Jong-Suk Chung

Modeling precision treatment of breast cancer

BackgroundFirst-generation molecular profiles for human breast cancers have enabled the identification of features that can predict therapeutic response; however, little is known about how the various data types can best be combined to yield optimal predictors. Collections of breast cancer cell lines mirror many aspects of breast cancer molecular pathobiology, and measurements of their omic and biological therapeutic responses are well-suited for development of strategies to identify the most predictive molecular feature sets.ResultsWe used least squares-support vector machines and random forest algorithms to identify molecular features associated with responses of a collection of 70 breast cancer cell lines to 90 experimental or approved therapeutic agents. The datasets analyzed included measurements of copy number aberrations, mutations, gene and isoform expression, promoter methylation and protein expression. Transcriptional subtype contributed strongly to response predictors for 25% of compounds, and adding other molecular data types improved prediction for 65%. No single molecular dataset consistently out-performed the others, suggesting that therapeutic response is mediated at multiple levels in the genome. Response predictors were developed and applied to TCGA data, and were found to be present in subsets of those patient samples.ConclusionsThese results suggest that matching patients to treatments based on transcriptional subtype will improve response rates, and inclusion of additional features from other profiling data types may provide additional benefit. Further, we suggest a systems biology strategy for guiding clinical trials so that patient cohorts most likely to respond to new therapies may be more efficiently identified.

Exome sequencing of desmoplastic melanoma identifies recurrent NFKBIE promoter mutations and diverse activating mutations in the MAPK pathway

Desmoplastic melanoma is an uncommon variant of melanoma with sarcomatous histology, distinct clinical behavior and unknown pathogenesis. We performed low-coverage genome and high-coverage exome sequencing of 20 desmoplastic melanomas, followed by targeted sequencing of 293 genes in a validation cohort of 42 cases. A high mutation burden (median of 62 mutations/Mb) ranked desmoplastic melanoma among the most highly mutated cancers. Mutation patterns strongly implicate ultraviolet radiation as the dominant mutagen, indicating a superficially located cell of origin. Newly identified alterations included recurrent promoter mutations of NFKBIE, encoding NF-κB inhibitor ɛ (IκBɛ), in 14.5% of samples. Common oncogenic mutations in melanomas, in particular in BRAF (encoding p.Val600Glu) and NRAS (encoding p.Gln61Lys or p.Gln61Arg), were absent. Instead, other genetic alterations known to activate the MAPK and PI3K signaling cascades were identified in 73% of samples, affecting NF1, CBL, ERBB2, MAP2K1, MAP3K1, BRAF, EGFR, PTPN11, MET, RAC1, SOS2, NRAS and PIK3CA, some of which are candidates for targeted therapies.

Phylogenetic analyses of melanoma reveal complex patterns of metastatic dissemination

Significance Subpopulations of cells in a primary melanoma sometimes disseminate and establish metastases, which usually cause mortality. By sequencing tumor samples from patients with metastatic melanoma never subjected to targeted therapies, we were able to trace the genetic evolution of cells in the primary that seed metastases. We show that distinct cells in the primary depart multiple times in parallel to seed metastases, often after evolving from a common, parental cell subpopulation. Intriguingly, we also determine that single metastases can be founded by more than one cell population found in the primary cancer. These mechanisms show how profound genetic diversity arises naturally among multiple metastases, driving growth and drug resistance, but also indicate that certain mutations may distinguish cells destined to metastasize. Melanoma is difficult to treat once it becomes metastatic. However, the precise ancestral relationship between primary tumors and their metastases is not well understood. We performed whole-exome sequencing of primary melanomas and multiple matched metastases from eight patients to elucidate their phylogenetic relationships. In six of eight patients, we found that genetically distinct cell populations in the primary tumor metastasized in parallel to different anatomic sites, rather than sequentially from one site to the next. In five of these six patients, the metastasizing cells had themselves arisen from a common parental subpopulation in the primary, indicating that the ability to establish metastases is a late-evolving trait. Interestingly, we discovered that individual metastases were sometimes founded by multiple cell populations of the primary that were genetically distinct. Such establishment of metastases by multiple tumor subpopulations could help explain why identical resistance variants are identified in different sites after initial response to systemic therapy. One primary tumor harbored two subclones with different oncogenic mutations in CTNNB1, which were both propagated to the same metastasis, raising the possibility that activation of wingless-type mouse mammary tumor virus integration site (WNT) signaling may be involved, as has been suggested by experimental models.

Transcription restores DNA repair to heterochromatin, determining regional mutation rates in cancer genomes

Somatic mutations in cancer are more frequent in heterochromatic and late-replicating regions of the genome. We report that regional disparities in mutation density are virtually abolished within transcriptionally silent genomic regions of cutaneous squamous cell carcinomas (cSCCs) arising in an XPC(-/-) background. XPC(-/-) cells lack global genome nucleotide excision repair (GG-NER), thus establishing differential access of DNA repair machinery within chromatin-rich regions of the genome as the primary cause for the regional disparity. Strikingly, we find that increasing levels of transcription reduce mutation prevalence on both strands of gene bodies embedded within H3K9me3-dense regions, and only to those levels observed in H3K9me3-sparse regions, also in an XPC-dependent manner. Therefore, transcription appears to reduce mutation prevalence specifically by relieving the constraints imposed by chromatin structure on DNA repair. We model this relationship among transcription, chromatin state, and DNA repair, revealing a new, personalized determinant of cancer risk.

The minimal amount of starting DNA for Agilent's hybrid capture-based targeted massively parallel sequencing.

Targeted capture massively parallel sequencing is increasingly being used in clinical settings, and as costs continue to decline, use of this technology may become routine in health care. However, a limited amount of tissue has often been a challenge in meeting quality requirements. To offer a practical guideline for the minimum amount of input DNA for targeted sequencing, we optimized and evaluated the performance of targeted sequencing depending on the input DNA amount. First, using various amounts of input DNA, we compared commercially available library construction kits and selected Agilent’s SureSelect-XT and KAPA Biosystems’ Hyper Prep kits as the kits most compatible with targeted deep sequencing using Agilent’s SureSelect custom capture. Then, we optimized the adapter ligation conditions of the Hyper Prep kit to improve library construction efficiency and adapted multiplexed hybrid selection to reduce the cost of sequencing. In this study, we systematically evaluated the performance of the optimized protocol depending on the amount of input DNA, ranging from 6.25 to 200 ng, suggesting the minimal input DNA amounts based on coverage depths required for specific applications.

Erratum to: Modeling precision treatment of breast cancer

During the type-setting of the final version of the article [1] some of the additional files were swapped. The correct files are republished in this Erratum.

Practical approach to determine sample size for building logistic prediction models using high-throughput data

An empirical method of sample size determination for building prediction models was proposed recently. Permutation method which is used in this procedure is a commonly used method to address the problem of overfitting during cross-validation while evaluating the performance of prediction models constructed from microarray data. But major drawback of such methods which include bootstrapping and full permutations is prohibitively high cost of computation required for calculating the sample size. In this paper, we propose that a single representative null distribution can be used instead of a full permutation by using both simulated and real data sets. During simulation, we have used a dataset with zero effect size and confirmed that the empirical type I error approaches to 0.05. Hence this method can be confidently applied to reduce overfitting problem during cross-validation. We have observed that pilot data set generated by random sampling from real data could be successfully used for sample size determination. We present our results using an experiment that was repeated for 300 times while producing results comparable to that of full permutation method. Since we eliminate full permutation, sample size estimation time is not a function of pilot data size. In our experiment we have observed that this process takes around 30min. With the increasing number of clinical studies, developing efficient sample size determination methods for building prediction models is critical. But empirical methods using bootstrap and permutation usually involve high computing costs. In this study, we propose a method that can reduce required computing time drastically by using representative null distribution of permutations. We use data from pilot experiments to apply this method for designing clinical studies efficiently for high throughput data.

Abstract 2968: Exome sequencing of desmoplastic melanoma reveals recurrent NFKBIE promoter mutations and diverse MAPK/PI3K pathway activating mutations

Desmoplastic melanomas (DMs) comprise 4% of the overall melanoma burden and have a 5-year survival rate of 85%. DMs are dermal tumors characterized by spindled melanocytes situated within abundant desomplastic stroma. These unusual histological features commonly lead to misdiagnosis. Currently, there are no known genetic drivers. A better understanding of the underlying biology of desmoplastic melanoma would provide biomarkers and therapeutic opportunities. Towards this goal, we performed low-coverage genome and high-coverage exome sequencing of 20 DMs in a discovery cohort, followed by targeted sequencing of 293 candidate genes on a validation cohort of 42 cases. Additionally, high-resolution aCGH was performed on samples from both cohorts. A high mutation burden (median 62 mutations/Mb) ranked desmoplastic melanoma among the most highly mutated cancers sequenced to date. Mutation patterns strongly indicate that UV-radiation is the dominant mutagen and implicate a superficially located cell of origin despite their predominantly intradermal presentation. Novel alterations included recurrent promoter mutations and amplification of NF-kappa B inhibitor epsilon, NFKBIE (IkBe) in 14.5% of samples. The promoter mutations typically affect both alleles and occur over a highly conserved DNA region. The mutations are predicted to disrupt a canonical Ets Like Factor 1 (ELF1) binding site. In total, these data imply aberrant NF-kappa B signaling as a pathogenic feature of desmoplastic melanoma. Commonly mutated oncogenes in melanomas, in particular BRAF V600E and NRAS Q61K/R, were absent. Instead, other genetic alterations known to activate the MAPK and PI3K signaling cascades were identified in 73% of samples, affecting NF1, CBL, ERBB2, MAP2K1, MAP3K1, BRAF, EGFR, PTPN11, MET, RAC1, SOS2, NRAS, and PIK3CA. Rb and p53 pathway alterations occurred respectively in 71% and 66% of tumors, affecting RB1, FBXW7, CDK4, PPP6C, CCND1, CDKN2A, TP53, and MDM2. Finally, TERT promoter mutations or amplifications occurred in 90% of tumors. The consequences of the mutations on protein expression levels was confirmed by immunostaining for NF1, EGFR, Rb, CDK4, CCND1, p16, p53, and Mdm2. Collectively, many of these oncogenic mutations are potentially druggable. In conclusion, desmoplastic melanomas harbor distinct genetic alterations that explain their unique biology, and this study illuminates genetic biomarkers and nominates targets for therapeutic intervention. Citation Format: Alan H. Shain, Maria Garrido, Thomas Botton, Eric Talevich, Iweh Yeh, Zack Sanborn, Jongsuk Chung, Nicholas Wang, Hojabr Kakavand, Graham Mann, John Thompson, Thomas Wiesner, Ritu Roy, Adam Olshen, Alexander Gagnon, Joe Gray, Nam Huh, Joe Hur, Klaus Busam, Richard Scolyer, Raymond Cho, Rajmohan Murali, Boris Bastian. Exome sequencing of desmoplastic melanoma reveals recurrent NFKBIE promoter mutations and diverse MAPK/PI3K pathway activating mutations. [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 2968. doi:10.1158/1538-7445.AM2015-2968