Opinion
Challenging Standard-of-Care Paradigms in
the Precision Oncology Era
Vivek Subbiah1,
* and Razelle Kurzrock2
The pace of genomic and immunological breakthroughs in oncology is accelerating,
making it likely that large randomized trials will increasingly become
outdated before their completion. Traditional clinical research/practice
paradigms must adapt to the reality unveiled by genomics, especially the need
for customized drug combinations, rather than one-size-fits-all monotherapy.
The raison-d'être of precision oncology is to offer ‘the right drug for the right
patient at the right time’, a process enabled by transformative tissue and bloodbased
genomic technologies. Genomically targeted therapies are most suitable
in early disease, when molecular heterogeneity is less pronounced, while
immunotherapy is most effective against tumors with unstable genomes.
Next-generation cancer research/practice models will need to overcome the
tyranny of tradition and emphasize an innovative, precise and personalized
patient-centric approach.
Clinical Trial Paradigms in the Era of Targeted Therapies and
Immunotherapies
“Victorious warriors win first and then go to war, while defeated warriors go to war first and
then seek to win” — Sun Tzu, The Art of War
Between 2003 and 2013, new cancer drugs approved by the European Medicines Agency
(EMA) or the [263_TD$DIFF]United States Food and Drug Administration (US FDA) produced a total mean
improvement in overall survival of only 3.4 months relative to the treatments that were available
in 2003 [1]. Routinely, new medicines that confer an additional survival of mere weeks with
statistical P value victories are hailed as major breakthroughs in oncology. The randomized
controlled trial (RCT), considered the gold standard for cancer clinical trials, has failed to render
cures or long-term survival for the majority of [264_TD$DIFF]patients suffering from advanced malignancies. In
diseases such as metastatic pancreatic cancer, >90% of patients are dead at 2 years, despite
a multitude of traditional trials [2]. The high costs of conventional trials, the large number of
patients receiving futile therapy on control arms, and the lack of biomarker (see Glossary)
selection hampers progress. In this Opinion, we critically appraise the state of standard-of-care
therapies, and present an overview of current clinical trial design paradigms in the era of
genomically targeted therapies and immunotherapy.
Targeted Therapies
Over 100 years ago, Paul Ehrlich introduced the concept of ‘magic bullet cures’ in oncology [3].
Realization of this idea remained elusive until the last decade, with the advent of drugs such as
imatinib targeting the altered Bcr-Abl tyrosine kinase, which is pathognomonic of chronic
myelogenous leukemia (CML). CML became a poster-child for precision oncology. Before
the imatinib era, median survival was $4 years; today, life expectancy for patients with CML
Highlights
The central tenet of the precision
oncology paradigm requires the delivery
of the right drug at the right time to
the right patient.
The current model for precision oncology
usually matches single agents to
patients with late-stage, refractory,
molecularly complex disease. This is
suboptimal.
Optimizing targeted therapy requires a
departure from traditional paradigms:
(i) deploying gene-targeted agents
early in the disease course when the
tumor is less complicated at the genomic
level; (ii) administration of immunetargeted
therapies to patients with
complex cancers harboring high tumor
mutational burden[262_TD$DIFF]; and (iii) moving
from monotherapy to customized
combinations.
Genomics represents the tip of the
iceberg. In the future, panomic testing
that includes transcriptomics, proteomics,
metabolomics, and immunogenomics
will paint a more complete
portrait of each tumor.
1
Department of Investigational Cancer
Therapeutics, Division of Cancer
Medicine, Unit 0455, The University of
Texas MD Anderson Cancer Center,
1515 Holcombe Boulevard, Houston,
TX 77030, USA
2
Division of Hematology & Oncology,
Center for Personalized Therapy &
Clinical Trials Office, UC San Diego –
Moores Cancer Center, 3855 Health
Sciences Drive, MC #0658, La Jolla,
CA 92093-0658, USA
*Correspondence:
vsubbiah@mdanderson.org
(V. Subbiah).
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© 2017 Elsevier Inc. All rights reserved.
TRECAN 228 No. of Pages 9
approaches normal, provided that treatment is started at the time of diagnosis [4]. Delaying
treatment until late-stage disease (as is standard in solid tumors) renders even the breakthrough
targeted therapies for CML ineffective. Other early examples of precision oncology
efforts included the success of trastuzumab in Her2-positive breast cancer, and epidermal
growth factor receptor (EGFR) and [265_TD$DIFF]anaplastic lymphoma kinase (ALK) inhibitors in EGFR- and
ALK-aberrant lung cancers [5–7]; all of which have significantly impacted outcome, albeit not to
the extent seen in CML.
In parallel, massive sequencing efforts have mapped the genome. The sequencing costs of a
single human genome have dropped in a breathtaking manner, from 3 billion US dollars over a
decade ago to about [266_TD$DIFF]one thousand US dollars today. Hundreds of actionable genes have been
discovered and thousands of new drugs with novel mechanisms of action, including genetargeted
agents and immunotherapy, are being identified. Yet, although we have witnessed a
few remarkable triumphs by utilizing genomics, other high-throughput omics technologies such
as proteomics, transciptomics, and metabolomics are in nascent stages.
Immunotherapies
Immunotherapy may be the ultimate example of a [267_TD$DIFF]precision treatment. Checkpoint inhibitors,
for instance, activate the immune machinery, enabling its innate ability to recognize and
destroy tumors [8,9]. The immune system is both personalized and precise. Furthermore, we
now realize that the immune apparatus distinguishes malignant cells from their normal counterparts
because the cancer cells present neoantigens, which are produced as a result of the
mutanome [10]. Additionally, specific genomic alterations, such as PD-L1 amplification (associated
with almost a 90% response rate in refractory Hodgkin’s disease treated with anti-PD-1
checkpoint inhibitors) and high tumor mutational burden are greatly predictive or response
[9,11–13]. Most striking is the ability of immunotherapy to induce durable complete remissions,
even in patients with advanced metastatic cancer. The recent US FDA approval of
pembrolizumab, an immune checkpoint inhibitor for microsatellite instability high
(MSI-H) cancers across all solid tumor types (histology-agnostic approval) in pediatric and
adult patients is an attestation to the power of precision medicinei
[261_TD$DIFF] [14–16]. This approval also
demonstrates that genomics and immunotherapy are wedded to each other, and their
successes epitomize the power and potential of this marriage.
Conventional Clinical Trial Paradigms
Unfortunately, conventional clinical trial strategies may not be the best way to evaluate the new
generation of genomically or immune-targeted agents. Indeed, genomics has unveiled a reality
that is incompatible with canonical trial design – every metastatic tumor is both unique and
complex at the molecular level [17–20] (Figure 1, Key Figure, Table 1). Furthermore, drugs that
are highly effective in small subpopulations of patients are not amenable to randomized trials in
unselected patient populations. Under such circumstances, trials must first identify response
biomarkers and then individualized combination therapy needs to be given.
The central premise of precision oncology is to offer ‘the right drug for the right patient at the
right time’ Ironically, traditional models for clinical research are almost diametrically opposed to
those needed based on the science of precision medicine: (i) in conventional models, commonalities
are found between patients in order for them to receive the same drug regimen,
instead of individualizing therapy; and (ii) targeted monotherapies are matched to one specific
molecular alteration in a patient’s tumor, rather than giving combination treatment optimally
tailored to the entirety of the tumor genomic portrait. Regarding timing of therapy, genomically
targeted agents are often applied to heavily pretreated patients, rather than early in the course
Glossary
Precision medicine: ‘an emerging
approach for disease treatment and
prevention that takes into account
individual variability in genes,
environment, and lifestyle for each
person’ (definition of the National
Institutes of Health, NIH); ‘a form of
medicine that uses information about
a person’s genes, proteins, and
environment to prevent, diagnose,
and treat disease’ (definition of the
National Cancer Institute, NCI).
Precision oncology: field in
oncology defined by customizing
treatment to an individual’s molecular
profile.
Biomarker: characteristic that is
objectively measured or evaluated as
an indicator of abnormal biological
processes or pharmacological/
biological responses to a therapeutic
intervention.
Randomized controlled trial: trial
in which two treatment groups (an
experimental group versus control
group; sometimes given a placebo or
a traditional therapy regimen) are
compared. The only expected
difference between the control and
experimental groups in RCTs is the
treatment effect of the experimental
therapy being studied.
Genomics: study of genes.
Targeted therapy: drugs that either
target molecular alterations specific
to cancer cells (e.g., mutated,
amplified or epigenetically up- and/or
downregulated signaling proteins), or
target immune cells to increase
anticancer immunity.
Immunotherapy: prevention or
treatment of disease with agents that
stimulate the immune response of
the host.
Tumor mutational burden: number
of mutations in a tumor.
Vemurafenib and dabrafenib:
tyrosine kinase inhibitor of aberrant
BRAF.
Trametinib and cobimetinib: MEK
inhibitor.
Panomics: informal name for
technological fields in biology that
end in omics, such as genomics,
proteomics, and metabolomics.
Proteomics: study of proteins.
Transcriptomics: study of
transcripts.
Metabolomics: study of
metabolism.
2 Trends in Cancer, Month Year, Vol. xx, No. yy
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Drug-centric approach: approach
to treatment centered on a drug or
drug regimen.
Patient centric approach:
approach to treatment centered on
the patient.
Checkpoint inhibitor: agent that
inhibits an immune checkpoint and
hence can reactivate the immune
system.
Pembrolizumab: antibody that
works as a checkpoint inhibitor.
Microsatellite instability:
microsatellites represent repeated
sequences of DNA that are one to
six base pairs in length. Microsatellite
instability is a condition of genetic
predisposition to mutation in
microsatellites that results from an
impaired DNA mismatch repair gene.
of the disease, when tumors are less heterogeneous, and the targeted drugs are more likely to
be effective [21,22]. Tumor mutational burden and complexity, on the other hand, may be an
advantage for immunotherapy. Importantly, standard-of-care therapies deny and/or delay
evaluation of new drugs in patients with lethal cancers by making the tumors more drug
resistant, impairing the immune system, and/or rendering the patients too sick to be eligible for
innovative treatment.
In order to unlock the potential of precision oncology, profound changes in our traditional
approaches need to occur. These changes start with universal genomic testing at the time of
diagnosis of cancer [23] (Table 2) and include customizing drug combinations, with genomically
targeted treatments given early in a patient’s disease course, and immunotherapy using
Key Figure
The Snowflake Theory and Changing Drug Development Paradigms
MetastaƟc cancer = Snowflakes at molecular level
Current paradigm Future paradigm
Drug-centric trial (tradiƟonal)
PaƟent-centric therapy
We already customize treatment
Drug
Regimen
A
PaƟent 1
PaƟent 2
One treatment for all Customized therapy
Meƞormin Meƞormin
FluoxeƟnePaƟent 1
PaƟent 2
PaƟent 3
Diabetes, CHF, RA
Diabetes, infecƟon,
depression
TofaciƟnib Clarithromycin
β-blockerStrategy: Find common feature between
paƟents (e.g. type of cancer or type of molecular
aberraƟon) and place all on same drugs
Figure 1. Top panel: cancers are akin to malignant snowflakes. No two snowflakes are identical, and it seems that it is also
extremely unusual for two metastatic tumors to have the same genomic fingerprint. As it turns out, if metastatic tumors are
akin to malignant snowflakes in their distinctiveness, individual tumors become the ultimate extrapolation of rare and ultrarare
tumors À n-of-one malignancies. Bottom panel: moving from drug-centric [258_TD$DIFF]to patient-centric trials and care[259_TD$DIFF]. If each
cancer is unique and complex, precisely targeting it requires personalized combination therapy regimens. Bottom panel
shows that personalized therapy is already routine in patient care outside the oncology setting. Abbreviations: CHF,
congestive heart failure; RA, rheumatoid arthritis.
Trends in Cancer, Month Year, Vol. xx, No. yy 3
TRECAN 228 No. of Pages 9
checkpoint inhibitors administered to patients with evolved cancers harboring high mutational
burdens or microsatellite instability.
Standard of Care, Standard of Proof, and Proof of Standards
Evidenced-based, standard-of-care guidelines/pathways are promulgated by a variety of
organizations and emphasize consistencyii
[24,25]. Departure from these guidelines may leave
the physician legally liable and justify insurers’ refusal to pay. Yet, the standard-of-care
oncology treatments are associated with >90% mortality at 2 years for some metastatic
cancers.
Importantly, in their present rendition, standard-of-care pathways, by virtue of their emphasis
on uniformity of management, are antithetical to precision oncology, which requires personalization
of therapy. Indeed, if each patient’s tumor is complex and unique, then, in order to
precisely target that tumor, one must apply medicines that affect the distinct alterations of the
tumor, and this requires customized treatment.
Moving Precision Oncology Forward
Precision oncology trials test feasibility of matching drugs to targeted therapy [26–29]. The
evidence for this matching strategy is rapidly accumulating, both from these trials and from
literature data mining [30,31]. Indeed, large meta-analyses of $85 000 participants in phase 1,
2, and 3 studies demonstrated that biomarker selection was the single most significant
independent factor predicting improvement in all outcome parameters. Of equal importance,
the use of genomically targeted therapy without a biomarker produced negligible response
rates, which were also worse than the results with cytotoxic agents [32–35].
Table 1. Redefining Clinical Trial Paradigms and Standard of Care
Subject matter Solution Challenge
The definition of personalized treatment is
inconsistent with canonical trial/practice
paradigms, where patients are grouped together
based on a biologic commonality.
A patient-centered, n-of-one approach is needed
to optimize therapy.
Current treatment paradigms, including precision
oncology trials, are drug centered rather than
patient centered.
Monotherapy is unlikely to cure patients with
advanced/complex malignancies
Combination therapies needed Matched customized combinations for n-of-one
tumors require evaluation of the strategy of
personalization or an algorithm for matching, rather
than the drug regimens themselves
The inimitability of tumors means that each cancer
is akin to a malignant snowflake – both unique and
complex in its genomic portrait
Unique/complex tumors require individualized
combination regimens
With 300 drugs, there are $4.5 million three-drug
regimens
Dosing of combinations of anticancer drugs has
traditionally required a phase I study
Outside of oncology, patients regularly receive de
novo combinations of drugs based on
understanding impact on metabolic enzymes etc.
The average oncology patient is already on eight
medications, which have not been assessed
together in a phase I study, but are given safely
together. Dosing algorithms for anticancer drug
combinations can be similarly derived from a variety
of sources including the literature [57–60].
The pathway to approval and payer acceptance of
drug combinations is unclear
If tumors are defined by their molecular makeup,
advanced molecular tests should be considered a
standard diagnostic tool for patients with cancer
Universal genomic testing of cancers Points and counterpoints in Table 2
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The Right Drug at the Right Time for the Right Patient
The [269_TD$DIFF]Right Drug
The discovery of BRAFV600E
[268_TD$DIFF] mutations as a bona fide oncogenic driver in 50% of melanomas
led to a drug development race in order to target the product of this gene. Treatment with the
potent BRAF inhibitor vemurafenib showed high response rates leading to FDA approval in
2011 [36,37]. Since then, the BRAF inhibitor dabrafenib and two MEK inhibitors (trametinib
and cobimetinib) have also been approved [38–40]. Yet, most patients fail to achieve
complete or long-term partial remission. This is likely due to the fact that the majority of
metastatic melanomas harbor several genomic alterations [41]. Hence, patients require combination
therapy tailored to the biomarker portfolio of their tumor. Indeed, a recent study
demonstrates that higher matching scores (number of matches divided by number of alterations)
independently correlates with better outcomes [26].
The Right Time
Timing is vital in cancer therapy. Tumor complexity increases with time and under the pressure
of therapy. CML epitomizes this evolution with three well-defined stages: chronic phase,
accelerated phase, and blast crisis. Other cancers almost certainly undergo a similar evolution,
but it is not as well delineated clinically [42]. In recent years, the clinical outcome of CML has
been transformed. Three major steps enabled this transformation: (i) discovery of the underlying
genetic defect (BCR-ABL); (ii) identification of a targeted agent (imatinib) that obviated the
aberrant enzymatic activity of Bcr-Abl; and (iii) administration of imatinib to patients with newly
diagnosed disease. The third step, that is treating early disease, is the one that is most
frequently not addressed in solid tumors.
As an example, BRAF inhibitors in patients with BRAF-mutant melanoma can result in
responses so remarkable that they have been designated as the oncological equivalent of
the Lazarus syndrome [43]. This syndrome refers to the spontaneous return of circulation
after failed attempts at resuscitation. Patients near death from melanoma can experience
Table 2. Case for Universal Genomic Testing of Tumors: Points and Counterpoints
Points Counterpoints Refs
Obtaining knowledge of genetic aberrations is not worthwhile if
no action can be taken in terms of treatment
Genomics is the diagnosis. Every patient with cancer deserves a
diagnosis.
Genetic abnormalities also predict prognosis.
Genomics can also predict contraindicated drugs, e.g., EGFR
therapy in KRAS-mutant colorectal cancer
[23]
Prohibitive cost precludes universal genomic testing Cost of testing has decreased precipitously
Financial burden of cancer therapy is massive
Cost of testing for a complete diagnosis and to select appropriate
therapy is tiny compared with the money squandered on illchosen
treatments
Genomic testing has not been validated in prospective trials In comprehensive meta-analyses of $85 000 patients treated on
clinical trials, genomic biomarkers were an independent factor
associated with improvement of all outcome variables
[33–35]
Genomic testing may benefit only a subgroup of patients or may
be germane to only rare diseases
Virtually impossible to know in advance of testing who will benefit
Options that may not exist at the time of a patient’s initial
diagnosis may become available before the patient’s disease
progresses
Universal genomic testing of malignancies will enable curating
clinically relevant data in large databases
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dramatic tumor reduction. Unfortunately, these patients are not usually cured, and the
disease almost inevitably returns after a few months and results in the patient’s demise. If
the experience with CML holds true, durable responses in solid tumors will require either
administration of targeted agents such as BRAF inhibitors to newly diagnosed disease and/or
giving customized combinations of drugs to patients with advanced disease in order to block
resistance pathways.
The Right Patient (and the Right Cancer)
Most novel drugs are tested in patients who have exhausted standard-of-care therapies. At this
time, not only is the cancer refractory, but the patient’s performance status and biological/
immune reserve may also be too poor to realistically expect the best outcomes. For these
reasons, patients should be treated with novel therapies earlier in their disease course.
Advanced Cancers Are Akin to Malignant Snowflakes – Complex and
Unique
No two snowflakes are identical, and it seems that it is also extremely unusual for two
metastatic tumors to have the same genomic fingerprint [17–20,44] (Figure 1). For example,
in 57 patients with advanced breast cancer, 216 somatic aberrations were observed (131 being
distinct) in 70 different genes; no two patients had the same molecular signature [17]. A study in
advanced osteosarcoma with multiple molecular profiling technologies showed similar results
[20]. Furthermore, we may be viewing only the tip of the iceberg. As new technologies emerge
beyond limited panel genomic sequencing, both the complexity and the individuality of tumors
are likely to be amplified (Figure 2).
Customized Combination Therapy[270_TD$DIFF]: From Drug-Centric to Patient-Centric
Research and Care
One of the major stumbling blocks in precision oncology is that there are intrinsic and acquired
resistance mechanisms to targeted therapy. One drug matched to a driver aberration may not
realistically be expected to cure patients or achieve remissions if each tumor has distinct and
complex alterations [31,41]. Other drugs must be added to overcome resistance [31,45,46]
A paradigm of individualized therapy means that the traditional way that drugs/drug regimens
become standard of care no longer works. Canonical drug development paradigms are drugcentered
(Figure 1). The drugs are the focus of the trial and each patient enrolled receives the
same regimen, regardless of their genomic and phenotypic heterogeneity. However, if each
tumor is different, we may need to test thousands of regimens in increasingly small subsets of
patients. Indeed, if there are $300 drugs in oncology, there are $45 000 two-drug regimens
and $4.5 million three-drug regimens. The traditional clinical trial design model breaks down.
However, the conundrum is solvable. Precision medicine implies patient-centered trials and
care. The patient is the focus and the drugs can therefore vary from patient to patient. In this
model, it is not the drug regimen that is evaluated, but rather the strategy of individualization.
The question then becomes what is the standard of proof for this strategy? In the era of
precision oncology, new clinical trial designs need to evaluate personalized care performance
so that standard-of-care guidelines can include, emphasize, or even mandate individualized
treatment.
The One-Size-Fits All Treatment Model in Oncology Is an Anomaly
In daily medical practice, physicians already use customized combinations to treat nonmalignant
conditions. A patient with diabetes, congestive heart failure, and rheumatoid arthritis
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receives a different set of drugs than a patient with diabetes, infection, and depression
(Figure 1). The drug doses are adjusted to prevent drug–drug interactions based on known
factors such as impact on metabolic enzymes. The average patient enters the oncology clinic
on approximately eight drugs tailored to their specific health problems. These individualized
drug combinations have never been formally tested in phase I studies; yet physicians safely and
effectively administer them on a regular basis to the benefit of their patients.
In oncology, however, there is a cultural precept that, if a new drug combination has not been
tested in phase I studies, it should not be used because its safety is unknown. This precept may
be a legacy of the cytotoxic era, since combining cytotoxics could have serious safety
concerns. However, modern anticancer agents have fewer prohibitive adverse effects and
our understanding of drug combinations has grown. One size fits all is not the norm in medicine,
and, since advanced cancers are heterogeneous, it should cease to be the norm in oncology
care.
Immunotherapy: Yet Another Paradigm Shift
One of the most important mechanisms by which cancer cells evade the immune system is
exploitation of checkpoints by the tumor to disable T cells. The PD-1/PD-L1 axis is of particular
interest because of rapidly emerging data suggesting that inhibition of this checkpoint can
restore anticancer immunity. Impressively, clinical responses with checkpoint inhibitors have
been observed in multiple different malignancies. Remarkably, some patients with advanced
tumors can achieve durable complete remission.
Metabolomics Transcriptomics Immunogenomics
Microbiomics Proteomics
Genomics
Figure 2. Six Blind Men and Elephants
[260_TD$DIFF]Beyond genomics – transcriptomics, proteomics, and more. The comprehensive molecular profile of the not-too-distant
future may include genomics, transcriptomics, proteomics, metabolomics, microbiomics, epigenomics, mutanomics,
lipidomics, and immunogenotyping, and may hence predict response to multiple modalities including immunotherapy and
chemotherapy [47–56]. Each of these modalities gives us a piece of the puzzle, akin to the parable of the six blind men who
each touch a different part of the elephant, such as the tusk versus the trunk, and therefore have vastly different views of the
elephant. Panomics testing is a requisite of comprehensive analysis and may require complex computer algorithms for
data integration and computation.
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Marriage of Genomics and Immunotherapy
The major predictive markers for checkpoint inhibitor response include high tumor mutational
burden, either associated with microsatellite instability or not, CD8 infiltrates, and PD-L1 overexpression
or amplification [9,11,12]. These markers reflect the coupling of the immune system
and genomics. Once the immune system is reactivated with the use of checkpoint inhibitors, T
cells must still be able to differentiate tumor cells from normal elements. T cells distinguish tumor
cellsfrom normal self in largepart through presentation of neoantigens created by the mutanome.
The more neoantigens, the better the chance of immune recognition. Hence, high tumor mutational
burden correlates with favorable outcome after checkpoint inhibitor treatment [13]. In
contrast, patients with lower number of genomic alterations appear to respond better to
gene-targeted therapy[26], presumably because, inmalignancies withmoregenomicalterations,
the presence of resistance mutations abrogate the effects of treatment.
Concluding Remarks
Breathtaking advances in our understanding of genomics and the immune system have
brought us to the threshold of a tipping point in cancer treatment. It appears, however, that
our established models for clinical research and practice are a suboptimal fit for the reality of
tumor heterogeneity (see Outstanding Questions). In order to overcome the cancer problem, it
is important to break free from the tyranny of tradition, and construct novel paradigms for the
management of neoplastic disease.
[271_TD$DIFF]Disclaimer Statement
Vivek Subbiah receives research funding for clinical trials from Novartis, Bayer, GSK, Nanocarrier, Vegenics, Northwest
Biotherapeutics, Berghealth, Incyte, Fujifilm, Pharmamar, D3, Pfizer, Multivir, Amgen, Abbvie, Bluprint Medicines, LOXO
and Roche/Genentech. Razelle Kurzrock receives consultant fees from X-biotech, Actuate Therapeutics, and Roche as
well as research funds from Genentech, Pfizer, Sequenom, Guardant, Foundation Medicine and Merck Serono, and has
an ownership interest in CureMatch Inc.
Resources
i
https://www.fda.gov/newsevents/newsroom/pressannouncements/ucm560167.htm
FDA (2017) FDA approves first cancer treatment for any solid tumor with a specific genetic feature
ii
https://www.nccn.org/professionals/physician_gls/f_guidelines.asp
NCCN (2016) NCCN Guidelines
References
1. Salas-Vega, S. et al. (2017) Assessment of overall survival, quality
of life, and safety benefits associated with new cancer medicines.
JAMA Oncol. 3, 382–390
2. Stewart, D.J. and Kurzrock, R. (2013) Fool’s gold, lost treasures,
and the randomized clinical trial. BMC Cancer 13, 193
3. Strebhardt, K. and Ullrich, A. (2008) Paul Ehrlich’s magic bullet
concept: 100 years of progress. Nat. Rev. Cancer 8, 473–480
4. Gambacorti-Passerini, C. et al. (2011) Multicenter independent
assessment of outcomes in chronic myeloid leukemia patients
treated with imatinib. J. Natl. Cancer Inst. 103, 553–561
5. Piccart-Gebhart, M.J. et al. (2005) Trastuzumab after adjuvant
chemotherapy in HER2-positive breast cancer. N. Engl. J. Med.
353, 1659–1672
6. Ciardiello, F. and Tortora, G. (2008) EGFR antagonists in cancer
treatment. N. Engl. J. Med. 358, 1160–1174
7. Shaw, A.T. et al. (2013) Crizotinib versus chemotherapy in
advanced ALK-positive lung cancer. N. Engl. J. Med. 368,
2385–2394
8. Farkona, S. et al. (2016) Cancer immunotherapy: the beginning of
the end of cancer? BMC Med. 14, 73
9. Goodman, A. et al. (2017) PD-1-PD-L1 immune-checkpoint
blockade in B-cell lymphomas. Nat. Rev. Clin. Oncol. 14, 203–
220
10. Champiat, S. et al. (2014) Exomics and immunogenics: bridging
mutational load and immune checkpoints efficacy. Oncoimmunology
3, e27817
11. Khagi, Y. et al. (2017) Next generation predictive biomarkers for
immune checkpoint inhibition. Cancer Metastasis Rev. 36, 179–
190
12. Patel, S.P. and Kurzrock, R. (2015) PD-L1 expression as a
predictive biomarker in cancer immunotherapy. Mol. Cancer
Ther. 14, 847–856
13. Rizvi, N.A. et al. (2015) Cancer immunology. Mutational landscape
determines sensitivity to PD-1 blockade in non-small cell
lung cancer. Science 348, 124–128
14. Le, D.T. et al. (2017) Mismatch-repair deficiency predicts
response of solid tumors to PD-1 blockade. Science 357,
409–413
15. Lemery, S. et al. (2017) First FDA approval agnostic of cancer site
– when a biomarker defines the indication. N. Engl. J. Med. 377,
1409–1412
16. (2017) First tissue-agnostic drug approval issued. Cancer Discov.
7, 656
17. Wheler, J.J. et al. (2014) Unique molecular signatures as a hallmark
of patients with metastatic breast cancer: implications for
current treatment paradigms. Oncotarget 5, 2349–2354
Outstanding Questions
Genomic sequencing is a basic diagnostic
tool that delineates the underpinnings
of malignancy and is therefore
crucial for classifying disease, predicting
prognosis, and directing therapy. If
a basic precept of medicine is that
each patient deserves an accurate
diagnosis, should not universal genomic
testing of tumors be necessary?
What adjustments to clinical trial
design and regulatory and care structures
are needed to move from a drugcentric
approach to a patient-centric
approach, wherein each tumor is prosecuted
with a customized combination
of drugs?
Would finding patients with identical or
near-identical tumors treated in the
same manner still be feasible with a
new form of interrogation based on
mining of large, well-annotated databases
using computerized and artificial
intelligence algorithms?
What is the optimal approach to identifying
immunogenic, mutanomederived
neoantigens that induce a Tcell
response?
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18. Kurzrock, R. and Giles, F.J. (2015) Precision oncology for patients
with advanced cancer: the challenges of malignant snowflakes.
Cell Cycle 14, 2219–2221
19. Wheler, J. et al. (2014) Unique molecular landscapes in cancer:
implications for individualized, curated drug combinations.
Cancer Res. 74, 7181–7184
20. Egas-Bejar, D. et al. (2014) Theranostic profiling for actionable
aberrations in advanced high risk osteosarcoma with aggressive
biology reveals high molecular diversity: the human fingerprint
hypothesis. Oncoscience 1, 167–179
21. Westin, J.R. and Kurzrock, R. (2012) It’s about time: lessons for
solid tumors from chronic myelogenous leukemia therapy. Mol.
Cancer Ther. 11, 2549–2555
22. Westin, J.R. et al. (2012) Treatment of chronic myelogenous
leukemia as a paradigm for solid tumors: how targeted agents
in newly diagnosed disease transformed outcomes. Am. Soc.
Clin. Oncol. Educ. Book 179–185
23. Subbiah, V. and Kurzrock, R. (2016) Universal genomic testing
needed to win the war against cancer: genomics IS the diagnosis.
JAMA Oncol. 2, 719–720
24. Moffett, P. and Moore, G. (2011) The standard of care: legal
history and definitions: the bad and good news. West J. Emerg.
Med. 12, 109–112
25. Markman, M. (2007) Standard of care versus standards of care in
oncology: a not so subtle distinction. J. Oncol. Pract. 3, 291
26. Wheler, J.J. et al. (2016) Cancer therapy directed by comprehensive
genomic profiling: a single center study. Cancer Res. 76,
3690–3701
27. Tsimberidou, A.M. et al. (2012) Personalized medicine in a phase I
clinical trials program: the MD Anderson Cancer Center initiative.
Clin. Cancer Res. 18, 6373–6383
28. Von Hoff, D.D. et al. (2010) Pilot study using molecular profiling of
patients’ tumors to find potential targets and select treatments for
their refractory cancers. J. Clin. Oncol. 28, 4877–4883
29. Roychowdhury, S. et al. (2011) Personalized oncology through
integrative high-throughput sequencing: a pilot study. Sci. Transl.
Med. 3, 111ra121
30. Diamond, E.L. et al. (2017) Vemurafenib for BRAF V600-mutant
Erdheim-Chester disease and Langerhans cell histiocytosis: analysis
of data from the histology-independent, phase 2, open-label
VE-BASKET study. JAMA Oncol. Published online November 29,
2017 https://doi.org/10.1001/jamaoncol.2017.5029
31. Hyman, D.M. et al. (2015) Vemurafenib in multiple nonmelanoma
cancers with BRAF V600 mutations. N. Engl. J. Med. 373, 726–
736
32. Schwaederle, M. et al. (2016) Precision oncology: The UC San
Diego Moores Cancer Center PREDICT experience. Mol. Cancer
Ther. 15, 743–752
33. Schwaederle, M. et al. (2015) Impact of precision medicine in
diverse cancers: a meta-analysis of phase II clinical trials. J. Clin.
Oncol. 33, 3817–3825
34. Schwaederle, M. et al. (2016) Association of biomarker-based
treatment strategies with response rates and progression-free
survival in refractory malignant neoplasms: a meta-analysis.
JAMA Oncol. 2, 1452–1459
35. Jardim, D.L. et al. (2015) Impact of a biomarker-based strategy
on oncology drug development: a meta-analysis of clinical trials
leading to FDA approval. J. Natl. Cancer Inst. 107, djv253
36. Bollag, G. et al. (2010) Clinical efficacy of a RAF inhibitor needs
broad target blockade in BRAF-mutant melanoma. Nature 467,
596–599
37. Flaherty, K.T. et al. (2010) Inhibition of mutated, activated BRAF in
metastatic melanoma. N. Engl. J. Med. 363, 809–819
38. Falchook, G.S. et al. (2012) Dabrafenib in patients with melanoma,
untreated brain metastases, and other solid tumours: a
phase 1 dose-escalation trial. Lancet 379, 1893–1901
39. Falchook, G.S. et al. (2012) Activity of the oral MEK inhibitor
trametinib in patients with advanced melanoma: a phase 1
dose-escalation trial. Lancet Oncol. 13, 782–789
40. Ascierto, P.A. et al. (2016) Cobimetinib combined with vemurafenib
in advanced BRAF(V600)-mutant melanoma (coBRIM):
updated efficacy results from a randomised, double-blind, phase
3 trial. Lancet Oncol. 17, 1248–1260
41. Sen, S. et al. (2017) Co-occurring genomic alterations and association
with progression-free survival in BRAFV600-mutated nonmelanoma
tumors. J. Natl. Cancer Inst. 109, djx094
42. Nowell, P.C. (1976) The clonal evolution of tumor cell populations.
Science 194, 23–28
43. Furmark, L. and Pavlick, A.C. (2015) BRAF inhibitors and the
“Lazarus syndrome” – an update and perspective. Am. J. Hemat.
Oncol. 11, 24–29
44. Groisberg, R. and Subbiah, V. (2017) The big, the bad, and the
exon 11: adjuvant imatinib for all gastro-intestinal stromal tumors
or just the ugly? Transl. Gastroenterol. Hepatol. 2, 81
45. Hong, D.S. et al. (2016) Phase IB study of vemurafenib in combination
with irinotecan and cetuximab in patients with metastatic
colorectal cancer with BRAFV600E mutation. Cancer Discov. 6,
1352–1365
46. Subbiah, V. et al. (2018) Dabrafenib and trametinib treatment in
patients with locally advanced or metastatic BRAF V600-mutant
anaplastic thyroid cancer. J. Clin. Oncol. 36, 7–13
47. Desai, N. et al. (2009) SPARC expression correlates with tumor
response to albumin-bound paclitaxel in head and neck cancer
patients. Transl. Oncol. 2, 59–64
48. Al-Ahmadie, H. et al. (2014) Synthetic lethality in ATM-deficient
RAD50-mutant tumors underlies outlier response to cancer therapy.
Cancer Discov. 4, 1014–1021
49. Komiya, K. et al. (2016) SPARC is a possible predictive marker for
albumin-bound paclitaxel in non-small-cell lung cancer. Onco.
Targets Ther. 9, 6663–6668
50. Mohni, K.N. et al. (2015) A synthetic lethal screen identifies
DNA repair pathways that sensitize cancer cells to combined
ATR inhibition and cisplatin treatments. PLoS One 10, e0125482
51. Subbiah, V. et al. (2012) Targeting the apoptotic pathway in
chondrosarcoma using recombinant human Apo2L/TRAIL
(dulanermin), a dual proapoptotic receptor (DR4/DR5) agonist.
Mol. Cancer Ther. 11, 2541–2546
52. Subbiah, V. et al. (2011) Targeted morphoproteomic profiling of
Ewing’s sarcoma treated with insulin-like growth factor 1 receptor
(IGF1R) inhibitors: response/resistance signatures. PLoS One 6,
e18424
53. Subbiah, V. et al. (2015) STUMP un “stumped”: anti-tumor
response to anaplastic lymphoma kinase (ALK) inhibitor based
targeted therapy in uterine inflammatory myofibroblastic tumor
with myxoid features harboring DCTN1-ALK fusion. J. Hematol.
Oncol. 8, 66
54. Jardim, D.L. et al. (2013) Comprehensive characterization of
malignant phyllodes tumor by whole genomic and proteomic
analysis: biological implications for targeted therapy opportunities.
Orphanet J. Rare Dis. 8, 112
55. Ding, K. et al. (2015) Leveraging a multi-omics strategy for prioritizing
personalized candidate mutation-driver genes: a proof-ofconcept
study. Sci. Rep. 5, 17564
56. Roychowdhury, S. and Chinnaiyan, A.M. (2016) Translating cancer
genomes and transcriptomes for precision oncology. CA
Cancer J. Clin. 66, 75–88
57. Nikanjam, M. et al. (2016) Dosing targeted and cytotoxic two-drug
combinations: lessons learned from analysis of 24,326 patients
reported 2010 through 2013. Int. J. Cancer 139, 2135–2141
58. Liu, S. et al. (2016) Dosing de novo combinations of two targeted
drugs: towards a customized precision medicine approach to
advanced cancers. Oncotarget 7, 11310–11320
59. Nikanjam, M. et al. (2017) Dosing immunotherapy combinations:
analysis of 3,526 patients for toxicity and response patterns.
Oncoimmunology 6, e1338997
60. Nikanjam, M. et al. (2017) Dosing three-drug combinations that
include targeted anti-cancer agents: analysis of 37,763 patients.
Oncologist 22, 576–584
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