despite the explosive increase in cancer genomics data , the actual application of genomics data and approach to clinics offers big challenges [ 1 , 2 ] .
the main reason for the delay in the application to clinics is non - connectivity between bioinformaticians , non - clinical scientists , and clinicians .
clinicians are not confident in how much additional information might be given through genomics technology compared to conventional tools , and non - clinical scientists and bioinformaticians are not aware of what clinical problems should be solved with priority .
cancer clinicians expect novel diagnostic , prognostic , and therapeutic values of this new technology .
now , the clinical field is aware that multiple assays using next - generation sequencing ( ngs ) has some advantage over single genetic tests [ 1 , 3 ] .
a recent cancer guideline from the national comprehensive cancer network ( nccn ) pointed out that epidermal growth factor receptor ( egfr ) and/or anaplastic lymphoma kinase ( alk ) testing should be conducted as part of multiplex / ngs in non - small - cell lung cancer ( nsclc ) .
the feasibility of an ngs diagnostic platform at cancer clinics was validated by cancer centers in the united states   and europe .
one platform showed that potential hurdles , time , cost , purity , and ethical problems could be overcome in implementing ngs using low - coverage whole - genome sequencing , whole - exome sequencing ( wes ) , and whole - transcriptome sequencing .
another platform sequenced clinical samples , including formalin - fixed , paraffin - embedded ones , and showed that this approach is practical and can give some benefits to the patients with lung cancer .
the wes approach in mendelian disorders showed that ~25% of sequencing can give a specific diagnosis based on the germline genetic make - up from the data of 250 probands , mostly consisting of neurological phenotypes in real practice .
the clinical application of cancer genomics should be considered in the context of unmet clinical needs to maximize the value of this novel paradigm . here , we dealt with the potential clinical application of cancer genomics in the aspect of practical management of cancer patients , along with a clinical scenario covering the whole course of cancer management .
we overviewed the potential of cancer genomics in the context of real cancer practice through this approach .
he quit smoking 10 years ago and had smoked an average of one pack per day for 30 years .
he had regular check - ups every year and had the last check - up with no abnormality 6 months ago .
he got a percutaneous needle aspiration ( pcna ) and biopsy but did not have any tumor cells .
he repeated the pcna and biopsy but developed pneumothorax ( air leakage from lung ) related to the diagnostic procedure .
after a staging work - up with a computed tomography ( ct ) scan , positron - emission tomography ( pet ) scan , brain magnetic resonance imaging scan , and bronchoscopy , his tumor was revealed as t2n0 clinical stage .
he got a surgery based on the result of the pathology and staging work - up . in this situation , we face unmet clinical needs of early cancer diagnosis .
he was diagnosed with symptoms related to the cancer , despite his regular check - up .
if we detect his malignancy before the symptoms , we can get a better result compared with the symptomatic case .
this imaginary patient was diagnosed with operable cancer , but as many as 60% to 70% of lung cancer patients are diagnosed with inoperable , advanced disease . to diagnose the cancer sooner ,
lots of trials failed to show an actual benefit of screening strategy in lung cancer [ 10 , 11 ] , but recently , a screening trial using low - dose chest ct of lung cancer was proven to be successful .
low - dose chest ct reduced the mortality from lung cancer through early detection in people with a history of at least 30 pack - years of smoking . despite this good approach to detect early lung cancer ,
this advantage should be counterbalanced with the potential harmful effects of radiation exposure or false positivity
many biomarkers were investigated for the early detection of lung cancer - for example , circulating tumor dna and rna .
but , false positivity and false negativity of single tests prevent these tests from clinical usage .
if we use multiple biomarkers using cancer genomics for the detection of early cancer , the false positives and negatives could be reduced to the level of clinical usage .
another effective approach of early diagnosis of cancer is to select a high - risk population for screening tests .
many genome - wide association studies identified some germline single - nucleotide polymorphism ( snps ) that are associated with the risk of cancers [ 14 - 16 ] , and these findings suggested important potential germline markers for the definition of high - risk populations .
mardis told the case of mike snyder as an example of an actual application of a germline genome test .
he sequenced his own genome and found a higher risk of type 2 diabetes ; since that time , he checked his glucose level regularly , and he detected high glucose levels at the very beginning .
the concept of early diagnosis , like the story of mike snyder , can be applied to the early detection of cancer , too .
if we define a high - risk population with germline genetic tests , we could focus on the use of screening tests in this population .
this 
 complication gives pain and discomfort of the sensation of dyspnea , prolongs the period of admission , and increases the cost of cancer management .
this problem originated from the invasive nature of the diagnostic procedure - pcna and biopsy using a needle , moving back and forth through the patient 's fragile lung tissue .
new diagnostic tools using cancer genomics - for example , identification of cancer - specific genetic alterations from circulating tumor dna - can avoid this type of complication from invasive procedures .
one pioneering study showed that circulating tumor dna could be combined with genomic technology to diagnose and monitor the status of cancer very safely .
his final pathological diagnosis was adenocarcinoma and revealed the involvement of some mediastinal lymph nodes ; therefore , the final pathological stage was t2n2 , iiia .
this stage required adjuvant chemotherapy , and he was referred to the department of medical oncology . a medical oncologist prescribed adjuvant chemotherapy - a navelbine and cisplatin regimen ( np ) for 4 cycles .
np 4 cycles has been prescribed to patients with high - risk clinical stage ib , iia , iib , iiia , and iiib , regardless of other clinicopathological factors .
the actual benefit from this treatment is only a 4% long - term survival advantage , with some hematological and non - hematological toxicities [ 19 , 20 ] .
the way to improve the risk - to - benefit ratio of adjuvant treatment is further selection of a patient population that could benefit from this treatment .
stage - based risk calculation - the current practice - cannot predict an accurate risk of relapse and attenuates the value of adjuvant treatment .
single patient - based risk calculation should be done to identify the adequate patients for adjuvant treatment .
cancer genomics will provide useful tools for a single patient - based risk calculation model .
this personalized adjuvant model will definitely increase the proportion of patients who benefit from it .
if he had been diagnosed as stage iiia preoperatively , he might have chosen a no - surgery option , such as concurrent chemoradiation , because no definite benefit of surgery was demonstrated until now , although this is controversial [ 21 , 22 ] .
we have relatively sensitive diagnostic methods , such as ct , pet , and endobronchial ultrasonography , but the sensitivity and specificity are still not perfect .
one focus of genomics technology should be a more accurate prognosis preoperatively , in addition to postoperatively .
prediction issues in cancer patients are not limited to survival , recurrence , and response to specific treatment .
the goal of cancer care is improvement of survival and , more importantly , quality of life .
predictive markers of bone metastasis or bone - related events , such as fracture or paralysis , are very important to improve the quality of life in cancer patients .
an unbiased approach is needed to discover good biomarker for these events , because we have very limited clues to dig into these problems .
the most important advantage of genomics approach is that is " unbiased " with regard to any hypotheses or theories .
that is why clinical problems with low - depth knowledge are a good candidate for the application of cancer genomics .
the connection between clinicians and genomic scientists can facilitate the development of clinical problems that are solved with a genomic approach .
he got 4 cycles of adjuvant chemotherapy and then followed up regularly with a physical examination , blood test , ct scan , and/or pet scan . at 6 months since the completion of adjuvant chemotherapy
, he presented with right - side chest pain , and his ct scan showed tumor relapse in the right lung and pleura .
he planned systemic anticancer treatment for life prolongation and palliation of symptom , not curative .
the role of ct scan and/or pet scan is not defined , but many clinicians use this high - cost image for monitoring relapse .
the reason for the popular use of this high - cost image is that there is no other alternative of monitoring cancer status .
recent studies showed the possibility of circulating tumor cell and cell - free dna as a tool for monitoring tumor status [ 18 , 25 ] .
cancer genomics enables circulating tumor cell or cell - free dna to detect relapse earlier than image findings or tumor markers .
the delicate delineation of circulating dna with ngs technologies is an essential part of this success story .
no tool is available with a definite clinical benefit for the monitoring of cancer ; therefore , we absolutely need new , non - invasive tools for it . because it takes at least several years to prove the benefit of monitoring
, we should start a well - designed prospective study using cancer genomics to prove it as soon as possible .
the medical oncologist who was in charge of him tested for egfr mutation and alk by fluorescence in situ hybridization to see rearrangements of alk .
he chose cytotoxic chemotherapy with pemetrexed and cisplatin according to the double - negative results of the egfr and alk tests .
the ultimate goal of the clinical application of cancer genomics should be therapeutics . there are increasing gene lists for nsclc that have matching targeted agents [ 26 , 27 ] , but many patients do not have genetic alterations that can be a target .
even for the same type of cancer - for example , lung adenocarcinoma - the genetic make - up is very heterogeneous .
the accurate definition of the genetic make - up of each patient will be the first step for personalized medicine .
many target panels have been developed by many vendors and hospitals for commercial or non - commercial use , but the usage is still very limited . as more cancer genome data are available [ 28 , 29 ] , the list of target panels will increase and be specified to tumor types .
now , we have two targeted agents to prolong the survival of lung cancer patients : the egfr tyrosine kinase inhibitor ( tki ) erlotinib or gefitinib for egfr mutations and the alk tki crizotinib for alk translocations . for this case
, there was no targeted agent , because he did not have egfr or alk alterations .
based on the histology of his tumor , he was treated with pemetrexed and cisplatin . despite the substantial toxicity of cytotoxic chemotherapy , many patients are prescribed these drugs with the individualized possibility of this agent not known .
this decision relies on population data from large - scale prospective trials . because clinical trials targeting specific genetic alterations are increasing , the clinical need for more genetic information is also increasing .
if the patient was diagnosed as stage iv initially , the amount of tissue for genetic testing is very limited .
the amount of tissue is enough for 2 or 3 genetic tests . for these patients ,
multiplexing or ngs technologies have important advantages to overcome the limitation of tissue amounts in the clinic .
this patient got adjuvant chemotherapy with np for 4 cycles but progressed 6 months after the completion .
the way to increase the benefit of adjuvant treatment could be the application of variable adjuvant treatment that is selected from individualized factors .
personalized cancer genomic analysis can show different and private profiles of individual cancers , and accumulation of these genomic data enables different adjuvant treatment strategies for each patient . despite the theoretical promise of personalized cancer genomic analysis for the selection of the right drug ,
one of important example is the story of lukas wartman , a doctor at washington university in st .
he suffered from a second relapse of acute lymphoblastic leukemia and sequenced the tumor dna and rna .
he found very high expression of flt3 and then figured out that sunitinib , a kidney cancer drug , could block tumor survival signals .
sunitinib induced complete remission in him , and thereafter , he received allogeneic stem cell transplantation and is now in remission . another story from the fox chase cancer center was reported on kit mutations from a pancreatic neuroendocrine tumor .
the patient had an advanced pancreatic neuroendocrine tumor and sequenced his tumor using a cancer panel .
he had a kit mutation and was treated with imatinib , a well - known kit inhibitor that is not approved for his tumor , for more than 2 years . if he had been in in routine practice without cancer panel sequencing , he would have died without imatinib treatment .
a pharmacogenomic approach could be used to minimize toxicity to cancer treatment , too . in this case
if the patient has a homozygous polymorphism in the ugt1a1 gene - the * 28 variant - he should be prescribed with a dose reduction to avoid severe , sometimes fatal , toxicity from irinotecan .
a genomic approach is required to properly assess pharmacogenomics in association with drug toxicity . a commercial chip , the affymetric dmet chip , covers 25 - 32% of genes of pharmacogenomics , and it is an important question whether more coverage with wes will be more beneficial .
he was treated with pemetrexed and cisplatin for 4 cycles , and his tumor shrank with the treatment .
but , his tumor rebounded , with a complaint of dyspnea and cough , 4 months after the completion of treatment .
after 4 cycles of second - line chemotherapy with docetaxel , he had stable disease . even in the case of a dramatic response to the first - line treatment
, nearly all cancers rebound with various relapse - free durations [ 30 , 34 ] .
the choice of second - line treatment depends on clinical guidelines , clinical parameters , and clinicians ' experiences .
any of the parameters may not be best if a specific patient 's situation is not considered .
we do not know the exact mechanism of resistance to the previous treatment and do not have any good way to assess the mechanism of resistance .
if cancer genomics is adopted in this situation , we can profile the tumor tissue that was resistant to the previous treatment .
the profile can help clinicians decide on a second - line treatment option and improve the individual 's outcome .
the resistance mechanism could vary , depending on multidimensional factors , such as the patient , tumor biology , and previous treatment .
especially , this situation should be approached individually with the concept of true personalized medicine .
he was in hospice care and died 1 year after the initial diagnosis of relapse .
after his death , his tumor was sequenced by whole - transcriptome analysis , and his tumor showed a ros1 translocation .
the median overall survival of metastatic lung cancer patients is 1 year if there is no genetic alteration for which targeted agents are available [ 30 , 34 ] , as in this case .
if the patient has an egfr - sensitive mutation or alk translocation , the median overall survival time reaches 2 years [ 35 , 36 ] .
a ros1 targeting agent , another example of successful targeting of oncogene addiction , is now available after his death .
if the ros1 translocation was known for his tumor and he was treated with a ros1 inhibitor , he could have a chance to live an additional one year or more .
tumors have very heterogeneous profiles [ 37 , 38 ] , and resistant tumors have even more heterogeneous ones .
this is why tumors should ultimately be approached on an individual basis , rather than a population basis .
now is the right time to think about how we can use cancer genomics for individual patients .
functional annotation of sequencing results , the way to integrate multi - dimensional data , and the exploration of the clinical meaning of sequencing variants for the routine clinical usage of cancer genomics . defining unmet clinical needs is as important as overcoming technological hurdles to maximize the benefit from cancer genomics .
early detection , non - invasive molecular diagnosis , risk prediction of relapse and tumor - related events , monitoring of disease status , matching good drugs , and finding out personal grade resistance mechanisms are good examples of unmet clinical needs that should be solved with cancer genomics .
it would speed up and optimize the application of genomics to cancer clinics if cancer biologists , bioinformaticians , and cancer clinicians cooperate towards one goal of achieving it .
 the large amount of data on cancer genome research has contributed to our understanding of cancer biology . 
 indeed , the genomics approach has a strong advantage for analyzing multi - factorial and complicated problems , such as cancer . 
 it is time to think about the actual usage of cancer genomics in the clinical field . 
 the clinical cancer field has lots of unmet needs in the management of cancer patients , which has been defined in the pre - genomic era . 
 unmet clinical needs are not well known to bioinformaticians and even non - clinician cancer scientists 
 . a personalized approach in the clinical field will bring potential additional challenges to cancer genomics , because most data to now have been population - based rather than individual - based . 
 we can maximize the use of cancer genomics in the clinical field if cancer scientists , bioinformaticians , and clinicians think and work together in solving unmet clinical needs . in this review , 
 we present one imaginary case of a cancer patient , with which we can think about unmet clinical needs to solve with cancer genomics in the diagnosis , prediction of prognosis , monitoring the status of cancer , and personalized treatment decision . 