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Review of precision cancer medicine: Evolution of the treatment paradigm

Open AccessPublished:March 31, 2020DOI:https://doi.org/10.1016/j.ctrv.2020.102019

      Highlights

      • Genomic studies have revealed that tumors are significantly heterogeneous and complex and therefore optimized therapy does not often result from classical clinical research and practice models.
      • Complete tumor and cell-free DNA profiling, exome sequencing, transcriptomics, proteomics, and exploration of the immune machinery hold the promise of complete characterization of drivers of carcinogenesis.
      • Precision oncology focuses on gene-directed, histology-agnostic treatments, which are individualized for each patient on the basis of biomarker analysis, and immunotherapy, including adoptive cell therapy.
      • Innovative trial designs, including “N of 1” models, will enable optimization of treatment for individual patients and to expedite drug discovery and approval.

      Abstract

      In recent years, biotechnological breakthroughs have led to identification of complex and unique biologic features associated with carcinogenesis. Tumor and cell-free DNA profiling, immune markers, and proteomic and RNA analyses are used to identify these characteristics for optimization of anticancer therapy in individual patients. Consequently, clinical trials have evolved, shifting from tumor type-centered to gene-directed, histology-agnostic, with innovative adaptive design tailored to biomarker profiling with the goal to improve treatment outcomes. A plethora of precision medicine trials have been conducted. The majority of these trials demonstrated that matched therapy is associated with superior outcomes compared to non-matched therapy across tumor types and in specific cancers. To improve the implementation of precision medicine, this approach should be used early in the course of the disease, and patients should have complete tumor profiling and access to effective matched therapy. To overcome the complexity of tumor biology, clinical trials with combinations of gene-targeted therapy with immune-targeted approaches (e.g., checkpoint blockade, personalized vaccines and/or chimeric antigen receptor T-cells), hormonal therapy, chemotherapy and/or novel agents should be considered. These studies should target dynamic changes in tumor biologic abnormalities, eliminating minimal residual disease, and eradicating significant subclones that confer resistance to treatment. Mining and expansion of real-world data, facilitated by the use of advanced computer data processing capabilities, may contribute to validation of information to predict new applications for medicines. In this review, we summarize the clinical trials and discuss challenges and opportunities to accelerate the implementation of precision oncology.

      Keywords

      Background

      The rapidly expanding body of knowledge about the roles of genomics and the immune system in cancer has enabled the development of therapies targeted to specific molecular alterations or other biologic characteristics, such as those implicated in immune suppression. However, genomics has also revealed a complicated reality about malignancies that requires a major shift in the therapy paradigm: away from tumor type-centered and toward gene-directed, histology-agnostic treatment, which is individualized for each patient on the basis of biomarker analysis. This paradigm shift is reflected by the emergence of precision medicine trials with innovative design [
      • Von Hoff D.D.
      • Stephenson Jr., J.J.
      • Rosen P.
      • et al.
      Pilot study using molecular profiling of patients' tumors to find potential targets and select treatments for their refractory cancers.
      ,
      • Tsimberidou A.M.
      • Iskander N.G.
      • Hong D.S.
      • et al.
      Personalized medicine in a phase I clinical trials program: the MD Anderson Cancer Center initiative.
      ,
      • Tsimberidou A.M.
      • Wen S.
      • Hong D.S.
      • et al.
      Personalized medicine for patients with advanced cancer in the phase I program at MD Anderson: validation and landmark analyses.
      ,
      • Tsimberidou A.M.
      • Hong D.S.
      • Ye Y.
      • et al.
      Initiative for Molecular Profiling and Advanced Cancer Therapy (IMPACT): An MD Anderson Precision Medicine Study. JCO Precis.
      ,
      • Le Tourneau C.
      • Delord J.P.
      • Goncalves A.
      • et al.
      Molecularly targeted therapy based on tumour molecular profiling versus conventional therapy for advanced cancer (SHIVA): a multicentre, open-label, proof-of-concept, randomised, controlled phase 2 trial.
      ,
      • Schwaederle M.
      • Parker B.A.
      • Schwab R.B.
      • et al.
      Precision Oncology: The UC San Diego Moores Cancer Center PREDICT Experience.
      ,
      • Wheler J.J.
      • Janku F.
      • Naing A.
      • et al.
      Cancer Therapy Directed by Comprehensive Genomic Profiling: A Single Center Study.
      ,
      • Stockley T.L.
      • Oza A.M.
      • Berman H.K.
      • et al.
      Molecular profiling of advanced solid tumors and patient outcomes with genotype-matched clinical trials: the Princess Margaret IMPACT/COMPACT trial.
      ,
      • Massard C.
      • Michiels S.
      • Ferte C.
      • et al.
      High-Throughput Genomics and Clinical Outcome in Hard-to-Treat Advanced Cancers: Results of the MOSCATO 01 Trial.
      ,
      • Hainsworth J.D.
      • Meric-Bernstam F.
      • Swanton C.
      • et al.
      Targeted Therapy for Advanced Solid Tumors on the Basis of Molecular Profiles: Results From MyPathway, an Open-Label, Phase IIa Multiple Basket Study.
      ,
      • Tredan O.
      • Wang Q.
      • Pissaloux D.
      • et al.
      Molecular screening program to select molecular-based recommended therapies for metastatic cancer patients: analysis from the ProfiLER trial.
      ,
      • Sicklick J.K.
      • Kato S.
      • Okamura R.
      • et al.
      Molecular profiling of cancer patients enables personalized combination therapy: the I-PREDICT study.
      ,
      • Rodon J.
      • Soria J.C.
      • Berger R.
      • et al.
      Genomic and transcriptomic profiling expands precision cancer medicine: the WINTHER trial.
      ,
      • Kim E.S.
      • Herbst R.S.
      • Wistuba I.I.
      • et al.
      The BATTLE trial: personalizing therapy for lung cancer.
      ,
      • Kris M.G.
      • Johnson B.E.
      • Berry L.D.
      • et al.
      Using multiplexed assays of oncogenic drivers in lung cancers to select targeted drugs.
      ,
      • Aisner D.L.
      • Sholl L.M.
      • Berry L.D.
      • et al.
      The Impact of Smoking and TP53 Mutations in Lung Adenocarcinoma Patients with Targetable Mutations-The Lung Cancer Mutation Consortium (LCMC2).
      ,
      • Papadimitrakopoulou V.
      • Lee J.J.
      • Wistuba I.I.
      • et al.
      The BATTLE-2 Study: A Biomarker-Integrated Targeted Therapy Study in Previously Treated Patients With Advanced Non-Small-Cell Lung Cancer.
      ,
      • Esserman L.J.
      • Berry D.A.
      • DeMichele A.
      • et al.
      Pathologic complete response predicts recurrence-free survival more effectively by cancer subset: results from the I-SPY 1 TRIAL–CALGB 150007/150012, ACRIN 6657.
      ,
      • 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).
      ,
      • Park J.W.
      • Liu M.C.
      • Yee D.
      • et al.
      Adaptive Randomization of Neratinib in Early Breast Cancer.
      ,
      • Rugo H.S.
      • Olopade O.I.
      • DeMichele A.
      • et al.
      Adaptive Randomization of Veliparib-Carboplatin Treatment in Breast Cancer.
      ]. Next-generation sequencing (NGS) of advanced cancers has demonstrated that genomic alterations do not fall neatly into categories defined by the tumor organ of origin. Furthermore, metastatic tumors harbor tremendously complex and individually unique genomic and immune landscapes [
      • Wheler J.
      • Lee J.J.
      • Kurzrock R.
      Unique molecular landscapes in cancer: implications for individualized, curated drug combinations.
      ,
      • Kurzrock R.
      • Giles F.J.
      Precision oncology for patients with advanced cancer: the challenges of malignant snowflakes.
      ]. Therefore, in order to target malignancies with “precision,” treatment needs to be personalized.
      Historically, phase II and III oncology clinical trials have measured outcomes histologically, but histological assessment cannot always capture the effects of gene-targeted agents or immunotherapy. Precision medicine approaches analyze patients’ circulating DNA (liquid biopsy), as well as immune markers and other biologic features, to assess efficacy and make treatment decisions. Genomic biomarkers have been the most successful to date, but other biomarkers, including protein assays and transcriptomics, are being developed and tested [
      • Rodon J.
      • Soria J.C.
      • Berger R.
      • et al.
      Genomic and transcriptomic profiling expands precision cancer medicine: the WINTHER trial.
      ,
      • Schwaederle M.
      • Zhao M.
      • Lee J.J.
      • et al.
      Association of Biomarker-Based Treatment Strategies With Response Rates and Progression-Free Survival in Refractory Malignant Neoplasms: A Meta-analysis.
      ,
      • Rosario S.R.
      • Long M.D.
      • Affronti H.C.
      • Rowsam A.M.
      • Eng K.H.
      • Smiraglia D.J.
      Pan-cancer analysis of transcriptional metabolic dysregulation using The Cancer Genome Atlas.
      ]. Several molecular alterations have been identified using sequencing and high-throughput technologies and have led to the approval of targeted agents by the Food and Drug Administration (FDA) [
      • Long G.V.
      • Stroyakovskiy D.
      • Gogas H.
      • et al.
      Combined BRAF and MEK inhibition versus BRAF inhibition alone in melanoma.
      ,
      • Drilon A.
      • Laetsch T.W.
      • Kummar S.
      • et al.
      Efficacy of Larotrectinib in TRK Fusion-Positive Cancers in Adults and Children.
      ]. Importantly, in recent years, the precision medicine paradigm has embraced immunotherapy and its interaction with genomics, as genomic characteristics, such as mismatch repair gene defects, are critical predictors of checkpoint blockade response [
      • Goodman A.M.
      • Kato S.
      • Bazhenova L.
      • et al.
      Tumor Mutational Burden as an Independent Predictor of Response to Immunotherapy in Diverse Cancers.
      ,
      • Le D.T.
      • Uram J.N.
      • Wang H.
      • et al.
      PD-1 Blockade in Tumors with Mismatch-Repair Deficiency.
      ,
      • Subbiah V.
      • Kurzrock R.
      The Marriage Between Genomics and Immunotherapy: Mismatch Meets Its Match.
      ].
      Herein, we review the rapid evolution of precision medicine in oncology and, in particular, the challenge and opportunity that genomic science has revealed vis-à-vis the need for “N-of-1” treatments. This treatment model does not conform to either canonical trial design or clinical practice, which seek to find commonalities between patients and treat them alike; instead, its goal is to provide optimized individualized treatment for each patient on the basis of biomarker analysis.

      History

      Survival improvement with gene- or immune-directed therapy was accelerated by several major discoveries. In particular, the introduction of imatinib mesylate (Abl tyrosine kinase inhibitor) for patients with Philadelphia chromosome [t(9;22)]–positive chronic myelogenous leukemia producing the enzymatically aberrant Bcr-Abl [
      • Kurzrock R.
      • Shtalrid M.
      • Romero P.
      • et al.
      A novel c-abl protein product in Philadelphia-positive acute lymphoblastic leukaemia.
      ,
      • Kurzrock R.
      • Gutterman J.U.
      • Talpaz M.
      The molecular genetics of Philadelphia chromosome-positive leukemias.
      ] resulted in near-normal life expectancy for patients with this previously fatal leukemia.
      In 2001, the human genome was sequenced [
      • Venter J.C.
      • Adams M.D.
      • Myers E.W.
      • et al.
      The sequence of the human genome.
      ]. Although this milestone represented an arduous and tremendously expensive endeavour, both the price and time required for sequencing have decreased precipitously, with technology advancing in a manner unparalleled in human history. A plethora of first- and second-generation precision medicine trials have since been conducted (Table 1, Table 2). They include, but are not limited to, the first pan-histology biomarker-driven trial using mostly protein markers [
      • Von Hoff D.D.
      • Stephenson Jr., J.J.
      • Rosen P.
      • et al.
      Pilot study using molecular profiling of patients' tumors to find potential targets and select treatments for their refractory cancers.
      ], the prospective molecular profiling of patients with advanced cancer in the phase I clinical trials setting (IMPACT trial) [
      • Tsimberidou A.M.
      • Iskander N.G.
      • Hong D.S.
      • et al.
      Personalized medicine in a phase I clinical trials program: the MD Anderson Cancer Center initiative.
      ,
      • Tsimberidou A.M.
      • Hong D.S.
      • Ye Y.
      • et al.
      Initiative for Molecular Profiling and Advanced Cancer Therapy (IMPACT): An MD Anderson Precision Medicine Study. JCO Precis.
      ], the SHIVA randomized trial [
      • Le Tourneau C.
      • Delord J.P.
      • Goncalves A.
      • et al.
      Molecularly targeted therapy based on tumour molecular profiling versus conventional therapy for advanced cancer (SHIVA): a multicentre, open-label, proof-of-concept, randomised, controlled phase 2 trial.
      ], trials assessing customized combinations [
      • Schwaederle M.
      • Parker B.A.
      • Schwab R.B.
      • et al.
      Precision Oncology: The UC San Diego Moores Cancer Center PREDICT Experience.
      ,
      • Sicklick J.K.
      • Kato S.
      • Okamura R.
      • et al.
      Molecular profiling of cancer patients enables personalized combination therapy: the I-PREDICT study.
      ], and trials including transcriptomics [
      • Rodon J.
      • Soria J.C.
      • Berger R.
      • et al.
      Genomic and transcriptomic profiling expands precision cancer medicine: the WINTHER trial.
      ].
      Table 1Examples of Precision Medicine Trials: Design and Outcomes.
      Year

      First/Last author
      Trial nameTrial typeNo of pts screened (N)Proportion of pts. matchedBiomarker(s)OutcomeInstitute(s)Comments
      Diverse treatment-refractory tumor types
      2010
      • Von Hoff D.D.
      • Stephenson Jr., J.J.
      • Rosen P.
      • et al.
      Pilot study using molecular profiling of patients' tumors to find potential targets and select treatments for their refractory cancers.


      Von Hoff D

      Penny R
      BisgroveProspective, navigational8677%IHC, FISH, microarray27% of 66 matched pts had a PFS2/PFS1 ratio* ≥1.3 (95% CI, 17% to 38%; p = 0.007).US

      (9 sites)
      2012
      • Tsimberidou A.M.
      • Iskander N.G.
      • Hong D.S.
      • et al.
      Personalized medicine in a phase I clinical trials program: the MD Anderson Cancer Center initiative.


      Tsimberidou A

      Kurzrock R
      IMPACT, first cohort

      Registry type,

      Navigational
      114415%PCR-based genomics,

      9 genes
      Matched vs unmatched

      RR, 27% vs. 5% (p < 0.0001),

      TTF: median, 5.2 vs. 2.2 mos (p < 0.0001)

      OS: median, 13.4 vs. 9.0 mos (p = 0.017)
      MD Anderson Cancer Center
      2014
      • Tsimberidou A.M.
      • Wen S.
      • Hong D.S.
      • et al.
      Personalized medicine for patients with advanced cancer in the phase I program at MD Anderson: validation and landmark analyses.


      Tsimberidou A Berry D
      IMPACT, second cohortRegistry type,

      navigational
      127611%PCR-based genomics,

      18–50 genes
      Matched vs unmatched

      RR, 11.9% vs. 5% (p < 0.0001),

      PFS: median, 3.9 vs. 2.2 mos, (p = 0.001);

      OS: median, 11.4 vs. 8.6 mos (p = 0.04)
      MD Anderson Cancer Center2-month landmark analyses, matched therapy group: OS, responders 30.5 months vs. 11.3 months for non-responders (p = 0.01).
      2017
      • Tsimberidou A.M.
      • Hong D.S.
      • Ye Y.
      • et al.
      Initiative for Molecular Profiling and Advanced Cancer Therapy (IMPACT): An MD Anderson Precision Medicine Study. JCO Precis.


      Tsimberidou AM

      Kurzrock R
      IMPACT, third cohortRegistry type,

      navigational
      143627%PCR-based genomics and NGS,

      11 to 182 genes
      Matched vs unmatched

      Higher rates of ORR (p = 0.0099), TTF (p = 0.0015), and OS (p = 0.04)
      MD Anderson Cancer Center

      2015
      • Le Tourneau C.
      • Delord J.P.
      • Goncalves A.
      • et al.
      Molecularly targeted therapy based on tumour molecular profiling versus conventional therapy for advanced cancer (SHIVA): a multicentre, open-label, proof-of-concept, randomised, controlled phase 2 trial.


      Le Tourneau

      Paoletti X
      SHIVAProspective,

      randomized
      74113%Targeted NGS, ~50 genesPFS not improved with matched therapy (p = 0.41)Institut Curie, 8 French sites~80% of patients received single-agent hormone modulators or everolimus
      2016
      • Schwaederle M.
      • Parker B.A.
      • Schwab R.B.
      • et al.
      Precision Oncology: The UC San Diego Moores Cancer Center PREDICT Experience.


      Schwaederle M

      Kurzrock R
      PREDICTRegistry type34725%NGS, 182 or 236 genesMatched vs unmatched

      Higher rates of SD ≥ 6 months/PR/CR (p = 0.02) and PFS (p < 0.04). Higher matching scores correlated with better OS: 15.7 vs 10.6 mos (p = 0.04)
      University of California San Diego
      2016
      • Wheler J.J.
      • Janku F.
      • Naing A.
      • et al.
      Cancer Therapy Directed by Comprehensive Genomic Profiling: A Single Center Study.


      Wheler JJ

      Kurzrock R
      MD Anderson Personalized Cancer Therapy InitiativeProspective,

      navigational
      50024%NGS,

      236 genes
      Higher matching scores correlated with higher rates of SD ≥ 6 months/PR/CR (p = 0.024), TTF (p = 0.0003), and OS (p = 0.05)MD Anderson Cancer Center
      2016
      • Stockley T.L.
      • Oza A.M.
      • Berman H.K.
      • et al.
      Molecular profiling of advanced solid tumors and patient outcomes with genotype-matched clinical trials: the Princess Margaret IMPACT/COMPACT trial.


      Stockley TL

      Bedard PL
      IMPACT/

      COMPACT
      Prospective1893

      5%Hot spot panel,

      23 genes
      Matched vs unmatched

      Higher ORR: 19% vs 9%, (p = 0.026).
      Princess Margaret, Canadian centers
      2017
      • Massard C.
      • Michiels S.
      • Ferte C.
      • et al.
      High-Throughput Genomics and Clinical Outcome in Hard-to-Treat Advanced Cancers: Results of the MOSCATO 01 Trial.


      Massard C

      Soria JC
      MOSCATOProspective103519%Targeted NGS,

      40–75 genes;

      aCGH; RNAseq
      PFS2/PFS1 ratio* was > 1.3 in 33% (63/193) of patientsInstitut Gustave Roussy
      2018
      • Hainsworth J.D.
      • Meric-Bernstam F.
      • Swanton C.
      • et al.
      Targeted Therapy for Advanced Solid Tumors on the Basis of Molecular Profiles: Results From MyPathway, an Open-Label, Phase IIa Multiple Basket Study.


      Hainsworth JD

      Kurzrock R
      MyPathwayProspective, Phase 2 basket251Not availableGenomic testing via any CLIA labMatched patients, ORR:

      All, 23%

      HER2-altered, 38%

      BRAF-altered, 43%
      Multiple sites, Genentech251 patients enrolled; 230 were treated; however, how many were screened pre-enrollment is unknown
      2019
      • Tredan O.
      • Wang Q.
      • Pissaloux D.
      • et al.
      Molecular screening program to select molecular-based recommended therapies for metastatic cancer patients: analysis from the ProfiLER trial.


      Tredan O

      Blay JY
      ProfilerProspective25796%NGS, 69 genesRR = 13% (23 of 182 treated)Four institutes (France)
      2019
      • Sicklick J.K.
      • Kato S.
      • Okamura R.
      • et al.
      Molecular profiling of cancer patients enables personalized combination therapy: the I-PREDICT study.


      Sicklick J

      Kurzrock R
      I-PREDICTProspective,

      navigational
      14949%NGS, 315 genes;

      ctDNA;

      PDL1 IHC
      Higher matching scores correlated with increased rates of SD ≥ 6 months/PR/CR: 50% vs 22.4% (p = 0.028), PFS (p = 0.0004), and OS (p = 0.038)University of California San Diego and Avera

      First trial to administer customized combination therapy (“N-of-1” matching)
      2019
      • Rodon J.
      • Soria J.C.
      • Berger R.
      • et al.
      Genomic and transcriptomic profiling expands precision cancer medicine: the WINTHER trial.


      Rodon J

      Kurzrock R
      WINTHERProspective, navigational30335%NGS, 236 genes;

      transcriptomics
      Higher matching scores correlated with longer PFS (p = 0.005) and OS (p = 0.03)Five countries (Spain, Israel, France, Canada, US)First solid tumor trial to include transcriptomics
      Specific tumors—Lung
      2011
      • Kim E.S.
      • Herbst R.S.
      • Wistuba I.I.
      • et al.
      The BATTLE trial: personalizing therapy for lung cancer.


      Kim ES

      Hong WK
      BATTLEProspective,

      adaptive, randomized
      255Not available11 biomarkers8-week disease control rate, 46%MD Anderson Cancer CenterIt is unclear how many patients were screened before consent
      2014
      • Kris M.G.
      • Johnson B.E.
      • Berry L.D.
      • et al.
      Using multiplexed assays of oncogenic drivers in lung cancers to select targeted drugs.


      Kris MG

      Bunn PA
      Lung cancer mutation consortiumProspective153717%Multiplex genotyping,

      10 genes
      Improved OS with matched vs unmatched therapy (p = 0.006)14 US sites
      2016
      • Aisner D.L.
      • Sholl L.M.
      • Berry L.D.
      • et al.
      The Impact of Smoking and TP53 Mutations in Lung Adenocarcinoma Patients with Targetable Mutations-The Lung Cancer Mutation Consortium (LCMC2).


      Aisner D

      Kwiatkowski DJ
      Lung Cancer Mutation Consortium IIProspective90412%NGS, minimum of 14 genesImproved survival with matched therapy (p < 0.001)16 sites
      2016
      • Papadimitrakopoulou V.
      • Lee J.J.
      • Wistuba I.I.
      • et al.
      The BATTLE-2 Study: A Biomarker-Integrated Targeted Therapy Study in Previously Treated Patients With Advanced Non-Small-Cell Lung Cancer.


      Papadimitrako-poulou V

      Herbst RS
      BATTLE-2Prospective,

      adaptive, randomized
      334Non-applicableALK, FISH,

      EGFR, and KRAS Sanger sequencing
      KRAS alterations: longer PFS without erlotinib (p = 0.04); KRAS wild-type tumors: longer OS on erlotinib (p = 0.03)MD Anderson

      Cancer Center
      Specific tumors—Breast
      2012
      • Esserman L.J.
      • Berry D.A.
      • DeMichele A.
      • et al.
      Pathologic complete response predicts recurrence-free survival more effectively by cancer subset: results from the I-SPY 1 TRIAL–CALGB 150007/150012, ACRIN 6657.


      Esserman LJ

      Hylton N
      I-SPY 1Neoadjuvant,

      correlative

      237Non-applicableIHCpCR differs by subsetMultiple US sites

      Aim was to develop biomarkers of response to conventional therapy
      2015
      • 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).


      Andre F

      Bonnefoi H
      SAFIR01/

      UNICANCER
      Prospective42313%Sanger sequencing (2 genes: PIK3CA and AKT); aCGHMatched group, ORR 9%18 centers in France
      2016
      • Park J.W.
      • Liu M.C.
      • Yee D.
      • et al.
      Adaptive Randomization of Neratinib in Early Breast Cancer.
      ,
      • Rugo H.S.
      • Olopade O.I.
      • DeMichele A.
      • et al.
      Adaptive Randomization of Veliparib-Carboplatin Treatment in Breast Cancer.


      Park JW

      Berry DA

      Rugo HS

      Esserman LJ
      I-SPY 2Phase 2 adaptive design, neoadjuvant

      Non-applicableNon-applicableIHC, MammaprintImproved pCR rates in 2 study arms with drug addition:

      HER2+, hormone receptor-negative: neratinib plus standard therapy (N = 115) vs standard therapy (N = 78): 56% vs 33%

      Triple–negative: veliparib plus carboplatin (N = 72) with standard therapy vs standard therapy (N = 44): 51% vs 26%
      Quantum-Leap Healthcare

      (US sites)
      Results for 2 arms of I-SPY-2 study available
      Specific tumors—Gastric
      2019
      • Lee J.
      • Kim S.T.
      • Kim K.
      • et al.
      Tumor Genomic Profiling Guides Patients with Metastatic Gastric Cancer to Targeted Treatment: The VIKTORY Umbrella Trial.


      Lee J

      WK Kang
      VICTORYProspective77214%NGS, IHC, PDL1, MMR and EBV statusImproved PFS and OS with matched vs unmatched therapy (p < 0.0001)Republic of KoreaThe trial included 10 phase II trials that operated independently (based on eight biomarkers)
      *PFS2/PFS1 ratio is defined by the PFS on the trial versus the PFS on the therapy immediately preceding the trial; in general, PFS is shorter with every subsequent therapy.
      **Οnly studies published as manuscripts, not just as abstracts, included.
      Abbreviations: aCGH = array comparative genomic hybridization, ASCO = American Society of Clinical Oncology, CLIA = clinical laboratory improvement amendment, cDNA MA = cDNA microarray, CGP = comprehensive genomic profiling, CR = complete remission, ctDNA = circulating tumor DNA, FISH = fluorescence in situ hybridization, IHC = immunohistochemistry, mos = months, NGS = next-generation sequencing, ORR = overall response rate, OS = overall survival, pCR = pathological complete response, PCR = polymerase chain reaction, PFS = progression-free survival, PR = partial remission; pts = patients, RR = response rate, RRPA = reverse phase protein array, SD = stable disease, TTF = time to treatment failure.
      Table 2Selected ongoing studies of precision medicine.
      Year startedTrial nameTrial typeCancer typeBiomarkerNCT numberInstitute(s)Comment
      2010
      • Park J.W.
      • Liu M.C.
      • Yee D.
      • et al.
      Adaptive Randomization of Neratinib in Early Breast Cancer.
      ,
      • Rugo H.S.
      • Olopade O.I.
      • DeMichele A.
      • et al.
      Adaptive Randomization of Veliparib-Carboplatin Treatment in Breast Cancer.
      I-SPY 2Prospective randomizedNeoadjuvant breast cancerIHC, MammaprintNCT01042379Quantum-Leap Healthcare, US sitesOngoing study with preliminary results (see Table 1)
      2012
      • Folprecht G.
      • Aust D.E.
      • Roth A.
      • et al.
      Improving access to molecularly defined clinical trials for patients with colorectal cancer: The EORTC SPECTAcolor platform.
      SPECTA-ColorRegistry typeAdvanced colorectal cancerNGS/IHCNCT01723969European hospitals
      2013MPACTProspectiveAdvanced cancerNGSNCT01827384NCI, US sites
      2014
      • Gerber D.E.
      • Oxnard G.R.
      • Mandrekar S.J.
      • et al.
      ALCHEMIST: a clinical trial platform to bring genomic discovery and molecularly targeted therapies to early-stage lung cancer.
      ALCHEMISTProspectiveEarly stage non-small cell lung cancerDirect sequencing, FISH, CLIA certified genotypingNCT02194738NCI, US sites
      2014
      • Herbst R.S.
      • Gandara D.R.
      • Hirsch F.R.
      • et al.
      Lung Master Protocol (Lung-MAP)-A Biomarker-Driven Protocol for Accelerating Development of Therapies for Squamous Cell Lung Cancer: SWOG S1400.
      Lung-MAPProspectiveAdvanced squamous cell lung cancerNGSNCT02154490

      NCT02785913

      NCT02965378

      NCT02785939

      NCT02785952

      NCT02926638

      NCT02766335
      NCI, US sites
      2014

      Aftimos PG, Antunes De Melo e Oliveira AM, Hilbers F, et al. 152OFirst report of AURORA, the breast international group (BIG) molecular screening initiative for metastatic breast cancer (MBC) patients (pts). Ann Oncol 2019;30.

      AURORARegistry typeMetastatic breast cancerNGS/RNAseqNCT02102165Institut Jules Bordet, Brussels, Belgium, European hospitals
      2014
      • Slosberg E.D.
      • Kang B.P.
      • Peguero J.
      • et al.
      Signature program: a platform of basket trials.
      SignatureProspectiveAdvanced cancersVariableCT02187783

      NCT02186821
      Novartis, multiple sites
      2014
      • Hainsworth J.D.
      • Meric-Bernstam F.
      • Swanton C.
      • et al.
      Targeted Therapy for Advanced Solid Tumors on the Basis of Molecular Profiles: Results From MyPathway, an Open-Label, Phase IIa Multiple Basket Study.
      MyPathwayProspectiveAdvanced cancersGenomic testingNCT02091141Genentech,

      US sites
      2014IMPACT2Prospective, randomizedMetastatic cancerGenomic testingNCT02152254MD Anderson Cancer Center
      2014
      • Joshi S.S.
      • Maron S.B.
      • Lomnicki S.
      • et al.
      Personalized antibodies for gastroesophageal adenocarcinoma (PANGEA): A phase II precision medicine trial (NCT02213289).
      PangeaProspectiveGastro-esophageal adenocarcinomaTumor biomarker profiling/cell-free DNANCT02213289University of Chicago
      2015
      • Krop I.E.
      • Jegede O.
      • Grilley-Olson J.E.
      • et al.
      Results from molecular analysis for therapy choice (MATCH) arm I: Taselisib for PIK3CA-mutated tumors.
      ,
      • Jhaveri K.L.
      • Wang X.V.
      • Makker V.
      • et al.
      Ado-trastuzumab emtansine in patients with HER2-amplified tumors excluding breast and gastric/gastroesophageal junction adenocarcinomas: results from the NCI-MATCH trial (EAY131) subprotocol Q.
      ,
      • Chae Y.K.
      • Vaklavas C.
      • Cheng H.H.
      • et al.
      Molecular analysis for therapy choice (MATCH) arm W: Phase II study of AZD4547 in patients with tumors with aberrations in the FGFR pathway.
      ,
      • Azad N.
      • Overman M.
      • Gray R.
      • et al.
      Nivolumab is effective in mismatch repair-deficient noncolorectal cancers: results from Arm Z1D-A subprotocol of the NCI-MATCH (EAY131) study.
      NCI-MATCHProspectiveAdvanced cancersNGSNCT02465060NCI, US sites
      2015
      • Sicklick J.K.
      • Kato S.
      • Okamura R.
      • et al.
      Molecular profiling of cancer patients enables personalized combination therapy: the I-PREDICT study.
      I-PREDICTProspective navigationalAdvanced cancers including treatment-naïve patientsCGPNCT02534675UC San Diego

      Avera
      Ongoing study with preliminary results (see Table 1)
      2016DARTProspectiveRare cancersNGS correlational testing: whole genomic, transcriptome, liquid biopsy (ctDNA), and immune signatureNCT02834013SWOG/NCI, multiple US sites
      2016
      • Mangat P.K.
      • Halabi S.
      • Bruinooge S.S.
      • et al.
      Rationale and Design of the Targeted Agent and Profiling Utilization Registry (TAPUR) Study. JCO Precis.
      TAPURProspectiveAdvanced cancersGenomic analysis or IHCNCT02693535ASCO, US sites
      2016DRUPProspectiveAdvanced cancersNGSNCT02925234Netherlands
      2017Pediatric MATCHProspectivePediatric advanced CancersCLIA-certified molecular testingNCT03155620NCI-COG, US sites
      2018Columbia University N-of-1 Clinical TrialsProspectiveMetastatic cancerComputational strategies (OncoTarget and OncoTreat)Columbia University
      Abbreviations: aCGH = array comparative genomic hybridization, ASCO = American Society of Clinical Oncology, CGP = comprehensive genomic profiling, CLIA = Clinical Laboratory Improvement Amendments, COG = Children’s Oncology Group, FISH = fluorescence in situ hybridization, IHC = immunohistochemistry, NCI = National Cancer Institute, NGS = next-generation sequencing, RNA seq = RNA sequencing; SWOG = Southwest Oncology Group.

      Innovative clinical trial designs for precision medicine

      Traditionally, oncology trials are drug-centered, aiming to identify common attributes among patients (e.g., their tumor type or, more recently, a shared genomic abnormality) and fit them into a trial with a specific drug regimen. The large variability in genomic subgroups, microenvironment, baseline characteristics, comorbidities, and other covariates resulted in tumor-specific clinical studies encompassing a tremendously heterogeneous population in histology-specific, gene-agnostic trials. Phase III randomized trials were often critical for regulatory approval of a novel agent/regimen, especially since the antitumor activity of a new drug/regimen was frequently only marginally better than the comparator arm (usually, conventional therapy), perhaps because the regimen was effective in only a small subgroup of the diverse population represented by any specific histology.

      Basket, umbrella, platform, octopus, and master protocols

      More recently, basket designs have emerged that target a common genetic defect [
      • Drilon A.
      • Laetsch T.W.
      • Kummar S.
      • et al.
      Efficacy of Larotrectinib in TRK Fusion-Positive Cancers in Adults and Children.
      ]. The 75% objective response rate noted across tumor types with larotrectinib, which targets NTRK fusions, best exemplifies the potential of the basket gene-directed, histology-agnostic model, though other single-gene targets have proven much less responsive [
      • Drilon A.
      • Laetsch T.W.
      • Kummar S.
      • et al.
      Efficacy of Larotrectinib in TRK Fusion-Positive Cancers in Adults and Children.
      ]. Umbrella trials involve a single histology and different treatments based on the genomic alterations in patient subgroups [
      • Herbst R.S.
      • Gandara D.R.
      • Hirsch F.R.
      • et al.
      Lung Master Protocol (Lung-MAP)-A Biomarker-Driven Protocol for Accelerating Development of Therapies for Squamous Cell Lung Cancer: SWOG S1400.
      ]. Other trial designs include platform trials, which use a single analytic technique, such as NGS, to identify genomic or other biomarkers in tumors with multiple histologies; octopus trials (also referred to as “complete phase I trials”) that have multiple arms testing different combinations featuring a particular drug; and master protocols, which encompass trials with several histologic arms (previously, “broad phase II trials”) or multiple platform, basket, or umbrella trials or sub-trials [
      • Tsimberidou A.M.
      • Iskander N.G.
      • Hong D.S.
      • et al.
      Personalized medicine in a phase I clinical trials program: the MD Anderson Cancer Center initiative.
      ,
      • Tsimberidou A.M.
      • Wen S.
      • Hong D.S.
      • et al.
      Personalized medicine for patients with advanced cancer in the phase I program at MD Anderson: validation and landmark analyses.
      ,
      • Tsimberidou A.M.
      • Hong D.S.
      • Ye Y.
      • et al.
      Initiative for Molecular Profiling and Advanced Cancer Therapy (IMPACT): An MD Anderson Precision Medicine Study. JCO Precis.
      ,
      • Schwaederle M.
      • Parker B.A.
      • Schwab R.B.
      • et al.
      Precision Oncology: The UC San Diego Moores Cancer Center PREDICT Experience.
      ] Randomization has also evolved, with the emergence of Bayesian adaptation, which allows dynamic modifications of randomization based on small numbers of patients and real-time outcomes.

      From drug-centered to patient-centered studies

      The ultimate goal of precision medicine is an individualized, patient-centered (rather than drug-centered) trial based on the best available biomarkers. In “N-of-1” trials, each patient’s treatment is considered separately on the basis of molecular, immune, and other biologic characteristics. These trials involve customized drug combinations tailored to individual patients [
      • Sicklick J.K.
      • Kato S.
      • Okamura R.
      • et al.
      Molecular profiling of cancer patients enables personalized combination therapy: the I-PREDICT study.
      ]. Determining efficacy in “N-of-1” trials requires assessing the “strategy” of matching patients to drugs, rather than treatments, which differ from patient to patient.

      Real-world data

      With advanced computer data “processing” capabilities, real-world registries and data mining are expanding. Two drug approvals by the FDA were based, at least in part, on such data: pembrolizumab for any solid tumor with a mismatch repair gene defect (https://www.fda.gov/Drugs/InformationOnDrugs/ApprovedDrugs/ucm56004”0.htm) and palbociclib for male breast cancer (https://www.fda.gov/NewsEvents/Newsroom/PressAnnouncements/ucm635276.htm). The stunning possibility exists that real-world data, if confirmed to accurately portray the anticipated results of prospective trials, will dramatically accelerate the drug approval process.

      Genomic and other biomarkers

      Genomics has been the cornerstone of precision medicine studies. Beyond genomics, RNA and protein profiling, with proteins being the effectors of signaling, also appear to be important in mediating biologic impact. Interestingly, matching patients to drugs on the basis of genomics has proven more effective in improving outcome than matching on the basis of protein assays, perhaps for technical reasons [
      • Schwaederle M.
      • Zhao M.
      • Lee J.J.
      • et al.
      Association of Biomarker-Based Treatment Strategies With Response Rates and Progression-Free Survival in Refractory Malignant Neoplasms: A Meta-analysis.
      ]. Despite the current practical limitations, protein and transcript assays may provide essential information when integrated with genomics [
      • Rodon J.
      • Soria J.C.
      • Berger R.
      • et al.
      Genomic and transcriptomic profiling expands precision cancer medicine: the WINTHER trial.
      ]. Recently, panels that incorporate immune signatures, based on DNA, RNA, and/or proteins, have also gained clinical significance [
      • Pabla S.
      • Conroy J.M.
      • Nesline M.K.
      • et al.
      Proliferative potential and resistance to immune checkpoint blockade in lung cancer patients.
      ].

      Genomics

      Given the advances in NGS technologies and the large number of laboratories in the US that perform Clinical Laboratory Improvement Amendments (CLIA)-certified NGS, optimization of the accuracy, reproducibility, and standardization of sequencing methods; variant annotation; and data interpretation is critical. Guidelines for the validation of NGS panels [
      • Jennings L.J.
      • Arcila M.E.
      • Corless C.
      • et al.
      Guidelines for Validation of Next-Generation Sequencing-Based Oncology Panels: A Joint Consensus Recommendation of the Association for Molecular Pathology and College of American Pathologists.
      ] and the interpretation and reporting of genomic variants have been developed [
      • Li M.M.
      • Datto M.
      • Duncavage E.J.
      • et al.
      Standards and Guidelines for the Interpretation and Reporting of Sequence Variants in Cancer: A Joint Consensus Recommendation of the Association for Molecular Pathology, American Society of Clinical Oncology, and College of American Pathologists.
      ]. Although whole-genome sequencing is not yet the standard practice in the clinic, the FDA has approved two NGS panels that include hundreds of genes [].
      Most genomic sequencing involves tissue, but blood-derived circulating tumor DNA (ctDNA), circulating tumor cells [
      • Salami S.S.
      • Singhal U.
      • Spratt D.E.
      • et al.
      Circulating Tumor Cells as a Predictor of Treatment Response in Clinically Localized Prostate Cancer.
      ], and exosomes [
      • Abd Elmageed Z.Y.
      • Yang Y.
      • Thomas R.
      • et al.
      Neoplastic reprogramming of patient-derived adipose stem cells by prostate cancer cell-associated exosomes.
      ] are increasingly used, with the latter two reflecting the contents of live cells.

      Blood-derived cell-free DNA analysis

      Clinical-grade ctDNA testing, which is non-invasive and reflects tumor heterogeneity (because tumor DNA may be leaked into the bloodstream from multiple metastatic lesions), is increasingly being used to select anti-cancer therapy and to monitor subclone dynamics during treatment [
      • Chabon J.J.
      • Simmons A.D.
      • Lovejoy A.F.
      • et al.
      Circulating tumour DNA profiling reveals heterogeneity of EGFR inhibitor resistance mechanisms in lung cancer patients.
      ,
      • Dagogo-Jack I.
      • Shaw A.T.
      Tumour heterogeneity and resistance to cancer therapies.
      ]. The discordance noted in some cases between results of ctDNA testing and tumor tissue genotyping analysis [
      • Merker J.D.
      • Oxnard G.R.
      • Compton C.
      • et al.
      Circulating Tumor DNA Analysis in Patients With Cancer: American Society of Clinical Oncology and College of American Pathologists Joint Review.
      ] could reflect technical issues but might be attributable to the following biologic reasons: (i) tumor NGS measures genomics in the small piece of tissue biopsied while ctDNA assesses shed DNA from multiple sites; (ii) ctDNA is associated with tumor load and can be detected at low levels.

      Blood-derived circulating tumor cell (CTC) analysis

      The presence of CTCs, which are epithelial tumor cells, has been independently associated with worse survival in several types of cancer [
      • Cristofanilli M.
      • Budd G.T.
      • Ellis M.J.
      • et al.
      Circulating tumor cells, disease progression, and survival in metastatic breast cancer.
      ,
      • Hiltermann T.J.
      • Pore M.M.
      • van den Berg A.
      • et al.
      Circulating tumor cells in small-cell lung cancer: a predictive and prognostic factor.
      ,
      • Hofman V.
      • Ilie M.I.
      • Long E.
      • et al.
      Detection of circulating tumor cells as a prognostic factor in patients undergoing radical surgery for non-small-cell lung carcinoma: comparison of the efficacy of the Cell Search Assay and the isolation by size of epithelial tumor cell method.
      ]. For example, in a prospective, multicenter, double-blind study, the number of CTCs in patients with untreated metastatic breast cancer correlated with shorter progression-free survival (PFS) and overall survival (OS) [
      • Cristofanilli M.
      • Budd G.T.
      • Ellis M.J.
      • et al.
      Circulating tumor cells, disease progression, and survival in metastatic breast cancer.
      ]. CTCs may also be a predictive biomarker for chemotherapy and immunotherapy [
      • Hiltermann T.J.
      • Pore M.M.
      • van den Berg A.
      • et al.
      Circulating tumor cells in small-cell lung cancer: a predictive and prognostic factor.
      ,
      • Tamminga M.
      • de Wit S.
      • Hiltermann T.J.N.
      • et al.
      Circulating tumor cells in advanced non-small cell lung cancer patients are associated with worse tumor response to checkpoint inhibitors.
      ]. However, the use of CTCs in clinical practice has not been fully established [

      Rossi E, Fabbri F. CTCs 2020: Great Expectations or Unreasonable Dreams. Cells 2019;8.

      ]. Finally, serial CTC analyses might enable real-time surveillance of the disease. A comparative study of five prospective randomized phase III trials in 6081 patients with metastatic castration-resistant prostate cancer assessed the prognostic value of CTCs compared to prostate-specific antigen [
      • Heller G.
      • McCormack R.
      • Kheoh T.
      • et al.
      Circulating Tumor Cell Number as a Response Measure of Prolonged Survival for Metastatic Castration-Resistant Prostate Cancer: A Comparison With Prostate-Specific Antigen Across Five Randomized Phase III Clinical Trials.
      ]. CTC ≥ 0 at baseline and at week 13 from treatment initiation was associated with OS. The investigators demonstrated that CTC monitoring was a robust and meaningful response endpoint for early-phase clinical trials in this setting [
      • Heller G.
      • McCormack R.
      • Kheoh T.
      • et al.
      Circulating Tumor Cell Number as a Response Measure of Prolonged Survival for Metastatic Castration-Resistant Prostate Cancer: A Comparison With Prostate-Specific Antigen Across Five Randomized Phase III Clinical Trials.
      ].

      Transcriptomics

      Transcriptomics refers to the study of RNA transcripts and their function. Transcriptomic analysis is performed using high-throughput technologies, including microarrays and RNA sequencing and it is a potentially valuable tool, particularly when there is discrepancy between genomic alterations and gene expression. Transcriptomics are utilized to identify prognostic and predictive gene expression signatures [
      • Sparano J.A.
      • Gray R.J.
      • Makower D.F.
      • et al.
      Adjuvant Chemotherapy Guided by a 21-Gene Expression Assay in Breast Cancer.
      ,
      • Paik S.
      • Shak S.
      • Tang G.
      • et al.
      A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer.
      ], to explore miRNAs and their role in mRNA regulation [
      • Buffa F.M.
      • Camps C.
      • Winchester L.
      • et al.
      microRNA-associated progression pathways and potential therapeutic targets identified by integrated mRNA and microRNA expression profiling in breast cancer.
      ,
      • Jacobsen A.
      • Silber J.
      • Harinath G.
      • Huse J.T.
      • Schultz N.
      • Sander C.
      Analysis of microRNA-target interactions across diverse cancer types.
      ] and to identify the tissue of origin in cancer of unknown primary [
      • Michuda J.
      • Igartua C.
      • Taxter T.
      • Bell J.S.
      • Pelossof R.
      • White K.
      Transcriptome-based cancer type prediction for tumors of unknown origin.
      ,
      • Bridgewater J.
      • van Laar R.
      • Floore A.
      • Van'T Veer L.
      Gene expression profiling may improve diagnosis in patients with carcinoma of unknown primary.
      ,
      • Tothill R.W.
      • Shi F.
      • Paiman L.
      • et al.
      Development and validation of a gene expression tumour classifier for cancer of unknown primary.
      ]. The first solid tumor precision medicine trial to use transcriptomics in the clinic---WINTHER---compared RNA expression in tumors to that in adjacent normal tissue and demonstrated that transcriptomics increased the number of patients that could be matched to therapy [
      • Rodon J.
      • Soria J.C.
      • Berger R.
      • et al.
      Genomic and transcriptomic profiling expands precision cancer medicine: the WINTHER trial.
      ]. Comparing tumor to normal tissue from the same patient may be necessary because of the large inter-patient variability in normal RNA expression. Other investigators have also used transcriptomics to select targeted treatments in patients with advanced solid tumors [
      • Weidenbusch B.
      • Richter G.H.S.
      • Kesper M.S.
      • et al.
      Transcriptome based individualized therapy of refractory pediatric sarcomas: feasibility, tolerability and efficacy.
      ,
      • Worst B.C.
      • van Tilburg C.M.
      • Balasubramanian G.P.
      • et al.
      Next-generation personalised medicine for high-risk paediatric cancer patients - The INFORM pilot study.
      ]. Challenges that prevent extensive use of transcriptomic biomarkers are degradation and fragmentation of RNA in formalin-fixed, paraffin-embedded tissue samples, complexity of required bioinformatic analysis of profiling data and low reproducibility of the results.

      Proteomics

      Proteomic analysis using immunohistochemical and other assays of tumors from patients with refractory metastatic cancer led to the identification of molecular targets that could guide therapeutic decisions and was associated with longer PFS compared to the patients’ PFS with their prior therapy (using patients as their own controls) [
      • Von Hoff D.D.
      • Stephenson Jr., J.J.
      • Rosen P.
      • et al.
      Pilot study using molecular profiling of patients' tumors to find potential targets and select treatments for their refractory cancers.
      ]. Proteomic assays are used in clinical practice to identify prognostic or predictive biomarkers for targeted treatments (hormone receptor expression, HER2 overexpression, ALK expression). However, the weaker correlation of proteomic markers, compared to genomic markers, with clinical outcomes suggests that technical issues should be addressed [
      • Schwaederle M.
      • Zhao M.
      • Lee J.J.
      • et al.
      Association of Biomarker-Based Treatment Strategies With Response Rates and Progression-Free Survival in Refractory Malignant Neoplasms: A Meta-analysis.
      ]. In a meta-analysis of phase 1 clinical trials of small molecules that used a genomic biomarker vs. those that used a protein biomarker, the median response rate was 41% vs. 25%, respectively (p = 0.05) [
      • Schwaederle M.
      • Zhao M.
      • Lee J.J.
      • et al.
      Association of Biomarker-Based Treatment Strategies With Response Rates and Progression-Free Survival in Refractory Malignant Neoplasms: A Meta-analysis.
      ]. Ongoing studies with targeted therapies include correlative analyses using peripheral blood and tumor tissue to identify proteomic biomarkers of response or resistance to treatment (LEEomic, NCT03613220 and BABST-C, NCT03743428).

      Immunotherapy and cellular therapy

      By reactivating the innate immune antitumor response, immunotherapy has provided a major breakthrough in oncology treatment [
      • Goodman A.M.
      • Kato S.
      • Bazhenova L.
      • et al.
      Tumor Mutational Burden as an Independent Predictor of Response to Immunotherapy in Diverse Cancers.
      ,
      • Rizvi N.A.
      • Hellmann M.D.
      • Snyder A.
      • et al.
      Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer.
      ]. Several novel approaches are currently being explored: checkpoint blockade, oncolytic viruses, cell-based products, modified cytokines, CD3-bispecific antibodies, vaccine platforms, and adoptive cell therapy [
      • Rosenberg S.A.
      • Restifo N.P.
      Adoptive cell transfer as personalized immunotherapy for human cancer.
      ].

      Checkpoint blockade

      There are seven FDA-approved checkpoint inhibitors: ipilimumab, pembrolizumab, nivolumab, avelumab, cemiplimab, durvalumab, and atezolizumab. Selected patients with advanced disease have remarkable response, including durable complete remission (CR). Despite the significant benefit noted in patients with diverse tumor types treated with checkpoint inhibitors, approximately 80% of patients across cancers do not experience beneficial effects. In the era of precision medicine, genomics, transcriptomics and other technologies are employed for the identification of biomarkers that predict benefit from immunotherapy. Interestingly, biomarkers predicting checkpoint inhibitor responsiveness are genomic: high tumor mutational burden (TMB) [
      • Goodman A.M.
      • Kato S.
      • Bazhenova L.
      • et al.
      Tumor Mutational Burden as an Independent Predictor of Response to Immunotherapy in Diverse Cancers.
      ,
      • Rizvi N.A.
      • Hellmann M.D.
      • Snyder A.
      • et al.
      Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer.
      ,
      • Jhaveri K.L.
      • Wang X.V.
      • Makker V.
      • et al.
      Ado-trastuzumab emtansine (T-DM1) in patients with HER2-amplified tumors excluding breast and gastric/gastroesophageal junction (GEJ) adenocarcinomas: results from the NCI-MATCH trial (EAY131) subprotocol Q.
      ], mismatch gene repair defects resulting in high microsatellite instability (MSI-H) (and, thus, high TMB) [
      • Le D.T.
      • Uram J.N.
      • Wang H.
      • et al.
      PD-1 Blockade in Tumors with Mismatch-Repair Deficiency.
      ,
      • Hellmann M.D.
      • Ciuleanu T.E.
      • Pluzanski A.
      • et al.
      Nivolumab plus Ipilimumab in Lung Cancer with a High Tumor Mutational Burden.
      ], PBRM1 alterations [
      • Miao D.
      • Margolis C.A.
      • Gao W.
      • et al.
      Genomic correlates of response to immune checkpoint therapies in clear cell renal cell carcinoma.
      ,
      • Otto G.
      PBRM1 loss promotes tumour response to immunotherapy.
      ], and PDL1 amplification [
      • Goodman A.M.
      • Piccioni D.
      • Kato S.
      • et al.
      Prevalence of PDL1 Amplification and Preliminary Response to Immune Checkpoint Blockade in Solid Tumors.
      ]. Specifically, TMB has been shown to predict clinical benefit from checkpoint inhibitors [
      • Goodman A.M.
      • Kato S.
      • Bazhenova L.
      • et al.
      Tumor Mutational Burden as an Independent Predictor of Response to Immunotherapy in Diverse Cancers.
      ]. In an analysis of 151 of 1638 patients who were treated with immunotherapeutic regimens and had TMB evaluation, high (≥20 mutations/mb) TBM was independently associated with significant improvement in PFS and OS compared to low to intermediate TMB [
      • Goodman A.M.
      • Kato S.
      • Bazhenova L.
      • et al.
      Tumor Mutational Burden as an Independent Predictor of Response to Immunotherapy in Diverse Cancers.
      ]. Other studies have however questioned the use of TMB as a biomarker [
      • Langer C.
      • Gadgeel S.
      • Borghaei H.
      • et al.
      OA04.05 KEYNOTE- 021: TMB and Outcomes for Carboplatin and Pemetrexed With or Without Pembrolizumab for Nonsquamous NSCLC.
      ,
      • Garassino M.
      • Rodriguez-Abreu D.
      • Gadgeel S.
      • et al.
      OA04.06 Evaluation of TMB in KEYNOTE- 189: Pembrolizumab Plus Chemotherapy vs Placebo Plus Chemotherapy for Nonsquamous NSCLC.
      ].
      Given its strong association with response to immunotherapy, MSI-H is an established biomarker for response to checkpoint inhibitors [
      • Marcus L.
      • Lemery S.J.
      • Keegan P.
      • Pazdur R.
      FDA Approval Summary: Pembrolizumab for the Treatment of Microsatellite Instability-High Solid Tumors.
      ,
      • Kim S.T.
      • Cristescu R.
      • Bass A.J.
      • et al.
      Comprehensive molecular characterization of clinical responses to PD-1 inhibition in metastatic gastric cancer.
      ]. MSI-H tumors have high TMB, often accumulating > 1000 non-synonymous genomic mutations, leading to tumor-specific proteins, known as neoantigens. Due to high clinical benefit rates, immunotherapeutic regimens have been approved by the FDA for the treatment of patients with advanced MSI-H colorectal cancer [
      • Overman M.J.
      • McDermott R.
      • Leach J.L.
      • et al.
      Nivolumab in patients with metastatic DNA mismatch repair-deficient or microsatellite instability-high colorectal cancer (CheckMate 142): an open-label, multicentre, phase 2 study.
      ,
      • Le D.T.
      • Kim T.W.
      • Cutsem E.V.
      • et al.
      Phase II Open-Label Study of Pembrolizumab in Treatment-Refractory, Microsatellite Instability–High/Mismatch Repair-Deficient Metastatic Colorectal Cancer: KEYNOTE-164.
      ,
      • Overman M.J.
      • Lonardi S.
      • Wong K.Y.M.
      • et al.
      Durable Clinical Benefit With Nivolumab Plus Ipilimumab in DNA Mismatch Repair-Deficient/Microsatellite Instability-High Metastatic Colorectal Cancer.
      ] or MSI-H tumors, irrespective of the organ of origin [
      • Marabelle A.
      • Le D.T.
      • Ascierto P.A.
      • et al.
      Efficacy of Pembrolizumab in Patients With Noncolorectal High Microsatellite Instability/Mismatch Repair-Deficient Cancer: Results From the Phase II KEYNOTE-158 Study.
      ]. Finally, defects in DNA proofreading proteins polymerase δ (POLD1) and polymerase ε (POLE) lead to increased TMB and are associated with response to immunotherapy [
      • Rizvi N.A.
      • Hellmann M.D.
      • Snyder A.
      • et al.
      Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer.
      ,
      • Gong J.
      • Wang C.
      • Lee P.P.
      • Chu P.
      • Fakih M.
      Response to PD-1 Blockade in Microsatellite Stable Metastatic Colorectal Cancer Harboring a POLE Mutation.
      ,
      • van Gool I.C.
      • Eggink F.A.
      • Freeman-Mills L.
      • et al.
      POLE Proofreading Mutations Elicit an Antitumor Immune Response in Endometrial Cancer.
      ]. For instance, of 4 patients with non–small cell lung cancer with deleterious mutations in POLD1 and POLE (whole-exome sequencing, [WES]), 3 patients with the highest TMB responded to pembrolizumab [
      • Rizvi N.A.
      • Hellmann M.D.
      • Snyder A.
      • et al.
      Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer.
      ]. Defects in other DNA repair systems might also be associated with response to immunotherapy. The predictive role of homologous recombination deficiency (HRD) is being evaluated in various tumors, including breast and ovarian cancer. Early phase clinical trials demonstrating that these patients may benefit from the addition of immunotherapy to poly ADP-ribose polymerase (PARP) inhibitors, should be confirmed with additional studies [
      • Domchek S.
      • Postel-Vinay S.
      • Im S.
      • et al.
      ,
      • Konstantinopoulos P.A.
      • Waggoner S.
      • Vidal G.A.
      • et al.
      Single-Arm Phases 1 and 2 Trial of Niraparib in Combination With Pembrolizumab in Patients With Recurrent Platinum-Resistant Ovarian Carcinoma.
      ].
      Furthermore, PBRM1 molecular alterations are evaluated as genomic biomarkers predicting checkpoint inhibitor responsiveness. Specifically, PBRM1 alterations were evaluated in a study of 35 patients with metastatic renal cell cancer treated with anti-programmed death-1 (PD-1) regimens [
      • Miao D.
      • Margolis C.A.
      • Gao W.
      • et al.
      Genomic correlates of response to immune checkpoint therapies in clear cell renal cell carcinoma.
      ]. WES revealed loss-of-function (LOF) mutations in the PBRM1 gene that predicted response to immunotherapy. Notably, the PBRM1 gene encodes for a protein of the chromatin remodeling complex, possibly interfering with hypoxia, and immune signaling pathways [
      • Miao D.
      • Margolis C.A.
      • Gao W.
      • et al.
      Genomic correlates of response to immune checkpoint therapies in clear cell renal cell carcinoma.
      ].
      Another biomarker that predicts benefit from immunotherapy is PD-L1 amplification [
      • Goodman A.M.
      • Piccioni D.
      • Kato S.
      • et al.
      Prevalence of PDL1 Amplification and Preliminary Response to Immune Checkpoint Blockade in Solid Tumors.
      ]. In a retrospective analysis, this marker was identified in 0.7% (843 of 118,187) patients of various tumor types and it did not always correlate with PD-L1 expression. Six of 9 (66.7%) patients with PD-L1-amplified solid tumors had an objective response to checkpoint inhibitors, and their median PFS was 15.2 months [
      • Goodman A.M.
      • Piccioni D.
      • Kato S.
      • et al.
      Prevalence of PDL1 Amplification and Preliminary Response to Immune Checkpoint Blockade in Solid Tumors.
      ]. PDL1 expression, assessed by immunohistochemistry on tumor cells or immune cells can be used as a response marker, albeit a suboptimal one [
      • Patel S.P.
      • Kurzrock R.
      PD-L1 Expression as a Predictive Biomarker in Cancer Immunotherapy.
      ]. Approximately 20% of FDA approvals of immunotherapeutic agents are based on companion PD-L1 diagnostic testing [
      • Davis A.A.
      • Patel V.G.
      The role of PD-L1 expression as a predictive biomarker: an analysis of all US Food and Drug Administration (FDA) approvals of immune checkpoint inhibitors.
      ].
      Genomic markers may also predict resistance---loss of JAK2 and beta 2 microglobulin mutations [
      • Zaretsky J.M.
      • Garcia-Diaz A.
      • Shin D.S.
      • et al.
      Mutations Associated with Acquired Resistance to PD-1 Blockade in Melanoma.
      ]—or hyper-progression (accelerated progression) after checkpoint blockade---MDM2 amplification and EGFR alterations [
      • Kato S.
      • Goodman A.
      • Walavalkar V.
      • Barkauskas D.A.
      • Sharabi A.
      • Kurzrock R.
      Hyperprogressors after Immunotherapy: Analysis of Genomic Alterations Associated with Accelerated Growth Rate.
      ]. WES of tumor tissue from 4 patients with advanced melanoma whose disease was resistant to anti–PD1 therapy, demonstrated LOF mutations in genes involved in interferon-receptor signaling and in antigen presentation (JAK1/2, β2-microglobulin) [
      • Zaretsky J.M.
      • Garcia-Diaz A.
      • Shin D.S.
      • et al.
      Mutations Associated with Acquired Resistance to PD-1 Blockade in Melanoma.
      ]. Importantly, PTEN loss is associated with resistance to immunotherapy in patients with melanoma, suggesting that targeting the PI3K/AKT/mTOR pathway may overcome resistance to immunotherapy [
      • Peng W.
      • Chen J.Q.
      • Liu C.
      • et al.
      Loss of PTEN Promotes Resistance to T Cell-Mediated Immunotherapy.
      ]. In our opinion, it is plausible that when PI3K/AKT/mTOR pathway alterations or PTEN loss are the key drivers of the disease, immunotherapy may have limited, if any, antitumor activity. Similarly, STK11 mutations and β-catenin pathway alterations are reportedly associated with resistance to immunotherapy [
      • Koyama S.
      • Akbay E.A.
      • Li Y.Y.
      • et al.
      STK11/LKB1 Deficiency Promotes Neutrophil Recruitment and Proinflammatory Cytokine Production to Suppress T-cell Activity in the Lung Tumor Microenvironment.
      ,
      • Spranger S.
      • Bao R.
      • Gajewski T.F.
      Melanoma-intrinsic β-catenin signalling prevents anti-tumour immunity.
      ].
      In summary, the available biomarkers are insufficient to adequately predict response to immunotherapy. Novel strategies may enhance our ability to identify biomarkers longitudinally, incorporating ctDNA analysis [
      • Said R.
      • Guibert N.
      • Oxnard G.R.
      • Tsimberidou A.M.
      Circulating tumor DNA analysis in the era of precision oncology.
      ] or tumor tissue immune, genomic, transcriptomic, and proteomic analysis.

      Adoptive cell therapy

      Adoptive cell therapy (ACT) is an innovative personalized treatment approach that enhances a patient’s immune system leading to specific tumor cell killing. Immune cells derived from a patient’s blood or tissue are expanded in vitro and then reinfused into the patient. These immune cells may be reprogrammed to recognize tumor-specific antigens [
      • Rosenberg S.A.
      • Restifo N.P.
      Adoptive cell transfer as personalized immunotherapy for human cancer.
      ,
      • Schumacher T.N.M.
      T-cell-receptor gene therapy.
      ]. Types of ACT include tumor-infiltrating lymphocyte (TIL) therapy, chimeric antigen receptor (CAR) T-cell therapy, engineered T-cell receptor (TCR) therapy and natural killer (NK) cell therapy.

      TILs

      ACT of TILs is based on the use of T-cells that have infiltrated a patient’s tumor. Autologous cells are being harvested and administered to patients after their expansion and activation. This approach has shown promising results in metastatic melanoma [
      • Rosenberg S.A.
      • Yang J.C.
      • Sherry R.M.
      • et al.
      Durable complete responses in heavily pretreated patients with metastatic melanoma using T-cell transfer immunotherapy.
      ,
      • Besser M.J.
      • Shapira-Frommer R.
      • Itzhaki O.
      • et al.
      Adoptive transfer of tumor-infiltrating lymphocytes in patients with metastatic melanoma: intent-to-treat analysis and efficacy after failure to prior immunotherapies.
      ,
      • Andersen R.
      • Donia M.
      • Ellebaek E.
      • et al.
      Long-Lasting Complete Responses in Patients with Metastatic Melanoma after Adoptive Cell Therapy with Tumor-Infiltrating Lymphocytes and an Attenuated IL2 Regimen.
      ,
      • Forget M.-A.
      • Haymaker C.
      • Hess K.R.
      • et al.
      Prospective Analysis of Adoptive TIL Therapy in Patients with Metastatic Melanoma: Response, Impact of Anti-CTLA4, and Biomarkers to Predict Clinical Outcome.
      ], nasopharyngeal, and cervical carcinoma [
      • Comoli P.
      • Pedrazzoli P.
      • Maccario R.
      • et al.
      Cell therapy of stage IV nasopharyngeal carcinoma with autologous Epstein-Barr virus-targeted cytotoxic T lymphocytes.
      ,
      • Stevanović S.
      • Draper L.M.
      • Langhan M.M.
      • et al.
      Complete regression of metastatic cervical cancer after treatment with human papillomavirus-targeted tumor-infiltrating T cells.
      ]. In three sequential clinical trials in patients with metastatic melanoma who had failed standard therapy, the use of autologous TILs was associated with objective response rates of 49%, 52%, and 72%, respectively; durable CRs were reported in 22% (20 of 93) of patients; and clinical benefit was observed irrespectively of prior therapy [
      • Rosenberg S.A.
      • Yang J.C.
      • Sherry R.M.
      • et al.
      Durable complete responses in heavily pretreated patients with metastatic melanoma using T-cell transfer immunotherapy.
      ]. Ongoing clinical trials assess the role of TIL therapy in various solid tumors (NCT03645928, NCT03935893, NCT03108495, NCT03083873).

      CAR T-cells

      CAR T-cells are a type of adoptive T-cell therapy in which autologous T-lymphocytes are genetically engineered to recognize the antigens expressed on malignant cells [
      • Maude S.L.
      • Frey N.
      • Shaw P.A.
      • et al.
      Chimeric antigen receptor T cells for sustained remissions in leukemia.
      ]. Adoptive T-cell therapy has resulted in remarkably high rates of durable CR in hematologic malignancies, including in patients with refractory disease. Therefore, the FDA has approved CAR T-cells for the treatment of pediatric patients and young adults with relapsed/refractory B-cell precursor acute lymphoblastic leukemia (Kymriah™, https://www.fda.gov/drugs/resources-information-approved-drugs/fda-approves-tisagenlecleucel-adults-relapsed-or-refractory-large-b-cell-lymphoma) and adult patients with relapsed/refractory diffuse large B-cell lymphoma (Yescarta™, https://www.fda.gov/vaccines-blood-biologics/cellular-gene-therapy-products/yescarta-axicabtagene-ciloleucel). CAR T-cells are currently being evaluated in solid tumors [

      ACTolog in Patients With Solid Cancers (ACTolog). 2016. (Accessed 6/24/2019, at https://clinicaltrials.gov/ct2/show/NCT02876510?cond=actolog&rank=1).

      ,
      • Brown C.E.
      • Alizadeh D.
      • Starr R.
      • et al.
      Regression of glioblastoma after Chimeric antigen receptor T-cell therapy.
      ].

      TCR therapy

      This approach uses T-cell receptor (TCR) engineered T-cells, and involves retroviruses that enable integration of new TCR transgene targeting antigens, which are expressed at high levels on different cancers into the genome of T-cells [
      • Govers C.
      • Sebestyén Z.
      • Coccoris M.
      • Willemsen R.A.
      • Debets R.
      T cell receptor gene therapy: strategies for optimizing transgenic TCR pairing.
      ]. TCR therapy has been assessed in hematologic and solid malignancies [
      • Johnson L.A.
      • Morgan R.A.
      • Dudley M.E.
      • et al.
      Gene therapy with human and mouse T-cell receptors mediates cancer regression and targets normal tissues expressing cognate antigen.
      ,
      • Morgan R.A.
      • Dudley M.E.
      • Wunderlich J.R.
      • et al.
      Cancer regression in patients after transfer of genetically engineered lymphocytes.
      ,
      • Chodon T.
      • Comin-Anduix B.
      • Chmielowski B.
      • et al.
      Adoptive transfer of MART-1 T-cell receptor transgenic lymphocytes and dendritic cell vaccination in patients with metastatic melanoma.
      ,
      • Kageyama S.
      • Ikeda H.
      • Miyahara Y.
      • et al.
      Adoptive Transfer of MAGE-A4 T-cell Receptor Gene-Transduced Lymphocytes in Patients with Recurrent Esophageal Cancer.
      ,
      • Parkhurst M.R.
      • Yang J.C.
      • Langan R.C.
      • et al.
      T cells targeting carcinoembryonic antigen can mediate regression of metastatic colorectal cancer but induce severe transient colitis.
      ]. Current trials evaluate treatment-associated toxicity, binding affinity to tumor antigens and efficacy in carefully selected patients with increased tumor burden.

      NK cell therapy

      Natural killer (NK) cells are cytotoxic lymphocytes that play a critical role in innate immunity. NK cells do not cause graft-versus-host disease, which makes them promising candidates for cancer treatment. Treatment of relapsed/refractory acute myeloid leukemia with haploidentical NK cells and recombinant human interleukin-15 induced CR in 32% of patients [
      • Cooley S.
      • He F.
      • Bachanova V.
      • et al.
      First-in-human trial of rhIL-15 and haploidentical natural killer cell therapy for advanced acute myeloid leukemia.
      ]. Clinical trials are currently evaluating CAR-NK cells in hematologic (NCT03056339, NCT00995137) and solid (NCT03656705, NCT03383978) malignancies.

      Personalized vaccines (vaccinomics)

      The accumulation of somatic mutations in cancer can generate cancer-specific neo-epitopes. Autologous T-cells often identify these neo-epitopes as foreign bodies, which makes them ideal cancer vaccine targets. Every cancer has its own unique mutations, but a small number of neo-antigens are shared between cancers. Theoretically, technological advances will soon result in rapid mapping of mutations within a genome, rational selection of vaccine targets such as neo-epitopes, and on-demand production of vaccines tailored to a patient's individual tumor. Alternatively, off-the-shelf vaccines for tumors with shared epitopes might also be exploitable.
      Several personalized vaccines are currently being evaluated in clinical trials [
      • Sahin U.
      • Derhovanessian E.
      • Miller M.
      • et al.
      Personalized RNA mutanome vaccines mobilize poly-specific therapeutic immunity against cancer.
      ,
      • Ott P.A.
      • Hu Z.
      • Keskin D.B.
      • et al.
      An immunogenic personal neoantigen vaccine for patients with melanoma.
      ]. For example, investigators used computational prediction of neo-epitopes to design personalized RNA mutanome vaccines for patients with metastatic melanoma [
      • Sahin U.
      • Derhovanessian E.
      • Miller M.
      • et al.
      Personalized RNA mutanome vaccines mobilize poly-specific therapeutic immunity against cancer.
      ]. Two of the five patients treated had objective responses to the vaccine alone, while a third patient had a CR to treatment with the vaccine combined with PD-1 blockade [
      • Sahin U.
      • Derhovanessian E.
      • Miller M.
      • et al.
      Personalized RNA mutanome vaccines mobilize poly-specific therapeutic immunity against cancer.
      ]. In another study of vaccine-induced polyfunctional CD4+ and CD8+ T-cells targeting unique neoantigens in patients with melanoma [
      • Ott P.A.
      • Hu Z.
      • Keskin D.B.
      • et al.
      An immunogenic personal neoantigen vaccine for patients with melanoma.
      ], four of six vaccinated patients had no recurrence at 25 months after vaccination [
      • Ott P.A.
      • Hu Z.
      • Keskin D.B.
      • et al.
      An immunogenic personal neoantigen vaccine for patients with melanoma.
      ].
      Sipuleucel-T, the first FDA-approved therapeutic cancer vaccine, is produced via ex vivo activation of autologous peripheral-blood mononuclear cells by a recombinant fusion protein comprised of prostatic acid phosphatase and granulocyte–macrophage colony-stimulating factor [
      • Kantoff P.W.
      • Higano C.S.
      • Shore N.D.
      • et al.
      Sipuleucel-T immunotherapy for castration-resistant prostate cancer.
      ]. Sipuleucel-T is used to treat metastatic castration-resistant prostate cancer on the basis of results of a randomized, double-blind, placebo-controlled phase III trial in which patients who received Sipuleucel-T had longer survival than those who received placebo (25.8 months vs. 21.7 months, respectively; p = 0.03) [
      • Kantoff P.W.
      • Higano C.S.
      • Shore N.D.
      • et al.
      Sipuleucel-T immunotherapy for castration-resistant prostate cancer.
      ].

      Challenges and solutions for the optimal implementation of precision medicine

      Genomic studies have unveiled the reality of tumors—they are tremendously heterogeneic and complex, and optimized therapy often does not result from classical clinical research and practice models.
      Precision medicine studies (Table 1, Table 2) demonstrate the major challenges in designing trials for this new paradigm. First, the rate of matching patients to drugs in these trials ranges from 5% to 49% and is mostly in the 15% to 20% range. Failure to match patients is attributed to (i) enrollment of individuals with end-stage disease, who deteriorate or die early; (ii) use of small gene panels that yield limited actionable alterations; (iii) delays in receiving and interpreting genomic results; and (iv) difficulty accessing targeted therapy drugs and/or limited drug availability. Some solutions provided by trials with higher matching rates, e.g., I-PREDICT 12 (matching rate, 49%), include: (i) use of clinical trial navigators and medication acquisition specialists; (ii) application of a large NGS panel with > 200 genes; (iii) creation of just-in-time electronic molecular tumor boards immediately upon physician request; and (iv) exploitation of biomarkers to match patients to chemotherapy, hormonal therapy, and immunotherapy (in addition to gene-targeted agents). The majority of these trials [
      • Tsimberidou A.M.
      • Iskander N.G.
      • Hong D.S.
      • et al.
      Personalized medicine in a phase I clinical trials program: the MD Anderson Cancer Center initiative.
      ,
      • Tsimberidou A.M.
      • Wen S.
      • Hong D.S.
      • et al.
      Personalized medicine for patients with advanced cancer in the phase I program at MD Anderson: validation and landmark analyses.
      ,
      • Sicklick J.K.
      • Kato S.
      • Okamura R.
      • et al.
      Molecular profiling of cancer patients enables personalized combination therapy: the I-PREDICT study.
      ,
      • Schwaederle M.
      • Zhao M.
      • Lee J.J.
      • et al.
      Association of Biomarker-Based Treatment Strategies With Response Rates and Progression-Free Survival in Refractory Malignant Neoplasms: A Meta-analysis.
      ] have shown improvement in clinical outcomes when treatments are matched to drugs compared to when they are not. Importantly, malignancies have complicated molecular biology, and use of personalized combinations of drugs that address a higher percentage of the aberrations present in an individual cancer is associated with better outcomes than more limited matching [
      • Schwaederle M.
      • Parker B.A.
      • Schwab R.B.
      • et al.
      Precision Oncology: The UC San Diego Moores Cancer Center PREDICT Experience.
      ,
      • Wheler J.J.
      • Janku F.
      • Naing A.
      • et al.
      Cancer Therapy Directed by Comprehensive Genomic Profiling: A Single Center Study.
      ,
      • Sicklick J.K.
      • Kato S.
      • Okamura R.
      • et al.
      Molecular profiling of cancer patients enables personalized combination therapy: the I-PREDICT study.
      ,
      • Rodon J.
      • Soria J.C.
      • Berger R.
      • et al.
      Genomic and transcriptomic profiling expands precision cancer medicine: the WINTHER trial.
      ].
      Other major hurdles encountered in the implementation of precision medicine include the following: (i) Potential differences in response to matched therapy depending on histology and/or genomic co-alterations. In contrast to molecular abnormalities that predict tumor agnostic response to treatment (e.g., NTRK fusions, MSI-H) [
      • Drilon A.
      • Laetsch T.W.
      • Kummar S.
      • et al.
      Efficacy of Larotrectinib in TRK Fusion-Positive Cancers in Adults and Children.
      ,
      • Le D.T.
      • Kim T.W.
      • Cutsem E.V.
      • et al.
      Phase II Open-Label Study of Pembrolizumab in Treatment-Refractory, Microsatellite Instability–High/Mismatch Repair-Deficient Metastatic Colorectal Cancer: KEYNOTE-164.
      ,
      • Marabelle A.
      • Le D.T.
      • Ascierto P.A.
      • et al.
      Efficacy of Pembrolizumab in Patients With Noncolorectal High Microsatellite Instability/Mismatch Repair-Deficient Cancer: Results From the Phase II KEYNOTE-158 Study.
      ], selected genomic biomarkers are predictive in specific tumor histologies [
      • Hyman D.M.
      • Puzanov I.
      • Subbiah V.
      • et al.
      Vemurafenib in multiple nonmelanoma cancers with BRAF V600 mutations.
      ,
      • Ross J.S.
      • Ali S.M.
      • Fasan O.
      • et al.
      ALK fusions in a wide variety of tumor types respond to anti-ALK targeted therapy.
      ]. (ii) The heterogeneity, complexity, and constant evolution of genomic landscapes. Due to significant heterogeneity between primary tumor and metastatic sites, molecular profiling of tumor tissue obtained from a single lesion may not always be representative of the systemic disease [
      • Lovly C.M.
      • Salama A.K.S.
      • Salgia R.
      Tumor Heterogeneity and Therapeutic Resistance.
      ,
      • Gerlinger M.
      • Rowan A.J.
      • Horswell S.
      • et al.
      Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing.
      ]. Additionally, under the pressure of targeted treatments, tumor molecular profile constantly evolves, with emerging resistant clones and new molecular alterations driving disease progression [
      • Kobayashi S.
      • Boggon T.J.
      • Dayaram T.
      • et al.
      EGFR mutation and resistance of non-small-cell lung cancer to gefitinib.
      ,
      • Napolitano A.
      • Vincenzi B.
      Secondary KIT mutations: the GIST of drug resistance and sensitivity.
      ]. (iii) The need to screen large numbers of patients in order to find specific/rare genomic defects (for instance, NTRK fusions) [
      • Drilon A.
      • Laetsch T.W.
      • Kummar S.
      • et al.
      Efficacy of Larotrectinib in TRK Fusion-Positive Cancers in Adults and Children.
      ,
      • Hyman D.M.
      • Puzanov I.
      • Subbiah V.
      • et al.
      Vemurafenib in multiple nonmelanoma cancers with BRAF V600 mutations.
      ,
      • Ross J.S.
      • Ali S.M.
      • Fasan O.
      • et al.
      ALK fusions in a wide variety of tumor types respond to anti-ALK targeted therapy.
      ]. (iv) Incomplete biologic/molecular profiles with which to select therapy; suboptimal technology and resources to understand completely the drivers of cancer in individual patients; (v) Considerable delays in the activation of clinical trials; (vi) differences in the metabolism and adverse effects of study drugs in various ethnic groups; (vii) lack of agreement between assays from different diagnostic companies/laboratories; and (viii) most importantly, lack of access to drugs for patients with limited resources as well as excessive eligibility criteria that rule out large swaths of patients with real-world co-morbidities. Approximately 3–5% of patients with cancer are enrolled on clinical trials and accrual is limited by overly restrictive eligibility criteria and limited access to drugs [
      • Murthy V.H.
      • Krumholz H.M.
      • Gross C.P.
      Participation in cancer clinical trials: race-, sex-, and age-based disparities.
      ]. ASCO, the Friends of Cancer Research, and the FDA recommended to broaden eligibility criteria to allow more patients to participate in clinical trials and gain benefit from novel investigational therapies [
      • Kim E.S.
      • Bruinooge S.S.
      • Roberts S.
      • et al.
      Broadening Eligibility Criteria to Make Clinical Trials More Representative: American Society of Clinical Oncology and Friends of Cancer Research Joint Research Statement.
      ]; and consequently participants will be representative of the actual patient population, increasing generalizability of the results. Patient enrollment could be enhanced by national and worldwide collaborations, as shown in multi-institutional trials [
      • Unger J.M.
      • Cook E.
      • Tai E.
      • Bleyer A.
      The Role of Clinical Trial Participation in Cancer Research: Barriers, Evidence, and Strategies.
      ,
      • Trimble E.L.
      • Abrams J.S.
      • Meyer R.M.
      • et al.
      Improving cancer outcomes through international collaboration in academic cancer treatment trials.
      ]. Finally, the Clinical Trials Transformation Initiative (CTTI), has been developed to examine the challenges and propose solutions to improve trial recruitment [
      • Huang G.D.
      • Bull J.
      • Johnston McKee K.
      • Mahon E.
      • Harper B.
      • Roberts J.N.
      Clinical trials recruitment planning: A proposed framework from the Clinical Trials Transformation Initiative.
      ].
      Several initiatives might help overcome the challenges introduced by our emerging understanding of cancer biology: (i) molecular profiling (tissue, blood) should be used at the time of diagnosis and during the course of the disease, the latter to monitor response and resistance; (ii) completion of molecular profiling should be expedited; and (iii) bioinformatic analysis should be optimized to include the key drivers of carcinogenesis.
      With the current excitement about the promise of immunotherapy, a large proportion of patients are assigned to immunotherapy trials without undergoing molecular profiling or immune marker identification. Although a significant minority of these patients will experience a clinical benefit and prolonged survival, the majority will have disease progression and/or significant adverse events. Therefore, the incorporation of biomarkers into the selection of patients for immunotherapy needs to be optimized.
      Finally, the immense potential of real-world data needs to be addressed. Validation of database information can be performed by comparing outcomes of clinical trials that led to approval with those in the database; if outcomes are similar, real-world data can then be used to rapidly predict new applications for medicines.

      Conclusions and future perspectives

      Remarkable biotechnological advances are transforming cancer care. Tumor and cell-free DNA profiling using NGS, as well as proteomic and RNA analysis, and a better understanding of immune mechanisms are optimizing cancer treatment selection. A major challenge in the therapeutic management of patients with advanced metastatic cancer is the complexity of tumor biology. This complexity is attributed to highly variable patterns of genetic and epigenetic diversity and clonal architecture associated with spatial expansion, proliferative self-renewal, migration, and invasion. The complexity is amplified by the dynamic, Darwinian evolutionary character of cancer cells, which undergo sequential searches for mechanisms to escape environmental constraints. Such cellular evolution involves the interplay of advantageous “driver” lesions, neutral or “passenger/hitchhiker” abnormalities, molecular changes in the tumor cells that increase the rate of other genomic anomalies, and modifications to the microenvironment and immune machinery that alter the fitness effects of other variables [
      • Greaves M.
      • Maley C.C.
      Clonal evolution in cancer.
      ]. Strategies to address tumor complexity include targeting self-renewing cancer stem cells to overcome their plasticity and adaptability, impacting the microenvironment, and turning cancer into a chronic disease (using cytostatic drugs to suppress cell division and new mutations). The complicated nature of tumor biology is also the result of interactions between the tumor, host, and local ecosystem, including HLA type, genetic polymorphisms, microbiome, immune cell repertoire, and tumor microenvironment [
      • Sahin U.
      • Tureci O.
      Personalized vaccines for cancer immunotherapy.
      ]. New strategies, some of which now have a proven track record, include gene-directed therapies and a host of immune-targeted approaches (e.g., checkpoint blockade, CAR T-cells, personalized vaccinomics) [
      • Sahin U.
      • Tureci O.
      Personalized vaccines for cancer immunotherapy.
      ,
      • van Rooij N.
      • van Buuren M.M.
      • Philips D.
      • et al.
      Tumor exome analysis reveals neoantigen-specific T-cell reactivity in an ipilimumab-responsive melanoma.
      ].
      An overarching theme is that optimized therapy may require the utilization of combinations of drugs and/or strategies that attack the tumor from multiple angles. It is time to recognize the possibility that advanced computer implementation could generate real-world data that expand our understanding of cancer, rapidly identify new treatments, and create personalized drugs or immune therapies.

      Authors' contributions

      All authors wrote and approved the paper.

      Funding

      NIH / NCI , award number P30 CA016672.

      Declaration of Competing Interest

      Dr. Apostolia-Maria Tsimberidou has the following financial relationships to disclose: Research Funding (Institution): Immatics, Parker Institute for Cancer Immunotherapy, Tempus, OBI Pharma, EMD Serono, Baxalta, ONYX, Bayer, Boston Biomedical, Placon Therapeutics, Karus Therapeutics, and Tvardi Therapeutics. Consulting or Advisory Role: Covance, Genentech, and Tempus.
      Dr. Elena Fountzilas has the following financial relationships to disclose: Travel grant from Merck and K.A.M Oncology/Hematology; stock ownership Deciphera Pharmaceuticals, Inc.
      Dr. Mina Nikanjam has the following financial relationships to disclose: Research Funding (Institution): Regeneron, Bristol Myers Squib, Immunocore, Idera, and Merck.
      Dr. Razelle Kurzrock has the following financial relationships to disclose: Research Funding (Institution): Incyte, Genentech, Merck Serono, Pfizer, Sequenom, Foundation Medicine, Konica Minolta, Grifols, Biologic Dynamics, and Guardant. Consulting role: X-Biotech, Loxo, and Actuate Therapeutics. Speaker fees: Roche. Ownership interest: IDbyDNA and Curematch, Inc.

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