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Review

Cancer Research Frontiers. 2017; 3(1): 29-50. doi: 10.17980/2017.29

Personalisation of Therapy – clinical impact and relevance of genetic mutations in tumours

Terri McVeigh*, Angela George.

Cancer Genetics Unit, Orchard House, The Royal Marsden NHS Foundation Trust, Downs Road, Sutton, SM2 5PT, London, UK.

 

*Corresponding author: Terri McVeigh, Clinical Research Fellow, Cancer Genetics Unit, Orchard House, The Royal Marsden NHS Foundation Trust, Downs Road, Sutton, SM2 5PT, London, UK. Tel: (+44) 0208 661 3375, email: terri.mcveigh@gmail.com

Citation: Terri McVeigh, et al. Personalisation of Therapy – clinical impact and relevance of genetic mutations in tumours. Cancer Research Frontiers. 2017; 3(1): 29-50. doi: 10.17980/2017.29

Copyright: @ 2017 Terri McVeigh, et al. 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.

Competing Interests: The authors declare no competing financial interests.

Received Oct 23, 2016; Revised Mar 9, 2017; Accepted Mar 13, 2017. Published Mar 25, 2017

 

Abstract

As technological advances in genetic sequencing and the parallel reduction in costs of sequencing make testing more accessible, genomic profiling of tumours is increasingly becoming integrated into routine clinical care. This personalisation of medical care is especially relevant in the area of oncology, where interest in tumour testing as part of standard care has dramatically increased. Tumour genomic profiles are particularly interesting, as they harbour mutations acquired temporally as somatic events, and less commonly, may reveal defects that have been inherited through the germline. Numerous techniques can be utilised to interrogate the tumour genomic landscape, ranging from tried and tested techniques, such as karyotyping, to full mutational analysis using more modern next and third generation technologies. The challenge for the clinician is no longer predominantly in accessing genomic technologies, but rather in interpreting complex reports, and separating relevant clinically actionable mutations from incidental mutations reflective of the damaged DNA repair mechanisms that are intrinsic to the neoplastic process. This may be especially difficult if the mutational spectrum includes variants in less well-studied genes, or in genes not commonly implicated as drivers of the cancer under investigation. Increasing utilisation of genomic profiling of cancers has informed our understanding that the timing of a mutation in a particular gene is as relevant as the gene in which it occurs in determining the neoplastic course. Once the key driver mutations in a cancer have been identified, the next challenge is to find and utilise an appropriate agent that targets the specific defect. Many hundreds of targeted agents have been designed and put through rigorous in vitro and phase 1 or even phase 2 trials, but few have made it into routine clinical use to date. In this review, we discuss the underlying mechanisms of genomic changes and mutational signatures that can be potentially targeted for therapeutic benefit, and some successful targeted agents that have been developed to date.

Keywords: Precision Medicine; Mutational Signature; Driver Mutations; inherited predisposition

 

Background

Over the last decade, there has been a huge increase in the applications of genetic testing for personalising patient treatment. The improvements in the available genetic sequencing techniques and expansion in access to testing has occurred in parallel with a substantial decrease in the cost of testing. Ten years ago, the cost of whole genome sequencing was in the order of $20,000,000; falling to less than $2000 by end of 2015 (1). Unsurprisingly, “genetic testing” is therefore becoming increasingly incorporated into mainstream medical care, particularly as information about the patient’s and their cancer’s genetic profile can provide improved prognostic information (2, 3); identify suitable patients for specific therapeutic agents (4, 5); and facilitate sub-classification (6), or identification of tumours of unknown origin (7). Several hundreds of “paradigm shifts” in medicine have been proposed in recent times, none as profound as the move towards precision medicine, and the n-of-1 clinical trial (8). Targeted therapy in cancer is not a new concept, but the drive to develop agents specific to the individual has gained significant momentum with the announcement of President Obama’s Precision Medicine Initiative in 2015 (9), heralding an “era of Precision Medicine” (10). This move is greatly supported by research funding agencies (11-13), and by the public (14). Surveys of public attitudes towards “genetic testing” suggest strong support for testing that can positively influence selection of targeted therapy, but reticence to participate in testing that might have implications for the wider family, or that might predict disease in the future (15, 16).

In this overview, we aim to discuss:

  1. The role of tumour suppressor genes and proto-oncogenes
  2. Driver and passenger mutations
  3. Chromosomal aberrations in cancer
  4. Techniques for molecular profiling in cancer
  5. Examples of targetable mutations and targeted therapies
  6. Inherited Tumour Predisposition
  7. Tumour mutational signatures
  8. Challenges in variant interpretation

 

1.       Tumour Suppressor Genes and Proto-oncogenes

The hallmarks of cancer include loss of response to normal controls of cell growth and programmed cell death, as well as immortality, self-sustained angiogenesis, invasion, and metastasis. These hallmarks can reflect defects in the genes involved in pathways of cell growth and division. Two broad classes of genes are implicated in tumourigenesis; tumour suppressor genes, and proto-oncogenes (table 1). Most proto-oncogenes (e.g. Ras) function in control of the cell cycle; without them, normal cell growth and development could not occur. Conversion of proto-oncogenes to oncogenes leads to abnormal over-activity or over-expression of the protein product. Such conversion may occur in a number of ways; including mutations in the gene sequence or in a gene regulatory region; gene amplification by localised DNA segmental duplication or multiplication (copy number variation); or by chromosomal translocation leading either to production of an abnormally and continually active fusion protein (e.g. BCR-ABL). These mechanisms may have a dominant effect, meaning that they may trigger oncogenesis even if only one allele is affected. Furthermore, epigenetic influences can lead to hyper- or hypo- methylation of cytosine residues, leading to over- or under-activity of a gene. These epigenetic influences may be somatic, but rarely can be inherited, leading to constitutional hyper- or hypo-methylation of a gene (e.g. constitutional MLH1 epimutations).

 

Tumour Suppressor genes (TSGs) (e.g. BRCA1, TP53) generally function to inhibit replication and proliferation of damaged cells, either by DNA repair or by control of cell cycle checkpoints or apoptosis. Loss-of-function mutations in such genes are oncogenic. Generally, the presence of one normal allele affords sufficient cellular control; but a “second hit” affecting this normal allele (loss of heterozygosity) disrupts control, leading to tumourigenesis. Knockout of the DNA repair function of one or more TSGs leads to sequential acquisition of more mutations, ultimately leading to dysplasia/neoplasia. Tumours arising in patients as a result of inherited defects in tumour suppressor genes, therefore, tend to have a very high mutational load. Pathways of which control is lost by virtue of loss of function of TSGs represent putative targets for therapies (17).

 

Table 1: Definitions of Genetic concepts discussed in this review

1668 TABLE1

 

 

2.       Drivers and passengers

Cancer cells may acquire 10s to 100,000s of mutations depending on the cell turnover rate of the organ in which they occur, which can be classified as driver or passenger mutations depending on whether they confer an advantage to the cell in terms of growth and/or survival (18). Separating driver mutations that contribute to progression and growth of the cancer from inconsequential passenger mutations can often be extremely challenging. Most somatic mutations are passengers, and will not confer a growth or selection advantage; and designing or applying therapies to exploit such mutations is of little value. It had been thought that several driver mutations are required to promote neoplasia. However, more recent evidence suggests that only three of these key driver mutations are required in certain cancer subtypes (19, 20), suggesting that a combination of a few appropriately targeted agents may be sufficient to combat the driving forces in these cancers.

 

Increased utility of somatic testing in tumours has implicated mutations in well-known TSGs and oncogenes as drivers of disease, but expanded gene panels have also led to detection of putative driver mutations in less well-characterized genes. Such genes can be classified as TSGs or oncogenes depending on a number of factors; whether the mutations identified confer loss- or gain-of function; and if one or both alleles are lost. Allelic status is not always informative, as wild-type alleles can be silenced in a number of ways, including by epigenetic mechanisms or silencing by a dominant-negative mutant allele. In certain instances, particularly in non-hereditary cases, bi-allelic loss of a TSG is not sufficient to promote oncogenesis, and other driver mutations are required. Depending on the context, a gene may function as either an oncogene or a tumour suppressor gene (e.g. NOTCH1) (21). Therefore, the allelic status of mutations in such genes must be considered in light of the clinical context to help classify the mutation as a driver or passenger. Analysis of both tumour and normal DNA is required to optimally classify mutations in TSGs (22).

 

3.       Chromosomal aberrations in cancer

Cancer cells display high rates of chromosomal copy number and structural instability compared to normal cells. The genetic aberrations that occur in cancer may involve whole chromosomes, limited segments of chromosomes, single genes, or related genes within a single pathway, or specific hotspot mutations. As such, “genetic testing” of tumours can be targeted to individual genetic loci or extensive across the entire tumour genome, depending on the clinical context, and the specific tumour type (23, 24). It has been shown that a limited number of specific chromosomes in a cancer genome may harbour many hundreds of clustered chromosomal rearrangements as a consequence of defective DNA repair or replication mechanisms, a phenomenon termed chromothripsis. This characteristic is commonly identified in cells with defective p53, permitting replication of cells with chromosomal mis-segregation (25). Foci of localised hypermutation (kataegis), with enrichment of C>T or C>G mutations, may also be found in association with certain genomic rearrangements; and may reflect aberrant activity of the APOBEC family of enzymes on dsDNA breaks (26). It has been shown that induction of di-centric chromosomes during telomeric crises induce chromothripsis and kataegis (27).

 

4.       Molecular Profiling of cancers

Transcriptomics

Gene expression analysis can be performed in a number of methods, ranging from low to high-throughput, depending on the clinical indication. Low throughput techniques include utilisation of a reporter gene assay (e.g. Luciferase); Northern or Western blots to measure RNA and protein levels respectively; Fluorescence in situ Hybridisation (FISH) to assess copy number variation; and Reverse-Transcription Polymerase Chain Reaction (RT-PCR), to detect and quantify mRNA from small volume samples. Higher throughput techniques include SAGE (serial analysis of gene expression); DNA microarray and RNA-Seq. SAGE and DNA microarray technologies serve to quantify expression of different isoforms of a large number of genes simultaneously. Different cancers have different gene expression profiles, and thus such methods may be used to sub-classify tumours. SAGE is more expensive, and more technically challenging than DNA microarray based studies, but provides more sensitive quantification of transcript expression, and can be used in discovery of novel mRNA transcripts, while DNA microarray probes must be designed based on known mRNA sequences. RNA-seq (whole transcriptome sequencing) also facilitates accurate quantification of gene expression; as well as detection of alternatively spliced or novel transcripts; post-transcriptional modifications; gene fusion, and mutations. Different approaches may be used in the same patient depending on the clinical context. For example, in breast cancer subtyping, gene expression analysis by DNA microarray is used to differentiate between luminal, non-luminal and normal-like subtypes, while gene expression analysis by RNA expression analysis using RT-PCR as part of the Oncotype DX test can predict those ER-positive/HER2-negative early stage tumours at highest risk of relapse (2).

 

Immunophenotyping

Immunohistochemistry (IHC) is routinely used to characterise tumours based on their immunophenotype. IHC can detect expression of particular antigens by virtue of binding with applied corresponding antibodies. Specific markers may be characteristic of different cellular processes, such as ki67 (proliferation) or CASP3 (apoptosis); or different tumour types. IHC is a simple, routine method that can be particularly useful in clarifying origin of tumours, particularly where morphological appearances are atypical, or if there is difficulty in differentiating between a primary tumour or metastatic deposit; for example, in the ovary, by analysis of the expression of proteins such as CK7, CK20, ER, WT1(28); or in the lung by IHC of CK7, CK30, B-catenin (29).

 

IHC can be used in the diagnostic work-up of patients with high-risk personal or familial cancer history. Loss of expression of mismatch repair proteins MLH1 and PMS2 in colorectal cancers, for example, may reflect germline mutations in MLH1, and more rarely germline missense mutations in PMS2; but such pattern of loss also occurs in tumours in association with somatic BRAF V600E mutation. Colorectal tumours displaying loss of MLH1 and PMS2 on IHC should routinely be tested for this variant before embarking on germline testing. Loss of MLH1/PMS2 can also be seen in endometrial cancers, particularly in the context of Lynch syndrome. BRAF mutations occur infrequently in endometrial cancers however, and the utility of BRAF testing in these tumours in predicting germline MMR defects is limited (30).

 

Cytogenetics

Molecular profiling techniques may include specific mutation testing, sequencing or expression analysis of specific genes; more extensive analysis of coding regions of all cancer genes as part of a next generation panel; analysis of coding regions of all genes as part of a whole exome analysis; or interrogation of coding and non-coding regions of all genes by whole genome analysis. Gross analysis of the genome can be undertaken by cytogenetic analysis using array CGH; or karyotype with or without Fluorescent in situ Hybridization (FISH). Karyotyping with FISH for recurrent genetic abnormalities remains a critical and recommended component of the diagnostic work-up of certain rapidly progressing haematological malignancies, such as acute myeloid (AML) (31)or acute lymphoblastic (ALL)(32) leukaemia and myelodysplastic syndrome (MDS) (33) supplemented by RT-PCR testing for fusion genes of interest, such as BCR-ABL in ALL; or sequencing of specific genes of interests using next generation multi-gene panels, e.g. KIT, NPM1 and CEBPA in AML; TP53, ASXL1, ETV6, RUNX1 and EZH2 in MDS. Low rates of cellular proliferation in other haematological malignancies, including Multiple Myeloma, limit the utility of karyotyping, and interphase FISH using probes specific to regions of interest is the preferred method of cytogenetic analysis of such cancers. Recurrent chromosomal aberrations can be recognised among different cancer types or in the organ in which they occur, for example; breast cancers often display gains of chromosomes 1q, 8q, 17q and 20, and losses of chromosomes 8p, 16q, 17p; gastric cancers display copy number gains of 8q and 20; and gliomas recurrently demonstrate gain of chromosome 7 and losses of 1p and 19q (34). Cytogenetic analysis of solid tumours by karyotyping has been eschewed in recent years in favour of more advanced technologies such as oligo- or SNP-arrays, in combination with specific gene testing or investigation for specific fusion genes of interest by RT-PCR or FISH. Cytogenetic studies do still have utility in certain solid cancers, for example, testing of somatic deletions of 1p19q for prognostication in oligodendroglioma (23) and for diagnosis and sub-classification of soft tissue sarcomas (24).

 

Microarray-based comparative genomic hybridisation (Array CGH) can detect copy number variation at a much higher resolution than standard karyotype (~80kB-v-5MB); but requires significant volumes of differentially labelled patient/tumour and reference DNA to co-hybridize to the array chip. The sensitivity of array-CGH is highly dependent on the proportion of the sample that is representative of the tumour compared to control; degree of tumour heterogeneity; and resolution of the selected microarray platform (number of probes).

 

Neither array-CGH nor SNP array detect balanced rearrangements, so other methods (PCR/karyotype/FISH) must be relied on for this purpose. SNP arrays are particularly useful in assessing copy number variation, loss of heterozygosity, and in detection of uniparental disomy (UPD); but rely on utilisation of well-described DNA sequences with known SNPs as probes.

 

Mutation analysis

Molecular genetic testing of solid organ tumours can be performed in a host of methods depending on the clinical question. Interrogation of tumour DNA can be performed by RT-PCR for specific recurrent mutations in particular genes, by sequencing of entire genes of interest by Sanger sequencing, MLPA or multi-gene panels, by sequencing of coding regions of all genes using exome sequencing, or by interrogation of introns and exons of all known genes using whole genome sequencing. More extensive sequencing increases the likelihood of detecting one/more pathogenic mutations; but inevitably increases the probability of detecting variants of uncertain significance; variants in poorly characterised genes; or heritable pathogenic variants arising from the germline. Molecular genetic analysis of tumours is challenging, because most methods require significant amounts of relatively good quality DNA. Tumour testing by next-generation sequencing, using multi-gene panels is increasingly being used in a diagnostic setting. Genomic DNA from a tumour sample is extracted, quantified and qualified, and amplified using PCR. These amplified products (DNA library) are then sequenced in parallel using fluorescently labelled nucleotides. Hybridisation of probes to the complementary strand is assessed by fluorescence. These synthetic sequences are then analysed in parallel using complex bio-informatics pathways. It is interpretation of results that is often the most complex step in the pathway (35), and robust reporting and classification of variants is essential.

 

5.       Targeted agents in Cancer

A comprehensive overview of the targeted agents currently available in all cancer types is beyond the scope of this article, but here we outline the application of targeted therapy in a common (Breast) and rare (Gastro-intestinal stromal tumours) cancer. Some common agents and the genes they target are outlined in table 2.

 

Table 2: Examples of Targeted therapies, and the gene they target

1668 TABLE2

 

Breast Cancer

The concept of personalised therapy in breast cancer is not a new one (15). The treatment of breast cancer, for example, has become increasingly personalised over the last several decades, with shifts towards less radical breast and axillary surgeries wherever possible, minimisation of systemic chemotherapy in patients at lowest risk, and utilisation of several targeted agents according to the molecular profile of the tumour. Tamoxifen, which targets oestrogen and progesterone receptors, was first developed in the 1960s during an attempt to develop new contraceptive agents (36) but is now a standard weapon in the armamentarium of breast cancer therapies for ER-positive disease. Increasing refinement of molecular profiling of tumours has led to the development of more and more targeted agents, in a more deliberate and considered manner than the Tamoxifen discovery effort. Other selective oestrogen receptor modulators include Raloxifene and Toremifene, the latter being currently utilised only in the metastatic context. Several lines of endocrine therapies have now been developed and incorporated into the routine management of patients with ER/PR-positive breast cancer; other endocrine agents include aromatase inhibitors (AIs), including non-steroidal AIs; Letrozole and Anastrozole; and steroidal aromatase inactivator, Exemestane; and luteinising hormone-releasing hormone (LHRH) analogs (Goserelin, Leuprolide). Application of these agents is dependent on menopausal status, response, or acquisition of resistance. Less commonly, agents such as Megestrol acetate, ethinyl estradiol or fluoxymestrone may also have a role.

 

In the metastatic setting, emergence of resistance to endocrine therapy can prove a significant clinical challenge. The agent Palbociclib alongside endocrine agents in ER-positive, HER2-negative advanced breast cancer has been supported by the results of the Phase II PALOMA-1 (37) and subsequent Phase III PALOMA-2 (38) trials. Palbociclib works to inhibit cyclin-dependent kinases-4 and -6; kinases implicated in the development of Endocrine resistance (39). Palbociclib preferentially inhibits proliferation of ER-positive breast cancer cells (40). The utility of Palbociclib was further evaluated in PALOMA-3, which compared combination Palbociclib and Fulvestrant to Fulvestrant monotherapy in endocrine-resistant ER-positive, HER2-negative metastatic breast cancer. This study was halted early because of efficacy (41).

 

Another mechanism of endocrine resistance is by aberrant signalling through the PI3K-AKT-mTOR pathway. This pathway can become overactive by virtue of loss-of-function mutations in genes such as PTEN, TSC1/TSC2 or STK11 (17), or by loss of the negative regulators PTEN or AKT. Over-activation of the same pathway can arise as a consequence of gain-of-function mutations in genes such as PI3KCA (17), which have been commonly identified in all types of breast cancer (42), particularly in ER/PR-positive subtypes. Agents that target the PI3K/AKT/mTOR pathway have therefore been of particular interest in cancers with such molecular aberrations, in different combinations with endocrine or other therapies; as part of a number of Phase I, Phase II, and Phase III trials (43). Everolimus, originally designed as an anti-fungal agent, has been utilised with in a variety of cancers with mutations in mTOR-signalling pathway, with proven efficacy in subependymal giant cell astrocytomas (SEGAs) occurring as part of Tuberous Sclerosis and in pancreatic neuroendocrine tumours(44, 45). A large Phase 3 study (BOLERO-2) provided robust data to support the use of Everolimus alongside endocrine therapy to reduce or reverse endocrine resistance in breast cancer (46); and current NCCN guidelines now recommend consideration of Everolimus in combination with Exemestane in patients with Stage IV disease fulfilling BOLERO-2 inclusion criteria (47). Other studies are ongoing investigating the utility of PI3K inhibition; including the FERGI phase II study (48), investigating the role of PI3K inhibitor Pictilisib in post-menopausal ER-positive/HER-2-negative advanced breast cancer in combination with Fulvestrant versus Fulvestrant monotherapy, although interestingly PI3K mutational status was not associated with differential response. BELLE-2 (49) and BELLE-3 studies similarly looked at Fulvestrant monotherapy versus Fulvestrant/PI3Ki combination, using Buparlisib (50).

 

The ERRB2 gene, encoding the ErbB2 (HER2/neu) protein, was discovered in the 1980s (51, 52). HER2 amplification is reported in 20-30% of breast tumours, and is a poor prognostic indicator. Over-expression of HER2 leads to ligand-independent activation and dimerization of HER2; inducing signalling through mTOR/PI3K and Raf/MEK pathways (53). Trastuzumab (Herceptin), a monoclonal antibody targeting HER2/neu was deliberately designed in 1990, with a view to blocking the increased proliferative activity conferred by amplified HER2, by obstructing activation of the receptor and inducing antibody-medicated cytotoxicity in cancer cells expressing the HER2 antigen (54). This agent is now routinely used as part of combination treatment of HER2-overexpressing breast, gastric and lung tumours. The use of Trastuzumab, alone or as part of the drug conjugate, Ado-trastuzumab emtansine, has been shown to improve patient disease-free and overall survival; but only in ~70% of patients in whom HER2-expression is over-amplified, a reflection of the highly intelligent nature of cancer cells in developing complex networks to promote growth, cell survival and resistance to therapy. Other monoclonal antibodies targeting HER2 have since been developed, including Pertuzumab (Perjeta), which inhibits dimerisation of HER2.

 

As of 2016, 28 small molecule kinase inhibitors were licenced by the FDA (55), of which two; Lapatinib and Palbocib, are utilised in trastuzumab-exposed HER2-overexpressing; and post-menopausal, ER-positive, HER2-negative breast cancer, respectively. Lapatinib is a tyrosine kinase inhibitor (TKI) which has targets both ErbB1 (encoded by EGFR) and ErbB2. Combination Lapatinib and Capecitabine therapy has been shown to halt disease progression more effectively, and improve survival compared to Capecitabine monotherapy (56, 57) in patients with advanced pre-treated HER2-overexpressing breast cancers. The combination of trastuzumab and lapatinib has been shown to be superior to lapatinib monotherapy in patients with advanced HER2-positive breast cancer, even in the context of heavy pre-treatment with Trastuzumab (58). A significant proportion of sporadic HER2-overexpressing breast cancers harbour TP53 mutations. Lapatinib has been shown to downregulate mutant p53 in HER2-overexpressing cells, suggesting that TP53 mutational status may be predictive of response to this targeted agent (59). Trastuzumab is also licenced for, and has been shown to have efficacy as an adjunct to chemotherapy in, advanced HER2-expressing gastric cancer (60), although other HER2-targeted agents such as Ado-trastuzumab emtansine and Lapatinib have not been shown to be improve overall survival for these patients (61, 62).

 

Consequences of inherited single gene mutations can also be exploited by targeted agents. PARP (Poly-ADP Ribose Polymerase) inhibitors, for example, work by inhibiting alternative pathways of DNA repair in BRCA-deficient cells; leading to increasing mutational burden to such a point as to induce cellular apoptosis (63) (synthetic lethality). Neighbouring BRCA-proficient cells are spared; by virtue of their intact BRCA-mediated DNA repair mechanisms. PARP inhibitors have been shown to significantly extend progression-free survival in patients with ovarian, breast and prostate cancers with germline or somatic BRCA deficiency (64-67).

 

Gastro-intestinal Stromal Tumours

GIST tumours are mesenchymal tumours of the gastro-intestinal tract. Most GISTs occur sporadically, but a small proportion arises as a result of germline susceptibility. Inherited predisposition to GIST can be in part explained by mutations in c-kit and PDGFRa; and to a lesser extent as part of a wider genetic syndrome, such as Neurofibromatosis type I (NF1) or Carney-Stratakis Dyad (SDH family). Sporadic GISTs harbour somatic events in these and other genes.

 

Approximately 85-90% of sporadic GISTs harbour primary somatic mutations in c-kit, and, similar to germline events, these occur most commonly in exon 11, encoding for the juxta-membrane domain, less commonly in exons 9, and rarely in exons 13 and 17(68). Small molecule kinase inhibitors, such as Imatinib, a tyrosine kinase inhibitor (TKI), has been associated with increased response rates in patients Gastro-intestinal stromal tumours harbouring c-kit activating mutations, as well as in CML associated with the BCR-ABL fusion protein as a consequence of somatic chromosomal rearrangements (69, 70). GISTs can go on to acquire secondary kinase mutations that can influence the development of resistance to therapy. It is has been shown that the response of GISTs to TKIs such as Imatinib and Sunitinib is dependent on the primary, and secondary c-kit mutational status of the tumour, with increased disease-free and overall survival post-Imatinib in patients with tumours with primary exon 11 mutations compared to tumours with exon 9 mutations (71). Conversely, progression-free and overall survival post-Sunitinib in Imatinib-resistant patients was noted to be greater in patients with tumours harbouring primary c-kit mutations in exon 9 versus those with mutations in exon 11 (72, 73). Between 5-7% of c-kit negative GISTs harbour mutations in the kit-homologous gene PDGFRa, predominantly in exons 12 and 18; encoding juxta-membrane domains and kinase activation loops, respectively. Most studies investigating the impact of mutational status of GISTs on treatment response include small numbers of patients with PDGFRa mutations, and so the evidence for exon-specific responses is scant. However, a recurrent mutation in exon 18 of PDGFRa, substitution D842V, which accounts for the majority of all PDGFRa mutations, is associated with Imatinib resistance (74, 75).

 

Approximately 10-15% of GISTs do not harbour mutations in PDGFRa or c-kit. Tumours without these mutations (“wild type” tumours) can be further subdivided into type I and type II wild-type GISTs depending on expression of SDHB (76). Type I GISTs, with in-tact SDHB include tumours arising in NF1 (77) as well as sporadic wild type tumours. Lack of SDHB expression in type II tumours can occur as a consequence of inactivating mutations in the SDH-family of genes, as part of the Carney-Stratakis Dyad (78). “Quadruple” wild-type tumours are those tumours without mutations in c-kit, PDGFRa, SDHx, or NF1 or other Ras family genes.

 

 A proportion of wild-type GISTs harbour the recurrent BRAF V600E hotspot mutation (79, 80) that is rarely identified in tumours with c-kit or PDGFRa over-expression. A number of BRAF-inhibitors have been designed to target common BRAF mutations V600E and V600K in melanoma, e.g. Dabrafenib and Vemurafenib, and have been applied with some success in patients with melanoma harbouring these hotspot mutations (81, 82). Sorafenib, a non-selective BRAF inhibitor, has had limited efficacy in melanoma (83), but proven efficacy in certain types of renal cancer (84). A single case report suggests that Dabrafenib may have a role in BRAF-mutated GIST (85). The results of a Phase II trial investigating the utility of Vemurafenib in BRAF-mutated malignancies is awaited (86). Numerous phase I and phase II trials are ongoing looking at the utility of other targeted agents in GIST including known agents such as Palbociclib and Buparlisib, and numerous novel such as KIT/PDGFRa inhibitors Ponatinib and Dasatinib, an comprehensive overview of which is provided by Szucz et al (87).

 

6.       Inherited Tumour Predisposition

Knowledge of an individual’s inherited predisposition to cancer is critical, as it facilitates strategies for prevention and may influence treatment, not just in the proband, but also in members of the wider family. It may also have implications for family planning, as most cancer predisposition syndromes are inherited in an autosomal dominant fashion. Genetic testing of both tumour and patient DNA is becoming more and more mainstream, a practice that has obvious merits in terms of prognostication and treatment. However, it is critical that this testing be performed with some insight into the relevance and potential implications of the result for the wider family. Tumour testing, in particular, may be difficult to interpret, as some apparently somatic mutations may be inherited or may reflect germline mutations in other genes. Several authors have noted that a significant proportion of tumours have such mutations (88-91). Other authors have noted that the follow up and onward referral of these patients to a Clinical Genetics professional is poor (92).

 

While testing of tumour DNA by next generation sequencing is a relatively new procedure, tumour testing by other methods has been in place in routine clinical practice for many years. However, it has been shown that, even routine tests such as IHC for MMR defects in patients with colorectal cancer is not uniform across centres, even within countries (93). Similarly, it has been shown that there may not be a standard procedure of referral for patients with abnormal IHC for further genetic assessment (94, 95). The new guideline from the National Institute for Health and Care Excellence provides a standardised framework to ensure investigation in a broader cohort of patients, and onward referral of individuals in whom MMR defects are detected (96).

 

7.       Tumour mutational signatures

Germline mutations can be associated with increased somatic mutational load as a consequence of impaired DNA repair mechanisms. The unique combination of mutations in any given tumour is referred to as a “mutational signature”. Twenty-one distinct recurrent somatic mutational signatures have been defined by Alexandrov et al (97), which may reflect the underlying tumourigenic defect, as well as the cancer aetiology. Such “signatures” can be differentiated by proportional representation of 96 different substitution types based on the six substitution types (C>A; C>G; C>T; T>A; T>G; T>C), and possible permutations and combinations of the bases immediately 5’ and 3’ of the mutation locus. Signature 6, for example, occurs primarily in cancer arising as a result of defective mismatch repair; with enrichment of C>T transitions at NpCpG sites. Comparatively, the mutational signature of tumours associated with germline pathogenic variants in BRCA1 and BRCA2 (signature 3) demonstrates an increased frequency of indels (signature 3), reflecting reduced efficacy in homologous recombination.

 

Other signatures can reflect exposure to environmental carcinogens, such as signature 7 in cancers associated with UV-exposure (melanoma, SCC); which has an over-representation of C>T transitions with bias for the non-transcribed strand, and signature 4, which is observed in cancers associated with smoking (cancers of the lung, head and neck, liver), and is likely to reflect the generation, and subsequent nucleotide excision repair of bulky adducts by toxins in smoke. Transversions of C>A are predominantly found in smokers, as carcinogens in cigarettes tend to form adducts on guanine (97). The most common of the mutational signatures, Signature 1A and 1B, reflect endogenous spontaneous deamination of methylated cytosine residues. These signatures occur commonly in both the germline and somatic cells, and correlate with the age of the individual in which the tumour arises. The relationships between cancer types, mutational processes and mutational signatures are complex. Certain mutational signatures are found only in specific classes of cancer; and similarly different tumours within certain classes of cancer express a limited number of different mutational signatures. However, different tumours within other classes of cancer can be heterogeneous in the mutational signatures they express (97). Considering mutagen-related signatures, signature-specific mutational burden can be correlated with the amount of exposure to the mutagen; heavy smokers have a higher burden of signature-4 specific mutations than light smokers; and melanomas in sun-exposed areas have higher burdens of UV-related signature-specific mutations (98).

 

Germline mutations in certain genes can also cause specific somatic mutations in other genes. For example, the MUTYH gene encodes a highly conserved post-replicative DNA glycosylase, which has dual adenine and 2-OH-A DNA glycosylase functions. Removal of adenine from in the DNA backbone by MUTYH at sites of mis-pairs with 8-oxoG is followed by base excision repair (BER) to form C:8-oxoG base pairs (99). OGG1 DNA glycosylase then acts to remove 8-oxoG paired opposite cytosine from DNA, restoring C:G pairing. Deficiency of these enzymes predispose to failure of repair of mutated DNA, which may then confer a risk of carcinogenesis. MUTYH mutations in particular fail to repair G à T transversions in other genes, namely APC (100-102) and KRAS (103) with a special predilection for guanine residues in particular hotspots. A particular somatic mutation c.34G>T in KRAS can reflect the presence of germline biallelic MUTYH mutations, which cause MUTYH-associated polyposis (MAP) (103). Because of the recessive nature of inheritance of this condition, patients with MAP often do not have a strong family history of cancer, and clinicians may therefore not always be alert to the possibility of an inherited cancer predisposition. Furthermore, patients with MAP can present with a colorectal cancer without preceding polyposis (104, 105). The KRAS proto-oncogene is one of the most commonly somatically mutated genes in cancer. Assessment of the KRAS gene, along with NRAS and BRAF, is performed routinely in colorectal cancers, to help determine application of anti-EGFR therapeutic agents. Generally, tumours mutations in codons 12 and 13 of KRAS will respond to this therapy, while tumours with wild-type KRAS are less likely to do so (106).

 

There are numerous methods by which KRAS testing can be performed, some of which target only common mutations, others that involve sequencing of the entire KRAS gene. Therefore, clinicians performing KRAS sequencing need to be cautious in interpreting KRAS sequencing data, as this subtle indicator of inherited cancer predisposition may easily be overlooked (107).

 

Gene expression profiles may also be modified by germline mutations. Germline mutations in BRCA1 or BRCA2 may not only have specific mutational signatures, but may also have an impact on the RNA expression levels of certain genes as measured by RT-PCR. The Oncotype DX assay is one of a number of multi-gene expression assays that have been developed in an attempt to identify patients at greatest risk of tumour recurrence, and therefore those in whom the need for systemic treatment is greatest (108). The Oncotype DX assay for breast cancer generates a recurrence score between 1 and 100, which correlates with 10-year risk of recurrence of disease (2, 109, 110). The use of this assay has been incorporated into the routine clinical assessment of patients with ER-positive, node negative breast cancer (2, 47). Tumours in carriers of BRCA1 and BRCA2 gene mutations are more likely to have intermediate- or high-risk recurrence score compared with tumours of similar molecular phenotype in age-matched controls with wild-type BRCA1 and BRCA2 (111, 112), suggesting that patients with BRCA1 and BRCA2 germline mutations have tumours of more aggressive biology, even if nodal disease and ER-negativity is not present.

 

8.       Challenges in tumour testing

Genomic profiling of tumours can be challenging for a number of reasons. Firstly, different methods of preservation can greatly impact the utility of different genomic testing techniques. Formalin fixation, for example, increases DNA cross-linking; and DNA extracted from such tissues is often significantly degraded. The optimum specimen on which to perform DNA testing is a fresh, or fresh frozen sample, but such samples are rarely available routinely. Furthermore, it is well documented that the genomic landscape of a cancer is dynamic, changing as the tumour evolves and acquires more and more DNA damage; meaning that there is significant tumour heterogeneity over space and time; within the main body of the tumour, and between the primary tumour and its metastases. Small samples of tumour provide only a snapshot of the landscape of mutations in that part of the tumour, at that point in time. Certain sub-clonal populations of tumour cells may prove more resistant to treatment than others. Patients in whom mixed response to therapy is observed may merit re-biopsy of areas with minimal response or progressive disease, to facilitate further genetic analysis.

 

Interpretation of somatic mutations requires consideration of a number of factors. The first challenge is to determine whether or not the mutation is pathogenic or not. Guidelines for the interpretation of sequence variants have been derived by consensus recommendations from The American college of Medical Genetics and Genomics, and the Association for Molecular Pathology (113), outlining the evidence, and strength of evidence, required to classify a variant into one of five categories of pathogenicity. Factors to be considered in determining pathogenicity of a germline mutation include population minor allele frequency, segregation and inheritance data, as well as computational and predictive data about the variant, functional data, and frequencies of the variant in different databases (114). Some of these factors must also be considered in interpretation of somatic variants. Interrogation of databases such as IARC or COSMIC will provide useful information regarding frequency of the variant in question in different cancer types. However, in somatic tumour testing, functional consequence must often be predicted from structural information rather than proven by biochemical analysis (115). Interpretation of somatic testing reports is becoming more and more challenging as panels expand to include more novel and less well-described genes. The expansion of panels certainly increases the likelihood of detecting a clinically actionable mutation, but also increases the likelihood of detecting variants of uncertain significance; which in turn increases the workload of the testing laboratories before a report can be generated. It is likely that the proportion of patients in whom clinically actionable mutations are detected by larger panels is outweighed by the proportion of patients in whom VUS, or pathogenic mutations to which targeted agents do not yet exist, are identified. Controversy regarding the use of large panels for germline testing exists, and as large scale tumour testing becomes part of the routine diagnostic workup, it is likely that similar controversies will emerge in that context.

 

Pathogenicity is not the only important factor to consider. The gene in which the mutation occurs may be an indicator as to whether it is a driver mutation or not, but is complicated by virtue of the fact that passenger mutations can occur in typical “driver” genes in different clinical contexts (21). The order in which mutations occur is also important, and may be dependent on the cancer type – for example mutations in genes in the RAS pathway are important drivers of melanoma (e.g. in BRAF), but arise in colorectal cancer (in KRAS) at a later stage than mutations in APC and in the APC pathway, which happen in early adenomatous development (116). It is important therefore to be mindful of the “cellular context” in applying targeted therapies; therapies directed against BRAF mutations may have high response rates in melanoma but not in colorectal cancers, even if the tumours are noted to have identical mutations (19).

 

Conclusions

Personalisation of therapy is an ambitious concept; but is becoming increasingly achievable given improvements in our understanding of tumour biology and the impact of different mutations in gene/genes in certain biological pathways, combined with rapidly improving technology and decreasing costs. Although our knowledge with respect to tumour mutational signatures, driver-passenger pathways, and the evolution of cancer phenotypes has improved dramatically over the past several decades, immense difficulties still exist. Separating the wheat from the chaff in terms of defining the driver mutations among tumours of high mutational burden, differentiating germline from somatic events, and defining the “cellular context” in which these mutations occur in less well-studied cancers remain challenges; not to mention the “unknown unknowns” including epigenetic factors, gene-environment interactions, and adaptation mechanisms of cancer cells in response to therapy. Our knowledge is perhaps not increasing as such a rapid rate as genomic technology, and it is important to apply the technology only in circumstances in which we are confident we will be able to interpret and utilise the results for the benefit of the patient, and to apply targeted therapies without inflicting undue toxicity or at the expense of potential benefit from proven standard approaches.

 

Acknowledgement:

Dr McVeigh is supported by the Irish Health Service Executive/Health Research Board (HSE/HRB) National Specialist Registrar Academic Fellowship (NSAFP/2014/1).

 

Abbreviations

AKT                                   V-Akt Murine Thymoma Viral Oncogene Homolog 1

ALK                                   Anaplastic Lymphoma Receptor Tyrosine Kinase gene

APC                                   Adenomatous Polyposis Coli gene

APOBEC                           apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like family of genes

ASXL1                               Additional Sex Combs Like 1, Transcriptional Regulator

ATM                                 Ataxia Telangiectasia Mutated serine/threonine kinase gene

ATR                                   Ataxia Telangiectasia and Rad3-related serine/threonine kinase gene

BCR-ABL                          fusion protein arising from chromosomal translocation approximating breakpoint cluster region protein gene/Abelson murine leukemia viral oncogene homolog 1 gene

BRAF                                 B-Raf proto-oncogene, serine/threonine kinase

BRCA1                              Breast Cancer 1, Early Onset

BRCA2                              Breast Cancer 2

CASP3                              Caspase 3

CEBPA                              CCAAT/Enhancer Binding Protein Alpha

CGH                                  Comparative Genomic Hybridisation

COSMIC                          Catalogue Of Somatic Mutations In Cancer

EGFR                                Epidermal Growth Factor Receptor 

ETV6                                 ETS Translocation Variant 6

EZH2                                 Enhancer of Zeste 2 Polycomb Repressive Complex 2 Subunit

FAP                                   Familial Adenomatous Polyposis

HER-2/neu                     Human Epidermal Growth Factor Receptor 2

IARC                                 International Agency for Research on Cancer

IHC                                    Immunohistochemistry

KIT                                     KIT Proto-Oncogene Receptor Tyrosine Kinase

KRAS                                Kirsten Rat Sarcoma Viral Oncogene Homolog

MAP                                 MUTYH-associated Polyposis

MAPK                              Mitogen-Activated Protein Kinase

mTOR                               mammalian target of Rapamycin

MUTYH                            MutY DNA Glycosylase

NF1                                   Neurofibromin 1

NOTCH1                          Notch (Drosophila) Homolog 1 (Translocation-Associated)

NPM1                              Nucleophosmin

PARP                                poly ADP ribose polymerase 

PCR                         polymerase chain reaction

PDGFRa                           Platelet Derived Growth Factor Receptor Alpha

PI3K                                  Phosphatidylinositol-4, 5-bisphosphate 3-kinase

PTEN                                Phosphatase And Tensin Homolog

RT-PCR                             Reverse transcription polymerase chain reaction 

RUNX1                             Runt Related Transcription Factor 1

SDHX                                Succinate Dehydrogenase Complex family

SEGA                                Subependymal giant cell astrocytoma

STK11                               Serine/Threonine Kinase 11

TP53                                 Tumour Protein P53

TSC1                                 Tuberous Sclerosis 1

TSC2                                 Tuberous Sclerosis 2

TSG                                   Tumour Suppressor Gene

VUS                                   Variant of Uncertain Significance

 

References

  1. National Human Genome Research Institute. Available from: https://www.genome.gov/27565109/the-cost-of-sequencing-a-human-genome
  2. Paik S, Tang G, Shak S, Kim C, Baker J, Kim W, et al. Gene expression and benefit of chemotherapy in women with node-negative, estrogen receptor-positive breast cancer. J Clin Oncol. 2006 Aug 10;24(23):3726-34. DOI: 10.1200/JCO.2005.04.7985.
  3. Planck M, Isaksson S, Veerla S, Staaf J. Identification of transcriptional subgroups in EGFR-mutated and EGFR/KRAS wild-type lung adenocarcinoma reveals gene signatures associated with patient outcome. Clin Cancer Res. 2013 Sep 15;19(18):5116-26. DOI: 10.1158/1078-0432.CCR-13-0928.
  4. Alexandrov LB, Nik-Zainal S, Siu HC, Leung SY, Stratton MR. A mutational signature in gastric cancer suggests therapeutic strategies. Nat Commun. 2015 Oct 29;6:8683. DOI: 10.1038/ncomms9683.
  5. Wood K, Luke JJ. Optimal Use of BRAF Targeting Therapy in the Immunotherapy Era. Curr Oncol Rep. 2016 Nov;18(11):67. DOI: 10.1007/s11912-016-0554-5.
  6. Sorlie T, Perou CM, Tibshirani R, Aas T, Geisler S, Johnsen H, et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci U S A. 2001 Sep 11;98(19):10869-74. DOI: 10.1073/pnas.191367098.
  7. Marquard AM, Birkbak NJ, Thomas CE, Favero F, Krzystanek M, Lefebvre C, et al. TumorTracer: a method to identify the tissue of origin from the somatic mutations of a tumor specimen. BMC Med Genomics. 2015 Oct 01;8(1):58. DOI: 10.1186/s12920-015-0130-0.
  8. Lillie EO, Patay B, Diamant J, Issell B, Topol EJ, Schork NJ. The n-of-1 clinical trial: the ultimate strategy for individualizing medicine? Per Med. 2011 Mar;8(2):161-73. DOI: 10.2217/pme.11.7.
  9. whitehouse.gov. FACT SHEET: President Obama’s Precision Medicine Initiative [Internet]. whitehouse.gov. 2015.
  10. Collins FS, Varmus H. A new initiative on precision medicine. N Engl J Med. 2015 Feb 26;372(9):793-5. DOI: 10.1056/NEJMp1500523.
  11. EU-EYE: Call on Personalised Medicine. Available from: http://www.eueye.org/call-on-personalised-medicine.
  12. European Commission: Horizon 2020: Health, Demographic Change and Wellbeing. Available from: https://ec.europa.eu/programmes/horizon2020/en/h2020-section/health-demographic-change-and-wellbeing
  13. European Commission: Towards an International Consortium for Personalised Medicine. Available from: https://ec.europa.eu/research/conferences/2016/permed2016/pdf/towards_ic_permed.pdf
  14. Kaufman DJ, Baker R, Milner LC, Devaney S, Hudson KL. A Survey of U.S Adults’ Opinions about Conduct of a Nationwide Precision Medicine Initiative(R) Cohort Study of Genes and Environment. PLoS ONE. 2016;11(8):e0160461. DOI: 10.1371/journal.pone.0160461.
  15. Henneman L, Vermeulen E, van El CG, Claassen L, Timmermans DR, Cornel MC. Public attitudes towards genetic testing revisited: comparing opinions between 2002 and 2010. Eur J Hum Genet. 2013 Aug;21(8):793-9. DOI: 10.1038/ejhg.2012.271.
  16. McVeigh TP, Sweeney KJ, Kerin MJ, Gallagher DJ. A qualitative analysis of the attitudes of Irish patients towards participation in genetic-based research. Ir J Med Sci. 2016 Nov;185(4):825-31. 
  17. Weichhart T, Hengstschlager M, Linke M. Regulation of innate immune cell function by mTOR. Nat Rev Immunol. 2015 Oct;15(10):599-614. DOI: 10.1038/nri3901.
  18. Stratton MR, Campbell PJ, Futreal PA. The cancer genome. Nature. 2009 Apr 09;458(7239):719-24. DOI: 10.1038/nature07943.
  19. Vogelstein B, Kinzler KW. The Path to Cancer –Three Strikes and You’re Out. N Engl J Med. 2015 Nov 12;373(20):1895-8. DOI: 10.1056/NEJMp1508811.
  20. Tomasetti C, Marchionni L, Nowak MA, Parmigiani G, Vogelstein B. Only three driver gene mutations are required for the development of lung and colorectal cancers. Proc Natl Acad Sci U S A. 2015 Jan 06;112(1):118-23. DOI: 10.1073/pnas.1421839112.
  21. Vogelstein B, Papadopoulos N, Velculescu VE, Zhou S, Diaz LA, Jr., Kinzler KW. Cancer genome landscapes. Science. 2013 Mar 29;339(6127):1546-58. DOI: 10.1126/science.1235122.
  22. Swanton C, Soria JC, Bardelli A, Biankin A, Caldas C, Chandarlapaty S, et al. Consensus on precision medicine for metastatic cancers: a report from the MAP conference. Ann Oncol. 2016 Aug;27(8):1443-8. DOI: 10.1093/annonc/mdw192.
  23. NCCN.org. NCCN Clinical Practice Guidelines in Oncology: Central Nervous System Cancers Version 1.2016.
  24. NCCN.org. NCCN Clinical Practice Guidelines in Oncology: Soft Tissue Sarcoma Version 1.2017.
  25. Forment JV, Kaidi A, Jackson SP. Chromothripsis and cancer: causes and consequences of chromosome shattering. Nat Rev Cancer. 2012 Oct;12(10):663-70. DOI: 10.1038/nrc3352.
  26. Nik-Zainal S, Alexandrov LB, Wedge DC, Van Loo P, Greenman CD, Raine K, et al. Mutational processes molding the genomes of 21 breast cancers. Cell. 2012 May 25;149(5):979-93. DOI: 10.1016/j.cell.2012.04.024.
  27. Maciejowski J, Li Y, Bosco N, Campbell PJ, de Lange T. Chromothripsis and Kataegis Induced by Telomere Crisis. Cell. 2015 Dec 17;163(7):1641-54. DOI: 10.1016/j.cell.2015.11.054.
  28. Kriplani D, Patel MM. Immunohistochemistry: A diagnostic aid in differentiating primary epithelial ovarian tumors and tumors metastatic to the ovary. South Asian J Cancer. 2013 Oct;2(4):254-8. DOI: 10.4103/2278-330X.119888.
  29. Ikeda S, Fujimori M, Shibata S, Okajima M, Ishizaki Y, Kurihara T, et al. Combined immunohistochemistry of beta-catenin, cytokeratin 7, and cytokeratin 20 is useful in discriminating primary lung adenocarcinomas from metastatic colorectal cancer. BMC Cancer. 2006 Feb 02;6(1):31. DOI: 10.1186/1471-2407-6-31.
  30. Metcalf AM, Spurdle AB. Endometrial tumour BRAF mutations and MLH1 promoter methylation as predictors of germline mismatch repair gene mutation status: a literature review. Fam Cancer. 2014 Mar;13(1):1-12. DOI: 10.1007/s10689-013-9671-6.
  31. NCCN.org. NCCN Clinical Practice Guidelines in Oncology: Acute Myeloid Leukaemia version 2.2016. Available from: https://www.nccn.org/professionals/physician_gls/pdf/aml.pdf.
  32. NCCN.org. NCCN Clinical Practice Guidelines in Oncology: Acute Lymphoblastic Leukaemia Version 2.2016.
  33. NCCN.org. NCCN Clinical Practice Guidelines in Oncology: Myelodysplastic Syndrome Version 2.2017.
  34. Nicholson JM, Cimini D. Cancer karyotypes: survival of the fittest. Front Oncol. 2013;3:148. DOI: 10.3389/fonc.2013.00148.
  35. Harris G, O’Toole S, George P, Browett P, Print C. Massive parallel sequencing of solid tumours – challenges and opportunities for pathologists. Histopathology. 2017 Jan;70(1):123-33. DOI: 10.1111/his.13067.
  36. Harper MJ, Walpole AL. A new derivative of triphenylethylene: effect on implantation and mode of action in rats. J Reprod Fertil. 1967 Feb;13(1):101-19.
  37. Finn RS, Crown JP, Lang I, Boer K, Bondarenko IM, Kulyk SO, et al. The cyclin-dependent kinase 4/6 inhibitor palbociclib in combination with letrozole versus letrozole alone as first-line treatment of oestrogen receptor-positive, HER2-negative, advanced breast cancer (PALOMA-1/TRIO-18): a randomised phase 2 study. The Lancet Oncology. 2015;16(1):25-35. DOI: 10.1016/s1470-2045(14)71159-3.
  38. Finn RS, Martin M, Rugo HS, Jones S, Im SA, Gelmon K, et al. Palbociclib and Letrozole in Advanced Breast Cancer. N Engl J Med. 2016 Nov 17;375(20):1925-36. DOI: 10.1056/NEJMoa1607303.
  39. Daldorff S, Mathiesen RM, Yri OE, Odegard HP, Geisler J. Cotargeting of CYP-19 (aromatase) and emerging, pivotal signalling pathways in metastatic breast cancer. Br J Cancer. 2017 Jan 03;116(1):10-20. DOI: 10.1038/bjc.2016.405.
  40. Finn RS, Dering J, Conklin D, Kalous O, Cohen DJ, Desai AJ, et al. PD 0332991, a selective cyclin D kinase 4/6 inhibitor, preferentially inhibits proliferation of luminal estrogen receptor-positive human breast cancer cell lines in vitro. Breast Cancer Res. 2009;11(5):R77. DOI: 10.1186/bcr2419.
  41. Cristofanilli M, Turner NC, Bondarenko I, Ro J, Im SA, Masuda N, et al. Fulvestrant plus palbociclib versus fulvestrant plus placebo for treatment of hormone-receptor-positive, HER2-negative metastatic breast cancer that progressed on previous endocrine therapy (PALOMA-3): final analysis of the multicentre, double-blind, phase 3 randomised controlled trial. Lancet Oncol. 2016 Apr;17(4):425-39. DOI: 10.1016/S1470-2045(15)00613-0.
  42. Chalhoub N, Baker SJ. PTEN and the PI3-kinase pathway in cancer. Annual Review of Pathology: Mechanisms of Disease2009. p. 127-50.
  43. Guerrero-Zotano A, Mayer IA, Arteaga CL. PI3K/AKT/mTOR: role in breast cancer progression, drug resistance, and treatment. Cancer Metastasis Rev. 2016 Dec;35(4):515-24. DOI: 10.1007/s10555-016-9637-x.
  44. Franz DN, Belousova E, Sparagana S, Bebin EM, Frost MD, Kuperman R, et al. Long-Term Use of Everolimus in Patients with Tuberous Sclerosis Complex: Final Results from the EXIST-1 Study. PLoS ONE. 2016;11(6):e0158476. DOI: 10.1371/journal.pone.0158476.
  45. Yao JC, Shah MH, Ito T, Bohas CL, Wolin EM, Van Cutsem E, et al. Everolimus for advanced pancreatic neuroendocrine tumors. N Engl J Med. 2011 Feb 10;364(6):514-23. DOI: 10.1056/NEJMoa1009290.
  46. Baselga J, Campone M, Piccart M, Burris HA, 3rd, Rugo HS, Sahmoud T, et al. Everolimus in postmenopausal hormone-receptor-positive advanced breast cancer. N Engl J Med. 2012 Feb 09;366(6):520-9. DOI: 10.1056/NEJMoa1109653.
  47. NCCN.org. Clinical Practice Guidlines in Oncology: Breast Cancer version 2.2016.
  48. Santa-Maria CA, Gradishar WJ. Changing Treatment Paradigms in Metastatic Breast Cancer: Lessons Learned. JAMA Oncol. 2015 Jul;1(4):528-34; quiz 49. DOI: 10.1001/jamaoncol.2015.1198.
  49. Clinicaltrials.gov. Available from: https://clinicaltrials.gov/ct2/show/NCT01610284.
  50. Massacesi C, Di Tomaso E, Urban P, Germa C, Quadt C, Trandafir L, et al. PI3K inhibitors as new cancer therapeutics: implications for clinical trial design. Onco Targets Ther. 2016 01/07;9:203-10. DOI: 10.2147/OTT.S89967.
  51. Shih C, Padhy LC, Murray M, Weinberg RA. Transforming genes of carcinomas and neuroblastomas introduced into mouse fibroblasts. Nature. 1981;290(5803):261-4. DOI: 10.1038/290261a0.
  52. 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 09;235(4785):177-82. DOI: 10.1126/science.3798106.
  53. Eccles SA. The epidermal growth factor receptor/Erb-B/HER family in normal and malignant breast biology. Int J Dev Biol. 2011;55(7-9):685-96. DOI: 10.1387/ijdb.113396se.
  54. Miller SM, Goulet DR, Johnson GL. Targeting the Breast Cancer Kinome. J Cell Physiol. 2017 Jan;232(1):53-60. DOI: 10.1002/jcp.25427.
  55. Wu P,Nielsen TE,Clausen MH. FDA-approved small-molecule kinase inhibitors. Trends Pharmacol Sci. 2015 Jul;36(7):422-39. doi: 10.1016/j.tips.2015.04.005. Epub 2015 May 12.
  56. Cameron D, Casey M, Oliva C, Newstat B, Imwalle B, Geyer CE. Lapatinib plus capecitabine in women with HER-2-positive advanced breast cancer: final survival analysis of a phase III randomized trial. Oncologist. 2010;15(9):924-34. DOI: 10.1634/theoncologist.2009-0181.
  57. Geyer CE, Forster J, Lindquist D, Chan S, Romieu CG, Pienkowski T, et al. Lapatinib plus capecitabine for HER2-positive advanced breast cancer. N Engl J Med. 2006 Dec 28;355(26):2733-43. DOI: 10.1056/NEJMoa064320.
  58. Blackwell KL, Burstein HJ, Storniolo AM, Rugo HS, Sledge G, Aktan G, et al. Overall survival benefit with lapatinib in combination with trastuzumab for patients with human epidermal growth factor receptor 2-positive metastatic breast cancer: final results from the EGF104900 Study. J Clin Oncol. 2012 Jul 20;30(21):2585-92. DOI: 10.1200/JCO.2011.35.6725.
  59. Li D, Marchenko ND. ErbB2 inhibition by lapatinib promotes degradation of mutant p53 protein in cancer cells. Oncotarget. 2017 Jan 24;8(4):5823-33. DOI: 10.18632/oncotarget.12878.
  60. Bang YJ, Van Cutsem E, Feyereislova A, Chung HC, Shen L, Sawaki A, et al. Trastuzumab in combination with chemotherapy versus chemotherapy alone for treatment of HER2-positive advanced gastric or gastro-oesophageal junction cancer (ToGA): a phase 3, open-label, randomised controlled trial. Lancet. 2010 Aug 28;376(9742):687-97. DOI: 10.1016/S0140-6736(10)61121-X.
  61. Kang Y-K, Shah MA, Ohtsu A, Van Cutsem E, Ajani JA, van der Horst T, et al. A randomized, open-label, multicenter, adaptive phase 2/3 study of trastuzumab emtansine (T-DM1) versus a taxane (TAX) in patients (pts) with previously treated HER2-positive locally advanced or metastatic gastric/gastroesophageal junction adenocarcinoma (LA/MGC/GEJC). Journal of Clinical Oncology. 2016;34(4_suppl):5-. DOI: 10.1200/jco.2016.34.4_suppl.5.
  62. Hecht JR, Bang YJ, Qin SK, Chung HC, Xu JM, Park JO, et al. Lapatinib in Combination With Capecitabine Plus Oxaliplatin in Human Epidermal Growth Factor Receptor 2-Positive Advanced or Metastatic Gastric, Esophageal, or Gastroesophageal Adenocarcinoma: TRIO-013/LOGiC–A Randomized Phase III Trial. J Clin Oncol. 2016 Feb 10;34(5):443-51. DOI: 10.1200/JCO.2015.62.6598.
  63. Rodon J, Iniesta MD, Papadopoulos K. Development of PARP inhibitors in oncology. Expert Opin Investig Drugs. 2009 Jan;18(1):31-43. DOI: 10.1517/13543780802525324.
  64. Fong PC, Yap TA, Boss DS, Carden CP, Mergui-Roelvink M, Gourley C, et al. Poly(ADP)-ribose polymerase inhibition: frequent durable responses in BRCA carrier ovarian cancer correlating with platinum-free interval. J Clin Oncol. 2010 May 20;28(15):2512-9. DOI: 10.1200/JCO.2009.26.9589.
  65. Ledermann J, Harter P, Gourley C, Friedlander M, Vergote I, Rustin G, et al. Olaparib maintenance therapy in patients with platinum-sensitive relapsed serous ovarian cancer: a preplanned retrospective analysis of outcomes by BRCA status in a randomised phase 2 trial. Lancet Oncol. 2014 Jul;15(8):852-61. DOI: 10.1016/S1470-2045(14)70228-1.
  66. Tutt A, Robson M, Garber JE, Domchek SM, Audeh MW, Weitzel JN, et al. Oral poly(ADP-ribose) polymerase inhibitor olaparib in patients with BRCA1 or BRCA2 mutations and advanced breast cancer: a proof-of-concept trial. Lancet. 2010 Jul 24;376(9737):235-44. DOI: 10.1016/S0140-6736(10)60892-6.
  67. Mateo J, Carreira S, Sandhu S, Miranda S, Mossop H, Perez-Lopez R, et al. DNA-Repair Defects and Olaparib in Metastatic Prostate Cancer. N Engl J Med. 2015 Oct 29;373(18):1697-708. DOI: 10.1056/NEJMoa1506859.
  68. Postow MA, Robson ME. Inherited gastrointestinal stromal tumor syndromes: mutations, clinical features, and therapeutic implications. Clin Sarcoma Res. 2012 Oct 04;2(1):16. DOI: 10.1186/2045-3329-2-16.
  69. Demetri GD, von Mehren M, Blanke CD, Van den Abbeele AD, Eisenberg B, Roberts PJ, et al. Efficacy and safety of imatinib mesylate in advanced gastrointestinal stromal tumors. N Engl J Med. 2002 Aug 15;347(7):472-80. DOI: 10.1056/NEJMoa020461.
  70. O’Brien SG, Guilhot F, Larson RA, Gathmann I, Baccarani M, Cervantes F, et al. Imatinib compared with interferon and low-dose cytarabine for newly diagnosed chronic-phase chronic myeloid leukemia. N Engl J Med. 2003 Mar 13;348(11):994-1004. DOI: 10.1056/NEJMoa022457.
  71. Heinrich MC, Corless CL, Demetri GD, Blanke CD, von Mehren M, Joensuu H, et al. Kinase mutations and imatinib response in patients with metastatic gastrointestinal stromal tumor. J Clin Oncol. 2003 Dec 01;21(23):4342-9. DOI: 10.1200/JCO.2003.04.190.
  72. Heinrich MC, Maki RG, Corless CL, Antonescu CR, Harlow A, Griffith D, et al. Primary and secondary kinase genotypes correlate with the biological and clinical activity of sunitinib in imatinib-resistant gastrointestinal stromal tumor. J Clin Oncol. 2008 Nov 20;26(33):5352-9. DOI: 10.1200/JCO.2007.15.7461.
  73. Reichardt P, Demetri GD, Gelderblom H, Rutkowski P, Im SA, Gupta S, et al. Correlation of KIT and PDGFRA mutational status with clinical benefit in patients with gastrointestinal stromal tumor treated with sunitinib in a worldwide treatment-use trial. BMC Cancer. 2016 Jan 15;16:22. DOI: 10.1186/s12885-016-2051-5.
  74. Corless CL, Schroeder A, Griffith D, Town A, McGreevey L, Harrell P, et al. PDGFRA mutations in gastrointestinal stromal tumors: frequency, spectrum and in vitro sensitivity to imatinib. J Clin Oncol. 2005 Aug 10;23(23):5357-64. DOI: 10.1200/JCO.2005.14.068.
  75. Lasota J, Miettinen M. Clinical significance of oncogenic KIT and PDGFRA mutations in gastrointestinal stromal tumours. Histopathology. 2008 Sep;53(3):245-66. DOI: 10.1111/j.1365-2559.2008.02977.x.
  76. Nannini M, Astolfi A, Urbini M, Indio V, Santini D, Heinrich MC, et al. Integrated genomic study of quadruple-WT GIST (KIT/PDGFRA/SDH/RAS pathway wild-type GIST). BMC Cancer. 2014 Sep 20;14(1):685. DOI: 10.1186/1471-2407-14-685.
  77. Gasparotto D, Rossi S, Polano M, Tamborini E, Lorenzetto E, Sbaraglia M, et al. Quadruple-Negative GIST Is a Sentinel for Unrecognized Neurofibromatosis Type 1 Syndrome. Clin Cancer Res. 2017 Jan 01;23(1):273-82. DOI: 10.1158/1078-0432.CCR-16-0152.
  78. Lasota J, Miettinen M. KIT and PDGFRA mutations in gastrointestinal stromal tumors (GISTs). Semin Diagn Pathol. 2006 May;23(2):91-102.
  79. Agaimy A, Terracciano LM, Dirnhofer S, Tornillo L, Foerster A, Hartmann A, et al. V600E BRAF mutations are alternative early molecular events in a subset of KIT/PDGFRA wild-type gastrointestinal stromal tumours. J Clin Pathol. 2009 Jul;62(7):613-6. DOI: 10.1136/jcp.2009.064550.
  80. Agaram NP, Wong GC, Guo T, Maki RG, Singer S, Dematteo RP, et al. Novel V600E BRAF mutations in imatinib-naive and imatinib-resistant gastrointestinal stromal tumors. Genes Chromosomes Cancer. 2008 Oct;47(10):853-9. DOI: 10.1002/gcc.20589.
  81. Banzi M, De Blasio S, Lallas A, Longo C, Moscarella E, Alfano R, et al. Dabrafenib: a new opportunity for the treatment of BRAF V600-positive melanoma. Onco Targets Ther. 2016;9:2725-33. DOI: 10.2147/OTT.S75104.
  82. Spain L, Julve M, Larkin J. Combination dabrafenib and trametinib in the management of advanced melanoma with BRAFV600 mutations. Expert Opin Pharmacother. 2016;17(7):1031-8. DOI: 10.1517/14656566.2016.1168805.
  83. Mangana J, Levesque MP, Karpova MB, Dummer R. Sorafenib in melanoma. Expert Opin Investig Drugs. 2012 Apr;21(4):557-68. DOI: 10.1517/13543784.2012.665872.
  84. Escudier B, Szczylik C, Hutson TE, Demkow T, Staehler M, Rolland F, et al. Randomized phase II trial of first-line treatment with sorafenib versus interferon Alfa-2a in patients with metastatic renal cell carcinoma. J Clin Oncol. 2009 Mar 10;27(8):1280-9. DOI: 10.1200/JCO.2008.19.3342.
  85. Falchook GS, Trent JC, Heinrich MC, Beadling C, Patterson J, Bastida CC, et al. BRAF mutant gastrointestinal stromal tumor: first report of regression with BRAF inhibitor dabrafenib (GSK2118436) and whole exomic sequencing for analysis of acquired resistance. Oncotarget. 2013 Feb;4(2):310-5. DOI: 10.18632/oncotarget.864.
  86. Blay JY, Labouret NH, Cropet C, Mazieres J, Nowak F, Bieche I, et al. Biomarker-driven access to vemurafenib in BRAF-positive cancers: Second study of the French National AcSé Program. Journal of Clinical Oncology. 2016;34(supplement)(2016 ASCO Annual Meeting Abstracts).
  87. Szucs Z, Thway K, Fisher C, Bulusu R, Constantinidou A, Benson C, et al. Promising novel therapeutic approaches in the management of gastrointestinal stromal tumors. Future Oncol. 2017 Jan;13(2):185-94. DOI: 10.2217/fon-2016-0194.
  88. Jones S, Anagnostou V, Lytle K, Parpart-Li S, Nesselbush M, Riley DR, et al. Personalized genomic analyses for cancer mutation discovery and interpretation. Sci Transl Med. 2015 Apr 15;7(283):283ra53. DOI: 10.1126/scitranslmed.aaa7161.
  89. Catenacci DV, Amico AL, Nielsen SM, Geynisman DM, Rambo B, Carey GB, et al. Tumor genome analysis includes germline genome: are we ready for surprises? Int J Cancer. 2015 Apr 01;136(7):1559-67. DOI: 10.1002/ijc.29128.
  90. Jain R, Savage MJ, Forman AD, Mukherji R, Hall MJ. The Relevance of Hereditary Cancer Risks to Precision Oncology: What Should Providers Consider When Conducting Tumor Genomic Profiling? J Natl Compr Canc Netw. 2016 Jun;14(6):795-806.
  91. Funchain P, Sohal, D., Khorana, A.A, Abraham, J., Pennell, N.A., Rini, B.I., et al. Hereditary implications of somatic tumor testing (abstract). Journal of Clinical Oncology. 2015;33((suppl; abstr 1523)).
  92. Ngeow J, Eng C. Precision medicine in heritable cancer: when somatic tumour testing and germline mutations meet. Npj Genomic Medicine. 2016 01/13/online;1(1):15006. DOI: 10.1038/npjgenmed.2015.6.
  93. Colling R, Church DN, Carmichael J, Murphy L, East J, Risby P, et al. Screening for Lynch syndrome and referral to clinical genetics by selective mismatch repair protein immunohistochemistry testing: an audit and cost analysis. J Clin Pathol. 2015 Dec;68(12):1036-9. DOI: 10.1136/jclinpath-2015-203083.
  94. Beamer LC, Grant ML, Espenschied CR, Blazer KR, Hampel HL, Weitzel JN, et al. Reflex immunohistochemistry and microsatellite instability testing of colorectal tumors for Lynch syndrome among US cancer programs and follow-up of abnormal results. J Clin Oncol. 2012 Apr 01;30(10):1058-63. DOI: 10.1200/JCO.2011.38.4719.
  95. O’Kane G, Flannery, F., O’Connor, K., Nolan, C., Farrell, M.P., Green, A.J., Meighan, B.M., McCormick, P., Kennedy, M.J., Muldoon, C., Gallagher, D.J. Routine screening for mismatch repair proteins: The impact on genetic testing. (abstract). Journal of Clinical Oncology. 2015;33(Suppl 3):abstr 525.
  96. NICE. National Institute for Health and Care Excellence- Diagnostics Assessment Programme: Molecular testing for Lynch syndrome in people with colorectal cancer [updated 2016]. Available from: https://www.nice.org.uk/guidance/GID-DG10001/documents/final-scope.
  97. Alexandrov LB, Nik-Zainal S, Wedge DC, Aparicio SA, Behjati S, Biankin AV, et al. Signatures of mutational processes in human cancer. Nature. 2013 Aug 22;500(7463):415-21. DOI: 10.1038/nature12477.
  98. Jia P, Pao W, Zhao Z. Patterns and processes of somatic mutations in nine major cancers. BMC Med Genomics. 2014 Feb 19;7(1):11. DOI: 10.1186/1755-8794-7-11.
  99. Nielsen M, Morreau H, Vasen HF, Hes FJ. MUTYH-associated polyposis (MAP). Crit Rev Oncol Hematol. 2011 Jul;79(1):1-16. DOI: 10.1016/j.critrevonc.2010.05.011.
  100. Al-Tassan N, Chmiel NH, Maynard J, Fleming N, Livingston AL, Williams GT, et al. Inherited variants of MYH associated with somatic G:C–>T:A mutations in colorectal tumors. Nat Genet. 2002 Feb;30(2):227-32. DOI: 10.1038/ng828.
  101. Oishi K, Hofmann S, Diaz GA, Brown T, Manwani D, Ng L, et al. Biallelic germline mutations in MYH predispose to multiple colorectal adenoma and somatic G:C→T:A mutations. Human Molecular Genetics. 2002;11(23):2961-7.
  102. Jones S, Emmerson P, Maynard J, Best JM, Jordan S, Williams GT, et al. Biallelic germline mutations in MYH predispose to multiple colorectal adenoma and somatic G:C–>T:A mutations. Hum Mol Genet. 2002 Nov 01;11(23):2961-7.
  103. Lipton L, Halford SE, Johnson V, Novelli MR, Jones A, Cummings C, et al. Carcinogenesis in MYH-associated polyposis follows a distinct genetic pathway. Cancer Res. 2003 Nov 15;63(22):7595-9.
  104. Wang L, Baudhuin LM, Boardman LA, Steenblock KJ, Petersen GM, Halling KC, et al. MYH mutations in patients with attenuated and classic polyposis and with young-onset colorectal cancer without polyps. Gastroenterology. 2004;127(1):9-16. DOI: 10.1053/j.gastro.2004.03.070.
  105. McVeigh TP, Duff M, Carroll C, Cody N, O’Shea R, Bradley L, et al. MUTYH-associated polyposis: The Irish experience. Irish Medical Journal. 2016;109(10):P485.
  106. Tan C, Du X. KRAS mutation testing in metastatic colorectal cancer. World J Gastroenterol. 2012 Oct 07;18(37):5171-80. DOI: 10.3748/wjg.v18.i37.5171.
  107. van Puijenbroek M, Nielsen M, Tops CM, Halfwerk H, Vasen HF, Weiss MM, et al. Identification of patients with (atypical) MUTYH-associated polyposis by KRAS2 c.34G > T prescreening followed by MUTYH hotspot analysis in formalin-fixed paraffin-embedded tissue. Clin Cancer Res. 2008 Jan 01;14(1):139-42. DOI: 10.1158/1078-0432.CCR-07-1705.
  108. Oakman C, Bessi S, Zafarana E, Galardi F, Biganzoli L, Di Leo A. Recent advances in systemic therapy: new diagnostics and biological predictors of outcome in early breast cancer. Breast Cancer Res. 2009;11(2):205. DOI: 10.1186/bcr2238.
  109. Cronin M, Pho M, Dutta D, Stephans JC, Shak S, Kiefer MC, et al. Measurement of gene expression in archival paraffin-embedded tissues: development and performance of a 92-gene reverse transcriptase-polymerase chain reaction assay. Am J Pathol. 2004 Jan;164(1):35-42. DOI: 10.1016/S0002-9440(10)63093-3.
  110. Carlson JJ, Roth JA. The impact of the Oncotype Dx breast cancer assay in clinical practice: a systematic review and meta-analysis. Breast Cancer Res Treat. 2013 Aug;141(1):13-22. DOI: 10.1007/s10549-013-2666-z.
  111. Shah PD, Patil S, Dickler MN, Offit K, Hudis CA, Robson ME. Twenty-one-gene recurrence score assay in BRCA-associated versus sporadic breast cancers: Differences based on germline mutation status. Cancer. 2016 Apr 15;122(8):1178-84. DOI: 10.1002/cncr.29903.
  112. Lewin R, Sulkes A, Shochat T, Tsoref D, Rizel S, Liebermann N, et al. Oncotype-DX recurrence score distribution in breast cancer patients with BRCA1/2 mutations. Breast Cancer Res Treat. 2016 Jun;157(3):511-6. DOI: 10.1007/s10549-016-3836-6.
  113. Richards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J, et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med. 2015 May;17(5):405-24. DOI: 10.1038/gim.2015.30.
  114. Wallis Y, Payne S, McAnulty C, Bodmer D, Sistermans E, Robertson K, et al. Practice Guidelines for the Evaluation of Pathogenicity and the Reporting of Sequence Variants in Clinical Molecular Genetics. Association for Clinical Genetic Science Guideline. 2013.
  115. Melloni GE, Ogier AG, de Pretis S, Mazzarella L, Pelizzola M, Pelicci PG, et al. DOTS-Finder: a comprehensive tool for assessing driver genes in cancer genomes. Genome Med. 2014;6(6):44. DOI: 10.1186/gm563.
  116. Kinzler KW, Vogelstein B. Lessons from hereditary colorectal cancer. Cell. 1996 Oct 18;87(2):159-70. DOI: 10.1016/S0092-8674(00)81333-1.
  117. Kwak EL, Bang YJ, Camidge DR, Shaw AT, Solomon B, Maki RG, et al. Anaplastic lymphoma kinase inhibition in non-small-cell lung cancer. N Engl J Med. 2010 Oct 28;363(18):1693-703. DOI: 10.1056/NEJMoa1006448.
  118. Talpaz M, Shah NP, Kantarjian H, Donato N, Nicoll J, Paquette R, et al. Dasatinib in imatinib-resistant Philadelphia chromosome-positive leukemias. N Engl J Med. 2006 Jun 15;354(24):2531-41. DOI: 10.1056/NEJMoa055229.
  119. Kantarjian H, Giles F, Wunderle L, Bhalla K, O’Brien S, Wassmann B, et al. Nilotinib in imatinib-resistant CML and Philadelphia chromosome-positive ALL. N Engl J Med. 2006 Jun 15;354(24):2542-51. DOI: 10.1056/NEJMoa055104.
  120. Chapman PB, Hauschild A, Robert C, Haanen JB, Ascierto P, Larkin J, et al. Improved survival with vemurafenib in melanoma with BRAF V600E mutation. N Engl J Med. 2011 Jun 30;364(26):2507-16. DOI: 10.1056/NEJMoa1103782.
  121. Hyman DM, Puzanov I, Subbiah V, Faris JE, Chau I, Blay JY, et al. Vemurafenib in Multiple Nonmelanoma Cancers with BRAF V600 Mutations. N Engl J Med. 2015 Aug 20;373(8):726-36. DOI: 10.1056/NEJMoa1502309.
  122. Mok TS, Wu YL, Thongprasert S, Yang CH, Chu DT, Saijo N, et al. Gefitinib or carboplatin-paclitaxel in pulmonary adenocarcinoma. N Engl J Med. 2009 Sep 3;361(10):947-57. DOI: 10.1056/NEJMoa0810699.
  123. Lynch TJ, Bell DW, Sordella R, Gurubhagavatula S, Okimoto RA, Brannigan BW, et al. Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib. N Engl J Med. 2004 May 20;350(21):2129-39. DOI: 10.1056/NEJMoa040938.
  124. Shepherd FA, Rodrigues Pereira J, Ciuleanu T, Tan EH, Hirsh V, Thongprasert S, et al. Erlotinib in previously treated non-small-cell lung cancer. N Engl J Med. 2005 Jul 14;353(2):123-32. DOI: 10.1056/NEJMoa050753.
  125. Piccart-Gebhart MJ, Procter M, Leyland-Jones B, Goldhirsch A, Untch M, Smith I, et al. Trastuzumab after adjuvant chemotherapy in HER2-positive breast cancer. N Engl J Med. 2005 Oct 20;353(16):1659-72. DOI: 10.1056/NEJMoa052306.
  126. Johnston S, Trudeau M, Kaufman B, Boussen H, Blackwell K, LoRusso P, et al. Phase II study of predictive biomarker profiles for response targeting human epidermal growth factor receptor 2 (HER-2) in advanced inflammatory breast cancer with lapatinib monotherapy. J Clin Oncol. 2008 Mar 01;26(7):1066-72. DOI: 10.1200/JCO.2007.13.9949.
  127. Swain SM, Baselga J, Kim SB, Ro J, Semiglazov V, Campone M, et al. Pertuzumab, trastuzumab, and docetaxel in HER2-positive metastatic breast cancer. N Engl J Med. 2015 Feb 19;372(8):724-34. DOI: 10.1056/NEJMoa1413513.
  128. Chan A, Delaloge S, Holmes FA, Moy B, Iwata H, Harvey VJ, et al. Neratinib after trastuzumab-based adjuvant therapy in patients with HER2-positive breast cancer (ExteNET): a multicentre, randomised, double-blind, placebo-controlled, phase 3 trial. Lancet Oncol. 2016 Mar;17(3):367-77. DOI: 10.1016/S1470-2045(15)00551-3.
  129. Motzer RJ, Escudier B, Oudard S, Hutson TE, Porta C, Bracarda S, et al. Phase 3 trial of everolimus for metastatic renal cell carcinoma : final results and analysis of prognostic factors. Cancer. 2010 Sep 15;116(18):4256-65. DOI: 10.1002/cncr.25219.
  130. Beaver JA, Park BH. The BOLERO-2 trial: the addition of everolimus to exemestane in the treatment of postmenopausal hormone receptor-positive advanced breast cancer. Future Oncol. 2012 Jun;8(6):651-7. DOI: 10.2217/fon.12.49.
  131. Mirza MR, Monk BJ, Herrstedt J, Oza AM, Mahner S, Redondo A, et al. Niraparib Maintenance Therapy in Platinum-Sensitive, Recurrent Ovarian Cancer. N Engl J Med. 2016 Dec 01;375(22):2154-64. DOI: 10.1056/NEJMoa1611310.
  132. Schmid P, Pinder SE, Wheatley D, Macaskill J, Zammit C, Hu J, et al. Phase II Randomized Preoperative Window-of-Opportunity Study of the PI3K Inhibitor Pictilisib Plus Anastrozole Compared With Anastrozole Alone in Patients With Estrogen Receptor-Positive Breast Cancer. J Clin Oncol. 2016 Jun 10;34(17):1987-94. DOI: 10.1200/JCO.2015.63.9179.
  133. Basset-Seguin N, Hauschild A, Grob JJ, Kunstfeld R, Dreno B, Mortier L, et al. Vismodegib in patients with advanced basal cell carcinoma (STEVIE): a pre-planned interim analysis of an international, open-label trial. Lancet Oncol. 2015 Jun;16(6):729-36. DOI: 10.1016/S1470-2045(15)70198-1.
  134. Wells SA, Jr., Robinson BG, Gagel RF, Dralle H, Fagin JA, Santoro M, et al. Vandetanib in patients with locally advanced or metastatic medullary thyroid cancer: a randomized, double-blind phase III trial. J Clin Oncol. 2012 Jan 10;30(2):134-41. DOI: 10.1200/JCO.2011.35.5040.
  135. Van Cutsem E, Tabernero J, Lakomy R, Prenen H, Prausova J, Macarulla T, et al. Addition of aflibercept to fluorouracil, leucovorin, and irinotecan improves survival in a phase III randomized trial in patients with metastatic colorectal cancer previously treated with an oxaliplatin-based regimen. J Clin Oncol. 2012 Oct 01;30(28):3499-506. DOI: 10.1200/JCO.2012.42.8201.
  136. Saltz LB, Clarke S, Diaz-Rubio E, Scheithauer W, Figer A, Wong R, et al. Bevacizumab in combination with oxaliplatin-based chemotherapy as first-line therapy in metastatic colorectal cancer: a randomized phase III study. J Clin Oncol. 2008 Apr 20;26(12):2013-9. DOI: 10.1200/JCO.2007.14.9930.
  137. Perren TJ, Swart AM, Pfisterer J, Ledermann JA, Pujade-Lauraine E, Kristensen G, et al. A phase 3 trial of bevacizumab in ovarian cancer. N Engl J Med. 2011 Dec 29;365(26):2484-96. DOI: 10.1056/NEJMoa1103799.
  138. Choueiri TK, Escudier B, Powles T, Tannir NM, Mainwaring PN, Rini BI, et al. Cabozantinib versus everolimus in advanced renal cell carcinoma (METEOR): final results from a randomised, open-label, phase 3 trial. Lancet Oncol. 2016 Jul;17(7):917-27. DOI: 10.1016/S1470-2045(16)30107-3.
  139. Viola D, Cappagli V, Elisei R. Cabozantinib (XL184) for the treatment of locally advanced or metastatic progressive medullary thyroid cancer. Future Oncol. 2013 Aug;9(8):1083-92. DOI: 10.2217/fon.13.128.
  140. Llovet JM, Ricci S, Mazzaferro V, Hilgard P, Gane E, Blanc JF, et al. Sorafenib in advanced hepatocellular carcinoma. N Engl J Med. 2008 Jul 24;359(4):378-90. DOI: 10.1056/NEJMoa0708857.
  141. Motzer RJ, Hutson TE, Tomczak P, Michaelson MD, Bukowski RM, Rixe O, et al. Sunitinib versus interferon alfa in metastatic renal-cell carcinoma. N Engl J Med. 2007 Jan 11;356(2):115-24. DOI: 10.1056/NEJMoa065044.

 

 

 

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