Article Tools
Clinical Trials
Article Tools
Precision Cancer Medicine: The Future Is Now, Only Better
89226
Abstract
The promise of precision medicine for cancer is already being realized with the recent introduction of many targeted therapies, some with companion diagnostic tests that identify patients most likely to benefit from treatment. The utility of molecular profiling of cancer to identify actionable aberrations has been suggested by several small clinical trials conducted in patients with advanced cancer and by many anecdotes but is yet to be proven in well-designed, prospective, randomized trials. Several trials that will definitively test this strategy are now underway or soon to be launched. Melanoma, a disease once largely untreatable when metastatic, may be a paradigm for understanding how the molecular drivers of a disease can lead to highly effective targeted therapies, as well as for realizing the enormous therapeutic potential of unleashing the immune system against cancer to produce long-term disease control. Looking to the future, advanced omics technologies and computational techniques will enable assessment of not only genomic variants, as performed today, but also of pathway and network aberrations that will greatly facilitate selection of drug combinations likely to benefit specific patients. As our deepening understanding of tumor biology converges with rapid advances in measurement science and technology and computational analysis, we have an enormous opportunity to create a future for precision medicine in oncology that provides for highly specific, minimally toxic, and dramatically effective treatment for each patient.
KEY POINTS
Data from retrospective analyses and early phase clinical trials have demonstrated that the strategy of matching targeted agents with genomic alterations is associated with encouraging results in the treatment of patients with various cancers.
Innovative prospective clinical trials that will more clearly define the value of this approach are ongoing or about to be launched.
The discovery of driver mutations in various melanoma subtypes has led to development of a number of pharmacologic inhibitors that target aberrant signal transduction and produce rapid clinical responses in a high proportion of patients.
Monoclonal antibody blockade of cytotoxic T-lymphocyte antigen 4 disrupts immune tolerance by downregulating T-regulatory cells and can induce long-lasting tumor regressions in some patients with advanced melanoma.
Programmed death-1 receptor and its ligand are highly promising new targets in cancer immunotherapy.
Genomic profiling is increasingly used in the management of patients with cancer to select appropriate targeted therapy. Data from retrospective analyses and early phase clinical trials have demonstrated that the strategy of matching targeted agents with genomic alterations is associated with encouraging results in the treatment of patients with various cancers. Nevertheless, several investigators have expressed the view that the use of individualized treatment strategies is not yet well validated and therefore not yet appropriate for routine clinical application.1,2 To address these concerns, prospective randomized controlled trials (RCTs) are being proposed. In 2012, the Institute of Medicine provided guidelines for developing omics-based tests and their use in clinical trials.3 Criteria for the use of omics-based predictors in clinical trials have been recently published.4
Some experts argue, however, that RCTs are too complex, expensive, and inefficient. In individualized medicine, the large number of new tests that are constantly evolving makes it difficult to assess them in randomized trials, particularly because the subpopulations of patients with targetable alterations who are treated with targeted agents are too small to analyze.
Despite these challenges, randomized studies are indispensable because they constitute the gold standard to provide solid evidence for the role of tumor molecular testing and treatment assignment.1 Innovative study designs have been proposed that explore different types of genetic profiling and modeling. The primary objective of these studies is to assess the effectiveness of molecular profiling to select targeted therapy. Observational and other nonrandomized clinical trials are also being conducted.
In June 2011, after preliminary results of the IMPACT (Initiative for Molecular Profiling and Advanced Cancer Therapy)5,6 study became available, the design of a randomized study in personalized cancer medicine began. In early 2014, the IMPACT II study, a randomized study evaluating molecular profiling and targeted agents in patients with metastatic cancer, will be initiated. The primary objective of this study is to determine whether patients treated with a targeted therapy selected on the basis of a genomic alteration in the tumor have longer progression-free survival (PFS) than those whose treatment is not selected on the basis of genomic analysis (GA). Approximately 1,400 patients with metastatic cancer (any tumor type) and up to three prior therapies will undergo tumor biopsy followed by molecular profiling (Fig. 1). If at least one molecular alteration is identified, the patient will be treated as follows: if there is a U.S. Food and Drug Administration (FDA)-approved drug within the labeled indication, the patient will receive it; if there is no approved drug for the alteration and the tumor type, but there is a commercially available targeted agent or appropriate clinical trial, patients will be randomly selected to receive targeted therapy versus treatment not selected on the basis of genetic profiling. Patients will be allowed to cross over to the other arm if they develop progressive disease on the assigned treatment or because of toxicity.7 A maximum of 300 patients will be enrolled in this randomized study; the first 100 patients will be randomly allocated with a 1:1 ratio and the remaining 200 patients will be adaptively assigned based on Bayesian hierarchical modeling.
FIG 1. Study design of IMPACT 2 (Initiative for Molecular Profiling and Advanced Cancer Therapy), a randomized study evaluating molecular profiling and targeted agents in patients with metastatic cancer.
Focusing on precision cancer medicine, the National Cancer Institute (NCI) has also planned a randomized trial to assess the utility of genome sequencing to determine therapy and improve patient outcomes in early-phase trials independent of tumor histology. The endpoint of the M-PACT (Molecular Profiling-based Assignment of Cancer Therapeutics) pilot study is to assess whether the rate of objective response (complete plus partial) and/or the 4-month PFS rate is improved following treatment with agents selected based on the presence of specific mutations in tumors of patients. Patients with refractory solid tumors with predefined mutations of interest that have progressed on one or more lines of standard therapy or for whom no standard treatment that has been shown to improve survival is available will be eligible. Study treatments will be chosen from a list of predefined regimens. Patients will be randomly assigned to receive treatment based on a study agent prospectively identified to target that mutation/pathway (arm A) versus treatment not known to target one of the detected mutations/pathways (arm B). Patients in arm B are allowed to cross over at the time of progression.
A randomized phase II trial comparing therapy based on tumor molecular profiling with conventional therapy in patients with refractory cancer (SHIVA) is being conducted by the Institut Curie. The molecular profile of the disease is first established from a tumor biopsy, and if a molecular abnormality is identified for which an approved targeted agent is available, patients are randomly assigned to receive targeted therapy based on molecular profiling or conventional therapy of the investigator's choice. The molecular profiling-based treatments and their targets are imatinib (KIT, ABL, and RET); everolimus (AKT, mTORC1/2, PTEN, and PI3K); vemurafenib (BRAF V600E); sorafenib (PDGFRA/B and FLT-3); erlotinib (epidermal growth factor receptor [EGFR]); lapatinib and trastuzumab (HER2); dasatinib (SRC, EPHA2, LCK, and YES); tamoxifen (or letrozole if contraindicated) (estrogen receptor, progesterone receptor); or abiraterone (AR). Crossover is allowed at disease progression.8
Several randomized trials in specific tumor types are being conducted. Clinical trials selected on the basis of their innovativeness and their potential influence on patient care are briefly described below.
BATTLE 2 is a targeted therapy study in previously treated patients with advanced stage, treatment refractory, non-small cell lung cancer (NSCLC). This is a two-institution biomarker-driven randomized phase II study. The primary objectives are to determine the 8-week disease control rate, and to identify prognostic and predictive markers and best individual treatment based on the patient's biomarker profile, in four treatment arms: erlotinib (arm 1), erlotinib plus Akt small molecule inhibitor (arm 2), Akt small molecule inhibitor plus mitogen-activated protein/extracellular signal-regulated kinase kinase (MEK) inhibitor (arm 3), or sorafenib (arm 4).9 The study is conducted in two stages. In stage I, 200 patients are to be adaptively randomly assigned based on their KRAS status. In stage II, an additional 200 patients will be adaptively randomly assigned based on biomarkers selected from stage I.
This integrated program for screening the target population of patients with early stage nonsquamous carcinoma of the lung aims to identify patients with tumors carrying mutations in the EGFR gene or rearrangements of the anaplastic lymphoma kinase (ALK) gene and enroll them on one of two specific adjuvant trials to test the benefit of adding erlotinib or crizotinib, respectively, to adjuvant therapy. The study will define biologic/molecular progression of nonsquamous NSCLC and will evaluate two promising therapies that target specific molecular subsets of the disease in the adjuvant setting. The primary endpoint is overall survival. Overall, 6,000 to 8,000 patients will be screened to identify and treat 378 and 430 patients with EGFR mutations and ALK rearrangements, respectively.
Patients with metastatic squamous cell lung cancer will undergo tumor molecular profiling and treatment will be assigned according to the marker or markers present. Patients with genetic alterations will be randomly assigned to receive investigational targeted therapy versus standard therapy (Fig. 2). A genotype- or phenotype-driven subgroup selection-refined strategy in a multiarm study has been proposed to improve operational efficiency: patient populations and eligibility criteria will be similar across different arms. A phase II-III design will be used to rapidly assess drug/biomarker matches for detection of large effects.
FIG 2. SWOG-1400: A biomarker-driven, multiarm phase II/III registration protocol in squamous lung cancer— second-line therapy.
A randomized phase III trial in patients with NSCLC will be launched in 2014 in a multicenter setting (UNICANCER). After four to six cycles of first-line chemotherapy, patients without progressive disease will be randomly assigned between GA-determined targeted therapy and pemetrexed in the case of nonsquamous cell lung cancers, and between GA-determined targeted therapy and EGFR inhibitors in the case of squamous cell lung cancer.
The I-SPY 2 TRIAL is a biomarker-driven study that compares the efficacy of novel drugs in combination with standard chemotherapy with the efficacy of standard therapy alone, aiming to identify patient subsets that benefit from specific treatments based on molecular characteristics. Using a Bayesian design, if novel agents are more effective than standard therapy they “graduate” from the trial with their corresponding biomarker signature(s) and are considered for further testing. New drugs enter as those that have undergone testing graduate or are dropped. Results of this randomized phase II clinical trial for women with HER2-positive and triple-negative breast cancer demonstrated that, when added to standard presurgery chemotherapy, veliparib combined with carboplatin improved the response rate in patients with triple-negative breast cancer. Neratinib also demonstrated a high probability for success in phase III studies in patients with HER2-positive/estrogen receptor-negative/progesterone receptor-negative disease.10-13
A randomized phase III trial will be launched in 2014 in a multicenter setting (UNICANCER) in patients with metastatic non-HER2+ breast cancer. Patients will receive six to eight cycles of chemotherapy. Genomic assessment will be performed on biopsies of metastatic lesions to identify molecular targets. Patients without progressive disease after chemotherapy will be randomly assigned to receive a targeted treatment based on GA or continued chemotherapy. In this trial 400 patients will be screened.
A prospective molecular analysis was offered to 108 patients with metastatic breast cancer for whom samples were collected prospectively or retrospectively from frozen or paraffin-embedded tissue.14 Analyses were performed using array comparative genomic hybridization (CGH; Agilent platform), and PIK3CA (exons 10 and 21) and AKT1 mutations were examined by standard Sanger sequencing. Genomic alterations were identified in 50% of patients, including 11 PIK3CA and 8 AKT1 mutations. Eighteen treatments (17 patients) were administered according to the molecular profile with nine showing evidence of activity. Reasons for not providing a genomic-driven treatment included absence of progressive disease (38%), investigator's choice (9%), rapid progressive disease (19%), and no drug access (21%). Array CGH correctly identified HER2 status in 97% of cases; failures were related to a low percentage of tumor cells. This trial showed that array CGH is feasible in the context of daily practice and, in combination with assessment of PIK3CA/AKT1 mutations, identifies a substantial number of actionable molecular alterations that allow matching with specific targeted agents.
For 427 patients with breast cancer presenting with a metastasis accessible to biopsy, array CGH (Agilent 4*180K or Affymetrix 6.0) and Sanger sequencing of PIK3CA (exon 10 and 21) and AKT1 (exon 4) were performed in five academic genomic centers.15 Patients were then offered targeted therapy based on the identified genomic alteration. The primary outcome measure was the percentage of patients for whom a targeted therapy could be offered. The primary objective was to include 30% of the patients in a clinical trial testing a drug matched to the genomic alteration. For the primary endpoint, analyses were performed on the overall population registered for the trial. Trial recruitment finished in July 2012. Sequencing and CGH arrays were feasible in 297 (70%) and 283 (67%) patients, respectively. A targetable genomic alteration was identified in 195 patients; the most frequent genomic alterations were PIK3CA (24%), CCND1 (18%), and FGFR1 (12%). In 117 patients a rare genomic alteration occurred in less than 5%, involving AKT1 mutation and EGFR, MDM2, FGFR2, AKT2, IGF1R, and MET high-level amplifications. Genomic analyses led to a personalized therapy in 55 out of 423 patients (13%). This study suggests that personalized medicine for metastatic breast cancer is feasible and confirms the high prevalence of rare targetable genomic alterations. This study also highlights the need to secure better access to relevant drugs in personalized medicine programs.
The MOSCATO-01 (Molecular profiling in Cancer for Treatment Optimization) trial is being conducted at Institut Gustave Roussy in patients with treatment-resistant progressive metastatic cancers with lesions accessible for biopsy to implement molecular screening by CGH, Secqan, and whole genome sequencing. The trial will include 900 patients. Preliminary results on the first 129 patients were presented at the 2013 American Society of Clinical Oncology (ASCO) annual meeting.16 DNA extracted from fresh tumor biopsies was analyzed by CGH (Agilent platform) and by sequencing 30 target genes. PFS using therapy based on GA was compared to PFS for the most recent therapy on which the patient had just experienced progression (PFS ratio). Of 129 consenting patients, 111 (86%) had a dedicated tumor biopsy. An actionable target was identified in 52 patients (40%). Among the 25 patients treated according to their GA, 5 (20%) showed a partial response (PR), 14 (56%) had stable disease, and 3 (12%) had progressive disease. High throughput molecular analysis is feasible in daily practice. It allows enrichment of phase I trials with specific GA, and leads to promising antitumor activity (20% PR compared to the typical 7% to 10% PR obtained in all-comers phase I trials). Currently, more than 600 patients have entered the trial.
To explore the role of genetic alterations in carcinogenesis and to develop therapeutic strategies based on tumor molecular profiles, the NCI is planning to initiate the Molecular Analysis for Therapy Choice (MATCH) trial, which uses a multiple-biomarker signal-finding design. Overall, 3,000 patients with advanced cancer and at least one prior therapy will be screened to identify approximately 1,000 patients for molecular targeted therapy. The study is not randomized. Molecular profiling will be performed in a network of Clinical Laboratory Improvement Amendments-certified laboratories. A targeted panel of mutations/amplifications will be used to determine eligibility. Whole-exome sequencing at baseline and at the time of progression will be performed for research purposes. The NCI will collaborate with multiple pharmaceutical companies that are contributing targeted agents (approved and investigational) to the study. The Eastern Cooperative Oncology Group–American College of Radiology Imaging Network will lead the trial with the cooperation of the NCI Clinical Trials Network. The projected study initiation date is mid- to late 2014. This approach is expected to provide more comprehensive genomic data than would typically be available if genotyping were limited to one or more mutations known to be associated with a particular cancer type.
The Worldwide Innovative Networking (WIN)-consortium has launched a phase II trial in 200 patients to address the question of whether patients with metastatic cancer can benefit from GA when failing standard systemic therapy. Based on GA of biopsies of both metastatic lesions and the organ of origin of the tumor, patients will receive either targeted therapy matched to a gene mutation (approximately 30% of patients) or, in the absence of a targetable mutation (70%), receive an algorithm-based treatment based on relative gene expression in the tumor compared to the normal tissue. The algorithm is based on in silico data analysis providing a ranking of drugs likely to be effective for the particular molecular profile.
Carefully designed innovative clinical trials aim to provide patients with new and effective therapies more quickly. The principal goal is to accelerate drug development and precision cancer medicine, and to provide more definitive evidence of the value of this approach in cancer treatment. If the use of personalized medicine proves superior to standard treatments, this approach has the potential to substantially reduce the cost and increase the efficiency of drug development and ultimately improve patient outcomes.
The discovery of driver mutations in various melanoma subtypes17-19 has led to development of an increasing number of pharmacologic inhibitors that target aberrant signal transduction molecules. These drugs are being explored in clinical trials in genetically defined subgroups of patients with melanoma. Although several new somatic mutations, including driver mutations, have been identified recently,20,21 few of them have shown therapeutic relevance.
Activating mutations and/or gene amplification of KIT have been found in certain subtypes of melanoma, in particular mucosal and acral melanoma.22,23 Case reports and several phase II studies evaluating the KIT inhibitors imatinib or nilotinib confirmed KIT as a therapeutic target.8,9 Response rates of approximately 20% to 25% are superior to those of dacarbazine, but still rather modest.24,25 A RCT comparing nilotinib to dacarbazine in patients with KIT-mutated melanoma is ongoing (NCT01028222).
BRAF mutations are found in 45% to 50% of melanomas, mostly those of the superficial spreading melanoma type, in younger individuals, and on nonchronically sun-damaged skin, and might be associated with poorer outcome compared to melanoma with wild type BRAF.26 Sorafenib is a nonselective multikinase inhibitor with weak BRAF blocking action that failed to improve outcomes in combination with chemotherapy in a RCT.27 The first selective BRAF-inhibitor was vemurafenib, which was approved as first-line therapy for advanced V600E-BRAF–mutated melanoma based on a RCT comparing vemurafenib with dacarbazine that showed a significant survival benefit (p < 0.001).28 Responses are frequent (48%) and rapid. The most common adverse events are arthralgia (56% of patients), fatigue (46%), rash (41%), photosensitivity (41%), and squamous cell carcinoma (SCC) of the skin (20% to 30%).
Dabrafenib, another BRAF-inhibitor, showed similar results in a RCT29 and also good responses in patients with brain metastases.30,31 Dabrafenib's toxicity profile seems better than that of vemurafenib with almost no photosensitivity and a lower rate of keratoacantomas/SCC (7% versus 20% to 30%).32-34
The major concern with BRAF-inhibitors is the short median duration of response of only approximately 6 months. Virtually all patients develop recurrence and the overall survival benefit as trial results mature is modest, with a hazard ratio of 0.70 at 12 months of follow up versus 0.37 at only 3.8 months of follow up.32 Various mechanisms of innate33 and acquired drug resistance, such as reactivation of the mitogen-activated protein kinase (MAPK) pathway or use of additional pathways33-35 have been described. Paradoxical activation of the MAPK pathway in cells without BRAF mutation could explain the appearance of keratoacanthomas and SCC,28 as well as new melanomas.36 For this reason, the risk of induction of other cancers should be closely monitored, especially in the context of adjuvant trials.
Intra-pathway inhibitor combinations targeting several genes on the MAPK pathway and inter-pathway inhibitor combinations (e.g., blocking both MAPK and PI3K pathways) may help to overcome some mechanisms of resistance.37,38
Treatment with the MEK-inhibitor trametinib led to a 22% response rate and a median PFS of 4.8 months.39 Trametinib is particularly promising in combination with a BRAF inhibitor, resulting in high response rates and almost no cases of keratoacanthoma or SCC.29 The combination of dabrafenib and trametenib has been shown to improve response rates to 76% with a good safety profile, and to significantly improve PFS compared to treatment with dabrafenib alone (p < 0.001).40 Results of a potentially confirmatory phase III trial comparing monotherapy to combination therapy are expected to be reported at the 2014 ASCO annual meeting.
Monoclonal antibodies that block cytotoxic T lymphocyte antigen 4 (CTLA4) weaken immune tolerance by downregulating T-regulatory cells and can induce tumor regression. Two such antibodies, tremelimumab and ipilimumab, had similar effects in phase I-II studies.41,42 Tremelimumab failed to prolong survival compared to dacarbazine in phase III and its development was abandoned.43 The dose schedule of only once per 3 months may have played a role, because more intensive schedules of ipilimumab succeeded both in second line and in first line.44,45 Ipilimumab was approved by the FDA and the European Medicines Agency. The optimal dose is not known. In a randomized phase II trial 10 mg/kg seemed to have the greatest activity, but at the cost of greater toxicity.46 The results of a RCT addressing this question are expected in the near future.
Characteristic features of ipilimumab treatment are (1) low objective response rates (less than 20%) but long duration of responses (median of approximately 20 months); (2) responses are usually delayed in onset (after 3 months or more); and (3) responses can occur after initial tumor progression or appearance of new lesions. Immune-related response criteria to avoid premature treatment cessation have been proposed, considering the possibility of early tumor progression and requiring confirmation of progressive disease.47,48
The absence of biomarkers that predict response and the toxicity profile associated with ipilimumab remain elements of some concern. Ipilimumab is a complex drug to handle: adverse events, mostly immune-related, occur in 40% of patients and include skin rashes, colitis, hepatitis, and hypophysitis. Grade 3-4 adverse events requiring cessation of treatment and high-dose corticosteroids occur in fewer than 10% of patients but can be fatal.
Various combinations of ipilimumab with other immunomodulating, antiangiogenic, chemotherapeutic, or targeted agents are being considered. A guiding principle for combination treatment designs could be to use drugs that induce immunogenic cell death.49 Interestingly, radiotherapy also causes immunogenic cell death, and in that context the observation of abscopal antitumor effects after radiotherapy and ipilimumab warrant further investigation.50
Programmed death-1 receptor (PD1) and its ligand (PD-L1) are highly promising new targets in immunotherapy. PD1 is an immune checkpoint protein that is expressed in many tumors in response to inflammation.51 The engagement of PD1 on the lymphocyte surface by PD-L1 on melanoma cells downregulates T-cell function.
Remarkable results of phase I trials evaluating two anti-PD1 antibodies (nivolumab and lambrolizumab) were recently reported, with objective response rates of more than 30% and a long-term benefit in the majority of responding patients.52,53 Furthermore, expression of PD-L1 on the surface of melanoma cells appears to be a predictive biomarker. Anti-PD-L1 antibody also has activity in melanoma.54 Importantly, the safety profile of these new agents seems very favorable compared to that of ipilimumab.
The efficacy indicators of the anti-PD-1 molecules nivolumab and lambrolizumab are quite superior to those of ipilimumab. Response rates range from 30% to 50%, and, importantly, the responses are similarly durable, if not superior to those of ipilimumab.55,56 Long-term follow-up of patients treated with nivolumab showed unprecedented 1-year and 2-year survival rates of 61% and 44%, respectively.57
Initial data for 53 patients indicate that ipilimumab combined with nivolumab may improve response rates even further.57 The caveat here is that World Health Organization criteria were used to assess response for the ipilimumab plus nivolumab combination instead of RECIST criteria, leading to a more favorable waterfall plot. It thus remains to be shown that the combination is better than treatment with anti-PD1 alone. This is clearly important as the combination is quite toxic and a RCT is currently addressing this question. In addition, the combination of ipilimumab and granulocyte macrophage colony-stimulating factor was reported to improve survival compared to ipilimumab alone. This somewhat surprising finding was associated with a more favorable toxicity profile of the combination.58 Anti-PD1 and combinations including anti-PD1 have the potential to become positioned as first-line treatment for all metastatic patients with melanoma.
We are on the verge of providing long-lasting responses in perhaps the majority of metastatic patients with melanoma. At present, immunotherapy provides a backbone for all patients and combination with MAPK pathway inhibitors offers an additional, sometimes primary, option for patients with mutations in that pathway. This dual approach in establishing precision cancer medicine for patients with melanoma will be a paradigm for many other tumor types.
It is clear from the preceding sections and the large number of targeted agents already introduced into clinical practice that precision medicine for cancer is here to stay. So what will the field look like in 50 years? In the words of the Nobel laureate Niels Bohr, “Prediction is very difficult, especially about the future.” About the only thing one can say with confidence is that precision medicine in the future will be even more precise, not just regarding therapy selection but also with respect to risk assessment, establishing prognosis, monitoring treatment effectiveness, and predicting its tolerability for each individual. Health care delivery will also change dramatically as point-of-care molecular diagnostics become commonplace and crowdsourcing of information affects clinical decision-making and patient engagement. Imagine the following scenario:
I am feeling unusually tired and weak and looking pale. I visit my nurse practitioner at my local pharmacy, who sticks my finger for a drop of blood to assess my blood counts (maybe in 50 years she just uses a noninvasive sensor). Within minutes he or she returns with my results: anemia and elevated white blood cell count. After detection of the elevated white blood cell count, a portion of my blood sample is automatically directed to a molecular profiler that detects a bcr-abl translocation and a quick scan of my genome detects no polymorphisms that would predict intolerance to tyrosine kinase inhibitors. I am informed of my diagnosis of chronic myeloid leukemia (CML), given a patient education sheet, and told to walk down the aisle to pick up a bottle of generic, over the counter imatinib to be taken according to the directions on the label (perhaps in 50 years I will visit my molecular oncologist to receive an infusion of molecular scissors that repairs the bcr-abl translocation in my bone marrow stem cells and cures my leukemia). My nurse practitioner tells me to return in 2 weeks for a repeat assessment of blood count and circulating bcr-abl level to monitor my progress. I return home, join a CML online support group, and learn more about my diagnosis by conversing with other patients like me.
Advanced omics technologies and computational techniques will enable assessment of not only genomic variants as performed today, but also of pathway and network aberrations that will greatly facilitate the selection of drug combinations likely to benefit specific patients. However, with literally millions of potential combinations that can be created among the currently available oncology drugs, it will be impossible to assess every combination in every cancer subtype through the usual clinical trial strategies. We will need to create an information collaborative that allows us to learn from every encounter with every patient and share that information across the medical community. Such rapid learning systems are being built today but their utility will depend on development of robust methodologies to extract clinically meaningful, robust, and reproducible information that can be applied to rapidly modify clinical practice guidelines and facilitate drug development and regulatory approval. Using the results of what will essentially become a series of “N of 1” trials to seek regulatory approval for use of a drug in a new indication will require ongoing engagement with the FDA and regulatory authorities worldwide. Issues to be considered are the level of evidence required to label a drug for use in treating tumors that harbor a particular molecular aberration, regardless of histology; the data that are necessary to demonstrate the clinical utility of complex molecular profiling tests such as next-generation sequencing; the definition of a “breakthrough drug” in a given clinical situation, such as a molecularly defined tumor subtype; and whether regulatory decisions could follow an “adaptive licensing” approval process, as some have advocated. Answers to some of these questions will come from the prospective trials described in the first part of this article, hopefully sooner than 50 years!
The pioneering computer scientist Alan Kay reminded us that “the best way to predict the future is to invent it.” We have done that throughout the history of medicine and, as our deepening understanding of tumor biology converges with rapid advances in measurement science and technology and computational analysis, we have an enormous opportunity to create a future for precision medicine in oncology that provides for highly specific, minimally toxic, and dramatically effective treatments for our patients. Whatever precision medicine looks like in 50 years, we can be assured that, in the words of the great sage Yogi Berra, “The future ain't what it used to be.”
Relationships are considered self-held and compensated unless otherwise noted. Relationships marked “L” indicate leadership positions. Relationships marked “I” are those held by an immediate family member; those marked “B” are held by the author and an immediate family member. Relationships marked “U” are uncompensated.
Employment or Leadership Position: None. Consultant or Advisory Role: Alexander M. Eggermont, Amgen; Bristol-Myers Squibb; GlaxoSmithKline; MedImmune; Merck. Apostolia M. Tsimberidou, Caris Life Sciences. Stock Ownership: None. Honoraria: Apostolia M. Tsimberidou, Caris Life Sciences. Research Funding: Apostolia M. Tsimberidou, Celgene; EMD Serono. Expert Testimony: None. Other Remuneration: None.
| 1. | Ioannidis JP, Khoury MJ. Are randomized trials obsolete or more important than ever in the genomic era? Genome Med. 2013;5:32. Medline, Google Scholar |
| 2. | Polley MY, Freidlin B, Korn EL, et al. Statistical and practical considerations for clinical evaluation of predictive biomarkers. J Natl Cancer Inst. 2013;105:1677-1683. Crossref, Medline, Google Scholar |
| 3. | Micheel CM, Nass SJ, Omenn GS (eds). Evolution of translational omics: lessons learned and the path forward. Washington DC: The National Academies Press; 2012. Crossref, Google Scholar |
| 4. | McShane LM, Cavenagh MM, Lively TG, et al. Criteria for the use of omics-based predictors in clinical trials. Nature. 2013;502:317-320. Crossref, Medline, Google Scholar |
| 5. | Tsimberidou AM, Iskander NG, Hong DS, et al. Personalized medicine in a phase I clinical trials program: the MD Anderson Cancer Center initiative. J Clin Oncol. 2011;29 (suppl; abstr CRA2500). Google Scholar |
| 6. | Tsimberidou AM, Iskander NG, Hong DS, et al. Personalized medicine in a phase I clinical trials program: the MD Anderson Cancer Center initiative. Clin Cancer Res. 2012;18:6373-6383. Crossref, Medline, Google Scholar |
| 7. | Druker BJ, Guilhot F, O'Brien SG, et al. Five-year follow-up of patients receiving imatinib for chronic myeloid leukemia. N Engl J Med. 2006;355:2408-2417. Crossref, Medline, Google Scholar |
| 8. | NCT01771458. A randomized phase II trial comparing therapy based on tumor molecular profiling versus conventional therapy in patients with refractory cancer (SHIVA). http://clinicaltrials.gov/ct2/show/NCT01771458. Accessed January 27, 2014. Google Scholar |
| 9. | NCT01248247. BATTLE-2 program: a biomarker-integrated targeted therapy study. http://clinicaltrials.gov/ct2/show/NCT01248247. Accessed January 27, 2014. Google Scholar |
| 10. | NCT01042379. I-SPY 2 TRIAL: neoadjuvant and personalized adaptive novel agents to treat breast cancer. http://clinicaltrials.gov/ct2/show/NCT01042379. Accessed January 27, 2014. Google Scholar |
| 11. | Rugo H, Olopade O, deMichele A, et al. Veliparib/carboplatin plus standard neoadjuvant therapy for high-risk breast cancer: first efficacy results from the I-SPY 2 Trial. Paper presented at: 36th Annual San Antonio Breast Cancer Symposium; 2013; San Antonio, TX. Google Scholar |
| 12. | DeMichele A, Berry DA, Zujewski J, et al. Developing safety criteria for introducing new agents into neoadjuvant trials. Clin Cancer Res. 2013;19:2817-2823. Medline, Google Scholar |
| 13. | Berry DA. Adaptive clinical trials in oncology. Nat Rev Clin Oncol. 2011;9:199-207. Crossref, Medline, Google Scholar |
| 14. | Arnedos M, Scott V, Job B, et al. Array CGH and PIK3CA/AKT1 mutations to drive patients to specific targeted agents: a clinical experience in 108 patients with metastatic breast cancer. Eur J Cancer. 2012;48:2293-2299. Medline, Google Scholar |
| 15. | Andre F, Bachelot T, Commo F, et al. Comparative genomic hybridisation array and DNA sequencing to direct treatment of metastatic breast cancer: a multicentre, prospective trial (SAFIR01/UNICANCER). Lancet Oncol. 2014;15:267-274. Crossref, Medline, Google Scholar |
| 16. | Hollebeque A, Massard C, De Baere T, et al. Molecular screening for cancer treatment optimization (MOSCATO 01): a prospective molecular triage trial—Interim results. J Clin Oncol. 2013;31 (suppl; abstr 2512). Google Scholar |
| 17. | Davies H, Bignell GR, Cox C, et al. Mutations of the BRAF gene in human cancer. Nature. 2002;417:949-954. Crossref, Medline, Google Scholar |
| 18. | Curtin JA, Fridlyand J, Kageshita T, et al. Distinct sets of genetic alterations in melanoma. N Engl J Med. 2005;353:2135-2147. Crossref, Medline, Google Scholar |
| 19. | Colombino M, Capone M, Lissia A, et al. BRAF/NRAS mutation frequencies among primary tumors and metastases in patients with melanoma. J Clin Oncol. 2012;30:2522-2529. Link, Google Scholar |
| 20. | Hodis E, Watson IR, Kryukov GV, et al. A landscape of driver mutations in melanoma. Cell. 2012;150:251-263. Crossref, Medline, Google Scholar |
| 21. | Berger MF, Hodis E, Heffernan TP, et al. Melanoma genome sequencing reveals frequent PREX2 mutations. Nature. 2012;485:502-506. Crossref, Medline, Google Scholar |
| 22. | Curtin JA, Busam K, Pinkel D, et al. Somatic activation of KIT in distinct subtypes of melanoma. J Clin Oncol. 2006;24:4340-4346. Link, Google Scholar |
| 23. | Minor DR, Kashani-Sabet M, Garrido M, et al. Sunitinib therapy for melanoma patients with KIT mutations. Clin Cancer Res. 2012;18:1457-1463. Crossref, Medline, Google Scholar |
| 24. | Guo J, Si L, Kong Y, et al. Phase II, open-label, single-arm trial of imatinib mesylate in patients with metastatic melanoma harboring c-Kit mutation or amplification. J Clin Oncol. 2011;29:2904-2409. Link, Google Scholar |
| 25. | Carvajal RD, Antonescu CR, Wolchok JD, et al. KIT as a therapeutic target in metastatic melanoma. JAMA. 2011;305:2327-2334. Crossref, Medline, Google Scholar |
| 26. | Long GV, Menzies AM, Nagrial AM, et al. Prognostic and clinicopathologic associations of oncogenic BRAF in metastatic melanoma. J Clin Oncol. 2011;29:1239-1246. Link, Google Scholar |
| 27. | Hauschild A, Agarwala SS, Trefzer U, et al. Results of a phase III, randomized, placebo-controlled study of sorafenib in combination with carboplatin and paclitaxel as second-line treatment in patients with unresectable stage III or stage IV melanoma. J Clin Oncol. 2009;27:2823-2830. Link, Google Scholar |
| 28. | Chapman PB, Hauschild A, Robert C, et al. Improved survival with vemurafenib in melanoma with BRAF V600E mutation. N Engl J Med. 2011;364:2507-2516. Crossref, Medline, Google Scholar |
| 29. | Hauschild A, Grob JJ, Demidov LV, et al. Dabrafenib in BRAF-mutated metastatic melanoma: a multicentre, open-label, phase 3 randomised controlled trial. Lancet. 2012;380:358-365. Crossref, Medline, Google Scholar |
| 30. | Falchook GS, Long GV, Kurzrock R, et al. Dabrafenib in patients with melanoma, untreated brain metastases, and other solid tumours: a phase 1 dose-escalation trial. Lancet. 2012;379:1893-1901. Crossref, Medline, Google Scholar |
| 31. | Long GV, Trefzer U, Davies MA, et al. Dabrafenib in patients with Val600Glu or Val600Lys BRAF-mutant melanoma metastatic to the brain (BREAK-MB): a multicentre, open-label, phase 2 trial. Lancet Oncol. 2012;13:1087-1095. Crossref, Medline, Google Scholar |
| 32. | Chapman PB, Hauschild A, Robert C, et al. Updated overall survival (OS) results for BRIM-3, a phase III randomized, open-label, multicenter trial comparing BRAF inhibitor vemurafenib (vem) with dacarbazine (DTIC) in previously untreated patients with BRAFV600E-mutated melanoma. J Clin Oncol. 2012;30 (suppl; abstr 8502). Medline, Google Scholar |
| 33. | Villanueva J, Vultur A, Lee JT, et al. Acquired resistance to BRAF inhibitors mediated by a RAF kinase switch in melanoma can be overcome by cotargeting MEK and IGF-1R/PI3K. Cancer Cell. 2010;18:683-695. Crossref, Medline, Google Scholar |
| 34. | Straussman R, Morikawa T, Shee K, et al. Tumour micro-environment elicits innate resistance to RAF inhibitors through HGF secretion. Nature. 2012;487:500-504. Crossref, Medline, Google Scholar |
| 35. | Poulikakos PI, Zhang C, Bollag G, et al. RAF inhibitors transactivate RAF dimers and ERK signalling in cells with wild-type BRAF. Nature. 2010;464:427-430. Crossref, Medline, Google Scholar |
| 36. | Zimmer L, Hillen U, Livingstone E, et al. Atypical melanocytic proliferations and new primary melanomas in patients with advanced melanoma undergoing selective BRAF inhibition. J Clin Oncol. 2012;30:2375-2383. Link, Google Scholar |
| 37. | Corcoran RB, Settleman J, Engelman JA. Potential therapeutic strategies to overcome acquired resistance to BRAF or MEK inhibitors in BRAF mutant cancers. Oncotarget. 2011;2:336-246. Medline, Google Scholar |
| 38. | Das Thakur M, Salangsang F, Landman AS, et al. Modelling vemurafenib resistance in melanoma reveals a strategy to forestall drug resistance. Nature. 2013; 494:251-255. Crossref, Medline, Google Scholar |
| 39. | Flaherty KT, Robert C, Hersey P, et al. Improved survival with MEK inhibition in BRAF-mutated melanoma. N Engl J Med. 2012;367:107-114. Crossref, Medline, Google Scholar |
| 40. | Flaherty KT, Infante JR, Daud A, et al. Combined BRAF and MEK inhibition in melanoma with BRAF V600 mutations. N Engl J Med. 2012;367:1694-1703. Crossref, Medline, Google Scholar |
| 41. | Hodi FS, O'Day SJ, McDermott DF, et al. Improved survival with ipilimumab in patients with metastatic melanoma. N Engl J Med. 2010;363:711-723. Crossref, Medline, Google Scholar |
| 42. | Ribas A. Clinical development of the anti-CTLA-4 antibody tremelimumab. Semin Oncol. 2010;37:450-454. Crossref, Medline, Google Scholar |
| 43. | Weber JS, O'Day S, Urba W, et al. Phase I/II study of ipilimumab for patients with metastatic melanoma. J Clin Oncol. 2008;26:5950-5956. Link, Google Scholar |
| 44. | Ribas A, Kefford R, Marshall MA, et al. Phase III randomized clinical trial comparing tremelimumab with standard-of-care chemotherapy in patients with advanced melanoma. J Clin Oncol. 2013;31:616-622. Link, Google Scholar |
| 45. | Robert C, Thomas L, Bondarenko I, et al. Ipilimumab plus dacarbazine for previously untreated metastatic melanoma. N Engl J Med. 2011;364:2517-2526. Crossref, Medline, Google Scholar |
| 46. | Wolchok JD, Neyns B, Linette G, et al. Ipilimumab monotherapy in patients with pretreated advanced melanoma: a randomised, double-blind, multicentre, phase 2, dose-ranging study. Lancet Oncol. 2010;11:155-164. Crossref, Medline, Google Scholar |
| 47. | Wolchok JD, Hoos A, O'Day S, et al. Guidelines for the evaluation of immune therapy activity in solid tumors: immune-related response criteria. Clin Cancer Res. 2009;15:7412-7420. Crossref, Medline, Google Scholar |
| 48. | Hoos A, Eggermont AM, Janetzki S, et al. Improved endpoints for cancer immunotherapy trials. J Natl Cancer Inst. 2010;102:1388-1397. Crossref, Medline, Google Scholar |
| 49. | Green DR, Ferguson T, Zitvogel L, et al. Immunogenic and tolerogenic cell death. Nat Rev Immunol. 2009;9:353-363. Crossref, Medline, Google Scholar |
| 50. | Postow MA, Callahan MK, Barker CA, et al. Immunologic correlates of the abscopal effect in a patient with melanoma. N Engl J Med. 2012;366:925-931. Crossref, Medline, Google Scholar |
| 51. | Topalian SL, Drake CG, Pardoll DM. Targeting the PD-1/B7-H1(PD-L1) pathway to activate anti-tumor immunity. Curr Opin Immunol. 2012;24:207-212. Crossref, Medline, Google Scholar |
| 52. | Patnaik A, Kang SP, Tolcher A W. Phase I study of MK-3475 (anti-PD-1 monoclonal antibody) in patients with advanced solid tumors. J Clin Oncol. 2012;30 (suppl; abstr 2512). Medline, Google Scholar |
| 53. | Topalian SL, Hodi FS, Brahmer JR, et al. Safety, activity, and immune correlates of anti-PD-1 antibody in cancer. N Engl J Med. 2012;366:2443-2454. Crossref, Medline, Google Scholar |
| 54. | Brahmer JR, Tykodi SS, Chow LQM, et al. Safety and activity of anti-PD-L1 antibody in patients with advanced cancer. N Engl J Med. 2012;366:2455-2465. Crossref, Medline, Google Scholar |
| 55. | Sznol M, Kluger HM, Hodi FS, et al. Survival and long-term follow-up of safety and response in patients (pts) with advanced melanoma (MEL) in a phase I trial of nivolumab (anti-PD-1; BMS-936558; ONO-4538). J Clin Oncol. 2013;31 (suppl; abstr CRA9006^). Google Scholar |
| 56. | Hamid O, Robert C, Daud A, et al. Safety and tumor responses with lambrolizumab (anti-PD-1) in melanoma. N Engl J Med. 2013;369:134-144. Crossref, Medline, Google Scholar |
| 57. | Wolchok JD, Kluger H, Callahan MK, et al. Nivolumab plus ipilimumab in advanced melanoma. N Engl J Med. 2013; 369:122-133. Crossref, Medline, Google Scholar |
| 58. | Hodi FS, Lee SJ, McDermott DF, et al. Multicenter, randomized phase II trial of GM-CSF (GM) plus ipilimumab (Ipi) versus Ipi alone in metastatic melanoma: E1608. J Clin Oncol. 2013;31 (suppl; abstr CRA9007). Medline, Google Scholar |
