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Background Multigene assays for breast cancer are becoming more commonly used for research, diagnostic, and treatment-selection purposes. These assays are commonly based on a single tumor sample, so this study assesses whether clinically-relevant multigene scores are subject to misclassification due to technical variation or intratumoral heterogeneity. Methods Using FFPE blocks, tumors from 37 patients were sampled at different pathologist-selected spatial locations, targeting regions with distinct histological appearance. A second pathologist assessed mitotic activity and presence of immune infiltration for each specimen. Gene expression was quantified using the PAM50 assay. Samples were classified with respect to intrinsic subtype (Luminal A, Luminal B, HER2-enriched, or Basal-like) and risk of recurrence with proliferation score (ROR-P, high vs. med/low). Euclidean distances between samples were calculated across the 50 genes, and the distribution of Euclidean distances were compared for samples that were concordant vs. discordant for each multigene classifier for PAM50 or ROR-P. Results Of the 37 tumors, 11 had samples with discordant PAM50 subtype and 7 had samples with discordant ROR-P group (75% and 83% agreement, respectively). The Euclidean distance between paired samples with discordant PAM50 intrinsic subtype or ROR-P score was significantly greater than that among concordant tumors. Samples discordant for ROR-P had more heterogeneity in histological characteristics, while discordance of PAM50 subtype call was generally not predicted by heterogeneity of these histological characteristics. Conclusion The concordance of PAM50 subtype calls and ROR-P, despite oversampling of heterogenous-appearing tumor regions, demonstrates reproducibility of the PAM50 classifier for breast cancer tumor intrinsic subtyping. However, assay sampling strategies for histologically heterogeneous tumors merit further consideration. Citation Format: Amber N. Hurson, Alina Hamilton, Linnea T. Olsson, Erin L. Kirk, Benjamin C. Calhoun, Joseph Geradts, Mark E. Sherman, Melissa A. Troester. Intratumoral heterogeneity of prognostic multigene signatures for breast cancer [abstract]. In: Proceedings of the AACR Virtual Special Conference on Tumor Heterogeneity: From Single Cells to Clinical Impact; 2020 Sep 17-18. Philadelphia (PA): AACR; Cancer Res 2020;80(21 Suppl):Abstract nr PO-068.

Background: Genome-wide analysis of Loss-of-heterozygosity (LoH) has been shown to be important in several contexts, including the assessment of BRCAness signature, a marker of homologous repair deficiency in several cancer types associated with efficacy of platinum therapy and PARP inhibitors. Methods to infer genome-wide LoH status require high-coverage whole-genome sequencing or low-pass sequencing of a large number of single cells. We present a method to infer genome-wide LoH status of as low as one single-cell from low-pass sequencing of libraries produced by Ampli1™ LowPass kit, at a fraction of the cost of current NGS-based methods. Methods: We used low-pass sequencing data generated from 294 single Circulating Tumor Cells (CTCs) from prostate, multiple myeloma and lung cancer patients, and from 24 single cells from 8 cell lines. DNA amplified with Ampli1™ WGA kit (based on ligation-mediated PCR) combined with Ampli1 LowPass kit for Illumina generates a reduced genome representation, whereby a significant fraction of fragments is expected to be covered by more than one read. We reasoned that, even at a mean coverage insufficient for variant calling (≤0.05x), a statistically significant decrease in loci showing 2 alleles should be expected in LoH regions. A pileup of mapped sequences at dbSNP loci was obtained (minor allele frequency ≥5%; read depth ≥2). Mono- and bi-allelic loci (pileup containing 1 or 2 different alleles, respectively), were counted in each uniform copy-number (CN) genome segment within a single chromosome arm. Regions with CN equal to cell ploidy were used as internal reference. A one-sided Fisher test on biallelic sites depletion (multiplicity corrected p-value threshold of 0.05) was used to call LoH on each region. Results: Compared with libraries obtained using random shearing, Ampli1 LowPass single-cell libraries showed an increase in mean coverage of covered genome from 1.1 to 1.9 starting from the same number of reads (1M) thus increasing the number of loci for LoH calling. Analysis of 10 patients' single cells with known LoH events, corresponding to regions at copy number 1, detected 1006 events out of 1142. Several (492) copy neutral LoH events or LoHs in copy gained regions were also detected. Representatives of such events were verified successfully by high pass sequencing of the same library. Performance was confirmed by analysis of 24 single cells from cell lines whose LoH status was inferred from CCLE database. Conclusions: This innovative method leverages reduced genome representation inherent in Ampli1 LowPass libraries, to compute genome-wide LoH along with copy number (CN) status, meeting the need for LoH/CN analysis at single-cell level on rare-cells such as CTCs. Citation Format: Alberto Ferrarini, Claudio Forcato, Marianna Garonzi, Genny Buson, Massimiliano Pellicano, Francesca Fontana, Nicolò Manaresi. Genome-wide loss-of-heterozygosity calling from low-pass sequencing of ligation-mediated whole genome amplified DNA in single tumor cells [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 1327.

The increased usage of circulating tumor DNA (ctDNA) sequencing for oncology clinical research demonstrates a critical need for sensitive and specific testing. While we have observed a high degree of concordance between single tumor mutations in tumor and plasma, several recent studies have highlighted a lack of concordance between plasma and tumor panel NGS gene panel testing due to biological and technical factors. To explore further these factors and benchmark ctDNA NGS testing services, a set of matched plasma, tumor, and normal samples from 24 subjects were acquired from three biobanking companies. Replicate 2 ml-plasma samples were tested by four ctDNA sequencing companies, and matching tumor/normal samples were tested by two tumor sequencing companies. Concordance was measured by comparing plasma mutations to tumor mutations as well as comparing mutations among the same plasma tested by the ctDNA companies. While our experience with NGS of matched samples from clinical trials typically identifies ~30% of patients with no detectable mutation and therefore likely not shedding tumor DNA, with the retrospectively collected commercial samples ~60% lacked detectable high confidence mutations, likely due to quality control issues with sample collection. We also found variation in the concordance of ctDNA mutation detection rates among the four vendors, due to significant differences in DNA yield and assay sensitivity. While factors such as tumor heterogeneity and timing of plasma-tumor collection can lower concordance rates, the majority of discordance in our study was due to technical rather than biological variation. Assay analytical variance and the impact of reporting false positive variants are key factors that need to be addressed as plasma-based NGS testing is more widely incorporated into translational and clinical research. Examples illustrating the complexity of the analyses and giving support for confidence in ctDNA testing results will be given. Citation Format: Daniel Stetson, Brian Dougherty, Ambar Ahmed, Tristan Lubinski, Aleksandra Markovets, Kenneth Thress, Robert McEwen, Gaia Schiavon, David Whitston, Barrett Nuttall, J. Carl Barrett. Examination of analytical factors impacting concordance of plasma-tumor testing by next-generation sequencing (NGS) [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 LB-249. doi:10.1158/1538-7445.AM2017-LB-249

Introduction: Genome-wide profiling of copy-number alterations (CNA) in single cells enables the characterization and investigation of genomic heterogeneity and evolution in tumor cell populations such as individual circulating tumor cells (CTCs), or disaggregated solid tumors. Ampli1™ WGA is a commercially available kit for whole-genome amplification from single cells suitable for array comparative genomic hybridization (aCGH), which is currently the gold standard for genetic diagnosis of CNA. However, labor and reagent costs limit aCGH applicability. Here we introduce for the first time a rapid, high-throughput low-pass Whole Genome Sequencing (WGS)-based method for CNA analysis (LPCNA) on single cells, enabling simultaneous profiling of multiple cells at lower cost and effort. Methods: Six single cells from NCI-H23, NCI-H661 and NCI-H1650 cell lines and 19 white blood cells (WBCs) were amplified using Ampli1™ WGA Kit. Only 5μl (1/10th of total WGA product) were used as sample input for LPCNA. Purified WGA products were processed for each sample in a single-tube, one-step reaction resulting in IonTorrent-compatible indexed libraries. Samples were pooled and size-selected to recover fragments with a length ranging from 300 to 400 bp. Following purification and template preparation, pools were sequenced on Ion 318™v2 Chips. Array CGH was performed for comparison, using Agilent SurePrint 4×180k arrays as previously reported. CNA analysis was performed using Control-FREEC for low-pass WGS data, or Agilent Genome Workbench for aCGH. Results: By exploiting the deterministic nature of Ampli1™ whole genome amplification, we devised a method to generate IonTorrent compatible libraries in a single reaction, drastically reducing time of bench work and cost. Method accuracy has been assessed by comparing the copy number profiles generated by our approach with about 870,000 reads/sample (average genome coverage of 0.064x, range 0.051-0.073) with those obtained by a validated aCGH method. Very good agreement in gain and loss profiles was achieved for all single tumor cells analyzed. As expected WBC analysis resulted in normal copy-number profiles with autosomes copy number = 2, with minimal false-positive rate ( Highlights: This innovative low-pass sequencing approach for CNA detection on single cells enables high level multiplexing on different NGS platforms. The approach is highly scalable and flexible. From few (6-8) samples (IonTorrent PGM, 318™v2 Chip), up to 96 samples (Ion Proton, Ion PI™ Chip v3) can be processed in a single run. This approach meets the need for rapid results ( This work has been carried out within IMI CANCER-ID consortium (www.cancer-id.eu). Citation Format: Genny Buson, Paola Tononi, Claudio Forcato, Francesca Fontana, Gianni Medoro, Rui Neves, Birte Möhlendick, Nikolas Stoecklein, Nicolò Manaresi. Scalable, rapid and affordable low-pass whole genome sequencing method for single-cell copy-number profiling on LM-PCR based WGA products. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2394.

Cells within a single tumor are known to display extensive phenotypic and functional heterogeneity. Many life-threatening features of cancer, including drug resistance, metastasis and relapse, are facets of intratumor heterogeneity. With emerging single-cell measurement technologies, the field is poised to make important strides in understanding and controlling this heterogeneity. However, these technologies require advances in analytical methods to interpret the complex data they produce. Using mass cytometry, which measures single cells in ~31 simultaneous proteomic features, we developed novel methods for analyzing phenotypic heterogeneity in cancer. We use AML as an example to demonstrate the power of our approach. The heart of our approach is Phenograph, a graph-based representation of single-cells which represents the phenotypic structure of the sample and can be partitioned into subsets of densely interconnected nodes, called communities. Using Phenograph, we deconstructed several AML samples into discrete phenotypes. Analyzing the resulting subpopulations provided insights into functional heterogeneity of AML. Phenograph can be applied to characterize heterogeneity and primitive subpopulations in additional cancers. Citation Format: Dana Pe'er. Computational dissection of phenotypic and functional heterogeneity in cancer. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr IA24.

Traditional approaches to evaluating antitumor agents using human tumor xenograft models have usually used 8-10 mice in control and treatment groups against a limited panel of tumor models. However, with increasing evidence of molecular/genetic subsets of tumors within a histotype, there is a need to establish panels of tumors that accurately reflect such heterogeneity. The consequences of evaluating agents in a large number of models is that it is highly resource consuming. An alternative approach is to use fewer animals per tumor line, allowing a greater number of models to be evaluated that capture greater molecular/genetic heterogeneity of the cancer type. We analyzed 67 agents evaluated by the PPTP to determine whether a single mouse, chosen by a random number generating routine in Excel, predicted the median response for groups of mice (solid tumors n = 10; acute lymphoblastic leukemias n = 8). Agents evaluated included standard cytotoxic agents, tyrosine kinase inhibitors, receptor-binding antibodies, and anti-angiogenic agents. Data were analyzed to determine predictive value for 79 xenograft models, and to identify classes of antitumor agent where predictive value of single mouse experiments was poor. The individual tumor response was compared to the group median response using response criteria developed in the PPTP. A total of 2106 comparisons were made. The single tumor response accurately predicted the group median response in 1684 comparisons (79.34%). Models had a range for accurate prediction (0.583 - 0.918). Allowing for mis-prediction of + or - one response category, the overall prediction rate increased to 95.63%. Single tumor results accurately predicted objective response rates (ORR) determined by group response, in 66 of 67 studies. There was a single study where single mouse data incorrectly predicted an agent as ‘active’ (>20% ORR). For most tumor models the single mouse appears to give results similar to groups of 8-10 mice. Importantly, even allowing for slight inaccuracy (± 1 response category), the single mouse experiment identified all active agents, and in the worse case over-predicted activity in one tumor histotype. The advantage of the single mouse experimental design is that it allows for inclusion of models that encompass greater molecular heterogeneity, thus recapitulating the clinical heterogeneity more accurately. Potentially, this design also reduces the numbers of mice used for experimentation. However, to generate these patient derived xenograft models, particularly solid tumors at diagnosis and relapse will require national and international coordination through organizations such as the Children's Oncology Group. Citation Format: Brendan Murphy, Han Yin, John M. Maris, Anders E. Kolb, Richard Gorlick, Patrick C. Reynolds, Min Kang, Stephen T. Keir, Raushan Kurmasheva, Igor Dvorchik, Jianrong Wu, Catherine Billups, Malcolm A. Smith, Peter J. Houghton. Analysis of single mouse tumor response results from the Pediatric Preclinical Testing Program (PPTP). [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 1617. doi:10.1158/1538-7445.AM2015-1617

The most prominent or most interesting genomic alteration events from an individual tumor sample can now be browsed and analyzed in the cBio Cancer Genomics Portal. With the rapid accumulation of detailed and comprehensive genomic maps of thousands of tumors in The Cancer Genome Atlas and other projects it has now become feasible to nominate the functionally most significant events affecting a tumor from an individual patient. The cBio Cancer Genomics Portal (http://cbioportal.org) allows interactive exploration of multidimensional cancer genomics data sets, currently for more than 6,000 tumor samples from 20 cancer genomics studies, including all TCGA projects. This information gateway significantly lowers the barrier between complex genomic data and their efficient use by cancer researchers for the development of biologic insights and clinical applications. In addition to gene-by-gene alteration maps across many samples and across diverse tumor types, one can now view genomic alterations in individual tumor samples. As there are potentially hundreds or thousands of genomic alterations in any single tumor sample, it is crucially important to select, for inspection and analysis, alteration events most likely to contribute to oncogenesis or affect the response to therapy. In the cBio portal patient view, this selection is done making use of recurrence statistics, background functional knowledge and predicted functional impact, under the control of the cancer researcher. All relevant data about a tumor are displayed on a single page, including clinical characteristics, summaries of the extent of mutations and copy-number alterations, as well as details about mutated, amplified, and deleted genes. Genomic alterations are filtered by the following criteria: recurrence of mutations or copy-number alterations across the tumor cohort (MutSig and GISTIC), mutation occurrence in COSMIC, and by cancer gene annotation (via the Sanger Cancer Gene Census and other sources). The patient view also provides information about drugs that target the altered genes and lists relevant clinical trials. The patient view is fully interactive and enables quick and easy assessment of all relevant genomic events in individual tumor samples. All data can be viewed in the context of the other tumors in the cohort, which facilitates classification of tumor samples by genomic criteria and can supplement standard pathology. Once characterization of genomic alterations in tumor samples becomes standard practice in patient care, this tool could be used to assess prognosis and guide treatment decisions, ideally the personalized choice of targeted therapies. Citation Format: Jianjiong Gao, Selcuk Onur Sumer, Gideon Dresdner, Bülent Arman Aksoy, Chris Sander, Nikolaus Schultz. Individual patient cancer profiles in the cBio Cancer Genomic Portal. [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 5140. doi:10.1158/1538-7445.AM2013-5140

We have demonstrated a novel somatic mutation enrichment methodology that allows DNA sequencing to work beyond its usual limit of detection to accurately profile solid tumors by detecting their mutation signature in plasma, even when the tumor DNA is present in abundances below 0.01%. Next Generation DNA Sequencing (NGS) is becoming the new standard for mutational profiling of tumor tissue, due to its flexibility, speed, and decreasing cost. While generally exceptional in performance, NGS suffers from a sequencing error rate of 0.1% - 1%, largely due to amplification induced artifacts in its workflow. While this does not constitute a significant problem in application of NGS to sequencing of most tumour tissues, it makes NGS impractical as a method to search for low abundance mutation signatures in plasma samples. Numerous publications have shown the presence of tumor signatures in the cell-free DNA (cfDNA) circulating in plasma, but concordance between the tumor signature and the plasma signature has been limited. This is likely due to limitations in the detection technologies used to search for cfDNA in plasma. To maximize concordance between plasma and tissue, it will be essential that sensitivities reaching 0.01% and below (as little as a single tumor mutant allele per sample) be achieved, and ideally that multiple mutational hot spots be analyzed to maximize the chance of detection. Current technologies are incapable of such sensitivity over a large number of mutation loci. We have developed a novel electrophoretic method that can enrich nucleic acid samples over 1,000,000-fold for up to 100 somatic mutations, enabling reliable profiling of samples containing as little as 0.01% mutant. By enriching nucleic acid samples for specific targets prior to amplification and sequencing, we enable the use of NGS in plasma-based mutation detection and profiling. We present data demonstrating highly sensitive multiplexed mutation detection in plasma samples, demonstrating 0.01% sensitivity. We also report on concordance studies between mutation incidence in tissue and matched plasma in human samples. Citation Format: Andre Marziali. Sensitive detection of tumor nucleic acids in plasma by mutation-enriched next generation sequencing. [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 4148. doi:10.1158/1538-7445.AM2013-4148