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IMPORTANCE: Alterations in the IKZF1 gene drive B-cell acute lymphoblastic leukemia (B-ALL) but are not routinely used to stratify patients by risk because of inconsistent associations with outcomes. We describe a novel deletion in 22q11.22 that was consistently associated with very poor outcomes in patients with B-ALL with IKZF1 alterations. OBJECTIVE: To determine whether focal deletions within the λ variable chain region in chromosome 22q11.22 were associated with patients with B-ALL with IKZF1 alterations with the highest risk of relapse and/or death. DESIGN, SETTING, AND PARTICIPANTS: This cohort study included 1310 primarily high-risk pediatric patients with B-ALL who were taken from 6 independent clinical cohorts, consisting of 3 multicenter cohorts (AALL0232 [2004-2011], P9906 [2000-2003], and patients with Down syndrome who were pooled from national and international studies) and 3 single-institution cohorts (University of Utah [Salt Lake City], Children’s Hospital of Philadelphia [Philadelphia, Pennsylvania], and St. Jude Children’s Hospital [Memphis, Tennessee]). Data analysis began in 2011 using patients from the older studies first, and data analysis concluded in 2021. EXPOSURES: Focal 22q11.22 deletions. MAIN OUTCOMES AND MEASURES: Event-free and overall survival was investigated. The hypothesis that 22q11.22 deletions stratified the prognostic effect of IKZF1 alterations was formulated while investigating nearby deletions in VPREB1 in 2 initial cohorts (n = 270). Four additional cohorts were then obtained to further study this association (n = 1040). RESULTS: This study of 1310 patients with B-ALL (717 male [56.1%] and 562 female patients [43.9%]) found that focal 22q11.22 deletions are frequent (518 of 1310 [39.5%]) in B-ALL and inconsistent with physiologic V(D)J recombination. A total of 299 of 1310 patients with B-ALL had IKZF1 alterations. Among patients with IKZF1 alterations, more than half shared concomitant focal 22q11.22 deletions (159 of 299 [53.0%]). Patients with combined IKZF1 alterations and 22q11.22 deletions had worse outcomes compared with patients with IKZF1 alterations and wild-type 22q11.22 alleles in every cohort examined (combined cohorts: 5-year event-free survival rates, 43.3% vs 68.5%; hazard ratio [HR], 2.18; 95% CI, 1.54-3.07; P

A novel machine learning system trained on The Cancer Genome Atlas (TCGA) data has been designed to help with classification, prognostic measures, and monitoring treatment responses in cancer management. The utility of this system will be demonstrated using a set of retrospective samples. Cytogenetic analysis has long been utilized in clinical cancer care with conventional cytogenetic methods such as chromosomal banding and FISH still standard protocols for hematologic malignancies. With the advent of microarrays, smaller and more complex variants have been discovered in cancer samples but identifying the potential impact of many alterations such as copy number variation (CNV) and loss of heterozygosity (LOH) has not been fully exploited. A typical cancer cytogenetic report can list many tens of variants with unclear clinical implications. To bridge this gap and assist with determining the clinical implications of such alterations, we have developed a novel machine learning system that has been trained using data from The Cancer Genome Atlas (TCGA). The TCGA data has been processed using the SNP-FASST2 segmentation algorithm followed by manual review for ploidy adjustment yielding high quality regions of CNV and LOH for over 6000 cases across 29 cancer types. Our novel neural network used this processed data to arrange samples based on their CNV and LOH profile across a two-dimensional map. The proximity of one sample to another on this map corresponds to its similarity in terms of CNV and LOH profile. To apply this system in a clinical setting, we have designed a case review system that aligns a new case with the learned cancer map to provide information on the similarity of the case with ones in TCGA. This enables rapid verification of tumor type or detection of a different origin for the sample than the one reported. In addition, the system can create a prognostic score using clinical survival data for the samples in TCGA. Here we will demonstrate the utility of the system in classifying new cases against the learned cancer map and estimating prognostic measures. Citation Format: Megan Roytman, Viren Wasnikar, Paul An, Raja Kashevan, Shalini Verma, Soheil Shams. Improving cancer cytogenetic case review with a new machine learning system [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2094.

ABSTRACT Aim: Neurofibromatosis type 2 (NF2)-associated vestibular schwannomas have variable size at presentation which presents a unique challenge in NF2 patient management. Therefore, we investigated the molecular signature characteristic of the differences in size for improved individualized precise therapy. Materials & methods: RNA expression analysis was performed on 15 small and 27 large NF2-associated vestibular schwannoma tumors using a microarray analyzing over 47,000 transcripts. Results: A signature of 11 genes was found to be correlated with NF2 tumor size. Conclusion: We have identified the genetic hallmark that differentiates large NF2-associated tumors from smaller tumors. This is the first time that these genes have been shown to be the hallmark for NF2 tumor size.

Approximately 10% of patients with chronic lymphocytic leukaemia (CLL) have a family history of the disease or a related lymphoproliferative disorder, yet the relationship of familial CLL to genomic abnormalities has not been characterized in detail. We therefore studied 75 CLL patients, half familial and half sporadic, using high-resolution array comparative genomic hybridization (CGH), in order to better define the relationship of genomic abnormalities to familial disease and other biological prognostic factors. Our results showed that the most common high-risk deletion in CLL, deletion 11q, was significantly associated with sporadic disease. Comparison of familial to sporadic disease additionally identified a copy number variant region near the centromere on 14q, proximal to IGH@, in which gains were associated both with familial CLL, and with mutated IGHV and homozygous deletion of 13q. Homozygous deletion of 13q was also found to be associated with mutated IGHV and low expression of ZAP-70, and a significantly longer time to first treatment compared to heterozygous deletion or lack of alteration. This study is the first high resolution effort to investigate and report somatic genetic differences between familial and sporadic CLL.

Meningiomas are one of the most common adult brain tumors. For most patients, surgical excision is curative. However, up to 20% recur. Currently, the molecular determinants predicting recurrence and malignant transformation are lacking. We performed retrospective global genetic and genomic analysis of 85 meningioma samples of various grades. Copy number alterations were assessed by 100K SNP arrays and correlated with gene expression, proliferation indices, and clinical outcome. In addition to chromosome 22q loss, which was detected in the majority of clinical samples, chromosome 6q and 14q loss was significantly more common in recurrent tumors and was associated with anaplastic histology. Five "classes" of meningiomas were detected by gene expression analysis that correlated with copy number alterations, recurrent status, and malignant histology. These classes more accurately identified recurrent tumors relative to Ki-67 index and extent of surgical resection, and highlight substantial expression heterogeneity between meningiomas. These data offer the most complete description of the genomic landscape of meningiomas and provide broad genomic information that may be used to further stratify meningioma patients into prognostic risk groups.

Next-generation sequencing (NGS) is mainly used to obtain sequence variants (SNVs). However, obtaining copy number results from NGS has gained momentum in both research and clinical applications. Targeted panel sequencing has been a popular method to achieve high depth of coverage for certain regions of interest at an affordable cost compared to whole genome sequencing. Shallow whole genome sequencing, where average read-depth can be as low as 0.1x, provides a cost savings-approach for identification of large copy number variant (CNV) events; it has been utilized in various application areas, including oncology. Here we introduce the BAM (MultiScale Reference) algorithm, currently in Nexus Copy Number, to function with shallow and targeted sequencing data, as well as WGS and WES, using a novel dynamic binning approach. This approach uses a Hidden Markov Model to segment the genome into target areas using the reads in targeted regions and the backbone areas using the off-target reads and additional areas. It uses coarse binning in the backbone areas that provides copy number base line as well as large copy number events and uses fine binning in target areas to provide high resolution copy number detection in targeted regions. Shallow WGS data and targeted panel NGS data, as well as WES with normal depth of coverage, were used for the testing. The results were compared with those from microarray and/or other algorithms in Nexus Copy Number, BAM ngCGH (matched) and BAM (pooled reference). GC correction schemes based on a range of window size and presence or absence of GC probe content were applied to the data and assessed for overall quality. Differences in overall read-depth resulted in variable sample quality across the cohorts, however most sample quality was adequate for copy number estimation and a quality threshold was assessed. Among the samples tested, the best quality after GC correction comes from the 50kb region size with or without the probes. Next, the copy number profiles of the samples from WES and microarray were compared for accuracy. Using microarray results as a reference for assessing calls greater than 5 MB, no false positive and one false negative call were observed; the single false negative call was attributable to low-level mosaicism in the tumor sample. Results indicate that relative copy number can be estimated and is comparable to the results achieved with microarray for the same targeted regions. This analysis series was then repeated using a secondary cohort of unrelated samples subjected to microarray and targeted panel NGS to validate results. The BAM (MultiScale Reference) method has been tested in a variety of cancer samples. This is an ideal tool for copy number estimation with NGS results in cancer samples because it provides a way for non-matched-pair analysis with genome, exome and targeted NGS. Citation Format: Andrea J. OHara, Zhiwei Che, Soheil Shams. Copy number estimation from targeted and shallow sequencing in cancer samples [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3582. doi:10.1158/1538-7445.AM2017-3582

Using the copy number and SNP- probes available in the TCGA level 1 data, reprocessed tumor profiles were more consistent with a control set in terms of median number of copy number events, sample ploidy, and breakpoint genes than the published “level 3” TCGA data. Probe-level data were analyzed using Nexus Copy Number SNP-FASST2 (a multi-state HMM algorithm that uses both SNP and copy number probes in making state assignments), with systematic correction applied to correct for GC biases. Additionally we performed manual baseline adjustment to correct for sample ploidy based on whole-genome B-alelle frequency data for each sample. The median number of copy number events in the TCGA data set was reduced from 377 (in the level 3 set) to 193, with a corresponding reduction in variance, and in line with our control samples (median CN changes of 173 and 142). After manual inspection, more than 15% of the TCGA BRCA samples available at level 3 were found to have incorrect baseline ploidy assignments. The resultant reanalyzed data set, along with associated clinical annotations for each sample, is available through Nexus DB repository to the general scientific community and should provide greater utility for further research. Citation Format: Louis J. Culot, Andrea O'Hara, Raja Keshavan, Zhiwei Che, Soheil Shams. Re-analysis of breast invasive carcinoma (BRCA) TCGA copy number data improves tumor profiles. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 5175. doi:10.1158/1538-7445.AM2014-5175

Molecular cytogenetic alterations, specifically monosomy 3, are strongly correlated with metastases and death in uveal melanoma (UVM). Although FISH can be used for the identification of monosomy 3, a subset of UVM exhibit only partial loss of chromosome 3 potentially missed with chromosome enumeration probes. Moreover, often limited material available can compromise detection of additional alterations with potential clinical relevance. Microarray analysis is an alternative method for the analysis of such specimens affording whole genome interrogation. In the current study we performed microarray to detect genomic changes in DNA from FFPET and FZT UVM samples (clinical trial NCT00952939). Of the available cases, 23.5% yielded DNA of sufficient quantity and quality to obtain interpretable microarray results, representing 28 patients. All but one case demonstrated significant abnormalities. Gains of 8q, consistent with an apparent i(8)(q10) karyotype, were the most common finding, seen in 21 patients. Monosomy 3 was detected by microarray in 15 cases. A single case showed partial loss of 3 (3q11.2q25.31), the only clinically significant abnormality detected in that case. All cases lacking chromosome 3 abnormalities showed a copy gain of 6p sharing a small 28.18 Mb region of overlap at 6p25.2p21.33. Additional findings included TERT and NEDD9 amplifications, and CDKN2A/B and LUM gene deletions. SNP analysis revealed three cases with unique regions of copy neutral LOH involving 5p15.33q35.3, 15q11.2q21.1 and one case with whole chromosome LOH for chromosomes 3, 4 and 6. The latter was detected in a FZT specimen, while the paired FFPET sample lacked evidence of LOH and showed monosomy 3, trisomy 4 and whole arm gains and losses of 6p and 6q, respectively, suggesting the presence of tumor heterogeneity. Microarray analysis has identified new recurrent abnormalities associated with UVM with potential relevance to UVM biology, diagnosis and prognosis. Conflict of Interest: Roger Schultz is an employee of Perkin Elmer.

Using data from The Cancer Genome Atlas (TCGA) project and HapMap consortium, we examined the hypothesis that existing copy number polymorphisms might predispose individuals to cancer and if different common CNVs bias toward different cancer types. This analysis was performed using a subset of the TCGA data, obtained from blood derived “normal” samples of cancer patients. All the data was generated using Affymetrix SNP 6.0 arrays (Affymetrix, Inc.) and processed using Nexus Copy Number ver. 6.1 (BioDiscovery, Inc.). The project contained 451 Glioblastoma (GBM), 353 Colon cancer, and 245 Skin cancer samples in addition to 211 HapMap samples which we used as reference since these individuals were supposed to be healthy. Using Fisher's Exact test, we compared each different cancer types to the HapMap samples to identify regions of copy number change with FDR corrected p-value of less than 0.05 and difference in frequency between each population of at least 10%. Many of the regions found in this process encompass regions identified as common CNV polymorphisms as reported in the Database of Genomic Variance (DGV). For example, a polymorphic region on 8p23.1 containing a number of genes from the Beta defensin family involved in defense response to bacterium were seen significantly gained in germline samples obtained from cancer patients as compared to the HapMap samples (11% in HapMap, 42% in Colon Cancer, 28% in GBM, and 32% in Skin cancer). Additionally, some interesting regions were identified that were not seen as polymorphic in the general population, did not show up in the HapMap samples or reported in DGV, but had high prevalence in the blood derived normal samples from cancer patients. Using gene enrichment analysis on genes discovered in these regions we identified a number of known cancer specific pathways that were highly enriched (e.g. KEGG Glioma and Melanoma pathways being statistically significantly enriched). Looking at the effected genes in these pathways we identified small focal and recurrent events that often only spanned a small portion of a gene covering a few exons. This included genes such as PIK3CA where a focal gain covering exons 10-14 were observed in none of the HapMap samples but in 21% of the germline of patients with Colon cancer, 9% of GBM and 14% of Skin cancer patients. A similar focal and recurrent gain was observed in KIT covering exons 2-7 with no gains in the HapMap samples and 55% in Colon, 15% in GBM, and 50% in Skin cancer patients. Similar observations were also made for other well-known cancer genes such as RAF1 and EGFR but to a lesser extent. We are in a process of comparing matched tumor/normal cases to further investigate possible evolution of these changes from the germline to the surrounding tissue (also available from the TCGA database) to the primary tumor samples. Citation Format: Soheil Shams, Raja Keshavan, Joshua D. Schiffman. Analysis of copy number and LOH in germline samples from different tumor types in The Cancer Genome Atlas (TCGA) project. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 3171. doi:10.1158/1538-7445.AM2013-3171

Cancer samples pose a challenging problem for analysis using microarray technology. The basic array CGH (aCGH) approach can confidently detect changes in copy number when a sample is measured against a normal control. However, the necessary normalization pre-processing step will essentially wash away the information associated with hyperdiploid samples. This can be an issue especially with cancer samples. Additionally, mosaic samples, due to different mixture of cells in the sample, can also severely complicate the analysis and interoperation of samples with microarray technology. One of the recently proposed approaches to simultaneously deal with these issues is called the Allele Specific Copy Number Analysis of Tumors (ASCAT) algorithm (1). The approach takes advantage of the SNP information from SNP microarrays to combine the change in total signal strength (log-R value) with the B-Allele Frequency (BAF) to create an optimization function that has local minimas at values corresponding to possible sample ploidy and percent normal contamination. Here we demonstrate an efficient implementation of the ASCAT algorithm on processing cancer samples generated by The Cancer Genome Atlas (TCGA) project. Using Affymetrix and Illumina SNP array data, results are compared between the different platforms as well as with and without application of the ASCAT algorithm. Characterization of a percentage of the cells as normal contaminants within the samples using the ASCAT algorithm reveals many more gains and losses in the aberration profile of the tumors than in samples processed without ASCAT. ASCAT also identified that a large percentage of samples contain high ploidy numbers. With the sample ploidy and extent of normal contamination identified using ASCAT, clearer and more accurate calls can be made for cancer samples. 1. Van Loo P, Nordgard SH, Lingjaerde OC, et al. Allele-specific copy number analysis of tumors. Proc Natl Acad Sci USA 2010;107:16910-16915.

Exploration of TCGA generated DNA copy number data in ovarian cancer points to interesting proapaptotic gene for survival prediction Using data generated by The Cancer Genome Atlas (TCGA) project, we investigated the use of copy number changes in prediction of both response to therapy as well as identification of possible prognostic survival bio-markers. The analysis was performed using an initial data set of 557 arrays (68 Affymetrix 6.0 and 489 Agilent 244K arrays). The raw data was processed using Nexus Copy Number version 5.1 software (BioDiscovery, El Segundo CA) and regions of copy number change were identified using the built-in FASST and SNP-FASST algorithms. As previously reported, using only the Agilent data set, we had identified a number of regions with statistically significant differences across between sample groups that responded to therapy as compared to those with progressive disease1. Performing gene enrichment analysis on these regions indicated a relationship with apoptosis. In our current study, we have also considered survival and have identified FAM105A located on the short arm of chromosome 5 as a possible component of Ovarian Cancer. A number of studies have identified the small 728Kb region between TRIO and ANKH gained in various cancers and hypothesized that these genes are possible oncognes2–3. Using Kaplan-Mier survival analysis, we have in fact been able to show that the FAM105A gene located between TRIO and ANKH is highly predictive of survival (p=0.00024) with the deletion causing a much poorer prognosis. This result was validated using the Affymetrix data on a different set of samples. Very little is known about this gene except the fact that using functional screening, it has demonstrated strong proapaptotic behavior which is in concordance with our findings here4. We are in the process of expanding the validation data set to several hundred samples. 2 Judith N Kloth, Jan Oosting, Tom van Wezel, Karoly Szuhai, Jeroen Knijnenburg, Arko Gorter, Gemma G Kenter, Gert Jan Fleuren, and Ekaterina S Jordanova, Combined array-comparative genomic hybridization and single-nucleotide polymorphism-loss of heterozygosity analysis reveals complex genetic alterations in cervical cancer, BMC Genomics. 2007; 8: 53. Published online 2007 February 20. doi: 10.1186/1471–2164–8-53. 3 Coe BP, Henderson LJ, Garnis C, Tsao MS, Gazdar AF, Minna J, Lam S, Macaulay C, Lam WL., High-resolution chromosome arm 5p array CGH analysis of small cell lung carcinoma cell lines, Genes Chromosomes Cancer. 2005 Mar;42(3):308–13. 4 Otto Mannherz, Daniel Mertens, Meinhard Hahn, Peter Lichter, Functional screening for proapoptotic genes by reverse transfection cell array technology, Genomics, Volume 87, Issue 5, May 2006, Pages 665–672, ISSN 0888–7543, DOI: 10.1016/j.ygeno.2005.12.009. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr LB-270. doi:10.1158/1538-7445.AM2011-LB-270