Citation: Topham JT, Marra MA (2016) Sequencing Strategies to Guide Decision Making in Cancer Treatment. PLoS Med13(12): e1002189. https://doi.org/10.1371/journal.pmed.1002189
Published: December 6, 2016
Copyright: © 2016 Topham, Marra. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: No specific funding received for this article
Competing interests: The authors have declared that no competing interests exist.
Abbreviations: CML, chronic myeloid leukemia; WGS, whole-genome sequencing
Provenance: Commissioned; not externally peer reviewed
Cancer is a complex genetic disease often associated with the accumulation of somatic DNA alterations . Genes that are targets of somatic alteration in cancer can be broadly classified as tumor suppressors or oncogenes, depending on whether they bear loss-of-function or gain-of-function alterations, respectively . Comprehensive efforts into cataloguing cancer genes have revealed that tumors demonstrate substantial variability in the genes that accumulate mutations both within and across cancer types [3,4]. Targeted agents have been developed to directly target tumors by acting on specific genomic alterations or gene products that are unique to the tumor cells, thereby offering a complementary approach to broad-acting cytotoxic chemotherapies.
Targeted treatments have become a major modality in the treatment of cancer and can offer advantages to the patient over standard chemotherapy alone [5,6]. However, given the heterogeneity of cancer, appropriate selection of targeted drugs relies on our ability to identify actionable genomic alterations within individual tumors . Rapid advances in genomic technologies have fueled translational cancer research, providing opportunities for acquisition of precision genomic information to help guide clinical management of cancer patients. Concomitant with decreasing costs, the advancement of genomic technologies has placed clinicians and researchers in a unique position to translate genomic results to the clinic.
The first assays to screen cancer patients for somatic alterations targetable by drugs were based on detection of single gene alterations . For example, early studies investigating the genomic landscapes of breast cancers identified HER2 copy number amplifications in ~30% of patients, noting that overexpression of HER2 was associated with poor prognosis . Trastuzumab was among the first monoclonal antibody-based targeted drugs developed, targeting HER2-expressing tumor cells. The success of clinical trials investigating the use of trastuzumab in the treatment of HER2-positive breast cancers has resulted in routine screening for HER2 amplifications in the management of breast cancer patients . Another early example of a successful single-gene assay is that of chronic myeloid leukemia (CML), in which the BCR–ABL fusion protein results from a chromosomal translocation event found in ~90% of CML patients . Imatinib, a tyrosine kinase inhibitor developed in the late 1990s, exhibited significant antitumor effects in cells harboring the BCR–ABL fusion . Screening for the presence of BCR–ABL fusion transcripts thus became routine in the use of imatinib for CML patients .
Continued discovery of recurrent driver alterations in different cancers, concomitant with further development of targeted drugs, eventually generated an impetus for the construction of multiplexed assays to detect multiple genomic alterations in cancer patients. Extending from single-gene-based assays, panel sequencing offered the opportunity to discover genomic alterations in tumors that were not known to harbor such alterations. Early applications of multi-gene assays aimed at guiding cancer treatment focused on a small number of genes directly associated with targeted agents, including examples such as a panel capable of detecting 41 alterations in 9 genes relevant in lung cancer , as well as a panel used to detect 120 mutations in 13 genes relevant across a broad spectrum of tumor types . Advancements in genomic technology have since led to development of panels encompassing as many as 4,000 alterations across 143 genes , and incorporation of panel methods has become routine in several cancer control organizations such as the BC Cancer Agency  and Memorial Sloan Kettering Cancer Center .
Investigations into the feasibility and efficacy of applying panel sequencing techniques to large populations of patients with advanced cancer are ongoing and include trials such as NCI-MATCH (Molecular Analysis for Therapy Choice) , TAPUR (Targeted Agent and Profiling Utilization Registry) , and TOP (The Oncopanel Pilot; clinicaltrials.gov/show/NCT02171286). Parallel to these efforts is the recently completed SHIVA trial, which compared clinical outcomes between patients receiving targeted agents, selected based on panel sequencing, and those given conventional chemotherapy, in patients with any type of metastatic solid tumor refractory to standard treatment . Results of SHIVA showed no or limited improvement in progression-free survival for patients receiving targeted therapy . However, it remains unclear whether these results from the SHIVA study can be attributed to an inability of panel sequencing to guide effective therapy rather than other factors, such as the selection of genes included in the panel or a restricted selection of targeted agents from which to choose.
While offering the potential to detect a broader range of actionable genomic alterations compared to single-gene assays, panel sequencing technologies generally do not survey all informative or actionable alterations. The obvious extension to panel-based methods are whole-genome sequencing (WGS) approaches, which are now being explored and offer the substantial advantage of comprehensive detection of genomic alterations that elude detection using panel sequencing. Among the first demonstrations of the use of WGS technology to guide treatment decision-making in cancer was a case of late-stage, metastatic adenocarcinoma of the tongue . Integration of whole-genome and transcriptome data provided a rationale for the selection of a targeted agent that would not have been otherwise considered and, in this case, was associated with maintenance of stable disease for several months . The application of WGS approaches in guiding personalized treatment of cancer has since been expanded to a broader patient population in the POG (Personalized OncoGenomics; clinicaltrials.gov/ct2/show/NCT02155621) trial, with preliminary results suggesting that WGS technology can inform treatment in ~34% of late-stage cancer patients with metastatic diseases . Whether WGS technology has the potential to guide treatment selection in a substantial proportion of cancer patients or to improve treatment outcomes remains unclear and may depend on the continued identification of driver alterations, development of additional targeted drugs to select from, broader clinical indications for existing targeted drugs, technology innovations that speed turnaround times and cost reductions, and development of clinical study designs able to rigorously evaluate treatment success.
Complicating the application of precision approaches to guide cancer treatment are results from detailed investigations into the clonal landscapes of tumors, which have revealed the dynamic nature of cancers resulting from evolution in response to selective pressures . Consequences of the inherent plasticity of tumors are witnessed in the high frequency of relapse for many cancers and drive the need to cease treating the disease as a static entity. The emergence of an evolutionary approach to cancer therapy is currently taking place. For example, the TRACERx (Tracking Cancer Evolution Through Treatment; clinicaltrials.gov/ct2/show/NCT01888601) trial follows lung cancer patients to assess the influence of therapies on the evolution of cancer over time . Thus, while sequencing technologies have clear potential to inform treatment decisions, the proportion of cases benefiting from such approaches will perhaps be maximized when strategies addressing the dynamic nature of tumors are implemented. Meanwhile, further advances in technology will continue to narrow the gap between genomics applications and the guidance of treatment planning in cancer, potentially offering benefit to patients, clinicians, and health systems.
- 1. Hanahan D, Weinberg RA. The hallmarks of cancer. Cell. 2000 Jan 7;100(1):57–70. pmid:10647931
- 2. Vogelstein B, Kinzler KW. Cancer genes and the pathways they control. Nat Med. 2004 Aug;10(8):789–99. pmid:15286780
- 3. Lawrence MS, Stojanov P, Mermel CH, Robinson JT, Garraway LA, Golub TR, et al. Discovery and saturation analysis of cancer genes across 21 tumour types. Nature. 2014 Jan 23;505(7484):495–501. pmid:24390350
- 4. Meyerson M, Gabriel S, Getz G. Advances in understanding cancer genomes through second-generation sequencing. Nat Rev Genet. 2010 Oct;11(10):685–96. pmid:20847746
- 5. Moore MJ, Goldstein D, Hamm J, Figer A, Hecht JR, Gallinger S, et al. Erlotinib plus gemcitabine compared with gemcitabine alone in patients with advanced pancreatic cancer: a phase III trial of the National Cancer Institute of Canada Clinical Trials Group. J Clin Oncol. 2007 May 20;25(15):1960–6. pmid:17452677
- 6. Romond EH, Perez EA, Bryant J, Suman VJ, Geyer CE, Davidson NE, et al. Trastuzumab plus Adjuvant Chemotherapy for Operable HER2-Positive Breast Cancer. N Engl J Med. 2005 Oct 20;353(16):1673–84. pmid:16236738
- 7. Hortobagyi GN. Opportunities and challenges in the development of targeted therapies. Semin Oncol. 2004 Feb;31(1 Suppl 3):21–7.
- 8. Sidorova JY, Saltykova LB, Lyschov AA, Zaritskey AY, Abdulkadyrov KM, Blinov MN. A rapid RT-PCR based method for the detection of BCR-ABL translocation. Mol Pathol. 1997 Oct;50(5):266–8. pmid:9497918
- 9. Slamon DJ, Clark GM, Wong SG, Levin WJ, Ullrich A, McGuire WL. Human breast cancer: correlation of relapse and survival with amplification of the HER-2/neu oncogene. Science. 1987 Jan 9;235(4785):177–82. pmid:3798106
- 10. Maughan KL, Lutterbie MA, Ham PS. Treatment of breast cancer. Am Fam Physician. 2010 Jun 1;81(11):1339–46. pmid:20521754
- 11. Nowell PC, Hungerford DA. Chromosome studies in human leukemia. II. Chronic granulocytic leukemia. J Natl Cancer Inst. 1961 Nov;27:1013–35. pmid:14480645
- 12. Skorski T, Nieborowska-Skorska M, Nicolaides NC, Szczylik C, Iversen P, Iozzo RV, et al. Suppression of Philadelphia1 leukemia cell growth in mice by BCR-ABL antisense oligodeoxynucleotide. Proc Natl Acad Sci U S A. 1994 May 10;91(10):4504–8. pmid:8183938
- 13. Branford S, Hughes TP, Rudzki Z. Monitoring chronic myeloid leukaemia therapy by real-time quantitative PCR in blood is a reliable alternative to bone marrow cytogenetics. Br J Haematol. 1999 Dec;107(3):587–99. pmid:10583264
- 14. Su Z, Dias-Santagata D, Duke M, Hutchinson K, Lin Y-L, Borger DR, et al. A Platform for Rapid Detection of Multiple Oncogenic Mutations With Relevance to Targeted Therapy in Non–Small-Cell Lung Cancer. J Mol Diagn. 2011 Jan;13(1):74–84. pmid:21227397
- 15. Dias-Santagata D, Akhavanfard S, David SS, Vernovsky K, Kuhlmann G, Boisvert SL, et al. Rapid targeted mutational analysis of human tumours: a clinical platform to guide personalized cancer medicine: Tumour genotyping for personalized cancer care. EMBO Mol Med. 2010 May;2(5):146–58. pmid:20432502
- 16. Mullard A. NCI-MATCH trial pushes cancer umbrella trial paradigm. Nat Rev Drug Discov. 2015 Aug;14(8):513–5. pmid:26228747
- 17. New genetic tests become standard of cancer care in BC. BC Cancer Agency. http://www.bccancer.bc.ca/about/news-stories/news/2016/new-genetic-tests-become-standard-of-cancer-care-in-bc. September 7, 2016.
- 18. Hyman DM, Solit DB, Arcila ME, Cheng DT, Sabbatini P, Baselga J, et al. Precision medicine at Memorial Sloan Kettering Cancer Center: clinical next-generation sequencing enabling next-generation targeted therapy trials. Drug Discov Today. 2015 Dec;20(12):1422–8. pmid:26320725
- 19. Do K, O’Sullivan Coyne G, Chen AP. An overview of the NCI precision medicine trials-NCI MATCH and MPACT. Chin Clin Oncol. 2015 Sep;4(3):31. pmid:26408298
- 20. Kim E. ASCO’s TAPUR Study: Off-Label Drugs for Actionable Mutations. Medscape. http://www.medscape.com/viewarticle/862427. April 29, 2016.
- 21. Le Tourneau C, Paoletti X, Servant N, Bièche I, Gentien D, Rio Frio T, et al. Randomised proof-of-concept phase II trial comparing targeted therapy based on tumour molecular profiling vs conventional therapy in patients with refractory cancer: results of the feasibility part of the SHIVA trial. Br J Cancer. 2014 Jul 1;111(1):17–24. pmid:24762958
- 22. Le Tourneau C, Delord J-P, Gonçalves A, Gavoille C, Dubot C, Isambert N, et al. Molecularly targeted therapy based on tumour molecular profiling versus conventional therapy for advanced cancer (SHIVA): a multicentre, open-label, proof-of-concept, randomised, controlled phase 2 trial. Lancet Oncol. 2015 Oct;16(13):1324–34. pmid:26342236
- 23. Jones SJ, Laskin J, Li YY, Griffith OL, An J, Bilenky M, et al. Evolution of an adenocarcinoma in response to selection by targeted kinase inhibitors. Genome Biol. 2010;11(8):R82. pmid:20696054
- 24. Laskin J, Jones S, Aparicio S, Chia S, Ch’ng C, Deyell R, et al. Lessons learned from the application of whole-genome analysis to the treatment of patients with advanced cancers. Cold Spring Harb Mol Case Stud. 2015 Oct;1(1):a000570. pmid:27148575
- 25. Gerlinger M, Horswell S, Larkin J, Rowan AJ, Salm MP, Varela I, et al. Genomic architecture and evolution of clear cell renal cell carcinomas defined by multiregion sequencing. Nat Genet. 2014 Feb 2;46(3):225–33. pmid:24487277
- 26. Jamal-Hanjani M, Hackshaw A, Ngai Y, Shaw J, Dive C, Quezada S, et al. Tracking genomic cancer evolution for precision medicine: the lung TRACERx study. PLoS Biol. 2014 Jul;12(7):e1001906 pmid:25003521