena National Cancer Institute (IRE). A set of 54 miRNAs with differential expression in tumor versus normal samples (T/N-deregulated) was identified in the IRE cohort and validated against data from the Cancer Genoma Atlas (TCGA) related to 563 OC patients and 8 non-neoplastic controls. The prognostic/predictive role of the selected 54 biomarkers was tested in reference to survival endpoints and platinum resistance (P-res).ResultsIn the IRE cohort, downregulation of the 2 miRNA-signature including miR-99a-5p and miR-320a held a negative prognostic relevance, while upregulation of miR-224-5p was predictive of less favorable event free survival (EFS) and P-res. Data from the TCGA showed that downregulation of 5 miRNAs, i.e., miR-150, miR-30d, miR-342, miR-424, and miR-502, was associated with more favorable EFS and overall survival outcomes, while miR-200a upregulation was predictive of P-res. The 9 miRNAs globally identified were all included into a single biologic signature, which was tested in enrichment analysis using predicted/validated miRNA target genes, followed by network representation of the miRNA-mRNA interactions.ConclusionsSpecific dysregulated microRNA sets in tumor tissue showed predictive/prognostic value in OC, and resulted in a promising biological signature for this disease.

Nuclear magnetic resonance-based chemical signature and nutritional discrimination of Macrotyloma uniflorum accessions from the alpine Himalayas.

Macrotyloma uniflorum (horsegram) is cultivated in the alpine Himalayas for its nutritional benefits. The chemical diversity of M. uniflorum has been explored by several research groups, but none has reported the chemical signature to monitor the identity and quality. To overcome quality control problems and provide more accurate chemotype information, proton nuclear magnetic resonance (1 H-NMR)-based chemical signature and tentative metabotype discrimination was studied. Metabolites were identified by characteristic chemical shifts and their correlations in one-dimensional (1D) and two-dimensional (2D) nuclear magnetic resonance (NMR) experiments. The identifications were confirmed by reference standards, with in-house databases, and with public databases. From the analysis of seed samples from eight locations, NMR detected 46 diversified metabolites, including 18 amino acids, nine carbohydrates, three vitamins, seven flavonoids, and nine miscellaneous molecules (fatty acids/choline/phenolics/organic acids/sterols). These metabolites generated a single snapshot chemical signature for M. uniflorum. Greater content of identified metabolites was found at high elevations of 1829 m (55.78 g kg-1 ) followed by 1000-1150 m (19.30-17.23 g kg-1 ) and 1150-1450 m (7.33-13.41 g kg-1 ). Principal component analysis of samples revealed that chemotypes grouped into three distinct clusters. 'Cluster-3' was positively correlated with metabolites and found the best nutritionally enriched chemotype. A proton nuclear magnetic resonance-based M. uniflorum chemical signature was generated for the first time. The environmental conditions of 1829 m were most suitable for obtaining nutritionally enriched M. uniflorum crops. Overall, this information could help in the conservation, quality control, product development, and agriculture inventions of M. uniflorum. © 2021 Society of Chemical Industry.

Abstract 2079: Prediction of DDR and other mutation signatures using targeted panels for FFPE samples

Abstract Mutational processes can cause driver mutations and are considered the primary cause of tumorigenesis. Cosmic signatures classify environmental or intrinsic mutational processes. Identification of signatures including DNA Damage Response (DDR) signatures enables research into the origin of cancer mutagenesis and into treatment optimization. Typically, Mutation signatures are identified using Whole Genome Sequencing or Whole Exome Sequencing. We demonstrate signature prediction of mutation signatures with two amplification-based targeted panels, which have a high sequencing success rate for FFPE samples. ~2000 FFPE Samples from a pan-solid tumor cohort were sequenced with either of two AmpliSeq panels (OCAplus and OTMLA), sizes 1.4 Mb and 1.7 Mb, to optimize a method to identify mutation signatures. First, we identified somatic SNVs by filtering out likely population germline mutations, and used these to construct the single base change substitution (SBS) matrix. The COSMIC cancer signatures use properties of the whole genome: we normalized to extend signature detection to targeted panel data. We adjusted the mutation frequencies observed using the ratio of trinucleotide counts in the genome and the ratio in the panel. Next, we measured the cosine similarity between the normalized sample and the COSMIC signatures. We selected the signatures with a strong match (&amp;gt;0.7) to the normalized sample. We further impute the signatures using a reduced candidate set, to assess the signature fit in the sample and reduce false positives. These steps provide the optimized signatures. We detected optimized signatures in 33% of the samples. A single signature was detected in 9% of the samples, 2 signatures in 6%, and &amp;gt;2 signatures in 17% of the samples. ~3% of samples showed at least one MMR signatures (SBS6, SBS14, SBS15, SBS20, SBS21, SBS26 and SBS44). In all 10 OCAPlus samples with MMR signatures, we also detected mutation(s) in an MMR gene. APOBEC signatures, SBS2 and SBS13, were observed in 3% of samples sequenced with OCAPlus panel and TML panel. We found 3 samples showing HRR signature SBS3. We successfully identified the putative causal DDR mutation in a number of samples. For example, of 62 samples with NTHL1 mutations, 60 had NTHL1 related signature SBS30. A sample with MUTYH mutations was assigned the SBS36 signature. We sequenced matched Tumor/Normal pairs; in most cases, the tumor sample showed a stronger mutational signature, with their corresponding normal sample showing either no signature or a weaker matching signature. We assessed reproducibility of the mutational signature. Looking at duplicate and triplicate replicates, we found that replicates consistently displayed the same mutational signatures. We show identification of mutation signatures using targeted panels designed for FFPE samples. Citation Format: Fiona Hyland, Ajithavalli Chellappan, Chintan Vora, Shilpa Nair, Jagannath Patro, Ritika Raj, Rushikesh Kanap. Prediction of DDR and other mutation signatures using targeted panels for FFPE samples [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2079.

Abstract 129: An integrated multi-omic cellular atlas of human breast cancers

Abstract Breast cancers are complex cellular ecosystems where heterotypic interactions play central roles in disease progression and response to therapy. However, our knowledge of the cellular composition and organization of breast cancer remains limited. We present a comprehensive single cell and spatially resolved transcriptomic atlas of human breast cancers. The 10X Genomics Chromium platform was used to generate single cell transcriptomic data (scRNA-Seq) from more than 120,000 cells sampled from 26 breast cancers. CITE-Seq was employed to simultaneously generate protein measurements using a panel of 157 antibodies against immune, stromal and epithelial cell surface markers and analysed using Seurat. Using single cell signatures, we estimated the cellular composition of more than 2000 breast cancers in the Metabric cohort using deconvolution methods. Spatial transcriptomics was conducted on 12 frozen tissues (Luminal, Her2+ and triple negative breast cancer (TNBC)) using the 10X genomics Visium solution. We also used a novel Spatial Whole Transcriptome Panel, targeting 18,000+ genes on the Nanostring GeoMX platform, to profile T cells and malignant cells across multiple tissue niches from 16 TNBC FFPE cases. Integrative scRNA-Seq analysis identifies recurrent gene modules driving neoplastic cell heterogeneity, including interferon signaling, estrogen receptor function and mutually exclusive patterns of proliferation versus EMT. We also develop a single cell classifier of intrinsic subtype (scSubtype) to reveal frequent intra-tumoral heterogeneity for breast cancer intrinsic subtypes. CITE-Seq revealed immune profiles at high resolution, leading to the identification of novel macrophage populations with high expression of PD-L1 and PD-L2 immune checkpoint ligands and associations with clinical outcome. We also observe enrichment of exhausted and proliferative CD8 T cells in TNBC, with unique patterns of cell-surface checkpoint protein expression when compared to other subtypes. Targeted analysis using the GeoMX revealed spatial segregation of T cell phenotypes, with exhausted and proliferative CD8 T cells forming small clusters adjacent to tumor cells with high interferon pathway activity. Analysis of scRNA-Seq data revealed that stromal cells generate diverse functions and cell surface protein expression through differentiation within 3 major lineages: fibroblast, endothelial and perivascular-like. Subsets of stromal cells had features associated with immune regulation and Visium data revealed that stromal-immune niches were spatially organized in tumors, offering insights into anti-tumor immune suppression by stromal cells. Finally, deconvolution stratified &amp;gt;2000 breast cancer cases in Metabric into nine clusters, termed ‘ecotypes', with distinct cellular compositions and clinical outcomes. This study provides a comprehensive atlas of the cellular architecture of breast cancer. Citation Format: Sunny Z. Wu, Daniel Roden, Ghamdan Al Eryani, Simon Junankar, Elgene Lim, Aatish Thennavan, Alma Andersson, Stephen Williams, Jingjing Gong, Robin Fropf, Kit Fuhrman, Joakim Lundeberg, Chuck Perou, Alexander Swarbrick. An integrated multi-omic cellular atlas of human breast cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 129.

Integrative Oncogenomic Analysis Combining Whole Genome, Transcriptome and Epigenome Identifies Altered Chromatin Accessibility Landscape in Multiple Myeloma

Multiple myeloma (MM) is a plasma cell malignancy characterized by clinical and genomic heterogeneity. Genomic investigations have identified few recurrently mutated protein-coding genes and various aberrations at the transcriptomic level. However, these changes do not accurately reflect the disease behavior. With accumulation of data in large patient population, it is becoming clear that analysis integrating various genomic correlates will be required to understand chromatin architecture that drives the disease behavior.To gain further insight into MM biology, we integrated whole-genome sequencing, transcriptome sequencing, and epigenome profile using various assays (including the assay for transposase-accessible chromatin sequencing [ATAC-seq] to detect open chromatin regions, multiplexed, ChIP-seq and indexed T7 ChIP-seq [Mint-ChIP] to identify active enhancers and super-enhancers) to characterize the chromatin state of MM. We have analyzed 33 samples including primary MM cells from 18 newly diagnosed and 5 relapsed patients, 5 normal plasma cells from healthy donors and 5 Myeloma cell lines.We first investigated the open chromatin landscape in myeloma cells, identifying over one million open chromatin regions (OCR) in our cohort, with an average of over 60,000 regions per sample. Despite a high heterogeneity between myeloma samples, unsupervised cluster analysis clearly separates primary myeloma samples, normal plasma cells and MM cell lines suggesting pathogenetic relevance. Although there is clear link between transcribed genes and OCR, we observe a significant number of OCR in the intergenic region with an as yet unclear biological significance. Furthermore, ATAC-seq data predicted several copy number abnormalities including the distinction of hyperdiploid from hypodiploid samples. We next characterized enhancers and super-enhancers in myeloma cells and their assigned genes using H3K27ac ChIP-Seq to identify important genes in MM biology. Despite heterogeneity between myeloma samples and myeloma cell lines, we could identify common SE-assigned genes as compared with normal plasma cells. These genes correspond to both known myeloma specific dependencies such as MYC, IRF4, CCND1, and TNSFR17 (BCMA) as well as new therapeutic targets that are currently under investigation.Next, we used WGS data from 10 samples matching with ATAC and RNA sequencing to evaluate the mutational landscape of open chromatin regions and the consequences of the mutational processes targeting myeloma epigenome. Of 53,798 single nucleotide substitutions (SNV) (average of 5380 per sample), 2.7% of SNVs involve OCR (total of 1470 SNV within ATAC peaks, median - 104 mutations involving OCR per sample). We evaluated the presence of mutations within OCR targeting known binding motifs for transcription factors (TFs). Out of 1470 mutations affecting OCR, we identified 547 (37%) SNV targeting a known binding motif for transcription factors such as Sp1, NFkB1 or YY1. Using RNA seq data, we filtered the mutations affecting expressed TFs and assigned genes and observed that the presence of these mutations could significantly impact the assigned gene expression level. Indeed, these mutations generate either a de novo binding motif or alter the binding motif of a given TFs and are potentially responsible for TF hi-jacking to modulate the expression of number of genes important for MM biology. Finally, using non-negative matrix factorization (NMF), we evaluated the mutational signatures affecting the OCR and observed that a single predominant mutational signature affects open chromatin regions, as opposed to other processes such as APOBEC, which targets other regions of the genome.In conclusion, global analysis of primary patient myeloma cells combining whole genome, transcriptome as well as epigenomic characterization reveals dysregulated chromatin landscape in myeloma that may be significantly influenced by specific mutational processes. Such integrated oncogenomic analysis provides an early view on perturbed genomic landscape with a view to understand the biology and specific targetable dependencies. DisclosuresAnderson:C4 Therapeutics: Other: scientific founder; Bristol-Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Gilead Sciences: Membership on an entity's Board of Directors or advisory committees; Oncopep: Other: scientific founder; Millenium Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; MedImmune: Membership on an entity's Board of Directors or advisory committees.

A Seven Gene Signature to Distinguish Bortezomib- and Lenalidomide-Responsive Myeloma: Rnaseq from the Padimac Study

BackgroundThe treatment of multiple myeloma has benefited from the adoption of several highly active novel therapies over the last 20 years. This has led to the challenge of how to rationally choose the best drug regimen for an individual patient. Two of the most important novel anti-myeloma drugs are the proteasome inhibitor (PI), bortezomib, and the immunomodulatory drug (IMiD), lenalidomide. However, whilst patients often respond much better to one or other of these agents, there are very few means for rationally choosing between them. The application of precision medicine to guide this choice would improve efficacy and reduce adverse drug effects. Microarray data can identify poor prognosis disease very effectively, but is agnostic to treatment and does not inform drug selection. RNA sequencing (RNAseq) offers several potential advantages over microarray profiling. However, it also poses specific challenges for deriving prediction signatures and RNAseq-based signatures have not so far been described in myeloma.MethodsIn the phase 2 PADIMAC trial, newly diagnosed transplant-eligible myeloma patients were treated with bortezomib, doxorubicin, and dexamethasone (PAD) therapy and stratified to receive autograft in partial remission or no further treatment (“watch and wait”) in very good partial remission (VGPR) or better. We extracted somatic RNA from CD138-selected cells prior to treatment and performed massively parallel RNAseq. We employed a novel combination of published techniques to transform the data and to perform training, validation, and testing.ResultsNine of 41 patients with RNAseq data achieved ≥VGPR sustained for over 12 months with no further treatment. We used these RNAseq data as a training/validation set to develop a signature for predicting good responses to bortezomib-based therapy in the absence of an IMiD. We developed several signatures, cross-validated within the PADIMAC dataset, and tested performance in identifying good responders to bortezomib-based therapy from an independent test set of 109 patients from the CoMMpass study. The signatures performed extremely well in testing (p=2.3x10-14, p=0.001, p<2.2x10-16, and p<2.2x10-16 for sensitivity, specificity, F-measure, and accuracy, respectively).Based on cross-validation performance, we identified a single seven-gene signature, trained it on the PADIMAC data, and applied it to the 109 bortezomib-treated patients within CoMMpass. Predicted good responders had superior survival to the remaining patients (p=0.016; Figure 1A). To investigate whether the signature identified generic good risk disease or whether it was treatment-specific, we tested it on 31 patients treated with lenalidomide-based therapies (without PI) within the CoMMpass study. To our surprise, when applied to lenalidomide-treated patients, the signature performed significantly worse than expected by chance, as did several other signatures trained on the PADIMAC data (p=3.2x10-9, p=3.6x10-6, p=3.8x10-12, and p=1.7x10-13 for sensitivity, specificity, F-measure, and accuracy, respectively). This implied that expectation of a good response to bortezomib was associated with a poor response when the patient received lenalidomide and vice - versa . We could not assess survival in lenalidomide-treated patients because of small numbers, so we instead trained our seven-gene signature on lenalidomide-treated patients and tested it on bortezomib-treated patients. This confirmed the reciprocal relationship; patients expected to respond well to lenalidomide, but receiving bortezomib, had an inferior survival to other bortezomib-treated patients (p=0.008; figure 1B). This reciprocity across independent datasets implies that our signature could select between bortezomib-based or lenalidomide-based therapies for individual patients.ConclusionsWe have identified a seven-gene RNAseq signature that can predict if newly diagnosed myeloma patients would benefit from initial bortezomib-based or lenalidomide-based therapy, both in terms of response and survival. This is, to our knowledge, the first example of such a signature in MM and could be employed for precision medicine in the future. [Display omitted] DisclosuresChapman:Takeda: Other: Travel sponsorship; Janssen: Honoraria; Celgene: Other: Travel sponsorship; Celgene: Honoraria. Popat:Janssen: Honoraria, Other: Travel support for meetings; Celgene: Honoraria, Other: Travel support for meetings; Amgen: Honoraria; Takeda: Honoraria, Other: Travel support for meetings. Cavenagh:Novartis: Consultancy; Janssen: Honoraria; Celgene: Consultancy; Takeda: Consultancy. Owen:Janssen: Consultancy, Other: Travel support; Takeda: Honoraria, Other: Travel Support; Celgene: Consultancy, Honoraria, Research Funding. D'Sa:Janssen Cilag: Consultancy, Honoraria, Other: Education grant, Research Funding; Amgen: Consultancy, Honoraria, Research Funding. Cook:Janssen: Honoraria, Other: Travel support, Research Funding; Takeda: Honoraria; Amgen: Honoraria, Other: Travel support; Celgene: Honoraria, Other: Travel support, Research Funding; Jazz Pharmaceuticals: Honoraria; Myeloma UK: Membership on an entity's Board of Directors or advisory committees. Yong:Amgen: Honoraria, Research Funding; Janssen: Honoraria, Research Funding.

Examining lysyl oxidase-like modulation of collagen architecture in 3D spheroid models of idiopathic pulmonary fibrosis via second-harmonic generation microscopy.

.Significance: Idiopathic pulmonary fibrosis (IPF) patients have a poor prognosis with short lifespan following diagnosis as there are limited effective treatment options. Despite matrix stiffening being the hallmark of the disease there remains a lack of knowledge surrounding the underlying collagen alterations in the disease. Specifically, while increased collagen crosslinking has been implicated, the resulting effects on collagen macro/supramolecular changes have not been explored.Aim: We sought to determine if second-harmonic generation (SHG) microscopy could characterize differences in the collagen architecture in 3D spheroid models of IPF grown under different crosslinking modulation conditions (promotion and inhibition).Approach: We used SHG metrics based on the fiber morphology, relative SHG brightness, and macro/supramolecular structure by SHG polarization analyses to compare the structure of the IPF spheroids.Results: Comparison of the fiber morphology of the spheroids showed that the control group had the longest, straightest, and thickest fibers. The spheroids with crosslink enhancement and inhibition had the highest and lowest SHG conversion efficiencies, respectively, consistent with the resulting harmonophore density. SHG polarization analyses showed that the peptide pitch angle, alignment of collagen molecules, and overall chirality were altered upon crosslink modulation and were also consistent with reduced organization relative to the control group.Conclusions: While no single SHG signature is associated with crosslinking, we show that the suite of metrics used here is effective in delineating alterations across the collagen architecture sizescales. The results largely mirror those of human tissues and demonstrate that the combination of 3D spheroid models and SHG analysis is a powerful approach for hypothesis testing the roles of operative cellular and molecular factors in IPF.

Genomic landscape of MSH6-mutated clinically advanced castrate-resistant prostate cancer (mCRPC).

5062 Background: Loss-of-function genomic alterations (GAs) in MSH6 have been associated with a unique subtype of hypermutated mCRPC that is often microsatellite stable (MSS) and may occur in either a sporadic or familial Lynch Syndrome-like clinical setting. Methods: 5,617 mCRPC cases were sequenced to evaluate all classes of GA using a hybrid capture-based FDA-approved comprehensive genomic profiling (CGP) assay. Tumor mutational burden (TMB) was determined on 0.8 Mb of sequenced DNA and microsatellite instability high (MSI-High) was determined on 95 loci. MSI-low status was not assessed. Results: 78 (1.4%) mCRPC were MSH6mut (Table). MSH6mut mCRPC included 73.1% short variant mutations, 23.1% biallelic deletions, 2.6% genomic rearrangements, and 1.3% multiple GAs/sample. Co-mutation of MSH2 was found in 28% of MSH6mut cases vs. 2% in MSH6wt cases (P &lt;.0001) and was most frequently caused by biallelic co-deletion of both genes (73% of co-mutated cases). MSI-High status was present in 46% of MSH6mut mCRPC, which was significantly greater than the 2% seen in MSH6wt cases (P &lt;.0001). An MMR single nucleotide mutational signature was observed in 65% of MSH6mut cases, compared to 3% MSH6wt cases (P &lt;.0001). Among MSH6mut cases with neither MSI-High nor MMR mutational signature, 87% did not have biallelic loss of MSH6 or any other MMR gene, confirming that monoallelic pathogenic mutations are insufficient to cause the MMR-D phenotype. For subjects whose variants could be classified, 45% (19/42) of pathogenic MSH6 alleles were germline; of these, 58% (11/19) had neither MSI-High nor an MMR single nucleotide signature. MSH6mut cases had fewer TMPRSS2: ERG fusions (P =.01), but harbored significantly higher frequencies of GAs in AR (P =.0002), ATM (P =.04), PIK3CA (P =.0003), APC (P =.005), ERBB2 (P =.001), and CDK6 (p =.046), likely at least partially attributable to the higher TMB in MSH6mutcases (P &lt;.0001). Conclusions: MSH6mut mCRPC is a unique disease that often features a hypermutated genomic signature, although only 46% of cases exhibited MSI-high status. This complex phenotype highlights the potential utility of multiple rather than single biomarkers to understand tumor biology and determine patients who may benefit from immunotherapy.[Table: see text]