Integrated Whole Exome and Transcriptome Sequencing in Cholesterol Metabolism in Melanoma: Systematic Review


Background: Melanoma is a highly malignant form of skin cancer that exhibits remarkable metabolic adaptability. Melanoma cells exhibit the capacity to adapt to specific conditions of the tumor microenvironment through the utilization of diverse energy sources, thereby facilitating the growth and advancement of the tumor. One of the notable characteristics of metabolic reprogramming is the heightened rate of lipid synthesis. This review was conducted to illustrate how the integration of whole exom and transcriptome sequencing will enhance the detection of the effect of cholesterol metabolism in melanoma.

Methods: The Cochrane database, Embase, PubMed, SCOPUS, Google Scholar, Ovid, and other databases were thoroughly searched for works addressing integrated whole exome and transcriptome sequencing in cholesterol metabolism in melanoma. Skin malignancy, melanoma progression, transcriptome sequencing, whole exome sequencing, transcriptome sequencing by RNA sequencing, and integrated transcriptome and whole exome sequencing were the key phrases employed. This article underwent a phased search for pertinent literature using a staged literature search methodology. Each section’s relevant papers were identified and summarized independently. The results have been condensed and narratively given in the pertinent sections of this thorough assessment.

Results: DNA-based analysis has proven to be ineffective in identifying numerous mutations that have an impact on splicing or gene expression. RNA-Sequencing, when combined with suitable bioinformatics, offers a reliable method for detecting supplementary mutations that aid in the genetic diagnosis of geno-dermatoses. Therefore, clinical RNA-Sequencing expands the scope of molecular diagnostics for rare genodermatoses, and it has the potential to serve as a dependable initial diagnostic method for expanding mutation databases in individuals with inheritable skin conditions.

Conclusion: The integration of patient-specific tumor RNA-sequencing and tumor DNA whole-exome sequencing (WES) would potentially enhance mutation detection capabilities compared to relying solely on DNA-WES.


skin malignancy, melanoma progression, whole exome sequencing

[1] Saginala, K., Barsouk, A., Aluru, J. S., Rawla, P., & Barsouk, A. (2021). Epidemiology of Melanoma. Medical Sciences : Open Access Journal, 9(4), 63.

[2] Shinzawa, K., Matsumoto, S., Sada, R., Harada, A., Saitoh, K., Kato, K., Ikeda, S., Hirayama, A., Yokoi, K., Tanemura, A., Nimura, K., Ikawa, M., Soga, T., & Kikuchi, A. (2023). GREB1 isoform 4 is specifically transcribed by MITF and required for melanoma proliferation. Oncogene, 42(42), 3142– 3156.

[3] Sumantran, V. N., Mishra, P., & Sudhakar, N. (2015). Microarray analysis of differentially expressed genes regulating lipid metabolism during melanoma progression. Indian Journal of Biochemistry & Biophysics, 52(2), 125–131.

[4] Jemal, A., Saraiya, M., Patel, P., Cherala, S. S., Barnholtz-Sloan, J., Kim, J., Wiggins, C. L., & Wingo, P. A. (2011). Recent trends in cutaneous melanoma incidence and death rates in the United States, 1992-2006. Journal of the American Academy of Dermatology, 65(5 Suppl 1), S17–25.e1-3.

[5] Siegel, R. L., Miller, K. D., & Jemal, A. (2020). Cancer statistics, 2020. CA: A Cancer Journal for Clinicians, 70(1), 7–30.

[6] Leonardi, G. C., Falzone, L., Salemi, R., Zanghì, A., Spandidos, D. A., Mccubrey, J. A., Candido, S., & Libra, M. (2018). Cutaneous melanoma: From pathogenesis to therapy (Review). International Journal of Oncology, 52(4), 1071–1080.

[7] Kaur, A., Webster, M. R., Marchbank, K., Behera, R., Ndoye, A., Kugel, C. H., III, Dang, V. M., Appleton, J., O’Connell, M. P., Cheng, P., Valiga, A. A., Morissette, R., McDonnell, N. B., Ferrucci, L., Kossenkov, A. V., Meeth, K., Tang, H. Y., Yin, X., Wood, W. H., III, . . . Weeraratna, A. T. (2016). sFRP2 in the aged microenvironment drives melanoma metastasis and therapy resistance. Nature, 532(7598), 250–254.

[8] Macdonald, J. B., Dueck, A. C., Gray, R. J., Wasif, N., Swanson, D. L., Sekulic, A., & Pockaj, B. A. (2011). Malignant melanoma in the elderly: Different regional disease and poorer prognosis. Journal of Cancer, 2(2), 538–543.

[9] Arozarena, I., & Wellbrock, C. (2017). Targeting invasive properties of melanoma cells. The FEBS Journal, 284(14), 2148–2162.

[10] Smith, M. P., Rowling, E. J., Miskolczi, Z., Ferguson, J., Spoerri, L., Haass, N. K., Sloss, O., McEntegart, S., Arozarena, I., von Kriegsheim, A., Rodriguez, J., Brunton, H., Kmarashev, J., Levesque, M. P., Dummer, R., Frederick, D. T., Andrews, M. C., Cooper, Z. A., Flaherty, K. T., . . . Wellbrock, C. (2017). Targeting endothelin receptor signalling overcomes heterogeneity driven therapy failure. EMBO Molecular Medicine, 9(8), 1011–1029.

[11] Campbell, N. R., Rao, A., Hunter, M. V., Sznurkowska, M. K., Briker, L., Zhang, M., Baron, M., Heilmann, S., Deforet, M., Kenny, C., Ferretti, L. P., Huang, T. H., Perlee, S., Garg, M., Nsengimana, J., Saini, M., Montal, E., Tagore, M., Newton-Bishop, J., . . . White, R. M. (2021). Cooperation between melanoma cell states promotes metastasis through heterotypic cluster formation. Developmental Cell, 56(20), 2808–2825.e10.

[12] Sharma, B., & Agnihotri, N. (2019). Role of cholesterol homeostasis and its efflux pathways in cancer progression. The Journal of Steroid Biochemistry and Molecular Biology, 191, 105377.

[13] Riscal, R., Skuli, N., & Simon, M. C. (2019). Even cancer cells watch their cholesterol! Molecular Cell, 76(2), 220–231.

[14] Schallreuter, K. U., Hasse, S., Rokos, H., Chavan, B., Shalbaf, M., Spencer, J. D., & Wood, J. M. (2009). Cholesterol regulates melanogenesis in human epidermal melanocytes and melanoma cells. Experimental Dermatology, 18(8), 680–688.

[15] Luo, J., Yang, H., & Song, B. L. (2020). Mechanisms and regulation of cholesterol homeostasis. Nature Reviews. Molecular Cell Biology, 21(4), 225–245.

[16] Rogers, M. A., Liu, J., Song, B. L., Li, B. L., Chang, C. C., & Chang, T. Y. (2015). Acyl-CoA: Cholesterol acyltransferases (ACATs/SOATs): Enzymes with multiple sterols as substrates and as activators. The Journal of Steroid Biochemistry and Molecular Biology, 151, 102–107.

[17] Xia, W., Wang, H., Zhou, X., Wang, Y., Xue, L., Cao, B., & Song, J. (2023). The role of cholesterol metabolism in tumor therapy, from bench to bed. Frontiers in Pharmacology, 14, 928821.

[18] Beloribi-Djefaflia, S., Vasseur, S., & Guillaumond, F. (2016). Lipid metabolic reprogramming in cancer cells. Oncogenesis, 5(1), e189.

[19] Huang, B., Song, B. L., & Xu, C. (2020). Cholesterol metabolism in cancer: Mechanisms and therapeutic opportunities. Nature Metabolism, 2(2), 132–141.

[20] Yamauchi, Y., Furukawa, K., Hamamura, K., & Furukawa, K. (2011). Positive feedback loop between PI3K-Akt-mTORC1 signaling and the lipogenic pathway boosts Akt signaling: Induction of the lipogenic pathway by a melanoma antigen. Cancer Research, 71(14), 4989–4997.

[21] Kuzu, O. F., Noory, M. A., & Robertson, G. P. (2016). The role of cholesterol in cancer. Cancer Research, 76(8), 2063–2070. 5472.CAN-15-2613

[22] Tian, W., Pang, W., Ge, Y., He, X., Wang, D., Li, X., Hou, H., Zhou, D., Feng, S., Chen, Z., & Yang, Y. (2018). Hepatocyte-generated 27-hydroxycholesterol promotes the growth of melanoma by activation of estrogen receptor alpha. Journal of Cellular Biochemistry, 119(3), 2929–2938.

[23] Pencheva, N., Buss, C. G., Posada, J., Merghoub, T., & Tavazoie, S. F. (2014). Broad-spectrum therapeutic suppression of metastatic melanoma through nuclear hormone receptor activation. Cell, 156(5), 986–1001.

[24] Restivo, G., Diener, J., Cheng, P. F., Kiowski, G., Bonalli, M., Biedermann, T., Reichmann, E., Levesque, M. P., Dummer, R., & Sommer, L. (2018). Publisher correction: The low affinity neurotrophin receptor CD271 regulates phenotype switching in melanoma. Nature Communications, 9(1), 314.

[25] Acton, S., Rigotti, A., Landschulz, K. T., Xu, S., Hobbs, H. H., & Krieger, M. (1996). Identification of scavenger receptor SR-BI as a high density lipoprotein receptor. Science, 271(5248), 518–520.

[26] Kinslechner, K., Schörghofer, D., Schütz, B., Vallianou, M., Wingelhofer, B., Mikulits, W., Röhrl, C., Hengstschläger, M., Moriggl, R., Stangl, H., & Mikula, M. (2018). Malignant phenotypes in metastatic melanoma are governed by SR-BI and its association with glycosylation and STAT5 activation. Molecular Cancer Research, 16(1), 135–146.

[27] Hoek, K. S., Schlegel, N. C., Eichhoff, O. M., Widmer, D. S., Praetorius, C., Einarsson, S. O., Valgeirsdottir, S., Bergsteinsdottir, K., Schepsky, A., Dummer, R., & Steingrimsson, E. (2008). Novel MITF targets identified using a two-step DNA microarray strategy. Pigment Cell & Melanoma Research, 21(6), 665–676.

[28] Li, Y. C., Park, M. J., Ye, S. K., Kim, C. W., & Kim, Y. N. (2006). Elevated levels of cholesterol-rich lipid rafts in cancer cells are correlated with apoptosis sensitivity induced by cholesterol-depleting agents. American Journal of Pathology, 168(4), 1107–1118.

[29] Wang, R., Bi, J., Ampah, K. K., Ba, X., Liu, W., & Zeng, X. (2013). Lipid rafts control human melanoma cell migration by regulating focal adhesion disassembly. Biochimica et Biophysica Acta, 1833(12), 3195–3205.

[30] Costa, G. A., de Souza, S. B., da Silva Teixeira, L. R., Okorokov, L. A., Arnholdt, A. C. V., Okorokova- Façanha, A. L., & Façanha, A. R. (2018). Tumor cell cholesterol depletion and V-ATPase inhibition as an inhibitory mechanism to prevent cell migration and invasiveness in melanoma. Biochimica et Biophysica Acta: General Subjects, 1862(3), 684– 691.

[31] Webb, B. A., Chimenti, M., Jacobson, M. P., & Barber, D. L. (2011). Dysregulated pH: A perfect storm for cancer progression. Nature Reviews Cancer, 11(9), 671–677.

[32] Caldieri, G., Giacchetti, G., Beznoussenko, G., Attanasio, F., Ayala, I., & Buccione, R. (2009). Invadopodia biogenesis is regulated by caveolin-mediated modulation of membrane cholesterol levels. Journal of Cellular and Molecular Medicine, 13(8B), 1728–1740. 4934.2008.00568.x

[33] Ayee, M. A., & Levitan, I. (2016). Paradoxical impact of cholesterol on lipid packing and cell stiffness. Frontiers in Bioscience (Landmark Edition), 21(6), 1245–1259.

[34] Zalba, S., & Ten Hagen, T. L. (2017). Cell membrane modulation as adjuvant in cancer therapy. Cancer Treatment Reviews, 52, 48–57.

[35] Zhao, W., Prijic, S., Urban, B. C., Tisza, M. J., Zuo, Y., Li, L., Tan, Z., Chen, X., Mani, S. A., & Chang, J. T. (2016). Candidate antimetastasis drugs suppress the metastatic capacity of breast cancer cells by reducing membrane fluidity. Cancer Research, 76(7), 2037–2049.

[36] Elborn, J. S. (2016). Cystic fibrosis. Lancet, 388(10059), 2519–2531.

[37] Walker, F. O. (2007). Huntington’s disease. Lancet, 369(9557), 218–228. 6736(07)60111-1

[38] Peltonen, L., Perola, M., Naukkarinen, J., & Palotie, A. (2006). Lessons from studying monogenic disease for common disease. Human Molecular Genetics, 15(Spec No 1, suppl_1), R67–R74.

[39] Petersen, B. S., Fredrich, B., Hoeppner, M. P., Ellinghaus, D., & Franke, A. (2017). Opportunities and challenges of whole-genome and - exome sequencing. BMC Genetics, 18(1), 14.

[40] Botstein, D., & Risch, N. (2003). Discovering genotypes underlying human phenotypes: Past successes for mendelian disease, future approaches for complex disease. Nature Genetics, 33(Suppl), 228–237.

[41] Ng, S. B., Turner, E. H., Robertson, P. D., Flygare, S. D., Bigham, A. W., Lee, C., Shaffer, T., Wong, M., Bhattacharjee, A., Eichler, E. E., Bamshad, M., Nickerson, D. A., & Shendure, J. (2009). Targeted capture and massively parallel sequencing of 12 human exomes. Nature, 461(7261), 272–276.

[42] Hodges, E., Xuan, Z., Balija, V., Kramer, M., Molla, M. N., Smith, S. W., Middle, C. M., Rodesch, M. J., Albert, T. J., Hannon, G. J., & McCombie, W. R. (2007). Genome-wide in situ exon capture for selective resequencing. Nature Genetics, 39(12), 1522–1527.

[43] Jennings, L. J., Arcila, M. E., Corless, C., Kamel- Reid, S., Lubin, I. M., Pfeifer, J., Temple-Smolkin, R. L., Voelkerding, K. V., & Nikiforova, M. N. (2017). Guidelines for validation of next-generation sequencing-based oncology panels: A joint consensus recommendation of the association for molecular pathology and college of american pathologists. The Journal of Molecular Diagnostics, 19(3), 341– 365.

[44] Woolston, A. L., Hsiao, P. C., Kuo, P. H., Wang, S. H., Lien, Y. J., Liu, C. M., Hwu, H. G., Lu, T. P., Chuang, E. Y., Chang, L. C., Chen, C. H., Wu, J. Y., Tsuang, M. T., & Chen, W. J. (2017). Genetic loci associated with an earlier age at onset in multiplex schizophrenia. Scientific Reports, 7(1), 6486.

[45] Génin, E., Feingold, J., & Clerget-Darpoux, F. (2008). Identifying modifier genes of monogenic disease: Strategies and difficulties. Human Genetics, 124(4), 357–368. 2

[46] Chiò, A., Mazzini, L., D’Alfonso, S., Corrado, L., Canosa, A., Moglia, C., Manera, U., Bersano, E., Brunetti, M., Barberis, M., Veldink, J. H., van den Berg, L. H., Pearce, N., Sproviero, W., McLaughlin, R., Vajda, A., Hardiman, O., Rooney, J., Mora, G., . . . Al-Chalabi, A. (2018). The multistep hypothesis of ALS revisited: The role of genetic mutations. Neurology, 91(7), e635–e642.

[47] Warr, A., Robert, C., Hume, D., Archibald, A., Deeb, N., & Watson, M. (2015). Exome Sequencing: Current and future perspectives. G3 (Bethesda, Md.), 5(8), 1543–1550.

[48] Marian, A. J. (2014). Sequencing your genome: What does it mean? Methodist DeBakey Cardiovascular Journal, 10(1), 3–6. 3

[49] Clark, M. J., Chen, R., Lam, H. Y., Karczewski, K. J., Chen, R., Euskirchen, G., Butte, A. J., & Snyder, M. (2011). Performance comparison of exome DNA sequencing technologies. Nature Biotechnology, 29(10), 908–914.

[50] Scatena, C., Murtas, D., & Tomei, S. (2021). Cutaneous melanoma classification: The importance of high-throughput genomic technologies. Frontiers in Oncology, 11, 635488.

[51] Gonzaga-Jauregui, C., Lupski, J. R., & Gibbs, R. A. (2012). Human genome sequencing in health and disease. Annual Review of Medicine, 63, 35–61.

[52] Teer, J. K., & Mullikin, J. C. (2010). Exome sequencing: The sweet spot before whole genomes. Human Molecular Genetics, 19(R2), R145–R151.

[53] Bashiardes, S., Veile, R., Helms, C., Mardis, E. R., Bowcock, A. M., & Lovett, M. (2005). Direct genomic selection. Nature Methods, 2(1), 63–69.

[54] Tadic, M., Kralj, S., Jagodic, M., Hanzel, D., & Makovec, D. (2014). Magnetic properties of novel superparamagnetic iron oxide nanoclusters and their peculiarity under annealing treatment. Applied Surface Science, 322, 255–264.

[55] Marian, A. J. (2011). Medical DNA sequencing. Current Opinion in Cardiology, 26(3), 175–180.

[56] Olson, N. D., Lund, S. P., Colman, R. E., Foster, J. T., Sahl, J. W., Schupp, J. M., Keim, P., Morrow, J. B., Salit, M. L., & Zook, J. M. (2015). Best practices for evaluating single nucleotide variant calling methods for microbial genomics. Frontiers in Genetics, 6, 235.

[57] Sanger, F., & Coulson, A. R. (1975). A rapid method for determining sequences in DNA by primed synthesis with DNA polymerase. Journal of Molecular Biology, 94(3), 441–448. 2836(75)90213-2

[58] van Vliet, A. H. (2010). Next generation sequencing of microbial transcriptomes: Challenges and opportunities. FEMS Microbiology Letters, 302(1), 1– 7.

[59] Kanrar, S., & Dhar, A. K. (2018). Complete genome sequence of a novel mutant strain of vibrio parahaemolyticus from Pacific White Shrimp (Penaeus vannamei). Genome Announcements, 6(24), e00497–18.

[60] Guo, Y., Long, J., He, J., Li, C. I., Cai, Q., Shu, X. O., Zheng, W., & Li, C. (2012). Exome sequencing generates high quality data in non-target regions. BMC Genomics, 13, 194. 2164-13-194

[61] Akintunde, O., Tucker, T., & Carabetta, V. J. (2023). The evolution of next-generation sequencing technologies. ArXiv.

[62] Buermans, H. P., & den Dunnen, J. T. (2014). Next generation sequencing technology: Advances and applications. Biochimica et Biophysica Acta, 1842(10), 1932–1941.

[63] Nolan, D., & Carlson, M. (2016). Whole exome sequencing in pediatric neurology patients: Clinical implications and estimated cost analysis. Journal of Child Neurology, 31(7), 887–894.

[64] Salazar-García, L., Pérez-Sayáns, M., García-García, A., Carracedo, Á., Cruz, R., Lozano, A., Sobrino, B., & Barros, F. (2018). Whole exome sequencing approach to analysis of the origin of cancer stem cells in patients with head and neck squamous cell carcinoma. Journal of Oral Pathology & Medicine, 47(10), 938–944.

[65] Cock, P. J., Fields, C. J., Goto, N., Heuer, M. L., & Rice, P. M. (2010). The Sanger FASTQ file format for sequences with quality scores, and the Solexa/Illumina FASTQ variants. Nucleic Acids Research, 38(6), 1767–1771.

[66] Bao, R., Huang, L., Andrade, J., Tan, W., Kibbe, W. A., Jiang, H., & Feng, G. (2014). Review of current methods, applications, and data management for the bioinformatics analysis of whole exome sequencing. Cancer Informatics, 13(Suppl 2), 67–82.

[67] He, B., Zhu, R., Yang, H., Lu, Q., Wang, W., Song, L., Sun, X., Zhang, G., Li, S., Yang, J., Tian, G., Bing, P., & Lang, J. (2020). Assessing the impact of data preprocessing on analyzing next generation sequencing data. Frontiers in Bioengineering and Biotechnology, 8, 817.

[68] Das, S., Biswas, N. K., & Basu, A. (2023). Mapinsights: Deep exploration of quality issues and error profiles in high-throughput sequence data. Nucleic Acids Research, 51(14), e75–e75.

[69] Meena, N., Mathur, P., Medicherla, K. M., & Suravajhala, P. A bioinformatics pipeline for whole exome sequencing: Overview of the processing and steps from raw data to downstream analysis. bioRxiv, 2017: p. 201145.

[70] Halim-Fikri, H., Syed-Hassan, S. R., Wan-Juhari, W. K., Assyuhada, M. G. S. N., Hernaningsih, Y., Yusoff, N. M., Merican, A. F., & Zilfalil, B. A. (2023). Central resources of variant discovery and annotation and its role in precision medicine. Asian Biomedicine: Research, Reviews and News, 16(6), 285–298.

[71] Xu, H., DiCarlo, J., Satya, R. V., Peng, Q., & Wang, Y. (2014). Comparison of somatic mutation calling methods in amplicon and whole exome sequence data. BMC Genomics, 15, 244.

[72] Hartman, M. L., Sztiller-Sikorska, M., & Czyz, M. (2019). Whole-exome sequencing reveals novel genetic variants associated with diverse phenotypes of melanoma cells. Molecular Carcinogenesis, 58(4), 588–602.

[73] Ferreira, P. G., Jares, P., Rico, D., Gómez-López, G., Martínez-Trillos, A., Villamor, N., Ecker, S., González- Pérez, A., Knowles, D. G., Monlong, J., Johnson, R., Quesada, V., Djebali, S., Papasaikas, P., López- Guerra, M., Colomer, D., Royo, C., Cazorla, M., Pinyol, M., . . . Guigó, R. (2014). Transcriptome characterization by RNA sequencing identifies a major molecular and clinical subdivision in chronic lymphocytic leukemia. Genome Research, 24(2), 212–226.

[74] Shi, H., Zhou, Y., Jia, E., Pan, M., Bai, Y., & Ge, Q. (2021). Bias in RNA-seq library preparation: Current challenges and solutions. BioMed Research International, 2021, 6647597.

[75] Yassour, M., Kaplan, T., Fraser, H. B., Levin, J. Z., Pfiffner, J., Adiconis, X., Schroth, G., Luo, S., Khrebtukova, I., Gnirke, A., Nusbaum, C., Thompson, D. A., Friedman, N., & Regev, A. (2009). Ab initio construction of a eukaryotic transcriptome by massively parallel mRNA sequencing. Proceedings of the National Academy of Sciences of the United States of America, 106(9), 3264–3269.

[76] Maher, C. A., Palanisamy, N., Brenner, J. C., Cao, X., Kalyana-Sundaram, S., Luo, S., Khrebtukova, I., Barrette, T. R., Grasso, C., Yu, J., Lonigro, R. J., Schroth, G., Kumar-Sinha, C., & Chinnaiyan, A. M. (2009). Chimeric transcript discovery by paired-end transcriptome sequencing. Proceedings of the National Academy of Sciences of the United States of America, 106(30), 12353–12358.

[77] Krishnakumar, S., Zheng, J., Wilhelmy, J., Faham, M., Mindrinos, M., & Davis, R. (2008). A comprehensive assay for targeted multiplex amplification of human DNA sequences. Proceedings of the National Academy of Sciences of the United States of America, 105(27), 9296–9301.

[78] Sugarbaker, D. J., Richards, W. G., Gordon, G. J., Dong, L., De Rienzo, A., Maulik, G., Glickman, J. N., Chirieac, L. R., Hartman, M. L., Taillon, B. E., Du, L., Bouffard, P., Kingsmore, S. F., Miller, N. A., Farmer, A. D., Jensen, R. V., Gullans, S. R., & Bueno, R. (2008). Transcriptome sequencing of malignant pleural mesothelioma tumors. Proceedings of the National Academy of Sciences of the United States of America, 105(9), 3521–3526.

[79] Zhang, H., He, L., & Cai, L. (2018). Transcriptome sequencing: RNA-Seq. Methods in Molecular Biology (Clifton, N.J.), 1754, 15–27.

[80] Pilia, G., Chen, W. M., Scuteri, A., Orrú, M., Albai, G., Dei, M., Lai, S., Usala, G., Lai, M., Loi, P., Mameli, C., Vacca, L., Deiana, M., Olla, N., Masala, M., Cao, A., Najjar, S. S., Terracciano, A., Nedorezov, T., . . . Schlessinger, D. (2006). Heritability of cardiovascular and personality traits in 6,148 Sardinians. PLOS Genetics, 2(8), e132.

[81] Rahalkar, A. R., & Hegele, R. A. (2008). Monogenic pediatric dyslipidemias: Classification, genetics and clinical spectrum. Molecular Genetics and Metabolism, 93(3), 282–294.

[82] Musunuru, K., Pirruccello, J. P., Do, R., Peloso, G. M., Guiducci, C., Sougnez, C., Garimella, K. V., Fisher, S., Abreu, J., Barry, A. J., Fennell, T., Banks, E., Ambrogio, L., Cibulskis, K., Kernytsky, A., Gonzalez, E., Rudzicz, N., Engert, J. C., DePristo, M. A., . . . Kathiresan, S. (2010). Exome sequencing, ANGPTL3 mutations, and familial combined hypolipidemia. The New England Journal of Medicine, 363(23), 2220– 2227.

[83] Lange, L. A., Hu, Y., Zhang, H., Xue, C., Schmidt, E. M., Tang, Z. Z., Bizon, C., Lange, E. M., Smith, J. D., Turner, E. H., Jun, G., Kang, H. M., Peloso, G., Auer, P., Li, K. P., Flannick, J., Zhang, J., Fuchsberger, C., Gaulton, K., . . . Willer, C. J., & the NHLBI Grand Opportunity Exome Sequencing Project. (2014). Whole-exome sequencing identifies rare and low-frequency coding variants associated with LDL cholesterol. American Journal of Human Genetics, 94(2), 233–245.

[84] Hodis, E., Watson, I. R., Kryukov, G. V., Arold, S. T., Imielinski, M., Theurillat, J. P., Nickerson, E., Auclair, D., Li, L., Place, C., Dicara, D., Ramos, A. H., Lawrence, M. S., Cibulskis, K., Sivachenko, A., Voet, D., Saksena, G., Stransky, N., Onofrio, R. C., . . . Chin, L. (2012). A landscape of driver mutations in melanoma. Cell, 150(2), 251–263.

[85] Amaral, T., Sinnberg, T., Meier, F., Krepler, C., Levesque, M., Niessner, H., & Garbe, C. (2017). The mitogen-activated protein kinase pathway in melanoma part I - Activation and primary resistance mechanisms to BRAF inhibition. European Journal of Cancer (Oxford, England), 73, 85–92.

[86] Berger, M. F., Levin, J. Z., Vijayendran, K., Sivachenko, A., Adiconis, X., Maguire, J., Johnson, L. A., Robinson, J., Verhaak, R. G., Sougnez, C., Onofrio, R. C., Ziaugra, L., Cibulskis, K., Laine, E., Barretina, J., Winckler, W., Fisher, D. E., Getz, G., Meyerson, M., . . . Garraway, L. A. (2010). Integrative analysis of the melanoma transcriptome. Genome Research, 20(4), 413–427.

[87] Pleasance, E., Titmuss, E., Williamson, L., Kwan, H., Culibrk, L., Zhao, E. Y., Dixon, K., Fan, K., Bowlby, R., Jones, M. R., Shen, Y., Grewal, J. K., Ashkani, J., Wee, K., Grisdale, C. J., Thibodeau, M. L., Bozoky, Z., Pearson, H., Majounie, E., . . . Marra, M. A. (2020). Pan-cancer analysis of advanced patient tumors reveals interactions between therapy and genomic landscapes. Nature Cancer, 1(4), 452–468.

[88] Horak, P., Heining, C., Kreutzfeldt, S., Hutter, B., Mock, A., Hüllein, J., Fröhlich, M., Uhrig, S., Jahn, A., Rump, A., Gieldon, L., Möhrmann, L., Hanf, D., Teleanu, V., Heilig, C. E., Lipka, D. B., Allgäuer, M., Ruhnke, L., Laßmann, A., . . . Fröhling, S. (2021). Comprehensive genomic and transcriptomic analysis for guiding therapeutic decisions in patients with rare cancers. Cancer Discovery, 11(11), 2780– 2795.

[89] Ulintz, P. J., Wu, W., & Gates, C. M. (2019). Bioinformatics analysis of whole exome sequencing data. Methods in Molecular Biology (Clifton, N.J.), 1881, 277–318. 4939-8876-1_21

[90] Bamshad, M. J., Ng, S. B., Bigham, A. W., Tabor, H. K., Emond, M. J., Nickerson, D. A., & Shendure, J. (2011). Exome sequencing as a tool for Mendelian disease gene discovery. Nature Reviews Genetics, 12(11), 745–755.

[91] Wang, Z., Gerstein, M., & Snyder, M. (2009). RNA-Seq: A revolutionary tool for transcriptomics. Nature Reviews Genetics, 10(1), 57–63.

[92] Ozsolak, F., & Milos, P. M. (2011). RNA sequencing: Advances, challenges and opportunities. Nature Reviews Genetics, 12(2), 87–98.

[93] Pickrell, J. K., Gilad, Y., & Pritchard, J. K. (2012). Comment on “Widespread RNA and DNA sequence differences in the human transcriptome”. Science, 335(6074), 1302.