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Review
. 2015 May;9(5):967-96.
doi: 10.1016/j.molonc.2014.09.011. Epub 2014 Oct 18.

Next-generation clinical trials: Novel strategies to address the challenge of tumor molecular heterogeneity (V体育2025版)

Daniel V T Catenacci  1
Affiliations
Review

Next-generation clinical trials: Novel strategies to address the challenge of tumor molecular heterogeneity

Daniel V T Catenacci. Mol Oncol. 2015 May.

"VSports注册入口" Abstract

The promise of 'personalized cancer care' with therapies toward specific molecular aberrations has potential to improve outcomes. However, there is recognized heterogeneity within any given tumor-type from patient to patient (inter-patient heterogeneity), and within an individual (intra-patient heterogeneity) as demonstrated by molecular evolution through space (primary tumor to metastasis) and time (after therapy). These issues have become hurdles to advancing cancer treatment outcomes with novel molecularly targeted agents. Classic trial design paradigms are challenged by heterogeneity, as they are unable to test targeted therapeutics against low frequency genomic 'oncogenic driver' aberrations with adequate power. Usual accrual difficulties to clinical trials are exacerbated by low frequencies of any given molecular driver. To address these challenges, there is need for innovative clinical trial designs and strategies implementing novel diagnostic biomarker technologies to account for inter-patient molecular diversity and scarce tissue for analysis. Importantly, there is also need for pre-defined treatment priority algorithms given numerous aberrations commonly observed within any one individual sample VSports手机版. Access to multiple available therapeutic agents simultaneously is crucial. Finally intra-patient heterogeneity through time may be addressed by serial biomarker assessment at the time of tumor progression. This report discusses various 'next-generation' biomarker-driven trial designs and their potentials and limitations to tackle these recognized molecular heterogeneity challenges. Regulatory hurdles, with respect to drug and companion diagnostic development and approval, are considered. Focus is on the 'Expansion Platform Design Types I and II', the latter demonstrated with a first example, 'PANGEA: Personalized Anti-Neoplastics for Gastro-Esophageal Adenocarcinoma'. Applying integral medium-throughput genomic and proteomic assays along with a practical biomarker assessment and treatment algorithm, 'PANGEA' attempts to address the problem of heterogeneity towards successful implementation of molecularly targeted therapies. .

Keywords: Esophagogastric cancer; Esophagus cancer; Expansion Platform Designs; Gastric cancer; Gastroesophageal cancer; Inter-patient heterogeneity; Intra-patient heterogeneity; Molecular heterogeneity; Next-generation clinical trials; PANGEA. V体育安卓版.

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"V体育ios版" Figures

Figure 1
Figure 1
The “run‐away 18‐wheeler truck” metaphor of cancer and current therapeutic strategies. ©Ion Medical Designs, LLC 2014. (A) In the untreated scenario, cancer is like a run‐away truck without brakes (loss of tumor suppressor) quickly and inappropriately accelerating down a hill. (B) In an attempt to slow down the truck (cancer cell), altering the slope (tumor environment) to ‘uphill’ has been employed {eg. anti‐angiogenesis}. (C) Stopping the driver from pushing the gas pedal {targeted inhibition towards the function of the oncogenic genomic driver} may relieve the inappropriate acceleration {eg. trastuzumab for HER2 gene amplification}, if only temporarily until another mechanism (inherent or acquired) to maintain the acceleration stimulus (oncogenic driver) moves to replace it. (D) Although loss of any back wheel (downstream effector) will likely not slow the truck given the presence of numerous wheels (redundant parallel escape signals), some wheels downstream can be critical, like when inducing a flat front tire (critical downstream hub) {eg. inhibition of DNA synthesis: classic cytotoxics; or inhibition of key protein: estrogen/androgen receptor antagonists}. (E) Reversing mechanisms of police (immune) evasion can re‐establish the ability to recognize and eliminate the abnormal ‘speedy truck’ {immunomodulation}. A combination of the strategies in (B–E) may be optimal to slow with significant magnitude and duration.
Figure 2
Figure 2
Intra‐patient tumor molecular evolution through space and/or time. (A) Intra‐patient heterogeneity ‘through space’ of Met by IHC (left) and MET gene copy by FISH (right) within the primary tumor (upper panel) to metastatic lymph node (lower panel. (Catenacci et al., 2014a) (B) Intra‐patient heterogeneity ‘through space’ of Her2 by IHC (left) and HER2 gene copy by FISH (right) from primary tumor (upper panel) to metastatic lymph node (lower panel). (Catenacci et al., 2014a) (C) Intra‐patient heterogeneity ‘through space’ of KRAS gene copy by FISH in primary tumor (upper panel) to metastatic peritoneal ascites (lower panel). (Catenacci et al., 2013) (D) Intra‐patient heterogeneity ‘through space and time’ of tumor cells and stromal elements within the primary tumor at diagnosis (upper panel) and metastatic peritoneal carcinomatosis implant after cisplatin/5FU chemotherapy (lower panel). FGFR2 is gene amplified only in the primary tumor, and MET is gene amplified only in the metastatic deposit. (Catenacci et al., 2014b) (E) Intra‐patient heterogeneity ‘through space and time’ of KRAS gene copy and expression prior to anti‐Met antibody therapy (upper panel, normal gene copy) and after (lower panel, gene amplified) suggesting a mechanism of resistance. (Catenacci et al., 2011a, 2014a; Catenacci et al., 2013).
Figure 3
Figure 3
Inter–patient tumor molecular heterogeneity. (Left panel) Genomic profiling using a ∼240 gene next‐generation sequencing (NGS) platform of a cohort of 50 stage IV GEC samples (upper panel) revealing few high frequency events (peak) and numerous low frequency events (tail); pie chart revealing profound inter‐patient molecular heterogeneity (see Table 3). (Catenacci et al., 2014a) (Right panel) Proteomic expression profiling of 100 GEC samples using multi‐plex (8 peptides shown) selected reaction monitoring (SRM) mass spectrometry (MS) revealing clear inter‐patient heterogeneity. (Catenacci et al., 2014a,b; Hembrough et al., 2012).
Figure 4
Figure 4
Classic biomarker‐focused clinical trial designs. (A) Retrospective‐Prospective. (B) Population Enriched, Histology Dependent. (C) Population Enriched, Histology Independent. (D) Biomarker Stratified.
Figure 5
Figure 5
Next‐generation clinical trial designs. (A) Exploratory Platform Design (e.g. ‘BATTLE’, ‘I‐SPY’). (B) Expansion Platform Design Type IA: Histology Dependent, Global, and Compartmentalized. (e.g. ‘FOCUS‐4’) (C) Expansion Platform Design Type IB: Histology Agnostic, Global, and Compartmentalized (e.g. ‘NCI‐MATCH’, ‘Signature’). (D) Expansion Platform Design Type IIA: Histology Dependent, Grass‐Roots, Holistic (e.g. PANGEA). (E) Expansion Platform Design Type IIB with Biologic Beyond Progression: Histology Dependent, Grass‐Roots, Holistic (e.g. PANGEA‐BBP). After first progression (PD1) patients undergo repeat biopsy of a progressing lesion and undergo repeat molecular testing and treatment assignment, which may allow cross‐over to a more appropriate biological group as directed by the prioritization algorithm (Figure 7). Patients on placebo remain on placebo at each progression point.
Figure 6
Figure 6
The ‘Expansion Platform Design Type II with BBP – PANGEA’. (A) Schema of the ongoing pilot ‘phase IIa’ trial called ‘PANGEA‐IMBBP’. (B) A planned future randomized placebo‐controlled phase IIb trial ‘PANGEA‐IIMBBP’ should the pilot trial meet endpoints. Molecular categorization is a stratification factor to ensure equal distribution between Arms A and B. HER2+ patients would receive trastuzumab in the first line, per clinical standards, then proceed with placebo for second/third line therapy.
Figure 7
Figure 7
The biomarker and treatment assignment algorithm is premised on optimizing the inhibition of ‘driver‐biology’. This 9‐point algorithm serves to prioritize treatment assignment should multiple aberrations (genomic and proteomic) be observed in an individual sample. Should multiple aberrations be present, priority could be given to higher allele frequency (for mutations) or higher gene copy/expression. The algorithm acts as a filter to create 5 distinct biomarker categories (with 9 tiers) that will receive 5 specific and most‐appropriately matched targeted therapies. Approximate hazard ratios (HR) anticipated for each categorized tier, as well as the aggregate HR (the primary endpoint of PANGEA), are indicated. This first iteration of the ‘PANGEA’ strategy is a compromise within the spectrum between the two extremes of ‘one‐size‐fits‐all’ and completely individualized therapy or ‘N‐of‐1’ (bottom panel). Rather than being a ‘tailored suit’, PANGEA can be considered fitting to ‘X‐large, large, medium, small and X‐small’. Future iterations could include more biomarker categories and treatment arms, consequently moving closer towards the ‘N‐of‐1’ limit.
Figure 8
Figure 8
Applications of next‐generation clinical trial designs, and total patients required, towards approval of ‘personalized’ treatment strategies that encompass both the drugs and companion diagnostics. Total numbers of patients required from phase II to phase III and FDA approval are approximated in the final right column, using a biomarker incidence of 20% and 7% as examples. For comparison purpose, the numbers reflect a median overall survival as the primary endpoint with target HR 0.67, two‐sided alpha 0.05, 80% power, randomization ratio 2:1, 12 month accrual and 24 month follow up. Total numbers for each trial design include estimated numbers for serial phase IIa, phase IIb, and then phase III trials in tandem. For the exploratory platform design, given the adaptive Bayesian statistics, a direct comparison is not possible. * The target total number for the ongoing BATTLE‐2 trial. **The target total number for the ongoing ISPY‐2 trial. ***Estimated numbers for a follow up randomized phase IIb trial for an identified biomarker/drug combination from either the phase IIa or Phase IIb Exploratory Platform design, with statistical endpoints as set above, performed prior to a full phase III. Numbers in parentheses indicate the target biomarker population subset that would be required to be identified from the entire patient population.
Figure 9
Figure 9
The ‘PANGEA’ strategy addressing inter–and intra–patient tumor molecular heterogeneity. The expansion platform type II design with biologics beyond progression is testing the ‘PANGEA personalized treatment strategy’. Obtaining baseline biopsies of metastatic disease and serially biopsies at each progression time‐point within the trial with repeat molecular testing and treatment assignment to match targeted therapies in real‐time may improve clinical outcomes, compared to a historical (phase IIa) or placebo (phase IIb) controlled standard therapy. Upon completion of each trial, an iterative process will allow to refine the treatment strategy (biomarker assays, molecular categories, treatment algorithms, and therapeutic agents) using knowledge gained from each previous trial and new technology and drugs developed in the interim.
Figure 10
Figure 10
Comparison of one‐size‐fits‐all accepted design strategy and the ‘Expansion Platform Type II Holistic Design’. (A) In the classic clinical trial design, administering an investigational agent to all‐comers versus placebo will lead to approval, should statistical endpoints be met. Often, statistical endpoints are met with only marginal clinical improvement in overall survival (∼1–2 months). Approval of agents in this scenario leads to large numbers of patients treated with the new agent that do not derive any benefit (top and bottom bracket at any time‐point (t) along the x‐axis). Often targeted agents applied using this trial design fail since only a small subset derive benefit which is not recognized due to dilutional effects of the other biomarker‐negative patients, along with too few numbers within the subset analysis for adequate statistical power. (B) The Expansion Platform Design Type II (with/without biologics beyond progression) uses targeted agents for targeted populations (middle panel), in attempt to improve (red line) over the natural outcome observed for each specific molecular group treated without the targeted agent (black line). Three of the 5 subgroups of PANGEA are shown here as theoretical outcomes that are hypothesized. Due to the large number of patients that would be required should each of the molecular groups within PANGEA be run as an individual compartmentalized stand‐alone trial (ie an Expansion Platform Design Type IA or B), the advantage of the type II design is that all patients screened are placed in a group that is most appropriate for them within the one trial, reducing total patients required. Results are pooled (right panel) for the primary endpoint of ‘personalized treatment strategy’ versus standard control to limit exposure of any agent to any patient not expected to derive benefit, while maximizing exposure to those that will (bracket). Since the total effect size is hypothesized to be large, particularly in the higher tiers of the algorithm (see Figure 7), fewer total patients are required for statistical endpoints.
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